{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOmHVpr1DuGr+8I/C4Jej85"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"a-WD1bKBXZ2l"},"source":["# Objetivos \n","- Aplicar la metodología CRISP - DM sobre un conjunto de datos de prueba\n","- Entender algunas librerias de visualización y preprocesamiento de datos"]},{"cell_type":"markdown","metadata":{"id":"7mpqHE3TXjn8"},"source":["\n","\n","# `1. Business Understanding`\n","Acá está el link que tiene la descripción de los datos con los que vamos a trabajar [Datos bancarios](https://www.kaggle.com/datasets/sahistapatel96/bankadditionalfullcsv)"]},{"cell_type":"markdown","metadata":{"id":"MnVyaBEUX9PM"},"source":["\n","\n","# `2. Data Understanding`"]},{"cell_type":"markdown","metadata":{"id":"ii6FCAc38t9H"},"source":["## Carga Datos"]},{"cell_type":"code","metadata":{"id":"QGxHIFa2YEqx","executionInfo":{"status":"ok","timestamp":1685969519723,"user_tz":180,"elapsed":1325,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["# Librerías para trabajar con datos \n","import pandas as pd \n","import numpy as np\n","# Librerías para visualización\n","import matplotlib.pyplot as plt\n","import seaborn as sns"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"6GncIi7OWRFF","executionInfo":{"status":"ok","timestamp":1685969536212,"user_tz":180,"elapsed":417,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["# Cargo el conjunto de datos\n","data_frame = pd.read_csv('bank-additional-full.csv', delimiter=';')"],"execution_count":3,"outputs":[]},{"cell_type":"code","source":["data_frame.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8Is2IyOSnb2n","executionInfo":{"status":"ok","timestamp":1685969557948,"user_tz":180,"elapsed":355,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"6bf86008-e4c5-404a-bfbc-6ff8e81bd092"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(41188, 21)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XLqLWgs3IXG8","executionInfo":{"status":"ok","timestamp":1685969564321,"user_tz":180,"elapsed":364,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"f09473e0-785f-45e6-fe6f-347ca6d74719"},"source":["print('Cantidad de filas: {}'.format(data_frame.shape[0]))\n","print('Cantidad de columnas: {}'.format(data_frame.shape[1]))"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Cantidad de filas: 41188\n","Cantidad de columnas: 21\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"6BERZhmUW8Js","executionInfo":{"status":"ok","timestamp":1685969613018,"user_tz":180,"elapsed":315,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"bc76e472-b227-48f8-9da3-40d860916a7c"},"source":["# overview de los datos\n","data_frame.sample(5)"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" age job marital education default housing loan \\\n","26871 32 admin. single university.degree no yes yes \n","24559 39 blue-collar married basic.9y no no no \n","19449 30 technician single university.degree no yes yes \n","40787 33 admin. married university.degree no yes yes \n","16667 36 entrepreneur married university.degree no no no \n","\n"," contact month day_of_week ... campaign pdays previous \\\n","26871 cellular nov thu ... 1 999 0 \n","24559 cellular nov mon ... 1 999 0 \n","19449 cellular aug thu ... 3 999 0 \n","40787 telephone sep wed ... 2 999 0 \n","16667 cellular jul wed ... 2 999 0 \n","\n"," poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m \\\n","26871 nonexistent -0.1 93.200 -42.0 4.076 \n","24559 nonexistent -0.1 93.200 -42.0 4.191 \n","19449 nonexistent 1.4 93.444 -36.1 4.968 \n","40787 nonexistent -1.1 94.199 -37.5 0.879 \n","16667 nonexistent 1.4 93.918 -42.7 4.963 \n","\n"," nr.employed y \n","26871 5195.8 no \n","24559 5195.8 no \n","19449 5228.1 yes \n","40787 4963.6 yes \n","16667 5228.1 yes \n","\n","[5 rows x 21 columns]"],"text/html":["\n","
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agejobmaritaleducationdefaulthousingloancontactmonthday_of_week...campaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
2687132admin.singleuniversity.degreenoyesyescellularnovthu...19990nonexistent-0.193.200-42.04.0765195.8no
2455939blue-collarmarriedbasic.9ynononocellularnovmon...19990nonexistent-0.193.200-42.04.1915195.8no
1944930techniciansingleuniversity.degreenoyesyescellularaugthu...39990nonexistent1.493.444-36.14.9685228.1yes
4078733admin.marrieduniversity.degreenoyesyestelephonesepwed...29990nonexistent-1.194.199-37.50.8794963.6yes
1666736entrepreneurmarrieduniversity.degreenononocellularjulwed...29990nonexistent1.493.918-42.74.9635228.1yes
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5 rows × 21 columns

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agejobmaritaleducationdefaulthousingloancontactmonthday_of_week...campaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
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0 rows × 21 columns

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Montero","userId":"08026561778973164523"}},"outputId":"dbbf2c4e-8c07-4b38-ae5e-a1bb5588b7cc"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 37120.000000\n","mean 93.575234\n","std 0.579372\n","min 92.201000\n","50% 93.749000\n","90% 94.465000\n","99% 94.465000\n","max 94.767000\n","Name: cons.price.idx, dtype: float64"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","source":["## Funciones de Preprocesamiento"],"metadata":{"id":"9F5PHsztQ7sh"}},{"cell_type":"code","source":["def truncar(df, column, bound='both'):\n"," #imputamos con el percentil 99 las columnas con outliers altos\n"," percentil_99=df[column].quantile(0.99)\n"," #imputamos con el percentil 1 las columnas con outliers bajos\n"," percentil_01=df[column].quantile(0.01)\n","\n"," if bound =='upper':\n"," df[column] = np.where(df[column]> percentil_99,percentil_99, df[column] )\n"," if bound =='lower':\n"," df[column] = np.where(df[column]< percentil_01,percentil_01, df[column] )\n"," if bound =='both':\n"," df[column] = np.where(df[column]> percentil_99,percentil_99, df[column] )\n"," df[column] = np.where(df[column]< percentil_01,percentil_01, df[column] )\n"," return df"],"metadata":{"id":"OLg7KWzRROKp","executionInfo":{"status":"ok","timestamp":1685969830949,"user_tz":180,"elapsed":5,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-8r1Ym9clmeN"},"source":["## Analisis del target"]},{"cell_type":"code","metadata":{"id":"FJEO-_TkDeBg","executionInfo":{"status":"ok","timestamp":1685969845289,"user_tz":180,"elapsed":304,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["# Defino el tamaño de los graficos\n","sns.set(rc={'figure.figsize':(5,5)})"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Slxh4q4d_5t7","executionInfo":{"status":"ok","timestamp":1685969846159,"user_tz":180,"elapsed":10,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"80ff6ec6-1d7a-4e65-ef93-a5c2e09dbd52"},"source":["# Distribución del target\n","df['y'].value_counts()"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["no 32933\n","yes 4187\n","Name: y, dtype: int64"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"_WTGoMfk70pw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1685969852018,"user_tz":180,"elapsed":258,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"96d7a83d-dbe3-4f58-ffd1-4faee89f9822"},"source":["# Cantidad de casos positivos\n","n_pos = df['y'].value_counts().loc['yes']\n","# Cantidad de casos negativos\n","n_neg = df['y'].value_counts().loc['no']\n","# Tasa de target\n","print('% de casos positivos: {:.1f} %'.format(n_pos/(n_pos+n_neg)*100))"],"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["% de casos positivos: 11.3 %\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8zys8PdKm2b6","executionInfo":{"status":"ok","timestamp":1685969863915,"user_tz":180,"elapsed":8,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"f9648c1d-4e08-4fcd-a7d0-cc78a2258e59"},"source":["# Genero un dataframe con solo los casos negativos de forma aleatoria con el total de n_pos \n","df_negative = df[df['y']=='no'].sample(n_pos)\n","# Genero un dataframe con solo los casos positivos\n","df_positive = df[df['y']=='yes']\n","# Creo el dataset balanceado ordenandolo de forma aleatoria (df_new)\n","df_new = pd.concat([df_negative, df_positive]).sample(frac=1).reset_index(drop=True)\n","# Chequeamos nuevamente el target\n","df_new['y'].value_counts()"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["no 4187\n","yes 4187\n","Name: y, dtype: int64"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":461},"id":"X5u_yFYhOYMO","executionInfo":{"status":"ok","timestamp":1685969875047,"user_tz":180,"elapsed":926,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"9ff1be8d-6454-42bb-8f0b-790aad634eac"},"source":["plt.pie(df_new['y'].value_counts(), \n"," labels = df_new['y'].unique(), \n"," autopct = '%1.1f%%',\n"," startangle= 80)\n","plt.title('Target Distribution')"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Target Distribution')"]},"metadata":{},"execution_count":25},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"leTA22VvCxZP"},"source":["## Analisis del variables"]},{"cell_type":"code","source":["plt.figure(figsize=(10,5))\n","df_job = pd.DataFrame({'count' :df_new.groupby(['job', 'y']).size()}).reset_index()\n","sns.barplot(x=\"job\", y=\"count\", hue=\"y\", data=df_job)\n","plt.xticks(rotation = 45)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":770},"id":"mYuqYNTM1Ali","executionInfo":{"status":"ok","timestamp":1685969889866,"user_tz":180,"elapsed":1484,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"a6697952-6d72-4393-c640-2c5e4721b136"},"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),\n"," [Text(0, 0, 'admin.'),\n"," Text(1, 0, 'blue-collar'),\n"," Text(2, 0, 'entrepreneur'),\n"," Text(3, 0, 'housemaid'),\n"," Text(4, 0, 'management'),\n"," Text(5, 0, 'retired'),\n"," Text(6, 0, 'self-employed'),\n"," Text(7, 0, 'services'),\n"," Text(8, 0, 'student'),\n"," Text(9, 0, 'technician'),\n"," Text(10, 0, 'unemployed'),\n"," Text(11, 0, 'unknown')])"]},"metadata":{},"execution_count":26},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"OjJFvNrxf5ou"},"source":["### Job"]},{"cell_type":"markdown","metadata":{"id":"1Q64EJRWEU9J"},"source":["*En que categorias se ven diferencias significativas?*"]},{"cell_type":"markdown","metadata":{"id":"ZWTak3z3fx0l"},"source":["### Age"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"YdPSX7GbY5Pf","executionInfo":{"status":"ok","timestamp":1685969944565,"user_tz":180,"elapsed":383,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"c2d6f772-84ea-4913-e078-778145bbd430"},"source":["sns.boxplot(x='y', y='age', data=df_new)"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":27},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"laDYj7cVGK8X"},"source":["### Marital "]},{"cell_type":"code","source":["df_new['marital'].value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dJY24IPU3lGO","executionInfo":{"status":"ok","timestamp":1685970003109,"user_tz":180,"elapsed":7,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"a5993b2e-96ac-48a3-fb2c-f56b5e273197"},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["married 4897\n","single 2564\n","divorced 898\n","unknown 15\n","Name: marital, dtype: int64"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":605},"id":"MADaCaVJsisP","executionInfo":{"status":"ok","timestamp":1685970006202,"user_tz":180,"elapsed":16,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"e9b5e4d6-ab4d-4724-b9b9-beef9ab313a1"},"source":["df_marital = pd.DataFrame({'count' : df_new.groupby( ['marital', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"marital\", y=\"count\", hue=\"y\", data=df_marital)\n","plt.xticks(rotation = 45)"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2, 3]),\n"," [Text(0, 0, 'divorced'),\n"," Text(1, 0, 'married'),\n"," Text(2, 0, 'single'),\n"," Text(3, 0, 'unknown')])"]},"metadata":{},"execution_count":33},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"Rp8QaX6RIPDl"},"source":["### Education"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ACHTbXuxIRgE","executionInfo":{"status":"ok","timestamp":1685970048795,"user_tz":180,"elapsed":288,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"1c9aafa7-0c3e-45de-a43c-471431484b81"},"source":["df_new['education'].value_counts()"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["university.degree 2710\n","high.school 1880\n","professional.course 1110\n","basic.9y 1049\n","basic.4y 825\n","basic.6y 416\n","unknown 378\n","illiterate 6\n","Name: education, dtype: int64"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":763},"id":"stcoHW_bIUkM","executionInfo":{"status":"ok","timestamp":1685970180944,"user_tz":180,"elapsed":1479,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"5ba05f8c-0cf3-4817-bccd-91e1e0867099"},"source":["plt.figure(figsize= (7,5))\n","df_education = pd.DataFrame({'count' : df_new.groupby( ['education', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"education\", y=\"count\", hue=\"y\", data=df_education)\n","plt.xticks(rotation = 'vertical')"],"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2, 3, 4, 5, 6, 7]),\n"," [Text(0, 0, 'basic.4y'),\n"," Text(1, 0, 'basic.6y'),\n"," Text(2, 0, 'basic.9y'),\n"," Text(3, 0, 'high.school'),\n"," Text(4, 0, 'illiterate'),\n"," Text(5, 0, 'professional.course'),\n"," Text(6, 0, 'university.degree'),\n"," Text(7, 0, 'unknown')])"]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"aX8KEcq4Cz3f"},"source":["En que categorias se ven diferencias significativas"]},{"cell_type":"markdown","metadata":{"id":"uDX6Vvi4HAvq"},"source":["### Housing\n","(como imputaria los valores unknown)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I5ayp1A1HF6-","executionInfo":{"status":"ok","timestamp":1685970150116,"user_tz":180,"elapsed":348,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"859a4c2a-85ce-4371-fb5b-2d8a63ace967"},"source":["df_new['housing'].value_counts()"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["yes 4454\n","no 3730\n","unknown 190\n","Name: housing, dtype: int64"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":508},"id":"mYji85qcsx37","executionInfo":{"status":"ok","timestamp":1685970163854,"user_tz":180,"elapsed":1426,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"a7498009-ee73-4206-e986-641003359ba1"},"source":["housing = pd.DataFrame({'count' : df_new.groupby( ['housing', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"housing\", y=\"count\", hue=\"y\", data=housing)\n","plt.xticks(rotation = 'horizontal')"],"execution_count":39,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2]),\n"," [Text(0, 0, 'no'), Text(1, 0, 'unknown'), Text(2, 0, 'yes')])"]},"metadata":{},"execution_count":39},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"RKK_GeDiHbZN"},"source":["### Loan\n","(como imputaría los valores unknown)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dRcMry-gIvRl","executionInfo":{"status":"ok","timestamp":1685970190933,"user_tz":180,"elapsed":7,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"907a673e-b02b-4f26-8dc0-794e75a15941"},"source":["df_new['loan'].value_counts()"],"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["no 6996\n","yes 1188\n","unknown 190\n","Name: loan, dtype: int64"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":508},"id":"dDmjelqas6yS","executionInfo":{"status":"ok","timestamp":1685970202268,"user_tz":180,"elapsed":879,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"6add5ab4-d721-43d9-ae5e-d713364751f7"},"source":["loan = pd.DataFrame({'count' : df_new.groupby( ['loan', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"loan\", y=\"count\", hue=\"y\", data=loan)\n","plt.xticks(rotation = 'horizontal')"],"execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2]),\n"," [Text(0, 0, 'no'), Text(1, 0, 'unknown'), Text(2, 0, 'yes')])"]},"metadata":{},"execution_count":42},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"hotIyqFTJCdn"},"source":["### Default"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oh8mBpEuJEFy","executionInfo":{"status":"ok","timestamp":1685970221500,"user_tz":180,"elapsed":713,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"f78b66df-0a12-416c-e154-5a6c4a9c4eef"},"source":["df_new['default'].value_counts()"],"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/plain":["no 7037\n","unknown 1337\n","Name: default, dtype: int64"]},"metadata":{},"execution_count":43}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":535},"id":"w63JoyLuJLH6","executionInfo":{"status":"ok","timestamp":1685970226075,"user_tz":180,"elapsed":1343,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"65b84f86-2ec3-4ff2-8964-541ee42707bd"},"source":["default = pd.DataFrame({'count' : df_new.groupby( ['default', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"default\", y=\"count\", hue=\"y\", data=default)\n","plt.xticks(rotation = 45)\n","# se escluye esta variable porque aunque ayuda a discriminar el target, tiene muchos valores unknown"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1]), [Text(0, 0, 'no'), Text(1, 0, 'unknown')])"]},"metadata":{},"execution_count":44},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"UHqs8l_BJrHv"},"source":["### Contact"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YMG5Mdr_Jwu0","executionInfo":{"status":"ok","timestamp":1685970242077,"user_tz":180,"elapsed":310,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"c482d513-e0e7-4f23-a679-402112c42e5b"},"source":["df_new['contact'].value_counts()"],"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["cellular 6034\n","telephone 2340\n","Name: contact, dtype: int64"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":552},"id":"MKsPW8GnJ2Ds","executionInfo":{"status":"ok","timestamp":1685970246984,"user_tz":180,"elapsed":806,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"fe3ef45c-7a18-4a82-beca-a4d2c5a4e0de"},"source":["contact = pd.DataFrame({'count' : df_new.groupby( ['contact', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"contact\", y=\"count\", hue=\"y\", data=contact)\n","plt.xticks(rotation = 90)"],"execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1]), [Text(0, 0, 'cellular'), Text(1, 0, 'telephone')])"]},"metadata":{},"execution_count":46},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"LpHCcb3IKItz"},"source":["### Month"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TlGvqONaKKJK","executionInfo":{"status":"ok","timestamp":1685970251814,"user_tz":180,"elapsed":324,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"fef8b789-9efa-4847-f441-cb225de4e387"},"source":["df_new['month'].value_counts()"],"execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/plain":["may 2338\n","jul 1295\n","aug 1224\n","jun 1015\n","nov 811\n","apr 716\n","oct 343\n","mar 286\n","sep 255\n","dec 91\n","Name: month, dtype: int64"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":683},"id":"deAy5sxvKRAZ","executionInfo":{"status":"ok","timestamp":1685970259062,"user_tz":180,"elapsed":1484,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"7756e805-9ba4-4d1c-e170-93efa2c957f3"},"source":["month = pd.DataFrame({'count' : df_new.groupby( ['month', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"month\", y=\"count\", hue=\"y\", data=month)\n","plt.xticks(rotation = 90)"],"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n"," [Text(0, 0, 'apr'),\n"," Text(1, 0, 'aug'),\n"," Text(2, 0, 'dec'),\n"," Text(3, 0, 'jul'),\n"," Text(4, 0, 'jun'),\n"," Text(5, 0, 'mar'),\n"," Text(6, 0, 'may'),\n"," Text(7, 0, 'nov'),\n"," Text(8, 0, 'oct'),\n"," Text(9, 0, 'sep')])"]},"metadata":{},"execution_count":48},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"mNGkWnidKj7D"},"source":["### day_of_week"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F1iEWyvkKlKa","executionInfo":{"status":"ok","timestamp":1685970277811,"user_tz":180,"elapsed":280,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"f4e022ac-a6ca-4593-cc42-a260865112da"},"source":["df_new['day_of_week'].value_counts()"],"execution_count":49,"outputs":[{"output_type":"execute_result","data":{"text/plain":["thu 1769\n","wed 1706\n","mon 1699\n","tue 1692\n","fri 1508\n","Name: day_of_week, dtype: int64"]},"metadata":{},"execution_count":49}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":596},"id":"MIh87dWwKpPB","executionInfo":{"status":"ok","timestamp":1685970287643,"user_tz":180,"elapsed":827,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"b6002ef4-99d3-42d4-aa18-a6c05ee7cf3e"},"source":["day_of_week = pd.DataFrame({'count' : df_new.groupby( ['day_of_week', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"day_of_week\", y=\"count\", hue=\"y\", data=day_of_week)\n","plt.xticks(rotation = 'vertical')"],"execution_count":50,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2, 3, 4]),\n"," [Text(0, 0, 'fri'),\n"," Text(1, 0, 'mon'),\n"," Text(2, 0, 'thu'),\n"," Text(3, 0, 'tue'),\n"," Text(4, 0, 'wed')])"]},"metadata":{},"execution_count":50},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"tXYu5omjK_ED"},"source":["### Duration"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"pgP1fIZvLAk5","executionInfo":{"status":"ok","timestamp":1685970292699,"user_tz":180,"elapsed":447,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"08d5b439-ad81-474b-94fd-f15191ef7410"},"source":["sns.boxplot(x='y', y='duration', data=df_new)"],"execution_count":51,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":51},{"output_type":"display_data","data":{"text/plain":["
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Montero","userId":"08026561778973164523"}},"outputId":"a756fba5-1020-483b-bcb9-b766eaeb9c55"},"source":["df_new[['duration']].describe(percentiles=[0.01, 0.99])"],"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" duration\n","count 8374.000000\n","mean 387.026152\n","std 356.770667\n","min 0.000000\n","1% 15.000000\n","50% 267.000000\n","99% 1622.270000\n","max 4199.000000"],"text/html":["\n","
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mean387.026152
std356.770667
min0.000000
1%15.000000
50%267.000000
99%1622.270000
max4199.000000
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duration
count8374.000000
mean382.451359
std333.784247
min0.000000
1%15.000000
50%267.000000
99%1622.072900
max1622.270000
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\n","
\n"," "]},"metadata":{},"execution_count":54}]},{"cell_type":"markdown","metadata":{"id":"l3ldckppPUJv"},"source":["### Campaign"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"k4QHIrG4PTxn","executionInfo":{"status":"ok","timestamp":1685970345507,"user_tz":180,"elapsed":312,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"ad27f2cd-f0e4-445e-9d67-4103e301b498"},"source":["sns.boxplot(x='y', y='campaign', data=df_new)"],"execution_count":56,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":56},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"P5UZz8VHPLnu","executionInfo":{"status":"ok","timestamp":1685970350255,"user_tz":180,"elapsed":12,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"86779f78-7f33-487f-d778-7b05276c72ed"},"source":["df_new[['campaign']].describe(percentiles=[0.01, 0.99])"],"execution_count":57,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" campaign\n","count 8374.000000\n","mean 2.331263\n","std 2.282588\n","min 1.000000\n","1% 1.000000\n","50% 2.000000\n","99% 12.000000\n","max 35.000000"],"text/html":["\n","
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campaign
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\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"asPaySTTQp_9"},"source":["# otro criterio de imputación\n","#print('Cantidad de clientes que no fueron contactados previamente: '+ str(df_new[df_new['pdays']==999].shape[0]))\n","#print('Cantidad de clientes del dataset: '+ str(df_new.shape[0])) \n","# decidimos excluir la variable\n","#df_new.drop(columns=['pdays'], inplace=True) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iGBvm31xQvNN","executionInfo":{"status":"ok","timestamp":1685970377785,"user_tz":180,"elapsed":8,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"f97c20ea-7293-4dae-fedc-37f3c888658d"},"source":["print('Cantidad de clientes del dataset: '+ str(df_new.shape[0])) \n","print('Cantidad de variables del dataset: '+ str(df_new.shape[1])) "],"execution_count":61,"outputs":[{"output_type":"stream","name":"stdout","text":["Cantidad de clientes del dataset: 8374\n","Cantidad de variables del dataset: 22\n"]}]},{"cell_type":"markdown","metadata":{"id":"NKubNC11SNT2"},"source":["### Previous"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"vIPIeYtDSDXw","executionInfo":{"status":"ok","timestamp":1685970383297,"user_tz":180,"elapsed":697,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"83a56aef-ffe9-4069-d0de-4ca2ff896a5a"},"source":["sns.boxplot(x='y', y='previous', data=df_new)"],"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":62},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["df_new['previous'].value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rNSSJMjH6dQq","executionInfo":{"status":"ok","timestamp":1685970390148,"user_tz":180,"elapsed":298,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"74fce453-b605-4151-8087-45f02f92a829"},"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0 6511\n","1 1325\n","2 358\n","3 126\n","4 40\n","5 11\n","6 3\n","Name: previous, dtype: int64"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lPMoK_18X8fX","executionInfo":{"status":"ok","timestamp":1685970394352,"user_tz":180,"elapsed":12,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"670a8d58-7cc9-40e4-8f4e-8c1a02f6f4a5"},"source":["df_new[df_new['y']=='yes']['previous'].describe(percentiles=[0.01, 0.99])\n","# conservamos los valores de la variable"],"execution_count":64,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 4187.000000\n","mean 0.488178\n","std 0.851093\n","min 0.000000\n","1% 0.000000\n","50% 0.000000\n","99% 4.000000\n","max 6.000000\n","Name: previous, dtype: float64"]},"metadata":{},"execution_count":64}]},{"cell_type":"markdown","metadata":{"id":"zRMppCqcYRCa"},"source":["### Poutcome"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":508},"id":"WKS818ivYTAX","executionInfo":{"status":"ok","timestamp":1685970417609,"user_tz":180,"elapsed":1027,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"e949cf81-7b46-48fc-be32-260c3afc97c2"},"source":["job = pd.DataFrame({'count' : df_new.groupby( ['poutcome', 'y'] ).size()}).reset_index()\n","sns.barplot(x=\"poutcome\", y=\"count\", hue=\"y\", data=job)\n","plt.xticks(rotation = 'horizontal')"],"execution_count":65,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2]),\n"," [Text(0, 0, 'failure'), Text(1, 0, 'nonexistent'), Text(2, 0, 'success')])"]},"metadata":{},"execution_count":65},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"DvOHd1hmbBcp"},"source":["### Social and Economic Context Attributes"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":721},"id":"X_CMsr59bE38","executionInfo":{"status":"ok","timestamp":1685970429264,"user_tz":180,"elapsed":1367,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"f00990aa-53e1-4ebc-eb1d-5e4c2dcc1e76"},"source":["import matplotlib.pyplot as plt\n","sns.set(rc={'figure.figsize':(10,8)})\n","fig, axes = plt.subplots(3, 2)\n","\n","sns.boxplot(data=df_new, x='y', y='emp.var.rate', ax=axes[0,0])\n","sns.boxplot(data=df_new, x='y', y='cons.price.idx', ax=axes[0,1])\n","sns.boxplot(data=df_new, x='y', y='cons.conf.idx', ax=axes[1,0])\n","sns.boxplot(data=df_new, x='y', y='euribor3m', ax=axes[1,1])\n","sns.boxplot(data=df_new, x='y', y='nr.employed', ax=axes[2,0])\n","sns.boxplot(data=df_new, x='y', y='age', ax=axes[2,1])"],"execution_count":66,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":66},{"output_type":"display_data","data":{"text/plain":["
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WaNGjdSoUSP761KlSumLL77QiRMnZDabVblyZXl7M+MTAMB458+f16VLl1S+fHkVKFDA6HAAAB7IkOW9b9y4oZEjR2rz5s1pgzGbVaNGDVWvXp2kDABguK+++kpPPPGEmjdvri5duuinn36SJF26dEkhISH66quvDI4QAOApDEnM8ufPr//+979KSEi4+84AABhg586dCgsLU9GiRTVixAj7cxiSFBAQoMDAQK1bt87ACAEAnsSQxEySGjZsqAMHDhhVPQAAdzRv3jw1atRIH3/8sfr27ZuuvH79+jp69KgBkQEAPJFhidmECRO0b98+zZw5U+fOnTMqDAAAMnTy5Em1a9cu0/LixYvr4sWLTowIAODJDHuQ68knn1RqaqoWLlyohQsXysvLSz4+Pmn2MZlM2rdvn0ERAgDysvz58+vGjRuZlv/+++/y9/d3XkAAAI9mWGLWtm1b1ksAALis4OBgbdy4Uf37909XduHCBUVEROixxx4zIDLj2awpRocAN0A7AbLGsMRs6tSpRlUNAMBdhYeHq2fPnurevbueeOIJmUwmffPNN/q///s/ffLJJ7LZbBoxYoTRYTrN7ZOfXDu50bhA4JZubz8AMmbYM2YAALiyypUra/Xq1fL399fs2bNls9m0ZMkSffDBB6pevbpWr16tsmXLGh0mAMBDuORiYampqYqJiZEklS5d2uBoAAB5VbVq1fTRRx8pNjZWZ86ckc1mU7ly5RQQEGB0aE53++MHBauFyGR2yT8h4EJs1hR77yqPrwB355L/qp45c0bt27eX2WzWkSNHjA4HAJDHFSlSRHXr1jU6DJdhMnuTmAGAgxkylNFms+nq1atKTEzMsLxw4cIKCQlR586dnRwZAAA3LV++XAMHDsy0fNCgQVq9erUTIwIAeDJDErPk5GQ99NBDWr58eYblJUqU0NSpUzVlyhQnRwYAwE1r165VlSpVMi2vWrWqIiIinBgRAMCTGZKY+fj4qHjx4unWLQMAwFX8/vvvd0zMKleurN9++82JEQEAPJlhszJ26dJFmzZtUlJSklEhAACQqXz58unChQuZlp8/f15mM5MbAwAcw7And4OCgrRjxw517NhRXbp0UZkyZeTr65tuvzZt2hgQHQAgr6tXr542bNigAQMGqFChQmnK4uPjtX79etWrV8+g6AAAnsawxOxf//qX/efZs2dnuI/JZNLRo0edFRIAAHYjR47U008/rZCQEPXv319Vq1aVJJ08eVLLli3ThQsXNGPGDIOjBAB4CsMSs8wm/gAAwBXUq1dPCxYs0IQJE/TWW2/Z12Gy2WwqW7as5s+frwYNGhgcJQDAUxiWmD300ENGVQ0AwD1p0qSJtm/friNHjtgn+ihfvrxq167NgrkAAIdidUgAAO7AbDbr/vvv1/333290KAAAD2ZoYnbhwgWtXbtWR44cUXx8vKxWa5pyk8mkZcuWGRQdACAv+eGHHyRJDz74YJrXd3NrfwAAcsKwxOzYsWPq16+fEhISVKlSJZ04cUJVq1ZVXFycYmJiVL58ed13331GhQcAyGNCQ0NlMpn0008/ycfHx/46MzabjUmqAAAOY1hiNmPGDBUoUEAbN26Ur6+vHnnkEY0fP14PP/ywvvjiC73xxhuaPn26UeEBAPKYW5NS+fj4pHkNAIAzGJaY7d+/X4MGDVLp0qV15coVSTfvPkpSu3bttG/fPr399ttauXKlUSECAPKQ2yelstlsqlWrlvLlyyeLxWJgVACAvMJsVMVWq1XFixeXJPn5+cnLy8ueoEk3F6A+fPiwQdEBAPKy5ORkPfTQQ/SaAQCcxrDErGzZsvrjjz9uBmE2q2zZsvruu+/s5fv371fhwoWNCg8AkIf5+PioePHi9mGNAADkNsMSs6ZNm2rr1q32171799ann36qAQMGqH///tq4caM6duxoVHgAgDyuS5cu2rRpk5KSkowOBQCQBxj2jNnQoUPVoUMHJScnK1++fOrfv7+uX7+ubdu2yWw2a/jw4RoyZIhR4QEA8rigoCDt2LFDHTt2VJcuXVSmTBn5+vqm269NmzYGRAcA8DSGJWZFihRRkSJF7K9NJpOGDx+u4cOHGxUSAAB2//rXv+w/z549O8N9mC4fAOAohiVmq1atUrt27RQQEGBUCAAAZIqJPwAAzmRYYvbmm29qypQpevDBB9W+fXs9/vjj8vf3NyocAADSuH36fAAAcpthk3988cUXGjp0qC5cuKDXXntNTZs21eDBg7Vx40ZdvXrVqLAAAEgjNTVVhw4dUmRkpCIjI3Xo0CGlpqYaHRYAwMMY1mNWqVIljRw5UiNHjtTJkye1ZcsWbd26VWPHjpWPj4+aNm2qDh06qEOHDkaFCADI49avX693331XFy9elM1mk3TzubKAgACNGjVK3bt3NzhCAICnMCwxu121atUUHh6u8PBwHTt2TFu2bNHq1av1n//8h8QMAGCINWvW6I033lDNmjU1cuRIVaxYUZJ0+vRpffLJJ3rttdeUnJys3r17GxsoAMAjuERidsuxY8f0xRdf6Msvv9S1a9cynJYYAABnWLRokRo1aqQPP/xQ+fLls29v3Lixunfvrv79+2vx4sUkZgAAhzA8MYuKilJkZKS++OIL/frrr/L29lbTpk0VFhamli1bGh0eACCP+vvvv/Xss8+mScpuyZcvnzp06KB33nnHgMgAAJ7IsMRs3rx52rp1q6KiouTl5aXGjRvrueeeU+vWrVW4cGGjwgIAQJJUs2ZNnT59OtPy06dPq0aNGk6MCADgyQxLzN5//309+OCDCg0N1eOPP66iRYsaFQoAAOm89tprGjJkiMqVK6eePXvah9cnJCRozZo12rp1qxYuXGhwlAAAT2FYYrZ7924VK1bMqOoBALijsWPHymw2a+rUqXrnnXdUsmRJSdL58+eVmpqqkiVL6uWXX05zjMlk0ubNm40IFwDg5gxLzEjKAACuzN/fX/7+/qpQoUKa7WXKlDEoIgCAJzN88o+MXL58WatWrZLJZNKIESOMDgcAkAetWLHC6BAAAHmIyyZmc+fOJTEDAACAQyWl2owOAW7AiHbikolZ6dKltXz5cqPDAAAAgAew2f75I3v63gsGRgJ3dHv7yU0umZj5+vrqoYceMjoMAAAyFB0drfbt28tsNuvIkSNGhwMA8AAukZjZbDZdunRJkhQQECCTyWRwRAAAZC5//vx68MEHjQ4DwD26/W/LMcEl5OPF35q4s6RUm7131Vm5iaGJWVRUlN577z3t2bNHCQkJkm72ljVr1kwjR45U9erVjQwPAIAMlSpVislBADfl42UiMYNLMiwx+/HHHzV48GBZrVa1atVKFStWlCSdPn1aO3fu1O7du7V48WI1atTIqBABAAAAwCkMS8wmT56sgIAArVy5UqVKlUpT9tdff6lv376aMmWK1q1bZ1CEAIC87OrVq4qPj09zjYqJidGaNWuUlJSktm3bqm7dugZGCADwJGajKo6KilKfPn3SJWXSzSEivXv3VlRUlAGRAQAgTZgwQS+88IL99dWrV9WzZ0/Nnz9fH374ofr27au9e/caGCEAwJMY1mNWunRpJSUlZVqenJys++67z4kRAQDwj3379qlnz57215s2bdL58+e1Zs0aVa1aVQMGDND8+fMVHBxsYJTGsFlTjA7BpdyaSpvJy9KinQBZY1hiNmLECE2ZMkUtWrRQzZo105QdOXJEK1eu1Pjx4w2KznXwj1paXPwy5qrt5GqSlQesb0P7zZirLvZ6+fJlBQYG2l/v3LlTDRs2VP369SVJISEhmjt3rkHRGevayY1GhwAAHsewxOynn35SsWLF1LVrVzVo0EAVKlSQJP366686ePCgqlWrpoMHD+rgwYNpjnv11VcNiNY4XPzgbm5fhPG9H/82MBK4I2ct4nkv/Pz89PffN9twQkKC9u3bp6FDh9rLvby87DMK36uvv/5a7733nk6ePKlixYqpW7duGjFihLy8vOz7LF68WJ9//rn++OMPpaSkqFy5curZs6f69u1LUg8AHsywxGzlypX2n/fv36/9+/enKT9x4oROnDiRZpvJZMpziRkAwBgNGjTQ6tWrVblyZe3Zs0eJiYlq1aqVvfzXX39N06N2NwcPHtTw4cPVoUMH/etf/1JUVJRmzZqlGzdu6OWXX7bvFx8fr/bt26tatWqyWCz67rvvNGnSJF29ejVNYuhsPj4WzZ+/1LD6XVViYqLCw4dJkmbNmi+LxWJwRK7Jx4fPBbgbwxKzY8eOGVW1y+PilzEufvfG6IufxeKr999fcsdnSPOixMREvfxyuCRp2rRZtN8M+Pj4yGLxNToMuzFjxujZZ59VWFiYJOmZZ55RtWrVJEmpqanaunWrmjVrds/nmzNnjmrWrKnp06dLkpo1ayabzaZ3331XAwcOVPHixSVJo0aNSnPcI488oj///FMbNmwwNDEzmUwu9ftxRRaLhc8IQLYZusC0kVJTU7V06VL95z//UVRUlGw2m4KCgvTCCy8YvnYaF7+74+Lnukwmk3x988vXN7/RobiUxMR/hrz5+fnRft1AhQoVtHXrVp06dUqFChVS2bJl7WU3btzQa6+9pho1atzz+Y4ePapu3bql2da0aVNNmzZN33zzjUJCQjI9tmjRokpOTs7yewAAuA+XSMysVqvi4+MzfLbA398/V+pMSEjQwoUL1aVLFw0ePFhms1kRERHq16+flixZoocffjhX6gUAuI98+fJlmHwVKlRIrVu3ztK5EhMT5ePjk2bbrdenTp1Kt39KSooSEhL0448/auPGjRo5cmSW6suIt7dhq+R4rNTUfz5Tb28zn7GLuv33BGSVs77bhiVmycnJWrRokdatW6dz587JarVmuN/Ro0dzpX5fX1999dVXKlKkiH1bkyZN1LFjRy1btozEDAAg6ea6m7///rtiY2MzLL9TT9ftKlSooEOHDqXZdmuCq/8995kzZ9SmTRv762HDhmnAgAH3HHNGzGaTihYtmKNzIL2EhH8mbvH3LyhfX3rDXdHtvycgq5z13TYsMZswYYI2btyoevXqqXXr1ipcuLBT6/fy8kqTlN3aFhQUpN9++82psQAAXM9vv/2mF198UYcOHcp0tkiTyXTPiVmfPn30yiuvaNmyZercubN98o/bZ2S8pVSpUlq7dq2uX7+uH3/8UYsWLZLZbNbzzz+f7fdjtdoUF3c928cjY7cPU75y5ZosllQDo0Fmbv89AVmVk++2n19+eXndW2+bYYnZ1q1b1blzZ02dOtWoENJJSUnRTz/9pIYNG+b4XAxlcDyGi8Cd0X7dz4QJE3TixAmNHz9ejRo1kp+fX47O17VrV504cUJvv/22Jk+erHz58mnkyJFatmyZSpYsmWZfHx8f1alTR5IUHBysQoUKadq0aerdu7dKlCiR7RhSUjIenYLsu/0zTUmxysuLz9gV0faRE876bhuWmOXPn1/16tUzqvoMLV68WDExMQwXcVEMF4E7o/26n/3792vIkCEKDQ11yPnMZrPGjx+vsLAwnT17VqVLl1ZKSopmzpx51+th7dq1lZqaqrNnz+YoMQMAuC7DErMOHTroP//5j3r37u2wc8bHx+v8+fN33a9cuXLpHsD+9ttvNWfOHA0fPlz3339/juJguEjuYLgI3Bnt1zmyMmTkbooWLZorw+wLFy5sn1Bk9uzZKlu2rB555JE7HrN//36ZTKY0M0MCADyLYYnZiy++qPHjx2vIkCHq1q2b7rvvvgzH2deuXfuez7l169Z7WoA6MjJSVapUsb8+fPiwwsLC1LFjR4fMeiXRZZ4bGC4Cd0b7dT+9evXS5s2b1bdv3wyvT1l16NAhff/996pZs6YSEhK0c+dObdq0SYsWLbKfPz4+XoMHD9aTTz6pChUqKCUlRXv37tXy5cvVs2dP+1pnALIvKTXjZ0bzqlvP0JpMJoMjcS1GtBPDErOkpCTZbDbt3r1bu3fvTldus9lkMpmyNCtjjx491KNHjyzFcebMGQ0ePFgNGjTQpEmTsnQsAMBzVaxYUVarVZ07d77jDcTbZ0+8k3z58mnbtm2aN2+eJKlevXpasWKFGjRoYN/HYrGoUqVK+uijjxQTEyNfX1+VL19eEydOvOdJRgDc2fS9F4wOAciQYYnZ+PHj9dVXX6l9+/aqV6+e02dllKTz58/r2WefValSpfTee+8pX758To8BAOCaRo0aZf952rRpGe6TlRuINWvWVERExB338fHx0ZQpU+49SACAxzAsMfvmm2/09NNPa/z48YbUn5CQoMGDB+vy5ct65ZVXdPLkSXuZj4+PatWqZUhcAADXsHz5cqNDAOAgPj4WzZ+/1OgwXE5iYqLCw4dJkmbNmi+LxWJwRK7Jx8c5n4thiVmhQoVUoUIFo6rX33//rWPHjkm6uXDn7cqUKaOdO3caERYAwEU89NBDRocAwEFMJpMsFmbDvROLxcJnZDDDErOnnnpKn3/+uXr16uWQh6qzqmzZsjp+/LjT6wUAuJ+oqCidPXtW0s2bd1WrVjU4IgCApzEsMatSpYp27NihLl26qEuXLjl+qBoAAEf76quvNHXqVHtSdkvZsmU1duxYtWrVyqDIAACexrDEzNEPVQMA4Ei7du3S888/r9KlS2vUqFH2ZVZOnTqliIgIhYWFacGCBXr00UcNjhQA4AkMS8x4qBoA4Mref/99BQUFadWqVSpQoIB9e6tWrfT000+rT58+mjdvHokZAMAhDEvMbj1UnZSUpMOHD+vixYt64IEHFBAQYFRIAADYHT9+XKNGjUqTlN1SoEABdenSRTNnzjQgMgCAJzIbWfny5cvVtGlT9enTR2FhYfbJOC5duqTg4GCtXbvWyPAAAHmYxWJRbGxspuWxsbFMLQ0AcBjDErN169Zp8uTJatasmd566y3ZbDZ7WUBAgBo3bqzIyEijwgMA5HHBwcFavny5Dhw4kK7sp59+0ooVK/Twww8bEBkAwBMZNpTxww8/VKtWrTRjxgxdvnw5XXnt2rW1YsUKAyIDAEB68cUX1atXL/Xp00d169ZVpUqVJEmnT5/WoUOHVKxYMY0ZM8bgKAEAnsKwHrMzZ87c8YFpf39/XblyxXkBAQBwm3Llymnz5s0KDQ1VbGysIiMjFRkZqdjYWPXr10+bNm1S2bJljQ4TAOAhDOsx8/Pzy7Cn7JaoqCiVKFHCiREBAPCPlJQUWSwWjR8/XuPHj09XfvXqVaWkpMjb27BLKQDAgxjWY/boo48qIiJCcXFx6cpOnjypTz/9VC1btjQgMgAApEmTJqlXr16Zlvfu3VtTp051YkQAAE9mWGIWHh6u1NRUdezYUbNmzZLJZNLGjRs1ZswYdevWTQEBARo+fLhR4QEA8rg9e/aobdu2mZa3bdtWu3fvdmJEAABPZlhiFhgYqPXr16tZs2b64osvZLPZtGnTJn399dfq0KGDIiIiWNMMAGCY8+fPKzAwMNPykiVLKiYmxokRAQA8maED44sVK6a33npLb731li5duiSr1aqAgACZzYYurwYAgPz9/XX69OlMy0+dOqVChQo5MSIAgCdzmQwoICBAxYsXJykDALiEZs2aac2aNTpy5Ei6ssOHDysiIuKOswsDAJAVTCUFAEAGXnjhBe3Zs0c9evRQy5YtVbVqVUk3J6j6+uuvFRAQoBdeeMHgKAEAnoLEDACADAQGBmrdunWaMWOGduzYoe3bt0uSChUqpE6dOmnUqFF3fAYNAICsIDEDACATJUuW1LRp02Sz2XTp0iVJN4fem0wmgyMDAHgaEjMAAO7CZDKpWLFiRocBAPBgzLQBAAAAAAYjMQMAAAAAg5GYAQAAAIDBSMwAAAAAwGAkZgAAAABgMBIzAAAAADAYiRkAAAAAGIzEDAAAAAAMRmIGAAAAAAYjMQMAAAAAg5GYAQAAAIDBSMwAAAAAwGAkZgAAAABgMBIzAAAAADAYiRkAAAAAGIzEDAAAAAAMRmIGAAAAAAYjMQMAAAAAg5GYAQAAAIDBSMwAAAAAwGDeRgcA12ez2ZSUlGh0GEpMTMzwZ6P5+FhkMpmMDgOZoP3eGe0X7orvdub4Xrs+2m/m8nL7NdlsNpvRQXia1FSrLl26ZnQYDmGz2TRlykRFRZ0wOhSXVbVqdY0b93qe/UfEldF+787T2m9AQEF5eTEYJCNcm/IOT/teexra7515WvvNynWJqxcAAAAAGIwes1zgSXclJdfpbpduxiLJpe6i5OUud3dA+70zT2u/9JhljmtT7nG177anfa89Ee03c57WfrNyXeIZM9yVyWSSxeJrdBhAttB+Ac/EdxvujPaLjHBbEQAAAAAMRmIGAAAAAAYjMQMAAAAAg5GYAQAAAIDBmJUxF9hsNlmtfKwA4Gxms8mjZvNyJK5NAOB8WbkukZgBAAAAgMEYyggAAAAABiMxAwAAAACDkZgBAAAAgMFIzAAAAADAYCRmAAAAAGAwEjMAAAAAMBiJGQAAAAAYjMQMAAAAAAxGYgYAAAAABiMxAwAAAACDkZgBAAAAgMFIzAAAAADAYCRmAAAAAGAwEjMAAAAAMBiJGQAAAAAYjMQMAAAAAAxGYgaXM3bsWHXs2FF79+5VSEiI6tevr+7du+uXX36x75OYmKgpU6aoadOmqlOnjjp37qzt27cbGDXyqp07dyooKEi//vprmu2xsbGqW7euVq1aJUk6cOCA+vXrp/r166thw4YaPXq0Ll68mOaYhQsX6vHHH1edOnXUuHFjDRgwQL///ruz3gqAO+DaBHfBdcl9kZjBJV24cEGTJk3SwIEDNWvWLCUmJmrkyJFKTk6WJI0ZM0affPKJBg0apHnz5qlq1aoKCwvTjh07DI4ceU3z5s0VGBiodevWpdn++eefS5I6deqkAwcOKDQ0VIULF9bMmTP15ptv6ueff9bw4cPt+2/cuFGzZ89W9+7dtXjxYk2aNEk1a9bUtWvXnPp+AGSOaxPcAdcl9+VtdABARmJjY7Vy5UpVq1ZNkpQ/f37169dPP/30kwoVKqRt27Zp4sSJ6tWrlyTp0Ucf1dmzZzVv3jy1atXKyNCRx3h5ealr165at26dwsPD5eXlJUlat26dHn/8cfn5+WnGjBm6//77NXfuXJlMJklS9erV1bFjR+3atUvNmzfXoUOHFBQUpCFDhtjP3bp1a0PeE4CMcW2CO+C65L7oMYNLKlmypP3CJ0lVq1aVJMXExGjfvn2SpCeeeCLNMe3atdORI0d0/fp15wUKSOrevbsuXLigPXv2SJKOHTumw4cPq3v37rpx44b279+vJ554QqmpqUpJSVFKSooqVqyoUqVK6eeff5Yk1apVS0eOHNGUKVP0448/2u/AA3AdXJvgLrguuSd6zOCS/Pz80rzOly+fpJvj92NjY5UvXz75+/un2ad48eKy2WyKj49XgQIFnBUqoLJly6pJkyZau3atWrRooXXr1qls2bJq3Lixzp8/r9TUVE2ZMkVTpkxJd+xff/0lSeratauuXbumiIgIffTRRypcuLBCQkI0ZswY+fr6OvstAcgA1ya4C65L7onEDG6nSJEiSk5OVmxsrIoUKWLf/vfff8tkMqlw4cIGRoe8qkePHhozZoxiYmL02WefKTQ01N4eTSaThgwZkuEQkKJFi0qSzGaz+vfvr/79+ysmJkZbtmzRjBkzVLRoUY0YMcLZbwdAFnFtgqvhuuR+SMzgdho2bChJ2rp1q3r27GnfvnXrVtWqVYs7kjBEq1at5Ofnp9GjRys2NlZdu3aVJBUoUED169dXdHS06tSpc0/nCgwM1LPPPqvPP/9c0dHRuRk2AAfh2gRXw3XJ/ZCYwe3UqFFDbdq00dSpU5WQkKBKlSpp8+bNOnDggN5//32jw0MelS9fPoWEhGjJkiVq2rSpSpUqZS976aWX1L9/f4WHh6tDhw7y8/PTuXPn9N///lddu3ZVcHCwJkyYID8/P9WvX19+fn7av3+/jh07pt69exv4rgDcK65NcDVcl9wPiRnc0jvvvKN3331XixYt0pUrV1S5cmW99957atmypdGhIQ97/PHHtWTJEnXr1i3N9gceeECrV6/WnDlzNG7cOCUnJ+u+++5T48aNVaFCBUlSgwYNFBERoU8//VQ3btxQuXLlNG7cOPXo0cOItwIgG7g2wdVwXXIvJpvNZjM6CADwBLNnz9bq1au1Z88e+fj4GB0OACCP47rkXugxA4Acio6O1unTp7Vy5Ur16dOHix8AwFBcl9wTPWYAkEOhoaE6ePCgmjVrpunTp/OQPwDAUFyX3BOJGQAAAAAYzGx0AAAAAACQ15GYAQAAAIDBSMwAAAAAwGAkZgAAAABgMBIzAAAAADAYiRkAAAAAGIzEDAAAAAAM5m10AJ7IZrPJamV5OABwNrPZJJPJZHQYLolrEwA4X1auSyRmucBqtenSpWtGhwEAeU5AQEF5eZGYZYRrEwA4X1auSwxlBAAAAACD0WMGt/Lss33sPy9dutrASAAAAADHoccMbmPs2FF3fA0AnmD9+vUKCgpK99/06dONDg0AkIvoMYPbOH8+5o6vAcCTLF68WIULF7a/DgwMNDAaAEBuIzGDW7h9COP/bmdII+7EZrMpKSnR6DAk3YxFkkvNGujjY3GpePCP2rVrKyAgwOgwAOQCrk2Zy8vXJRIzuLxjx47dtbxGjRpOigbuxGazacqUiYqKOmF0KC6ratXqGjfu9Tx7EQQAZ+PadGd5+bpEYgaX9/bb/75rOb1mADxNx44ddfnyZZUuXVpPPfWUBg0aJC8vL6PDchn0OGQuL/c4AO7MZLv1rwkcJjXVyloxDnTs2LE7JmcvvTSBHjNkylX+eEtMTFR4+DBJ0qxZ82WxWAyO6CZP+wPu5nox7j2v1Z49e/TTTz+pXr16MplM2rlzpz7++GP17t1bEyZMyPZ5U1Otiou7ke3jXeW7JN2MZdq0t3TqVJTRobikqlWr6aWXxrvUd9vT/q3JKVf5PiUmJmrkyCGSpLlzP3CJa5OntRU/v/z3fF2ixwwu725JF0kZ7sRkMsli8TU6jDQsFovLxQTX0axZMzVr1sz+umnTprJYLFq2bJmGDh2qkiVLZuu8ZrNJRYsWzHZcCQkJ6t+/d7aPh/NERZ3Uc889Y3QYaXz66afy9eXfvbQKGR2AEhIS7D8HBgbwOzIYiRncwtKlqzOcAIQhjADygnbt2mnp0qU6evRothMzq9WmuLjr2Y4hMTHh7jsBmbhy5ZosllSjw8D/uP17ze8od9BjBo9UsmRgminyS5Zk6mgAyIqUFKtDji1YLUQmM39C3OJqz5i5Cps1RddObpR0s/14eWW//SF33P695ndkPPceiI88ZerUmXd8DQCeKjIyUl5eXqpVq5bRoSADJpOJpAxAjnG7C26FoYsAPN3AgQMVHBysoKAgSdKOHTsUERGhfv36qUSJEobFdftcYbd6QYB7xVxzwN2RmAEA4EIqVaqkdevW6dy5c7JarapYsaLGjx+v0NBQo0MDAOQiEjMAAFzIq6++anQIGbp9qB7PmOFe3P6MGUM9gbvjX1UAAJAlJrM3iRkAOBiTfwAAAACAwbjdBQAAAI9ms9mUlJRodBguJzExMcOfkZaPj8Upw3FJzAAAAODRkpISNWzYs0aH4dLCw4cZHYLLmj9/qSwW31yvh6GMAAAAAGAweswAAACQZ4wJLiEfL2aJvOXWGnPMnJlWUqpN0/decGqdJGYAAADIM3y8TCRmafBZuAqPHcq4ePFihYSEqFGjRqpfv746deqklStXplt53mazaeHChWrRooXq1q2rnj176uDBg8YEDQAAACBP8tges/j4eLVv317VqlWTxWLRd999p0mTJunq1asaOnSofb9Fixbpvffe05gxYxQUFKRVq1bp2Wef1aZNm1SuXDkD3wEAAACAvMJjE7NRo0alef3II4/ozz//1IYNG+yJWWJioj744AM9++yzGjBggCSpYcOGeuKJJ7RkyRK98cYbTo7aNbnSFLOuOA7aWVOoAgAAwHN5bGKWkaJFiyo5Odn+ev/+/bp69aratWtn3+bj46PHH39c27dvNyJEl2Oz2TRlykRFRZ0wOhSXVbVqdY0b9zrJGQAAALLN4xOzlJQUJSQk6Mcff9TGjRs1cuRIe1l0dLQkqXLlymmOqVKlipYtW6aEhAT5+mZvzQJvb894fM9ms5Fw3IXJZJK3t5nPCXeUmvrPvwne3maP+TcCAAA4hkcnZmfOnFGbNm3sr4cNG2YfsihJcXFx8vHxkcViSXOcn5+fbDabYmNjs5WYmc0mFS1aMNtxu5oZM95xidXgExISFBoaKklasWJFtpNmR7NYGMqIu0tI8LL/7O9f0GXaLwAAcA1uk5jFx8fr/Pnzd92vXLly8vHxkSSVKlVKa9eu1fXr1/Xjjz9q0aJFMpvNev7553M1VqvVpri467laR16UmJhq/zkhIVU2W+od9naeGzf4XePuEhMT7D9fuXJNFotrtF9P4+eXX15e9EYCANyP2yRmW7du1auvvnrX/SIjI1WlShVJN58Xq1OnjiQpODhYhQoV0rRp09S7d2+VKFFCfn5+SkpKUmJiYppes7i4OJlMJhUpUiTb8aakWLN9LDJ2+2eakmKVlxefMdwH7RcAANyJ2yRmPXr0UI8ePXJ0jtq1ays1NVVnz55ViRIl7M+WnT59WjVq1LDvFx0drdKlSzPUCAAAAIBT5KnxHvv375fJZFLZsmUlSQ888IAKFSqkL774wr5PcnKytm3bpkcffdSoMAEAAADkMW7TY5YV8fHxGjx4sJ588klVqFBBKSkp2rt3r5YvX66ePXuqePHikm5O2jBkyBDNmTNHAQEBql69uj7++GNduXJFAwcONPhdAAAAAMgrPDIxs1gsqlSpkj766CPFxMTI19dX5cuX18SJExUSEpJm38GDB8tms2np0qW6dOmSatasqSVLlqhcuXLGBA8AAAAgz/HIxMzHx0dTpky5p31NJpOGDBmiIUOG5HJUAAAAAJCxPPWMGQAAAAC4IhIzAAAAADCYwxOz3bt333WfJUuWOLpaAAAAAHBbDk/MnnvuOU2YMEHXrl1LV3bmzBn16tVL06dPd3S1AAAAAOC2HJ6YhYeHa8OGDXryySe1d+9e+/aPPvpIISEh+uOPPzRv3jxHVwsAAAAAbsvhidnQoUO1du1a+fn56ZlnntHrr7+up59+WlOnTlXLli31+eefq2XLlo6uFgAAj3Tt2jU9+uijCgoK0s8//2x0OACAXJIr0+UHBQUpIiJCffv21SeffCKTyaTRo0dr8ODBuVEdAAAe6/3331dqaqrRYQAAclmuzMoYExOjoUOH6tChQ2rRooWKFSum999/X6tWrcqN6gAA8EinTp3S6tWrFRYWZnQoAIBc5vDEbP369erUqZN++eUXvfvuu1qwYIE+//xztWjRQm+++aaeeeYZnTt3ztHVAgDgcSZNmqRevXqpUqVKRocCAMhlDh/KOH78eLVo0UKTJk1S8eLFJUn+/v6aOXOm2rZtqzfeeEOdOnXSDz/84OiqAQDwGFu3btWJEyc0Z84cHT582CHn9PbO/v3Y1FSWPkX2eXubc9T+cor2i5xwVvt1eGI2efJkde3aNcOyJ554Qg8++KAmTpzo6GoBAPAYN27c0NSpUzVq1CgVKlTIIec0m00qWrRgto9PSPBySBzIm/z9C8rX19ew+mm/yAlntV+HJ2aZJWW3FCtWTO+9956jqwUAwGPMnz9fxYoVU7du3Rx2TqvVpri469k+PjExwWGxIO+5cuWaLBbjJrGh/SInctJ+/fzyy8vr3nrbcmVWRgAAkD1nz57V0qVLNW/ePMXHx0uSrl+/bv//tWvXVLBg9nq+UlKs2Y4rJ8cCKSlWeXkZ14Zov8gJZ7XfHCdmNWrUkMlkyvJxR48ezWnVAAC4hOTkZMXExCguLk42my1dee3ate/5XH/88YeSk5P13HPPpSvr16+f6tWrp4iIiBzFCwBwPTlOzEaMGJEuMdu+fbuioqLUtGlT+0xS0dHR+vbbb1WtWjW1bt06p9UCAGC4uLg4TZs2TZ999pmSk5PTldtsNplMpizdjKxZs6aWL1+eZtvRo0c1ZcoUTZw4UXXq1Mlx3AAA15PjxOx/11b55JNPdPHiRX322WeqXLlymrJTp06pf//+KlmyZE6rBQDAcGPHjtXXX3+t9u3bq169eipcuHCOz+nn56fg4OAMy2rXrp2l3jcAgPtw+DNmS5Ys0dNPP50uKZOkKlWqqG/fvlq8eLGeeuopR1cNAIBTffvttwoNDdX48eONDgUA4OYcnpidO3dO3t6Zn9bb25sFpgEAHsHf318VKlTI9XqCg4N1/PjxXK8HAGAch6+UVq1aNa1evVoxMTHpys6dO6ePP/5Y1atXd3S1AAA43VNPPaUtW7bIamXGNwBAzji8x2zcuHEaNGiQ2rZtq9atW9vvJP7666/asWOHbDab3n77bUdXCwCA040YMUJJSUnq1q2bOnfurMDAQHl5pV/Itk2bNgZEBwBwJw5PzBo1aqSIiAjNnj1bX331lRISbi7o5+vrq6ZNmyosLExBQUGOrhYAAKeLiYnR3r17dfTo0UxnXszqrIwAgLwpVxaYrl69uubNmyer1apLly5JkgICAmQ2O3zkJAAAhhk/frwOHz6sIUOGqG7dug6ZlREAkDflSmJ2i9lsVvHixXOzCgAADLNv3z4NHjxYzz//vNGhAADcXI4Ts7lz58pkMmnYsGEym82aO3fuXY8xmUwaMWJETqsGAMBQxYsXV5EiRYwOAwDgARyWmA0ePFg+Pj4kZgCAPOOZZ57RmjVr1L17dxUsWNDocAAAbizHidmxY8fu+BoAAE+VlJQkb29vtWnTRu3atdN9992XblZGk8mkAQMGGBMgAMBt5OozZgAAeLJp06bZf165cmWG+5CYAQDuBYkZAADZtGPHDqNDAAB4CKcmZhcuXNC7774rk8mkyZMnO7NqAAAcrkyZMkaHAADwEE5dWCw+Pl4bNmzQhg0bnFktAAAAALi0HCdmI0eO1I8//mh//cMPP9gXlf5f5cuX144dO/TVV1/ltFoAAAxz/PhxvfHGGxo9erTWrl0rSUpJSdHbb7+tZs2aqUGDBgoNDdVPP/1kcKQAAHeR46GMO3bsUJs2beyv+/Xrp7fffludOnVKX5m3N8M+AABu7eTJk+rZs6dSUlLk6+uryMhI/fnnn0pISNDmzZsVHBysxMREfffdd+rfv78+/fRTVatWzeiwAQAuLseJWWBgoI4ePaonn3xSkmSz2WQymXIcGAD3ZLPZlJSUaHQYLicxMTHDn5GWj4/F5a8hc+bMUcmSJfXxxx8rICBAr7zyipYtW6YqVaooMjJSfn5+kqQ//vhD3bp10wcffKDp06cbHDUAwNXlODFr3769li5dqi+++EKFCxeWJM2YMUMffPBBpseYTCZt3rw5p1UDcEFJSYkaNuxZo8NwaeHhw4wOwWXNn79UFouv0WHc0U8//aQ+ffqoWLFikqQBAwZo/fr16tq1qz0pk6SyZcuqW7du2rJli1GhAgDcSI4Ts9GjR6tChQrau3evLl68KJPJpPz588vf398B4eVN9DhkjB6He+MOPQ6AO7t06ZKKFy9uf33r53LlyqXbt1KlSrp48aLTYgMAuK8cJ2ZeXl7q2bOnevbsKUmqUaOGhg0bluEzZrg39DjcHT0OmXOlHocxwSXk40WSeIvNZpMkEuf/kZRq0/S9F4wO4575+/vr8uXL9tf58uVTpUqVVKhQoXT7xsbGqmDBgs4MDwDgphy+jtmOHTsUEBDg6NMCcEM+XiYSszT4LDxB9erVdfToUfvrwoUL64svvshw36NHj6p8+fLOCg0A4MYcnpjdPuvitWvXFBcXZ79LfLvSpUs7umqPVLBaiExmp64D7tLocciYzZqiayc3Gh0GkCf07t1bx48fv+t+ly5d0v/93/+pd+/eTogKAODuHP4Xf2JioubOnau1a9fqypUrme53+91GZM5k9iYxuw3pGACjtW7dWq1bt77rfgEBAfr222+dEBEAwBM4/C/+N954Qxs3blTr1q3VsGFDFSlSxNFVAABguBs3bujFF19UmzZt7EvGAACQXQ5PzLZv364ePXro3//+t6NPDQCAy8ifP7/++9//6tFHHzU6FACABzA7+oQmk0m1atVy9GkBAHA5DRs21IEDB4wOAwDgARyemLVq1Ur//e9/HX1aAABczoQJE7Rv3z7NnDlT586dc8g5d+3apaefflqNGzfW/fffr1atWmnKlCmKj493yPkBAK7J4UMZhw8frvDwcL322mvq2bOnSpcuLbM5ff7HAtQAAHf35JNPKjU1VQsXLtTChQvl5eUlHx+fNPuYTCbt27fvns955coV1a1bV6GhofL399fJkyc1Z84cnTx5UkuXLnX0WwAAuAiHJ2Zt2rSRJB05ckRr167NdD9mZQQAuLu2bds6fPmOzp07p3kdHBwsHx8fvfbaa4qJiVFgYKBD6wMAuAaHJ2YjRoxgjSkAQJ4wdepUp9Rza5RJcnKyU+oDADifwxOzsLAwR58SAIA8JzU1VSkpKYqKitK8efPUsmVLlS1bNkfn9PbO/qPlqakOfywdeYi3tzlH7S+naL/ICWe131xfuTghIUGS5Ovrm9tVAQDgdFevXtVHH32k//znP/rzzz8lSaVLl1aLFi00YMAAFSpUKFvnfeyxxxQTEyNJatasmWbMmJGjOM1mk4oWLZjt4xMSvHJUP/I2f/+Chv4tSPtFTjir/eZKYvbnn39qzpw52rVrly5fvixJKlq0qJo3b66RI0eqTJkyuVEtAABOFRMTo759++qPP/5Q5cqV9cADD0iSTp8+rblz52rTpk1atWqVSpYsmeVzL1y4UDdu3FBUVJTmz5+voUOH6sMPP5SXV/b+wLRabYqLu56tYyUpMTEh28cCV65ck8WSalj9tF/kRE7ar59ffnl53Vtvm8MTs1OnTqlPnz6Kj4/XI488oipVqkiSoqOjtWnTJn399ddavXq1Kleu7OiqAQBwqunTp+vvv//WBx98oObNm6cp27Vrl8LDwzVjxgxNmzYty+euUaOGJKlBgwaqU6eOOnfurO3bt+uJJ57IdrwpKVZDjgVSUqzy8jKuDdF+kRPOar8OT8xmzJghs9msDRs2KCgoKE3ZiRMnNGDAAM2YMUPz5s1zdNUAADjVnj171L9//3RJmSQ1b95coaGhioiIyHE9QUFBypcvn3777bccnwsA4Joc/hTbDz/8oNDQ0HRJmSRVr15dffv21ffff+/oagEAcLobN26oWLFimZYXL15cN27cyHE9P/30k5KTk3M8+QcAwHU5vMcsJSXljg/H5c+fXykpKY6uFgAAp6tSpYq2bNmiXr16pVtYOjk5WVu2bLEP6b9XI0eO1P3336+goCD5+vrq2LFjWrJkiYKCgtS6dWtHhg8AcCEOT8xq1qypTz/9VD169FDhwoXTlF29elVr165VrVq1HF0tAABON3jwYI0aNUo9evRQnz59VLFiRUk3J/9Ys2aNjh8/rpkzZ2bpnHXr1lVkZKQWLlwom82mMmXKqEePHho4cGC65A8A4DlyZR2zwYMHq127duratWuai9SGDRt05coVTZgwwdHVAgDgdO3atdONGzc0Y8YMvf766zKZTJIkm82mYsWKafLkyVmerOO5557Tc889lxvhAgBcmMMTs4cfflgLFy7U22+/rYULF6Ypq1mzpt555x01btzY0dUCAGCIrl276sknn9TPP/+sv/76S9LNdczuv/9+eXvn+nKhAAAPkStXjEceeUQbN27UhQsX0iy2WaJEidyo7q5++eUX9ejRQ76+vjpw4ECasqSkJM2cOVObN2/WtWvX1KBBA7322mtM5w8AuGfe3t5q0KCBGjRoYHQoAAA3lau38kqUKGFYMnaLzWbTm2++qYCAAF2/nn5hzUmTJikyMlJjx45VYGCgFixYoAEDBmjLli3pnpEDAOB/JSUlKSIiQrt27dLZs2clSWXKlFHz5s3Vo0cPWSwWgyMEALgDh0+Xv3z5cg0cODDT8kGDBmn16tWOrjZT69at0+XLl9WtW7d0ZefOndPatWv14osvqnv37mrWrJnmzZun+Ph4rVmzxmkxAgDc07lz59S5c2dNmjRJx44dU0BAgAICAnTs2DFNmjRJnTt31rlz54wOEwDgBhyemK1du/aOUwNXrVrVIYtt3ou4uDjNmDFD48aNU758+dKVf/PNN7JarWkezPb391eTJk20e/dup8QIAHBfEydO1J9//qlZs2Zpz549WrlypVauXKk9e/Zo5syZ+uuvvzRx4kSjwwQAuAGHD2X8/fff1bdv30zLK1eu7LTEbNasWapdu7Yee+wx/fLLL+nKo6OjVaxYMRUpUiTN9ipVqmjt2rU5qtvbO/s5b2qqw/Nl5CHe3uYctb+cov0iJ4xuv1n1f//3fxowYECGMy+2a9dOR44c0cqVKw2IDADgbhyemOXLl08XLlzItPz8+fMym3P/onv06FGtXbtWGzZsyHSfuLi4DJ8j8/PzU2xsbLbrNptNKlq0YLaPT0jwyvaxgL9/wTsu8p7baL/ICaPbb1YVLFhQAQEBmZYXL15cBQtm/3oAAMg7HJ6Y1atXTxs2bNCAAQNUqFChNGXx8fFav3696tWrl+XzxsfH6/z583fdr1y5csqXL58mTpyoPn363HFYZW6xWm2Ki0s/0ci9SkxMcGA0yGuuXLkmiyXVsPpvb79JqTbD4oD7uL2d5LT9+vnll5eX83rcunbtqg0bNuipp55S/vz505Rdu3ZN69evz/AZZwAA/pfDE7ORI0fq6aefVkhIiPr376+qVatKkk6ePKlly5bpwoULmjFjRpbPu3XrVr366qt33S8yMlLHjh1TdHS0ZsyYobi4OElSYmKipJu9ZBaLRRaLRX5+frp69Wq6c8TFxaUb3phVKSlWQ44FUlKs8vIyrg0lJ//zR/X0vZn3ngMZSU5ONbT93s22bdvSvK5Zs6b+85//qF27dgoJCVGFChUkSb/++qs2bdqkIkWKKCgoyIhQAQBuJld6zBYsWKAJEyborbfekslkknRz2vqyZctq/vz52VrnpUePHurRo8c97RsZGanY2Fi1bNkyXdmDDz6owYMHa8yYMapcubL+/vtvxcbGpknEoqOjWccMAJDO888/L5PJJJvtZi/f7T8vWLAg3f7nzp3T6NGj1b59e6fGCQBwP7myjlmTJk20fft2HTlyRL/99pskqXz58qpdu7Y9UctNXbp00UMPPZRm24YNGxQZGalFixapdOnSkqSmTZvKbDZr27Zt9qQvNjZW33zzjYYPH57rcQKe6Pbv+JjgEvLxyv3vPNxbUqrN3rvqjGtETixfvtzoEAAAHirXFpg2m826//77df/99+dWFZkqW7asypYtm2bb999/Ly8vLwUHB9u33Xffferevbvefvttmc1mBQYG6oMPPlDhwoXVq1cvZ4cNeBwfLxOJGTzK/970AwDAUXItMXMXr776qgoWLKgZM2bo2rVreuCBB/Thhx9mOFsjAAAAAOQGpyZm0dHRat++vcxms44cOeLMqhUWFqawsLB02318fPTyyy/r5Zdfdmo8AAD3ExoaKrPZrCVLlsjb21v9+vW76zEmk0nLli1zQnQAAHfm1MQsf/78evDBB51ZJQAADmW1/jNr5K2JP+7kXvYBAMCpiVmpUqW0YsUKZ1YJAIDD/O81jGsaAMBRnLcKJwAAHiQhIUFTpkzRzp07jQ4FAOABHN5jdvXqVcXHx6tUqVL2bTExMVqzZo2SkpLUtm1b1a1b19HVeiybNcXoEOAGaCeA8/n6+uqTTz5R1apVjQ4FAOABHJ6YTZgwQX/88YciIiIk3UzUevbsqXPnzslsNmv58uVavHhxmmnrkdbtzyNcO7nRuEDglnieBXCe2rVr68SJE0aHAQDwAA4fyrhv3z61aNHC/nrTpk06f/681qxZo++//15BQUGaP3++o6sFAMDpxo8fr8jISH366adKSaHnGgCQfQ7vMbt8+bICAwPtr3fu3KmGDRuqfv36kqSQkBDNnTvX0dV6FJPpnwV5C1YLkcmc55ebw13YrCn23tXb2w+A3DV27FiZTCZNmDBBkyZNUmBgoCwWS5p9TCaTNm/ebFCEAAB34fC/+P38/PT3339Luvlg9L59+zR06FB7uZeXlxISEhxdrccymb1JzADARfn7+8vf31+VKlUyOhQAgJtz+F/8DRo00OrVq1W5cmXt2bNHiYmJatWqlb38119/TdOjBgCAu2K6fACAozg8MRszZoyeffZZhYWFSZKeeeYZVatWTZKUmpqqrVu3qlmzZo6u1mMx215atya2YLheWrQTAAAA9+bwxKxChQraunWrTp06pUKFCqls2bL2shs3bui1115TjRo1HF2tx2JWRgBwXT/88MM97ffggw/e8zm/+OILbd68WYcPH1ZcXJwqVKig0NBQdevWjZtSAODBcuXhpXz58mWYfBUqVEitW7fOjSoBAHC60NDQe0qWjh49es/n/Oijj1SmTBmNHTtWRYsW1X//+1+99tprOnfunEaOHJmTcAEALizXZpWIiorS77//rtjY2AzLQ0JCcqtqt+fjY9H8+UuNDsPlJCYmKjx8mCRp1qz56WY+w00+PnwugLMsX7483bbU1FSdPXtWERERslqtGj16dJbOOX/+fAUEBNhfP/zww7py5Yo+/PBDDR8+XGazw1e6AQC4AIcnZr/99ptefPFFHTp0KNOFbk0mE4nZHZhMJlksvkaH4dIsFgufEQDDPfTQQ5mWde3aVX369NH333+vhx9++J7PeXtSdkvNmjUVERGh69evq1ChQtmKFQDg2hyemE2YMEEnTpzQ+PHj1ahRI/n5+Tm6CgAAXJ7ZbFaHDh30wQcf6IUXXsjRufbt26fAwECSMgDwYA5PzPbv368hQ4YoNDTU0acGAMCtxMbGKj4+Pkfn+PHHHxUZGamXX345x/F4e2d/GGRqKkMokX3e3uYctb+cov0iJ5zVfh2emBUtWlSFCxd29GkBAHA5f/75Z4bb4+Li9OOPP2rJkiVq1KhRts9/7tw5jRo1SsHBwerXr1+2zyNJZrNJRYsWzPbxCQleOaofeZu/f0H5+hr3CALtFznhrPbr8MSsV69e2rx5s/r27SsvL74EAADP1bJly0xnZbTZbKpfv74mTpyYrXPHxcVp8ODB8vf315w5c3I86YfValNc3PVsH5+YmJCj+pG3XblyTRZLqmH1036REzlpv35++eXldW//fjs8MatYsaKsVqs6d+6sbt266b777sswQWvTpo2jqwYAwKkmT56cLjEzmUzy8/NT+fLlVbVq1WydNyEhQUOGDFF8fLw++eQTh41ESUmxGnIskJJilZeXcW2I9ouccFb7dXhiNmrUKPvP06ZNy3Afk8mUpTVdAABwRV27dpUkJSUl6fDhw7p48aIeeOCBDGdWvFcpKSkKDw9XdHS0Vq1apcDAQEeFCwBwYQ5PzDJa0wUAAE+1fPlyzZ071z7Jx9KlS/Xwww/r0qVLateunV588UV17979ns83ceJEff311xo7dqyuXr2qgwcP2stq1aolHx8fR78FAIALcHhidqc1XQAA8CTr1q3T5MmT1aFDBzVp0kTjx4+3lwUEBKhx48aKjIzMUmL27bffSpKmTp2armzHjh0qW7ZszgMHALgchydmt4uKitLZs2clSWXKlMn2WHsAAFzRhx9+qFatWmnGjBm6fPlyuvLatWtrxYoVWTrnzp07HRUeAMCN5Epi9tVXX2nq1Kn2pOyWsmXLauzYsWrVqlVuVAsAgFOdOXPmjut2+vv768qVK84LCADgthyemO3atUvPP/+8SpcurVGjRqlKlSqSpFOnTikiIkJhYWFasGCBHn30UUdXDQCAU/n5+WXYU3ZLVFSUSpQo4cSIAADuyuGJ2fvvv6+goCCtWrVKBQoUsG9v1aqVnn76afXp00fz5s0jMQMAuL1HH31UERER6tOnT7qykydP6tNPP1W3bt0MiAwA4G5ytlplBo4fP66QkJA0SdktBQoUUJcuXXT8+HFHVwsAgNOFh4crNTVVHTt21KxZs2QymbRx40aNGTNG3bp1U0BAgIYPH250mAAAN+DwxMxisSg2NjbT8tjYWFksFkdXCwCA0wUGBmr9+vVq1qyZvvjiC9lsNm3atElff/21OnTooIiIiBytaQYAyDscPpQxODhYy5cvV7NmzdSgQYM0ZT/99JNWrFihJk2aOLpaAAAMUaxYMb311lt66623dOnSJVmtVgUEBMhsdvi9TwCAB3N4Yvbiiy+qV69e6tOnj+rWratKlSpJkk6fPq1Dhw6pWLFiGjNmjKOrBQDAcPSOAQCyy+G388qVK6fNmzcrNDRUsbGxioyMVGRkpGJjY9WvXz9t2rSJxTEBAAAA4DYO7zFLSUmRxWLR+PHjNX78+HTlV69eVUpKiry9c3VtawAAAABwGw7vMZs0aZJ69eqVaXnv3r01depUR1cLAAAAAG7L4d1We/bsUUhISKblbdu21ebNmx1dLQAXlJRqMzoEl2Kz3fw8TCaTwZG4FtoJAAC5kJidP39egYGBmZaXLFlSMTExjq4WgAuavveC0SEAAAC4BYcPZfT399fp06czLT916pQKFSrk6GoBAAAAwG05vMesWbNmWrNmjTp16qRatWqlKTt8+LAiIiL0xBNPOLpaAC7Cx8ei+fOXGh2Gy0lMTFR4+DBJ0qxZ82WxWAyOyDX5+PC5uAObNcXoEFwKw5QzRjsBssbhidkLL7ygPXv2qEePHmrZsqWqVq0qSTp58qS+/vprBQQE6IUXXnB0tQBchMlkksXia3QYLs1isfAZwa1dO7nR6BAAwOM4PDELDAzUunXrNGPGDO3YsUPbt2+XJBUqVEidOnXSqFGj7vgMGgAAAADkNbmymFjJkiU1bdo02Ww2Xbp0SZIUEBBAFz8AAG6KYcoZY5jyvWGYMnB3ubrKs8lkUrFixXKzCgAA4AQMU747hikDyIlcTcwAAAAAV8LaibgXRrQTEjMAAAB4tFszZ0qssYmsu7395CaHr2MGAAAAAMgaeswAAADg0W6fgG5McAn5eDEhHe4sKdVm71111gSGJGYAAADIM3y8TCRmcEkMZQQAAAAAg5GYAQAAAIDBGMoIAICLOXPmjJYsWaKffvpJJ0+eVOXKlfX5558bHRYAIBeRmAEA4GJOnjypXbt2qV69erJarU6bqhkAYByGMgIA4GJatmypXbt26b333lPt2rWNDgcA4AQkZgAAuBizmcszAOQ1DGUEACCP8PYm4XO01NR/PlNvbzOfsYu6/fcEZJWzvtskZgAA5AFms0lFixY0OgyPk5DgZf/Z37+gfH19DYwGmbn99wRklbO+2yRmAADkAVarTXFx140Ow+MkJibYf75y5ZosllQDo0Fmbv89AVmVk++2n19+eXndW28biRkAAHlESorV6BA8zu2faUqKVV5efMauiLaPnHDWd9tjB9yOHTtWQUFB6f7bvXt3mv2SkpI0bdo0NWnSRPXr19czzzyj6Ohog6IGAAAAkBd5dI9ZuXLlNH369DTbqlSpkub1pEmTFBkZqbFjxyowMFALFizQgAEDtGXLFhUuXNiZ4QIAACCXJaWyLuDtbq2TaDKZDI7EtRjRTjw6MfP19VX9+vUzLT937pzWrl2r119/Xd27d5ck1alTR4899pjWrFmjwYMHOylSAAD+cePGDe3atUuSdPbsWV29elVbt26VJD300EMKCAgwMjzArU3fe8HoEIAMeXRidjfffPONrFarnnjiCfs2f39/NWnSRLt37yYxAwAY4uLFi3rhhRfSbLv1evny5QoODjYiLABALvLoxOzMmTNq2LChEhMTVb16dQ0fPlytW7e2l0dHR6tYsWIqUqRImuOqVKmitWvX5qhu1jFxPNaKgTuj/SIrypYtq+PHjxsdBuAxfHwsmj9/qdFhuJzExESFhw+TJM2aNV8Wi8XgiFyTj49zPhePTcxq1qypOnXqqGrVqoqPj9fHH3+sESNGaPbs2fYesri4uAyfI/Pz81NsbGy262atmNzBWjFwZ7RfADCOyWSSxcK/u3disVj4jAzmNolZfHy8zp8/f9f9ypUrJx8fH/Xv3z/N9pYtW6pXr15677330gxdzA2sFZM7WCsG7oz26xxZWS8GAABX4jaJ2datW/Xqq6/edb/IyMh0My9KktlsVps2bfTOO+8oISFBvr6+8vPz09WrV9PtGxcXl254Y1axXobjsVYM3BntFwAA3InbJGY9evRQjx49HHrOypUr6++//1ZsbGyaRCw6OlqVK1d2aF0AAAAAkBm3Scxyymq1auvWrapWrZr92Y6mTZvKbDZr27Zt9qQvNjZW33zzjYYPH25kuC7FZrMpKSnR6DCUmJiY4c9G8/GxsPaHC6P93hntF+6K73bm+F67Ptpv5vJy+zXZbq0q50HOnj2rsWPHqkOHDqpQoYJiY2P18ccfa+/evZozZ44ef/xx+74TJkzQF198YV9g+oMPPtCZM2dytMB0aqpVly5dc9TbMZTNZtOUKRMVFXXC6FBcVtWq1TVu3Ot59h8RV0b7vTtPa78BAQV5xiwTXJvyDk/7Xnsa2u+deVr7zcp1ySN7zAoWLKhChQpp/vz5unjxovLly6f7779fixYtUrNmzdLs++qrr6pgwYKaMWOGrl27pgceeEAffvhhtpMyAAAAAMgqj+wxM5on3ZWUXKe7XboZiySXuouSl7vc3QHt9848rf3SY5Y5rk25x9W+2572vfZEtN/MeVr7zfM9ZnAs1v6AO6P9Ap6J7zbcGe0XGeG2IgAAAAAYjMQMAAAAAAxGYgYAAAAABiMxAwAAAACDMStjLrDZbLJa+VgBwNnMZpNHzeblSFybAMD5snJdIjEDAAAAAIMxlBEAAAAADEZiBgAAAAAGIzEDAAAAAIORmAEAAACAwUjMAAAAAMBgJGYAAAAAYDASMwAAAAAwGIkZAAAAABiMxAwAAAAADEZiBgAAAAAGIzEDAAAAAIORmAEAAACAwUjMAAAAAMBgJGYAAAAAYDASMwAAAAAwGIkZAAAAABiMxAwuZ+zYserYsaP27t2rkJAQ1a9fX927d9cvv/xi3ycxMVFTpkxR06ZNVadOHXXu3Fnbt283MGrkVTt37lRQUJB+/fXXNNtjY2NVt25drVq1SpJ04MAB9evXT/Xr11fDhg01evRoXbx4Mc0xCxcu1OOPP646deqocePGGjBggH7//XdnvRUAd8C1Ce6C65L7IjGDS7pw4YImTZqkgQMHatasWUpMTNTIkSOVnJwsSRozZow++eQTDRo0SPPmzVPVqlUVFhamHTt2GBw58prmzZsrMDBQ69atS7P9888/lyR16tRJBw4cUGhoqAoXLqyZM2fqzTff1M8//6zhw4fb99+4caNmz56t7t27a/HixZo0aZJq1qypa9euOfX9AMgc1ya4A65L7svb6ACAjMTGxmrlypWqVq2aJCl//vzq16+ffvrpJxUqVEjbtm3TxIkT1atXL0nSo48+qrNnz2revHlq1aqVkaEjj/Hy8lLXrl21bt06hYeHy8vLS5K0bt06Pf744/Lz89OMGTN0//33a+7cuTKZTJKk6tWrq2PHjtq1a5eaN2+uQ4cOKSgoSEOGDLGfu3Xr1oa8JwAZ49oEd8B1yX3RYwaXVLJkSfuFT5KqVq0qSYqJidG+ffskSU888USaY9q1a6cjR47o+vXrzgsUkNS9e3dduHBBe/bskSQdO3ZMhw8fVvfu3XXjxg3t379fTzzxhFJTU5WSkqKUlBRVrFhRpUqV0s8//yxJqlWrlo4cOaIpU6boxx9/tN+BB+A6uDbBXXBdck/0mMEl+fn5pXmdL18+STfH78fGxipfvnzy9/dPs0/x4sVls9kUHx+vAgUKOCtUQGXLllWTJk20du1atWjRQuvWrVPZsmXVuHFjnT9/XqmpqZoyZYqmTJmS7ti//vpLktS1a1ddu3ZNERER+uijj1S4cGGFhIRozJgx8vX1dfZbApABrk1wF1yX3BOJGdxOkSJFlJycrNjYWBUpUsS+/e+//5bJZFLhwoUNjA55VY8ePTRmzBjFxMTos88+U2hoqL09mkwmDRkyJMMhIEWLFpUkmc1m9e/fX/3791dMTIy2bNmiGTNmqGjRohoxYoSz3w6ALOLaBFfDdcn9kJjB7TRs2FCStHXrVvXs2dO+fevWrapVqxZ3JGGIVq1ayc/PT6NHj1ZsbKy6du0qSSpQoIDq16+v6Oho1alT557OFRgYqGeffVaff/65oqOjczNsAA7CtQmuhuuS+yExg9upUaOG2rRpo6lTpyohIUGVKlXS5s2bdeDAAb3//vtGh4c8Kl++fAoJCdGSJUvUtGlTlSpVyl720ksvqX///goPD1eHDh3k5+enc+fO6b///a+6du2q4OBgTZgwQX5+fqpfv778/Py0f/9+HTt2TL179zbwXQG4V1yb4Gq4LrkfEjO4pXfeeUfvvvuuFi1apCtXrqhy5cp677331LJlS6NDQx72+OOPa8mSJerWrVua7Q888IBWr16tOXPmaNy4cUpOTtZ9992nxo0bq0KFCpKkBg0aKCIiQp9++qlu3LihcuXKady4cerRo4cRbwVANnBtgqvhuuReTDabzWZ0EADgCWbPnq3Vq1drz5498vHxMTocAEAex3XJvdBjBgA5FB0drdOnT2vlypXq06cPFz8AgKG4LrkneswAIIdCQ0N18OBBNWvWTNOnT+chfwCAobguuScSMwAAAAAwmNnoAAAAAAAgryMxAwAAAACDkZgBAAAAgMFIzAAAAADAYCRmAAAAAGAwEjMAAAAAMBgLTOcCm80mq5VVCADA2cxmk0wmk9Fh3NGOHTu0YMECRUVFqWDBgmrYsKHGjBmjcuXKpdnv008/1eLFi/Xnn3+qUqVKGjVqlB577LFs18u1CQCcLyvXJdYxywWpqVZdunTN6DAAIM8JCCgoLy/XHQyyd+9eDRgwQCEhIerUqZOuXLmi2bNny2q16rPPPpOvr68kacuWLRo9erSGDh2qxo0bKzIyUuvWrdOqVatUv379bNXNtQkAnC8r1yV6zAAAcJItW7aodOnSmjx5sv0OakBAgPr3769ffvlFjRo1kiS999576tChg8LDwyVJjRs31okTJzRv3jwtWrTIqPABALmIxAwAACdJSUlRwYIF0wxrKVy4sKSbQw0l6ffff9evv/6qF198Mc2x7du319tvv62kpCT5+Pg4L2jc1aBBobJaU2U2e2nx4hVGhwNkybPP9rH/vHTpagMjgeuO9wAAwMN07dpVp06d0qpVqxQfH6/ff/9d7777rmrVqqUHHnhAkhQdHS1JqlSpUppjq1SpouTkZP3+++9OjxuZ+7//+1ZWa6okyWpN1f/937cGRwTcu9uTsoxew7noMQMAwEkaNWqkuXPnavTo0fr3v/8tSapZs6YWL14sLy8vSVJsbKwkyc/PL82xt17fKs8Ob2/uxzrawoXz0r1u2rSZQdEAOce/E8YhMXNBNptNSUmJRodhdzOeJKPDcFk+Pj4uNQucj4/FpeIxmit9n24NVXOl3w/txbn279+vl156SU899ZRatGihK1eu6P3339dzzz2n1atX2yf/yA1ms0lFixbMtfPnRaNGjcpw+7///apmzpzp5GiArOnUqVOG2/v166XPPvvMydFAIjFzSUlJiRo27Fmjw4Cbmj9/qSyW3Pvjzp3YbDZNmTJRUVEnjA7FZVWtWl3jxr1OcuYkkyZNUuPGjTV27Fj7tvr166tFixbatGmTevbsqSJFikiS4uPjVaJECft+cXFxkmQvzyqr1aa4uOs5iB63u379uqKiojIsi4qK0tmzF1SgQAEnRwXcm88///yO5StWfKKOHTs6KRrP5ueXn1kZAQBwNadOnVKrVq3SbLvvvvtUtGhR/fbbb5KkypUrS7r5rNmtn2+9zpcvX7r1zrIiJcWa7WOR1sSJr961/K23pjspGiBrIiJW3rX8iSfaOyka3EJi5uIKVguRyWzsr8lms0m2VENjcGkmL8N7G2zWFF07udHQGFyRyWTSuHGvu8RQxsTERIWHD5MkzZo1XxaLxeCIbmIoo3OVLl1aR44cSbPt7Nmzunz5ssqUKSNJKleunCpWrKitW7eqdevW9v0iIyP18MMPMyOji3jllX9r5MhBdywHMmP0MPsOHbpqy5b1dyxPTExwYkT/yMvXJRIzF2cyexuemN38auQzNAYgu0wmk8sN7bRYLC4XE5yjV69emjx5siZNmqSWLVvqypUrmj9/vooVK6Z27drZ9wsLC9OYMWNUvnx5BQcHKzIyUocOHdLKlXe+yw3nudvNFVe5+QLX4w7D7LdsWX/HxC035eUh9iRmAAA4Sb9+/eTj46OPP/5Y69atU8GCBVW/fn3NmjVLRYsWte/XsWNH3bhxQ4sWLdLChQtVqVIlzZ07Vw0aNDAwetxu166ddy1v2fJxJ0UDwBOQmAEA4CQmk0m9e/dW796977pvjx491KNHDydEhexo3rylVq9eJqs1/XN7ZrNZzZu3NCAquANXGmaf0WRz8+cvNSCSfzCUEQAAAPfMbDarVKnSOnv2j3RlpUqVkdnMWlDInKsMs58/f2ma5Gzp0tUGRgP+1QAAAMiiv/76M8OkTJLOnv1df/31p5MjAnLG6J4ykJgBAABkWWDgffLy8sqwzMvLS4GB9zk5IgDujsQMAAAgi37++Selpma8lExqaqp+/vknJ0cEwN2RmAEAAGRR3br15eub8TNCvr75VbdufecGBMDtkZgBAAA4lM3oAAC4IRIzAACALDp06KASEhIyLEtISNChQwedGxAAt0diBgAAkEV169aXj49PhmU+PhaGMgLIMpdKzNavX6+goKB0/02fPl2SdPXqVc2ZM0fdu3dXo0aN9Mgjj2jo0KE6fvx4unPFx8dr/Pjxeuihh9SgQQM9//zzOn/+fLr99u/fr549e6pu3bp67LHHtHDhQtlsDEEAAACZs9lsSklJybAsJSWZvyUAZJlLLjC9ePFiFS5c2P46MDBQkvTnn3/qk08+Ubdu3RQeHq7ExEQtXbpUPXv21Lp161SlShX7MeHh4YqKitIbb7whi8WiWbNmafDgwVq3bp28vW++7TNnzmjgwIFq0qSJwsPDdfz4cU2fPl1eXl4aOHCgc980AABwG7t27ZTVas2wzGq1ateunWrZ8nEnRwXAnblkYla7dm0FBASk2162bFlt375d+fPnt29r3LixWrZsqdWrV+u1116TJB04cEDffPONlixZoqZNm0qSKlWqpPbt22vbtm1q3769JGnJkiUqWrSo3n33Xfn4+Ojhhx/WpUuXtGDBAoWGhmY6RAEAAORtTZo8qpUrP7xjOQBkhUsNZbybAgUKpEnKJKlgwYIqX758mmGKu3fvlp+fn5o0aWLfVrlyZdWsWVO7d+9Os1+rVq3SJGDt27dXXFycDhw4kIvvBAAAuLPIyM05KgeA/+WSPWYdO3bU5cuXVbp0aT311FMaNGiQvLy8Mtw3Li5OJ0+e1COPPGLfFh0drUqVKslkMqXZt3LlyoqOjpYkXb9+XX/99ZcqV66cbh+TyaTo6GgFBwdn+z14e2c/501Ndat8GS7G29uco/aH3HH795rfEeD+OnXqos8+23DHcgDICpdKzEqUKKGwsDDVq1dPJpNJO3fu1KxZsxQTE6MJEyZkeMw777wjk8mk3r1727fFxcWleUbtliJFiuiXX36RdHNyEEny8/NLs4+Pj4/y58+v2NjYbL8Ps9mkokULZvv4hISMk1DgXvj7F8x00VMY5/bvNb8jwP3dbXIPJv8AkFUulZg1a9ZMzZo1s79u2rSpLBaLli1bpqFDh6pkyZJp9l+3bp0iIiI0depU3Xfffc4ON1NWq01xcdezfXxiYsbrogD34sqVa7JYUo0OA//j9u81v6Pc4+eXX15e9EYi93388fK7lvfrx0RiAO6dSyVmGWnXrp2WLl2qo0ePpknMdu3apQkTJmj48OHq0iXtcAE/Pz+dO3cu3bliY2NVpEgRSbL3qN3qObslKSlJN27csO+XXSkpGc/UlNvHAikpVnl50YZcze3fa35HgPvr3buf/vOfHXcsB4CscPnELCMHDx7UCy+8oJCQEL3wwgvpyitXrqzvvvtONpstzXNmp0+fVvXq1SXdnEikVKlS9mfObt/HZrOle/bMKDZrxmukALdzpXZis9mUlJRodBguJzExMcOfkZaPjyXd88GAK/Ly8pK3t3eGa5l5e3tn+mw8AGTG5ROzyMhIeXl5qVatWpKkqKgoDRkyRI0bN9bEiRMzPObRRx/V+++/r++++84+Kcjp06d15MgRDRo0KM1+O3bs0Isvvqh8+fLZ6/Pz81ODBg1y+Z1l7vZx6ddObjQsDrgno59rSEpK1LBhzxoag6sLDx9mdAgua/78pbJYeP4Oru/QoYN3WGA6RYcOHVT9+g84OSoA7sylErOBAwcqODhYQUFBkqQdO3YoIiJC/fr1U4kSJXTx4kUNHDhQFotF/fv3t0/kIUmFChVS1apVJUkNGjRQ06ZNNX78eL388suyWCyaOXOmgoKC1KZNmzT1ffbZZxo9erR69+6tEydOaMmSJRo1ahRrmAEAgEzVqFErR+UA8L9cKjGrVKmS1q1bp3PnzslqtapixYoaP368QkNDJd3sLbv17NiAAQPSHPvQQw9pxYoV9tezZs3SlClTNGHCBKWkpKhp06Z69dVX5e39z1uuUKGClixZoqlTp+q5555TQECAnn/+eT37rLF3+28fxlOwWohMZpf6NcEF2awp9t5VVxoGNia4hHy8XCceo93qzXSl35ErSEq1afreC0aHAWTJhx8uvGv5sGHPOykaAJ7Apf7if/XVV+9YHhwcrOPHj9/TuQoXLqzJkydr8uTJd9zvgQceUERExD3H6GwmszeJGdyWj5eJxCwNPgvAUYx+nvXppwfohx/+747lRs2yzLOagHviL34AAOBWbDabpkyZqKioE0aHkqkXXhhqWN1Vq1bXuHGvk5wBbobFXgAAAADAYPSYAQAAt2IymTRu3OsusTTHsWNHNXv2O/bXL744XpUrVzUwIoYyAu6KxAwAALgdk8nkEksr1KhR0/6zv39R1ax5v4HRAHBnDGUEAABwgClTZhgdAgA3RmIGAAAAAAYjMQMAAAAAg5GYAQAAAIDBSMwAAAAAwGAkZgAAAABgsHueLr9GjRrZWhPj6NGjWT4GAAAAAPKSe07MRowYkS4x2759u6KiotS0aVNVqlRJkhQdHa1vv/1W1apVU+vWrR0bLQAAAAB4oHtOzMLCwtK8/uSTT3Tx4kV99tlnqly5cpqyU6dOqX///ipZsqRjogQAAAAAD5btZ8yWLFmip59+Ol1SJklVqlRR3759tXjx4hwFBwAAAAB5QbYTs3PnzsnbO/MON29vb507dy67pwcAAACAPCPbiVm1atW0evVqxcTEpCs7d+6cPv74Y1WvXj1HwQEAAABAXnDPz5j9r3HjxmnQoEFq27atWrdurQoVKkiSfv31V+3YsUM2m01vv/22wwIFAAAAAE+V7cSsUaNGioiI0OzZs/XVV18pISFBkuTr66umTZsqLCxMQUFBDgsUAAAAADxVthMzSapevbrmzZsnq9WqS5cuSZICAgJkNrNuNQAAAADcqxwlZreYzWZZLBYVKFCApAwAAAAAsihHWdTPP/+sgQMHql69egoODtb3338vSbp06ZKGDRumvXv3OiRIAAA8yYYNGxQSEqI6deooODhYgwYNsj8SIEk7d+7Uk08+qTp16qht27Zat26dgdECAJwh24nZ/v371adPH505c0ZPPvmkrFarvSwgIEBXr17VJ5984pAgAQDwFPPnz9ebb76p9u3ba8mSJfr3v/+tsmXLKjU1VZL0448/auTIkapfv74WLVqkdu3a6ZVXXtHWrVsNjhwAkJuyPZRx5syZqlKliiIiInT16lV9+umnacqDg4O1YcOGHAcIAICniI6O1ty5c/X++++refPm9u1t27a1/zx//nzVrVtX//73vyVJjRs31u+//6733ntPTzzxhNNjBgA4R7Z7zH7++Wd17dpVPj4+MplM6coDAwP1999/5yg4AAA8yfr161W2bNk0SdntkpKStHfv3nQJWPv27XXq1Cn98ccfzggTAGCAbPeYeXt7pxm++L9iYmJUoECB7J4eAACP89NPP6l69ep6//33tWLFCsXHx+v+++/XuHHjVK9ePf32229KTk5W5cqV0xxXpUoVSTd73MqWLZvt+r29maDL0VJT//lMvb3NfMZwK7Rf15LtxKxevXr68ssvNWDAgHRl169f1/r16/Xggw/mJDYAADzKhQsX9Msvv+jEiRN6/fXXlT9/fi1YsEDPPvustm3bptjYWEmSn59fmuNuvb5Vnh1ms0lFixbMfvDIUEKCl/1nf/+C8vX1NTAaIGtov64l24nZ888/r6efflrPPfecOnToIEk6fvy4/vjjDy1ZskSXLl3S8OHDHRYoAADuzmaz6fr165o9e7Zq1Kgh6eaNzpYtW2rlypVq2rRprtVttdoUF3c9186fVyUm/jOb5pUr12SxpBoYDZA1tN/c5+eXX15e99YTmaMes4ULF+qNN97Qyy+/LEmaOnWqJKl8+fJauHCh/aIDAABu9nz5+/unuT76+/urVq1aioqKst/ojI+PT3NcXFycJKlIkSI5qj8lJfNHEJA9t3+mKSlWeXnxGcN90H5dS44WmH744Yf15Zdf6ujRo/r1119ls9lUrlw53X///RlOCAIAQF5WtWpV/fbbbxmWJSYmqnz58sqXL5+io6PVrFkze1l0dLQkpXv2DADgObL9hN/58+ftP9esWVPt2rVT+/btVadOHZIyAAAy8Nhjj+nKlSs6evSofdvly5d1+PBh1a5dWz4+PgoODtaXX36Z5rjIyEhVqVIlRxN/AABcW7YTsxYtWujZZ5/Vxo0bdf06Y9YBALib1q1bq06dOnr++ecVGRmpHTt2aOjQofLx8VGfPn0kScOGDdPBgwf1xhtvaO/evXrvvff0+eefKywszODoAQC5KduJ2fPPP6/z589r7NixatKkicaMGaPdu3ffcQp9AADyMrPZrIULF6p+/fqaMGGC/vWvf6lQoUJatWqVSpQoIUlq1KiR5syZo3379mngwIH6/PPPNWnSJLVr187g6AEAuSnbz5gNHTpUQ4cO1ZEjR/TZZ59py5Yt+vzzz1WsWDF16NBBnTp1Up06dRwZKwAAbi8gIEDvvPPOHfdp1aqVWrVq5aSIAACuIEeTf0hSrVq1VKtWLb300kv6v//7P3322Wdav369VqxYoUqVKunJJ5/Uk08+qdKlSzsiXgAAAADwOA5b3ttkMqlhw4Zq3ry56tWrJ5vNpjNnzmju3Llq3bq1fegjAAAAACCtHPeYSbL3lG3btk1Xr15V9erV9fLLL6tTp07y8vLS+vXr9cEHH+ill17SRx995IgqAQAAAMBjZDsxO3bsmDZv3qwtW7bo/PnzKl68uLp3766QkBAFBQWl2XfgwIGyWCyaNm1ajgMGAAAAAE+T7cQsJCREvr6+atWqlUJCQtSkSROZzZmPjKxatarq16+f3eoAAAAAwGNlOzGbPHmy2rZtq4IFC97T/o0bN1bjxo2zWx0AAAAAeKxsJ2Zdu3Z1ZBwAAAAAkGflaPKPq1ev6qOPPtJ//vMf/fnnn5Kk0qVLq0WLFhowYIAKFSrkkCABAAAAwJNle7r8mJgYhYSEaO7cubp+/boeeOABPfDAA7px44bmzp2rLl26MD0+AAAAANyDbPeYTZ8+XX///bc++OADNW/ePE3Zrl27FB4erhkzZjATIwAAAADcRbZ7zPbs2aP+/funS8okqXnz5goNDdWuXbtyFBwAAAAA5AXZTsxu3LihYsWKZVpevHhx3bhxI7unBwAAAIA8I9uJWZUqVbRlyxYlJSWlK0tOTtaWLVtUpUqVHAUHAICR/vzzT02YMEFt27bVQw89pB9++EGSdOnSJU2aNElHjhwxOEIAgKfI9jNmgwcP1qhRo9SjRw/16dNHFStWlCSdPn1aa9as0fHjxzVz5kxHxQkAgFNFRUWpb9++slqtqlu3rn777TelpKRIkgICArRv3z5dv35dkydPNjhSAIAnyHZi1q5dO924cUMzZszQ66+/LpPJJEmy2WwqVqyYJk+erCeeeMJhgQIA4EzvvPOOChcurIiICEnSI488kqa8efPm+uKLL4wIDQDggXK0jlnXrl315JNP6pdffkmzjtn9998vb+8cnRoAAEP98MMPGjFihAICAnT58uV05aVLl1ZMTIwBkQEAPFGOsydvb2/Vr19f9evXz3Ew69ev17hx49JtHzx4sMaMGSNJioyM1BdffKGffvpJMTExeumllzRw4MB0x8THx2vKlCn66quvlJycrGbNmunVV19VyZIl0+y3f/9+TZs2TUePHlWxYsXUu3dvDR482N4DCADIm2w2m3x9fTMtv3Tpknx8fJwYEYDsstlsSkpKNDoMl5OYmJjhz0jLx8filNzgnhOzWw88Z9WDDz6Y5WMWL16swoUL218HBgbaf966dat+//13tWjRQp988kmm5wgPD1dUVJTeeOMNWSwWzZo1S4MHD9a6devsvXlnzpzRwIED1aRJE4WHh+v48eOaPn26vLy8Mkz2AAB5R61atbRr1y717ds3XVlKSoq2bNmievXqGRAZgKxKSkrUsGHPGh2GSwsPH2Z0CC5r/vylslgyv1HnKPecmIWGhmYpU7TZbDKZTDp69GiWg6pdu7YCAgIyLJs1a5bM5puTSWaWmB04cEDffPONlixZoqZNm0qSKlWqpPbt22vbtm1q3769JGnJkiUqWrSo3n33Xfn4+Ojhhx/WpUuXtGDBAoWGhnInFADysOeee05Dhw7V66+/rg4dOkiSLl68qP/+979asGCBoqOjNWHCBIOjBAB4intOzJYvX56bcdyzW0nZnezevVt+fn5q0qSJfVvlypVVs2ZN7d69256Y7d69W48//niaBKx9+/b64IMPdODAAQUHBzv+DQAA3ELz5s01ZcoUTZ482T4ByIsvviibzaZChQpp2rRp2RoVAsBYY4JLyMeLR1ZusdlsksRjPP8jKdWm6XsvOLXOe07MHnroodyMI42OHTvq8uXLKl26tJ566ikNGjRIXl5e93x8dHS0KlWqlK6BVa5cWdHR0ZKk69ev66+//lLlypXT7WMymRQdHZ2jxMzbO9tLxCk19Z9jbdaUbJ/HE/GPR8Zubyfe3uYctb+cur39JqXaDIsD7uP2dmJ0+/1fISEhatOmjb799ludOXNGVqtV5cuXV9OmTVWoUCGjwwOQDT5eJhKzNPgsXIVDpk68ePGizp49K0kqU6aMihUrlq3zlChRQmFhYapXr55MJpN27typWbNmKSYmJkvDReLi4tI8o3ZLkSJF9Msvv0i6OTmIJPn5+aXZx8fHR/nz51dsbGy23oMkmc0mFS1aMNvHJyT8k4ReO7kx2+dB3uTvX/COExbkths3/vmj2tl3muD+ihQpoPz58xsdRhoFChTQ448/bnQYAAAPl6PE7LvvvtM777yT7jmymjVrasyYMenWfLmbZs2aqVmzZvbXTZs2lcVi0bJlyzR06NB0Myq6KqvVpri469k+PjExwYHRIK+5cuWaLJZUw+pPSLhhWN1wf1euXFNCgjXbx/v55ZeXl2N63G4tA5MZk8kki8WiokWL0osPAMixbCdm27dv1wsvvKBixYpp0KBBqlixoiTp9OnT2rRpkwYPHqxZs2bl+C5ju3bttHTpUh09evSeEzM/Pz+dO3cu3fbY2FgVKVJEkuw9ard6zm5JSkrSjRs37PtlV0pK9v+wMJvzaf78pTmq3xMlJibaZwyaNWu+LBaLwRG5JrM5X47aX06l3jYsjXH8uBe3j+NPTbUZ2n5v17Jly3tKuCwWixo2bKjhw4erYcOGTogMAOCJsp2YzZo1S9WqVdOqVavSjbMfOnSoevfu7ZDELDsqV66s7777zj4z5C2nT59W9erVJd0cmlKqVCn7M2e372Oz2dI9e+ZMN+/CGjcUzR1YLBY+IzfAOH64s7feeksrVqzQX3/9pU6dOqlChQqSbi618tlnn6lMmTLq2rWrzpw5o82bN6t///5avHixGjdubHDkuYN1oDLGOlD3xlnrQAHuLNuJ2e+//67Ro0dn+PBzoUKF1L17d7377rs5Ck66uaC0l5eXatWqdc/HPProo3r//ff13Xff2YdTnj59WkeOHNGgQYPS7Ldjxw69+OKLypcvn70+Pz8/NWjQIMexAwDc1/nz55WcnKzt27enex555MiR6tOnjxISEvTKK69o+PDh6tatm+bNm+exiRnrQN0d60BlzlnrQAHuLNuJWeXKlXXp0qVMyy9evGgf3nivBg4cqODgYAUFBUmSduzYoYiICPXr108lSpSQJEVFRSkqKsp+zIkTJ7R161blz59fzZs3lyQ1aNBATZs21fjx4/Xyyy/LYrFo5syZCgoKUps2bdLU99lnn2n06NHq3bu3Tpw4oSVLlmjUqFGsYQYAedyaNWv0zDPPpEvKJMnf3189evTQ8uXLNWjQIBUtWlRdu3bVkiVLDIgUAOAJsp2Yvfjii/rXv/6lOnXqqHXr1mnKtm/frk8++UQzZ87M0jkrVaqkdevW6dy5c7JarapYsaLGjx+v0NBQ+z5ffPGF5s6da3+9ceNGbdy4UWXKlNHOnTvt22fNmqUpU6ZowoQJSklJUdOmTfXqq6/K2/uft1yhQgUtWbJEU6dO1XPPPaeAgAA9//zzevZZ7ggCQF535coV3biR+WQ2169fT3OD8tYNxLygYLUQmcwOmdjZI7CUS8Zs1hRmlwayINv/qq5YsUJFixZVWFiYSpYsqfLly0uSfvvtN50/f14VK1bU8uXL0yxMbTKZNH/+/EzP+eqrr9613rCwMIWFhd11v8KFC2vy5MmaPHnyHfd74IEH7AuHAgBwS506dbR8+XK1bNnSPpLjlmPHjmnlypWqW7eufdupU6cUGBjo7DANYTJ7k5jdhnQMgCNk+1/VEydOSJJKlSolSfZ1zLy8vFSqVCklJiba97mFO0kAAHfx6quvqn///urSpYvq16+fZvKPgwcPqlChQnrllVck3Zz04fvvv1fbtm2NDBkA4MaynZjdPmwQAABPU6NGDW3evFmLFi3Snj179PPPP0uSSpcurT59+mjQoEG67777JN2cKXbjxo0GRgsAcHeMQwAAIBOBgYH3NMweAICcynFilpycrJiYGMXFxdkffr1d7dq1c1oFAAAAAHi0bCdmcXFxmjZtmj777DMlJyenK7+1uPPRo0dzFCAAAEZJTEzUl19+qSNHjig+Pl5WqzVNuclkuuskUwAA3ItsJ2Zjx47V119/rfbt26tevXoqXLiwI+MCAMBQZ8+eVb9+/XT27Fn5+fkpPj5eRYoUUXx8vFJTU1W0aFEVKFDA6DABAB4i24nZt99+q9DQUI0fP96R8QAA4BLefvttXb16VRERESpbtqweeeQRzZw5Uw0bNtTy5cu1atUqFpQGADiMObsH+vv726cOBgDA0/zf//2fevfurbp168ps/udy6ePjo0GDBqlx48YMYwQAOEy2E7OnnnpKW7ZsSTfeHgAAT5CQkKAyZcpIkgoVKiSTyaT4+Hh7eYMGDbRv3z6jwgMAeJhsD2UcMWKEkpKS1K1bN3Xu3FmBgYHy8vJKt1+bNm1yFCAAAEYoVaqUYmJiJEne3t4KDAzUwYMH7de1qKgoWSwWI0MEAHiQbCdmMTEx2rt3r44ePZrpzIvMyggAcFeNGzfWjh07NHLkSElSly5dtHDhQsXFxclqtWrz5s3q3LmzwVECADxFthOz8ePH6/DhwxoyZIjq1q3LrIwAAI/y3HPP6eeff1ZSUpJ8fHw0dOhQnT9/Xl9++aXMZrM6duyocePGZfv8165dU7t27RQTE6O1a9eqTp069rJPP/1Uixcv1p9//qlKlSpp1KhReuyxxxzxtgAALirbidm+ffs0ePBgPf/8846MBwAAl1C6dGmVLl3a/tpiseitt97SW2+95ZDzv//++0pNTU23fcuWLXrttdc0dOhQNW7cWJGRkRo5cqRWrVql+vXrO6RuAIDryfbkH8WLF1eRIkUcGQsAAHnCqVOntHr1aoWFhaUre++999ShQweFh4ercePG+ve//606depo3rx5BkQKAHCWbCdmzzzzjNauXatr1645Mh4AADzepEmT1KtXL1WqVCnN9t9//12//vqr2rVrl2Z7+/bt9d133ykpKcmZYQIAnCjbQxmTkpLk7e2tNm3aqF27drrvvvvSzcpoMpk0YMCAnMYIAIDH2Lp1q06cOKE5c+bo8OHDacqio6MlKV3CVqVKFSUnJ+v3339XlSpVsl23t3e278cqNTX7xwLe3uYctb+cov0iJ5zVfrOdmE2bNs3+88qVKzPch8QMAIB/3LhxQ1OnTtWoUaNUqFChdOWxsbGSJD8/vzTbb72+VZ4dZrNJRYsWzPbxCQnpl8QB7pW/f0H5+voaVj/tFznhrPab7cRsx44djowDAACPN3/+fBUrVkzdunVzet1Wq01xcdezfXxiYoIDo0Fec+XKNVks6Se7cRbaL3IiJ+3Xzy+/vLzurbct24lZmTJlsnsoAAB5ztmzZ7V06VLNmzdP8fHxkqTr16/b/3/t2jX7pFrx8fEqUaKE/di4uDhJyvGkWykpVkOOBVJSrPLyMq4N0X6RE85qv9lOzG6JiYnRDz/8oIsXL6pt27a67777lJqaqvj4eBUuXDjdc2cAAORFf/zxh5KTk/Xcc8+lK+vXr5/q1aunGTNmSLr5rFnlypXt5dHR0cqXL5/KlSvntHjvxGZNMToEuAHaCZA12U7MbDabpk6dqlWrViklJUUmk0nVq1fXfffdp+vXr6tly5Z6/vnnecYMAABJNWvW1PLly9NsO3r0qKZMmaKJEyeqTp06KleunCpWrKitW7eqdevW9v0iIyP18MMPy8fHx9lh29lsNvvP105uNCwOuKfb2w+AjGU7MVu8eLGWL1+uwYMH6+GHH9YzzzxjLytcuLDatGmjbdu2kZgBAKCbE3gEBwdnWFa7dm3Vrl1bkhQWFqYxY8aofPnyCg4OVmRkpA4dOpTpRFsAAM+Q7cTs008/VUhIiP71r3/p8uXL6cqDgoK0e/fuHAUHAEBe07FjR924cUOLFi3SwoULValSJc2dO1cNGjQwNC6TyWT/uWC1EJnMOX4aAh7OZk2x967e3n6MlpRK7x3uzoh2ku1/Vf/66687XiTy58+vq1evZvf0AAB4vODgYB0/fjzd9h49eqhHjx4GRHRvTGZvEjO4lduHUk7fe8HASOCOnDUUN9srpRUrVkx//fVXpuWHDx9WqVKlsnt6AAAAAMgzsn276/HHH9eaNWvUtWtX+yKZt7qpv/nmG23YsEEDBw50TJQAAABANt0+lHJMcAn5eLnO0Eq4pqRUm7131VlDcbOdmD3//PPau3evOnfurEaNGslkMmnRokWaPXu2Dh48qJo1a2ro0KGOjBUAAADIER8vE4kZXFK2hzIWLlxYERERGjRokGJiYmSxWPTDDz8oPj5eI0aM0OrVq5U/f35HxgoAAAAAHilHT+76+vpq+PDhGj58uKPiAQAAAIA8J9s9ZgAAAAAAx8iVuW6vXr2qr776SpIUEhKSG1UAAAAAgMfIlcTs/PnzGjt2rEwmE4kZAAAAANxFthKzpKQk7dmzR2XKlFGNGjXSlZcsWVJTpkzJcXAAAAAAkBdk6xmzfPny6YUXXtCBAwcyLC9UqJC6dOmiLl265Cg4AAAAAMgLspWYmUwmVaxYUZcvX3Z0PAAAAACQ52T7GbMhQ4Zo6tSp+v/27j+2qvr+4/jr9ra9gPb2R8QKo0BbtNKt2kpCSyh1Gz+kpULD6NAlhUVUFKm5hmYCASabsSRDrUjFUOtcUjQCXQiFrhGRDIfMTCigIvA1F1yzWGRjva2l9Of9/kF6pWvlR23v59ze5yMxufeez733fY6nvPO6n889d/bs2UpISBjImgAAgIV5uzpMl2ApXq9X0pUPrvEdzhPg5vQ7mB0/flxRUVF68MEHNXnyZP3oRz/SsGHDeo1bs2bNDyoQAABYS/P/7TJdAgAMOf0OZhUVFb7bhw8f7nOMzWYjmAEAAADAdfQ7mJ06dWog6wAAABYWHu7Qli1vmi7DclpbW+VyPSlJKinZIofDYbgiawoPt85xaev0mi7BUliK2zcT58mA/I5Zc3OzGhsbff9jrzZ69OiBeAsAAGCQzWaTw9H7Kwv4jsPh4BgFgI0fXzBdAtCnfgez1tZWbd68WTt37lRDQ8P3jvviiy/6+xYAAAAAEBT6Hcyee+457dq1SzNmzNCkSZMUGRk5kHUBGAJYLtITy0X6xnkCYLCxFLdvLMW9Mf5aitvvYLZv3z7l5+frd7/73UDWA2AIYbkIAMAKWIp7fSzFNa9fPzAtXTnBk5OTB7IWAAAAAAhK/Z4xmz59uj766CM99NBDA1kPgADHcpG+sVzkxljpym0AAPhTv4PZsmXL5HK5tHbtWi1cuFCjR49WSEjvCbioqKgfUh+AAMNyketjuQgAAPhf/Q5ms2bNkiSdPHlSO3fu/N5xXJURAAAAAK6t38Hsqaee4spiAAAAADAA+h3MCgsLB7IOAAAAAAha/b4q42D485//rKSkpF7/bdy4sce4HTt26IEHHlBKSormzp2rAwcO9HqtpqYmrV69WpMnT1ZaWpqefvppffPNN73GHT16VAsXLtQ999yjn/3sZ9q6davvt4YAAAAAwB/6PWM2mN544w1FRET47sfGxvpu7927V2vXrtUTTzyhjIwMVVdXa/ny5dq2bZtSU1N941wul7788ks999xzcjgcKikp0WOPPabKykqFhl7Z7a+++kpLlizR1KlT5XK5dPr0aW3cuFF2u11Llizx2/4CAAAACG6WDGY//vGPFRMT0+e2TZs2ac6cOXK5XJKkjIwMnTlzRqWlpSorK5Mk1dbW6m9/+5vKy8uVmZkpSYqPj1dOTo7ee+895eTkSJLKy8sVHR2tl156SeHh4ZoyZYouXryo119/XQUFBQoPDx/8nQUAAAAQ9Cy1lPF66urqdO7cOWVnZ/d4PCcnR4cPH1ZbW5sk6eDBg3I6nZo6dapvTEJCgiZOnKiDBw/6Hjt48KCmT5/eI4Dl5OSosbFRtbW1g7w3AAAAAHCFJWfMcnNz9d///lejR4/WL3/5Sz366KOy2+1yu92Srsx+XS0xMVHt7e2qq6tTYmKi3G634uPje101MiEhwfcaly5d0tdff62EhIReY2w2m9xut9LT0/u9D6GhAZV5A0Jn53fHNDQ0hGOMgML5CwAArsVSwWzkyJEqLCzUvffeK5vNpg8++EAlJSU6f/681q1bJ4/HI0lyOp09ntd9v3t7Y2Njj++odYuMjNRnn30m6crFQfp6rfDwcA0fPtz3Wv0REmJTdPQt/X4++nb5st13OyrqFg0bxg/0InBw/gIAgGuxVDCbNm2apk2b5rufmZkph8OhP/3pT3riiScMVnZzurq8amy8ZLqMIae19bLvdkNDsxyOToPVADeH89c/nM7hstuZjQQABB5LBbO+ZGdn680339QXX3yhyMhISVdmu0aOHOkb09jYKEm+7U6nU/X19b1ey+Px+MZ0z6h1z5x1a2trU0tLi29cf3V0dP2g56O3q49pR0eX7HaOMQIH5y8AALiWgPpYsfv7YN3fE+vmdrsVFhamuLg437izZ8/2+j2ys2fP+l5jxIgRGjVqVK/X6n7e/373DAAAAAAGi+WDWXV1tex2u5KTkxUXF6fx48erpqam15gpU6b4rq6YlZUlj8ejw4cP+8acPXtWJ0+eVFZWlu+xrKws7d+/X+3t7T1ey+l0Ki0tbZD3DAAAAACusNRSxiVLlig9PV1JSUmSpP3792v79u1atGiRb+liYWGhioqKNHbsWKWnp6u6ulonTpxQRUWF73XS0tKUmZmp1atX69lnn5XD4dDLL7+spKQkzZo1q8f7VVVVacWKFXr44Yd15swZlZeX65lnnuE3zAAAAAD4jaWCWXx8vCorK1VfX6+uri6NHz9eq1evVkFBgW9Mbm6uWlpaVFZWpq1btyo+Pl6bN2/uNcNVUlKi4uJirVu3Th0dHcrMzNSaNWsUGvrdLo8bN07l5eXasGGDHn/8ccXExOjpp5/WI4884rd9BgAAAABLBbM1a9bc0Lj8/Hzl5+dfc0xERIReeOEFvfDCC9ccd99992n79u03XCMAAAAADDTLf8cMAAAAAIY6ghkAAAAAGEYwAwDAT/7yl7/oySefVFZWllJTUzVv3jzt3Lmz18+77NixQw888IBSUlI0d+5cHThwwFDFAAB/IZgBAOAnb731loYPH66VK1dqy5YtysrK0tq1a1VaWuobs3fvXq1du1bZ2dkqKytTamqqli9frmPHjpkrHAAw6Cx18Q8AAIayLVu2KCYmxnd/ypQpamho0B//+EctW7ZMISEh2rRpk+bMmSOXyyVJysjI0JkzZ1RaWqqysjJDlQMABhszZgAA+MnVoazbxIkT9e233+rSpUuqq6vTuXPnlJ2d3WNMTk6ODh8+rLa2Nn+VCgDwM2bMAAAw6MiRI4qNjdWtt96qI0eOSLryu55XS0xMVHt7u+rq6pSYmNjv9woN5fPYgdbZ+d0xDQ0N4RgjoHD+WgvBDAAAQz755BNVV1fr2WeflSR5PB5JktPp7DGu+3739v4ICbEpOvqWfj8ffbt82e67HRV1i4YNG2awGuDmcP5aC8EMAAAD6uvr9cwzzyg9PV2LFi0a9Pfr6vKqsfHSoL9PsGltvey73dDQLIej02A1wM3h/B18Tudw2e03NhNJMAMAwM8aGxv12GOPKSoqSq+++qpCQq407cjISElSU1OTRo4c2WP81dv7q6Oj6wc9H71dfUw7Orpkt3OMETg4f62FYAYAgB9dvnxZS5cuVVNTk959911FRET4tiUkJEiS3G6373b3/bCwMMXFxfm9Xqvyer1qa2s1XYZaW1v7vG1SeLhDNpvNdBkAbhLBDAAAP+no6JDL5ZLb7da2bdsUGxvbY3tcXJzGjx+vmpoazZgxw/d4dXW1pkyZovDwcH+XbEler1fFxev15ZdnTJfSg8v1pOkSJEkTJtylVat+SzgDAgzBDAAAP1m/fr0OHDiglStX6ttvv+3xo9HJyckKDw9XYWGhioqKNHbsWKWnp6u6ulonTpxQRUWFucIBAIOOYIbrYrnItbFkxNo4f6+N89e/Dh06JEnasGFDr2379+/XmDFjlJubq5aWFpWVlWnr1q2Kj4/X5s2blZaW5u9yLctms2nVqt9a4m9buvLvjCTL/C3xd2199KbvF8znr83b/a8JBkxnZ5cuXmw2XcaAsOpyESthyYh1cf5e31A7f2Nibrnhq18Fm6HUm4BARm+6tmDuS3QvAAAAADCMGbNBMNQ+lbTKdLtkveUiUnBPuQcCzt9rG2rnLzNm32+o9SYgkNGbvl8w9yW+Y4brstlscjj4JXgEJs5fAIDV0JvQFz5WBAAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGFclXEQeL1edXVxWAHA30JCbEPqal4Did4EAP53M32JYAYAAAAAhrGUEQAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMlrNy5Url5ubq448/Vl5enlJTU7VgwQJ99tlnvjGtra0qLi5WZmamUlJSNG/ePO3bt89g1QhWH3zwgZKSknTu3Lkej3s8Ht1zzz3atm2bJKm2tlaLFi1SamqqJk2apBUrVug///lPj+ds3bpVM2fOVEpKijIyMvTrX/9adXV1/toVANdAb0KgoC8FLoIZLOnChQt6/vnntWTJEpWUlKi1tVXLly9Xe3u7JKmoqEjvvvuuHn30UZWWlmrChAkqLCzU/v37DVeOYHP//fcrNjZWlZWVPR7fs2ePJOnBBx9UbW2tCgoKFBERoZdfflm///3v9emnn2rZsmW+8bt27dIrr7yiBQsW6I033tDzzz+viRMnqrm52a/7A+D70ZsQCOhLgSvUdAFAXzwejyoqKnTnnXdKkoYPH65Fixbp+PHjuvXWW/Xee+9p/fr1euihhyRJWVlZ+te//qXS0lJNnz7dZOkIMna7XfPnz1dlZaVcLpfsdrskqbKyUjNnzpTT6dSLL76on/zkJ9q8ebNsNpsk6a677lJubq7++te/6v7779eJEyeUlJSkpUuX+l57xowZRvYJQN/oTQgE9KXAxYwZLOn222/3NT5JmjBhgiTp/PnzOnLkiCRp9uzZPZ6TnZ2tkydP6tKlS/4rFJC0YMECXbhwQR9++KEk6dSpU/r888+1YMECtbS06OjRo5o9e7Y6OzvV0dGhjo4OjR8/XqNGjdKnn34qSUpOTtbJkydVXFysTz75xPcJPADroDchUNCXAhMzZrAkp9PZ435YWJikK+v3PR6PwsLCFBUV1WPMbbfdJq/Xq6amJo0YMcJfpQIaM2aMpk6dqp07d+qnP/2pKisrNWbMGGVkZOibb75RZ2eniouLVVxc3Ou5X3/9tSRp/vz5am5u1vbt2/XWW28pIiJCeXl5Kioq0rBhw/y9SwD6QG9CoKAvBSaCGQJOZGSk2tvb5fF4FBkZ6Xv83//+t2w2myIiIgxWh2CVn5+voqIinT9/XlVVVSooKPCdjzabTUuXLu1zCUh0dLQkKSQkRIsXL9bixYt1/vx57d27Vy+++KKio6P11FNP+Xt3ANwkehOshr4UeAhmCDiTJk2SJNXU1GjhwoW+x2tqapScnMwnkjBi+vTpcjqdWrFihTwej+bPny9JGjFihFJTU+V2u5WSknJDrxUbG6tHHnlEe/bskdvtHsyyAQwQehOshr4UeAhmCDh33323Zs2apQ0bNujy5cuKj4/X7t27VVtbq9dee810eQhSYWFhysvLU3l5uTIzMzVq1Cjftt/85jdavHixXC6X5syZI6fTqfr6en300UeaP3++0tPTtW7dOjmdTqWmpsrpdOro0aM6deqUHn74YYN7BeBG0ZtgNfSlwEMwQ0D6wx/+oJdeekllZWVqaGhQQkKCNm3apJ///OemS0MQmzlzpsrLy/WLX/yix+P33Xef3n77bb366qtatWqV2tvbdccddygjI0Pjxo2TJKWlpWn79u3asWOHWlpaFBcXp1WrVik/P9/ErgDoB3oTrIa+FFhsXq/Xa7oIABgKXnnlFb399tv68MMPFR4ebrocAECQoy8FFmbMAOAHcrvdOnv2rCoqKvSrX/2K5gcAMIq+FJiYMQOAH6igoEDHjh3TtGnTtHHjRr7kDwAwir4UmAhmAAAAAGBYiOkCAAAAACDYEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMgCD397//XUlJSdq3b1+vbVVVVUpKSlJtba2BygAAwYrehGBEMAOCXHp6ukaNGqWqqqpe26qqqjR27FilpaUZqAwAEKzoTQhGBDMgyNlsNs2dO1cHDhxQU1OT7/GLFy/q0KFDmjt3rsHqAADBiN6EYEQwA6B58+apra1NNTU1vseqq6vV0dFB8wMAGEFvQrAhmAFQYmKiUlJSeiwZqaqqUmpqqsaNG2ewMgBAsKI3IdgQzABIkvLy8vSPf/xD9fX1+uc//6ljx47xiSQAwCh6E4IJwQyAJCknJ0d2u1179uzR7t27FRYWpuzsbNNlAQCCGL0JwSTUdAEArCEmJkbTpk3T7t271draqszMTMXExJguCwAQxOhNCCbMmAHwycvL0+nTp3Xu3DmWigAALIHehGBh83q9XtNFALCGtrY2ZWZmqqurS4cOHZLD4TBdEgAgyNGbECxYygjAJyQkRHa7XTNmzKDxAQAsgd6EYMFSRgA+77//vi5evKi8vDzTpQAAIInehODBjBkAHT9+XKdPn9Zrr72m5ORkTZ482XRJAIAgR29CsCGYAdA777yj3bt36+6779aGDRtMlwMAAL0JQYeLfwAAAACAYXzHDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGDY/wO4m6qI5EzDzQAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"M0smd8vic7F1"},"source":["## Matriz de correlaciones"]},{"cell_type":"code","metadata":{"id":"oFATdUSabn49","executionInfo":{"status":"ok","timestamp":1685973321287,"user_tz":180,"elapsed":357,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["atributos_numericos = df_new[['age', 'duration', 'campaign', 'previous', 'emp.var.rate',\n"," 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed']]\n"],"execution_count":67,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":793},"id":"m16-zq4tc_Pk","executionInfo":{"status":"ok","timestamp":1685973327686,"user_tz":180,"elapsed":2206,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"8e5ab0de-769a-4a07-b349-0da54c67ee51"},"source":["f, ax = plt.subplots(figsize=(11, 12))\n","sns.heatmap(atributos_numericos.corr(), annot = True, cbar_kws= {'orientation': 'horizontal'})"],"execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":68},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"HMORMNWWgF9s"},"source":["*De las 3 variables mas correlacionadas nos quedamos con 1* nr.employed\n"]},{"cell_type":"markdown","metadata":{"id":"NscHju0YjMsZ"},"source":["## Dispersogramas"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"HfWekroagKIw","executionInfo":{"status":"ok","timestamp":1685973518798,"user_tz":180,"elapsed":1824,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"811c3f01-e5fb-4810-ea0e-bfa5d06adf40"},"source":["import matplotlib.pyplot as plt\n","sns.set(rc={'figure.figsize':(10,5)})\n","\n","fig, (ax1, ax2) = plt.subplots(1, 2)\n","\n","sns.scatterplot(data=df_new, x=\"nr.employed\", y=\"euribor3m\", hue=\"y\",ax=ax1)\n","sns.scatterplot(data=df_new, x=\"nr.employed\", y=\"emp.var.rate\", hue=\"y\",ax=ax2)\n"],"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":69},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"Lo4GnBsCjUqQ"},"source":["\n","\n","# `3. Data Preparation`"]},{"cell_type":"markdown","metadata":{"id":"wcn2xqs2kBln"},"source":["## Estandarización de variables numericas"]},{"cell_type":"code","metadata":{"id":"2BGd44xthWYw","executionInfo":{"status":"ok","timestamp":1685975616061,"user_tz":180,"elapsed":359,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["from sklearn.preprocessing import StandardScaler"],"execution_count":70,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":374},"id":"zFiQqZ-_jl44","executionInfo":{"status":"ok","timestamp":1685975734510,"user_tz":180,"elapsed":22,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"d212b1ce-d9b7-4d16-c8f6-b38eb9181fd9"},"source":["X = df_new[['age', 'duration', 'campaign', 'previous',\n"," 'cons.price.idx', 'cons.conf.idx']]\n","target =df_new[['y']]\n","\n","#reemplazo yes por 1 y no por 0\n","target['y'] =np.where(target['y']=='yes', 1, 0)\n","\n","# instanciamos StandardScaler\n","scaler = StandardScaler()\n","\n","X_scaled = pd.DataFrame(data=scaler.fit_transform(atributos_numericos), \n"," columns=atributos_numericos.columns)\n","X_scaled.head()"],"execution_count":73,"outputs":[{"output_type":"stream","name":"stderr","text":[":6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," target['y'] =np.where(target['y']=='yes', 1, 0)\n"]},{"output_type":"execute_result","data":{"text/plain":[" age duration campaign previous emp.var.rate cons.price.idx \\\n","0 0.890906 -1.085950 1.379772 -0.454035 1.110626 -0.040859 \n","1 0.134729 -0.235050 -0.652631 -0.454035 1.110626 0.709745 \n","2 0.806887 -0.507698 0.871671 -0.454035 -0.749416 -0.913397 \n","3 0.386788 -1.058985 0.363571 -0.454035 -0.749416 -0.913397 \n","4 0.638847 0.418105 0.363571 -0.454035 -1.388805 -0.802548 \n","\n"," cons.conf.idx euribor3m nr.employed \n","0 0.755045 1.066971 1.075392 \n","1 -0.469830 1.065382 1.075392 \n","2 -1.119385 -0.893281 -0.414431 \n","3 -1.119385 -0.850909 -0.414431 \n","4 -0.117214 -0.891163 -0.678903 "],"text/html":["\n","
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agedurationcampaignpreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employed
00.890906-1.0859501.379772-0.4540351.110626-0.0408590.7550451.0669711.075392
10.134729-0.235050-0.652631-0.4540351.1106260.709745-0.4698301.0653821.075392
20.806887-0.5076980.871671-0.454035-0.749416-0.913397-1.119385-0.893281-0.414431
30.386788-1.0589850.363571-0.454035-0.749416-0.913397-1.119385-0.850909-0.414431
40.6388470.4181050.363571-0.454035-1.388805-0.802548-0.117214-0.891163-0.678903
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\n"," "]},"metadata":{},"execution_count":73}]},{"cell_type":"markdown","metadata":{"id":"oJwiHEIIqyjT"},"source":["## Dummies categorias"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n6-d2kgnrU1q","executionInfo":{"status":"ok","timestamp":1685975737498,"user_tz":180,"elapsed":13,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"0c561efa-7442-4a40-feb6-cae55f5109b3"},"source":["df_new.dtypes"],"execution_count":74,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age float64\n","job object\n","marital object\n","education object\n","default object\n","housing object\n","loan object\n","contact object\n","month object\n","day_of_week object\n","duration float64\n","campaign float64\n","pdays int64\n","previous int64\n","poutcome object\n","emp.var.rate float64\n","cons.price.idx float64\n","cons.conf.idx float64\n","euribor3m float64\n","nr.employed float64\n","y object\n","rand int64\n","dtype: object"]},"metadata":{},"execution_count":74}]},{"cell_type":"code","metadata":{"id":"yiw0fSEMrZ3K","executionInfo":{"status":"ok","timestamp":1685975739598,"user_tz":180,"elapsed":432,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["job_dummies = pd.get_dummies(df_new.job, prefix='Job')\n","marital_dummies = pd.get_dummies(df_new.marital, prefix='Marital')\n","education_dummies = pd.get_dummies(df_new.education, prefix='Education')\n","#default_dummies = pd.get_dummies(df_new.default, prefix='Default')\n","housing_dummies = pd.get_dummies(df_new.housing, prefix='Housing')\n","loan_dummies = pd.get_dummies(df_new.loan, prefix='Loan')\n","contact_dummies = pd.get_dummies(df_new.contact, prefix='Contact')\n","month_dummies = pd.get_dummies(df_new.month, prefix='Month')\n","day_of_week_dummies = pd.get_dummies(df_new.day_of_week, prefix='Day_of_Week')\n","poutcome_dummies = pd.get_dummies(df_new.poutcome, prefix='Poutcome')\n"," \n","df_new2 = (\n"," X_scaled.join(job_dummies)\n"," .join(marital_dummies)\n"," .join(education_dummies)\n"," #.join(default_dummies)\n"," .join(housing_dummies)\n"," .join(loan_dummies)\n"," .join(contact_dummies)\n"," .join(month_dummies)\n"," .join(day_of_week_dummies)\n"," .join(poutcome_dummies)\n",") .join(target)"],"execution_count":75,"outputs":[]},{"cell_type":"code","source":["job_dummies.head(1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162},"id":"XBuup8Un_D7C","executionInfo":{"status":"ok","timestamp":1685975768243,"user_tz":180,"elapsed":12,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"6cb401f2-1541-439e-d808-06b6ddb3c725"},"execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Job_admin. Job_blue-collar Job_entrepreneur Job_housemaid \\\n","0 0 0 0 0 \n","\n"," Job_management Job_retired Job_self-employed Job_services Job_student \\\n","0 0 0 0 1 0 \n","\n"," Job_technician Job_unemployed Job_unknown \n","0 0 0 0 "],"text/html":["\n","
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Job_admin.Job_blue-collarJob_entrepreneurJob_housemaidJob_managementJob_retiredJob_self-employedJob_servicesJob_studentJob_technicianJob_unemployedJob_unknown
0000000010000
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agejobmaritaleducationdefaulthousingloancontactmonthday_of_week...pdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedyrand
051.0servicesmarriedhigh.schoolunknownnoyescellularaugtue...9990nonexistent1.493.444-36.14.9655228.1no7566
142.0managementmarriedbasic.6yunknownyesnocellularjultue...9990nonexistent1.493.918-42.74.9625228.1no781
250.0admin.divorceduniversity.degreenonoyescellularmaymon...9990nonexistent-1.892.893-46.21.2645099.1yes24693
345.0entrepreneurdivorcedbasic.9ynoyesnocellularmaytue...9990nonexistent-1.892.893-46.21.3445099.1no25164
448.0admin.marrieduniversity.degreenoyesnotelephonejunfri...9990nonexistent-2.992.963-40.81.2685076.2yes29115
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5 rows × 22 columns

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\n"," "]},"metadata":{},"execution_count":78}]},{"cell_type":"markdown","metadata":{"id":"GPmcWf1gmMDv"},"source":["\n","# `4. Modeling`\n","\n"]},{"cell_type":"markdown","metadata":{"id":"LZaACNaZmOXo"},"source":["## Decision Tree"]},{"cell_type":"code","metadata":{"id":"eJXvgL6zi3Id","executionInfo":{"status":"ok","timestamp":1685976951490,"user_tz":180,"elapsed":319,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["from sklearn.model_selection import train_test_split\n","from sklearn import tree\n","from sklearn.ensemble import RandomForestClassifier"],"execution_count":124,"outputs":[]},{"cell_type":"code","metadata":{"id":"os2gHZfRmQQb","executionInfo":{"status":"ok","timestamp":1685976951822,"user_tz":180,"elapsed":6,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["X = df_new2.loc[:, df_new2.columns != 'y']\n","y = df_new2.loc[:, df_new2.columns == 'y']"],"execution_count":125,"outputs":[]},{"cell_type":"code","metadata":{"id":"F0-XAjskmWt8","executionInfo":{"status":"ok","timestamp":1685976957636,"user_tz":180,"elapsed":4,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=42)"],"execution_count":126,"outputs":[]},{"cell_type":"code","metadata":{"id":"xr94RJZymYaV","colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"status":"ok","timestamp":1685976958513,"user_tz":180,"elapsed":14,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"23ba1663-dbc0-4d15-f302-311da0fc1d6c"},"source":["dt = tree.DecisionTreeClassifier()\n","dt.fit(X_train, y_train)"],"execution_count":127,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier()"],"text/html":["
DecisionTreeClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"]},"metadata":{},"execution_count":127}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"lF4isfjImZUl","executionInfo":{"status":"ok","timestamp":1685976962509,"user_tz":180,"elapsed":1719,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"f65ec899-a5a1-424b-d7bb-7be43d44e718"},"source":["import graphviz \n","dot_data = tree.export_graphviz(dt, out_file=None) \n","dot_data = tree.export_graphviz(dt, out_file=None, \n"," feature_names=X.columns,\n"," class_names=df_new['y'].astype(str).unique())\n","graph = graphviz.Source(dot_data) \n","graph"],"execution_count":128,"outputs":[{"output_type":"execute_result","data":{"image/svg+xml":"\n\n\n\n\n\nTree\n\n\n\n0\n\nduration <= -0.551\ngini = 0.5\nsamples = 5861\nvalue = [2951, 2910]\nclass = no\n\n\n\n1\n\nnr.employed <= -0.547\ngini = 0.311\nsamples = 2204\nvalue = [1780, 424]\nclass = no\n\n\n\n0->1\n\n\nTrue\n\n\n\n326\n\nnr.employed <= -0.547\ngini = 0.435\nsamples = 3657\nvalue = [1171, 2486]\nclass = yes\n\n\n\n0->326\n\n\nFalse\n\n\n\n2\n\nduration <= -0.905\ngini = 0.445\nsamples = 457\nvalue = [153, 304]\nclass = yes\n\n\n\n1->2\n\n\n\n\n\n193\n\ncons.conf.idx <= -1.203\ngini = 0.128\nsamples = 1747\nvalue = [1627, 120]\nclass = no\n\n\n\n1->193\n\n\n\n\n\n3\n\nJob_retired <= 0.5\ngini = 0.241\nsamples = 50\nvalue = [43, 7]\nclass = no\n\n\n\n2->3\n\n\n\n\n\n24\n\nduration <= -0.662\ngini = 0.394\nsamples = 407\nvalue = [110, 297]\nclass = yes\n\n\n\n2->24\n\n\n\n\n\n4\n\ncons.price.idx <= -1.615\ngini = 0.215\nsamples = 49\nvalue = [43, 6]\nclass = no\n\n\n\n3->4\n\n\n\n\n\n23\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n3->23\n\n\n\n\n\n5\n\nduration <= -0.917\ngini = 0.415\nsamples = 17\nvalue = [12, 5]\nclass = no\n\n\n\n4->5\n\n\n\n\n\n18\n\nPoutcome_success <= 0.5\ngini = 0.061\nsamples = 32\nvalue = [31, 1]\nclass = no\n\n\n\n4->18\n\n\n\n\n\n6\n\nJob_services <= 0.5\ngini = 0.278\nsamples = 12\nvalue = [10, 2]\nclass = no\n\n\n\n5->6\n\n\n\n\n\n13\n\nPoutcome_nonexistent <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n5->13\n\n\n\n\n\n7\n\nPoutcome_success <= 0.5\ngini = 0.165\nsamples = 11\nvalue = [10, 1]\nclass = no\n\n\n\n6->7\n\n\n\n\n\n12\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n6->12\n\n\n\n\n\n8\n\ngini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = no\n\n\n\n7->8\n\n\n\n\n\n9\n\nduration <= -0.942\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n7->9\n\n\n\n\n\n10\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n9->10\n\n\n\n\n\n11\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n9->11\n\n\n\n\n\n14\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n13->14\n\n\n\n\n\n15\n\nDay_of_Week_tue <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n13->15\n\n\n\n\n\n16\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n15->16\n\n\n\n\n\n17\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n15->17\n\n\n\n\n\n19\n\ngini = 0.0\nsamples = 25\nvalue = [25, 0]\nclass = no\n\n\n\n18->19\n\n\n\n\n\n20\n\nduration <= -0.935\ngini = 0.245\nsamples = 7\nvalue = [6, 1]\nclass = no\n\n\n\n18->20\n\n\n\n\n\n21\n\ngini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = no\n\n\n\n20->21\n\n\n\n\n\n22\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n20->22\n\n\n\n\n\n25\n\ncons.price.idx <= 0.905\ngini = 0.463\nsamples = 244\nvalue = [89, 155]\nclass = yes\n\n\n\n24->25\n\n\n\n\n\n138\n\nContact_telephone <= 0.5\ngini = 0.224\nsamples = 163\nvalue = [21, 142]\nclass = yes\n\n\n\n24->138\n\n\n\n\n\n26\n\nPoutcome_success <= 0.5\ngini = 0.439\nsamples = 218\nvalue = [71, 147]\nclass = yes\n\n\n\n25->26\n\n\n\n\n\n127\n\nDay_of_Week_wed <= 0.5\ngini = 0.426\nsamples = 26\nvalue = [18, 8]\nclass = no\n\n\n\n25->127\n\n\n\n\n\n27\n\nduration <= -0.776\ngini = 0.476\nsamples = 164\nvalue = [64, 100]\nclass = yes\n\n\n\n26->27\n\n\n\n\n\n110\n\nEducation_basic.6y <= 0.5\ngini = 0.226\nsamples = 54\nvalue = [7, 47]\nclass = yes\n\n\n\n26->110\n\n\n\n\n\n28\n\nprevious <= 1.696\ngini = 0.5\nsamples = 77\nvalue = [38, 39]\nclass = yes\n\n\n\n27->28\n\n\n\n\n\n67\n\nduration <= -0.695\ngini = 0.419\nsamples = 87\nvalue = [26, 61]\nclass = yes\n\n\n\n27->67\n\n\n\n\n\n29\n\nMonth_nov <= 0.5\ngini = 0.497\nsamples = 72\nvalue = [33, 39]\nclass = yes\n\n\n\n28->29\n\n\n\n\n\n66\n\ngini = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = no\n\n\n\n28->66\n\n\n\n\n\n30\n\nEducation_basic.9y <= 0.5\ngini = 0.499\nsamples = 61\nvalue = [32, 29]\nclass = no\n\n\n\n29->30\n\n\n\n\n\n63\n\nDay_of_Week_wed <= 0.5\ngini = 0.165\nsamples = 11\nvalue = [1, 10]\nclass = yes\n\n\n\n29->63\n\n\n\n\n\n31\n\neuribor3m <= -1.209\ngini = 0.492\nsamples = 57\nvalue = [32, 25]\nclass = no\n\n\n\n30->31\n\n\n\n\n\n62\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n30->62\n\n\n\n\n\n32\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n31->32\n\n\n\n\n\n33\n\nduration <= -0.851\ngini = 0.483\nsamples = 54\nvalue = [32, 22]\nclass = no\n\n\n\n31->33\n\n\n\n\n\n34\n\ncons.price.idx <= -1.686\ngini = 0.497\nsamples = 24\nvalue = [11, 13]\nclass = yes\n\n\n\n33->34\n\n\n\n\n\n49\n\neuribor3m <= -0.938\ngini = 0.42\nsamples = 30\nvalue = [21, 9]\nclass = no\n\n\n\n33->49\n\n\n\n\n\n35\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n34->35\n\n\n\n\n\n36\n\nduration <= -0.857\ngini = 0.488\nsamples = 19\nvalue = [11, 8]\nclass = no\n\n\n\n34->36\n\n\n\n\n\n37\n\nage <= -0.621\ngini = 0.391\nsamples = 15\nvalue = [11, 4]\nclass = no\n\n\n\n36->37\n\n\n\n\n\n48\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n36->48\n\n\n\n\n\n38\n\nage <= -0.747\ngini = 0.5\nsamples = 6\nvalue = [3, 3]\nclass = no\n\n\n\n37->38\n\n\n\n\n\n43\n\nDay_of_Week_wed <= 0.5\ngini = 0.198\nsamples = 9\nvalue = [8, 1]\nclass = no\n\n\n\n37->43\n\n\n\n\n\n39\n\nDay_of_Week_thu <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n38->39\n\n\n\n\n\n42\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n38->42\n\n\n\n\n\n40\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n39->40\n\n\n\n\n\n41\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n39->41\n\n\n\n\n\n44\n\ngini = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = no\n\n\n\n43->44\n\n\n\n\n\n45\n\nMonth_oct <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n43->45\n\n\n\n\n\n46\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n45->46\n\n\n\n\n\n47\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n45->47\n\n\n\n\n\n50\n\nJob_management <= 0.5\ngini = 0.245\nsamples = 21\nvalue = [18, 3]\nclass = no\n\n\n\n49->50\n\n\n\n\n\n57\n\nDay_of_Week_tue <= 0.5\ngini = 0.444\nsamples = 9\nvalue = [3, 6]\nclass = yes\n\n\n\n49->57\n\n\n\n\n\n51\n\nDay_of_Week_fri <= 0.5\ngini = 0.18\nsamples = 20\nvalue = [18, 2]\nclass = no\n\n\n\n50->51\n\n\n\n\n\n56\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n50->56\n\n\n\n\n\n52\n\ngini = 0.0\nsamples = 14\nvalue = [14, 0]\nclass = no\n\n\n\n51->52\n\n\n\n\n\n53\n\nEducation_university.degree <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [4, 2]\nclass = no\n\n\n\n51->53\n\n\n\n\n\n54\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n53->54\n\n\n\n\n\n55\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n53->55\n\n\n\n\n\n58\n\nEducation_high.school <= 0.5\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = yes\n\n\n\n57->58\n\n\n\n\n\n61\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n57->61\n\n\n\n\n\n59\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n58->59\n\n\n\n\n\n60\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n58->60\n\n\n\n\n\n64\n\ngini = 0.0\nsamples = 10\nvalue = [0, 10]\nclass = yes\n\n\n\n63->64\n\n\n\n\n\n65\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n63->65\n\n\n\n\n\n68\n\neuribor3m <= -1.198\ngini = 0.335\nsamples = 61\nvalue = [13, 48]\nclass = yes\n\n\n\n67->68\n\n\n\n\n\n95\n\nduration <= -0.686\ngini = 0.5\nsamples = 26\nvalue = [13, 13]\nclass = no\n\n\n\n67->95\n\n\n\n\n\n69\n\neuribor3m <= -1.217\ngini = 0.49\nsamples = 7\nvalue = [4, 3]\nclass = no\n\n\n\n68->69\n\n\n\n\n\n74\n\nJob_entrepreneur <= 0.5\ngini = 0.278\nsamples = 54\nvalue = [9, 45]\nclass = yes\n\n\n\n68->74\n\n\n\n\n\n70\n\nDay_of_Week_fri <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n69->70\n\n\n\n\n\n73\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n69->73\n\n\n\n\n\n71\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n70->71\n\n\n\n\n\n72\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n70->72\n\n\n\n\n\n75\n\ncons.price.idx <= -1.868\ngini = 0.256\nsamples = 53\nvalue = [8, 45]\nclass = yes\n\n\n\n74->75\n\n\n\n\n\n94\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n74->94\n\n\n\n\n\n76\n\nDay_of_Week_thu <= 0.5\ngini = 0.444\nsamples = 15\nvalue = [5, 10]\nclass = yes\n\n\n\n75->76\n\n\n\n\n\n83\n\nJob_admin. <= 0.5\ngini = 0.145\nsamples = 38\nvalue = [3, 35]\nclass = yes\n\n\n\n75->83\n\n\n\n\n\n77\n\nLoan_no <= 0.5\ngini = 0.18\nsamples = 10\nvalue = [1, 9]\nclass = yes\n\n\n\n76->77\n\n\n\n\n\n80\n\nJob_services <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [4, 1]\nclass = no\n\n\n\n76->80\n\n\n\n\n\n78\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n77->78\n\n\n\n\n\n79\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n77->79\n\n\n\n\n\n81\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n80->81\n\n\n\n\n\n82\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n80->82\n\n\n\n\n\n84\n\ngini = 0.0\nsamples = 26\nvalue = [0, 26]\nclass = yes\n\n\n\n83->84\n\n\n\n\n\n85\n\nHousing_unknown <= 0.5\ngini = 0.375\nsamples = 12\nvalue = [3, 9]\nclass = yes\n\n\n\n83->85\n\n\n\n\n\n86\n\nEducation_university.degree <= 0.5\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n85->86\n\n\n\n\n\n93\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n85->93\n\n\n\n\n\n87\n\nage <= -0.159\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n86->87\n\n\n\n\n\n92\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n86->92\n\n\n\n\n\n88\n\nMonth_oct <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n87->88\n\n\n\n\n\n91\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n87->91\n\n\n\n\n\n89\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n88->89\n\n\n\n\n\n90\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n88->90\n\n\n\n\n\n96\n\nEducation_unknown <= 0.5\ngini = 0.245\nsamples = 7\nvalue = [6, 1]\nclass = no\n\n\n\n95->96\n\n\n\n\n\n99\n\nemp.var.rate <= -1.418\ngini = 0.465\nsamples = 19\nvalue = [7, 12]\nclass = yes\n\n\n\n95->99\n\n\n\n\n\n97\n\ngini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = no\n\n\n\n96->97\n\n\n\n\n\n98\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n96->98\n\n\n\n\n\n100\n\nPoutcome_failure <= 0.5\ngini = 0.469\nsamples = 8\nvalue = [5, 3]\nclass = no\n\n\n\n99->100\n\n\n\n\n\n105\n\ncampaign <= -0.399\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n99->105\n\n\n\n\n\n101\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n100->101\n\n\n\n\n\n102\n\nMarital_divorced <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n100->102\n\n\n\n\n\n103\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n102->103\n\n\n\n\n\n104\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n102->104\n\n\n\n\n\n106\n\nHousing_no <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n105->106\n\n\n\n\n\n109\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n105->109\n\n\n\n\n\n107\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n106->107\n\n\n\n\n\n108\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n106->108\n\n\n\n\n\n111\n\nDay_of_Week_mon <= 0.5\ngini = 0.174\nsamples = 52\nvalue = [5, 47]\nclass = yes\n\n\n\n110->111\n\n\n\n\n\n126\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n110->126\n\n\n\n\n\n112\n\nage <= -0.663\ngini = 0.083\nsamples = 46\nvalue = [2, 44]\nclass = yes\n\n\n\n111->112\n\n\n\n\n\n121\n\nJob_admin. <= 0.5\ngini = 0.5\nsamples = 6\nvalue = [3, 3]\nclass = no\n\n\n\n111->121\n\n\n\n\n\n113\n\nage <= -0.916\ngini = 0.208\nsamples = 17\nvalue = [2, 15]\nclass = yes\n\n\n\n112->113\n\n\n\n\n\n120\n\ngini = 0.0\nsamples = 29\nvalue = [0, 29]\nclass = yes\n\n\n\n112->120\n\n\n\n\n\n114\n\ngini = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = yes\n\n\n\n113->114\n\n\n\n\n\n115\n\nMonth_aug <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n113->115\n\n\n\n\n\n116\n\nduration <= -0.843\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n115->116\n\n\n\n\n\n119\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n115->119\n\n\n\n\n\n117\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n116->117\n\n\n\n\n\n118\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n116->118\n\n\n\n\n\n122\n\ncons.conf.idx <= 2.166\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n121->122\n\n\n\n\n\n125\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n121->125\n\n\n\n\n\n123\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n122->123\n\n\n\n\n\n124\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n122->124\n\n\n\n\n\n128\n\nMarital_divorced <= 0.5\ngini = 0.278\nsamples = 18\nvalue = [15, 3]\nclass = no\n\n\n\n127->128\n\n\n\n\n\n133\n\nPoutcome_failure <= 0.5\ngini = 0.469\nsamples = 8\nvalue = [3, 5]\nclass = yes\n\n\n\n127->133\n\n\n\n\n\n129\n\ngini = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = no\n\n\n\n128->129\n\n\n\n\n\n130\n\nMonth_oct <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n128->130\n\n\n\n\n\n131\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n130->131\n\n\n\n\n\n132\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n130->132\n\n\n\n\n\n134\n\nJob_blue-collar <= 0.5\ngini = 0.278\nsamples = 6\nvalue = [1, 5]\nclass = yes\n\n\n\n133->134\n\n\n\n\n\n137\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n133->137\n\n\n\n\n\n135\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n134->135\n\n\n\n\n\n136\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n134->136\n\n\n\n\n\n139\n\nduration <= -0.556\ngini = 0.194\nsamples = 156\nvalue = [17, 139]\nclass = yes\n\n\n\n138->139\n\n\n\n\n\n188\n\ncons.conf.idx <= 0.532\ngini = 0.49\nsamples = 7\nvalue = [4, 3]\nclass = no\n\n\n\n138->188\n\n\n\n\n\n140\n\ncons.conf.idx <= -1.852\ngini = 0.177\nsamples = 153\nvalue = [15, 138]\nclass = yes\n\n\n\n139->140\n\n\n\n\n\n185\n\nJob_self-employed <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n139->185\n\n\n\n\n\n141\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n140->141\n\n\n\n\n\n142\n\nage <= -1.336\ngini = 0.167\nsamples = 152\nvalue = [14, 138]\nclass = yes\n\n\n\n140->142\n\n\n\n\n\n143\n\nemp.var.rate <= -1.534\ngini = 0.42\nsamples = 10\nvalue = [3, 7]\nclass = yes\n\n\n\n142->143\n\n\n\n\n\n148\n\nduration <= -0.56\ngini = 0.143\nsamples = 142\nvalue = [11, 131]\nclass = yes\n\n\n\n142->148\n\n\n\n\n\n144\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n143->144\n\n\n\n\n\n145\n\nEducation_basic.9y <= 0.5\ngini = 0.219\nsamples = 8\nvalue = [1, 7]\nclass = yes\n\n\n\n143->145\n\n\n\n\n\n146\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n145->146\n\n\n\n\n\n147\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n145->147\n\n\n\n\n\n149\n\nduration <= -0.614\ngini = 0.124\nsamples = 135\nvalue = [9, 126]\nclass = yes\n\n\n\n148->149\n\n\n\n\n\n180\n\ncons.price.idx <= 0.141\ngini = 0.408\nsamples = 7\nvalue = [2, 5]\nclass = yes\n\n\n\n148->180\n\n\n\n\n\n150\n\nEducation_basic.6y <= 0.5\ngini = 0.213\nsamples = 66\nvalue = [8, 58]\nclass = yes\n\n\n\n149->150\n\n\n\n\n\n175\n\ncons.conf.idx <= 2.193\ngini = 0.029\nsamples = 69\nvalue = [1, 68]\nclass = yes\n\n\n\n149->175\n\n\n\n\n\n151\n\nnr.employed <= -0.983\ngini = 0.192\nsamples = 65\nvalue = [7, 58]\nclass = yes\n\n\n\n150->151\n\n\n\n\n\n174\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n150->174\n\n\n\n\n\n152\n\ncons.conf.idx <= 2.193\ngini = 0.091\nsamples = 42\nvalue = [2, 40]\nclass = yes\n\n\n\n151->152\n\n\n\n\n\n161\n\nEducation_high.school <= 0.5\ngini = 0.34\nsamples = 23\nvalue = [5, 18]\nclass = yes\n\n\n\n151->161\n\n\n\n\n\n153\n\nMonth_may <= 0.5\ngini = 0.05\nsamples = 39\nvalue = [1, 38]\nclass = yes\n\n\n\n152->153\n\n\n\n\n\n158\n\nEducation_basic.9y <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n152->158\n\n\n\n\n\n154\n\ngini = 0.0\nsamples = 35\nvalue = [0, 35]\nclass = yes\n\n\n\n153->154\n\n\n\n\n\n155\n\nMarital_single <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n153->155\n\n\n\n\n\n156\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n155->156\n\n\n\n\n\n157\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n155->157\n\n\n\n\n\n159\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n158->159\n\n\n\n\n\n160\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n158->160\n\n\n\n\n\n162\n\nage <= 1.857\ngini = 0.255\nsamples = 20\nvalue = [3, 17]\nclass = yes\n\n\n\n161->162\n\n\n\n\n\n171\n\neuribor3m <= -0.994\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n161->171\n\n\n\n\n\n163\n\nage <= -0.705\ngini = 0.188\nsamples = 19\nvalue = [2, 17]\nclass = yes\n\n\n\n162->163\n\n\n\n\n\n170\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n162->170\n\n\n\n\n\n164\n\nduration <= -0.626\ngini = 0.408\nsamples = 7\nvalue = [2, 5]\nclass = yes\n\n\n\n163->164\n\n\n\n\n\n169\n\ngini = 0.0\nsamples = 12\nvalue = [0, 12]\nclass = yes\n\n\n\n163->169\n\n\n\n\n\n165\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n164->165\n\n\n\n\n\n166\n\nJob_admin. <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n164->166\n\n\n\n\n\n167\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n166->167\n\n\n\n\n\n168\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n166->168\n\n\n\n\n\n172\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n171->172\n\n\n\n\n\n173\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n171->173\n\n\n\n\n\n176\n\ngini = 0.0\nsamples = 65\nvalue = [0, 65]\nclass = yes\n\n\n\n175->176\n\n\n\n\n\n177\n\nJob_technician <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n175->177\n\n\n\n\n\n178\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n177->178\n\n\n\n\n\n179\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n177->179\n\n\n\n\n\n181\n\nMonth_dec <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n180->181\n\n\n\n\n\n184\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n180->184\n\n\n\n\n\n182\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n181->182\n\n\n\n\n\n183\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n181->183\n\n\n\n\n\n186\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n185->186\n\n\n\n\n\n187\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n185->187\n\n\n\n\n\n189\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n188->189\n\n\n\n\n\n190\n\nMarital_single <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n188->190\n\n\n\n\n\n191\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n190->191\n\n\n\n\n\n192\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n190->192\n\n\n\n\n\n194\n\nduration <= -0.908\ngini = 0.498\nsamples = 181\nvalue = [96, 85]\nclass = no\n\n\n\n193->194\n\n\n\n\n\n271\n\nMonth_oct <= 0.5\ngini = 0.044\nsamples = 1566\nvalue = [1531, 35]\nclass = no\n\n\n\n193->271\n\n\n\n\n\n195\n\nJob_entrepreneur <= 0.5\ngini = 0.128\nsamples = 29\nvalue = [27, 2]\nclass = no\n\n\n\n194->195\n\n\n\n\n\n204\n\neuribor3m <= -0.783\ngini = 0.496\nsamples = 152\nvalue = [69, 83]\nclass = yes\n\n\n\n194->204\n\n\n\n\n\n196\n\neuribor3m <= -0.651\ngini = 0.069\nsamples = 28\nvalue = [27, 1]\nclass = no\n\n\n\n195->196\n\n\n\n\n\n203\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n195->203\n\n\n\n\n\n197\n\ngini = 0.0\nsamples = 24\nvalue = [24, 0]\nclass = no\n\n\n\n196->197\n\n\n\n\n\n198\n\nHousing_yes <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n196->198\n\n\n\n\n\n199\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n198->199\n\n\n\n\n\n200\n\nduration <= -0.935\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n198->200\n\n\n\n\n\n201\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n200->201\n\n\n\n\n\n202\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n200->202\n\n\n\n\n\n205\n\nJob_blue-collar <= 0.5\ngini = 0.492\nsamples = 110\nvalue = [62, 48]\nclass = no\n\n\n\n204->205\n\n\n\n\n\n258\n\ncampaign <= 3.666\ngini = 0.278\nsamples = 42\nvalue = [7, 35]\nclass = yes\n\n\n\n204->258\n\n\n\n\n\n206\n\neuribor3m <= -0.806\ngini = 0.5\nsamples = 92\nvalue = [46, 46]\nclass = no\n\n\n\n205->206\n\n\n\n\n\n253\n\neuribor3m <= -0.811\ngini = 0.198\nsamples = 18\nvalue = [16, 2]\nclass = no\n\n\n\n205->253\n\n\n\n\n\n207\n\nDay_of_Week_wed <= 0.5\ngini = 0.494\nsamples = 79\nvalue = [35, 44]\nclass = yes\n\n\n\n206->207\n\n\n\n\n\n248\n\ncampaign <= 0.364\ngini = 0.26\nsamples = 13\nvalue = [11, 2]\nclass = no\n\n\n\n206->248\n\n\n\n\n\n208\n\nprevious <= 0.263\ngini = 0.499\nsamples = 63\nvalue = [33, 30]\nclass = no\n\n\n\n207->208\n\n\n\n\n\n243\n\ncampaign <= 0.11\ngini = 0.219\nsamples = 16\nvalue = [2, 14]\nclass = yes\n\n\n\n207->243\n\n\n\n\n\n209\n\nduration <= -0.858\ngini = 0.494\nsamples = 54\nvalue = [24, 30]\nclass = yes\n\n\n\n208->209\n\n\n\n\n\n242\n\ngini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = no\n\n\n\n208->242\n\n\n\n\n\n210\n\ngini = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = no\n\n\n\n209->210\n\n\n\n\n\n211\n\nContact_telephone <= 0.5\ngini = 0.475\nsamples = 49\nvalue = [19, 30]\nclass = yes\n\n\n\n209->211\n\n\n\n\n\n212\n\neuribor3m <= -0.835\ngini = 0.444\nsamples = 45\nvalue = [15, 30]\nclass = yes\n\n\n\n211->212\n\n\n\n\n\n241\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n211->241\n\n\n\n\n\n213\n\nJob_management <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [4, 1]\nclass = no\n\n\n\n212->213\n\n\n\n\n\n216\n\nJob_management <= 0.5\ngini = 0.399\nsamples = 40\nvalue = [11, 29]\nclass = yes\n\n\n\n212->216\n\n\n\n\n\n214\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n213->214\n\n\n\n\n\n215\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n213->215\n\n\n\n\n\n217\n\nduration <= -0.571\ngini = 0.346\nsamples = 36\nvalue = [8, 28]\nclass = yes\n\n\n\n216->217\n\n\n\n\n\n238\n\nage <= -1.21\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n216->238\n\n\n\n\n\n218\n\nLoan_yes <= 0.5\ngini = 0.32\nsamples = 35\nvalue = [7, 28]\nclass = yes\n\n\n\n217->218\n\n\n\n\n\n237\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n217->237\n\n\n\n\n\n219\n\nMarital_married <= 0.5\ngini = 0.291\nsamples = 34\nvalue = [6, 28]\nclass = yes\n\n\n\n218->219\n\n\n\n\n\n236\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n218->236\n\n\n\n\n\n220\n\ngini = 0.0\nsamples = 15\nvalue = [0, 15]\nclass = yes\n\n\n\n219->220\n\n\n\n\n\n221\n\nEducation_university.degree <= 0.5\ngini = 0.432\nsamples = 19\nvalue = [6, 13]\nclass = yes\n\n\n\n219->221\n\n\n\n\n\n222\n\nDay_of_Week_mon <= 0.5\ngini = 0.5\nsamples = 10\nvalue = [5, 5]\nclass = no\n\n\n\n221->222\n\n\n\n\n\n231\n\nage <= 0.933\ngini = 0.198\nsamples = 9\nvalue = [1, 8]\nclass = yes\n\n\n\n221->231\n\n\n\n\n\n223\n\nDay_of_Week_fri <= 0.5\ngini = 0.469\nsamples = 8\nvalue = [3, 5]\nclass = yes\n\n\n\n222->223\n\n\n\n\n\n230\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n222->230\n\n\n\n\n\n224\n\nHousing_no <= 0.5\ngini = 0.408\nsamples = 7\nvalue = [2, 5]\nclass = yes\n\n\n\n223->224\n\n\n\n\n\n229\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n223->229\n\n\n\n\n\n225\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n224->225\n\n\n\n\n\n226\n\nage <= 0.597\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n224->226\n\n\n\n\n\n227\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n226->227\n\n\n\n\n\n228\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n226->228\n\n\n\n\n\n232\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n231->232\n\n\n\n\n\n233\n\nHousing_no <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n231->233\n\n\n\n\n\n234\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n233->234\n\n\n\n\n\n235\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n233->235\n\n\n\n\n\n239\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n238->239\n\n\n\n\n\n240\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n238->240\n\n\n\n\n\n244\n\ngini = 0.0\nsamples = 13\nvalue = [0, 13]\nclass = yes\n\n\n\n243->244\n\n\n\n\n\n245\n\nJob_retired <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n243->245\n\n\n\n\n\n246\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n245->246\n\n\n\n\n\n247\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n245->247\n\n\n\n\n\n249\n\nEducation_unknown <= 0.5\ngini = 0.153\nsamples = 12\nvalue = [11, 1]\nclass = no\n\n\n\n248->249\n\n\n\n\n\n252\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n248->252\n\n\n\n\n\n250\n\ngini = 0.0\nsamples = 11\nvalue = [11, 0]\nclass = no\n\n\n\n249->250\n\n\n\n\n\n251\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n249->251\n\n\n\n\n\n254\n\ngini = 0.0\nsamples = 15\nvalue = [15, 0]\nclass = no\n\n\n\n253->254\n\n\n\n\n\n255\n\nHousing_no <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n253->255\n\n\n\n\n\n256\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n255->256\n\n\n\n\n\n257\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n255->257\n\n\n\n\n\n259\n\nJob_blue-collar <= 0.5\ngini = 0.219\nsamples = 40\nvalue = [5, 35]\nclass = yes\n\n\n\n258->259\n\n\n\n\n\n270\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n258->270\n\n\n\n\n\n260\n\nduration <= -0.831\ngini = 0.184\nsamples = 39\nvalue = [4, 35]\nclass = yes\n\n\n\n259->260\n\n\n\n\n\n269\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n259->269\n\n\n\n\n\n261\n\nJob_management <= 0.5\ngini = 0.469\nsamples = 8\nvalue = [3, 5]\nclass = yes\n\n\n\n260->261\n\n\n\n\n\n266\n\nJob_self-employed <= 0.5\ngini = 0.062\nsamples = 31\nvalue = [1, 30]\nclass = yes\n\n\n\n260->266\n\n\n\n\n\n262\n\nDay_of_Week_mon <= 0.5\ngini = 0.278\nsamples = 6\nvalue = [1, 5]\nclass = yes\n\n\n\n261->262\n\n\n\n\n\n265\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n261->265\n\n\n\n\n\n263\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n262->263\n\n\n\n\n\n264\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n262->264\n\n\n\n\n\n267\n\ngini = 0.0\nsamples = 30\nvalue = [0, 30]\nclass = yes\n\n\n\n266->267\n\n\n\n\n\n268\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n266->268\n\n\n\n\n\n272\n\nPoutcome_success <= 0.5\ngini = 0.028\nsamples = 1551\nvalue = [1529, 22]\nclass = no\n\n\n\n271->272\n\n\n\n\n\n323\n\nduration <= -0.909\ngini = 0.231\nsamples = 15\nvalue = [2, 13]\nclass = yes\n\n\n\n271->323\n\n\n\n\n\n273\n\nMonth_dec <= 0.5\ngini = 0.023\nsamples = 1544\nvalue = [1526, 18]\nclass = no\n\n\n\n272->273\n\n\n\n\n\n320\n\neuribor3m <= -0.875\ngini = 0.49\nsamples = 7\nvalue = [3, 4]\nclass = yes\n\n\n\n272->320\n\n\n\n\n\n274\n\neuribor3m <= -0.887\ngini = 0.022\nsamples = 1543\nvalue = [1526, 17]\nclass = no\n\n\n\n273->274\n\n\n\n\n\n319\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n273->319\n\n\n\n\n\n275\n\nDay_of_Week_tue <= 0.5\ngini = 0.117\nsamples = 112\nvalue = [105, 7]\nclass = no\n\n\n\n274->275\n\n\n\n\n\n296\n\nMonth_nov <= 0.5\ngini = 0.014\nsamples = 1431\nvalue = [1421, 10]\nclass = no\n\n\n\n274->296\n\n\n\n\n\n276\n\nDay_of_Week_wed <= 0.5\ngini = 0.072\nsamples = 107\nvalue = [103, 4]\nclass = no\n\n\n\n275->276\n\n\n\n\n\n291\n\nduration <= -0.678\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n275->291\n\n\n\n\n\n277\n\nduration <= -0.602\ngini = 0.038\nsamples = 104\nvalue = [102, 2]\nclass = no\n\n\n\n276->277\n\n\n\n\n\n288\n\nduration <= -0.81\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n276->288\n\n\n\n\n\n278\n\nEducation_professional.course <= 0.5\ngini = 0.02\nsamples = 100\nvalue = [99, 1]\nclass = no\n\n\n\n277->278\n\n\n\n\n\n285\n\nJob_student <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n277->285\n\n\n\n\n\n279\n\ngini = 0.0\nsamples = 85\nvalue = [85, 0]\nclass = no\n\n\n\n278->279\n\n\n\n\n\n280\n\nduration <= -0.713\ngini = 0.124\nsamples = 15\nvalue = [14, 1]\nclass = no\n\n\n\n278->280\n\n\n\n\n\n281\n\ngini = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = no\n\n\n\n280->281\n\n\n\n\n\n282\n\nJob_blue-collar <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n280->282\n\n\n\n\n\n283\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n282->283\n\n\n\n\n\n284\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n282->284\n\n\n\n\n\n286\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n285->286\n\n\n\n\n\n287\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n285->287\n\n\n\n\n\n289\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n288->289\n\n\n\n\n\n290\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n288->290\n\n\n\n\n\n292\n\nJob_blue-collar <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n291->292\n\n\n\n\n\n295\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n291->295\n\n\n\n\n\n293\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n292->293\n\n\n\n\n\n294\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n292->294\n\n\n\n\n\n297\n\nduration <= -0.569\ngini = 0.005\nsamples = 1249\nvalue = [1246, 3]\nclass = no\n\n\n\n296->297\n\n\n\n\n\n310\n\neuribor3m <= 0.665\ngini = 0.074\nsamples = 182\nvalue = [175, 7]\nclass = no\n\n\n\n296->310\n\n\n\n\n\n298\n\nduration <= -0.602\ngini = 0.002\nsamples = 1217\nvalue = [1216, 1]\nclass = no\n\n\n\n297->298\n\n\n\n\n\n305\n\ncons.conf.idx <= 0.727\ngini = 0.117\nsamples = 32\nvalue = [30, 2]\nclass = no\n\n\n\n297->305\n\n\n\n\n\n299\n\ngini = 0.0\nsamples = 1142\nvalue = [1142, 0]\nclass = no\n\n\n\n298->299\n\n\n\n\n\n300\n\nduration <= -0.599\ngini = 0.026\nsamples = 75\nvalue = [74, 1]\nclass = no\n\n\n\n298->300\n\n\n\n\n\n301\n\nage <= 0.723\ngini = 0.245\nsamples = 7\nvalue = [6, 1]\nclass = no\n\n\n\n300->301\n\n\n\n\n\n304\n\ngini = 0.0\nsamples = 68\nvalue = [68, 0]\nclass = no\n\n\n\n300->304\n\n\n\n\n\n302\n\ngini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = no\n\n\n\n301->302\n\n\n\n\n\n303\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n301->303\n\n\n\n\n\n306\n\ngini = 0.0\nsamples = 29\nvalue = [29, 0]\nclass = no\n\n\n\n305->306\n\n\n\n\n\n307\n\nEducation_university.degree <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n305->307\n\n\n\n\n\n308\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n307->308\n\n\n\n\n\n309\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n307->309\n\n\n\n\n\n311\n\nJob_housemaid <= 0.5\ngini = 0.011\nsamples = 175\nvalue = [174, 1]\nclass = no\n\n\n\n310->311\n\n\n\n\n\n316\n\nDay_of_Week_tue <= 0.5\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = yes\n\n\n\n310->316\n\n\n\n\n\n312\n\ngini = 0.0\nsamples = 170\nvalue = [170, 0]\nclass = no\n\n\n\n311->312\n\n\n\n\n\n313\n\nEducation_basic.4y <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [4, 1]\nclass = no\n\n\n\n311->313\n\n\n\n\n\n314\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n313->314\n\n\n\n\n\n315\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n313->315\n\n\n\n\n\n317\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n316->317\n\n\n\n\n\n318\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n316->318\n\n\n\n\n\n321\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n320->321\n\n\n\n\n\n322\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n320->322\n\n\n\n\n\n324\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n323->324\n\n\n\n\n\n325\n\ngini = 0.0\nsamples = 13\nvalue = [0, 13]\nclass = yes\n\n\n\n323->325\n\n\n\n\n\n327\n\nPoutcome_success <= 0.5\ngini = 0.137\nsamples = 1162\nvalue = [86, 1076]\nclass = yes\n\n\n\n326->327\n\n\n\n\n\n572\n\nduration <= 0.33\ngini = 0.492\nsamples = 2495\nvalue = [1085, 1410]\nclass = yes\n\n\n\n326->572\n\n\n\n\n\n328\n\nduration <= -0.069\ngini = 0.178\nsamples = 750\nvalue = [74, 676]\nclass = yes\n\n\n\n327->328\n\n\n\n\n\n523\n\ncampaign <= 1.126\ngini = 0.057\nsamples = 412\nvalue = [12, 400]\nclass = yes\n\n\n\n327->523\n\n\n\n\n\n329\n\nduration <= -0.072\ngini = 0.23\nsamples = 369\nvalue = [49, 320]\nclass = yes\n\n\n\n328->329\n\n\n\n\n\n454\n\neuribor3m <= -1.224\ngini = 0.123\nsamples = 381\nvalue = [25, 356]\nclass = yes\n\n\n\n328->454\n\n\n\n\n\n330\n\nContact_telephone <= 0.5\ngini = 0.221\nsamples = 364\nvalue = [46, 318]\nclass = yes\n\n\n\n329->330\n\n\n\n\n\n451\n\nPoutcome_failure <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = no\n\n\n\n329->451\n\n\n\n\n\n331\n\neuribor3m <= -0.992\ngini = 0.196\nsamples = 326\nvalue = [36, 290]\nclass = yes\n\n\n\n330->331\n\n\n\n\n\n432\n\nMonth_oct <= 0.5\ngini = 0.388\nsamples = 38\nvalue = [10, 28]\nclass = yes\n\n\n\n330->432\n\n\n\n\n\n332\n\nage <= -1.672\ngini = 0.163\nsamples = 268\nvalue = [24, 244]\nclass = yes\n\n\n\n331->332\n\n\n\n\n\n401\n\nage <= -0.243\ngini = 0.328\nsamples = 58\nvalue = [12, 46]\nclass = yes\n\n\n\n331->401\n\n\n\n\n\n333\n\nnr.employed <= -1.408\ngini = 0.5\nsamples = 4\nvalue = [2, 2]\nclass = no\n\n\n\n332->333\n\n\n\n\n\n336\n\nage <= 2.613\ngini = 0.153\nsamples = 264\nvalue = [22, 242]\nclass = yes\n\n\n\n332->336\n\n\n\n\n\n334\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n333->334\n\n\n\n\n\n335\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n333->335\n\n\n\n\n\n337\n\nprevious <= 4.564\ngini = 0.12\nsamples = 233\nvalue = [15, 218]\nclass = yes\n\n\n\n336->337\n\n\n\n\n\n390\n\nMonth_may <= 0.5\ngini = 0.35\nsamples = 31\nvalue = [7, 24]\nclass = yes\n\n\n\n336->390\n\n\n\n\n\n338\n\nage <= -0.916\ngini = 0.107\nsamples = 229\nvalue = [13, 216]\nclass = yes\n\n\n\n337->338\n\n\n\n\n\n385\n\nduration <= -0.433\ngini = 0.5\nsamples = 4\nvalue = [2, 2]\nclass = no\n\n\n\n337->385\n\n\n\n\n\n339\n\nduration <= -0.287\ngini = 0.226\nsamples = 54\nvalue = [7, 47]\nclass = yes\n\n\n\n338->339\n\n\n\n\n\n358\n\ncons.price.idx <= -1.868\ngini = 0.066\nsamples = 175\nvalue = [6, 169]\nclass = yes\n\n\n\n338->358\n\n\n\n\n\n340\n\nEducation_unknown <= 0.5\ngini = 0.105\nsamples = 36\nvalue = [2, 34]\nclass = yes\n\n\n\n339->340\n\n\n\n\n\n349\n\neuribor3m <= -1.127\ngini = 0.401\nsamples = 18\nvalue = [5, 13]\nclass = yes\n\n\n\n339->349\n\n\n\n\n\n341\n\nMarital_single <= 0.5\ngini = 0.059\nsamples = 33\nvalue = [1, 32]\nclass = yes\n\n\n\n340->341\n\n\n\n\n\n346\n\ncons.price.idx <= -1.472\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n340->346\n\n\n\n\n\n342\n\nEducation_university.degree <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n341->342\n\n\n\n\n\n345\n\ngini = 0.0\nsamples = 30\nvalue = [0, 30]\nclass = yes\n\n\n\n341->345\n\n\n\n\n\n343\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n342->343\n\n\n\n\n\n344\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n342->344\n\n\n\n\n\n347\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n346->347\n\n\n\n\n\n348\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n346->348\n\n\n\n\n\n350\n\nMonth_mar <= 0.5\ngini = 0.153\nsamples = 12\nvalue = [1, 11]\nclass = yes\n\n\n\n349->350\n\n\n\n\n\n353\n\nJob_admin. <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [4, 2]\nclass = no\n\n\n\n349->353\n\n\n\n\n\n351\n\ngini = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = yes\n\n\n\n350->351\n\n\n\n\n\n352\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n350->352\n\n\n\n\n\n354\n\nJob_technician <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [4, 1]\nclass = no\n\n\n\n353->354\n\n\n\n\n\n357\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n353->357\n\n\n\n\n\n355\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n354->355\n\n\n\n\n\n356\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n354->356\n\n\n\n\n\n359\n\nMarital_married <= 0.5\ngini = 0.202\nsamples = 35\nvalue = [4, 31]\nclass = yes\n\n\n\n358->359\n\n\n\n\n\n374\n\nMonth_oct <= 0.5\ngini = 0.028\nsamples = 140\nvalue = [2, 138]\nclass = yes\n\n\n\n358->374\n\n\n\n\n\n360\n\ngini = 0.0\nsamples = 14\nvalue = [0, 14]\nclass = yes\n\n\n\n359->360\n\n\n\n\n\n361\n\nJob_technician <= 0.5\ngini = 0.308\nsamples = 21\nvalue = [4, 17]\nclass = yes\n\n\n\n359->361\n\n\n\n\n\n362\n\nDay_of_Week_tue <= 0.5\ngini = 0.198\nsamples = 18\nvalue = [2, 16]\nclass = yes\n\n\n\n361->362\n\n\n\n\n\n371\n\nDay_of_Week_mon <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n361->371\n\n\n\n\n\n363\n\neuribor3m <= -1.096\ngini = 0.117\nsamples = 16\nvalue = [1, 15]\nclass = yes\n\n\n\n362->363\n\n\n\n\n\n368\n\nJob_management <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n362->368\n\n\n\n\n\n364\n\ngini = 0.0\nsamples = 13\nvalue = [0, 13]\nclass = yes\n\n\n\n363->364\n\n\n\n\n\n365\n\nEducation_university.degree <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n363->365\n\n\n\n\n\n366\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n365->366\n\n\n\n\n\n367\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n365->367\n\n\n\n\n\n369\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n368->369\n\n\n\n\n\n370\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n368->370\n\n\n\n\n\n372\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n371->372\n\n\n\n\n\n373\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n371->373\n\n\n\n\n\n375\n\ngini = 0.0\nsamples = 111\nvalue = [0, 111]\nclass = yes\n\n\n\n374->375\n\n\n\n\n\n376\n\neuribor3m <= -1.17\ngini = 0.128\nsamples = 29\nvalue = [2, 27]\nclass = yes\n\n\n\n374->376\n\n\n\n\n\n377\n\ngini = 0.0\nsamples = 18\nvalue = [0, 18]\nclass = yes\n\n\n\n376->377\n\n\n\n\n\n378\n\nHousing_no <= 0.5\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n376->378\n\n\n\n\n\n379\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n378->379\n\n\n\n\n\n380\n\ncampaign <= -0.399\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n378->380\n\n\n\n\n\n381\n\nage <= 1.899\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n380->381\n\n\n\n\n\n384\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n380->384\n\n\n\n\n\n382\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n381->382\n\n\n\n\n\n383\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n381->383\n\n\n\n\n\n386\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n385->386\n\n\n\n\n\n387\n\neuribor3m <= -1.084\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n385->387\n\n\n\n\n\n388\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n387->388\n\n\n\n\n\n389\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n387->389\n\n\n\n\n\n391\n\neuribor3m <= -1.098\ngini = 0.285\nsamples = 29\nvalue = [5, 24]\nclass = yes\n\n\n\n390->391\n\n\n\n\n\n400\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n390->400\n\n\n\n\n\n392\n\neuribor3m <= -1.182\ngini = 0.147\nsamples = 25\nvalue = [2, 23]\nclass = yes\n\n\n\n391->392\n\n\n\n\n\n397\n\nDay_of_Week_thu <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n391->397\n\n\n\n\n\n393\n\neuribor3m <= -1.184\ngini = 0.32\nsamples = 10\nvalue = [2, 8]\nclass = yes\n\n\n\n392->393\n\n\n\n\n\n396\n\ngini = 0.0\nsamples = 15\nvalue = [0, 15]\nclass = yes\n\n\n\n392->396\n\n\n\n\n\n394\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n393->394\n\n\n\n\n\n395\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n393->395\n\n\n\n\n\n398\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n397->398\n\n\n\n\n\n399\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n397->399\n\n\n\n\n\n402\n\nJob_blue-collar <= 0.5\ngini = 0.161\nsamples = 34\nvalue = [3, 31]\nclass = yes\n\n\n\n401->402\n\n\n\n\n\n415\n\nage <= -0.075\ngini = 0.469\nsamples = 24\nvalue = [9, 15]\nclass = yes\n\n\n\n401->415\n\n\n\n\n\n403\n\neuribor3m <= -0.9\ngini = 0.117\nsamples = 32\nvalue = [2, 30]\nclass = yes\n\n\n\n402->403\n\n\n\n\n\n412\n\nEducation_professional.course <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n402->412\n\n\n\n\n\n404\n\nEducation_university.degree <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n403->404\n\n\n\n\n\n407\n\nduration <= -0.506\ngini = 0.064\nsamples = 30\nvalue = [1, 29]\nclass = yes\n\n\n\n403->407\n\n\n\n\n\n405\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n404->405\n\n\n\n\n\n406\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n404->406\n\n\n\n\n\n408\n\nJob_admin. <= 0.5\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = yes\n\n\n\n407->408\n\n\n\n\n\n411\n\ngini = 0.0\nsamples = 23\nvalue = [0, 23]\nclass = yes\n\n\n\n407->411\n\n\n\n\n\n409\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n408->409\n\n\n\n\n\n410\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n408->410\n\n\n\n\n\n413\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n412->413\n\n\n\n\n\n414\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n412->414\n\n\n\n\n\n416\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n415->416\n\n\n\n\n\n417\n\nduration <= -0.109\ngini = 0.408\nsamples = 21\nvalue = [6, 15]\nclass = yes\n\n\n\n415->417\n\n\n\n\n\n418\n\nHousing_yes <= 0.5\ngini = 0.375\nsamples = 20\nvalue = [5, 15]\nclass = yes\n\n\n\n417->418\n\n\n\n\n\n431\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n417->431\n\n\n\n\n\n419\n\nJob_blue-collar <= 0.5\ngini = 0.473\nsamples = 13\nvalue = [5, 8]\nclass = yes\n\n\n\n418->419\n\n\n\n\n\n430\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n418->430\n\n\n\n\n\n420\n\nEducation_professional.course <= 0.5\ngini = 0.5\nsamples = 10\nvalue = [5, 5]\nclass = no\n\n\n\n419->420\n\n\n\n\n\n429\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n419->429\n\n\n\n\n\n421\n\ncampaign <= -0.399\ngini = 0.469\nsamples = 8\nvalue = [3, 5]\nclass = yes\n\n\n\n420->421\n\n\n\n\n\n428\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n420->428\n\n\n\n\n\n422\n\nLoan_no <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = no\n\n\n\n421->422\n\n\n\n\n\n427\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n421->427\n\n\n\n\n\n423\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n422->423\n\n\n\n\n\n424\n\nJob_admin. <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n422->424\n\n\n\n\n\n425\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n424->425\n\n\n\n\n\n426\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n424->426\n\n\n\n\n\n433\n\nage <= -1.42\ngini = 0.305\nsamples = 32\nvalue = [6, 26]\nclass = yes\n\n\n\n432->433\n\n\n\n\n\n448\n\nage <= -0.369\ngini = 0.444\nsamples = 6\nvalue = [4, 2]\nclass = no\n\n\n\n432->448\n\n\n\n\n\n434\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n433->434\n\n\n\n\n\n435\n\nMonth_mar <= 0.5\ngini = 0.231\nsamples = 30\nvalue = [4, 26]\nclass = yes\n\n\n\n433->435\n\n\n\n\n\n436\n\ncons.conf.idx <= 1.748\ngini = 0.185\nsamples = 29\nvalue = [3, 26]\nclass = yes\n\n\n\n435->436\n\n\n\n\n\n447\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n435->447\n\n\n\n\n\n437\n\neuribor3m <= -0.893\ngini = 0.08\nsamples = 24\nvalue = [1, 23]\nclass = yes\n\n\n\n436->437\n\n\n\n\n\n442\n\nduration <= -0.491\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n436->442\n\n\n\n\n\n438\n\ngini = 0.0\nsamples = 21\nvalue = [0, 21]\nclass = yes\n\n\n\n437->438\n\n\n\n\n\n439\n\nHousing_no <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n437->439\n\n\n\n\n\n440\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n439->440\n\n\n\n\n\n441\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n439->441\n\n\n\n\n\n443\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n442->443\n\n\n\n\n\n444\n\ncampaign <= 0.11\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n442->444\n\n\n\n\n\n445\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n444->445\n\n\n\n\n\n446\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n444->446\n\n\n\n\n\n449\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n448->449\n\n\n\n\n\n450\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n448->450\n\n\n\n\n\n452\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n451->452\n\n\n\n\n\n453\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n451->453\n\n\n\n\n\n455\n\ncampaign <= -0.399\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n454->455\n\n\n\n\n\n460\n\neuribor3m <= -1.175\ngini = 0.115\nsamples = 375\nvalue = [23, 352]\nclass = yes\n\n\n\n454->460\n\n\n\n\n\n456\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n455->456\n\n\n\n\n\n457\n\nEducation_unknown <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n455->457\n\n\n\n\n\n458\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n457->458\n\n\n\n\n\n459\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n457->459\n\n\n\n\n\n461\n\nMarital_divorced <= 0.5\ngini = 0.016\nsamples = 122\nvalue = [1, 121]\nclass = yes\n\n\n\n460->461\n\n\n\n\n\n468\n\nHousing_yes <= 0.5\ngini = 0.159\nsamples = 253\nvalue = [22, 231]\nclass = yes\n\n\n\n460->468\n\n\n\n\n\n462\n\ngini = 0.0\nsamples = 104\nvalue = [0, 104]\nclass = yes\n\n\n\n461->462\n\n\n\n\n\n463\n\nJob_admin. <= 0.5\ngini = 0.105\nsamples = 18\nvalue = [1, 17]\nclass = yes\n\n\n\n461->463\n\n\n\n\n\n464\n\ngini = 0.0\nsamples = 16\nvalue = [0, 16]\nclass = yes\n\n\n\n463->464\n\n\n\n\n\n465\n\nMonth_mar <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n463->465\n\n\n\n\n\n466\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n465->466\n\n\n\n\n\n467\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n465->467\n\n\n\n\n\n469\n\nprevious <= 3.847\ngini = 0.084\nsamples = 137\nvalue = [6, 131]\nclass = yes\n\n\n\n468->469\n\n\n\n\n\n484\n\nMarital_single <= 0.5\ngini = 0.238\nsamples = 116\nvalue = [16, 100]\nclass = yes\n\n\n\n468->484\n\n\n\n\n\n470\n\neuribor3m <= -1.17\ngini = 0.071\nsamples = 136\nvalue = [5, 131]\nclass = yes\n\n\n\n469->470\n\n\n\n\n\n483\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n469->483\n\n\n\n\n\n471\n\nage <= 0.009\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = yes\n\n\n\n470->471\n\n\n\n\n\n474\n\nprevious <= 1.696\ngini = 0.046\nsamples = 128\nvalue = [3, 125]\nclass = yes\n\n\n\n470->474\n\n\n\n\n\n472\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n471->472\n\n\n\n\n\n473\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n471->473\n\n\n\n\n\n475\n\nJob_housemaid <= 0.5\ngini = 0.017\nsamples = 120\nvalue = [1, 119]\nclass = yes\n\n\n\n474->475\n\n\n\n\n\n480\n\neuribor3m <= -1.085\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = yes\n\n\n\n474->480\n\n\n\n\n\n476\n\ngini = 0.0\nsamples = 115\nvalue = [0, 115]\nclass = yes\n\n\n\n475->476\n\n\n\n\n\n477\n\nage <= 2.277\ngini = 0.32\nsamples = 5\nvalue = [1, 4]\nclass = yes\n\n\n\n475->477\n\n\n\n\n\n478\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n477->478\n\n\n\n\n\n479\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n477->479\n\n\n\n\n\n481\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n480->481\n\n\n\n\n\n482\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n480->482\n\n\n\n\n\n485\n\nage <= 0.429\ngini = 0.307\nsamples = 74\nvalue = [14, 60]\nclass = yes\n\n\n\n484->485\n\n\n\n\n\n516\n\nEducation_basic.6y <= 0.5\ngini = 0.091\nsamples = 42\nvalue = [2, 40]\nclass = yes\n\n\n\n484->516\n\n\n\n\n\n486\n\nage <= 0.093\ngini = 0.411\nsamples = 38\nvalue = [11, 27]\nclass = yes\n\n\n\n485->486\n\n\n\n\n\n503\n\nJob_management <= 0.5\ngini = 0.153\nsamples = 36\nvalue = [3, 33]\nclass = yes\n\n\n\n485->503\n\n\n\n\n\n487\n\nJob_technician <= 0.5\ngini = 0.353\nsamples = 35\nvalue = [8, 27]\nclass = yes\n\n\n\n486->487\n\n\n\n\n\n502\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n486->502\n\n\n\n\n\n488\n\nEducation_high.school <= 0.5\ngini = 0.454\nsamples = 23\nvalue = [8, 15]\nclass = yes\n\n\n\n487->488\n\n\n\n\n\n501\n\ngini = 0.0\nsamples = 12\nvalue = [0, 12]\nclass = yes\n\n\n\n487->501\n\n\n\n\n\n489\n\nage <= -0.663\ngini = 0.498\nsamples = 17\nvalue = [8, 9]\nclass = yes\n\n\n\n488->489\n\n\n\n\n\n500\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n488->500\n\n\n\n\n\n490\n\nDay_of_Week_wed <= 0.5\ngini = 0.278\nsamples = 6\nvalue = [5, 1]\nclass = no\n\n\n\n489->490\n\n\n\n\n\n493\n\ncons.conf.idx <= 0.421\ngini = 0.397\nsamples = 11\nvalue = [3, 8]\nclass = yes\n\n\n\n489->493\n\n\n\n\n\n491\n\ngini = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = no\n\n\n\n490->491\n\n\n\n\n\n492\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n490->492\n\n\n\n\n\n494\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n493->494\n\n\n\n\n\n495\n\ncampaign <= -0.399\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = no\n\n\n\n493->495\n\n\n\n\n\n496\n\nage <= -0.117\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n495->496\n\n\n\n\n\n499\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n495->499\n\n\n\n\n\n497\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n496->497\n\n\n\n\n\n498\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n496->498\n\n\n\n\n\n504\n\nage <= 2.739\ngini = 0.108\nsamples = 35\nvalue = [2, 33]\nclass = yes\n\n\n\n503->504\n\n\n\n\n\n515\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n503->515\n\n\n\n\n\n505\n\nDay_of_Week_tue <= 0.5\ngini = 0.059\nsamples = 33\nvalue = [1, 32]\nclass = yes\n\n\n\n504->505\n\n\n\n\n\n512\n\neuribor3m <= -1.104\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n504->512\n\n\n\n\n\n506\n\ngini = 0.0\nsamples = 24\nvalue = [0, 24]\nclass = yes\n\n\n\n505->506\n\n\n\n\n\n507\n\nJob_admin. <= 0.5\ngini = 0.198\nsamples = 9\nvalue = [1, 8]\nclass = yes\n\n\n\n505->507\n\n\n\n\n\n508\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n507->508\n\n\n\n\n\n509\n\nage <= 0.639\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n507->509\n\n\n\n\n\n510\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n509->510\n\n\n\n\n\n511\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n509->511\n\n\n\n\n\n513\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n512->513\n\n\n\n\n\n514\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n512->514\n\n\n\n\n\n517\n\nDay_of_Week_mon <= 0.5\ngini = 0.048\nsamples = 41\nvalue = [1, 40]\nclass = yes\n\n\n\n516->517\n\n\n\n\n\n522\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n516->522\n\n\n\n\n\n518\n\ngini = 0.0\nsamples = 38\nvalue = [0, 38]\nclass = yes\n\n\n\n517->518\n\n\n\n\n\n519\n\nPoutcome_failure <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n517->519\n\n\n\n\n\n520\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n519->520\n\n\n\n\n\n521\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n519->521\n\n\n\n\n\n524\n\nduration <= 3.396\ngini = 0.049\nsamples = 401\nvalue = [10, 391]\nclass = yes\n\n\n\n523->524\n\n\n\n\n\n569\n\nduration <= -0.326\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n523->569\n\n\n\n\n\n525\n\nJob_technician <= 0.5\ngini = 0.044\nsamples = 397\nvalue = [9, 388]\nclass = yes\n\n\n\n524->525\n\n\n\n\n\n566\n\nMarital_single <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n524->566\n\n\n\n\n\n526\n\nage <= 3.117\ngini = 0.03\nsamples = 333\nvalue = [5, 328]\nclass = yes\n\n\n\n525->526\n\n\n\n\n\n553\n\ncons.price.idx <= 0.543\ngini = 0.117\nsamples = 64\nvalue = [4, 60]\nclass = yes\n\n\n\n525->553\n\n\n\n\n\n527\n\nJob_self-employed <= 0.5\ngini = 0.019\nsamples = 312\nvalue = [3, 309]\nclass = yes\n\n\n\n526->527\n\n\n\n\n\n544\n\nMonth_may <= 0.5\ngini = 0.172\nsamples = 21\nvalue = [2, 19]\nclass = yes\n\n\n\n526->544\n\n\n\n\n\n528\n\nMonth_dec <= 0.5\ngini = 0.013\nsamples = 304\nvalue = [2, 302]\nclass = yes\n\n\n\n527->528\n\n\n\n\n\n541\n\nemp.var.rate <= -0.517\ngini = 0.219\nsamples = 8\nvalue = [1, 7]\nclass = yes\n\n\n\n527->541\n\n\n\n\n\n529\n\neuribor3m <= -1.221\ngini = 0.007\nsamples = 293\nvalue = [1, 292]\nclass = yes\n\n\n\n528->529\n\n\n\n\n\n536\n\nMarital_married <= 0.5\ngini = 0.165\nsamples = 11\nvalue = [1, 10]\nclass = yes\n\n\n\n528->536\n\n\n\n\n\n530\n\nDay_of_Week_mon <= 0.5\ngini = 0.111\nsamples = 17\nvalue = [1, 16]\nclass = yes\n\n\n\n529->530\n\n\n\n\n\n535\n\ngini = 0.0\nsamples = 276\nvalue = [0, 276]\nclass = yes\n\n\n\n529->535\n\n\n\n\n\n531\n\ngini = 0.0\nsamples = 14\nvalue = [0, 14]\nclass = yes\n\n\n\n530->531\n\n\n\n\n\n532\n\ncampaign <= 0.11\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n530->532\n\n\n\n\n\n533\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n532->533\n\n\n\n\n\n534\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n532->534\n\n\n\n\n\n537\n\nDay_of_Week_mon <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n536->537\n\n\n\n\n\n540\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n536->540\n\n\n\n\n\n538\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n537->538\n\n\n\n\n\n539\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n537->539\n\n\n\n\n\n542\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n541->542\n\n\n\n\n\n543\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n541->543\n\n\n\n\n\n545\n\nEducation_university.degree <= 0.5\ngini = 0.1\nsamples = 19\nvalue = [1, 18]\nclass = yes\n\n\n\n544->545\n\n\n\n\n\n550\n\nMarital_married <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n544->550\n\n\n\n\n\n546\n\ngini = 0.0\nsamples = 17\nvalue = [0, 17]\nclass = yes\n\n\n\n545->546\n\n\n\n\n\n547\n\nMonth_oct <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n545->547\n\n\n\n\n\n548\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n547->548\n\n\n\n\n\n549\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n547->549\n\n\n\n\n\n551\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n550->551\n\n\n\n\n\n552\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n550->552\n\n\n\n\n\n554\n\nDay_of_Week_fri <= 0.5\ngini = 0.287\nsamples = 23\nvalue = [4, 19]\nclass = yes\n\n\n\n553->554\n\n\n\n\n\n565\n\ngini = 0.0\nsamples = 41\nvalue = [0, 41]\nclass = yes\n\n\n\n553->565\n\n\n\n\n\n555\n\nDay_of_Week_wed <= 0.5\ngini = 0.172\nsamples = 21\nvalue = [2, 19]\nclass = yes\n\n\n\n554->555\n\n\n\n\n\n564\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n554->564\n\n\n\n\n\n556\n\nDay_of_Week_mon <= 0.5\ngini = 0.1\nsamples = 19\nvalue = [1, 18]\nclass = yes\n\n\n\n555->556\n\n\n\n\n\n561\n\nLoan_yes <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n555->561\n\n\n\n\n\n557\n\ngini = 0.0\nsamples = 16\nvalue = [0, 16]\nclass = yes\n\n\n\n556->557\n\n\n\n\n\n558\n\nemp.var.rate <= -1.069\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n556->558\n\n\n\n\n\n559\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n558->559\n\n\n\n\n\n560\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n558->560\n\n\n\n\n\n562\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n561->562\n\n\n\n\n\n563\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n561->563\n\n\n\n\n\n567\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n566->567\n\n\n\n\n\n568\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n566->568\n\n\n\n\n\n570\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n569->570\n\n\n\n\n\n571\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n569->571\n\n\n\n\n\n573\n\ncons.conf.idx <= -1.203\ngini = 0.409\nsamples = 1197\nvalue = [854, 343]\nclass = no\n\n\n\n572->573\n\n\n\n\n\n892\n\nduration <= 0.68\ngini = 0.293\nsamples = 1298\nvalue = [231, 1067]\nclass = yes\n\n\n\n572->892\n\n\n\n\n\n574\n\neuribor3m <= -0.82\ngini = 0.39\nsamples = 222\nvalue = [59, 163]\nclass = yes\n\n\n\n573->574\n\n\n\n\n\n641\n\nemp.var.rate <= 0.587\ngini = 0.301\nsamples = 975\nvalue = [795, 180]\nclass = no\n\n\n\n573->641\n\n\n\n\n\n575\n\nduration <= -0.404\ngini = 0.068\nsamples = 57\nvalue = [2, 55]\nclass = yes\n\n\n\n574->575\n\n\n\n\n\n584\n\neuribor3m <= -0.766\ngini = 0.452\nsamples = 165\nvalue = [57, 108]\nclass = yes\n\n\n\n574->584\n\n\n\n\n\n576\n\nduration <= -0.419\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n575->576\n\n\n\n\n\n583\n\ngini = 0.0\nsamples = 46\nvalue = [0, 46]\nclass = yes\n\n\n\n575->583\n\n\n\n\n\n577\n\nEducation_basic.9y <= 0.5\ngini = 0.18\nsamples = 10\nvalue = [1, 9]\nclass = yes\n\n\n\n576->577\n\n\n\n\n\n582\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n576->582\n\n\n\n\n\n578\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n577->578\n\n\n\n\n\n579\n\nPoutcome_success <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n577->579\n\n\n\n\n\n580\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n579->580\n\n\n\n\n\n581\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n579->581\n\n\n\n\n\n585\n\nage <= -1.168\ngini = 0.497\nsamples = 122\nvalue = [56, 66]\nclass = yes\n\n\n\n584->585\n\n\n\n\n\n636\n\nduration <= 0.108\ngini = 0.045\nsamples = 43\nvalue = [1, 42]\nclass = yes\n\n\n\n584->636\n\n\n\n\n\n586\n\ngini = 0.0\nsamples = 17\nvalue = [0, 17]\nclass = yes\n\n\n\n585->586\n\n\n\n\n\n587\n\nDay_of_Week_mon <= 0.5\ngini = 0.498\nsamples = 105\nvalue = [56, 49]\nclass = no\n\n\n\n585->587\n\n\n\n\n\n588\n\nEducation_university.degree <= 0.5\ngini = 0.489\nsamples = 80\nvalue = [34, 46]\nclass = yes\n\n\n\n587->588\n\n\n\n\n\n633\n\nage <= 1.269\ngini = 0.211\nsamples = 25\nvalue = [22, 3]\nclass = no\n\n\n\n587->633\n\n\n\n\n\n589\n\nage <= 0.849\ngini = 0.498\nsamples = 53\nvalue = [28, 25]\nclass = no\n\n\n\n588->589\n\n\n\n\n\n622\n\nDay_of_Week_wed <= 0.5\ngini = 0.346\nsamples = 27\nvalue = [6, 21]\nclass = yes\n\n\n\n588->622\n\n\n\n\n\n590\n\nJob_technician <= 0.5\ngini = 0.472\nsamples = 42\nvalue = [26, 16]\nclass = no\n\n\n\n589->590\n\n\n\n\n\n617\n\nDay_of_Week_thu <= 0.5\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n589->617\n\n\n\n\n\n591\n\nage <= -0.831\ngini = 0.422\nsamples = 33\nvalue = [23, 10]\nclass = no\n\n\n\n590->591\n\n\n\n\n\n612\n\nHousing_yes <= 0.5\ngini = 0.444\nsamples = 9\nvalue = [3, 6]\nclass = yes\n\n\n\n590->612\n\n\n\n\n\n592\n\nJob_blue-collar <= 0.5\ngini = 0.49\nsamples = 7\nvalue = [3, 4]\nclass = yes\n\n\n\n591->592\n\n\n\n\n\n597\n\nPoutcome_success <= 0.5\ngini = 0.355\nsamples = 26\nvalue = [20, 6]\nclass = no\n\n\n\n591->597\n\n\n\n\n\n593\n\nDay_of_Week_tue <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n592->593\n\n\n\n\n\n596\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n592->596\n\n\n\n\n\n594\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n593->594\n\n\n\n\n\n595\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n593->595\n\n\n\n\n\n598\n\nJob_housemaid <= 0.5\ngini = 0.32\nsamples = 25\nvalue = [20, 5]\nclass = no\n\n\n\n597->598\n\n\n\n\n\n611\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n597->611\n\n\n\n\n\n599\n\ncampaign <= -0.399\ngini = 0.278\nsamples = 24\nvalue = [20, 4]\nclass = no\n\n\n\n598->599\n\n\n\n\n\n610\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n598->610\n\n\n\n\n\n600\n\ngini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = no\n\n\n\n599->600\n\n\n\n\n\n601\n\nEducation_basic.9y <= 0.5\ngini = 0.391\nsamples = 15\nvalue = [11, 4]\nclass = no\n\n\n\n599->601\n\n\n\n\n\n602\n\nEducation_high.school <= 0.5\ngini = 0.278\nsamples = 12\nvalue = [10, 2]\nclass = no\n\n\n\n601->602\n\n\n\n\n\n607\n\nJob_blue-collar <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n601->607\n\n\n\n\n\n603\n\ngini = 0.0\nsamples = 8\nvalue = [8, 0]\nclass = no\n\n\n\n602->603\n\n\n\n\n\n604\n\nMarital_married <= 0.5\ngini = 0.5\nsamples = 4\nvalue = [2, 2]\nclass = no\n\n\n\n602->604\n\n\n\n\n\n605\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n604->605\n\n\n\n\n\n606\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n604->606\n\n\n\n\n\n608\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n607->608\n\n\n\n\n\n609\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n607->609\n\n\n\n\n\n613\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n612->613\n\n\n\n\n\n614\n\ncampaign <= 0.618\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n612->614\n\n\n\n\n\n615\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n614->615\n\n\n\n\n\n616\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n614->616\n\n\n\n\n\n618\n\nJob_technician <= 0.5\ngini = 0.18\nsamples = 10\nvalue = [1, 9]\nclass = yes\n\n\n\n617->618\n\n\n\n\n\n621\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n617->621\n\n\n\n\n\n619\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n618->619\n\n\n\n\n\n620\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n618->620\n\n\n\n\n\n623\n\nDay_of_Week_fri <= 0.5\ngini = 0.18\nsamples = 20\nvalue = [2, 18]\nclass = yes\n\n\n\n622->623\n\n\n\n\n\n628\n\nage <= -0.243\ngini = 0.49\nsamples = 7\nvalue = [4, 3]\nclass = no\n\n\n\n622->628\n\n\n\n\n\n624\n\ngini = 0.0\nsamples = 17\nvalue = [0, 17]\nclass = yes\n\n\n\n623->624\n\n\n\n\n\n625\n\ncampaign <= -0.399\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n623->625\n\n\n\n\n\n626\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n625->626\n\n\n\n\n\n627\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n625->627\n\n\n\n\n\n629\n\nPoutcome_failure <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n628->629\n\n\n\n\n\n632\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n628->632\n\n\n\n\n\n630\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n629->630\n\n\n\n\n\n631\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n629->631\n\n\n\n\n\n634\n\ngini = 0.0\nsamples = 22\nvalue = [22, 0]\nclass = no\n\n\n\n633->634\n\n\n\n\n\n635\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n633->635\n\n\n\n\n\n637\n\ngini = 0.0\nsamples = 41\nvalue = [0, 41]\nclass = yes\n\n\n\n636->637\n\n\n\n\n\n638\n\nMarital_married <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n636->638\n\n\n\n\n\n639\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n638->639\n\n\n\n\n\n640\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n638->640\n\n\n\n\n\n642\n\neuribor3m <= 0.697\ngini = 0.454\nsamples = 368\nvalue = [240, 128]\nclass = no\n\n\n\n641->642\n\n\n\n\n\n775\n\nduration <= -0.063\ngini = 0.157\nsamples = 607\nvalue = [555, 52]\nclass = no\n\n\n\n641->775\n\n\n\n\n\n643\n\neuribor3m <= -0.887\ngini = 0.429\nsamples = 347\nvalue = [239, 108]\nclass = no\n\n\n\n642->643\n\n\n\n\n\n770\n\nJob_management <= 0.5\ngini = 0.091\nsamples = 21\nvalue = [1, 20]\nclass = yes\n\n\n\n642->770\n\n\n\n\n\n644\n\neuribor3m <= -0.898\ngini = 0.492\nsamples = 96\nvalue = [42, 54]\nclass = yes\n\n\n\n643->644\n\n\n\n\n\n681\n\nDay_of_Week_mon <= 0.5\ngini = 0.338\nsamples = 251\nvalue = [197, 54]\nclass = no\n\n\n\n643->681\n\n\n\n\n\n645\n\nduration <= -0.293\ngini = 0.451\nsamples = 32\nvalue = [21, 11]\nclass = no\n\n\n\n644->645\n\n\n\n\n\n662\n\nDay_of_Week_thu <= 0.5\ngini = 0.441\nsamples = 64\nvalue = [21, 43]\nclass = yes\n\n\n\n644->662\n\n\n\n\n\n646\n\ngini = 0.0\nsamples = 10\nvalue = [10, 0]\nclass = no\n\n\n\n645->646\n\n\n\n\n\n647\n\ncampaign <= 1.634\ngini = 0.5\nsamples = 22\nvalue = [11, 11]\nclass = no\n\n\n\n645->647\n\n\n\n\n\n648\n\nEducation_university.degree <= 0.5\ngini = 0.475\nsamples = 18\nvalue = [7, 11]\nclass = yes\n\n\n\n647->648\n\n\n\n\n\n661\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n647->661\n\n\n\n\n\n649\n\nMarital_divorced <= 0.5\ngini = 0.355\nsamples = 13\nvalue = [3, 10]\nclass = yes\n\n\n\n648->649\n\n\n\n\n\n658\n\nage <= -0.831\ngini = 0.32\nsamples = 5\nvalue = [4, 1]\nclass = no\n\n\n\n648->658\n\n\n\n\n\n650\n\nEducation_unknown <= 0.5\ngini = 0.18\nsamples = 10\nvalue = [1, 9]\nclass = yes\n\n\n\n649->650\n\n\n\n\n\n653\n\nEducation_high.school <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n649->653\n\n\n\n\n\n651\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n650->651\n\n\n\n\n\n652\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n650->652\n\n\n\n\n\n654\n\nHousing_no <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n653->654\n\n\n\n\n\n657\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n653->657\n\n\n\n\n\n655\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n654->655\n\n\n\n\n\n656\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n654->656\n\n\n\n\n\n659\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n658->659\n\n\n\n\n\n660\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n658->660\n\n\n\n\n\n663\n\nDay_of_Week_tue <= 0.5\ngini = 0.188\nsamples = 38\nvalue = [4, 34]\nclass = yes\n\n\n\n662->663\n\n\n\n\n\n670\n\nduration <= 0.168\ngini = 0.453\nsamples = 26\nvalue = [17, 9]\nclass = no\n\n\n\n662->670\n\n\n\n\n\n664\n\ngini = 0.0\nsamples = 29\nvalue = [0, 29]\nclass = yes\n\n\n\n663->664\n\n\n\n\n\n665\n\nJob_admin. <= 0.5\ngini = 0.494\nsamples = 9\nvalue = [4, 5]\nclass = yes\n\n\n\n663->665\n\n\n\n\n\n666\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n665->666\n\n\n\n\n\n667\n\nPoutcome_nonexistent <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [4, 1]\nclass = no\n\n\n\n665->667\n\n\n\n\n\n668\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n667->668\n\n\n\n\n\n669\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n667->669\n\n\n\n\n\n671\n\nprevious <= 1.696\ngini = 0.255\nsamples = 20\nvalue = [17, 3]\nclass = no\n\n\n\n670->671\n\n\n\n\n\n680\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n670->680\n\n\n\n\n\n672\n\nJob_services <= 0.5\ngini = 0.188\nsamples = 19\nvalue = [17, 2]\nclass = no\n\n\n\n671->672\n\n\n\n\n\n679\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n671->679\n\n\n\n\n\n673\n\nEducation_basic.4y <= 0.5\ngini = 0.105\nsamples = 18\nvalue = [17, 1]\nclass = no\n\n\n\n672->673\n\n\n\n\n\n678\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n672->678\n\n\n\n\n\n674\n\ngini = 0.0\nsamples = 16\nvalue = [16, 0]\nclass = no\n\n\n\n673->674\n\n\n\n\n\n675\n\nContact_telephone <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n673->675\n\n\n\n\n\n676\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n675->676\n\n\n\n\n\n677\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n675->677\n\n\n\n\n\n682\n\nduration <= 0.024\ngini = 0.239\nsamples = 195\nvalue = [168, 27]\nclass = no\n\n\n\n681->682\n\n\n\n\n\n743\n\ncampaign <= 0.11\ngini = 0.499\nsamples = 56\nvalue = [29, 27]\nclass = no\n\n\n\n681->743\n\n\n\n\n\n683\n\nEducation_professional.course <= 0.5\ngini = 0.111\nsamples = 152\nvalue = [143, 9]\nclass = no\n\n\n\n682->683\n\n\n\n\n\n720\n\ncampaign <= -0.399\ngini = 0.487\nsamples = 43\nvalue = [25, 18]\nclass = no\n\n\n\n682->720\n\n\n\n\n\n684\n\nPoutcome_success <= 0.5\ngini = 0.073\nsamples = 131\nvalue = [126, 5]\nclass = no\n\n\n\n683->684\n\n\n\n\n\n707\n\nduration <= -0.313\ngini = 0.308\nsamples = 21\nvalue = [17, 4]\nclass = no\n\n\n\n683->707\n\n\n\n\n\n685\n\nMarital_divorced <= 0.5\ngini = 0.061\nsamples = 127\nvalue = [123, 4]\nclass = no\n\n\n\n684->685\n\n\n\n\n\n704\n\nage <= -0.916\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n684->704\n\n\n\n\n\n686\n\nEducation_high.school <= 0.5\ngini = 0.035\nsamples = 112\nvalue = [110, 2]\nclass = no\n\n\n\n685->686\n\n\n\n\n\n699\n\nJob_technician <= 0.5\ngini = 0.231\nsamples = 15\nvalue = [13, 2]\nclass = no\n\n\n\n685->699\n\n\n\n\n\n687\n\ngini = 0.0\nsamples = 83\nvalue = [83, 0]\nclass = no\n\n\n\n686->687\n\n\n\n\n\n688\n\nHousing_yes <= 0.5\ngini = 0.128\nsamples = 29\nvalue = [27, 2]\nclass = no\n\n\n\n686->688\n\n\n\n\n\n689\n\ngini = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = no\n\n\n\n688->689\n\n\n\n\n\n690\n\nJob_services <= 0.5\ngini = 0.219\nsamples = 16\nvalue = [14, 2]\nclass = no\n\n\n\n688->690\n\n\n\n\n\n691\n\nLoan_yes <= 0.5\ngini = 0.133\nsamples = 14\nvalue = [13, 1]\nclass = no\n\n\n\n690->691\n\n\n\n\n\n696\n\nDay_of_Week_tue <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n690->696\n\n\n\n\n\n692\n\ngini = 0.0\nsamples = 10\nvalue = [10, 0]\nclass = no\n\n\n\n691->692\n\n\n\n\n\n693\n\nMarital_married <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n691->693\n\n\n\n\n\n694\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n693->694\n\n\n\n\n\n695\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n693->695\n\n\n\n\n\n697\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n696->697\n\n\n\n\n\n698\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n696->698\n\n\n\n\n\n700\n\nJob_management <= 0.5\ngini = 0.133\nsamples = 14\nvalue = [13, 1]\nclass = no\n\n\n\n699->700\n\n\n\n\n\n703\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n699->703\n\n\n\n\n\n701\n\ngini = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = no\n\n\n\n700->701\n\n\n\n\n\n702\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n700->702\n\n\n\n\n\n705\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n704->705\n\n\n\n\n\n706\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n704->706\n\n\n\n\n\n708\n\ngini = 0.0\nsamples = 11\nvalue = [11, 0]\nclass = no\n\n\n\n707->708\n\n\n\n\n\n709\n\nHousing_yes <= 0.5\ngini = 0.48\nsamples = 10\nvalue = [6, 4]\nclass = no\n\n\n\n707->709\n\n\n\n\n\n710\n\nDay_of_Week_thu <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n709->710\n\n\n\n\n\n715\n\nage <= 0.009\ngini = 0.32\nsamples = 5\nvalue = [4, 1]\nclass = no\n\n\n\n709->715\n\n\n\n\n\n711\n\ncons.conf.idx <= -0.73\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n710->711\n\n\n\n\n\n714\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n710->714\n\n\n\n\n\n712\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n711->712\n\n\n\n\n\n713\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n711->713\n\n\n\n\n\n716\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n715->716\n\n\n\n\n\n717\n\nduration <= -0.201\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n715->717\n\n\n\n\n\n718\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n717->718\n\n\n\n\n\n719\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n717->719\n\n\n\n\n\n721\n\nduration <= 0.252\ngini = 0.476\nsamples = 23\nvalue = [9, 14]\nclass = yes\n\n\n\n720->721\n\n\n\n\n\n732\n\nJob_entrepreneur <= 0.5\ngini = 0.32\nsamples = 20\nvalue = [16, 4]\nclass = no\n\n\n\n720->732\n\n\n\n\n\n722\n\nJob_services <= 0.5\ngini = 0.498\nsamples = 17\nvalue = [9, 8]\nclass = no\n\n\n\n721->722\n\n\n\n\n\n731\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n721->731\n\n\n\n\n\n723\n\nMarital_single <= 0.5\ngini = 0.49\nsamples = 14\nvalue = [6, 8]\nclass = yes\n\n\n\n722->723\n\n\n\n\n\n730\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n722->730\n\n\n\n\n\n724\n\nage <= 0.723\ngini = 0.48\nsamples = 10\nvalue = [6, 4]\nclass = no\n\n\n\n723->724\n\n\n\n\n\n729\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n723->729\n\n\n\n\n\n725\n\nage <= -0.831\ngini = 0.245\nsamples = 7\nvalue = [6, 1]\nclass = no\n\n\n\n724->725\n\n\n\n\n\n728\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n724->728\n\n\n\n\n\n726\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n725->726\n\n\n\n\n\n727\n\ngini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = no\n\n\n\n725->727\n\n\n\n\n\n733\n\nEducation_basic.6y <= 0.5\ngini = 0.208\nsamples = 17\nvalue = [15, 2]\nclass = no\n\n\n\n732->733\n\n\n\n\n\n740\n\nDay_of_Week_wed <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n732->740\n\n\n\n\n\n734\n\nJob_admin. <= 0.5\ngini = 0.117\nsamples = 16\nvalue = [15, 1]\nclass = no\n\n\n\n733->734\n\n\n\n\n\n739\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n733->739\n\n\n\n\n\n735\n\ngini = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = no\n\n\n\n734->735\n\n\n\n\n\n736\n\nDay_of_Week_tue <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n734->736\n\n\n\n\n\n737\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n736->737\n\n\n\n\n\n738\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n736->738\n\n\n\n\n\n741\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n740->741\n\n\n\n\n\n742\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n740->742\n\n\n\n\n\n744\n\nPoutcome_success <= 0.5\ngini = 0.444\nsamples = 39\nvalue = [26, 13]\nclass = no\n\n\n\n743->744\n\n\n\n\n\n765\n\neuribor3m <= -0.86\ngini = 0.291\nsamples = 17\nvalue = [3, 14]\nclass = yes\n\n\n\n743->765\n\n\n\n\n\n745\n\nduration <= 0.142\ngini = 0.401\nsamples = 36\nvalue = [26, 10]\nclass = no\n\n\n\n744->745\n\n\n\n\n\n764\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n744->764\n\n\n\n\n\n746\n\ncons.conf.idx <= -0.73\ngini = 0.328\nsamples = 29\nvalue = [23, 6]\nclass = no\n\n\n\n745->746\n\n\n\n\n\n759\n\nage <= -0.411\ngini = 0.49\nsamples = 7\nvalue = [3, 4]\nclass = yes\n\n\n\n745->759\n\n\n\n\n\n747\n\neuribor3m <= -0.86\ngini = 0.457\nsamples = 17\nvalue = [11, 6]\nclass = no\n\n\n\n746->747\n\n\n\n\n\n758\n\ngini = 0.0\nsamples = 12\nvalue = [12, 0]\nclass = no\n\n\n\n746->758\n\n\n\n\n\n748\n\ngini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = no\n\n\n\n747->748\n\n\n\n\n\n749\n\nContact_cellular <= 0.5\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = yes\n\n\n\n747->749\n\n\n\n\n\n750\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n749->750\n\n\n\n\n\n751\n\nduration <= -0.38\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = yes\n\n\n\n749->751\n\n\n\n\n\n752\n\nJob_services <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n751->752\n\n\n\n\n\n757\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n751->757\n\n\n\n\n\n753\n\nduration <= -0.442\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n752->753\n\n\n\n\n\n756\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n752->756\n\n\n\n\n\n754\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n753->754\n\n\n\n\n\n755\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n753->755\n\n\n\n\n\n760\n\nMarital_married <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n759->760\n\n\n\n\n\n763\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n759->763\n\n\n\n\n\n761\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n760->761\n\n\n\n\n\n762\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n760->762\n\n\n\n\n\n766\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n765->766\n\n\n\n\n\n767\n\ncons.price.idx <= -0.67\ngini = 0.124\nsamples = 15\nvalue = [1, 14]\nclass = yes\n\n\n\n765->767\n\n\n\n\n\n768\n\ngini = 0.0\nsamples = 14\nvalue = [0, 14]\nclass = yes\n\n\n\n767->768\n\n\n\n\n\n769\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n767->769\n\n\n\n\n\n771\n\ngini = 0.0\nsamples = 18\nvalue = [0, 18]\nclass = yes\n\n\n\n770->771\n\n\n\n\n\n772\n\ncons.conf.idx <= -0.191\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n770->772\n\n\n\n\n\n773\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n772->773\n\n\n\n\n\n774\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n772->774\n\n\n\n\n\n776\n\nJob_student <= 0.5\ngini = 0.046\nsamples = 422\nvalue = [412, 10]\nclass = no\n\n\n\n775->776\n\n\n\n\n\n815\n\neuribor3m <= 1.067\ngini = 0.351\nsamples = 185\nvalue = [143, 42]\nclass = no\n\n\n\n775->815\n\n\n\n\n\n777\n\neuribor3m <= 1.066\ngini = 0.042\nsamples = 418\nvalue = [409, 9]\nclass = no\n\n\n\n776->777\n\n\n\n\n\n812\n\nduration <= -0.313\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n776->812\n\n\n\n\n\n778\n\nage <= -1.168\ngini = 0.019\nsamples = 308\nvalue = [305, 3]\nclass = no\n\n\n\n777->778\n\n\n\n\n\n791\n\nContact_telephone <= 0.5\ngini = 0.103\nsamples = 110\nvalue = [104, 6]\nclass = no\n\n\n\n777->791\n\n\n\n\n\n779\n\nJob_blue-collar <= 0.5\ngini = 0.208\nsamples = 17\nvalue = [15, 2]\nclass = no\n\n\n\n778->779\n\n\n\n\n\n784\n\nJob_entrepreneur <= 0.5\ngini = 0.007\nsamples = 291\nvalue = [290, 1]\nclass = no\n\n\n\n778->784\n\n\n\n\n\n780\n\ngini = 0.0\nsamples = 12\nvalue = [12, 0]\nclass = no\n\n\n\n779->780\n\n\n\n\n\n781\n\neuribor3m <= 1.064\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = no\n\n\n\n779->781\n\n\n\n\n\n782\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n781->782\n\n\n\n\n\n783\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n781->783\n\n\n\n\n\n785\n\ngini = 0.0\nsamples = 276\nvalue = [276, 0]\nclass = no\n\n\n\n784->785\n\n\n\n\n\n786\n\nEducation_professional.course <= 0.5\ngini = 0.124\nsamples = 15\nvalue = [14, 1]\nclass = no\n\n\n\n784->786\n\n\n\n\n\n787\n\ngini = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = no\n\n\n\n786->787\n\n\n\n\n\n788\n\nLoan_yes <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n786->788\n\n\n\n\n\n789\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n788->789\n\n\n\n\n\n790\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n788->790\n\n\n\n\n\n792\n\ncons.price.idx <= 0.334\ngini = 0.076\nsamples = 101\nvalue = [97, 4]\nclass = no\n\n\n\n791->792\n\n\n\n\n\n807\n\nage <= -0.495\ngini = 0.346\nsamples = 9\nvalue = [7, 2]\nclass = no\n\n\n\n791->807\n\n\n\n\n\n793\n\ncampaign <= 2.65\ngini = 0.024\nsamples = 83\nvalue = [82, 1]\nclass = no\n\n\n\n792->793\n\n\n\n\n\n798\n\nduration <= -0.082\ngini = 0.278\nsamples = 18\nvalue = [15, 3]\nclass = no\n\n\n\n792->798\n\n\n\n\n\n794\n\ngini = 0.0\nsamples = 80\nvalue = [80, 0]\nclass = no\n\n\n\n793->794\n\n\n\n\n\n795\n\nDay_of_Week_fri <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n793->795\n\n\n\n\n\n796\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n795->796\n\n\n\n\n\n797\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n795->797\n\n\n\n\n\n799\n\nEducation_basic.9y <= 0.5\ngini = 0.208\nsamples = 17\nvalue = [15, 2]\nclass = no\n\n\n\n798->799\n\n\n\n\n\n806\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n798->806\n\n\n\n\n\n800\n\nJob_technician <= 0.5\ngini = 0.117\nsamples = 16\nvalue = [15, 1]\nclass = no\n\n\n\n799->800\n\n\n\n\n\n805\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n799->805\n\n\n\n\n\n801\n\ngini = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = no\n\n\n\n800->801\n\n\n\n\n\n802\n\nage <= -0.621\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n800->802\n\n\n\n\n\n803\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n802->803\n\n\n\n\n\n804\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n802->804\n\n\n\n\n\n808\n\ngini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = no\n\n\n\n807->808\n\n\n\n\n\n809\n\nJob_services <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n807->809\n\n\n\n\n\n810\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n809->810\n\n\n\n\n\n811\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n809->811\n\n\n\n\n\n813\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n812->813\n\n\n\n\n\n814\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n812->814\n\n\n\n\n\n816\n\nDay_of_Week_mon <= 0.5\ngini = 0.266\nsamples = 158\nvalue = [133, 25]\nclass = no\n\n\n\n815->816\n\n\n\n\n\n883\n\nduration <= 0.124\ngini = 0.466\nsamples = 27\nvalue = [10, 17]\nclass = yes\n\n\n\n815->883\n\n\n\n\n\n817\n\nage <= -1.378\ngini = 0.208\nsamples = 127\nvalue = [112, 15]\nclass = no\n\n\n\n816->817\n\n\n\n\n\n866\n\nage <= 0.513\ngini = 0.437\nsamples = 31\nvalue = [21, 10]\nclass = no\n\n\n\n816->866\n\n\n\n\n\n818\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n817->818\n\n\n\n\n\n819\n\nHousing_unknown <= 0.5\ngini = 0.198\nsamples = 126\nvalue = [112, 14]\nclass = no\n\n\n\n817->819\n\n\n\n\n\n820\n\ncons.conf.idx <= -0.386\ngini = 0.177\nsamples = 122\nvalue = [110, 12]\nclass = no\n\n\n\n819->820\n\n\n\n\n\n863\n\neuribor3m <= 1.038\ngini = 0.5\nsamples = 4\nvalue = [2, 2]\nclass = no\n\n\n\n819->863\n\n\n\n\n\n821\n\nDay_of_Week_tue <= 0.5\ngini = 0.312\nsamples = 31\nvalue = [25, 6]\nclass = no\n\n\n\n820->821\n\n\n\n\n\n838\n\nduration <= 0.252\ngini = 0.123\nsamples = 91\nvalue = [85, 6]\nclass = no\n\n\n\n820->838\n\n\n\n\n\n822\n\nage <= 0.849\ngini = 0.42\nsamples = 20\nvalue = [14, 6]\nclass = no\n\n\n\n821->822\n\n\n\n\n\n837\n\ngini = 0.0\nsamples = 11\nvalue = [11, 0]\nclass = no\n\n\n\n821->837\n\n\n\n\n\n823\n\neuribor3m <= 1.063\ngini = 0.346\nsamples = 18\nvalue = [14, 4]\nclass = no\n\n\n\n822->823\n\n\n\n\n\n836\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n822->836\n\n\n\n\n\n824\n\nEducation_basic.9y <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n823->824\n\n\n\n\n\n827\n\nEducation_unknown <= 0.5\ngini = 0.231\nsamples = 15\nvalue = [13, 2]\nclass = no\n\n\n\n823->827\n\n\n\n\n\n825\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n824->825\n\n\n\n\n\n826\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n824->826\n\n\n\n\n\n828\n\nduration <= -0.027\ngini = 0.142\nsamples = 13\nvalue = [12, 1]\nclass = no\n\n\n\n827->828\n\n\n\n\n\n833\n\nLoan_yes <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n827->833\n\n\n\n\n\n829\n\ncampaign <= -0.399\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n828->829\n\n\n\n\n\n832\n\ngini = 0.0\nsamples = 10\nvalue = [10, 0]\nclass = no\n\n\n\n828->832\n\n\n\n\n\n830\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n829->830\n\n\n\n\n\n831\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n829->831\n\n\n\n\n\n834\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n833->834\n\n\n\n\n\n835\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n833->835\n\n\n\n\n\n839\n\ncampaign <= 1.634\ngini = 0.077\nsamples = 75\nvalue = [72, 3]\nclass = no\n\n\n\n838->839\n\n\n\n\n\n854\n\nLoan_yes <= 0.5\ngini = 0.305\nsamples = 16\nvalue = [13, 3]\nclass = no\n\n\n\n838->854\n\n\n\n\n\n840\n\nEducation_professional.course <= 0.5\ngini = 0.055\nsamples = 71\nvalue = [69, 2]\nclass = no\n\n\n\n839->840\n\n\n\n\n\n851\n\nEducation_high.school <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n839->851\n\n\n\n\n\n841\n\neuribor3m <= 1.01\ngini = 0.032\nsamples = 61\nvalue = [60, 1]\nclass = no\n\n\n\n840->841\n\n\n\n\n\n848\n\nduration <= -0.009\ngini = 0.18\nsamples = 10\nvalue = [9, 1]\nclass = no\n\n\n\n840->848\n\n\n\n\n\n842\n\nJob_blue-collar <= 0.5\ngini = 0.142\nsamples = 13\nvalue = [12, 1]\nclass = no\n\n\n\n841->842\n\n\n\n\n\n847\n\ngini = 0.0\nsamples = 48\nvalue = [48, 0]\nclass = no\n\n\n\n841->847\n\n\n\n\n\n843\n\ngini = 0.0\nsamples = 10\nvalue = [10, 0]\nclass = no\n\n\n\n842->843\n\n\n\n\n\n844\n\neuribor3m <= 1.009\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n842->844\n\n\n\n\n\n845\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n844->845\n\n\n\n\n\n846\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n844->846\n\n\n\n\n\n849\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n848->849\n\n\n\n\n\n850\n\ngini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = no\n\n\n\n848->850\n\n\n\n\n\n852\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n851->852\n\n\n\n\n\n853\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n851->853\n\n\n\n\n\n855\n\nEducation_basic.6y <= 0.5\ngini = 0.231\nsamples = 15\nvalue = [13, 2]\nclass = no\n\n\n\n854->855\n\n\n\n\n\n862\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n854->862\n\n\n\n\n\n856\n\nJob_services <= 0.5\ngini = 0.133\nsamples = 14\nvalue = [13, 1]\nclass = no\n\n\n\n855->856\n\n\n\n\n\n861\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n855->861\n\n\n\n\n\n857\n\ngini = 0.0\nsamples = 12\nvalue = [12, 0]\nclass = no\n\n\n\n856->857\n\n\n\n\n\n858\n\nEducation_unknown <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n856->858\n\n\n\n\n\n859\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n858->859\n\n\n\n\n\n860\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n858->860\n\n\n\n\n\n864\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n863->864\n\n\n\n\n\n865\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n863->865\n\n\n\n\n\n867\n\nduration <= 0.033\ngini = 0.384\nsamples = 27\nvalue = [20, 7]\nclass = no\n\n\n\n866->867\n\n\n\n\n\n880\n\nJob_admin. <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n866->880\n\n\n\n\n\n868\n\ngini = 0.0\nsamples = 10\nvalue = [10, 0]\nclass = no\n\n\n\n867->868\n\n\n\n\n\n869\n\nJob_admin. <= 0.5\ngini = 0.484\nsamples = 17\nvalue = [10, 7]\nclass = no\n\n\n\n867->869\n\n\n\n\n\n870\n\ncampaign <= -0.399\ngini = 0.5\nsamples = 14\nvalue = [7, 7]\nclass = no\n\n\n\n869->870\n\n\n\n\n\n879\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n869->879\n\n\n\n\n\n871\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n870->871\n\n\n\n\n\n872\n\nJob_blue-collar <= 0.5\ngini = 0.463\nsamples = 11\nvalue = [7, 4]\nclass = no\n\n\n\n870->872\n\n\n\n\n\n873\n\nage <= -0.369\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n872->873\n\n\n\n\n\n878\n\ngini = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = no\n\n\n\n872->878\n\n\n\n\n\n874\n\nduration <= 0.246\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n873->874\n\n\n\n\n\n877\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n873->877\n\n\n\n\n\n875\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n874->875\n\n\n\n\n\n876\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n874->876\n\n\n\n\n\n881\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n880->881\n\n\n\n\n\n882\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n880->882\n\n\n\n\n\n884\n\nduration <= -0.034\ngini = 0.142\nsamples = 13\nvalue = [1, 12]\nclass = yes\n\n\n\n883->884\n\n\n\n\n\n887\n\nage <= -0.159\ngini = 0.459\nsamples = 14\nvalue = [9, 5]\nclass = no\n\n\n\n883->887\n\n\n\n\n\n885\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n884->885\n\n\n\n\n\n886\n\ngini = 0.0\nsamples = 12\nvalue = [0, 12]\nclass = yes\n\n\n\n884->886\n\n\n\n\n\n888\n\nEducation_high.school <= 0.5\ngini = 0.408\nsamples = 7\nvalue = [2, 5]\nclass = yes\n\n\n\n887->888\n\n\n\n\n\n891\n\ngini = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = no\n\n\n\n887->891\n\n\n\n\n\n889\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n888->889\n\n\n\n\n\n890\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n888->890\n\n\n\n\n\n893\n\nContact_telephone <= 0.5\ngini = 0.438\nsamples = 299\nvalue = [97, 202]\nclass = yes\n\n\n\n892->893\n\n\n\n\n\n1044\n\nduration <= 1.354\ngini = 0.232\nsamples = 999\nvalue = [134, 865]\nclass = yes\n\n\n\n892->1044\n\n\n\n\n\n894\n\nage <= -1.252\ngini = 0.404\nsamples = 221\nvalue = [62, 159]\nclass = yes\n\n\n\n893->894\n\n\n\n\n\n993\n\neuribor3m <= 1.014\ngini = 0.495\nsamples = 78\nvalue = [35, 43]\nclass = yes\n\n\n\n893->993\n\n\n\n\n\n895\n\nage <= -1.42\ngini = 0.444\nsamples = 9\nvalue = [6, 3]\nclass = no\n\n\n\n894->895\n\n\n\n\n\n902\n\nMarital_married <= 0.5\ngini = 0.389\nsamples = 212\nvalue = [56, 156]\nclass = yes\n\n\n\n894->902\n\n\n\n\n\n896\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n895->896\n\n\n\n\n\n897\n\neuribor3m <= 1.065\ngini = 0.375\nsamples = 8\nvalue = [6, 2]\nclass = no\n\n\n\n895->897\n\n\n\n\n\n898\n\ngini = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = no\n\n\n\n897->898\n\n\n\n\n\n899\n\nJob_services <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n897->899\n\n\n\n\n\n900\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n899->900\n\n\n\n\n\n901\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n899->901\n\n\n\n\n\n903\n\nEducation_basic.4y <= 0.5\ngini = 0.293\nsamples = 84\nvalue = [15, 69]\nclass = yes\n\n\n\n902->903\n\n\n\n\n\n932\n\nduration <= 0.414\ngini = 0.435\nsamples = 128\nvalue = [41, 87]\nclass = yes\n\n\n\n902->932\n\n\n\n\n\n904\n\nduration <= 0.673\ngini = 0.267\nsamples = 82\nvalue = [13, 69]\nclass = yes\n\n\n\n903->904\n\n\n\n\n\n931\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n903->931\n\n\n\n\n\n905\n\nage <= -0.327\ngini = 0.252\nsamples = 81\nvalue = [12, 69]\nclass = yes\n\n\n\n904->905\n\n\n\n\n\n930\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n904->930\n\n\n\n\n\n906\n\neuribor3m <= -0.858\ngini = 0.156\nsamples = 47\nvalue = [4, 43]\nclass = yes\n\n\n\n905->906\n\n\n\n\n\n915\n\neuribor3m <= -0.827\ngini = 0.36\nsamples = 34\nvalue = [8, 26]\nclass = yes\n\n\n\n905->915\n\n\n\n\n\n907\n\nduration <= 0.457\ngini = 0.42\nsamples = 10\nvalue = [3, 7]\nclass = yes\n\n\n\n906->907\n\n\n\n\n\n910\n\nduration <= 0.647\ngini = 0.053\nsamples = 37\nvalue = [1, 36]\nclass = yes\n\n\n\n906->910\n\n\n\n\n\n908\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n907->908\n\n\n\n\n\n909\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n907->909\n\n\n\n\n\n911\n\ngini = 0.0\nsamples = 35\nvalue = [0, 35]\nclass = yes\n\n\n\n910->911\n\n\n\n\n\n912\n\nduration <= 0.668\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n910->912\n\n\n\n\n\n913\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n912->913\n\n\n\n\n\n914\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n912->914\n\n\n\n\n\n916\n\nPoutcome_failure <= 0.5\ngini = 0.117\nsamples = 16\nvalue = [1, 15]\nclass = yes\n\n\n\n915->916\n\n\n\n\n\n921\n\nEducation_professional.course <= 0.5\ngini = 0.475\nsamples = 18\nvalue = [7, 11]\nclass = yes\n\n\n\n915->921\n\n\n\n\n\n917\n\ngini = 0.0\nsamples = 14\nvalue = [0, 14]\nclass = yes\n\n\n\n916->917\n\n\n\n\n\n918\n\nJob_admin. <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n916->918\n\n\n\n\n\n919\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n918->919\n\n\n\n\n\n920\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n918->920\n\n\n\n\n\n922\n\nduration <= 0.462\ngini = 0.43\nsamples = 16\nvalue = [5, 11]\nclass = yes\n\n\n\n921->922\n\n\n\n\n\n929\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n921->929\n\n\n\n\n\n923\n\nage <= -0.159\ngini = 0.198\nsamples = 9\nvalue = [1, 8]\nclass = yes\n\n\n\n922->923\n\n\n\n\n\n926\n\nage <= 0.177\ngini = 0.49\nsamples = 7\nvalue = [4, 3]\nclass = no\n\n\n\n922->926\n\n\n\n\n\n924\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n923->924\n\n\n\n\n\n925\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n923->925\n\n\n\n\n\n927\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n926->927\n\n\n\n\n\n928\n\ngini = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = no\n\n\n\n926->928\n\n\n\n\n\n933\n\nduration <= 0.358\ngini = 0.499\nsamples = 29\nvalue = [15, 14]\nclass = no\n\n\n\n932->933\n\n\n\n\n\n946\n\nage <= 1.269\ngini = 0.387\nsamples = 99\nvalue = [26, 73]\nclass = yes\n\n\n\n932->946\n\n\n\n\n\n934\n\nMonth_nov <= 0.5\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n933->934\n\n\n\n\n\n939\n\ncons.conf.idx <= -1.203\ngini = 0.401\nsamples = 18\nvalue = [13, 5]\nclass = no\n\n\n\n933->939\n\n\n\n\n\n935\n\nduration <= 0.333\ngini = 0.18\nsamples = 10\nvalue = [1, 9]\nclass = yes\n\n\n\n934->935\n\n\n\n\n\n938\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n934->938\n\n\n\n\n\n936\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n935->936\n\n\n\n\n\n937\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n935->937\n\n\n\n\n\n940\n\nDay_of_Week_fri <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n939->940\n\n\n\n\n\n943\n\nDay_of_Week_fri <= 0.5\ngini = 0.153\nsamples = 12\nvalue = [11, 1]\nclass = no\n\n\n\n939->943\n\n\n\n\n\n941\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n940->941\n\n\n\n\n\n942\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n940->942\n\n\n\n\n\n944\n\ngini = 0.0\nsamples = 11\nvalue = [11, 0]\nclass = no\n\n\n\n943->944\n\n\n\n\n\n945\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n943->945\n\n\n\n\n\n947\n\nage <= 1.185\ngini = 0.414\nsamples = 89\nvalue = [26, 63]\nclass = yes\n\n\n\n946->947\n\n\n\n\n\n992\n\ngini = 0.0\nsamples = 10\nvalue = [0, 10]\nclass = yes\n\n\n\n946->992\n\n\n\n\n\n948\n\neuribor3m <= -0.877\ngini = 0.4\nsamples = 87\nvalue = [24, 63]\nclass = yes\n\n\n\n947->948\n\n\n\n\n\n991\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n947->991\n\n\n\n\n\n949\n\nDay_of_Week_thu <= 0.5\ngini = 0.188\nsamples = 19\nvalue = [2, 17]\nclass = yes\n\n\n\n948->949\n\n\n\n\n\n954\n\neuribor3m <= 0.86\ngini = 0.438\nsamples = 68\nvalue = [22, 46]\nclass = yes\n\n\n\n948->954\n\n\n\n\n\n950\n\ngini = 0.0\nsamples = 16\nvalue = [0, 16]\nclass = yes\n\n\n\n949->950\n\n\n\n\n\n951\n\nEducation_basic.4y <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n949->951\n\n\n\n\n\n952\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n951->952\n\n\n\n\n\n953\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n951->953\n\n\n\n\n\n955\n\nage <= 0.597\ngini = 0.496\nsamples = 33\nvalue = [15, 18]\nclass = yes\n\n\n\n954->955\n\n\n\n\n\n974\n\nJob_admin. <= 0.5\ngini = 0.32\nsamples = 35\nvalue = [7, 28]\nclass = yes\n\n\n\n954->974\n\n\n\n\n\n956\n\nduration <= 0.536\ngini = 0.494\nsamples = 27\nvalue = [15, 12]\nclass = no\n\n\n\n955->956\n\n\n\n\n\n973\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n955->973\n\n\n\n\n\n957\n\nduration <= 0.484\ngini = 0.355\nsamples = 13\nvalue = [10, 3]\nclass = no\n\n\n\n956->957\n\n\n\n\n\n962\n\nage <= -0.579\ngini = 0.459\nsamples = 14\nvalue = [5, 9]\nclass = yes\n\n\n\n956->962\n\n\n\n\n\n958\n\nduration <= 0.462\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n957->958\n\n\n\n\n\n961\n\ngini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = no\n\n\n\n957->961\n\n\n\n\n\n959\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n958->959\n\n\n\n\n\n960\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n958->960\n\n\n\n\n\n963\n\nEducation_basic.6y <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n962->963\n\n\n\n\n\n966\n\nage <= 0.345\ngini = 0.32\nsamples = 10\nvalue = [2, 8]\nclass = yes\n\n\n\n962->966\n\n\n\n\n\n964\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n963->964\n\n\n\n\n\n965\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n963->965\n\n\n\n\n\n967\n\nduration <= 0.598\ngini = 0.198\nsamples = 9\nvalue = [1, 8]\nclass = yes\n\n\n\n966->967\n\n\n\n\n\n972\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n966->972\n\n\n\n\n\n968\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n967->968\n\n\n\n\n\n969\n\nduration <= 0.616\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n967->969\n\n\n\n\n\n970\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n969->970\n\n\n\n\n\n971\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n969->971\n\n\n\n\n\n975\n\nEducation_university.degree <= 0.5\ngini = 0.423\nsamples = 23\nvalue = [7, 16]\nclass = yes\n\n\n\n974->975\n\n\n\n\n\n990\n\ngini = 0.0\nsamples = 12\nvalue = [0, 12]\nclass = yes\n\n\n\n974->990\n\n\n\n\n\n976\n\nJob_unemployed <= 0.5\ngini = 0.363\nsamples = 21\nvalue = [5, 16]\nclass = yes\n\n\n\n975->976\n\n\n\n\n\n989\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n975->989\n\n\n\n\n\n977\n\neuribor3m <= 1.069\ngini = 0.32\nsamples = 20\nvalue = [4, 16]\nclass = yes\n\n\n\n976->977\n\n\n\n\n\n988\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n976->988\n\n\n\n\n\n978\n\neuribor3m <= 1.066\ngini = 0.266\nsamples = 19\nvalue = [3, 16]\nclass = yes\n\n\n\n977->978\n\n\n\n\n\n987\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n977->987\n\n\n\n\n\n979\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n978->979\n\n\n\n\n\n980\n\nEducation_high.school <= 0.5\ngini = 0.42\nsamples = 10\nvalue = [3, 7]\nclass = yes\n\n\n\n978->980\n\n\n\n\n\n981\n\nDay_of_Week_wed <= 0.5\ngini = 0.219\nsamples = 8\nvalue = [1, 7]\nclass = yes\n\n\n\n980->981\n\n\n\n\n\n986\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n980->986\n\n\n\n\n\n982\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n981->982\n\n\n\n\n\n983\n\nHousing_yes <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n981->983\n\n\n\n\n\n984\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n983->984\n\n\n\n\n\n985\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n983->985\n\n\n\n\n\n994\n\nHousing_no <= 0.5\ngini = 0.496\nsamples = 55\nvalue = [30, 25]\nclass = no\n\n\n\n993->994\n\n\n\n\n\n1033\n\nJob_management <= 0.5\ngini = 0.34\nsamples = 23\nvalue = [5, 18]\nclass = yes\n\n\n\n993->1033\n\n\n\n\n\n995\n\nduration <= 0.364\ngini = 0.444\nsamples = 21\nvalue = [7, 14]\nclass = yes\n\n\n\n994->995\n\n\n\n\n\n1010\n\nage <= 0.429\ngini = 0.438\nsamples = 34\nvalue = [23, 11]\nclass = no\n\n\n\n994->1010\n\n\n\n\n\n996\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n995->996\n\n\n\n\n\n997\n\nduration <= 0.671\ngini = 0.388\nsamples = 19\nvalue = [5, 14]\nclass = yes\n\n\n\n995->997\n\n\n\n\n\n998\n\neuribor3m <= 1.011\ngini = 0.291\nsamples = 17\nvalue = [3, 14]\nclass = yes\n\n\n\n997->998\n\n\n\n\n\n1009\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n997->1009\n\n\n\n\n\n999\n\nDay_of_Week_mon <= 0.5\ngini = 0.153\nsamples = 12\nvalue = [1, 11]\nclass = yes\n\n\n\n998->999\n\n\n\n\n\n1004\n\nDay_of_Week_thu <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n998->1004\n\n\n\n\n\n1000\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n999->1000\n\n\n\n\n\n1001\n\nJob_blue-collar <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n999->1001\n\n\n\n\n\n1002\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1001->1002\n\n\n\n\n\n1003\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1001->1003\n\n\n\n\n\n1005\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1004->1005\n\n\n\n\n\n1006\n\nEducation_basic.9y <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1004->1006\n\n\n\n\n\n1007\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1006->1007\n\n\n\n\n\n1008\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1006->1008\n\n\n\n\n\n1011\n\neuribor3m <= 1.011\ngini = 0.346\nsamples = 27\nvalue = [21, 6]\nclass = no\n\n\n\n1010->1011\n\n\n\n\n\n1026\n\neuribor3m <= 1.01\ngini = 0.408\nsamples = 7\nvalue = [2, 5]\nclass = yes\n\n\n\n1010->1026\n\n\n\n\n\n1012\n\nduration <= 0.372\ngini = 0.188\nsamples = 19\nvalue = [17, 2]\nclass = no\n\n\n\n1011->1012\n\n\n\n\n\n1019\n\nLoan_no <= 0.5\ngini = 0.5\nsamples = 8\nvalue = [4, 4]\nclass = no\n\n\n\n1011->1019\n\n\n\n\n\n1013\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1012->1013\n\n\n\n\n\n1014\n\nduration <= 0.608\ngini = 0.105\nsamples = 18\nvalue = [17, 1]\nclass = no\n\n\n\n1012->1014\n\n\n\n\n\n1015\n\ngini = 0.0\nsamples = 15\nvalue = [15, 0]\nclass = no\n\n\n\n1014->1015\n\n\n\n\n\n1016\n\nduration <= 0.631\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1014->1016\n\n\n\n\n\n1017\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1016->1017\n\n\n\n\n\n1018\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1016->1018\n\n\n\n\n\n1020\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1019->1020\n\n\n\n\n\n1021\n\nJob_blue-collar <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n1019->1021\n\n\n\n\n\n1022\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1021->1022\n\n\n\n\n\n1023\n\nEducation_basic.9y <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1021->1023\n\n\n\n\n\n1024\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1023->1024\n\n\n\n\n\n1025\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1023->1025\n\n\n\n\n\n1027\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1026->1027\n\n\n\n\n\n1028\n\nJob_services <= 0.5\ngini = 0.278\nsamples = 6\nvalue = [1, 5]\nclass = yes\n\n\n\n1026->1028\n\n\n\n\n\n1029\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n1028->1029\n\n\n\n\n\n1030\n\neuribor3m <= 1.01\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1028->1030\n\n\n\n\n\n1031\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1030->1031\n\n\n\n\n\n1032\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1030->1032\n\n\n\n\n\n1034\n\nMonth_aug <= 0.5\ngini = 0.245\nsamples = 21\nvalue = [3, 18]\nclass = yes\n\n\n\n1033->1034\n\n\n\n\n\n1043\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1033->1043\n\n\n\n\n\n1035\n\nEducation_basic.6y <= 0.5\ngini = 0.18\nsamples = 20\nvalue = [2, 18]\nclass = yes\n\n\n\n1034->1035\n\n\n\n\n\n1042\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1034->1042\n\n\n\n\n\n1036\n\nEducation_unknown <= 0.5\ngini = 0.1\nsamples = 19\nvalue = [1, 18]\nclass = yes\n\n\n\n1035->1036\n\n\n\n\n\n1041\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1035->1041\n\n\n\n\n\n1037\n\ngini = 0.0\nsamples = 16\nvalue = [0, 16]\nclass = yes\n\n\n\n1036->1037\n\n\n\n\n\n1038\n\nduration <= 0.411\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n1036->1038\n\n\n\n\n\n1039\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1038->1039\n\n\n\n\n\n1040\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1038->1040\n\n\n\n\n\n1045\n\nduration <= 1.347\ngini = 0.302\nsamples = 442\nvalue = [82, 360]\nclass = yes\n\n\n\n1044->1045\n\n\n\n\n\n1238\n\nDay_of_Week_mon <= 0.5\ngini = 0.169\nsamples = 557\nvalue = [52, 505]\nclass = yes\n\n\n\n1044->1238\n\n\n\n\n\n1046\n\ncons.price.idx <= 0.77\ngini = 0.298\nsamples = 440\nvalue = [80, 360]\nclass = yes\n\n\n\n1045->1046\n\n\n\n\n\n1237\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1045->1237\n\n\n\n\n\n1047\n\nDay_of_Week_mon <= 0.5\ngini = 0.265\nsamples = 337\nvalue = [53, 284]\nclass = yes\n\n\n\n1046->1047\n\n\n\n\n\n1182\n\nDay_of_Week_fri <= 0.5\ngini = 0.387\nsamples = 103\nvalue = [27, 76]\nclass = yes\n\n\n\n1046->1182\n\n\n\n\n\n1048\n\nduration <= 0.767\ngini = 0.226\nsamples = 262\nvalue = [34, 228]\nclass = yes\n\n\n\n1047->1048\n\n\n\n\n\n1141\n\nEducation_unknown <= 0.5\ngini = 0.378\nsamples = 75\nvalue = [19, 56]\nclass = yes\n\n\n\n1047->1141\n\n\n\n\n\n1049\n\nEducation_professional.course <= 0.5\ngini = 0.046\nsamples = 42\nvalue = [1, 41]\nclass = yes\n\n\n\n1048->1049\n\n\n\n\n\n1056\n\nduration <= 0.785\ngini = 0.255\nsamples = 220\nvalue = [33, 187]\nclass = yes\n\n\n\n1048->1056\n\n\n\n\n\n1050\n\ngini = 0.0\nsamples = 37\nvalue = [0, 37]\nclass = yes\n\n\n\n1049->1050\n\n\n\n\n\n1051\n\ncons.price.idx <= -0.526\ngini = 0.32\nsamples = 5\nvalue = [1, 4]\nclass = yes\n\n\n\n1049->1051\n\n\n\n\n\n1052\n\nDay_of_Week_thu <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1051->1052\n\n\n\n\n\n1055\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1051->1055\n\n\n\n\n\n1053\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1052->1053\n\n\n\n\n\n1054\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1052->1054\n\n\n\n\n\n1057\n\ncampaign <= -0.399\ngini = 0.49\nsamples = 7\nvalue = [4, 3]\nclass = no\n\n\n\n1056->1057\n\n\n\n\n\n1062\n\nHousing_no <= 0.5\ngini = 0.235\nsamples = 213\nvalue = [29, 184]\nclass = yes\n\n\n\n1056->1062\n\n\n\n\n\n1058\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1057->1058\n\n\n\n\n\n1059\n\nEducation_high.school <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n1057->1059\n\n\n\n\n\n1060\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1059->1060\n\n\n\n\n\n1061\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1059->1061\n\n\n\n\n\n1063\n\nDay_of_Week_wed <= 0.5\ngini = 0.295\nsamples = 128\nvalue = [23, 105]\nclass = yes\n\n\n\n1062->1063\n\n\n\n\n\n1122\n\nMarital_single <= 0.5\ngini = 0.131\nsamples = 85\nvalue = [6, 79]\nclass = yes\n\n\n\n1062->1122\n\n\n\n\n\n1064\n\nJob_unemployed <= 0.5\ngini = 0.238\nsamples = 94\nvalue = [13, 81]\nclass = yes\n\n\n\n1063->1064\n\n\n\n\n\n1101\n\nduration <= 0.796\ngini = 0.415\nsamples = 34\nvalue = [10, 24]\nclass = yes\n\n\n\n1063->1101\n\n\n\n\n\n1065\n\nEducation_professional.course <= 0.5\ngini = 0.213\nsamples = 91\nvalue = [11, 80]\nclass = yes\n\n\n\n1064->1065\n\n\n\n\n\n1098\n\nLoan_no <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1064->1098\n\n\n\n\n\n1066\n\nduration <= 1.33\ngini = 0.162\nsamples = 79\nvalue = [7, 72]\nclass = yes\n\n\n\n1065->1066\n\n\n\n\n\n1091\n\neuribor3m <= -0.85\ngini = 0.444\nsamples = 12\nvalue = [4, 8]\nclass = yes\n\n\n\n1065->1091\n\n\n\n\n\n1067\n\ncampaign <= 0.11\ngini = 0.144\nsamples = 77\nvalue = [6, 71]\nclass = yes\n\n\n\n1066->1067\n\n\n\n\n\n1088\n\ncons.price.idx <= 0.042\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1066->1088\n\n\n\n\n\n1068\n\nLoan_no <= 0.5\ngini = 0.204\nsamples = 52\nvalue = [6, 46]\nclass = yes\n\n\n\n1067->1068\n\n\n\n\n\n1087\n\ngini = 0.0\nsamples = 25\nvalue = [0, 25]\nclass = yes\n\n\n\n1067->1087\n\n\n\n\n\n1069\n\ngini = 0.0\nsamples = 15\nvalue = [0, 15]\nclass = yes\n\n\n\n1068->1069\n\n\n\n\n\n1070\n\neuribor3m <= -0.82\ngini = 0.272\nsamples = 37\nvalue = [6, 31]\nclass = yes\n\n\n\n1068->1070\n\n\n\n\n\n1071\n\nEducation_basic.6y <= 0.5\ngini = 0.1\nsamples = 19\nvalue = [1, 18]\nclass = yes\n\n\n\n1070->1071\n\n\n\n\n\n1076\n\nJob_admin. <= 0.5\ngini = 0.401\nsamples = 18\nvalue = [5, 13]\nclass = yes\n\n\n\n1070->1076\n\n\n\n\n\n1072\n\ngini = 0.0\nsamples = 16\nvalue = [0, 16]\nclass = yes\n\n\n\n1071->1072\n\n\n\n\n\n1073\n\nDay_of_Week_tue <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n1071->1073\n\n\n\n\n\n1074\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1073->1074\n\n\n\n\n\n1075\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1073->1075\n\n\n\n\n\n1077\n\nage <= 0.051\ngini = 0.245\nsamples = 14\nvalue = [2, 12]\nclass = yes\n\n\n\n1076->1077\n\n\n\n\n\n1084\n\nduration <= 0.797\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n1076->1084\n\n\n\n\n\n1078\n\ngini = 0.0\nsamples = 9\nvalue = [0, 9]\nclass = yes\n\n\n\n1077->1078\n\n\n\n\n\n1079\n\nEducation_basic.4y <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n1077->1079\n\n\n\n\n\n1080\n\ncons.conf.idx <= 0.208\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1079->1080\n\n\n\n\n\n1083\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1079->1083\n\n\n\n\n\n1081\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1080->1081\n\n\n\n\n\n1082\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1080->1082\n\n\n\n\n\n1085\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1084->1085\n\n\n\n\n\n1086\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1084->1086\n\n\n\n\n\n1089\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1088->1089\n\n\n\n\n\n1090\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1088->1090\n\n\n\n\n\n1092\n\nduration <= 0.941\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n1091->1092\n\n\n\n\n\n1095\n\nduration <= 0.877\ngini = 0.219\nsamples = 8\nvalue = [1, 7]\nclass = yes\n\n\n\n1091->1095\n\n\n\n\n\n1093\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1092->1093\n\n\n\n\n\n1094\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1092->1094\n\n\n\n\n\n1096\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1095->1096\n\n\n\n\n\n1097\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n1095->1097\n\n\n\n\n\n1099\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1098->1099\n\n\n\n\n\n1100\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1098->1100\n\n\n\n\n\n1102\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1101->1102\n\n\n\n\n\n1103\n\nage <= -0.831\ngini = 0.375\nsamples = 32\nvalue = [8, 24]\nclass = yes\n\n\n\n1101->1103\n\n\n\n\n\n1104\n\nage <= -0.916\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = no\n\n\n\n1103->1104\n\n\n\n\n\n1107\n\nLoan_unknown <= 0.5\ngini = 0.302\nsamples = 27\nvalue = [5, 22]\nclass = yes\n\n\n\n1103->1107\n\n\n\n\n\n1105\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1104->1105\n\n\n\n\n\n1106\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1104->1106\n\n\n\n\n\n1108\n\nJob_entrepreneur <= 0.5\ngini = 0.26\nsamples = 26\nvalue = [4, 22]\nclass = yes\n\n\n\n1107->1108\n\n\n\n\n\n1121\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1107->1121\n\n\n\n\n\n1109\n\nEducation_basic.4y <= 0.5\ngini = 0.211\nsamples = 25\nvalue = [3, 22]\nclass = yes\n\n\n\n1108->1109\n\n\n\n\n\n1120\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1108->1120\n\n\n\n\n\n1110\n\nMarital_divorced <= 0.5\ngini = 0.153\nsamples = 24\nvalue = [2, 22]\nclass = yes\n\n\n\n1109->1110\n\n\n\n\n\n1119\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1109->1119\n\n\n\n\n\n1111\n\nMarital_married <= 0.5\ngini = 0.087\nsamples = 22\nvalue = [1, 21]\nclass = yes\n\n\n\n1110->1111\n\n\n\n\n\n1116\n\nage <= -0.159\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1110->1116\n\n\n\n\n\n1112\n\nage <= 0.009\ngini = 0.278\nsamples = 6\nvalue = [1, 5]\nclass = yes\n\n\n\n1111->1112\n\n\n\n\n\n1115\n\ngini = 0.0\nsamples = 16\nvalue = [0, 16]\nclass = yes\n\n\n\n1111->1115\n\n\n\n\n\n1113\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n1112->1113\n\n\n\n\n\n1114\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1112->1114\n\n\n\n\n\n1117\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1116->1117\n\n\n\n\n\n1118\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1116->1118\n\n\n\n\n\n1123\n\nage <= -0.663\ngini = 0.065\nsamples = 59\nvalue = [2, 57]\nclass = yes\n\n\n\n1122->1123\n\n\n\n\n\n1130\n\nage <= -0.117\ngini = 0.26\nsamples = 26\nvalue = [4, 22]\nclass = yes\n\n\n\n1122->1130\n\n\n\n\n\n1124\n\ncampaign <= -0.399\ngini = 0.32\nsamples = 10\nvalue = [2, 8]\nclass = yes\n\n\n\n1123->1124\n\n\n\n\n\n1129\n\ngini = 0.0\nsamples = 49\nvalue = [0, 49]\nclass = yes\n\n\n\n1123->1129\n\n\n\n\n\n1125\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n1124->1125\n\n\n\n\n\n1126\n\nDay_of_Week_thu <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1124->1126\n\n\n\n\n\n1127\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1126->1127\n\n\n\n\n\n1128\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1126->1128\n\n\n\n\n\n1131\n\nage <= -1.252\ngini = 0.153\nsamples = 24\nvalue = [2, 22]\nclass = yes\n\n\n\n1130->1131\n\n\n\n\n\n1140\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1130->1140\n\n\n\n\n\n1132\n\nJob_blue-collar <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1131->1132\n\n\n\n\n\n1135\n\nage <= -0.411\ngini = 0.087\nsamples = 22\nvalue = [1, 21]\nclass = yes\n\n\n\n1131->1135\n\n\n\n\n\n1133\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1132->1133\n\n\n\n\n\n1134\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1132->1134\n\n\n\n\n\n1136\n\ngini = 0.0\nsamples = 18\nvalue = [0, 18]\nclass = yes\n\n\n\n1135->1136\n\n\n\n\n\n1137\n\nage <= -0.327\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n1135->1137\n\n\n\n\n\n1138\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1137->1138\n\n\n\n\n\n1139\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1137->1139\n\n\n\n\n\n1142\n\nage <= -0.579\ngini = 0.357\nsamples = 73\nvalue = [17, 56]\nclass = yes\n\n\n\n1141->1142\n\n\n\n\n\n1181\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1141->1181\n\n\n\n\n\n1143\n\nMonth_nov <= 0.5\ngini = 0.159\nsamples = 23\nvalue = [2, 21]\nclass = yes\n\n\n\n1142->1143\n\n\n\n\n\n1150\n\nduration <= 1.166\ngini = 0.42\nsamples = 50\nvalue = [15, 35]\nclass = yes\n\n\n\n1142->1150\n\n\n\n\n\n1144\n\nduration <= 1.266\ngini = 0.087\nsamples = 22\nvalue = [1, 21]\nclass = yes\n\n\n\n1143->1144\n\n\n\n\n\n1149\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1143->1149\n\n\n\n\n\n1145\n\ngini = 0.0\nsamples = 20\nvalue = [0, 20]\nclass = yes\n\n\n\n1144->1145\n\n\n\n\n\n1146\n\nMarital_divorced <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1144->1146\n\n\n\n\n\n1147\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1146->1147\n\n\n\n\n\n1148\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1146->1148\n\n\n\n\n\n1151\n\nduration <= 1.115\ngini = 0.449\nsamples = 44\nvalue = [15, 29]\nclass = yes\n\n\n\n1150->1151\n\n\n\n\n\n1180\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n1150->1180\n\n\n\n\n\n1152\n\nage <= -0.495\ngini = 0.414\nsamples = 41\nvalue = [12, 29]\nclass = yes\n\n\n\n1151->1152\n\n\n\n\n\n1179\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1151->1179\n\n\n\n\n\n1153\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1152->1153\n\n\n\n\n\n1154\n\ncons.conf.idx <= -1.203\ngini = 0.381\nsamples = 39\nvalue = [10, 29]\nclass = yes\n\n\n\n1152->1154\n\n\n\n\n\n1155\n\nHousing_yes <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = no\n\n\n\n1154->1155\n\n\n\n\n\n1160\n\nduration <= 0.773\ngini = 0.327\nsamples = 34\nvalue = [7, 27]\nclass = yes\n\n\n\n1154->1160\n\n\n\n\n\n1156\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1155->1156\n\n\n\n\n\n1157\n\nEducation_basic.4y <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n1155->1157\n\n\n\n\n\n1158\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1157->1158\n\n\n\n\n\n1159\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1157->1159\n\n\n\n\n\n1161\n\ngini = 0.0\nsamples = 10\nvalue = [0, 10]\nclass = yes\n\n\n\n1160->1161\n\n\n\n\n\n1162\n\nduration <= 0.794\ngini = 0.413\nsamples = 24\nvalue = [7, 17]\nclass = yes\n\n\n\n1160->1162\n\n\n\n\n\n1163\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1162->1163\n\n\n\n\n\n1164\n\neuribor3m <= -0.875\ngini = 0.351\nsamples = 22\nvalue = [5, 17]\nclass = yes\n\n\n\n1162->1164\n\n\n\n\n\n1165\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1164->1165\n\n\n\n\n\n1166\n\ncons.conf.idx <= -0.405\ngini = 0.308\nsamples = 21\nvalue = [4, 17]\nclass = yes\n\n\n\n1164->1166\n\n\n\n\n\n1167\n\ngini = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = yes\n\n\n\n1166->1167\n\n\n\n\n\n1168\n\nduration <= 1.025\ngini = 0.408\nsamples = 14\nvalue = [4, 10]\nclass = yes\n\n\n\n1166->1168\n\n\n\n\n\n1169\n\ncampaign <= -0.399\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n1168->1169\n\n\n\n\n\n1176\n\nduration <= 1.061\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1168->1176\n\n\n\n\n\n1170\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1169->1170\n\n\n\n\n\n1171\n\nLoan_yes <= 0.5\ngini = 0.18\nsamples = 10\nvalue = [1, 9]\nclass = yes\n\n\n\n1169->1171\n\n\n\n\n\n1172\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n1171->1172\n\n\n\n\n\n1173\n\nduration <= 0.868\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1171->1173\n\n\n\n\n\n1174\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1173->1174\n\n\n\n\n\n1175\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1173->1175\n\n\n\n\n\n1177\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1176->1177\n\n\n\n\n\n1178\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1176->1178\n\n\n\n\n\n1183\n\nEducation_high.school <= 0.5\ngini = 0.436\nsamples = 78\nvalue = [25, 53]\nclass = yes\n\n\n\n1182->1183\n\n\n\n\n\n1230\n\ncampaign <= 0.618\ngini = 0.147\nsamples = 25\nvalue = [2, 23]\nclass = yes\n\n\n\n1182->1230\n\n\n\n\n\n1184\n\nMarital_married <= 0.5\ngini = 0.391\nsamples = 60\nvalue = [16, 44]\nclass = yes\n\n\n\n1183->1184\n\n\n\n\n\n1217\n\nDay_of_Week_mon <= 0.5\ngini = 0.5\nsamples = 18\nvalue = [9, 9]\nclass = no\n\n\n\n1183->1217\n\n\n\n\n\n1185\n\nduration <= 1.076\ngini = 0.48\nsamples = 25\nvalue = [10, 15]\nclass = yes\n\n\n\n1184->1185\n\n\n\n\n\n1200\n\nduration <= 1.294\ngini = 0.284\nsamples = 35\nvalue = [6, 29]\nclass = yes\n\n\n\n1184->1200\n\n\n\n\n\n1186\n\neuribor3m <= 1.037\ngini = 0.444\nsamples = 12\nvalue = [8, 4]\nclass = no\n\n\n\n1185->1186\n\n\n\n\n\n1195\n\neuribor3m <= 1.063\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = yes\n\n\n\n1185->1195\n\n\n\n\n\n1187\n\nJob_student <= 0.5\ngini = 0.32\nsamples = 10\nvalue = [8, 2]\nclass = no\n\n\n\n1186->1187\n\n\n\n\n\n1194\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1186->1194\n\n\n\n\n\n1188\n\nage <= 0.261\ngini = 0.198\nsamples = 9\nvalue = [8, 1]\nclass = no\n\n\n\n1187->1188\n\n\n\n\n\n1193\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1187->1193\n\n\n\n\n\n1189\n\ngini = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = no\n\n\n\n1188->1189\n\n\n\n\n\n1190\n\nJob_admin. <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1188->1190\n\n\n\n\n\n1191\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1190->1191\n\n\n\n\n\n1192\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1190->1192\n\n\n\n\n\n1196\n\nJob_student <= 0.5\ngini = 0.153\nsamples = 12\nvalue = [1, 11]\nclass = yes\n\n\n\n1195->1196\n\n\n\n\n\n1199\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1195->1199\n\n\n\n\n\n1197\n\ngini = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = yes\n\n\n\n1196->1197\n\n\n\n\n\n1198\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1196->1198\n\n\n\n\n\n1201\n\nage <= 0.009\ngini = 0.251\nsamples = 34\nvalue = [5, 29]\nclass = yes\n\n\n\n1200->1201\n\n\n\n\n\n1216\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1200->1216\n\n\n\n\n\n1202\n\ngini = 0.0\nsamples = 13\nvalue = [0, 13]\nclass = yes\n\n\n\n1201->1202\n\n\n\n\n\n1203\n\ncampaign <= 0.618\ngini = 0.363\nsamples = 21\nvalue = [5, 16]\nclass = yes\n\n\n\n1201->1203\n\n\n\n\n\n1204\n\nduration <= 0.724\ngini = 0.278\nsamples = 18\nvalue = [3, 15]\nclass = yes\n\n\n\n1203->1204\n\n\n\n\n\n1213\n\neuribor3m <= 1.011\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1203->1213\n\n\n\n\n\n1205\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1204->1205\n\n\n\n\n\n1206\n\nJob_admin. <= 0.5\ngini = 0.208\nsamples = 17\nvalue = [2, 15]\nclass = yes\n\n\n\n1204->1206\n\n\n\n\n\n1207\n\nage <= 1.479\ngini = 0.117\nsamples = 16\nvalue = [1, 15]\nclass = yes\n\n\n\n1206->1207\n\n\n\n\n\n1212\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1206->1212\n\n\n\n\n\n1208\n\ngini = 0.0\nsamples = 14\nvalue = [0, 14]\nclass = yes\n\n\n\n1207->1208\n\n\n\n\n\n1209\n\ncons.price.idx <= 1.203\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1207->1209\n\n\n\n\n\n1210\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1209->1210\n\n\n\n\n\n1211\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1209->1211\n\n\n\n\n\n1214\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1213->1214\n\n\n\n\n\n1215\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1213->1215\n\n\n\n\n\n1218\n\ncampaign <= 0.11\ngini = 0.426\nsamples = 13\nvalue = [9, 4]\nclass = no\n\n\n\n1217->1218\n\n\n\n\n\n1229\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n1217->1229\n\n\n\n\n\n1219\n\nLoan_yes <= 0.5\ngini = 0.49\nsamples = 7\nvalue = [3, 4]\nclass = yes\n\n\n\n1218->1219\n\n\n\n\n\n1228\n\ngini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = no\n\n\n\n1218->1228\n\n\n\n\n\n1220\n\nJob_admin. <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n1219->1220\n\n\n\n\n\n1227\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1219->1227\n\n\n\n\n\n1221\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1220->1221\n\n\n\n\n\n1222\n\nemp.var.rate <= 1.023\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1220->1222\n\n\n\n\n\n1223\n\nduration <= 1.07\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1222->1223\n\n\n\n\n\n1226\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1222->1226\n\n\n\n\n\n1224\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1223->1224\n\n\n\n\n\n1225\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1223->1225\n\n\n\n\n\n1231\n\ngini = 0.0\nsamples = 19\nvalue = [0, 19]\nclass = yes\n\n\n\n1230->1231\n\n\n\n\n\n1232\n\ncampaign <= 1.38\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n1230->1232\n\n\n\n\n\n1233\n\neuribor3m <= 1.01\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1232->1233\n\n\n\n\n\n1236\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1232->1236\n\n\n\n\n\n1234\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1233->1234\n\n\n\n\n\n1235\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1233->1235\n\n\n\n\n\n1239\n\nJob_technician <= 0.5\ngini = 0.191\nsamples = 459\nvalue = [49, 410]\nclass = yes\n\n\n\n1238->1239\n\n\n\n\n\n1384\n\nEducation_basic.6y <= 0.5\ngini = 0.059\nsamples = 98\nvalue = [3, 95]\nclass = yes\n\n\n\n1238->1384\n\n\n\n\n\n1240\n\nduration <= 1.819\ngini = 0.173\nsamples = 398\nvalue = [38, 360]\nclass = yes\n\n\n\n1239->1240\n\n\n\n\n\n1359\n\nMonth_nov <= 0.5\ngini = 0.296\nsamples = 61\nvalue = [11, 50]\nclass = yes\n\n\n\n1239->1359\n\n\n\n\n\n1241\n\nduration <= 1.816\ngini = 0.249\nsamples = 144\nvalue = [21, 123]\nclass = yes\n\n\n\n1240->1241\n\n\n\n\n\n1300\n\nEducation_university.degree <= 0.5\ngini = 0.125\nsamples = 254\nvalue = [17, 237]\nclass = yes\n\n\n\n1240->1300\n\n\n\n\n\n1242\n\nage <= 1.353\ngini = 0.232\nsamples = 142\nvalue = [19, 123]\nclass = yes\n\n\n\n1241->1242\n\n\n\n\n\n1299\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1241->1299\n\n\n\n\n\n1243\n\neuribor3m <= 1.01\ngini = 0.217\nsamples = 137\nvalue = [17, 120]\nclass = yes\n\n\n\n1242->1243\n\n\n\n\n\n1296\n\nduration <= 1.582\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n1242->1296\n\n\n\n\n\n1244\n\ncons.conf.idx <= 0.18\ngini = 0.295\nsamples = 50\nvalue = [9, 41]\nclass = yes\n\n\n\n1243->1244\n\n\n\n\n\n1265\n\nEducation_basic.9y <= 0.5\ngini = 0.167\nsamples = 87\nvalue = [8, 79]\nclass = yes\n\n\n\n1243->1265\n\n\n\n\n\n1245\n\nJob_self-employed <= 0.5\ngini = 0.227\nsamples = 46\nvalue = [6, 40]\nclass = yes\n\n\n\n1244->1245\n\n\n\n\n\n1262\n\nduration <= 1.762\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n1244->1262\n\n\n\n\n\n1246\n\nage <= 0.345\ngini = 0.198\nsamples = 45\nvalue = [5, 40]\nclass = yes\n\n\n\n1245->1246\n\n\n\n\n\n1261\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1245->1261\n\n\n\n\n\n1247\n\nage <= -1.336\ngini = 0.105\nsamples = 36\nvalue = [2, 34]\nclass = yes\n\n\n\n1246->1247\n\n\n\n\n\n1256\n\ncampaign <= -0.399\ngini = 0.444\nsamples = 9\nvalue = [3, 6]\nclass = yes\n\n\n\n1246->1256\n\n\n\n\n\n1248\n\nEducation_basic.9y <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n1247->1248\n\n\n\n\n\n1251\n\nLoan_yes <= 0.5\ngini = 0.059\nsamples = 33\nvalue = [1, 32]\nclass = yes\n\n\n\n1247->1251\n\n\n\n\n\n1249\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1248->1249\n\n\n\n\n\n1250\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1248->1250\n\n\n\n\n\n1252\n\ngini = 0.0\nsamples = 28\nvalue = [0, 28]\nclass = yes\n\n\n\n1251->1252\n\n\n\n\n\n1253\n\ncons.price.idx <= -0.769\ngini = 0.32\nsamples = 5\nvalue = [1, 4]\nclass = yes\n\n\n\n1251->1253\n\n\n\n\n\n1254\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1253->1254\n\n\n\n\n\n1255\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n1253->1255\n\n\n\n\n\n1257\n\nDay_of_Week_thu <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = no\n\n\n\n1256->1257\n\n\n\n\n\n1260\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n1256->1260\n\n\n\n\n\n1258\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1257->1258\n\n\n\n\n\n1259\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1257->1259\n\n\n\n\n\n1263\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1262->1263\n\n\n\n\n\n1264\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1262->1264\n\n\n\n\n\n1266\n\nDay_of_Week_tue <= 0.5\ngini = 0.128\nsamples = 73\nvalue = [5, 68]\nclass = yes\n\n\n\n1265->1266\n\n\n\n\n\n1287\n\nMarital_divorced <= 0.5\ngini = 0.337\nsamples = 14\nvalue = [3, 11]\nclass = yes\n\n\n\n1265->1287\n\n\n\n\n\n1267\n\nLoan_yes <= 0.5\ngini = 0.068\nsamples = 57\nvalue = [2, 55]\nclass = yes\n\n\n\n1266->1267\n\n\n\n\n\n1278\n\nage <= -0.747\ngini = 0.305\nsamples = 16\nvalue = [3, 13]\nclass = yes\n\n\n\n1266->1278\n\n\n\n\n\n1268\n\ngini = 0.0\nsamples = 46\nvalue = [0, 46]\nclass = yes\n\n\n\n1267->1268\n\n\n\n\n\n1269\n\nHousing_yes <= 0.5\ngini = 0.298\nsamples = 11\nvalue = [2, 9]\nclass = yes\n\n\n\n1267->1269\n\n\n\n\n\n1270\n\ncampaign <= 0.618\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n1269->1270\n\n\n\n\n\n1277\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n1269->1277\n\n\n\n\n\n1271\n\nDay_of_Week_fri <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1270->1271\n\n\n\n\n\n1276\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1270->1276\n\n\n\n\n\n1272\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1271->1272\n\n\n\n\n\n1273\n\nEducation_university.degree <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1271->1273\n\n\n\n\n\n1274\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1273->1274\n\n\n\n\n\n1275\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1273->1275\n\n\n\n\n\n1279\n\nage <= -0.916\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1278->1279\n\n\n\n\n\n1282\n\nage <= 0.849\ngini = 0.142\nsamples = 13\nvalue = [1, 12]\nclass = yes\n\n\n\n1278->1282\n\n\n\n\n\n1280\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1279->1280\n\n\n\n\n\n1281\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1279->1281\n\n\n\n\n\n1283\n\ngini = 0.0\nsamples = 10\nvalue = [0, 10]\nclass = yes\n\n\n\n1282->1283\n\n\n\n\n\n1284\n\nduration <= 1.491\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n1282->1284\n\n\n\n\n\n1285\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1284->1285\n\n\n\n\n\n1286\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1284->1286\n\n\n\n\n\n1288\n\nduration <= 1.575\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = yes\n\n\n\n1287->1288\n\n\n\n\n\n1295\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1287->1295\n\n\n\n\n\n1289\n\nJob_blue-collar <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n1288->1289\n\n\n\n\n\n1294\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n1288->1294\n\n\n\n\n\n1290\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1289->1290\n\n\n\n\n\n1291\n\ncons.price.idx <= 1.143\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1289->1291\n\n\n\n\n\n1292\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1291->1292\n\n\n\n\n\n1293\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1291->1293\n\n\n\n\n\n1297\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1296->1297\n\n\n\n\n\n1298\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1296->1298\n\n\n\n\n\n1301\n\nJob_management <= 0.5\ngini = 0.161\nsamples = 193\nvalue = [17, 176]\nclass = yes\n\n\n\n1300->1301\n\n\n\n\n\n1358\n\ngini = 0.0\nsamples = 61\nvalue = [0, 61]\nclass = yes\n\n\n\n1300->1358\n\n\n\n\n\n1302\n\neuribor3m <= 1.063\ngini = 0.139\nsamples = 187\nvalue = [14, 173]\nclass = yes\n\n\n\n1301->1302\n\n\n\n\n\n1355\n\nduration <= 2.111\ngini = 0.5\nsamples = 6\nvalue = [3, 3]\nclass = no\n\n\n\n1301->1355\n\n\n\n\n\n1303\n\ncampaign <= 2.904\ngini = 0.193\nsamples = 111\nvalue = [12, 99]\nclass = yes\n\n\n\n1302->1303\n\n\n\n\n\n1342\n\nMarital_divorced <= 0.5\ngini = 0.051\nsamples = 76\nvalue = [2, 74]\nclass = yes\n\n\n\n1302->1342\n\n\n\n\n\n1304\n\nage <= -0.916\ngini = 0.18\nsamples = 110\nvalue = [11, 99]\nclass = yes\n\n\n\n1303->1304\n\n\n\n\n\n1341\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1303->1341\n\n\n\n\n\n1305\n\nage <= -1.084\ngini = 0.397\nsamples = 11\nvalue = [3, 8]\nclass = yes\n\n\n\n1304->1305\n\n\n\n\n\n1312\n\ncampaign <= 1.126\ngini = 0.149\nsamples = 99\nvalue = [8, 91]\nclass = yes\n\n\n\n1304->1312\n\n\n\n\n\n1306\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n1305->1306\n\n\n\n\n\n1307\n\nJob_admin. <= 0.5\ngini = 0.5\nsamples = 6\nvalue = [3, 3]\nclass = no\n\n\n\n1305->1307\n\n\n\n\n\n1308\n\ncons.conf.idx <= -1.203\ngini = 0.375\nsamples = 4\nvalue = [3, 1]\nclass = no\n\n\n\n1307->1308\n\n\n\n\n\n1311\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1307->1311\n\n\n\n\n\n1309\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1308->1309\n\n\n\n\n\n1310\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1308->1310\n\n\n\n\n\n1313\n\ncampaign <= -0.399\ngini = 0.121\nsamples = 93\nvalue = [6, 87]\nclass = yes\n\n\n\n1312->1313\n\n\n\n\n\n1336\n\nMarital_divorced <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = yes\n\n\n\n1312->1336\n\n\n\n\n\n1314\n\ngini = 0.0\nsamples = 37\nvalue = [0, 37]\nclass = yes\n\n\n\n1313->1314\n\n\n\n\n\n1315\n\nJob_student <= 0.5\ngini = 0.191\nsamples = 56\nvalue = [6, 50]\nclass = yes\n\n\n\n1313->1315\n\n\n\n\n\n1316\n\nJob_entrepreneur <= 0.5\ngini = 0.165\nsamples = 55\nvalue = [5, 50]\nclass = yes\n\n\n\n1315->1316\n\n\n\n\n\n1335\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1315->1335\n\n\n\n\n\n1317\n\nJob_retired <= 0.5\ngini = 0.14\nsamples = 53\nvalue = [4, 49]\nclass = yes\n\n\n\n1316->1317\n\n\n\n\n\n1332\n\nage <= 0.135\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1316->1332\n\n\n\n\n\n1318\n\nEducation_basic.4y <= 0.5\ngini = 0.113\nsamples = 50\nvalue = [3, 47]\nclass = yes\n\n\n\n1317->1318\n\n\n\n\n\n1329\n\nduration <= 3.304\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n1317->1329\n\n\n\n\n\n1319\n\nDay_of_Week_fri <= 0.5\ngini = 0.081\nsamples = 47\nvalue = [2, 45]\nclass = yes\n\n\n\n1318->1319\n\n\n\n\n\n1326\n\nage <= 0.177\ngini = 0.444\nsamples = 3\nvalue = [1, 2]\nclass = yes\n\n\n\n1318->1326\n\n\n\n\n\n1320\n\ngini = 0.0\nsamples = 34\nvalue = [0, 34]\nclass = yes\n\n\n\n1319->1320\n\n\n\n\n\n1321\n\neuribor3m <= 1.01\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = yes\n\n\n\n1319->1321\n\n\n\n\n\n1322\n\nEducation_basic.9y <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]\nclass = yes\n\n\n\n1321->1322\n\n\n\n\n\n1325\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n1321->1325\n\n\n\n\n\n1323\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1322->1323\n\n\n\n\n\n1324\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1322->1324\n\n\n\n\n\n1327\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1326->1327\n\n\n\n\n\n1328\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1326->1328\n\n\n\n\n\n1330\n\ngini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = yes\n\n\n\n1329->1330\n\n\n\n\n\n1331\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1329->1331\n\n\n\n\n\n1333\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1332->1333\n\n\n\n\n\n1334\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1332->1334\n\n\n\n\n\n1337\n\nduration <= 2.249\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1336->1337\n\n\n\n\n\n1340\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1336->1340\n\n\n\n\n\n1338\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1337->1338\n\n\n\n\n\n1339\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1337->1339\n\n\n\n\n\n1343\n\nMonth_aug <= 0.5\ngini = 0.028\nsamples = 70\nvalue = [1, 69]\nclass = yes\n\n\n\n1342->1343\n\n\n\n\n\n1350\n\nage <= -0.117\ngini = 0.278\nsamples = 6\nvalue = [1, 5]\nclass = yes\n\n\n\n1342->1350\n\n\n\n\n\n1344\n\ngini = 0.0\nsamples = 54\nvalue = [0, 54]\nclass = yes\n\n\n\n1343->1344\n\n\n\n\n\n1345\n\nduration <= 2.288\ngini = 0.117\nsamples = 16\nvalue = [1, 15]\nclass = yes\n\n\n\n1343->1345\n\n\n\n\n\n1346\n\nduration <= 2.082\ngini = 0.375\nsamples = 4\nvalue = [1, 3]\nclass = yes\n\n\n\n1345->1346\n\n\n\n\n\n1349\n\ngini = 0.0\nsamples = 12\nvalue = [0, 12]\nclass = yes\n\n\n\n1345->1349\n\n\n\n\n\n1347\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1346->1347\n\n\n\n\n\n1348\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1346->1348\n\n\n\n\n\n1351\n\ngini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = yes\n\n\n\n1350->1351\n\n\n\n\n\n1352\n\nduration <= 3.118\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1350->1352\n\n\n\n\n\n1353\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1352->1353\n\n\n\n\n\n1354\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1352->1354\n\n\n\n\n\n1356\n\ngini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = yes\n\n\n\n1355->1356\n\n\n\n\n\n1357\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1355->1357\n\n\n\n\n\n1360\n\nduration <= 3.578\ngini = 0.208\nsamples = 51\nvalue = [6, 45]\nclass = yes\n\n\n\n1359->1360\n\n\n\n\n\n1377\n\nEducation_professional.course <= 0.5\ngini = 0.5\nsamples = 10\nvalue = [5, 5]\nclass = no\n\n\n\n1359->1377\n\n\n\n\n\n1361\n\nage <= -0.327\ngini = 0.153\nsamples = 48\nvalue = [4, 44]\nclass = yes\n\n\n\n1360->1361\n\n\n\n\n\n1374\n\nMarital_married <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = no\n\n\n\n1360->1374\n\n\n\n\n\n1362\n\ngini = 0.0\nsamples = 27\nvalue = [0, 27]\nclass = yes\n\n\n\n1361->1362\n\n\n\n\n\n1363\n\nage <= 0.177\ngini = 0.308\nsamples = 21\nvalue = [4, 17]\nclass = yes\n\n\n\n1361->1363\n\n\n\n\n\n1364\n\nContact_telephone <= 0.5\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = yes\n\n\n\n1363->1364\n\n\n\n\n\n1373\n\ngini = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = yes\n\n\n\n1363->1373\n\n\n\n\n\n1365\n\nEducation_university.degree <= 0.5\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = yes\n\n\n\n1364->1365\n\n\n\n\n\n1372\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1364->1372\n\n\n\n\n\n1366\n\nprevious <= 0.263\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = yes\n\n\n\n1365->1366\n\n\n\n\n\n1371\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1365->1371\n\n\n\n\n\n1367\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n1366->1367\n\n\n\n\n\n1368\n\nMarital_single <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = no\n\n\n\n1366->1368\n\n\n\n\n\n1369\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1368->1369\n\n\n\n\n\n1370\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1368->1370\n\n\n\n\n\n1375\n\ngini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = no\n\n\n\n1374->1375\n\n\n\n\n\n1376\n\ngini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = yes\n\n\n\n1374->1376\n\n\n\n\n\n1378\n\ngini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = no\n\n\n\n1377->1378\n\n\n\n\n\n1379\n\nDay_of_Week_thu <= 0.5\ngini = 0.408\nsamples = 7\nvalue = [2, 5]\nclass = yes\n\n\n\n1377->1379\n\n\n\n\n\n1380\n\nage <= -0.117\ngini = 0.278\nsamples = 6\nvalue = [1, 5]\nclass = yes\n\n\n\n1379->1380\n\n\n\n\n\n1383\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1379->1383\n\n\n\n\n\n1381\n\ngini = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = yes\n\n\n\n1380->1381\n\n\n\n\n\n1382\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1380->1382\n\n\n\n\n\n1385\n\ncons.conf.idx <= 0.727\ngini = 0.022\nsamples = 88\nvalue = [1, 87]\nclass = yes\n\n\n\n1384->1385\n\n\n\n\n\n1390\n\nLoan_yes <= 0.5\ngini = 0.32\nsamples = 10\nvalue = [2, 8]\nclass = yes\n\n\n\n1384->1390\n\n\n\n\n\n1386\n\ngini = 0.0\nsamples = 81\nvalue = [0, 81]\nclass = yes\n\n\n\n1385->1386\n\n\n\n\n\n1387\n\nduration <= 2.607\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = yes\n\n\n\n1385->1387\n\n\n\n\n\n1388\n\ngini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = yes\n\n\n\n1387->1388\n\n\n\n\n\n1389\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1387->1389\n\n\n\n\n\n1391\n\nage <= 1.017\ngini = 0.198\nsamples = 9\nvalue = [1, 8]\nclass = yes\n\n\n\n1390->1391\n\n\n\n\n\n1394\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1390->1394\n\n\n\n\n\n1392\n\ngini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = yes\n\n\n\n1391->1392\n\n\n\n\n\n1393\n\ngini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = no\n\n\n\n1391->1393\n\n\n\n\n\n","text/plain":[""]},"metadata":{},"execution_count":128}]},{"cell_type":"markdown","metadata":{"id":"4tZB5BOFmtpt"},"source":["## Random Forest"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"id":"8XIEUXucmtRm","executionInfo":{"status":"ok","timestamp":1685977092916,"user_tz":180,"elapsed":1247,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"15a668ce-ae8a-456c-b6df-4044294aa088"},"source":["rf = RandomForestClassifier(random_state=1234)\n","rf.fit(X_train, y_train)"],"execution_count":138,"outputs":[{"output_type":"stream","name":"stderr","text":[":2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"," rf.fit(X_train, y_train)\n"]},{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(random_state=1234)"],"text/html":["
RandomForestClassifier(random_state=1234)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"]},"metadata":{},"execution_count":138}]},{"cell_type":"markdown","metadata":{"id":"GR6KrfI9myes"},"source":["# `5.Evaluation`\n",""]},{"cell_type":"markdown","metadata":{"id":"u0pq8KA-m3Zt"},"source":["## Arbol de Decision"]},{"cell_type":"code","metadata":{"id":"-jfaYjCnm3zr","executionInfo":{"status":"ok","timestamp":1685977107610,"user_tz":180,"elapsed":471,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["from sklearn.metrics import (auc,\n"," accuracy_score, \n"," roc_auc_score,\n"," confusion_matrix,\n"," ConfusionMatrixDisplay,\n"," roc_curve,\n"," RocCurveDisplay)"],"execution_count":139,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WQeYpCF9m43k","executionInfo":{"status":"ok","timestamp":1685977107991,"user_tz":180,"elapsed":8,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"d35e1af1-ee32-4e46-8eb8-097360684689"},"source":["y_pred = dt.predict(X_test)\n","y_prob = dt.predict_proba(X_test)\n","\n","print('Accuracy: '+ round(accuracy_score(y_test, y_pred),2).astype(str))"],"execution_count":140,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.83\n"]}]},{"cell_type":"code","source":["fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n","roc_auc = auc(fpr, tpr)\n","display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc)\n","display.plot()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":473},"id":"PEkJMnEdBAL8","executionInfo":{"status":"ok","timestamp":1685977112013,"user_tz":180,"elapsed":17,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"cb50e438-0a17-41a0-e47e-e1ce5b015708"},"execution_count":141,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"YrpJWMSvm7jT","executionInfo":{"status":"ok","timestamp":1685977112767,"user_tz":180,"elapsed":35,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"474930dd-f8b0-403b-992d-f7ee36f128cd"},"source":["cm = confusion_matrix(y_test, y_pred)\n","disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n"," display_labels=dt.classes_)\n","disp.plot()"],"execution_count":142,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":142},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["accuracy = accuracy_score(y_test,y_pred)\n","print('Accuracy Test: ', accuracy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nnHguLqnBv4S","executionInfo":{"status":"ok","timestamp":1685977112769,"user_tz":180,"elapsed":25,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"5dbbdd40-2286-4a53-8377-89cd8069b73b"},"execution_count":143,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy Test: 0.8265021886191802\n"]}]},{"cell_type":"markdown","metadata":{"id":"AdOCrEXXpnQj"},"source":["## Random Forest"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Zmw_W6w0pmpc","executionInfo":{"status":"ok","timestamp":1685977113284,"user_tz":180,"elapsed":12,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"767716c9-4ce6-4f4f-9530-b6b67bf423fa"},"source":["y_pred_rf = rf.predict(X_test)\n","y_prob_rf = rf.predict_proba(X_test)\n","print('Accuracy: '+ round(accuracy_score(y_test, y_pred_rf),2).astype(str))"],"execution_count":144,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.89\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":473},"id":"9yY9sGL8nE-M","executionInfo":{"status":"ok","timestamp":1685977113813,"user_tz":180,"elapsed":537,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"46ea1763-a52c-4988-dd60-545115a0a2b4"},"source":["fpr, tpr, thresholds = roc_curve(y_test, y_pred_rf)\n","roc_auc = auc(fpr, tpr)\n","display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc)\n","display.plot()\n","plt.show()"],"execution_count":145,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"cUHe22WUptcj","executionInfo":{"status":"ok","timestamp":1685977119575,"user_tz":180,"elapsed":969,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"357de801-3f4f-456d-e1ad-b7c3536eb442"},"source":["cm = confusion_matrix(y_test, y_pred_rf)\n","disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n"," display_labels=rf.classes_)\n","\n","disp.plot()"],"execution_count":146,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":146},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["y_prob_rf[:,1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xF_8w02zHD4-","executionInfo":{"status":"ok","timestamp":1685977122242,"user_tz":180,"elapsed":355,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"3d3d9bae-e863-4fe8-bbf5-2dfb8802001f"},"execution_count":147,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.42, 0.98, 0.98, ..., 0.02, 0.02, 0.7 ])"]},"metadata":{},"execution_count":147}]},{"cell_type":"markdown","metadata":{"id":"DyOeCac0qLGc"},"source":["# 6. Deployment"]},{"cell_type":"markdown","metadata":{"id":"Q6Xinsw-qNiF"},"source":["¿Qué estrategia de implementación podemos seguir?\n","Usar los puntos de corte definidos en test"]},{"cell_type":"code","metadata":{"id":"2GILGYKzqLmQ","executionInfo":{"status":"ok","timestamp":1685977130392,"user_tz":180,"elapsed":419,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["df_bin = y_test.reset_index(drop=True)\n","\n","# transformo a dataframe la predicción y la probabilidad\n","df_pred = pd.DataFrame(y_pred_rf, columns=['Predicion'])\n","df_prob = pd.DataFrame(y_prob_rf[:, 1], columns=['Probabilidad'])\n","\n","# añado la predicción y la probabilidad\n","df_bin = (df_bin.join(df_pred)\n"," .join(df_prob))\n","df_bin=df_bin.sort_values(by=['Probabilidad'])\n","\n","# creo 10 grupos en base a la probabilidad\n","df_bin['percentil']=pd.qcut(df_bin['Probabilidad'], 10)\n","bines= pd.DataFrame({'percentil':df_bin.percentil.unique()})\n","bines['Bin'] =bines.index\n","df_bin =pd.merge(df_bin, bines,how='left', on =['percentil','percentil'])"],"execution_count":148,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"P--6_ERbqT_z"},"source":["## Tabla en base a la probabilidad"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"UoRmS10C_Im0","executionInfo":{"status":"ok","timestamp":1685977132241,"user_tz":180,"elapsed":14,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}},"outputId":"31a34e89-96a5-4af4-c0ad-15666da7c768"},"source":["agg_func = {'Probabilidad':['min', 'max', 'count'],\n"," 'y':['sum']}\n"," \n","df_res = df_bin.groupby('Bin').agg(agg_func).reset_index()\n","df_res.columns = df_res.columns.droplevel()\n","df_res.rename(columns ={'count':'totales',\n"," 'max':'max_prob',\n"," 'min':'min_prob',\n"," 'sum':'positivos',\n"," '':'Bin'}, inplace=True)\n","df_res"],"execution_count":149,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Bin min_prob max_prob totales positivos\n","0 0 0.00 0.03 307 0\n","1 1 0.04 0.06 258 2\n","2 2 0.07 0.10 196 4\n","3 3 0.11 0.32 250 22\n","4 4 0.33 0.61 254 137\n","5 5 0.62 0.72 244 197\n","6 6 0.73 0.81 287 246\n","7 7 0.82 0.87 239 215\n","8 8 0.88 0.93 239 225\n","9 9 0.94 1.00 239 229"],"text/html":["\n","
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Binmin_probmax_probtotalespositivos
000.000.033070
110.040.062582
220.070.101964
330.110.3225022
440.330.61254137
550.620.72244197
660.730.81287246
770.820.87239215
880.880.93239225
990.941.00239229
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\n"," "]},"metadata":{},"execution_count":149}]},{"cell_type":"code","metadata":{"id":"Vf2hPLJv-AuX","executionInfo":{"status":"ok","timestamp":1685977135404,"user_tz":180,"elapsed":496,"user":{"displayName":"Eduardo Montero","userId":"08026561778973164523"}}},"source":["# guarda modelo\n","from joblib import dump, load\n","dump(rf, 'RF_Bank.joblib') \n","# cargar modelo\n","loaded_model = load('RF_Bank.joblib') "],"execution_count":150,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"r_TGeg3FGkc_"},"execution_count":null,"outputs":[]}]}