Predicción_Abandono_de_Clientes.ipynb

{"cells":[{"cell_type":"markdown","source":["# **Predicción Abandono de Banco**\n","\n","---\n","\n"],"metadata":{"id":"MWiD_lJk5x8V"}},{"cell_type":"code","execution_count":255,"metadata":{"id":"E34bhn83DwXm","executionInfo":{"status":"ok","timestamp":1715373918310,"user_tz":180,"elapsed":669,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns"]},{"cell_type":"markdown","source":["## **Reolectar datos**"],"metadata":{"id":"OhMZSPYs5uE1"}},{"cell_type":"code","execution_count":256,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"executionInfo":{"elapsed":87,"status":"ok","timestamp":1715373918746,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"rXepGMq9ER1C","outputId":"65d6341f-7f87-45c7-84a4-03498c81eed1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n","0          1    15634602  Hargrave          619    France  Female   42   \n","1          2    15647311      Hill          608     Spain  Female   41   \n","2          3    15619304      Onio          502    France  Female   42   \n","3          4    15701354      Boni          699    France  Female   39   \n","4          5    15737888  Mitchell          850     Spain  Female   43   \n","\n","   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n","0       2       0.00              1          1               1   \n","1       1   83807.86              1          0               1   \n","2       8  159660.80              3          1               0   \n","3       1       0.00              2          0               0   \n","4       2  125510.82              1          1               1   \n","\n","   EstimatedSalary  Exited  \n","0        101348.88       1  \n","1        112542.58       0  \n","2        113931.57       1  \n","3         93826.63       0  \n","4         79084.10       0  "],"text/html":["\n","  <div id=\"df-7b21e36d-3cbc-493b-945d-e6de33598089\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>RowNumber</th>\n","      <th>CustomerId</th>\n","      <th>Surname</th>\n","      <th>CreditScore</th>\n","      <th>Geography</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Tenure</th>\n","      <th>Balance</th>\n","      <th>NumOfProducts</th>\n","      <th>HasCrCard</th>\n","      <th>IsActiveMember</th>\n","      <th>EstimatedSalary</th>\n","      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France  Female    42   \n","1         2    15647311      Hill                 608   Spain  Female    41   \n","\n","   antiguedad_años     saldo  num_productos  tiene_tarj_credito  es_activo  \\\n","0                2      0.00              1                   1          1   \n","1                1  83807.86              1                   0          1   \n","\n","   salario_estimado  salió  \n","0         101348.88      1  \n","1         112542.58      0  "],"text/html":["\n","  <div id=\"df-0a485f54-bed3-4b18-9fca-bcb11897cdcf\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      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\"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"apellido\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2932,\n        \"samples\": [\n          \"McGuirk\",\n          \"Torkelson\",\n          \"Rapuluchukwu\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"puntaje_crediticio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96,\n        \"min\": 350,\n        \"max\": 850,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          754,\n          533,\n          744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pais\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"France\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"edad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 92,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          61,\n          42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antiguedad_a\\u00f1os\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"saldo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62397.405202385955,\n        \"min\": 0.0,\n        \"max\": 250898.09,\n        \"num_unique_values\": 6382,\n        \"samples\": [\n          117707.18,\n          133050.97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_productos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tiene_tarj_credito\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"es_activo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salario_estimado\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57510.49281769816,\n        \"min\": 11.58,\n        \"max\": 199992.48,\n        \"num_unique_values\": 9999,\n        \"samples\": [\n          100809.99,\n          95273.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sali\\u00f3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":257}],"source":["#cambio los nombres de las columnas al español\n","df1 = df.copy()\n","df1.columns = ['num_fila', 'id_cliente', 'apellido', 'puntaje_crediticio', 'pais', 'genero', 'edad', 'antiguedad_años', 'saldo', 'num_productos', 'tiene_tarj_credito', 'es_activo', 'salario_estimado', 'salió']\n","df1.head(2)"]},{"cell_type":"code","execution_count":258,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":77,"status":"ok","timestamp":1715373918749,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"hGMSvqYoa7ho","outputId":"8ea19d99-9bdb-4767-d4c9-8b4fdd993164"},"outputs":[{"output_type":"stream","name":"stdout","text":["Datos a analizar: (10000, 14)\n"]}],"source":["print('Datos a analizar:', df1.shape)"]},{"cell_type":"code","execution_count":259,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":67,"status":"ok","timestamp":1715373918753,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"E-r5db2uacLX","outputId":"0003acc0-3ba1-4a13-b835-f523071256a1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["num_fila              0\n","id_cliente            0\n","apellido              0\n","puntaje_crediticio    0\n","pais                  0\n","genero                0\n","edad                  0\n","antiguedad_años       0\n","saldo                 0\n","num_productos         0\n","tiene_tarj_credito    0\n","es_activo             0\n","salario_estimado      0\n","salió                 0\n","dtype: int64"]},"metadata":{},"execution_count":259}],"source":["#verifico que no hayan valores Nan\n","df1.isna().sum()"]},{"cell_type":"code","execution_count":260,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":61,"status":"ok","timestamp":1715373918755,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"qn6nJwbBanPw","outputId":"890c6ae2-8146-4bca-e12b-125ef2db8b6a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["num_fila              10000\n","id_cliente            10000\n","apellido               2932\n","puntaje_crediticio      460\n","pais                      3\n","genero                    2\n","edad                     70\n","antiguedad_años          11\n","saldo                  6382\n","num_productos             4\n","tiene_tarj_credito        2\n","es_activo                 2\n","salario_estimado       9999\n","salió                     2\n","dtype: int64"]},"metadata":{},"execution_count":260}],"source":["#Verifio la cantidad de resultados posibles para cada columna\n","df1.nunique()"]},{"cell_type":"code","execution_count":261,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":132},"executionInfo":{"elapsed":53,"status":"ok","timestamp":1715373918756,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"LAFfxyGMbIVs","outputId":"8774d082-2994-4884-b9c4-d547c5487101"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   puntaje_crediticio    pais  genero  edad  antiguedad_años     saldo  \\\n","0                 619  France  Female    42                2      0.00   \n","1                 608   Spain  Female    41                1  83807.86   \n","\n","   num_productos  tiene_tarj_credito  es_activo  salario_estimado  salió  \n","0              1                   1          1         101348.88      1  \n","1              1                   0          1         112542.58      0  "],"text/html":["\n","  <div id=\"df-2a031a3f-6c61-45d5-bc85-9054d9b57fcb\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>puntaje_crediticio</th>\n","      <th>pais</th>\n","      <th>genero</th>\n","      <th>edad</th>\n","      <th>antiguedad_años</th>\n","      <th>saldo</th>\n","      <th>num_productos</th>\n","      <th>tiene_tarj_credito</th>\n","      <th>es_activo</th>\n","      <th>salario_estimado</th>\n","      <th>salió</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      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\"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"edad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 92,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          61,\n          42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antiguedad_a\\u00f1os\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"saldo\",\n      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\"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"es_activo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salario_estimado\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57510.49281769816,\n        \"min\": 11.58,\n        \"max\": 199992.48,\n        \"num_unique_values\": 9999,\n        \"samples\": [\n          100809.99,\n          95273.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sali\\u00f3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":261}],"source":["#Borro las columnas que no me son utiles para el analisis\n","df2 = df1.drop(['num_fila', 'id_cliente', 'apellido'], axis = 1)\n","df2.head(2)"]},{"cell_type":"markdown","source":["## **Explorar datos**"],"metadata":{"id":"yQivGxSR9C9a"}},{"cell_type":"code","execution_count":262,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"executionInfo":{"elapsed":543,"status":"ok","timestamp":1715373919251,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"UAqF1AyKdj2E","outputId":"f4efb39e-f9e6-421b-d07e-ddc5f20a67d0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#comparo valores de cuantos de los clientes se fueron y cuantos no.\n","labels= ['no abandonaron', 'abandonaron']\n","y = [df2.salió[df2['salió']==0].count(), df2.salió[df2['salió']==1].count()]\n","explode = (0, 0.1)\n","\n","fig = plt.figure(figsize=(6,6))\n","ax = fig.add_subplot()\n","\n","ax.pie(y,labels=labels, explode=explode, autopct='%1.1f%%', shadow=True, startangle=90)\n","ax.set_title('Proporción de clientes que abandonaron y que no abandonaron', size= 15)\n","plt.show()"]},{"cell_type":"code","execution_count":263,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":853},"executionInfo":{"elapsed":1270,"status":"ok","timestamp":1715373920486,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"hq3TCm18cX4C","outputId":"30686c1d-9e92-46fd-fac7-f036cbf1aa8e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x1000 with 4 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#se puede ver que hay variables continuas y variables categoricas\n","# primero vamos a examinar las variables categoricas en relación con los clientes que salieron\n","#('pais', 'genero', 'tiene_tarj_credito',\t'es_activo')\n","fig = plt.figure(figsize= (12, 10))\n","list_cat = ['pais', 'genero', 'tiene_tarj_credito', 'es_activo']\n","c= 0\n","for i in list_cat:\n","    ax = fig.add_subplot(2,2,c+1)\n","    sns.countplot(data=df2, x=i, hue='salió', ax=ax)\n","    ax.set_facecolor('#F5F5F5')\n","    ax.set_title(i, size=16)\n","    c+=1\n","plt.show()"]},{"cell_type":"code","execution_count":264,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":3940,"status":"ok","timestamp":1715373924370,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"rPCbIlBvd4Ov","outputId":"f361cc2c-871b-4d28-ba27-f025cb305471"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x1600 with 6 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#examino las variables continuas discriminando por la columna 'salió'\n","#(puntaje_crediticio, edad, antiguedad_años, saldo, num_productos, salario_estimado)\n","fig = plt.figure(figsize= (12, 16))\n","list_con= ['puntaje_crediticio', 'edad', 'antiguedad_años', 'saldo', 'num_productos', 'salario_estimado']\n","c=0\n","\n","for i in list_con:\n","    ax = fig.add_subplot(3,2,c+1)\n","    sns.kdeplot(data= df2, x=i, hue= 'salió', fill= True, alpha= 0.2, ax=ax)\n","\n","    ax.set_facecolor('#F5F5F5')\n","    ax.set_title(i, size=17)\n","\n","    c += 1\n","plt.show()"]},{"cell_type":"markdown","source":["### **Codificar columnas categoricas**"],"metadata":{"id":"nMs3nblF_X0z"}},{"cell_type":"code","execution_count":265,"metadata":{"id":"B32kAo_yeG0V","executionInfo":{"status":"ok","timestamp":1715373924372,"user_tz":180,"elapsed":234,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#antes de proseguir creando el modelo de machine learning,\n","#primero voy a cambiar las columnas categoricas en texto a número('genero', 'pais')"]},{"cell_type":"code","execution_count":266,"metadata":{"id":"cqdML_9CwPY8","executionInfo":{"status":"ok","timestamp":1715373924378,"user_tz":180,"elapsed":231,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["df_cod= df2.copy()"]},{"cell_type":"code","execution_count":267,"metadata":{"id":"hXgzBmEVxAIg","executionInfo":{"status":"ok","timestamp":1715373924379,"user_tz":180,"elapsed":229,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn.preprocessing import LabelEncoder\n","#genero función para codificar las columnas categoricas ordinales\n","#('genero','pais')\n","def label_encoding(df, column):\n","    #hago una copia del df pasado como parametro\n","    df_copy = df.copy()\n","\n","    #genero el LabelEncoder\n","    le = LabelEncoder()\n","\n","    #aplico LabelEncoder a la columna pasada como parametro\n","    label_encoding= le.fit_transform(df_copy[column])\n","\n","    #borro la columna pasada como parametro\n","    df_copy = df_copy.drop([column], axis=1)\n","\n","    #genero otra columna con el mismo nombre de la columna pasada como parametro\n","    #que contenga los datos ya codificados\n","    df_copy[column]= label_encoding\n","\n","    #retorno el dataframe ya codificado y el label encoder generado\n","    return df_copy, le"]},{"cell_type":"code","execution_count":268,"metadata":{"id":"puxen8SFz71J","executionInfo":{"status":"ok","timestamp":1715373924380,"user_tz":180,"elapsed":223,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#aplico label encoder a las columnas categoricas ordinales\n","#(genero, pais)\n","df_cod, le_genero = label_encoding(df_cod, 'genero')\n","df_cod, le_pais = label_encoding(df_cod, 'pais')"]},{"cell_type":"code","execution_count":269,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"executionInfo":{"elapsed":223,"status":"ok","timestamp":1715373924382,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"vW9-dv_D1pYH","outputId":"20d7267d-2a83-4c28-bd48-79ec4e3addef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   puntaje_crediticio  edad  antiguedad_años      saldo  num_productos  \\\n","0                 619    42                2       0.00              1   \n","1                 608    41                1   83807.86              1   \n","2                 502    42                8  159660.80              3   \n","3                 699    39                1       0.00              2   \n","4                 850    43                2  125510.82              1   \n","\n","   tiene_tarj_credito  es_activo  salario_estimado  salió  genero  pais  \n","0                   1          1         101348.88      1       0     0  \n","1                   0          1         112542.58      0       0     2  \n","2                   1          0         113931.57      1       0     0  \n","3                   0          0          93826.63      0       0     0  \n","4                   1          1          79084.10      0       0     2  "],"text/html":["\n","  <div id=\"df-ce524029-730f-4da4-a713-4fbf0031d253\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>puntaje_crediticio</th>\n","      <th>edad</th>\n","      <th>antiguedad_años</th>\n","      <th>saldo</th>\n"," 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},\n    {\n      \"column\": \"antiguedad_a\\u00f1os\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          2,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"saldo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62397.405202385955,\n        \"min\": 0.0,\n        \"max\": 250898.09,\n        \"num_unique_values\": 6382,\n        \"samples\": [\n          117707.18,\n          133050.97,\n          124271.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_productos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tiene_tarj_credito\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"es_activo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salario_estimado\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57510.49281769816,\n        \"min\": 11.58,\n        \"max\": 199992.48,\n        \"num_unique_values\": 9999,\n        \"samples\": [\n          100809.99,\n          95273.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sali\\u00f3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genero\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pais\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":269}],"source":["df_cod.head()"]},{"cell_type":"code","execution_count":270,"metadata":{"id":"mXutznIWXFlc","executionInfo":{"status":"ok","timestamp":1715373924388,"user_tz":180,"elapsed":217,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#Ahora voy a aplicarle OneHotEncoding a todas las columnas categoricas\n","#para ello voy a generar otra función que sea para aplicar OneHotEncoder\n","from sklearn.preprocessing import OneHotEncoder\n","\n","def one_hot_encoding(df,column):\n","    #genero copia de df pasado como parametro\n","    df_copy= df.copy()\n","\n","    #genero el OneHotEncoder\n","    ohe= OneHotEncoder(sparse=False)\n","    #Aplico el ohe a la columna pasada como parametro\n","    one_hot_encoding= ohe.fit_transform(df_copy[[column]])\n","\n","    #creo las columnas ya codificadas y le agrego sufujo\n","    one_hot_encoding_df= pd.DataFrame(one_hot_encoding, dtype=int).add_prefix(column+'_')\n","\n","    #uno las columnas al dataframe\n","    df_copy = df_copy.join(one_hot_encoding_df)\n","\n","    #borro la columna original\n","    df_copy= df_copy.drop([column], axis=1)\n","\n","    return df_copy, ohe"]},{"cell_type":"code","execution_count":271,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":211,"status":"ok","timestamp":1715373924389,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"_zsNGSVl42Eq","outputId":"659a0e50-b30d-48dc-801f-67edb03d0a8e"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n","  warnings.warn(\n"]}],"source":["#le aplico la función a las columnas categoricas de entrada\n","#('pais','genero','tiene_tarj_credito','es_activo')\n","\n","df_cod, ohe_pais = one_hot_encoding(df_cod, 'pais')\n","df_cod, ohe_genero = one_hot_encoding(df_cod, 'genero')\n","df_cod, ohe_tiene_tarj_credito = one_hot_encoding(df_cod, 'tiene_tarj_credito')\n","df_cod, ohe_es_activo = one_hot_encoding(df_cod, 'es_activo')"]},{"cell_type":"code","execution_count":272,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"executionInfo":{"elapsed":196,"status":"ok","timestamp":1715373924390,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"TX62YUnV5Qz_","outputId":"148897df-b02a-4f52-e16e-83e28ad78d39"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   puntaje_crediticio  edad  antiguedad_años      saldo  num_productos  \\\n","0                 619    42                2       0.00              1   \n","1                 608    41                1   83807.86              1   \n","2                 502    42                8  159660.80              3   \n","3                 699    39                1       0.00              2   \n","4                 850    43                2  125510.82              1   \n","\n","   salario_estimado  salió  pais_0  pais_1  pais_2  genero_0  genero_1  \\\n","0         101348.88      1       1       0       0         1         0   \n","1         112542.58      0       0       0       1         1         0   \n","2         113931.57      1       1       0       0         1         0   \n","3          93826.63      0       1       0       0         1         0   \n","4          79084.10      0       0       0       1         1         0   \n","\n","   tiene_tarj_credito_0  tiene_tarj_credito_1  es_activo_0  es_activo_1  \n","0                     0                     1            0            1  \n","1                     1                     0            0            1  \n","2                     0                     1    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\"\"\n      }\n    },\n    {\n      \"column\": \"es_activo_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":275}],"source":["#segundo, normalizo las columnas continuas para tener una mejor predicción.\n","#como nos esncontramos con algunos datos con outliers y no todos los datos\n","# estan balanceados, voy a usar RobustScaler para normalizar los datos continuos\n","from sklearn.preprocessing import RobustScaler\n","\n","scaler = RobustScaler()\n","\n","#para normalizar las columnas, voy a usar list_con que genere anteriormente\n","df_norm[list_con] = scaler.fit_transform(df_norm[list_con])\n","\n","df_norm.head()"]},{"cell_type":"markdown","source":["### **Balanceo de datos con Sobre-muestreo (Oversampling)**"],"metadata":{"id":"32U8DVQZ_tB3"}},{"cell_type":"markdown","metadata":{"id":"CecrYZ5dV-qZ"},"source":["Ahora si, como un último paso antes de probar diferentes modelos de clasificación, debemos recordar que nuestros datos se encuentran desbalanceados en relación con nuestra columna target. Esto quiere decir que, al momento de crear nuestro modelo de clasificación, este nos va a dar resultados erróneos ya que le dimos para entrenar muchos datos donde las personas no abandonaron, y muy pocos sobre personas que si se fueron, por lo que vamos a tener en su mayoría, predicciones con falsos negativos (personas que no se fueron, pero en realidad sí).\n","\n","Para ello, existen las técnicas de sobre-muestreo (oversampling) y sub-muestreo(undersampling), en este caso, voy a usar el sobre-muestreo para no perder datos que pueden ser de calidad para nuestra predicción.\n"]},{"cell_type":"code","execution_count":276,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":465},"executionInfo":{"elapsed":1237,"status":"ok","timestamp":1715373925484,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"A2TS3NkYWNDI","outputId":"304c9ac5-cb7f-4165-c109-513a0f37e76e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 500x500 with 1 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\n"},"metadata":{}}],"source":["fig, ax = plt.subplots(figsize=(5, 5))\n","sns.countplot(x = 'salió', data = df_norm, ax=ax)\n","plt.show()"]},{"cell_type":"code","execution_count":277,"metadata":{"id":"Tyo-EZuhWVXZ","executionInfo":{"status":"ok","timestamp":1715373925485,"user_tz":180,"elapsed":173,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#primero separo los datos por clase:\n","datos_salieron = df_norm[df_norm['salió']==1]\n","datos_quedaron = df_norm[df_norm['salió']==0]"]},{"cell_type":"code","execution_count":278,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":172,"status":"ok","timestamp":1715373925486,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"FcEPP-fVWdqO","outputId":"0cbfb626-7393-4c3d-cb5a-57a3f4ce4d57"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8000, 16)"]},"metadata":{},"execution_count":278}],"source":["#sobre_muestreo con remplazo:\n","sobremuestreo_salieron = datos_salieron.sample(n=8000, replace=True, random_state=0)\n","sobremuestreo_salieron.shape"]},{"cell_type":"code","execution_count":279,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":152,"status":"ok","timestamp":1715373925487,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"FcPoCVocWlxZ","outputId":"0be41bef-9092-484e-fb76-5e1a2c800c65"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(15963, 16)"]},"metadata":{},"execution_count":279}],"source":["#concateno los dataframe datos_quedaron y sobremuestreo_salieron\n","df_bal = pd.concat([datos_quedaron, sobremuestreo_salieron])\n","df_bal.shape"]},{"cell_type":"code","execution_count":280,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":465},"executionInfo":{"elapsed":151,"status":"ok","timestamp":1715373925497,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"FwScugZBDfUU","outputId":"a89e3743-45e4-41c8-f6ae-fc5d79fb815a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 500x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig, ax = plt.subplots(figsize=(5, 5))\n","sns.countplot(x = 'salió', data = df_bal, ax=ax)\n","plt.show()"]},{"cell_type":"markdown","source":["## **Entrenar modelo**"],"metadata":{"id":"VaGcj0bv9bDq"}},{"cell_type":"code","execution_count":281,"metadata":{"id":"9USz4IsI0NhK","executionInfo":{"status":"ok","timestamp":1715373925498,"user_tz":180,"elapsed":135,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n","\n","#creo una función que me genere el y_hat del modelo y me muestre distintas metricas\n","def validation(df, model, X_test, y_test, name):\n","    y_hat = model.predict(X_test)\n","\n","    accuracy = accuracy_score(y_test, y_hat, normalize=True)\n","    f1 = f1_score(y_test, y_hat)\n","    precision =  precision_score(y_test, y_hat)\n","    recall= recall_score(y_test, y_hat)\n","\n","    df_valid = pd.DataFrame({'model': [name.upper()], 'accuracy':[accuracy],\n","                             'f1_score':[f1], 'precision_score':[precision],\n","                             'recall_score':[recall]})\n","    df_valid = pd.concat([df, df_valid])\n","\n","    return df_valid, y_hat, accuracy"]},{"cell_type":"code","execution_count":282,"metadata":{"id":"uD2m_8EHgUqN","executionInfo":{"status":"ok","timestamp":1715373925499,"user_tz":180,"elapsed":134,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#comienzo a crear el modelo\n","#para ello , primero creo las variables x e y\n","X = df_bal.drop('salió', axis=1).values\n","y = df_bal['salió'].values"]},{"cell_type":"code","execution_count":283,"metadata":{"id":"CG24GyBwgeC8","executionInfo":{"status":"ok","timestamp":1715373925501,"user_tz":180,"elapsed":114,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#creo el campo de entrenamiento, 70% entrenamiento, 30% test\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"]},{"cell_type":"markdown","source":["### **Modelo K-Nearest Neigbours (KNN)**"],"metadata":{"id":"g95m37aW9jtG"}},{"cell_type":"code","execution_count":284,"metadata":{"id":"BUV6my90eQRo","executionInfo":{"status":"ok","timestamp":1715373925508,"user_tz":180,"elapsed":118,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn.neighbors import KNeighborsClassifier\n","knn = KNeighborsClassifier(n_neighbors=5)\n","knn = knn.fit(X_train, y_train)"]},{"cell_type":"markdown","source":["### **DecisionTreeClassifier**"],"metadata":{"id":"gOLjPLSh92Fm"}},{"cell_type":"code","execution_count":285,"metadata":{"id":"Lkm9Cml-eRUI","executionInfo":{"status":"ok","timestamp":1715373925511,"user_tz":180,"elapsed":114,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn.tree import DecisionTreeClassifier\n","dtc = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n","dtc = dtc.fit(X_train, y_train)"]},{"cell_type":"markdown","source":["### **Random Forest**"],"metadata":{"id":"naw2y9c7-G3s"}},{"cell_type":"code","execution_count":286,"metadata":{"id":"I1rWJyYMzLYp","executionInfo":{"status":"ok","timestamp":1715373925513,"user_tz":180,"elapsed":113,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn.ensemble import RandomForestClassifier\n","rf = RandomForestClassifier(n_estimators=5, criterion='entropy', max_depth=5, random_state=0)\n","rf = rf.fit(X_train, y_train)"]},{"cell_type":"markdown","source":["### **Sopport Vector Machine (SVM)**"],"metadata":{"id":"W0olFWZm-S4o"}},{"cell_type":"code","execution_count":287,"metadata":{"id":"SQKAbJWO2yjT","executionInfo":{"status":"ok","timestamp":1715373929565,"user_tz":180,"elapsed":4163,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn import svm\n","svc = svm.SVC(kernel='poly')\n","svc = svc.fit(X_train, y_train)"]},{"cell_type":"markdown","source":["### **Logistic Regression**"],"metadata":{"id":"r6csD3F_-fBa"}},{"cell_type":"code","execution_count":288,"metadata":{"id":"izpBmkXz7C_I","executionInfo":{"status":"ok","timestamp":1715373929566,"user_tz":180,"elapsed":40,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn.linear_model import LogisticRegression\n","lr = LogisticRegression(solver='sag')\n","lr = lr.fit(X_train, y_train)"]},{"cell_type":"markdown","source":["## **Validación de modelos**"],"metadata":{"id":"NQGHkaRK-o1S"}},{"cell_type":"code","source":["df_model = pd.DataFrame(columns = ['model', 'accuracy', 'f1_score', 'precision_score',\n","                         'recall_score'])"],"metadata":{"id":"bWPOQH5w6xY9","executionInfo":{"status":"ok","timestamp":1715373929566,"user_tz":180,"elapsed":36,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"execution_count":289,"outputs":[]},{"cell_type":"code","execution_count":290,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":1824,"status":"ok","timestamp":1715373931356,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"i18Mkjqvg9kC","outputId":"90cb9830-65f6-4c4c-aee9-3f73cb16cd81"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  model  accuracy  f1_score  precision_score  recall_score\n","0   KNN  0.809981  0.819731         0.783712      0.859219\n","0   DTC  0.776362  0.770320         0.796452      0.745847\n","0    RF  0.755272  0.750320         0.770341      0.731312\n","0   SVC  0.798914  0.797391         0.808102      0.786960\n","0    LR  0.710169  0.707296         0.718509      0.696429"],"text/html":["\n","  <div id=\"df-597a70b7-dbba-43c5-ae25-78f741a72ada\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>f1_score</th>\n","      <th>precision_score</th>\n","      <th>recall_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>KNN</td>\n","      <td>0.809981</td>\n","      <td>0.819731</td>\n","      <td>0.783712</td>\n","      <td>0.859219</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>DTC</td>\n","      <td>0.776362</td>\n","      <td>0.770320</td>\n","      <td>0.796452</td>\n","      <td>0.745847</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>RF</td>\n","      <td>0.755272</td>\n","      <td>0.750320</td>\n","      <td>0.770341</td>\n","      <td>0.731312</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>SVC</td>\n","      <td>0.798914</td>\n","      <td>0.797391</td>\n","      <td>0.808102</td>\n","      <td>0.786960</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.710169</td>\n","      <td>0.707296</td>\n","      <td>0.718509</td>\n","      <td>0.696429</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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'svc')\n","df_model, y_hat_lr, accuracy_lr = validation(df_model, lr, X_test, y_test, 'lr')\n","\n","df_model"]},{"cell_type":"code","execution_count":291,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"executionInfo":{"elapsed":743,"status":"ok","timestamp":1715373932082,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"VbsVQ2F2g_mH","outputId":"19744909-467f-4cad-a529-e43533202531"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#me quedo con el mejor modelo y muestro metrica de confusión\n","from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n","cm = confusion_matrix(y_test, y_hat_knn)\n","\n","cmd = ConfusionMatrixDisplay(cm, display_labels= rf.classes_)\n","cmd.plot(cmap=plt.cm.Blues)\n","plt.show()"]},{"cell_type":"markdown","source":["## **Automatización de modelos**"],"metadata":{"id":"0B_tqqBt-zeJ"}},{"cell_type":"code","execution_count":292,"metadata":{"id":"KINoguyu-j1m","executionInfo":{"status":"ok","timestamp":1715373932083,"user_tz":180,"elapsed":29,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["from sklearn.model_selection import GridSearchCV"]},{"cell_type":"code","execution_count":293,"metadata":{"id":"lH5Z_dks-wt9","executionInfo":{"status":"ok","timestamp":1715373932085,"user_tz":180,"elapsed":25,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#Función para visualizar los datos de los modelos creados\n","def grid_model(model,name):\n","    print(f'\\nMODELO {name.upper()}:')\n","    print(f'Mejor modelo:', model.best_estimator_)\n","    print(f'Mejores parametros:', model.best_params_)\n","    print(f'Mejor score:', model.best_score_)"]},{"cell_type":"code","execution_count":294,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"executionInfo":{"elapsed":14072,"status":"ok","timestamp":1715373946134,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"},"user_tz":180},"id":"lk4EETcMhUsn","outputId":"14f46675-43b4-4df4-e6a0-1f3acdf506b0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=25, estimator=KNeighborsClassifier(),\n","             param_grid={'n_neighbors': [2, 4, 5, 7, 8, 10]},\n","             scoring='accuracy')"],"text/html":["<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=25, estimator=KNeighborsClassifier(),\n","             param_grid={&#x27;n_neighbors&#x27;: [2, 4, 5, 7, 8, 10]},\n","             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=25, estimator=KNeighborsClassifier(),\n","             param_grid={&#x27;n_neighbors&#x27;: [2, 4, 5, 7, 8, 10]},\n","             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":294}],"source":["parameters_knn= {\"n_neighbors\":[2,4,5,7,8,10]}\n","knn = KNeighborsClassifier()\n","grid_knn = GridSearchCV(knn, parameters_knn, scoring = 'accuracy',cv=25)\n","grid_knn.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":295,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"id":"LBw18rwlCbKF","executionInfo":{"status":"ok","timestamp":1715374156443,"user_tz":180,"elapsed":210341,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"95235aa8-b497-4a65-e9d0-8306069481ed"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=25, estimator=RandomForestClassifier(),\n","             param_grid={'criterion': ['gini', 'entropy'],\n","                         'max_depth': [4, 5, 8, 10, 12, 15, 20],\n","                         'n_estimators': [4, 5, 8, 10, 12, 15]},\n","             scoring='accuracy')"],"text/html":["<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=25, estimator=RandomForestClassifier(),\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [4, 5, 8, 10, 12, 15, 20],\n","                         &#x27;n_estimators&#x27;: [4, 5, 8, 10, 12, 15]},\n","             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=25, estimator=RandomForestClassifier(),\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [4, 5, 8, 10, 12, 15, 20],\n","                         &#x27;n_estimators&#x27;: [4, 5, 8, 10, 12, 15]},\n","             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":295}],"source":["parameters_rfc = {\"criterion\":[\"gini\", \"entropy\"],\n","                  \"n_estimators\": [4,5,8,10,12,15],\n","                  \"max_depth\": [4,5,8,10,12,15,20]}\n","rfc = RandomForestClassifier()\n","grid_rfc= GridSearchCV(rfc, parameters_rfc, scoring='accuracy',cv=25)\n","grid_rfc.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":296,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"id":"9kABJyYgCd0S","executionInfo":{"status":"ok","timestamp":1715374171436,"user_tz":180,"elapsed":15023,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"83e0ee0d-6edf-49ea-d226-62381133c693"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=25, estimator=DecisionTreeClassifier(),\n","             param_grid={'criterion': ['gini', 'entropy'],\n","                         'max_depth': [4, 5, 8, 10, 12, 15]},\n","             scoring='accuracy')"],"text/html":["<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=25, estimator=DecisionTreeClassifier(),\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [4, 5, 8, 10, 12, 15]},\n","             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=25, estimator=DecisionTreeClassifier(),\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [4, 5, 8, 10, 12, 15]},\n","             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":296}],"source":["parameters_dtc = {\"criterion\":[\"gini\", \"entropy\"],\n","                  \"max_depth\": [4,5,8,10,12,15]}\n","dtc = DecisionTreeClassifier()\n","grid_dtc= GridSearchCV(dtc, parameters_dtc, scoring='accuracy',cv=25)\n","grid_dtc.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":297,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"id":"cz496EpqdjvC","executionInfo":{"status":"ok","timestamp":1715375070346,"user_tz":180,"elapsed":898923,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"0f30c41d-f02c-47c8-87c7-71b91a44d1e0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=5, estimator=SVC(),\n","             param_grid={'C': [1, 7, 10, 15, 18, 20],\n","                         'kernel': ['linear', 'poly', 'rbf']},\n","             scoring='accuracy')"],"text/html":["<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n","             param_grid={&#x27;C&#x27;: [1, 7, 10, 15, 18, 20],\n","                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]},\n","             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n","             param_grid={&#x27;C&#x27;: [1, 7, 10, 15, 18, 20],\n","                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]},\n","             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":297}],"source":["#en caso de querer automatizar aun mas el modelo, podemos agregar el siguiente\n","#parametro: \"gamma\" : [0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1,5]\n","parameters_svc = {\"C\":[1,7,10,15,18,20],\n","                  \"kernel\":[\"linear\", \"poly\", \"rbf\"]}\n","svc = svm.SVC()\n","grid_svc= GridSearchCV(svc, parameters_svc, scoring='accuracy', cv=5)\n","grid_svc.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":298,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"id":"WEJ3apNgCnw1","executionInfo":{"status":"ok","timestamp":1715375075386,"user_tz":180,"elapsed":5051,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"fd91645e-4062-4a79-897e-e32fcd495394"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=25, estimator=LogisticRegression(),\n","             param_grid={'solver': ('lbfgs', 'sag')}, scoring='accuracy')"],"text/html":["<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=25, estimator=LogisticRegression(),\n","             param_grid={&#x27;solver&#x27;: (&#x27;lbfgs&#x27;, &#x27;sag&#x27;)}, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=25, estimator=LogisticRegression(),\n","             param_grid={&#x27;solver&#x27;: (&#x27;lbfgs&#x27;, &#x27;sag&#x27;)}, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":298}],"source":["parameters_lr = {'solver':('lbfgs', 'sag')}\n","lr = LogisticRegression()\n","grid_lr= GridSearchCV(lr, parameters_lr, scoring='accuracy',cv=25)\n","grid_lr.fit(X_train, y_train)"]},{"cell_type":"markdown","source":["## **Validación de los modelos automatizados**"],"metadata":{"id":"GOiQaxSn-9yo"}},{"cell_type":"code","execution_count":299,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FelarVlrD3hk","executionInfo":{"status":"ok","timestamp":1715375075386,"user_tz":180,"elapsed":29,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"8607ceec-1fd7-440c-fe2f-c7ff209de397"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","MODELO KNN:\n","Mejor modelo: KNeighborsClassifier(n_neighbors=2)\n","Mejores parametros: {'n_neighbors': 2}\n","Mejor score: 0.8746186334406758\n","\n","MODELO RFC:\n","Mejor modelo: RandomForestClassifier(max_depth=20, n_estimators=10)\n","Mejores parametros: {'criterion': 'gini', 'max_depth': 20, 'n_estimators': 10}\n","Mejor score: 0.9343999357951867\n","\n","MODELO DTC:\n","Mejor modelo: DecisionTreeClassifier(max_depth=15)\n","Mejores parametros: {'criterion': 'gini', 'max_depth': 15}\n","Mejor score: 0.8882218276301401\n","\n","MODELO SVC:\n","Mejor modelo: SVC(C=20)\n","Mejores parametros: {'C': 20, 'kernel': 'rbf'}\n","Mejor score: 0.8126905922102787\n","\n","MODELO LR:\n","Mejor modelo: LogisticRegression()\n","Mejores parametros: {'solver': 'lbfgs'}\n","Mejor score: 0.7027864888995897\n"]}],"source":["#validando los modelos con la función grid_model(model,name):\n","grid_model(grid_knn, 'knn')\n","grid_model(grid_rfc, 'rfc')\n","grid_model(grid_dtc, 'dtc')\n","grid_model(grid_svc, 'svc')\n","grid_model(grid_lr, 'lr')"]},{"cell_type":"code","source":["df_model_grid = pd.DataFrame(columns = ['model', 'accuracy', 'f1_score', 'precision_score',\n","                         'recall_score'])"],"metadata":{"id":"CTNYzDkQ-3VM","executionInfo":{"status":"ok","timestamp":1715375075387,"user_tz":180,"elapsed":17,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"execution_count":300,"outputs":[]},{"cell_type":"code","execution_count":301,"metadata":{"id":"2riw3pruEjef","executionInfo":{"status":"ok","timestamp":1715375077909,"user_tz":180,"elapsed":2536,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"outputs":[],"source":["#validando los modelos con la función validation\n","df_model_grid, y_hat_grid_knn, accuracy_grid_knn = validation(df_model_grid, grid_knn, X_test, y_test, 'knn')\n","df_model_grid, y_hat_grid_rfc, accuracy_grid_rfc = validation(df_model_grid, grid_rfc, X_test, y_test, 'rfc')\n","df_model_grid, y_hat_grid_dtc, accuracy_grid_dtc = validation(df_model_grid, grid_dtc, X_test, y_test, 'dtc')\n","df_model_grid, y_hat_grid_svc, accuracy_grid_svc = validation(df_model_grid, grid_svc, X_test, y_test, 'svc')\n","df_model_grid, y_hat_grid_lr, accuracy_grid_lr = validation(df_model_grid, grid_lr, X_test, y_test, 'lr')"]},{"cell_type":"code","source":["df_model_grid"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"63DYyJcfDZOF","executionInfo":{"status":"ok","timestamp":1715375077918,"user_tz":180,"elapsed":29,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"8ef01dc3-7383-4dbb-d57d-d9f284fa63a2"},"execution_count":302,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  model  accuracy  f1_score  precision_score  recall_score\n","0   KNN  0.881186  0.881136         0.886507      0.875831\n","0   RFC  0.938818  0.940143         0.925211      0.955565\n","0   DTC  0.890374  0.896552         0.853018      0.944767\n","0   SVC  0.832115  0.835650         0.822866      0.848837\n","0    LR  0.710169  0.707296         0.718509      0.696429"],"text/html":["\n","  <div id=\"df-c5f7e779-c8cd-43c9-bcf0-00e8d3044301\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>f1_score</th>\n","      <th>precision_score</th>\n","      <th>recall_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>KNN</td>\n","      <td>0.881186</td>\n","      <td>0.881136</td>\n","      <td>0.886507</td>\n","      <td>0.875831</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>RFC</td>\n","      <td>0.938818</td>\n","      <td>0.940143</td>\n","      <td>0.925211</td>\n","      <td>0.955565</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>DTC</td>\n","      <td>0.890374</td>\n","      <td>0.896552</td>\n","      <td>0.853018</td>\n","      <td>0.944767</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>SVC</td>\n","      <td>0.832115</td>\n","      <td>0.835650</td>\n","      <td>0.822866</td>\n","      <td>0.848837</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>LR</td>\n","      <td>0.710169</td>\n","      <td>0.707296</td>\n","      <td>0.718509</td>\n","      <td>0.696429</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5f7e779-c8cd-43c9-bcf0-00e8d3044301')\"\n","            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\n"},"metadata":{}}],"source":["#Comparar los resultados de los modelos dependiendo de su accuracy con grafico de barra\n","#lista de los modelos generados\n","model_name= ['KNeighborsClassifier', 'RandomForestClassifier', 'DecisionTreeClassifier',\n","             'SupportVectorMachine', 'LogisticRegression']\n","\n","#accuracies de los modelos\n","accuracy = [accuracy_grid_knn, accuracy_grid_rfc, accuracy_grid_dtc,\n","              accuracy_grid_svc, accuracy_grid_lr]\n","\n","fig = plt.figure(figsize= (10,5))\n","ax= sns.barplot(y=model_name, x=accuracy, color= '#6A5ACD')\n","\n","#destaco la barra con mejor accuracy\n","color ='#00CED1'\n","accuracy_max = max(accuracy)\n","barra_max = accuracy.index(accuracy_max)\n","ax.patches[barra_max].set_facecolor(color)\n","\n","#titulos\n","ax.set_title('Mejor modelo')\n","ax.set_ylabel('Modelos')\n","ax.set_xlabel('Accuracies')\n","\n","ax.set_xticks(list(np.linspace(0,1,11)))\n","\n","#fondo\n","ax.set_facecolor('#F8F8FF')\n","plt.show()"]},{"cell_type":"code","execution_count":304,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"KobrX0bidPS5","executionInfo":{"status":"ok","timestamp":1715375078981,"user_tz":180,"elapsed":553,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"d9496d9a-f5f2-4a78-ddec-12d976d41563"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#confusion_matrix del mejor modelo\n","cm = confusion_matrix(y_test, y_hat_grid_rfc)\n","\n","cmd = ConfusionMatrixDisplay(cm)\n","cmd.plot(cmap=plt.cm.Blues)\n","\n","plt.show()"]},{"cell_type":"markdown","source":["## **Probar modelo**"],"metadata":{"id":"hDbZJZzk_IE3"}},{"cell_type":"code","source":["df2.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":132},"id":"qflKoHPBGonS","executionInfo":{"status":"ok","timestamp":1715375078983,"user_tz":180,"elapsed":83,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"e85db836-7680-407b-d56f-5df2ee7417cd"},"execution_count":305,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   puntaje_crediticio    pais  genero  edad  antiguedad_años     saldo  \\\n","0                 619  France  Female    42                2      0.00   \n","1                 608   Spain  Female    41                1  83807.86   \n","\n","   num_productos  tiene_tarj_credito  es_activo  salario_estimado  salió  \n","0              1                   1          1         101348.88      1  \n","1              1                   0          1         112542.58      0  "],"text/html":["\n","  <div id=\"df-8fcb6c00-3fa0-4715-946a-b8ae0cf58f49\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>puntaje_crediticio</th>\n","      <th>pais</th>\n","      <th>genero</th>\n","      <th>edad</th>\n","      <th>antiguedad_años</th>\n","      <th>saldo</th>\n","      <th>num_productos</th>\n","      <th>tiene_tarj_credito</th>\n","      <th>es_activo</th>\n","      <th>salario_estimado</th>\n","      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antiguedad_años    saldo  \\\n","0                 600  Spain  Female    30                2  73203.9   \n","\n","   num_productos  tiene_tarj_credito  es_activo  salario_estimado  \n","0              2                   1          1          101220.3  "],"text/html":["\n","  <div id=\"df-f5e4f42c-0b9d-48e2-8b49-c90b64228f36\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>puntaje_crediticio</th>\n","      <th>pais</th>\n","      <th>genero</th>\n","      <th>edad</th>\n","      <th>antiguedad_años</th>\n","      <th>saldo</th>\n","      <th>num_productos</th>\n","      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\"es_activo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salario_estimado\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 101220.3,\n        \"max\": 101220.3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          101220.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":306}]},{"cell_type":"code","source":["#ahora debemos hacer el mismo proceso que hicimos en un principio\n","#primero creo una funcion para aplicar label_encoder a las columnas categoricas ordinales\n","def df_le(df,le,column):\n","\n","    df_copy = df.copy()\n","\n","    label_encoding= le.transform(df_copy[column])\n","\n","    #borro la columna pasada como parametro\n","    df_copy = df_copy.drop([column], axis=1)\n","\n","    #genero otra columna con el mismo nombre de la columna pasada como parametro\n","    #que contenga los datos ya codificados\n","    df_copy[column]= label_encoding\n","\n","    #retorno el dataframe ya codificado y el label encoder generado\n","\n","    return df_copy"],"metadata":{"id":"fi5Pv1CdJsmZ","executionInfo":{"status":"ok","timestamp":1715375078985,"user_tz":180,"elapsed":73,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"execution_count":307,"outputs":[]},{"cell_type":"code","source":["df_prub= prueba.copy()"],"metadata":{"id":"5P91vBKmLEn4","executionInfo":{"status":"ok","timestamp":1715375078986,"user_tz":180,"elapsed":72,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"execution_count":308,"outputs":[]},{"cell_type":"code","source":["df_prub = df_le(df_prub, le_genero, 'genero')\n","df_prub = df_le(df_prub, le_pais, 'pais')"],"metadata":{"id":"pmRkM8cpK_ZG","executionInfo":{"status":"ok","timestamp":1715375078987,"user_tz":180,"elapsed":71,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"execution_count":309,"outputs":[]},{"cell_type":"code","source":["df_prub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":109},"id":"rvzjlkTKLeO8","executionInfo":{"status":"ok","timestamp":1715375078988,"user_tz":180,"elapsed":69,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"c4de9cab-d39f-4f62-f2dd-895ea9f93655"},"execution_count":310,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   puntaje_crediticio  edad  antiguedad_años    saldo  num_productos  \\\n","0                 600    30                2  73203.9              2   \n","\n","   tiene_tarj_credito  es_activo  salario_estimado  genero  pais  \n","0                   1          1          101220.3       0     2  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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antiguedad_a\\u00f1os\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"saldo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 73203.9,\n        \"max\": 73203.9,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          73203.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_productos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n 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\"samples\": [\n          101220.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genero\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pais\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":310}]},{"cell_type":"code","source":["#luego creo funcion para aplicar onehotencoder a todas las columnas categoricas\n","def df_ohe(df,ohe,column):\n","    df_copy = df.copy()\n","\n","    ohe_transform = ohe.transform(df[[column]])\n","\n","    #se crean las columnas con los valores de one_hot_encoder\n","    df_ohe = pd.DataFrame(ohe_transform, dtype= int).add_prefix(column+'_')\n","\n","    #se unen las nuevas columnas al dataset original\n","    df_copy = df_copy.join(df_ohe)\n","\n","    #y finalmente se elimina la columna vieja\n","    df_copy = df_copy.drop([column],axis=1)\n","\n","    return df_copy"],"metadata":{"id":"7J3K4G4TKF-w","executionInfo":{"status":"ok","timestamp":1715375078990,"user_tz":180,"elapsed":67,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"execution_count":311,"outputs":[]},{"cell_type":"code","source":["df_prub = df_ohe(df_prub,ohe_pais,'pais')\n","df_prub = df_ohe(df_prub,ohe_genero,'genero')\n","df_prub = df_ohe(df_prub,ohe_tiene_tarj_credito,'tiene_tarj_credito')\n","df_prub = df_ohe(df_prub,ohe_es_activo,'es_activo')"],"metadata":{"id":"1C458BwtK80n","executionInfo":{"status":"ok","timestamp":1715375078991,"user_tz":180,"elapsed":66,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}}},"execution_count":312,"outputs":[]},{"cell_type":"code","source":["df_prub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":109},"id":"gWR61F9aMQ77","executionInfo":{"status":"ok","timestamp":1715375078992,"user_tz":180,"elapsed":65,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"4466654a-4b6d-4bd7-c1d1-5f841a85d44c"},"execution_count":313,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   puntaje_crediticio  edad  antiguedad_años    saldo  num_productos  \\\n","0                 600    30                2  73203.9              2   \n","\n","   salario_estimado  pais_0  pais_1  pais_2  genero_0  genero_1  \\\n","0          101220.3       0       0       1         1         0   \n","\n","   tiene_tarj_credito_0  tiene_tarj_credito_1  es_activo_0  es_activo_1  \n","0                     0                     1            0            1  "],"text/html":["\n","  <div id=\"df-a8d73823-5643-4ab5-8e20-51fcec982ee8\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>puntaje_crediticio</th>\n","      <th>edad</th>\n","      <th>antiguedad_años</th>\n","      <th>saldo</th>\n","      <th>num_productos</th>\n","      <th>salario_estimado</th>\n","      <th>pais_0</th>\n","      <th>pais_1</th>\n","      <th>pais_2</th>\n","      <th>genero_0</th>\n","      <th>genero_1</th>\n","  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\"genero_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tiene_tarj_credito_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tiene_tarj_credito_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"es_activo_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"es_activo_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":315}]},{"cell_type":"code","source":["#hora de predecir nuestro df de prueba con el mejor modelo!\n","X_prueba = df_prub.values\n","y_hat_prueba= grid_rfc.predict(X_prueba)\n","\n","if y_hat_prueba == [1]:\n","    print('Has abandonado')\n","else:\n","    print('No has abandonado')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nkRoMqeKMwcC","executionInfo":{"status":"ok","timestamp":1715375079475,"user_tz":180,"elapsed":89,"user":{"displayName":"Tatiana CR","userId":"05835673470100029923"}},"outputId":"539a941b-3c4b-4bdf-f379-c2b6cb84f706"},"execution_count":316,"outputs":[{"output_type":"stream","name":"stdout","text":["No has abandonado\n"]}]}],"metadata":{"colab":{"provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyMjequlxvVlpAjzx7GD4TBG"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}