Telecom Customer Churn Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importamos librerias necesarias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, Markdown\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, KFold\n",
    "from sklearn.metrics import confusion_matrix, precision_score, r2_score, recall_score, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "## Eliminamos warnings de sklearn\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "warnings.filterwarnings('ignore'\n",
    "                        , category = ConvergenceWarning\n",
    "                        , module = 'sklearn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funciones:\n",
    "* Crear gráficos para analizar el peso en churn de cada variable.\n",
    "* Pasar a porcentajes la matriz de confusión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(df, col, Var = 'Churn'):\n",
    "    df_temp = df.groupby([col], as_index = True).mean()[Var].sort_values(ascending = True)\n",
    "    \n",
    "    Grafico = df_temp.plot(kind = 'barh'\n",
    "                       , width = 0.5\n",
    "                       , color = plt.get_cmap('Blues')(np.linspace(start = 0.15\n",
    "                                           , stop = 0.85\n",
    "                                           , num = len(df_temp)))\n",
    "                       , stacked = True\n",
    "                       , legend = False\n",
    "                       , fontsize = 10)\n",
    "    \n",
    "    Grafico.set_xlim([0,0.55])\n",
    "    Grafico.set_ylabel('')\n",
    "    Grafico.grid(axis='x',alpha=0.25)\n",
    "    Grafico.set_xticklabels(['{:3.0f} %'.format(x * 100) for x in Grafico.get_xticks()])\n",
    "    [spine.set_visible(False) for spine in Grafico.spines.values()]\n",
    "    Grafico.spines['left'].set_visible(True)\n",
    "\n",
    "    plt.tick_params(left = False, bottom = False)\n",
    "    plt.title(col)\n",
    "    df_temp = None\n",
    "    \n",
    "def matriz(y, pred, Modelo = ''):\n",
    "    mc = confusion_matrix(y, pred)\n",
    "    Total = sum(np.transpose(mc))\n",
    "    mcr = np.transpose(np.round(np.transpose(mc) / Total * 100,2))\n",
    "    MC = pd.DataFrame(mc, columns=['No', 'Si'])\n",
    "\n",
    "    MC['index'] = ['No', 'Si']\n",
    "    MC['No'] *= -1\n",
    "\n",
    "    Grafico = MC.set_index('index').sort_values(['index']\n",
    "                                                , ascending=False).plot(kind = 'barh'\n",
    "                                                                        , stacked = True\n",
    "                                                                        , width = 0.75\n",
    "                                                                        , color=[['#c9c9c9','#e60000'],['#00c800','#c9c9c9']]\n",
    "                                                                        , legend = False\n",
    "                                                                        , figsize = (3,1.2))\n",
    "    \n",
    "    plt.gca().set_xticks([])\n",
    "    plt.gca().set_yticks([])\n",
    "    \n",
    "    plt.gca().grid(axis='x',alpha=0)\n",
    "    plt.gca().grid(axis='y',alpha=0)\n",
    "    plt.gca().set_xlabel('{:.2f} % - '.format(np.mean(y == pred) * 100) + Modelo\n",
    "                         , fontsize = 12\n",
    "                         , alpha = 0.6)\n",
    "    plt.gca().set_ylabel('')\n",
    "    \n",
    "    [spine.set_visible(False) for spine in plt.gca().spines.values()]\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def markdown(txt):\n",
    "    display(Markdown(txt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cargamos datos y eliminamos valores faltantes en _TotalCharges_.\n",
    "* Creamos Churn_b con valores binarios.\n",
    "* Mezclamos el DataFrame para hacer los análisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3281</th>\n",
       "      <td>6322-PJJDJ</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>94.80</td>\n",
       "      <td>4690.65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>1262-OPMFY</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>75.75</td>\n",
       "      <td>655.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4606</th>\n",
       "      <td>1734-ZMNTZ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.00</td>\n",
       "      <td>300.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "3281  6322-PJJDJ    Male              0     Yes         No      49   \n",
       "1625  1262-OPMFY  Female              0     Yes         No       9   \n",
       "4606  1734-ZMNTZ  Female              0     Yes        Yes      11   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...  \\\n",
       "3281          Yes            No     Fiber optic                   No  ...   \n",
       "1625          Yes            No     Fiber optic                   No  ...   \n",
       "4606          Yes           Yes              No  No internet service  ...   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "3281                   No                   No                  Yes   \n",
       "1625                  Yes                   No                   No   \n",
       "4606  No internet service  No internet service  No internet service   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling  \\\n",
       "3281                  Yes        Two year              Yes   \n",
       "1625                   No  Month-to-month              Yes   \n",
       "4606  No internet service        Two year               No   \n",
       "\n",
       "                  PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "3281           Electronic check          94.80       4690.65     0  \n",
       "1625  Bank transfer (automatic)          75.75         655.9     1  \n",
       "4606               Mailed check          25.00         300.7     0  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder() \n",
    "\n",
    "df = pd.read_csv('data.csv')\n",
    "\n",
    "df['TotalCharges'].replace(' ', np.nan, inplace=True)\n",
    "df = df.dropna(subset = ['TotalCharges'])\n",
    "df = df.sample(frac = 1\n",
    "               , random_state = 12345)\n",
    "   \n",
    "df['Churn'] =  df[['Churn']].apply(le.fit_transform)\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Churn</th>\n",
       "      <th>customerID</th>\n",
       "      <th>Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5163</td>\n",
       "      <td>0.734215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1869</td>\n",
       "      <td>0.265785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Churn  customerID     Ratio\n",
       "0      0        5163  0.734215\n",
       "1      1        1869  0.265785"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Churn = df.groupby(by = ['Churn'])['customerID'].count().reset_index()\n",
    "df_Churn['Ratio'] = df_Churn.customerID / df_Churn.customerID.sum()\n",
    "df_Churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creamos lista de columnas y eliminamos las que contienen datos numéricos.\n",
    "* Creamos lista de las variables a analizar impacto en Churn.\n",
    "* Creamos gráfico para ver impacto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x2160 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columnsList = df.columns.tolist()\n",
    "excluir = ['customerID','TotalCharges','tenure','MonthlyCharges','Churn','Churn_b']\n",
    "\n",
    "for i in excluir:\n",
    "    if i in columnsList:\n",
    "        columnsList.remove(i)\n",
    "\n",
    "fig = plt.figure(figsize =(5, 30)) \n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "plt.subplots_adjust(hspace = .7)\n",
    "\n",
    "for i in range(1,len(columnsList) + 1):    \n",
    "    plt.subplot(len(columnsList), 1, i)\n",
    "    plot(df, columnsList[i -1])\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis de variables numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E2FxV7xvaNXH1Jjk4ycq2/WTgxQxeTLseOK0Nm4haq+q8qlpdVWsYHJ/XVdWrmcBak+yX5Fe2bzNYH76VCTwGquq7wNYkz2pdJwFfZwJrHfIqfrkkA+OqddwvPMziBYpTgG8yWHN9x7jr2aG2jwP3Aj9jMNNYz2C99VrgjnZ74LjrbLX+NoOlga8Ct7SPUyaxXuA3gJtbrbcCf9v6jwS+BGxh8Kfv3uOudYe6TwCumtRaW01faR+3bf95msRjoNV1FPDldhx8BjhggmvdF/g+8NShvrHU6tsPSFKHlsqyjCRpFgx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI69H/gN7BomXwiEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Var_Continuas = ['TotalCharges','tenure','MonthlyCharges']\n",
    "\n",
    "for i in Var_Continuas:\n",
    "    df[[i]].apply(pd.to_numeric).hist(bins = 50\n",
    "                                      , grid = False\n",
    "                                      , color = '#86bf91'\n",
    "                                      , zorder = 2\n",
    "                                      , figsize = (6, 3)\n",
    "                                      , rwidth = 0.8)\n",
    "    \n",
    "    [spine.set_visible(False) for spine in plt.gca().spines.values()]\n",
    "    plt.gca().spines['bottom'].set_visible(True)\n",
    "    plt.tick_params(left = False, bottom = False)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creamos arbol de decisión de la variable Tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df[['tenure']]\n",
    "y = df['Churn'] \n",
    "\n",
    "tree = DecisionTreeClassifier(criterion = 'entropy'\n",
    "                                 , min_samples_split = 2000\n",
    "                                 , max_depth = 2 )\n",
    "\n",
    "tree = tree.fit(X, y)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12, 6)\n",
    "Grafico = plot_tree(tree\n",
    "                    , fontsize = 12\n",
    "                    , max_depth = 7\n",
    "                    , impurity = True\n",
    "                    , rounded = True\n",
    "                    , filled= True\n",
    "                    , class_names = ['No', 'Yes'] )\n",
    "\n",
    "plt.savefig('tree.png', dpi = 210)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "puntos de corte 17.5 y 59.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Tenure</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alto</td>\n",
       "      <td>1483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bajo</td>\n",
       "      <td>2626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>medio</td>\n",
       "      <td>2923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Tenure  Churn\n",
       "0   alto   1483\n",
       "1   bajo   2626\n",
       "2  medio   2923"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tenure'] = df.tenure.map(lambda x: 'bajo' if x <= 17.5 else ('medio' if x <= 59.5 else 'alto'))\n",
    "columnsList.append('Tenure')\n",
    "\n",
    "df.groupby(['Tenure'])['Churn'].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[columnsList]\n",
    "y = df['Churn']\n",
    "\n",
    "XE, XT, ye, yt = train_test_split(X\n",
    "                                  , y\n",
    "                                  , test_size = 0.25\n",
    "                                  , random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Params = {'Random Forest': RandomForestClassifier(n_estimators = 250\n",
    "                                                   , random_state = 0)\n",
    "           , 'SGD': SGDClassifier(max_iter = 5\n",
    "                                  , tol = None)\n",
    "           , 'Perceptron': MLPClassifier(random_state = 0)\n",
    "           , 'KNN': KNeighborsClassifier()\n",
    "           , 'Tree Classifier': DecisionTreeClassifier(criterion = 'entropy'\n",
    "                                                       , random_state = 0)\n",
    "           , 'Estra Tree Classifier': ExtraTreesClassifier(random_state = 0)\n",
    "           , 'Gradient Boosting Classifier': GradientBoostingClassifier(random_state = 0)\n",
    "           , 'SVC': SVC(kernel = 'linear'\n",
    "                        , random_state = 0\n",
    "                        , gamma = 'scale' )\n",
    "           , 'Regresión logística': LogisticRegression(solver = 'lbfgs'\n",
    "                                                       , multi_class = 'multinomial'\n",
    "                                                       , class_weight = 'balanced'\n",
    "                                                       , random_state = 0)}\n",
    "\n",
    "Nums = Pipeline(\n",
    "    steps = [\n",
    "        ('imputer', SimpleImputer(strategy = 'median')), \n",
    "        ('scaler' , StandardScaler())])\n",
    "\n",
    "Text = Pipeline(\n",
    "    steps = [\n",
    "        ('imputer', SimpleImputer(strategy = 'constant', fill_value = 'missing')), \n",
    "        ('onehot' , OneHotEncoder(handle_unknown = 'ignore'))])\n",
    "\n",
    "Pipe = ColumnTransformer(\n",
    "    transformers = [\n",
    "        ('num', Nums, X.select_dtypes(include = ['int64', 'float64']).columns), \n",
    "        ('cat', Text, X.select_dtypes(include = ['object']).columns)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Comparativa de los distintos modelos:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/markdown": [
       "* Matríz de confusión:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x86.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* Precisión modelos sobre base test:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7906712172923777\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>Modelo</th>\n",
       "      <th>Precisión (accuracy)</th>\n",
       "      <th>Cross Val</th>\n",
       "      <th>Score (True)</th>\n",
       "      <th>R Cuadrado</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>79.07</td>\n",
       "      <td>0.800339</td>\n",
       "      <td>61.73</td>\n",
       "      <td>-0.0912164</td>\n",
       "      <td>[0.89, 0.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>77.65</td>\n",
       "      <td>0.78972</td>\n",
       "      <td>58.96</td>\n",
       "      <td>-0.165348</td>\n",
       "      <td>[0.89, 0.45]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>77.42</td>\n",
       "      <td>0.769055</td>\n",
       "      <td>57.59</td>\n",
       "      <td>-0.177209</td>\n",
       "      <td>[0.88, 0.48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>77.02</td>\n",
       "      <td>0.778914</td>\n",
       "      <td>56</td>\n",
       "      <td>-0.197966</td>\n",
       "      <td>[0.86, 0.52]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Estra Tree Classifier</td>\n",
       "      <td>76.34</td>\n",
       "      <td>0.765074</td>\n",
       "      <td>55.15</td>\n",
       "      <td>-0.233549</td>\n",
       "      <td>[0.87, 0.46]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNN</td>\n",
       "      <td>75.09</td>\n",
       "      <td>0.757483</td>\n",
       "      <td>51.86</td>\n",
       "      <td>-0.298785</td>\n",
       "      <td>[0.83, 0.52]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Regresión logística</td>\n",
       "      <td>73.72</td>\n",
       "      <td>0.748775</td>\n",
       "      <td>49.51</td>\n",
       "      <td>-0.369951</td>\n",
       "      <td>[0.72, 0.78]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tree Classifier</td>\n",
       "      <td>72.7</td>\n",
       "      <td>0.733028</td>\n",
       "      <td>47.13</td>\n",
       "      <td>-0.423326</td>\n",
       "      <td>[0.82, 0.45]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SGD</td>\n",
       "      <td>65.98</td>\n",
       "      <td>0.7539</td>\n",
       "      <td>41.48</td>\n",
       "      <td>-0.773227</td>\n",
       "      <td>[0.62, 0.76]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Modelo Precisión (accuracy) Cross Val Score (True)  \\\n",
       "0  Gradient Boosting Classifier                79.07  0.800339        61.73   \n",
       "1                           SVC                77.65   0.78972        58.96   \n",
       "2                 Random Forest                77.42  0.769055        57.59   \n",
       "3                    Perceptron                77.02  0.778914           56   \n",
       "4         Estra Tree Classifier                76.34  0.765074        55.15   \n",
       "5                           KNN                75.09  0.757483        51.86   \n",
       "6           Regresión logística                73.72  0.748775        49.51   \n",
       "7               Tree Classifier                 72.7  0.733028        47.13   \n",
       "8                           SGD                65.98    0.7539        41.48   \n",
       "\n",
       "  R Cuadrado        Recall  \n",
       "0 -0.0912164   [0.89, 0.5]  \n",
       "1  -0.165348  [0.89, 0.45]  \n",
       "2  -0.177209  [0.88, 0.48]  \n",
       "3  -0.197966  [0.86, 0.52]  \n",
       "4  -0.233549  [0.87, 0.46]  \n",
       "5  -0.298785  [0.83, 0.52]  \n",
       "6  -0.369951  [0.72, 0.78]  \n",
       "7  -0.423326  [0.82, 0.45]  \n",
       "8  -0.773227  [0.62, 0.76]  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Result, classifiersList = [{},[]]\n",
    "\n",
    "kf = KFold(n_splits = 10)\n",
    "           \n",
    "markdown(f'### Comparativa de los distintos modelos:') \n",
    "           \n",
    "for i in Params:\n",
    "    Score, lista = [[],[]]\n",
    "    clf = Pipeline(steps=[('Prepo', Pipe), \n",
    "                          ('Modelo', Params[i])])\n",
    "    clf.fit(XE, ye)\n",
    "    Score.append(round(np.mean(yt == clf.predict(XT)) * 100, 2))\n",
    "    Score.append(cross_val_score(clf, XE, ye, cv=kf, scoring = 'accuracy').mean())\n",
    "    Score.append(round(precision_score(yt, clf.predict(XT), labels = [0, 1], pos_label = 1) * 100, 2))\n",
    "    Score.append(r2_score(yt, clf.predict(XT)))\n",
    "    Score.append(recall_score(yt, clf.predict(XT), average = None).round(2))\n",
    "    Result[i] = Score\n",
    "\n",
    "    lista.append(i)\n",
    "    lista.append(clf)\n",
    "    classifiersList.append(tuple(lista))\n",
    "\n",
    "Result = (pd.DataFrame(Result, index=['Precisión (accuracy)'\n",
    "                                      , 'Cross Val' \n",
    "                                      , 'Score (True)'\n",
    "                                      , 'R Cuadrado'\n",
    "                                      , 'Recall'])\n",
    "          .transpose()\n",
    "          .sort_values(by = 'Precisión (accuracy)'\n",
    "                       , ascending = False)\n",
    "          .reset_index()\n",
    "          .rename(columns = {'index':'Modelo'})\n",
    "         )\n",
    "\n",
    "markdown(f'* Matríz de confusión:')\n",
    "\n",
    "Best = Pipeline(steps=[('Prepo', Pipe),\n",
    "                      ('Modelo', Params[Result['Modelo'][0]])])\n",
    "\n",
    "Best.fit(XE, ye)\n",
    "\n",
    "Grafico = matriz(yt, Best.predict(XT), Result['Modelo'][0])        \n",
    "markdown(f'* Precisión modelos sobre base test:')\n",
    "print(accuracy_score(yt, Best.predict(XT)))\n",
    "Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7770193401592719\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "clf = VotingClassifier(classifiersList, n_jobs=-1)\n",
    "clf.fit(XE, ye)\n",
    "predictions = clf.predict(XT)\n",
    "print(accuracy_score(yt, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86, 0.53])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(yt, clf.predict(XT), average = None).round(2)"
   ]
  }
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