code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, recall_score, classification_report, roc_auc_score, confusion_matrix\n",
    "\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Código"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carga de archivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataframe: (32618, 3).\n"
     ]
    }
   ],
   "source": [
    "clients_cdp = pd.read_csv(\"data/cliente_planes - cliente_cdp.csv\")\n",
    "print(f\"Shape of the dataframe: {clients_cdp.shape}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataframe: (10901, 7).\n"
     ]
    }
   ],
   "source": [
    "all_clients = pd.read_csv(\"data/datos_clientes - dataset.csv\")\n",
    "print(f\"Shape of the dataframe: {all_clients.shape}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataframe: (10900, 3).\n"
     ]
    }
   ],
   "source": [
    "call_billing = pd.read_csv(\"data/facturacion_duracion_llamadas_trimestre - f.csv\")\n",
    "print(f\"Shape of the dataframe: {call_billing.shape}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataframe: (1389, 4).\n"
     ]
    }
   ],
   "source": [
    "segments = pd.read_csv(\"data/segmentos.csv\")\n",
    "print(f\"Shape of the dataframe: {segments.shape}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unión y exploración de la data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(21718)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clients_cdp[\"nro_cliente\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_clients[\"nro_cliente\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>mes</th>\n",
       "      <th>nro_cliente</th>\n",
       "      <th>Plan_mes</th>\n",
       "      <th>user_id</th>\n",
       "      <th>install_time</th>\n",
       "      <th>platform</th>\n",
       "      <th>country_region</th>\n",
       "      <th>city</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>95729</td>\n",
       "      <td>10045a3c</td>\n",
       "      <td>2018-07-03 0:02:10</td>\n",
       "      <td>Android</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>Avellaneda</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>96410</td>\n",
       "      <td>1004d93d</td>\n",
       "      <td>2018-07-06 23:09:52</td>\n",
       "      <td>Android</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>San Isidro</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>96400</td>\n",
       "      <td>1006ac06</td>\n",
       "      <td>2018-07-05 23:03:47</td>\n",
       "      <td>Android</td>\n",
       "      <td>Cordoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>95729</td>\n",
       "      <td>100bc465</td>\n",
       "      <td>2018-07-07 17:02:46</td>\n",
       "      <td>Android</td>\n",
       "      <td>Formosa</td>\n",
       "      <td>Formosa</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>95420</td>\n",
       "      <td>100bcd07</td>\n",
       "      <td>2018-06-30 3:21:43</td>\n",
       "      <td>Android</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Lanus</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mes  nro_cliente  Plan_mes   user_id         install_time platform  \\\n",
       "0    1            1     95729  10045a3c   2018-07-03 0:02:10  Android   \n",
       "1    1            3     96410  1004d93d  2018-07-06 23:09:52  Android   \n",
       "2    1            2     96400  1006ac06  2018-07-05 23:03:47  Android   \n",
       "3    1            5     95729  100bc465  2018-07-07 17:02:46  Android   \n",
       "4    1            4     95420  100bcd07   2018-06-30 3:21:43  Android   \n",
       "\n",
       "  country_region        city  gender  \n",
       "0       Santa Fe  Avellaneda    male  \n",
       "1   Buenos Aires  San Isidro  female  \n",
       "2        Cordoba     Córdoba    male  \n",
       "3        Formosa     Formosa  female  \n",
       "4   Buenos Aires       Lanus    male  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(left=clients_cdp, right=all_clients, on=\"nro_cliente\", how=\"left\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_billing[\"nro_cliente\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mes</th>\n",
       "      <th>nro_cliente</th>\n",
       "      <th>Plan_mes</th>\n",
       "      <th>user_id</th>\n",
       "      <th>install_time</th>\n",
       "      <th>platform</th>\n",
       "      <th>country_region</th>\n",
       "      <th>city</th>\n",
       "      <th>gender</th>\n",
       "      <th>sum_fact</th>\n",
       "      <th>sum_duracion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>95729</td>\n",
       "      <td>10045a3c</td>\n",
       "      <td>2018-07-03 0:02:10</td>\n",
       "      <td>Android</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>Avellaneda</td>\n",
       "      <td>male</td>\n",
       "      <td>645.007199</td>\n",
       "      <td>330.775190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>96410</td>\n",
       "      <td>1004d93d</td>\n",
       "      <td>2018-07-06 23:09:52</td>\n",
       "      <td>Android</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>San Isidro</td>\n",
       "      <td>female</td>\n",
       "      <td>520.514551</td>\n",
       "      <td>565.763348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>96400</td>\n",
       "      <td>1006ac06</td>\n",
       "      <td>2018-07-05 23:03:47</td>\n",
       "      <td>Android</td>\n",
       "      <td>Cordoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>male</td>\n",
       "      <td>1330.167700</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>95729</td>\n",
       "      <td>100bc465</td>\n",
       "      <td>2018-07-07 17:02:46</td>\n",
       "      <td>Android</td>\n",
       "      <td>Formosa</td>\n",
       "      <td>Formosa</td>\n",
       "      <td>female</td>\n",
       "      <td>555.171059</td>\n",
       "      <td>99.929049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>95420</td>\n",
       "      <td>100bcd07</td>\n",
       "      <td>2018-06-30 3:21:43</td>\n",
       "      <td>Android</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Lanus</td>\n",
       "      <td>male</td>\n",
       "      <td>1534.166310</td>\n",
       "      <td>371.433155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mes  nro_cliente  Plan_mes   user_id         install_time platform  \\\n",
       "0    1            1     95729  10045a3c   2018-07-03 0:02:10  Android   \n",
       "1    1            3     96410  1004d93d  2018-07-06 23:09:52  Android   \n",
       "2    1            2     96400  1006ac06  2018-07-05 23:03:47  Android   \n",
       "3    1            5     95729  100bc465  2018-07-07 17:02:46  Android   \n",
       "4    1            4     95420  100bcd07   2018-06-30 3:21:43  Android   \n",
       "\n",
       "  country_region        city  gender     sum_fact  sum_duracion  \n",
       "0       Santa Fe  Avellaneda    male   645.007199    330.775190  \n",
       "1   Buenos Aires  San Isidro  female   520.514551    565.763348  \n",
       "2        Cordoba     Córdoba    male  1330.167700     30.000000  \n",
       "3        Formosa     Formosa  female   555.171059     99.929049  \n",
       "4   Buenos Aires       Lanus    male  1534.166310    371.433155  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(right=call_billing, on=\"nro_cliente\", how=\"left\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segments[\"user_id\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dups_users_segments = segments.loc[segments[\"user_id\"].duplicated(), \"user_id\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>target_churn_indicator</th>\n",
       "      <th>range_group</th>\n",
       "      <th>n_previous_registers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>539</th>\n",
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       "      <td>1</td>\n",
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       "      <th>540</th>\n",
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       "      <td>b</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>75d198ce</td>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
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       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>75d198ce</td>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>75d198ce</td>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>85ef5dfe</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>85ef5dfe</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>c103db74</td>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>c103db74</td>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  target_churn_indicator range_group  n_previous_registers\n",
       "539   6f1e1709                       0           b                     1\n",
       "540   6f1e1709                       0           b                     2\n",
       "587   75d198ce                       0           b                     1\n",
       "588   75d198ce                       0           b                     2\n",
       "589   75d198ce                       0           b                     3\n",
       "679   85ef5dfe                       0           a                     1\n",
       "680   85ef5dfe                       0           a                     2\n",
       "1003  c103db74                       1           b                     1\n",
       "1004  c103db74                       1           b                     2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segments[segments[\"user_id\"].isin(dups_users_segments)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya que *n_previous_registers* hace referencia a la *cantidad de veces que un cliente estuvo en campaña de upgrade*, se procede a eliminar los duplicados por *user_id* de menor cantidad de registros previos (esta variable es acumulativa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "segments = segments.sort_values(by=[\"user_id\", \"n_previous_registers\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>target_churn_indicator</th>\n",
       "      <th>range_group</th>\n",
       "      <th>n_previous_registers</th>\n",
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       "      <td>2</td>\n",
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       "      <th>587</th>\n",
       "      <td>75d198ce</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "      user_id  target_churn_indicator range_group  n_previous_registers\n",
       "589  75d198ce                       0           b                     3\n",
       "588  75d198ce                       0           b                     2\n",
       "587  75d198ce                       0           b                     1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segments[segments[\"user_id\"]==\"75d198ce\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "segments = segments.drop_duplicates(subset=[\"user_id\"], keep=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>user_id</th>\n",
       "      <th>target_churn_indicator</th>\n",
       "      <th>range_group</th>\n",
       "      <th>n_previous_registers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>75d198ce</td>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  target_churn_indicator range_group  n_previous_registers\n",
       "589  75d198ce                       0           b                     3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segments[segments[\"user_id\"]==\"75d198ce\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segments[\"user_id\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>mes</th>\n",
       "      <th>nro_cliente</th>\n",
       "      <th>Plan_mes</th>\n",
       "      <th>user_id</th>\n",
       "      <th>install_time</th>\n",
       "      <th>platform</th>\n",
       "      <th>country_region</th>\n",
       "      <th>city</th>\n",
       "      <th>gender</th>\n",
       "      <th>sum_fact</th>\n",
       "      <th>sum_duracion</th>\n",
       "      <th>target_churn_indicator</th>\n",
       "      <th>range_group</th>\n",
       "      <th>n_previous_registers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>95729</td>\n",
       "      <td>10045a3c</td>\n",
       "      <td>2018-07-03 0:02:10</td>\n",
       "      <td>Android</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>Avellaneda</td>\n",
       "      <td>male</td>\n",
       "      <td>645.007199</td>\n",
       "      <td>330.775190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>96410</td>\n",
       "      <td>1004d93d</td>\n",
       "      <td>2018-07-06 23:09:52</td>\n",
       "      <td>Android</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>San Isidro</td>\n",
       "      <td>female</td>\n",
       "      <td>520.514551</td>\n",
       "      <td>565.763348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>96400</td>\n",
       "      <td>1006ac06</td>\n",
       "      <td>2018-07-05 23:03:47</td>\n",
       "      <td>Android</td>\n",
       "      <td>Cordoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>male</td>\n",
       "      <td>1330.167700</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>b</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>95729</td>\n",
       "      <td>100bc465</td>\n",
       "      <td>2018-07-07 17:02:46</td>\n",
       "      <td>Android</td>\n",
       "      <td>Formosa</td>\n",
       "      <td>Formosa</td>\n",
       "      <td>female</td>\n",
       "      <td>555.171059</td>\n",
       "      <td>99.929049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>95420</td>\n",
       "      <td>100bcd07</td>\n",
       "      <td>2018-06-30 3:21:43</td>\n",
       "      <td>Android</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Lanus</td>\n",
       "      <td>male</td>\n",
       "      <td>1534.166310</td>\n",
       "      <td>371.433155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mes  nro_cliente  Plan_mes   user_id         install_time platform  \\\n",
       "0    1            1     95729  10045a3c   2018-07-03 0:02:10  Android   \n",
       "1    1            3     96410  1004d93d  2018-07-06 23:09:52  Android   \n",
       "2    1            2     96400  1006ac06  2018-07-05 23:03:47  Android   \n",
       "3    1            5     95729  100bc465  2018-07-07 17:02:46  Android   \n",
       "4    1            4     95420  100bcd07   2018-06-30 3:21:43  Android   \n",
       "\n",
       "  country_region        city  gender     sum_fact  sum_duracion  \\\n",
       "0       Santa Fe  Avellaneda    male   645.007199    330.775190   \n",
       "1   Buenos Aires  San Isidro  female   520.514551    565.763348   \n",
       "2        Cordoba     Córdoba    male  1330.167700     30.000000   \n",
       "3        Formosa     Formosa  female   555.171059     99.929049   \n",
       "4   Buenos Aires       Lanus    male  1534.166310    371.433155   \n",
       "\n",
       "   target_churn_indicator range_group  n_previous_registers  \n",
       "0                     NaN         NaN                   NaN  \n",
       "1                     NaN         NaN                   NaN  \n",
       "2                     0.0           b                   1.0  \n",
       "3                     NaN         NaN                   NaN  \n",
       "4                     NaN         NaN                   NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(right=segments, on=\"user_id\", how=\"left\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se verifica que la cantidad de filas del dataframe df sea igual a la cantidad de clientes en clients_cdp\n",
    "df.shape[0] == clients_cdp.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mes                           0\n",
       "nro_cliente                   0\n",
       "Plan_mes                      0\n",
       "user_id                       0\n",
       "install_time                  0\n",
       "platform                      0\n",
       "country_region                0\n",
       "city                          0\n",
       "gender                        0\n",
       "sum_fact                      0\n",
       "sum_duracion                  0\n",
       "target_churn_indicator    30100\n",
       "range_group               30100\n",
       "n_previous_registers      30100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se asume a continuación que si el cliente no está en la tabla de segmentos es porque NUNCA ha hecho parte de las campañas.\n",
    "\n",
    "En este sentido, se asume que *target_churn_indicator = 0* y *n_previous_registers = 0*; *range_group* no se puede identificar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna({\"target_churn_indicator\": 0}, inplace=True)\n",
    "df.fillna({\"n_previous_registers\": 0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32618 entries, 0 to 32617\n",
      "Data columns (total 14 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   mes                     32618 non-null  int64  \n",
      " 1   nro_cliente             32618 non-null  int64  \n",
      " 2   Plan_mes                32618 non-null  int64  \n",
      " 3   user_id                 32618 non-null  object \n",
      " 4   install_time            32618 non-null  object \n",
      " 5   platform                32618 non-null  object \n",
      " 6   country_region          32618 non-null  object \n",
      " 7   city                    32618 non-null  object \n",
      " 8   gender                  32618 non-null  object \n",
      " 9   sum_fact                32618 non-null  float64\n",
      " 10  sum_duracion            32618 non-null  float64\n",
      " 11  target_churn_indicator  32618 non-null  float64\n",
      " 12  range_group             2518 non-null   object \n",
      " 13  n_previous_registers    32618 non-null  float64\n",
      "dtypes: float64(4), int64(3), object(7)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Resumen genérico del dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mes                         int64\n",
       "nro_cliente                 int64\n",
       "Plan_mes                    int64\n",
       "user_id                    object\n",
       "install_time               object\n",
       "platform                   object\n",
       "country_region             object\n",
       "city                       object\n",
       "gender                     object\n",
       "sum_fact                  float64\n",
       "sum_duracion              float64\n",
       "target_churn_indicator    float64\n",
       "range_group                object\n",
       "n_previous_registers      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se cambian los tipos de datos de algunas variables\n",
    "df[\"install_time\"] = pd.to_datetime(df[\"install_time\"])\n",
    "df[\"nro_cliente\"] = df[\"nro_cliente\"].astype(\"category\")\n",
    "df[\"mes\"] = df[\"mes\"].astype(\"category\")\n",
    "df[\"Plan_mes\"] = df[\"Plan_mes\"].astype(\"category\")\n",
    "df[\"target_churn_indicator\"] = df[\"target_churn_indicator\"].astype('int64')\n",
    "df[\"n_previous_registers\"] = df[\"n_previous_registers\"].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Las variables con tipo de datos object se convierten a categóricas\n",
    "df = df.astype({col: 'category' for col in df.select_dtypes(include='object').columns})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pregunta 1: ¿cuántos clientes tuvieron al menos un cambio de plan?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se agrupa la información según la pregunta\n",
    "number_plans_per_client = df.groupby([\"nro_cliente\"], as_index=False)[\"Plan_mes\"].nunique()\n",
    "number_plans_per_client = number_plans_per_client.rename(columns={\"Plan_mes\": \"num_planes\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
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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>nro_cliente</th>\n",
       "      <th>num_planes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10859</th>\n",
       "      <td>10860</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10860</th>\n",
       "      <td>10861</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10863</th>\n",
       "      <td>10864</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10894</th>\n",
       "      <td>10895</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10898</th>\n",
       "      <td>10899</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5847 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      nro_cliente  num_planes\n",
       "0               1           2\n",
       "3               4           2\n",
       "4               5           2\n",
       "5               6           2\n",
       "7               8           2\n",
       "...           ...         ...\n",
       "10859       10860           2\n",
       "10860       10861           2\n",
       "10863       10864           2\n",
       "10894       10895           2\n",
       "10898       10899           2\n",
       "\n",
       "[5847 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muestra de clientes específicos que tienen más de un plan\n",
    "number_plans_per_client[number_plans_per_client[\"num_planes\"]>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5847"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número total de clientes únicos que tienen más de un plan\n",
    "number_plans_per_client[number_plans_per_client[\"num_planes\"]>1].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>nro_cliente</th>\n",
       "      <th>Plan_mes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13277</th>\n",
       "      <td>10861</td>\n",
       "      <td>96420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32578</th>\n",
       "      <td>10861</td>\n",
       "      <td>96916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nro_cliente Plan_mes\n",
       "13277       10861    96420\n",
       "32578       10861    96916"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejemplo 1 de cliente que cambió de plan al menos una vez en el trimestre\n",
    "df.loc[df[\"nro_cliente\"]==10861, [\"nro_cliente\", \"Plan_mes\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>nro_cliente</th>\n",
       "      <th>Plan_mes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>94497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13263</th>\n",
       "      <td>8</td>\n",
       "      <td>97120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21725</th>\n",
       "      <td>8</td>\n",
       "      <td>97120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nro_cliente Plan_mes\n",
       "6               8    94497\n",
       "13263           8    97120\n",
       "21725           8    97120"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejemplo 2 de cliente que cambió de plan al menos una vez en el trimestre\n",
    "df.loc[df[\"nro_cliente\"]==8, [\"nro_cliente\", \"Plan_mes\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Conclusión**\n",
    "\n",
    "Del total de clientes, hay 5847 que cambiaron de plan durante el trimestre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pregunta 2: ¿existe alguna relación entre country_region y los cambios de planes en el trimestre?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# En un nuevo dataframe extraigo únicamente las columnas que necesito\n",
    "clients_vs_regions = df.loc[:, [\"nro_cliente\", \"country_region\"]].drop_duplicates().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unión de este nuevo dataframe con el de la pregunta 1 (number_plans_per_client)\n",
    "df_q2 = pd.merge(left=clients_vs_regions, right=number_plans_per_client, on=\"nro_cliente\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se crea una variable que indique si el cliente cambió de plan (el número de planes no es relevante para esta pregunta)\n",
    "df_q2[\"cambio_plan\"] = df_q2[\"num_planes\"].apply(lambda x: 1 if x > 1 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_region\n",
       "Buenos Aires           3658\n",
       "Buenos Aires F.D.      1690\n",
       "Cordoba                1114\n",
       "Entre Rios              675\n",
       "Santa Fe                507\n",
       "Tucuman                 422\n",
       "Salta                   399\n",
       "Mendoza                 384\n",
       "Chaco                   211\n",
       "Misiones                209\n",
       "Corrientes              184\n",
       "Neuquen                 155\n",
       "San Luis                148\n",
       "Formosa                 133\n",
       "La Rioja                131\n",
       "Chubut                  122\n",
       "San Juan                117\n",
       "Santiago del Estero     114\n",
       "Jujuy                   113\n",
       "Rio Negro               105\n",
       "La Pampa                 94\n",
       "Catamarca                86\n",
       "Santa Cruz               66\n",
       "Tierra del Fuego         47\n",
       "Entre Ríos Province       8\n",
       "Santa Fe Province         2\n",
       "Cordoba Province          2\n",
       "Chaco Province            1\n",
       "Neuquén Province          1\n",
       "Río Negro Province        1\n",
       "Tucumán Province          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muestra general de todas las regiones disponibles -> Es necesario recategorizar para encontrar alguna conclusión general\n",
    "df_q2[\"country_region\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.8118348623853211)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se evidencia que más del 80% de los clientes se ubican en 8 regiones (Pareto)\n",
    "df_q2[\"country_region\"].value_counts(normalize=True)[0:8].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se almacenan las regiones más usuales en una lista\n",
    "list_main_regions = df_q2[\"country_region\"].value_counts(normalize=True)[0:8].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Buenos Aires',\n",
       " 'Buenos Aires F.D.',\n",
       " 'Cordoba',\n",
       " 'Entre Rios',\n",
       " 'Santa Fe',\n",
       " 'Tucuman',\n",
       " 'Salta',\n",
       " 'Mendoza']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muestra de tales regiones\n",
    "list_main_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recategorización de la región asociada a los clientes\n",
    "df_q2[\"country_region_recat\"] = df_q2[\"country_region\"].apply(lambda x: x if x in list_main_regions else \"Otra\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>cambio_plan</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_region_recat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mendoza</th>\n",
       "      <td>0.406250</td>\n",
       "      <td>0.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buenos Aires F.D.</th>\n",
       "      <td>0.454438</td>\n",
       "      <td>0.545562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Otra</th>\n",
       "      <td>0.457825</td>\n",
       "      <td>0.542175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salta</th>\n",
       "      <td>0.461153</td>\n",
       "      <td>0.538847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buenos Aires</th>\n",
       "      <td>0.465555</td>\n",
       "      <td>0.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Fe</th>\n",
       "      <td>0.469428</td>\n",
       "      <td>0.530572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cordoba</th>\n",
       "      <td>0.477558</td>\n",
       "      <td>0.522442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entre Rios</th>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.518519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tucuman</th>\n",
       "      <td>0.492891</td>\n",
       "      <td>0.507109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cambio_plan                  0         1\n",
       "country_region_recat                    \n",
       "Mendoza               0.406250  0.593750\n",
       "Buenos Aires F.D.     0.454438  0.545562\n",
       "Otra                  0.457825  0.542175\n",
       "Salta                 0.461153  0.538847\n",
       "Buenos Aires          0.465555  0.534445\n",
       "Santa Fe              0.469428  0.530572\n",
       "Cordoba               0.477558  0.522442\n",
       "Entre Rios            0.481481  0.518519\n",
       "Tucuman               0.492891  0.507109"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabla cruzada entre la región recategorizada y el cambio de plan (0: no cambió de plan; 1: cambió de plan), con proporciones por filas\n",
    "crosstab_q2 = pd.crosstab(df_q2[\"country_region_recat\"], df_q2[\"cambio_plan\"], normalize=\"index\")\n",
    "crosstab_q2 = crosstab_q2.sort_values(by=[1], ascending=False)\n",
    "crosstab_q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot con barras apiladas para comparar visualmente los resultados de la tabla cruzada anterior\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.bar(crosstab_q2.index, crosstab_q2[0], label=0, color=\"steelblue\")\n",
    "ax.bar(crosstab_q2.index, crosstab_q2[1], bottom=crosstab_q2[0], label=1, color=\"orange\")\n",
    "\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.ylabel(\"Proporción\")\n",
    "plt.title(\"Proporción de clientes con cambio de plan por región\")\n",
    "plt.legend(title=\"Cambio de plan\")\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.5364220183486239)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proporción media de clientes que han cambiado de plan, sin discriminar por región\n",
    "df_q2[\"cambio_plan\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Conclusión**\n",
    "\n",
    "A nivel descriptivo y considerando las principales regiones de Argentina, es posible decir que Mendoza presenta la mayor proporción de clientes con cambio de plan (0.59) que, sin embargo, no parece diferir en gran medida de la proporción media de clientes en todas las regiones que lo hicieron (0.54). El resto de las regiones principales se mantienen más o menos constantes en cuanto a dicha proporción, oscilando en un mínimo de 0.51 para Tucuman y un máximo de 0.55 para Buenos Aires F.D.\n",
    "\n",
    "Por lo tanto, contestando a la pregunta inicial, a nivel descriptivo no parece existir una relación entre la variable *country_region* y los cambios de planes en el trimestre, exceptuando tal vez el caso puntual de la región de Mendoza.\n",
    "\n",
    "Más adelante se revisará si esta región específica (y también el resto de regiones), tienen alguna relación significativa con los clientes que cambiaron de plan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pregunta 3: ¿cuáles serían las tres principales características de los clientes que realizan cambios de plan?\n",
    "\n",
    "#### Usando estadísticas y gráficos descriptivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se crea un nuevo dataframe para esta pregunta específica eliminando variables que hacen ruido\n",
    "# Se elimina el mes porque se revisará en general a los clientes, no desagregados según el periodo\n",
    "# Se eliminar el rango de edad (range_group) por la alta cantidad de valores faltantes sin posibilidad de imputar adecuadamente\n",
    "df_q3 = df.drop(columns=[\"mes\", \"user_id\", \"Plan_mes\", \"range_group\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se eliminan duplicados\n",
    "df_q3 = df_q3.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nuevamente se crea la variable cambio_plan\n",
    "df_q3 = df_q3.merge(right=number_plans_per_client, on=\"nro_cliente\", how=\"inner\")\n",
    "df_q3[\"cambio_plan\"] = df_q3[\"num_planes\"].apply(lambda x: 1 if x > 1 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                            10900\n",
       "mean     2018-07-04 01:33:35.075229440\n",
       "min                2018-06-30 00:01:41\n",
       "25%      2018-07-01 23:58:54.750000128\n",
       "50%         2018-07-04 00:02:49.500000\n",
       "75%         2018-07-06 01:23:18.500000\n",
       "max                2018-07-07 23:59:47\n",
       "Name: install_time, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisión de la variable install_time, que se decide eliminar debido a que el rango entre la fecha mín. y máx. es apenas de 8 días\n",
    "df_q3[\"install_time\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminación de estas columnas adicionales\n",
    "df_q3 = df_q3.drop(columns=[\"num_planes\", \"install_time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se establece el nro. del cliente como índice\n",
    "df_q3 = df_q3.set_index(\"nro_cliente\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recategorización de la región nuevamente\n",
    "df_q3[\"country_region_recat\"] = df_q3[\"country_region\"].apply(lambda x: x if x in list_main_regions else \"Otra\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Buenos Aires             1516\n",
       "Córdoba                   731\n",
       "Villa Angelica            447\n",
       "San Miguel de Tucumán     370\n",
       "Salta                     354\n",
       "                         ... \n",
       "Villa Iris                  1\n",
       "Villa La Angostura          1\n",
       "Villa Ocampo                1\n",
       "Villa Reduccion             1\n",
       "Acassuso                    1\n",
       "Name: count, Length: 670, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisión de la cantidad de categorías presentes en la columna city\n",
    "df_q3[\"city\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Debido al gran número de ciudades presentes, tomo únicamente aquellas que representen al menos al 1.5% de los clientes\n",
    "city_counts = df_q3[\"city\"].value_counts(normalize=True)\n",
    "cities_above_threshold = city_counts[city_counts > 0.015].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se recategoriza la variable city\n",
    "df_q3[\"city_recat\"] = df_q3[\"city\"].apply(lambda x: x if x in cities_above_threshold else \"Otra\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city_recat\n",
       "Otra                     0.669083\n",
       "Buenos Aires             0.139083\n",
       "Córdoba                  0.067064\n",
       "Villa Angelica           0.041009\n",
       "San Miguel de Tucumán    0.033945\n",
       "Salta                    0.032477\n",
       "Mendoza                  0.017339\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vista en proporciones de la variable city_recat\n",
    "df_q3[\"city_recat\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se eliminan de este nuevo dataframe las variables crudas / sin recategorizar\n",
    "df_q3 = df_q3.drop(columns=[\"city\", \"country_region\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cambio en el tipo de estas variables\n",
    "df_q3[\"city_recat\"] = df_q3[\"city_recat\"].astype(\"category\")\n",
    "df_q3[\"country_region_recat\"] = df_q3[\"country_region_recat\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se recategoriza la variable n_previous_registers, ya que hay muy pocos clientes que estuvieron 2 o más veces presentes en campañas de upgrade\n",
    "df_q3[\"n_previous_registers\"] = df_q3[\"n_previous_registers\"].apply(lambda x: '0' if x==0 else '1 o más')\n",
    "df_q3[\"n_previous_registers\"] = df_q3[\"n_previous_registers\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# División de columnas según su tipo\n",
    "cat_int_cols = df_q3.drop(columns=[\"cambio_plan\"]).select_dtypes(include=['category', 'int64']).columns.tolist()\n",
    "float_cols = df_q3.drop(columns=[\"cambio_plan\"]).select_dtypes(include=['float64']).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pieza de código para extraer gráficos descriptivos sobre la relación entre la variable cambio_plan y el resto\n",
    "for col in df_q3.columns:\n",
    "\n",
    "    if col != \"cambio_plan\":\n",
    "\n",
    "        if col in cat_int_cols:\n",
    "            prop_df = df_q3.groupby([col, \"cambio_plan\"]).size().unstack()\n",
    "            prop_df = prop_df.div(prop_df.sum(axis=1), axis=0)\n",
    "            sorted_index = prop_df[1].sort_values(ascending=False).index\n",
    "            prop_df = prop_df.loc[sorted_index]\n",
    "            prop_df.plot(kind=\"bar\", stacked=False, colormap=\"Blues\", edgecolor=\"black\")\n",
    "            \n",
    "            plt.xlabel(col, fontsize=10)\n",
    "            plt.ylabel(\"Proporción\", fontsize=10)\n",
    "            plt.title(f\"Proporción de {col} por cambio_plan\", fontsize=12, fontweight=\"bold\")\n",
    "            plt.legend(title=\"Cambio plan\", loc=\"upper right\", fontsize=10, title_fontsize=10)\n",
    "            \n",
    "        else:\n",
    "            sns.boxplot(x=df_q3[\"cambio_plan\"], y=df_q3[col], palette=\"Blues\", width=0.6, linewidth=1.2, showfliers=False)\n",
    "            \n",
    "            plt.xlabel(\"Cambio plan\", fontsize=10)\n",
    "            plt.ylabel(col, fontsize=10)\n",
    "            plt.title(f\"Distribución de {col} por cambio_plan\", fontsize=12, fontweight=\"bold\")\n",
    "\n",
    "        plt.xticks(rotation=45, fontsize=10)\n",
    "        plt.yticks(fontsize=10)\n",
    "        plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Conclusión a partir de las gráficas**\n",
    "\n",
    "Las gráficas permiten concluir que la característica más importante que diferencia los clientes que cambiaron de plan vs. los que no lo hicieron es el total de facturación en $ durante todo el trimestre; es probable que dada la cantidad facturada, quisieran cambiar a un plan más económico.\n",
    "\n",
    "Por otro lado, los clientes de la ciudad y región de Mendoza son los que más cambios de planes han tenido; particularmente en la ciudad, más del 60% ha cambiado su plan en el último trimestre, mientras que en la región esta proporción llega casi al 60%.\n",
    "\n",
    "Finalmente, cabe resaltar que la proporción de clientes que cambia de plan y que además está en campañas de *churn* es un poco mayor a la proporción de clientes que no está en tales campañas (valor cercano a 0.6), hecho que podría estar relacionado con una efectividad en estos mecanismos, ya que es posible que estos clientes hayan intentado cancelar algún servicio pero con la campaña dirigida hayan decidido tomar algún otro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pregunta 3: ¿cuáles serían las tres principales características de los clientes que realizan cambios de plan? \n",
    "#### Ahora usando una regresión logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cambio_plan\n",
       "1    0.536422\n",
       "0    0.463578\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evidencia de que no existe un desbalance marcado en la variable independiente\n",
    "df_q3[\"cambio_plan\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definición de mis predictores y variable de respuesta\n",
    "X = df_q3.drop(columns=[\"cambio_plan\"])\n",
    "y = df_q3[\"cambio_plan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conversión de las variables categóricas a variables dummy\n",
    "X = pd.get_dummies(X, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# División de la data en entrenamiento y prueba\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establecimiento del estandarizados para las vairables numéricas\n",
    "scaler = StandardScaler()\n",
    "X_train[X_train.select_dtypes(include=['float64']).columns] = scaler.fit_transform(X_train.select_dtypes(include=['float64']))\n",
    "X_test[X_test.select_dtypes(include=['float64']).columns] = scaler.transform(X_test.select_dtypes(include=['float64']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>sum_fact</th>\n",
       "      <th>sum_duracion</th>\n",
       "      <th>target_churn_indicator</th>\n",
       "      <th>platform_iOS</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_unknown</th>\n",
       "      <th>n_previous_registers_1 o más</th>\n",
       "      <th>country_region_recat_Buenos Aires F.D.</th>\n",
       "      <th>country_region_recat_Cordoba</th>\n",
       "      <th>country_region_recat_Entre Rios</th>\n",
       "      <th>...</th>\n",
       "      <th>country_region_recat_Otra</th>\n",
       "      <th>country_region_recat_Salta</th>\n",
       "      <th>country_region_recat_Santa Fe</th>\n",
       "      <th>country_region_recat_Tucuman</th>\n",
       "      <th>city_recat_Córdoba</th>\n",
       "      <th>city_recat_Mendoza</th>\n",
       "      <th>city_recat_Otra</th>\n",
       "      <th>city_recat_Salta</th>\n",
       "      <th>city_recat_San Miguel de Tucumán</th>\n",
       "      <th>city_recat_Villa Angelica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nro_cliente</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4863</th>\n",
       "      <td>-0.128051</td>\n",
       "      <td>-0.341513</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10401</th>\n",
       "      <td>0.219772</td>\n",
       "      <td>0.079211</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>-0.972194</td>\n",
       "      <td>0.567343</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sum_fact  sum_duracion  target_churn_indicator  platform_iOS  \\\n",
       "nro_cliente                                                                 \n",
       "4863        -0.128051     -0.341513                       0         False   \n",
       "10401        0.219772      0.079211                       0         False   \n",
       "2507        -0.972194      0.567343                       0         False   \n",
       "\n",
       "             gender_male  gender_unknown  n_previous_registers_1 o más  \\\n",
       "nro_cliente                                                              \n",
       "4863               False           False                         False   \n",
       "10401              False           False                         False   \n",
       "2507               False           False                         False   \n",
       "\n",
       "             country_region_recat_Buenos Aires F.D.  \\\n",
       "nro_cliente                                           \n",
       "4863                                          False   \n",
       "10401                                         False   \n",
       "2507                                           True   \n",
       "\n",
       "             country_region_recat_Cordoba  country_region_recat_Entre Rios  \\\n",
       "nro_cliente                                                                  \n",
       "4863                                False                            False   \n",
       "10401                               False                            False   \n",
       "2507                                False                            False   \n",
       "\n",
       "             ...  country_region_recat_Otra  country_region_recat_Salta  \\\n",
       "nro_cliente  ...                                                          \n",
       "4863         ...                      False                       False   \n",
       "10401        ...                      False                       False   \n",
       "2507         ...                      False                       False   \n",
       "\n",
       "             country_region_recat_Santa Fe  country_region_recat_Tucuman  \\\n",
       "nro_cliente                                                                \n",
       "4863                                 False                         False   \n",
       "10401                                 True                         False   \n",
       "2507                                 False                         False   \n",
       "\n",
       "             city_recat_Córdoba  city_recat_Mendoza  city_recat_Otra  \\\n",
       "nro_cliente                                                            \n",
       "4863                      False               False             True   \n",
       "10401                     False               False             True   \n",
       "2507                      False               False            False   \n",
       "\n",
       "             city_recat_Salta  city_recat_San Miguel de Tucumán  \\\n",
       "nro_cliente                                                       \n",
       "4863                    False                             False   \n",
       "10401                   False                             False   \n",
       "2507                    False                             False   \n",
       "\n",
       "             city_recat_Villa Angelica  \n",
       "nro_cliente                             \n",
       "4863                             False  \n",
       "10401                            False  \n",
       "2507                             False  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vista del dataframe de entrenamiento\n",
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generación y ajuste del modelo de regresión logística, añadiendo una penalización L1 (Lasso)\n",
    "model = LogisticRegression(penalty=\"l1\", max_iter=1000, solver=\"liblinear\")\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.81005829, -0.31935995,  0.14951579, -0.04646017,  0.03257078,\n",
       "         0.0104537 , -0.01009794, -0.02422041, -0.04372396, -0.02089325,\n",
       "         0.30234197,  0.06278763,  0.1456904 , -0.01226637, -0.18608084,\n",
       "        -0.17418619,  0.        , -0.10138562, -0.34799655,  0.        ,\n",
       "         0.02310663]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muestra de los coeficientes del modelo\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se añaden los coeficientes resultantes del modelo a un dataframe\n",
    "# Se calculan los odds ratio, que permiten mayor interpretabilidad\n",
    "coefficients = pd.DataFrame({\"feature\": X.columns, \"coefficient\": model.coef_[0]})\n",
    "coefficients[\"odds_ratio\"] = np.exp(coefficients[\"coefficient\"])\n",
    "coefficients = coefficients.sort_values(by=\"odds_ratio\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>feature</th>\n",
       "      <th>coefficient</th>\n",
       "      <th>odds_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sum_fact</td>\n",
       "      <td>0.810058</td>\n",
       "      <td>2.248039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>country_region_recat_Mendoza</td>\n",
       "      <td>0.302342</td>\n",
       "      <td>1.353024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>target_churn_indicator</td>\n",
       "      <td>0.149516</td>\n",
       "      <td>1.161272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>country_region_recat_Salta</td>\n",
       "      <td>0.145690</td>\n",
       "      <td>1.156838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>country_region_recat_Otra</td>\n",
       "      <td>0.062788</td>\n",
       "      <td>1.064801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gender_male</td>\n",
       "      <td>0.032571</td>\n",
       "      <td>1.033107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>city_recat_Villa Angelica</td>\n",
       "      <td>0.023107</td>\n",
       "      <td>1.023376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gender_unknown</td>\n",
       "      <td>0.010454</td>\n",
       "      <td>1.010509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>city_recat_Mendoza</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>city_recat_San Miguel de Tucumán</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>n_previous_registers_1 o más</td>\n",
       "      <td>-0.010098</td>\n",
       "      <td>0.989953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>country_region_recat_Santa Fe</td>\n",
       "      <td>-0.012266</td>\n",
       "      <td>0.987809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>country_region_recat_Entre Rios</td>\n",
       "      <td>-0.020893</td>\n",
       "      <td>0.979323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>country_region_recat_Buenos Aires F.D.</td>\n",
       "      <td>-0.024220</td>\n",
       "      <td>0.976071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>country_region_recat_Cordoba</td>\n",
       "      <td>-0.043724</td>\n",
       "      <td>0.957218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>platform_iOS</td>\n",
       "      <td>-0.046460</td>\n",
       "      <td>0.954603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>city_recat_Otra</td>\n",
       "      <td>-0.101386</td>\n",
       "      <td>0.903585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>city_recat_Córdoba</td>\n",
       "      <td>-0.174186</td>\n",
       "      <td>0.840140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>country_region_recat_Tucuman</td>\n",
       "      <td>-0.186081</td>\n",
       "      <td>0.830206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sum_duracion</td>\n",
       "      <td>-0.319360</td>\n",
       "      <td>0.726614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>city_recat_Salta</td>\n",
       "      <td>-0.347997</td>\n",
       "      <td>0.706101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   feature  coefficient  odds_ratio\n",
       "0                                 sum_fact     0.810058    2.248039\n",
       "10            country_region_recat_Mendoza     0.302342    1.353024\n",
       "2                   target_churn_indicator     0.149516    1.161272\n",
       "12              country_region_recat_Salta     0.145690    1.156838\n",
       "11               country_region_recat_Otra     0.062788    1.064801\n",
       "4                              gender_male     0.032571    1.033107\n",
       "20               city_recat_Villa Angelica     0.023107    1.023376\n",
       "5                           gender_unknown     0.010454    1.010509\n",
       "16                      city_recat_Mendoza     0.000000    1.000000\n",
       "19        city_recat_San Miguel de Tucumán     0.000000    1.000000\n",
       "6             n_previous_registers_1 o más    -0.010098    0.989953\n",
       "13           country_region_recat_Santa Fe    -0.012266    0.987809\n",
       "9          country_region_recat_Entre Rios    -0.020893    0.979323\n",
       "7   country_region_recat_Buenos Aires F.D.    -0.024220    0.976071\n",
       "8             country_region_recat_Cordoba    -0.043724    0.957218\n",
       "3                             platform_iOS    -0.046460    0.954603\n",
       "17                         city_recat_Otra    -0.101386    0.903585\n",
       "15                      city_recat_Córdoba    -0.174186    0.840140\n",
       "14            country_region_recat_Tucuman    -0.186081    0.830206\n",
       "1                             sum_duracion    -0.319360    0.726614\n",
       "18                        city_recat_Salta    -0.347997    0.706101"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muestra de los coeficientes resultantes, organizados según su importancia\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicciones de las etiquetas y probabilidades de la variable de respuesta según el modelo entrenado\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_proba = model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5912844036697248\n",
      "Recall: 0.6526946107784432\n",
      "AUC: 0.6482507642620651\n",
      "\n",
      "Reporte general:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.52      0.54      1011\n",
      "           1       0.61      0.65      0.63      1169\n",
      "\n",
      "    accuracy                           0.59      2180\n",
      "   macro avg       0.59      0.59      0.59      2180\n",
      "weighted avg       0.59      0.59      0.59      2180\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Muestra de la precisión, recall, AUC y reporte general de métricas\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred))\n",
    "print(\"AUC:\", roc_auc_score(y_test, y_pred_proba))\n",
    "print(\"\\nReporte general:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matriz de confusión resultante\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "plt.xlabel(\"Predichos\")\n",
    "plt.ylabel(\"Actuales\")\n",
    "plt.title(\"Matriz de confusión\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Conclusión a partir del modelo de regresión logística**\n",
    "\n",
    "Al ajustar un modelo de regresión logística con el fin de validar a un mayor nivel estadístico los resultados obtenidos por las gráficas anteriores, los resultados confirman lo previamente mencionado:\n",
    "\n",
    "1. La suma total facturada en el trimestre tiene un impacto sobre los clientes que realizan al menos un cambio en el plan. Particularmente, por cada peso ($) que aumenta el total facturado para un cliente, hay un aumento de 2.25 veces en los *odds* de cambiar de plan (lo que se relaciona con una probabilidad mayor de cambiar de plan).\n",
    "\n",
    "2. Las personas que pertenecen a la región de Mendoza también tienen una mayor probabilidad de cambiar de plan en comparación a personas que residen en otras regiones (*odds ratio* de 1.35).\n",
    "\n",
    "3. De la misma manera, si un cliente está dentro de las campañas de *churn*, sus odds de cambiar de plan son 1.16 veces mayores en comparación a quienes no hacen parte de estas campañas.\n",
    "\n",
    "Adicionalmente, vale la pena mencionar que este modelo tiene un área bajo la curva (AUC) de 0.65, que si bien no es un mal resultado, podría ser mejor si se estudiara la posibilidad de agregar más datos o de probar utilizando otro modelo de clasificación, por ejemplo.\n",
    "\n",
    "Por otro lado, si bien el accuracy no es muy alto (0.59), el recall es de 0.65, que también tiene oportunidades de mejora pero que evidencia que el modelo es aceptable para identificar a los clientes que realmente van a cambiar de plan."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "_venv_strata",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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