PRA2_Limpieza_y_validación_de_los_datos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zpJhDV4jJSFX"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EFvPqJwILotP"
   },
   "source": [
    "**Asignatura**: Tipología y ciclo de vida de los datos (Máster Ciencia de Datos)\n",
    "\n",
    "**Dataset**: Para la presente práctica, se ha utilizado la información de los pasajeros del Titanic, disponible en Kaggle. Se ha tomado el conjunto de datos de entrenamiento (train), por tanto trabajamos con una muestra de los pasajeros y no con la población completa (el total de los pasajeros que viajaban en el Titanic)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iaND0z8LIQSN"
   },
   "source": [
    "# Descripción del dataset\n",
    "\n",
    "El Titanic fue un transatlántico británico que se hundió en el Atlántico Norte en la madrugada del 15 de abril de 1912, tras colisionar con un iceberg durante su viaje inaugural de Southampton a Nueva York. Se calcula que había 2224 pasajeros y tripulantes a bordo del barco, y que murieron más de 1500, lo que lo convierte en uno de los desastres marítimos comerciales más grandes de la historia moderna. El Titanic era el mayor barco a flote en el momento en que entró en servicio y fue el segundo de los tres transatlánticos de clase olímpica operados por la White Star Line.\n",
    "\n",
    "El dataset elegido contiene información demográfica y de un total de 891 de los 2224 pasajeros y tripulantes que viajaban a bordo del Titanic, por tanto el dataset contiene únicamente una muestra de los datos de todos los pasajeros (población) que viajaron en el barco.\n",
    "\n",
    "La información contenida en este dataset resulta útil para investigar por ejemplo la relación entre el precio del billete que pagó cada tripulante en base a sus atributos, o los factores más importantes que determinaron la probabilidad de supervivencia al hundimiento del Titanic.\n",
    "\n",
    "Variables:\n",
    "\n",
    "*   **PassengerId**: identificador del pasajero.\n",
    "*   **Survived**: valor booleano que indica si el pasajero sobrevivió o no. El valor 0 indica que no sobrevivió y 1 indica que sí.\n",
    "*   **Pclass**: Categoría en la que viajaba el pasajero (un proxy de su clase social).\n",
    "*   **Sex**: sexo del pasajero.\n",
    "*   **Age**: edad del pasajero.\n",
    "*   **Sibsp**: número de hermanos o cónyuges a bordo del Titanic.\n",
    "*   **Parch**: número de padres o hijos a bordo del Titanic.\n",
    "*   **Ticket**: número de billete.\n",
    "*   **Fare**: tarifa del pasajero.\n",
    "*   **Cabin**: número de cabina.\n",
    "*   **Embarked**: puerto de embarque.\n",
    "\n",
    "Puesto que la mayoría de análisis realizados que se pueden encontrar en la web pretenden analizar la probabilidad de supervivencia de los pasajeros, en nuestro caso vamos a tratar de dar respuesta a preguntas del tipo:\n",
    "\n",
    "\n",
    "*   ¿El lugar en el que embarcó el pasajero tiene relación con el precio del billete?\n",
    "*   ¿Cuál es la influencia del sexo en el precio del billete?\n",
    "* ¿Qué influye más en la probabilidad de supervivencia, el precio del billete o la edad del pasajero?\n",
    "\n",
    "\n",
    "En primer lugar, vamos a importar las librerías que necesitaremos para realizar el ejercicio, así como el conjunto de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "8fhp39alYAbD",
    "outputId": "bff931ca-2039-4265-998b-0575942d842d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Imputador valores perdidos\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "# Tests estadísticos\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols, glm\n",
    "\n",
    "# Representación gráfica\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_palette(\"Set2\")\n",
    "\n",
    "file_name = \"titanic_data.csv\"\n",
    "\n",
    "# Cargamos y mostramos los datos\n",
    "data = pd.read_csv(file_name)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WVecHgVlVgLX"
   },
   "source": [
    "# Integración y selección de los datos de interés a analizar\n",
    "\n",
    "Pese a que con la información de la cabina posiblemente se podría construir nuevos atributos (feature engineering), en nuestro caso descartaremos tanto el identificador del pasajero como el de la cabina, ya que no aportan valor al análisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "KDGOHdH9VuhV"
   },
   "outputs": [],
   "source": [
    "# Columnas que no son útiles (para descartar)\n",
    "drop_cols = [\"PassengerId\", \"Cabin\"]\n",
    "\n",
    "data = data.drop(columns = drop_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lf0RLb3GIeuy"
   },
   "source": [
    "# Limpieza de datos\n",
    "\n",
    "En esta etapa, trataremos de realizar imputaciones en aquellos registros con valores ausentes y corregiremos las posibles anomalías (outliers) que detectemos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9iOF_N3HIiA_"
   },
   "source": [
    "## ¿Los datos contienen ceros o elementos vacíos? ¿Cómo gestionarías cada uno de estos casos?\n",
    "\n",
    "Podemos utilizar la función *describe* para conocer alguna información básica de cada atributo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "7NV_awApm1tR",
    "outputId": "ad17c05c-5112-4775-8255-5c7749322232"
   },
   "outputs": [
    {
     "data": {
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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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>Klaber, Mr. Herman</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.125</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>891</td>\n",
       "      <td>681</td>\n",
       "      <td>1601</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count unique                 top freq       mean        std   min  \\\n",
       "Survived  891.0    NaN                 NaN  NaN   0.383838   0.486592   0.0   \n",
       "Pclass    891.0    NaN                 NaN  NaN   2.308642   0.836071   1.0   \n",
       "Name        891    891  Klaber, Mr. Herman    1        NaN        NaN   NaN   \n",
       "Sex         891      2                male  577        NaN        NaN   NaN   \n",
       "Age       714.0    NaN                 NaN  NaN  29.699118  14.526497  0.42   \n",
       "SibSp     891.0    NaN                 NaN  NaN   0.523008   1.102743   0.0   \n",
       "Parch     891.0    NaN                 NaN  NaN   0.381594   0.806057   0.0   \n",
       "Ticket      891    681                1601    7        NaN        NaN   NaN   \n",
       "Fare      891.0    NaN                 NaN  NaN  32.204208  49.693429   0.0   \n",
       "Embarked    889      3                   S  644        NaN        NaN   NaN   \n",
       "\n",
       "             25%      50%   75%       max  \n",
       "Survived     0.0      0.0   1.0       1.0  \n",
       "Pclass       2.0      3.0   3.0       3.0  \n",
       "Name         NaN      NaN   NaN       NaN  \n",
       "Sex          NaN      NaN   NaN       NaN  \n",
       "Age       20.125     28.0  38.0      80.0  \n",
       "SibSp        0.0      0.0   1.0       8.0  \n",
       "Parch        0.0      0.0   0.0       6.0  \n",
       "Ticket       NaN      NaN   NaN       NaN  \n",
       "Fare      7.9104  14.4542  31.0  512.3292  \n",
       "Embarked     NaN      NaN   NaN       NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Podemos mostrar una descripción general del dataset\n",
    "data.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, vamos a inspeccionar las columnas que tienen valores nulos así como la cantidad de valores nulos por columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "EZIE618Po57x",
    "outputId": "e8489be4-86f0-4e00-f258-e2827a1eaecb"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>Valores nulos?</th>\n",
       "      <th>Total valores nulos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "      <th>Pclass</th>\n",
       "      <td>False</td>\n",
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       "      <th>Name</th>\n",
       "      <td>False</td>\n",
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       "      <th>Sex</th>\n",
       "      <td>False</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>True</td>\n",
       "      <td>177</td>\n",
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       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>False</td>\n",
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       "      <th>Parch</th>\n",
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       "      <th>Ticket</th>\n",
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       "      <th>Fare</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Valores nulos?  Total valores nulos\n",
       "Survived           False                    0\n",
       "Pclass             False                    0\n",
       "Name               False                    0\n",
       "Sex                False                    0\n",
       "Age                 True                  177\n",
       "SibSp              False                    0\n",
       "Parch              False                    0\n",
       "Ticket             False                    0\n",
       "Fare               False                    0\n",
       "Embarked            True                    2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MissD = pd.DataFrame(data.isna().any())\n",
    "MissD.columns = ['Valores nulos?']\n",
    "MissD['Total valores nulos'] = data.isna().sum()\n",
    "MissD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el caso de la edad, vemos que tenemos un total de 177 valores nulos, puesto que es un número elevado de registros vamos a realizar una imputación en vez de descartarlos. En concreto, vamos a reemplazar los valores faltantes por el promedio de edad de los datos disponibles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "yp1izQa_0GtP"
   },
   "outputs": [],
   "source": [
    "# Imputación media de edad \n",
    "data_alt = data\n",
    "data['Age'] = data['Age'].fillna(data['Age'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternativamente, podemos utilizar el algoritmo k-nearest neighbors para imputar los valores perdidos de la edad (Age). En este caso se utiliza también la media, pero de los N=5 valores más cercanos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "D5NcjkteZ6uW"
   },
   "outputs": [],
   "source": [
    "# k-nearest neighbors para imputar los valore perdidos de Age.\n",
    "imputer = KNNImputer(n_neighbors=5, weights=\"uniform\")\n",
    "\n",
    "\n",
    "# Ajustamos el modelo e imputamos los missing values\n",
    "imputer.fit(data_alt[[\"Age\"]])\n",
    "data_alt[\"Age\"] = imputer.transform(data_alt[[\"Age\"]]).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el caso del puerto de embarque, solo hay dos datos faltantes así que podemos completarlo con el valor más frecuente (moda). Otra aproximación, teniendo en cuenta que son únicamente dos registros, podrían ser descartarlos, aunque en nuestro caso hemos preferido no deshacernos de ningún registro puesto que el tamaño inicial de la muestra no es muy elevado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "C0is3fmStHsR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    646\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputación valores nulos Embarked \n",
    "data['Embarked']=data['Embarked'].fillna(data['Embarked'].value_counts().index[0])\n",
    "\n",
    "data['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, volvemos a comprobar que tras las imputaciones no existe ningún atributo con valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "Hd6PHaDDzwaM",
    "outputId": "bccc3cf5-cdcd-43d3-ddb4-5c62902856f5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Valores nulos?</th>\n",
       "      <th>Total valores nulos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "      <th>Name</th>\n",
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       "      <th>Age</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Valores nulos?  Total valores nulos\n",
       "Survived           False                    0\n",
       "Pclass             False                    0\n",
       "Name               False                    0\n",
       "Sex                False                    0\n",
       "Age                False                    0\n",
       "SibSp              False                    0\n",
       "Parch              False                    0\n",
       "Ticket             False                    0\n",
       "Fare               False                    0\n",
       "Embarked           False                    0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vuelvo a comprobar los valores faltantes\n",
    "MissD=pd.DataFrame(data.isna().any())\n",
    "MissD.columns=['Valores nulos?']\n",
    "MissD['Total valores nulos']=data.isna().sum()\n",
    "MissD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "95JNvnEvIhzk"
   },
   "source": [
    "## Identificación y tratamiento de valores extremos\n",
    "\n",
    "En este apartado trataremos de identificar si existen valores extremos (outliers) y veremos qué tratamiento aplicar a cada uno de ellos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3NdskkYH3Wmp"
   },
   "source": [
    "### Edad\n",
    "\n",
    "Visualizamos la distribución de los datos a través de un histograma y un boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 263
    },
    "id": "NYJpSI4c3av2",
    "outputId": "0473a5c0-07e9-4930-d274-b98ef35525d9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(2,2,1)\n",
    "sns.histplot(data['Age'])\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data=data['Age'], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FTcNf1Uy4P11"
   },
   "source": [
    "A partir del boxplot podemos ver que hay algunos valores que podrían interpretarse como outliers. Como la distribución es aproximadamente normal y, además, aplicando el TCL podemos asumir normalidad; vamos a aplicar un método de detección de outliers basado en la ditribución normal:\n",
    "\n",
    "Aquellos punto que se encuentren por debajo de `mean-3*(sigma)` o por encima de `mean+3*(sigma)` serán considerados outliers y serán reemplazados por el valor límite en cada caso.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ETHd_JnD4N2j",
    "outputId": "4ac9a5fc-342c-4a85-e7b6-a71d654ee631"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Limite superior: 69.0\n",
      "Limite inferior: -9.0\n"
     ]
    }
   ],
   "source": [
    "upper_limit = data['Age'].mean() + 3*data['Age'].std()\n",
    "lower_limit = data['Age'].mean() - 3*data['Age'].std()\n",
    "\n",
    "print(\"Limite superior:\", round(upper_limit,0))\n",
    "print(\"Limite inferior:\", round(lower_limit,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reemplazamos los valores de los outliers por los nuevos valores que hemos calculados y de nuevo representamos los resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "xXmZP2AM7wPT"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Imputamos valores a outliers\n",
    "data['Age'] = np.where(\n",
    "    data['Age']>upper_limit,\n",
    "    upper_limit,\n",
    "    np.where(\n",
    "        data['Age']<lower_limit,\n",
    "        lower_limit,\n",
    "        data['Age']\n",
    "    )\n",
    ")\n",
    "\n",
    "# Volvemos a visualizar los datos corregidos\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(2,2,1)\n",
    "sns.histplot(data['Age'])\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data=data['Age'], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "maxdJyXO9H9K"
   },
   "source": [
    "### Fare\n",
    "\n",
    "Como en el apartado anterior, visualizamos la distribución de los datos a través de un histograma y un boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 260
    },
    "id": "u8Jj9zLq9O5O",
    "outputId": "53ec574d-9e9b-48a9-d45d-4e5eb9f11298"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(2,2,1)\n",
    "sns.histplot(data['Fare'])\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data=data['Fare'], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1UeC8c9q9WUV"
   },
   "source": [
    "En el caso de la tarifa del billete vemos, a partir del boxplot, que hay valores extremos. Sin embargo, contrariamente a lo que sucedía con la variable edad, en este caso la distribución está sesgada y no parece seguir una normal, por lo cual deberemos aplicar un método distinto. Para estos casos podemos utilizar la regla del rango intercuartílico (IRQ):\n",
    "\n",
    "Los datos que caen por debajo de `Q1 – 1.5 IQR` o por encima de `Q3 + 1.5 IQR` serán considerados outliers. Q1 y Q3  representan el percentil 25 y 50, respectivamente, y IQR representa el rango intercuartílico calculado como `Q3 – Q1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XwFZl-ce_Dog",
    "outputId": "e83a6717-89df-4fed-de7e-245318735e5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentil 25: 7.9104\n",
      "percentil 75: 31.0\n",
      "IQR: 23.0896\n"
     ]
    }
   ],
   "source": [
    "# calculo Q1, Q3 y el IQR\n",
    "\n",
    "percentile25 = data['Fare'].quantile(0.25)\n",
    "percentile75 = data['Fare'].quantile(0.75)\n",
    "iqr = percentile75 - percentile25\n",
    "\n",
    "print(\"percentil 25:\", percentile25)\n",
    "print(\"percentil 75:\", percentile75)\n",
    "print(\"IQR:\", iqr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_-C4pTq__7GG"
   },
   "source": [
    "De nuevo, imputamos los nuevos valores a los outliers que hemos identificado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "dWqEICeN_9ye"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculamos los límites\n",
    "upper_limit = percentile75 + 1.5 * iqr\n",
    "lower_limit = percentile25 - 1.5 * iqr\n",
    "\n",
    "# imputación de los datos\n",
    "data['Fare'] = np.where(\n",
    "    data['Fare'] > upper_limit,\n",
    "    upper_limit,\n",
    "    np.where(\n",
    "        data['Fare'] < lower_limit,\n",
    "        lower_limit,\n",
    "        data['Fare']\n",
    "    )\n",
    ")\n",
    "\n",
    "# Visualizo nuevamente los datos\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(2,2,1)\n",
    "sns.histplot(data['Fare'])\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data=data['Fare'], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SibSp y Parch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para el caso de estas dos variables, no las vamos a utilizar en nuestro análisis posterior, por lo que decidimos no tratarlas. Sin embargo, al inspeccionar el histograma y el boxplot podemos ver que también contienen algunos registros que pueden ser considerados extremos.\n",
    "\n",
    "Dependiendo del tipo de análisis que se implemente, podrían o no tratarse (imputarse o exlcluir) estos registros. Desde nuestro punto de vista, no serían reales *outliers* y deberían analizarse sin ser tratados. Pero de nuevo, esto depende del tipo de análisis a realizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(2,2,1)\n",
    "sns.histplot(data['SibSp'])\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data=data['SibSp'], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "4     18\n",
       "3     16\n",
       "8      7\n",
       "5      5\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.SibSp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(2,2,1)\n",
    "sns.histplot(data['Parch'])\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data=data['Parch'], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    678\n",
       "1    118\n",
       "2     80\n",
       "3      5\n",
       "5      5\n",
       "4      4\n",
       "6      1\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Parch.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, exportamos los datos procesados en esta etapa de limpieza a un nuevo fichero csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"datos_procesados.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3wZM7kKSiHbj"
   },
   "source": [
    "# Análisis de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Aw3V_ToAS84W"
   },
   "source": [
    "## Selección de los grupos de datos que se quieren analizar/comparar (planificación de los análisis a aplicar)\n",
    "\n",
    "En este punto vamos a comparar diferentes grupos de datos.\n",
    "\n",
    "1. Medidas de tendencia central y dispersión de los atributos numéricos del dataset (Análisis estadístico descriptivo).\n",
    "\n",
    "2. Analizar si existen diferencias significativas entre el precio medio (*Fare*) de hombres y mujeres (Comparación de dos grupos, análisis estadístico inferencial).\n",
    "\n",
    "3. Correlación entre la edad y la tarifa (correlación de Pearson, análisis estadístico inferencial).\n",
    "\n",
    "4. Analizar si existen diferencias significativas entre las tarifas (*Fare*) en función del puerto de embarque (*Embarked*) (Comparación de varios grupos, análisis estadístico inferencial).\n",
    "\n",
    "5. Regresión logística para ver si existe relación entre los grupos supervivientes/fallecidos y la edad y tarifa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2X_pJi19TNDV"
   },
   "source": [
    "## Comprobación de la normalidad y homogeneidad de la varianza\n",
    "\n",
    "En primer lugar, vamos a comprobar para los diferentes atributos que vamos a analizar si los valores siguen una distribución normal y si para los diferentes grupos tenemos homogenedidad de varianza (homocedasticidad) o no. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SmuQIjZQEJ6-"
   },
   "source": [
    "### Normalidad\n",
    "\n",
    "- **Tarifa (*Fare*)**: Puesto que p-value es menor que 0.05, podemos rechazar la hipótesis nula H0, por lo que no podemos asumir nomalidad en la distribución de la tarifa (Fare). **No obstante, puesto que el conjunto de datos se compone de un número de registros\n",
    "suficientemente grande, por el teorema central del límite podemos considerar que los datos siguen una distribución normal**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "8Xzu6bMcb2IR",
    "outputId": "6a3fc607-ab43-4f18-96ee-caff7df29998"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tarifa (Fare)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "res = stats.probplot(data.Fare, plot=ax)\n",
    "\n",
    "# Test de Shapiro-Wilk -> p-value\n",
    "p_value = stats.shapiro(data.Fare)[1]\n",
    "\n",
    "_ = ax.set_title(\"Saphiro-Wilk: p-value = {}\".format(p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tiVx0_lAE65X"
   },
   "source": [
    "- **Edad (*Age*)**: vemos que no podemos asumir normalidad ya que el p-value resultante del test es menor al nivel de significancia (0.05) y por tanto podemos descartar la hipótesis nula de normalidad de los datos. Sin embargo, por el TCL vamos a asumir normalidad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "aAsFS6XtE6s1",
    "outputId": "9e9b0126-26ed-45c3-d140-069b55da55d6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Edad\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "res = stats.probplot(data.Age, plot=ax)\n",
    "\n",
    "# Test de Shapiro-Wilk -> p-value\n",
    "p_value = stats.shapiro(data.Age)[1]\n",
    "\n",
    "_ = ax.set_title(\"Saphiro-Wilk: p-value = {}\".format(p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sz82LjdOgZVt"
   },
   "source": [
    "### Comprobación homogeneidad de varianzas\n",
    "\n",
    "*   **Género (*Sex*)**: realizamos el test de Levene para contrastar si existe igualdad de varianza. En este caso obtenemos que p-value < 0.05, por lo que podemos descartar H0 y no asumimos igualdad de varianza.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z1i6I7vEe0tU",
    "outputId": "ae809126-5cc4-4835-f982-67ab71262a9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.026724076950801e-06"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Levene (asumimos normalidad TCL) para homocedasticidad tarifa según género\n",
    "stat, p = stats.levene(data.loc[data.Sex == 'female', \"Fare\"],\n",
    "                       data.loc[data.Sex == 'male', \"Fare\"])\n",
    "\n",
    "# p-value < 0.05 por lo que descartamos H0 y no asumimos igualdad de varianza\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U7OaV55RE0g-"
   },
   "source": [
    "*   **Puerto de embarque (*Embarked*)**: de igual forma que para la variable Sex, realizamos el test de Levene para contrastar si existe igualdad de varianza. En este caso obtenemos que p-value < 0.05, por lo que podemos descartar H0 y no asumimos igualdad de varianza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W8cS2flbqIkd",
    "outputId": "d79f264b-1c49-4557-b1c7-9ecbcb7ee3af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.605467542531367e-15"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Levene (asumimos normalidad TCL) para homocedasticidad tarifa según puerto embarque\n",
    "stat, p = stats.levene(data.loc[data.Embarked == 'C', \"Fare\"],\n",
    "                       data.loc[data.Embarked == 'S', \"Fare\"],\n",
    "                       data.loc[data.Embarked == 'Q', \"Fare\"])\n",
    "\n",
    "# p-value < 0.05 por lo que descartamos H0 y no asumimos igualdad de varianza\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1qtAAneTTVc5"
   },
   "source": [
    "## Aplicación de pruebas estadísticas para comparar los grupos de datos.\n",
    "\n",
    "En función de los datos y el objetivo del estudio, aplicar pruebas de contraste de hipótesis, correlaciones, regresiones, etc. Aplicar al menos tres métodos de análisis diferentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xhiebqPfgEsT"
   },
   "source": [
    "### Tendencia central y dispersión.\n",
    "\n",
    "Un primer análisis estadístico preliminar que nos ayuda a entender los atributos numéricos son las medidas de tendencia central (como por ejemplo la media o la mediana) y de dispersión (como por ejemplo la desviación estándar, varianza e IQC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "CW7n-uyELw7V",
    "outputId": "1e293aa2-184a-434c-9292-487acfd53b37"
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>891.0</td>\n",
       "      <td>29.670426</td>\n",
       "      <td>12.908011</td>\n",
       "      <td>0.42</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>35.0</td>\n",
       "      <td>68.705163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>24.046813</td>\n",
       "      <td>20.481625</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>31.0</td>\n",
       "      <td>65.634400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count       mean        std   min      25%        50%   75%        max\n",
       "Age   891.0  29.670426  12.908011  0.42  22.0000  29.699118  35.0  68.705163\n",
       "Fare  891.0  24.046813  20.481625  0.00   7.9104  14.454200  31.0  65.634400"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resumen de los principales estadísticos (cuartiles, media, desviación estándar, min. y máx.)\n",
    "data[[\"Age\", \"Fare\"]].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que la edad (Age) media de los pasajeros es de 29.7 años, con una desviación estándar de 12.9 años. El pasajero más mayor tenía 68 años.\n",
    "\n",
    "Respecto a la tarifa (Fare), el valor medio es de 24.04, con una desviación estándar de 20.4, lo que nos da a entender que las variaciones entre los precios que pagaron los diferentes pasajeros está muy dispersa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SElJ2ItUf6xi"
   },
   "source": [
    "### Precio medio billete (Fare) de hombres y mujeres.\n",
    "\n",
    "Hemos visto que podemos asumir normalidad por el TCL, pero tenemos heterocedasticidad, por lo que aplicamos test de Mann-Whitney (tipo no paramétrico) para ver si existen diferencias significativas entre el precio medio del billete para hombres y mujeres.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oLDnJmQiTdlK",
    "outputId": "8208e681-f614-4832-9248-c2dd30371eef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0751625976690705e-15"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stats.mannwhitneyu(data.loc[data.Sex == 'female', \"Fare\"],\n",
    "                       data.loc[data.Sex == 'male', \"Fare\"])\n",
    "\n",
    "# p-value < 0.05 por lo que podemos descartar H0, concluyendo diferencias significativas\n",
    "# del precio medio del billete entre hombres y mujeres.\n",
    "res.pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MB8AzZz7FD39"
   },
   "source": [
    "Puesto que el p-value < 0.05 podemos descartar H0, concluyendo diferencias significativas del precio medio del billete entre hombres y mujeres."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eCN1XujAgG00"
   },
   "source": [
    "### Correlación entre la edad y tarifa.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lAuCLp94FPK0"
   },
   "source": [
    "Vemos que la correlación entre la edad (Age) y la tarifa (Fare) no es muy fuerte. Esto se desprende de que el valor de correlación que obtenemos se aleja de +1 o -1, situándose en este caso cercano al cero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "qzopQ9v0gIyp",
    "outputId": "dddbe65f-dd5a-4f7f-ae42-14689cff6156"
   },
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.138454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.138454</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age      Fare\n",
       "Age   1.000000  0.138454\n",
       "Fare  0.138454  1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_matrix = data[[\"Age\", \"Fare\"]].corr(method=\"pearson\")\n",
    "cor_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R0PGiqJlgJGU"
   },
   "source": [
    "### Diferencias significativas entre las tarifas (Fare) en función del puerto de embarque (Embarked).\n",
    "\n",
    "Asumimos normalidad en los datos (TCL) y tenemos heterocedasticidad, por lo que podemos aplicar el test de Kruskal-Wallis para comparar los tres grupos (prueba no paramétrica)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rCLjxnPygK7N",
    "outputId": "8f00191e-40af-4cb8-ce32-6ca862da2dc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.223021894742521e-16"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stats.kruskal(data.loc[data.Embarked == 'C', \"Fare\"],\n",
    "                   data.loc[data.Embarked == 'S', \"Fare\"],\n",
    "                   data.loc[data.Embarked == 'Q', \"Fare\"])\n",
    "\n",
    "\n",
    "res.pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CxeJPlGYFXO7"
   },
   "source": [
    "Ya que p-value < 0.05 podemos descartar la hipótesis nula H0, concluyendo que existen diferencias en el precio medio del billete en función del puerto de embarque."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n5h2HigKMk9E"
   },
   "source": [
    "### Regresión logística para ver si existe relación entre los grupos supervivientes / fallecidos frente a la edad y tarifa.\n",
    "\n",
    "En base a los resultados de los modelos creados, vemos que la variable edad (*Age*) explica peor si un individuo sobrevive o no al accidente que el precio del billete (*Fare*). Usando la combinación de ambas, el valor del test AIC se reduce y por tanto el modelo mejora, aunque ligeramente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H6I_bxuHOehK",
    "outputId": "f5418399-b191-4007-b52a-8431e819f180"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AIC Age: 1186.1836099093052\n",
      " AIC Fare: 1101.47882232241\n",
      " AIC Fare + Age: 1090.4586897743307\n"
     ]
    }
   ],
   "source": [
    "glm_age = glm(\"Survived ~ Age\", data=data, family=sm.families.Binomial()).fit()\n",
    "glm_fare = glm(\"Survived ~ Fare\", data=data, family=sm.families.Binomial()).fit()\n",
    "glm_conjunta = glm(\"Survived ~ Fare + Age\", data=data, family=sm.families.Binomial()).fit()\n",
    "\n",
    "print(\"AIC Age: {}\\n AIC Fare: {}\\n AIC Fare + Age: {}\"\\\n",
    "      .format(glm_age.aic, glm_fare.aic, glm_conjunta.aic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Fo-YnyObOjw"
   },
   "source": [
    "# Representación de los resultados\n",
    "\n",
    "A continuación vamos a representar los valores de los análisis realizados en el punto anterior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i5RwOU6jFoLm"
   },
   "source": [
    "## Tendencia central y dispersión\n",
    "\n",
    "Para las medidas de la tendencia central y dispersión de las variables cuantitativas (Fare y Age), vamos a representar un boxplot que nos ayuda a entender de una forma fácilmente interpretable cómo se distribuye el valor de ambos atributos a lo largo de nuestra muestra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "id": "Hxj50GdXbRLB",
    "outputId": "6e78acd2-8a1f-40ad-acd9-71746164d262"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Boxplot Tarifa (Fare)')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tendencia central y dispersión.\n",
    "fig, axs = plt.subplots(1, 2, figsize=(16, 2))\n",
    "\n",
    "sns.boxplot(x=\"Age\", data=data, ax=axs[0])\n",
    "sns.boxplot(x=\"Fare\", data=data, ax=axs[1])\n",
    "\n",
    "axs[0].set_xlabel(\"Boxplot Edad (Age)\")\n",
    "axs[1].set_xlabel(\"Boxplot Tarifa (Fare)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXQhlN34FzPa"
   },
   "source": [
    "## Precio del billete según el Sexo\n",
    "\n",
    "Como hemos visto con la realización del test, tenemos diferencias significativas en el precio medio del billete entre hombres y mujeres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "r85-A5mAbsMm",
    "outputId": "551e06be-a91a-421c-841f-6811f5d609e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot tarifa por sexo')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Precio del billete según el Sexo\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "sns.histplot(x=\"Fare\", hue=\"Sex\", data=data, ax=axs[0])\n",
    "sns.boxplot(x=\"Sex\", y=\"Fare\", data=data, ax=axs[1])\n",
    "\n",
    "axs[0].set_title(\"Histograma tarifa por sexo\")\n",
    "axs[1].set_title(\"Boxplot tarifa por sexo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C7vxq9eUF4GL"
   },
   "source": [
    "## Correlación Edad y Tarifa\n",
    "\n",
    "Podemos realizar un *scatter plot* (gráfico de dispersión) para ver si se intuye alguna relación en los datos entre el precio del billete (Fare) y la edad del pasajero. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "A7FM9McreXSo",
    "outputId": "8941f638-6780-4a41-c0d0-85e70a7a23f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlación y dispersión\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "sns.heatmap(cor_matrix, annot=True, ax=axs[0])\n",
    "sns.scatterplot(x=\"Age\", y=\"Fare\", data=data, ax=axs[1])\n",
    "\n",
    "axs[0].set_title(\"Matriz de correlación\")\n",
    "axs[1].set_title(\"Scatter plot Tarifa vs Edad\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos identificar cierta concentración de observaciones en la franja de edad 20 - 40 años que han pagado una tarifa entre 10 y 20, sin embargo no vemos que exista una fuerte correlación entre ambos atributos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EzjKjMo1F88g"
   },
   "source": [
    "## Precio del billete según puerto de embarque.\n",
    "\n",
    "Con la representación gráfica de los resultados, al igual que hicimos con el test, podemos ver si existen diferencias significativas en el precio del billete (Fare) según el puerto de embarque. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "pHMspY30cZG7",
    "outputId": "adeddb70-1b1b-485b-9148-f648b9abef75"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot tarifa por puerto de embarque')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Precio del billete según puerto de embarque.\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "sns.histplot(x=\"Fare\", hue=\"Embarked\", data=data, ax=axs[0])\n",
    "sns.boxplot(x=\"Embarked\", y=\"Fare\", data=data, ax=axs[1])\n",
    "\n",
    "axs[0].set_title(\"Histograma tarifa por puerto de embarque\")\n",
    "axs[1].set_title(\"Boxplot tarifa por puerto de embarque\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver a simple vista (confirmado con el test que hemos realizado anteriormente), que existen diferencias significativas en el precio medio del billete en función del puerto de embarque del pasajero, siendo el precio medio del billete más alto para los que han embarcado en 'C' (Cherbourg)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qn_kN9MoIx2F"
   },
   "source": [
    "# Resolución del problema\n",
    "\n",
    "Con el análisis que hemos realizado, podemos dar respuesta a las preguntas que se plantearon al inicio, en particular, sabemos que el precio medio del billete varía significativamente en función de los valores de los atributos de cada pasajero, **siendo el precio medio superior (casi tres veces) para aquellos pasajeros que embarcaron en Cherbourg**. \n",
    "\n",
    "También hemos podido averiguar que **las mujeres pagaron de media casi el doble del precio medio de los billetes de los hombres.** Adicionalmente, hemos tratado de identificar si existe correlación entre la edad y la tarifa, concluyendo que no existe una relación fuerte entre ambos atributos.\n",
    "\n",
    "Una vez analizado el precio del billete y las diferencias entre grupos de individuos, hemos puesto el foco en la posibilidad de supervivencia tras el hundimiento, analizando qué factor tenía más relación con la supervivencia, deduciendo que **el precio del billete explica mejor que la edad si un individuo sobrevive al accidente o no.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y0RS0PasI7qh"
   },
   "source": [
    "# Código\n",
    "\n",
    "El código, así como el conjunto de datos que se ha empleado para la elaboración de la práctica se puede encontrar en el siguiente repositorio de GitHub:\n",
    "\n",
    "https://github.com/ntaxus/LIMPIEZA-DE-DATOS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vídeo\n",
    "\n",
    "El video explicativo se encuentra en la siguiente carpeta del Drive:\n",
    "\n",
    "https://drive.google.com/drive/folders/1M-aivwnVJzKqnlhpnUy4ZpCrLke8VWLQ?usp=sharing"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "Nicolás C. Caruso"
   },
   {
    "name": "Antonio Gutiérrez Blanco"
   }
  ],
  "colab": {
   "collapsed_sections": [],
   "name": "PRA2 - Limpieza y validación de los datos.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "title": "Práctica 2: Limpieza y análisis de datos"
 },
 "nbformat": 4,
 "nbformat_minor": 1
}