Correlacion de Pearson.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f2f1fb9-dc41-4728-bf72-4ee4cc97ea5c",
   "metadata": {},
   "source": [
    "**El coeficiente de correlacion de Pearson**\n",
    "-----------------\n",
    "-----------------\n",
    "**Definicion**\n",
    "\n",
    "El coeficiente de correlación de Pearson, también conocido como coeficiente de correlación producto-momento de Pearson, es una medida estadística que cuantifica la\n",
    "intensidad y dirección de la relación lineal entre dos variables cuantitativas. Matemáticamente, se denota como 𝑟 y toma valores en el intervalo [-1, 1].\n",
    "\n",
    "El coeficiente de correlación de Pearson entre dos variables 𝑋 y 𝑌 se define como: Cov(𝑋𝑌)/(σX.σ𝑌)\n",
    "- Cov(𝑋𝑌) es la covarianza entre 𝑋 y 𝑌\n",
    "- σX es la desviación estandart de X\n",
    "- σ𝑌 es la desviación estandart de Y\n",
    "\n",
    "La definicion formal del coeficiente de correlacion de Pearson es:\n",
    "\n",
    "$r = \\frac{\\sum_{i=1}^{n} (X_i - \\bar{X})(Y_i - \\bar{Y})}{\\sqrt{\\sum_{i=1}^{n} (X_i - \\bar{X})^2 \\sum_{i=1}^{n} (Y_i - \\bar{Y})^2}} \\$\n",
    "-------------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "donde:\n",
    "- $n$ es el número de pares de datos.\n",
    "- $X_i$ e $Y_i$ son los valores individuales de las variables  $X$ e $Y$.\n",
    "- $\\bar{X}$ y $\\bar{Y}$ son las medias de $X$ e $Y$, respectivamente.\n",
    "\n",
    "**Interpretacion de sus resultados**\n",
    "\n",
    "$r$=1: Indica una correlación positiva perfecta. A medida que 𝑋 aumenta, 𝑌 también aumenta de manera proporcional.\n",
    "\n",
    "$r$=-1: Indica una correlación negativa perfecta. A medida que 𝑋 aumenta, 𝑌 disminuye de manera proporcional.\n",
    "\n",
    "$r$=0: Indica que no hay una correlación lineal entre 𝑋 e 𝑌.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e017d51f-30ee-4fb2-b758-b0f90fa296d0",
   "metadata": {},
   "source": [
    "Cargamos las librerias y bases de datos\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb365a11-fd65-4308-a15b-3842a7862fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Cargar los datos\n",
    "ratings = pd.read_csv(\"https://s3-us-west-2.amazonaws.com/recommender-tutorial/ratings.csv\")\n",
    "movies = pd.read_csv(\"https://s3-us-west-2.amazonaws.com/recommender-tutorial/movies.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22091c24-fc0f-4500-9b56-fd30e03a9c8a",
   "metadata": {},
   "source": [
    "Revisamos rápidamente que todo esté funcionando bien\n",
    "----------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7ac3e0ca-90a4-4695-badf-21a6d4820981",
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploración de los datos\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "158729b7-c7ee-44ee-92dc-d674cd636535",
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589c5403-923d-4f27-b128-d44821e7c6a8",
   "metadata": {},
   "source": [
    "Creamos una matriz de usuarios y peliculas\n",
    "--------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "059e40ff-d189-453f-a910-cc3289f8e5c4",
   "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>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>193565</th>\n",
       "      <th>193567</th>\n",
       "      <th>193571</th>\n",
       "      <th>193573</th>\n",
       "      <th>193579</th>\n",
       "      <th>193581</th>\n",
       "      <th>193583</th>\n",
       "      <th>193585</th>\n",
       "      <th>193587</th>\n",
       "      <th>193609</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</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>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>4.0</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</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>4.0</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>3.0</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",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 9724 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "1           4.0     NaN     4.0     NaN     NaN     4.0     NaN     NaN   \n",
       "2           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5           4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "...         ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "606         2.5     NaN     NaN     NaN     NaN     NaN     2.5     NaN   \n",
       "607         4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "608         2.5     2.0     2.0     NaN     NaN     NaN     NaN     NaN   \n",
       "609         3.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "610         5.0     NaN     NaN     NaN     NaN     5.0     NaN     NaN   \n",
       "\n",
       "movieId  9       10      ...  193565  193567  193571  193573  193579  193581  \\\n",
       "userId                   ...                                                   \n",
       "1           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "...         ...     ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "606         NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "607         NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "608         NaN     4.0  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "609         NaN     4.0  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "610         NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "movieId  193583  193585  193587  193609  \n",
       "userId                                   \n",
       "1           NaN     NaN     NaN     NaN  \n",
       "2           NaN     NaN     NaN     NaN  \n",
       "3           NaN     NaN     NaN     NaN  \n",
       "4           NaN     NaN     NaN     NaN  \n",
       "5           NaN     NaN     NaN     NaN  \n",
       "...         ...     ...     ...     ...  \n",
       "606         NaN     NaN     NaN     NaN  \n",
       "607         NaN     NaN     NaN     NaN  \n",
       "608         NaN     NaN     NaN     NaN  \n",
       "609         NaN     NaN     NaN     NaN  \n",
       "610         NaN     NaN     NaN     NaN  \n",
       "\n",
       "[610 rows x 9724 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear la matriz de usuarios y películas\n",
    "user_movie_matrix = ratings.pivot(index='userId', columns='movieId', values='rating')\n",
    "user_movie_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a54caf-6d84-4815-a098-124f35adbf91",
   "metadata": {},
   "source": [
    "Buscamos las 10 peliculas mejor calificadas por el usuario 4\n",
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e638894-c623-4a3d-93d4-b16457cca58c",
   "metadata": {},
   "source": [
    "Seleccionar las 10 peliculas mejor calificadas por el usuario 4 es una operacion arbitraria. Podriamos haber seleccionado las Top5 o las Top20. \n",
    "\n",
    "Si quisieramos complejizar mas el problema, podriamos tomar la ultima calificacion que tenga 4 o mas estrellas y recomendarle peliculas similares a esa.\n",
    "\n",
    "A los fines de ejemplificar el uso del coeficiente de correlacion de pearson, utilizaremos un ejemplo mas sencillo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4efaa274-0387-4bed-862b-77899d4625cf",
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4967</td>\n",
       "      <td>No Man's Land (2001)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>898</td>\n",
       "      <td>Philadelphia Story, The (1940)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1203</td>\n",
       "      <td>12 Angry Men (1957)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3508</td>\n",
       "      <td>Outlaw Josey Wales, The (1976)</td>\n",
       "      <td>Action|Adventure|Drama|Thriller|Western</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1197</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>Action|Adventure|Comedy|Fantasy|Romance</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1196</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1188</td>\n",
       "      <td>Strictly Ballroom (1992)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2599</td>\n",
       "      <td>Election (1999)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1103</td>\n",
       "      <td>Rebel Without a Cause (1955)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1086</td>\n",
       "      <td>Dial M for Murder (1954)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                                              title  \\\n",
       "0     4967                               No Man's Land (2001)   \n",
       "1      898                     Philadelphia Story, The (1940)   \n",
       "2     1203                                12 Angry Men (1957)   \n",
       "3     3508                     Outlaw Josey Wales, The (1976)   \n",
       "4     1197                         Princess Bride, The (1987)   \n",
       "5     1196  Star Wars: Episode V - The Empire Strikes Back...   \n",
       "6     1188                           Strictly Ballroom (1992)   \n",
       "7     2599                                    Election (1999)   \n",
       "8     1103                       Rebel Without a Cause (1955)   \n",
       "9     1086                           Dial M for Murder (1954)   \n",
       "\n",
       "                                    genres  rating  \n",
       "0                                Drama|War     5.0  \n",
       "1                     Comedy|Drama|Romance     5.0  \n",
       "2                                    Drama     5.0  \n",
       "3  Action|Adventure|Drama|Thriller|Western     5.0  \n",
       "4  Action|Adventure|Comedy|Fantasy|Romance     5.0  \n",
       "5                  Action|Adventure|Sci-Fi     5.0  \n",
       "6                           Comedy|Romance     5.0  \n",
       "7                                   Comedy     5.0  \n",
       "8                                    Drama     5.0  \n",
       "9                   Crime|Mystery|Thriller     5.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtrar las calificaciones del usuario 4\n",
    "user_4_ratings = ratings[ratings['userId'] == 4]\n",
    "\n",
    "# Ordenar las calificaciones de manera descendente\n",
    "user_4_ratings_sorted = user_4_ratings.sort_values(by='rating', ascending=False)\n",
    "\n",
    "# Seleccionar las 10 primeras películas\n",
    "top_10_user_4_ratings = user_4_ratings_sorted.head(10)\n",
    "\n",
    "# Hacer un merge con el DataFrame movies para obtener los títulos y géneros\n",
    "top_10_user_4_movies = top_10_user_4_ratings.merge(movies, on='movieId')\n",
    "\n",
    "# Seleccionar solo las columnas 'title', 'genres' y 'rating'\n",
    "top_10_user_4_movies_info = top_10_user_4_movies[['movieId','title', 'genres', 'rating']]\n",
    "\n",
    "# Mostrar el resultado\n",
    "top_10_user_4_movies_info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9069d2-148e-4e4a-9b06-21cbfe157f5f",
   "metadata": {},
   "source": [
    "Recomendamos películas similares a lo ya visto\n",
    "------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33bef7b8-8bdd-4e03-8a18-884df68bbdd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4967"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtener el ID de la primera película del top10\n",
    "target_movie_id = top_10_user_4_movies_info.iloc[0]['movieId']\n",
    "target_movie_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c92d03-d470-4f24-bc8f-ec0846bf777e",
   "metadata": {},
   "source": [
    "No podemos aplicar el coeficiente de correlacion de pearson entre dos peliculas, si una de ellas tiene muy pocas calificaciones. Tenemos que eliminar esas columnas.\n",
    "\n",
    "El efecto positivo de esta operacion: nos quedamos con una matriz más reducida y esto el calculo del coeficiente de correlacion de pearson va a ser computacionalmente menos costoso.\n",
    "\n",
    "El efecto negativo de esta operacion: las peliculas con menos de 5 recomendaciones no van a formar parte de la lista de items recomendados. Si quisieramos recomendar peliculas que no son tan populares y no marcan tendencia, tendriamos que combinar este procedimiento con otros algoritmos de recomendacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a00a0f1-143a-4e31-b9b7-6f6391323cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contar el número de calificaciones por película\n",
    "rating_counts = user_movie_matrix.notna().sum()\n",
    "\n",
    "# Filtrar las columnas que tenga menos de 5 calificaciones\n",
    "valid_movies = rating_counts[rating_counts >= 5].index\n",
    "user_movie_matrix_reduced = user_movie_matrix[valid_movies]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e081cb9-4583-41f0-9e70-e2ec32d602e4",
   "metadata": {},
   "source": [
    "La matriz original era de 610 filas × 9724 columnas. Ahora vamos a tener una matriz reducida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97c11941-62ce-4442-9ef5-5208a8f1661c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de:  (610, 3650)\n"
     ]
    }
   ],
   "source": [
    "print(\"Matriz de: \", user_movie_matrix_reduced.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9b5fd4-63fb-4cba-9579-1c11070ee4c2",
   "metadata": {},
   "source": [
    "El siguiente paso es calcular el coeficiente de correlación de pearson entre la pelicula objetivo y todas aquellas peliculas que:\n",
    "1) Tengan mas de 5 calificaciones (por el paso anterior)\n",
    "2) Como minimo, hayan sido calificadas por 3 usuarios que tambien calificaron a la pelicula objetivo\n",
    "3) Tengan una desviacion estandar superior a 0\n",
    "\n",
    "La justificacion para implementar el paso 2: las correlaciones calculadas a partir de muestras muy pequeñas (menos de 3) son poco confiables y pueden ser muy sensibles a valores atípicos. Necesitar al menos 3 puntos de datos proporciona una mínima base para un cálculo más estable.\n",
    "\n",
    "La justificacion para implementar el paso 3: dado que la desviación estándar mide la dispersión de los datos, una desviación estándar de cero indica que no hay variabilidad en las calificaciones. En términos de la fórmula de la correlación de Pearson, esto significa que el denominador será cero, resultando en un NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d177fc33-11b1-4345-9712-eb3e89509f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtener la columna de la película objetivo\n",
    "target_movie_ratings = user_movie_matrix_reduced[target_movie_id]\n",
    "\n",
    "# Calcular el coeficiente de correlación de Pearson\n",
    "correlation_dict = {}\n",
    "for movie_id in user_movie_matrix_reduced.columns:\n",
    "    if movie_id == target_movie_id:\n",
    "        continue\n",
    "\n",
    "    # Obtener las calificaciones para la película comparada\n",
    "    other_movie_ratings = user_movie_matrix_reduced[movie_id]\n",
    "    \n",
    "    # Verificar que ambas columnas tengan al menos 3 calificaciones en común\n",
    "    common_ratings = user_movie_matrix_reduced[[target_movie_id, movie_id]].dropna()\n",
    "    if len(common_ratings) >= 3:\n",
    "        # Verificar las estadísticas de las calificaciones comunes\n",
    "        stats = common_ratings.describe()\n",
    "        if stats.loc['std', target_movie_id] == 0 or stats.loc['std', movie_id] == 0:\n",
    "            #Podemos usar esta linea para mostrar que peliculas eliminamos de la lista por tener desv estandar = 0\n",
    "            #print(f\"Skipping movie_id: {movie_id} due to zero standard deviation in common ratings\")\n",
    "            continue\n",
    "\n",
    "        # Calcular la correlación de Pearson\n",
    "        correlation = common_ratings.corr().iloc[0, 1]\n",
    "        if not np.isnan(correlation):  # Verificar si la correlación no es NaN\n",
    "            correlation_dict[movie_id] = correlation\n",
    "        else:\n",
    "            print(f\"NaN correlation for movie_id: {movie_id} with target_movie_id: {target_movie_id}\")\n",
    "            print(common_ratings)\n",
    "            print(\"Statistics for common ratings:\")\n",
    "            print(stats)\n",
    "            \n",
    "# Convertir el diccionario a un DataFrame para una mejor visualización y ordenar desendentemente\n",
    "correlation_df = pd.DataFrame(list(correlation_dict.items()), columns=['movieId', 'pearson_correlation'])\n",
    "correlation_df = correlation_df.sort_values(by='pearson_correlation', ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a9d5fff-7ac9-4adb-9500-3482485d1267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peliculas similares a la pelicula objetivo\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>pearson_correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3262</td>\n",
       "      <td>Twin Peaks: Fire Walk with Me (1992)</td>\n",
       "      <td>Crime|Drama|Mystery|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69844</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (2009)</td>\n",
       "      <td>Adventure|Fantasy|Mystery|Romance|IMAX</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91500</td>\n",
       "      <td>The Hunger Games (2012)</td>\n",
       "      <td>Action|Adventure|Drama|Sci-Fi|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7099</td>\n",
       "      <td>Nausicaä of the Valley of the Wind (Kaze no ta...</td>\n",
       "      <td>Adventure|Animation|Drama|Fantasy|Sci-Fi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>4370</td>\n",
       "      <td>A.I. Artificial Intelligence (2001)</td>\n",
       "      <td>Adventure|Drama|Sci-Fi</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>2001</td>\n",
       "      <td>Lethal Weapon 2 (1989)</td>\n",
       "      <td>Action|Comedy|Crime|Drama</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>3450</td>\n",
       "      <td>Grumpy Old Men (1993)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>54272</td>\n",
       "      <td>Simpsons Movie, The (2007)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>1275</td>\n",
       "      <td>Highlander (1986)</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>902 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                                              title  \\\n",
       "0       3262               Twin Peaks: Fire Walk with Me (1992)   \n",
       "1      69844      Harry Potter and the Half-Blood Prince (2009)   \n",
       "2      91500                            The Hunger Games (2012)   \n",
       "3         18                                  Four Rooms (1995)   \n",
       "4       7099  Nausicaä of the Valley of the Wind (Kaze no ta...   \n",
       "..       ...                                                ...   \n",
       "897     4370                A.I. Artificial Intelligence (2001)   \n",
       "898     2001                             Lethal Weapon 2 (1989)   \n",
       "899     3450                              Grumpy Old Men (1993)   \n",
       "900    54272                         Simpsons Movie, The (2007)   \n",
       "901     1275                                  Highlander (1986)   \n",
       "\n",
       "                                       genres  pearson_correlation  \n",
       "0                Crime|Drama|Mystery|Thriller                  1.0  \n",
       "1      Adventure|Fantasy|Mystery|Romance|IMAX                  1.0  \n",
       "2      Action|Adventure|Drama|Sci-Fi|Thriller                  1.0  \n",
       "3                                      Comedy                  1.0  \n",
       "4    Adventure|Animation|Drama|Fantasy|Sci-Fi                  1.0  \n",
       "..                                        ...                  ...  \n",
       "897                    Adventure|Drama|Sci-Fi                 -1.0  \n",
       "898                 Action|Comedy|Crime|Drama                 -1.0  \n",
       "899                                    Comedy                 -1.0  \n",
       "900                          Animation|Comedy                 -1.0  \n",
       "901                  Action|Adventure|Fantasy                 -1.0  \n",
       "\n",
       "[902 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge con el DataFrame movies para obtener títulos y géneros\n",
    "recommended_movies = correlation_df.merge(movies[['movieId', 'title', 'genres']], on='movieId')\n",
    "\n",
    "# Seleccionar solo las columnas 'movieId', 'title', 'genres', y 'pearson_correlation'\n",
    "recommended_movies_info = recommended_movies[['movieId', 'title', 'genres', 'pearson_correlation']]\n",
    "\n",
    "print(\"Peliculas similares a la pelicula objetivo\")\n",
    "recommended_movies_info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cea3489-b36e-4d9d-a322-552d98d6febf",
   "metadata": {},
   "source": [
    "Vamos a eliminar todas las peliculas que ya fueron vistas por el usuario 4, para recomendarle peliculas nuevas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "463f4fcf-2c80-49a7-a09e-9af77ec1076c",
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>pearson_correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3262</td>\n",
       "      <td>Twin Peaks: Fire Walk with Me (1992)</td>\n",
       "      <td>Crime|Drama|Mystery|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69844</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (2009)</td>\n",
       "      <td>Adventure|Fantasy|Mystery|Romance|IMAX</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91500</td>\n",
       "      <td>The Hunger Games (2012)</td>\n",
       "      <td>Action|Adventure|Drama|Sci-Fi|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7099</td>\n",
       "      <td>Nausicaä of the Valley of the Wind (Kaze no ta...</td>\n",
       "      <td>Adventure|Animation|Drama|Fantasy|Sci-Fi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>4370</td>\n",
       "      <td>A.I. Artificial Intelligence (2001)</td>\n",
       "      <td>Adventure|Drama|Sci-Fi</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>2001</td>\n",
       "      <td>Lethal Weapon 2 (1989)</td>\n",
       "      <td>Action|Comedy|Crime|Drama</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>3450</td>\n",
       "      <td>Grumpy Old Men (1993)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>54272</td>\n",
       "      <td>Simpsons Movie, The (2007)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>1275</td>\n",
       "      <td>Highlander (1986)</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>733 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                                              title  \\\n",
       "0       3262               Twin Peaks: Fire Walk with Me (1992)   \n",
       "1      69844      Harry Potter and the Half-Blood Prince (2009)   \n",
       "2      91500                            The Hunger Games (2012)   \n",
       "3         18                                  Four Rooms (1995)   \n",
       "4       7099  Nausicaä of the Valley of the Wind (Kaze no ta...   \n",
       "..       ...                                                ...   \n",
       "897     4370                A.I. Artificial Intelligence (2001)   \n",
       "898     2001                             Lethal Weapon 2 (1989)   \n",
       "899     3450                              Grumpy Old Men (1993)   \n",
       "900    54272                         Simpsons Movie, The (2007)   \n",
       "901     1275                                  Highlander (1986)   \n",
       "\n",
       "                                       genres  pearson_correlation  \n",
       "0                Crime|Drama|Mystery|Thriller                  1.0  \n",
       "1      Adventure|Fantasy|Mystery|Romance|IMAX                  1.0  \n",
       "2      Action|Adventure|Drama|Sci-Fi|Thriller                  1.0  \n",
       "3                                      Comedy                  1.0  \n",
       "4    Adventure|Animation|Drama|Fantasy|Sci-Fi                  1.0  \n",
       "..                                        ...                  ...  \n",
       "897                    Adventure|Drama|Sci-Fi                 -1.0  \n",
       "898                 Action|Comedy|Crime|Drama                 -1.0  \n",
       "899                                    Comedy                 -1.0  \n",
       "900                          Animation|Comedy                 -1.0  \n",
       "901                  Action|Adventure|Fantasy                 -1.0  \n",
       "\n",
       "[733 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtener los movieId de las películas ya calificadas por el usuario 4\n",
    "seen_movie_ids = user_4_ratings['movieId'].tolist()\n",
    "\n",
    "# Eliminar las películas ya vistas por el usuario 4 de las recomendaciones\n",
    "filtered_recommendations = recommended_movies_info[~recommended_movies_info['movieId'].isin(seen_movie_ids)]\n",
    "filtered_recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ca0cfd-7158-4c93-bf9b-755c1e1b5093",
   "metadata": {},
   "source": [
    "Nos quedamos con las top10 peliculas no vistas por el usuario 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "073824fb-e3f6-4bee-97e9-e5ebb073643f",
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>pearson_correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3262</td>\n",
       "      <td>Twin Peaks: Fire Walk with Me (1992)</td>\n",
       "      <td>Crime|Drama|Mystery|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69844</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (2009)</td>\n",
       "      <td>Adventure|Fantasy|Mystery|Romance|IMAX</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91500</td>\n",
       "      <td>The Hunger Games (2012)</td>\n",
       "      <td>Action|Adventure|Drama|Sci-Fi|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7099</td>\n",
       "      <td>Nausicaä of the Valley of the Wind (Kaze no ta...</td>\n",
       "      <td>Adventure|Animation|Drama|Fantasy|Sci-Fi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40278</td>\n",
       "      <td>Jarhead (2005)</td>\n",
       "      <td>Action|Drama|War</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6755</td>\n",
       "      <td>Bubba Ho-tep (2002)</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4776</td>\n",
       "      <td>Training Day (2001)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>101</td>\n",
       "      <td>Bottle Rocket (1996)</td>\n",
       "      <td>Adventure|Comedy|Crime|Romance</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5254</td>\n",
       "      <td>Blade II (2002)</td>\n",
       "      <td>Action|Horror|Thriller</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieId                                              title  \\\n",
       "0      3262               Twin Peaks: Fire Walk with Me (1992)   \n",
       "1     69844      Harry Potter and the Half-Blood Prince (2009)   \n",
       "2     91500                            The Hunger Games (2012)   \n",
       "3        18                                  Four Rooms (1995)   \n",
       "4      7099  Nausicaä of the Valley of the Wind (Kaze no ta...   \n",
       "5     40278                                     Jarhead (2005)   \n",
       "6      6755                                Bubba Ho-tep (2002)   \n",
       "7      4776                                Training Day (2001)   \n",
       "8       101                               Bottle Rocket (1996)   \n",
       "10     5254                                    Blade II (2002)   \n",
       "\n",
       "                                      genres  pearson_correlation  \n",
       "0               Crime|Drama|Mystery|Thriller                  1.0  \n",
       "1     Adventure|Fantasy|Mystery|Romance|IMAX                  1.0  \n",
       "2     Action|Adventure|Drama|Sci-Fi|Thriller                  1.0  \n",
       "3                                     Comedy                  1.0  \n",
       "4   Adventure|Animation|Drama|Fantasy|Sci-Fi                  1.0  \n",
       "5                           Action|Drama|War                  1.0  \n",
       "6                              Comedy|Horror                  1.0  \n",
       "7                       Crime|Drama|Thriller                  1.0  \n",
       "8             Adventure|Comedy|Crime|Romance                  1.0  \n",
       "10                    Action|Horror|Thriller                  1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_recommendations = filtered_recommendations.head(10)\n",
    "top_10_recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad8aeb4-53ce-4dd3-b454-3569a787a55b",
   "metadata": {},
   "source": [
    "Adicional: verificar que recomendamos peliculas no vistas anteriormente\n",
    "-----------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c83b71b-f184-4a49-a01f-bbdaf56a21cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paso 1: Extraer los movieId de top_10_recommendations\n",
    "top_10_movie_ids = top_10_recommendations['movieId'].tolist()\n",
    "\n",
    "# Paso 2: Verificar que esas columnas en user_movie_matrix no tengan calificaciones en la fila 4\n",
    "user_4_ratings = user_movie_matrix.loc[4, top_10_movie_ids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cfd73778-1f3f-4f03-b7f0-4d92bfa2ab10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "3262    NaN\n",
       "69844   NaN\n",
       "91500   NaN\n",
       "18      NaN\n",
       "7099    NaN\n",
       "40278   NaN\n",
       "6755    NaN\n",
       "4776    NaN\n",
       "101     NaN\n",
       "5254    NaN\n",
       "Name: 4, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_4_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d4de36f-2531-44f6-a9a9-9d07ffc9b510",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}