model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cargue de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos las librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from surprise import Dataset, Reader, SVD, accuracy\n",
    "from surprise.model_selection import train_test_split\n",
    "from surprise.model_selection import cross_validate\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv('ml-latest-small/ratings.csv')\n",
    "movies = pd.read_csv('ml-latest-small/movies.csv')\n",
    "tags = pd.read_csv('ml-latest-small/tags.csv')\n",
    "links = pd.read_csv('ml-latest-small/links.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisis exploratorio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspección Inicial de los Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
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       "      <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",
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      ],
      "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>movieId</th>\n",
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       "  <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",
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       "</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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>60756</td>\n",
       "      <td>funny</td>\n",
       "      <td>1445714994</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>1445714996</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>60756</td>\n",
       "      <td>will ferrell</td>\n",
       "      <td>1445714992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>89774</td>\n",
       "      <td>Boxing story</td>\n",
       "      <td>1445715207</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   userId  movieId              tag   timestamp\n",
       "0       2    60756            funny  1445714994\n",
       "1       2    60756  Highly quotable  1445714996\n",
       "2       2    60756     will ferrell  1445714992\n",
       "3       2    89774     Boxing story  1445715207\n",
       "4       2    89774              MMA  1445715200"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>movieId</th>\n",
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       "      <td>8844.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "      <td>15602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "      <td>31357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "      <td>11862.0</td>\n",
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      ],
      "text/plain": [
       "   movieId  imdbId   tmdbId\n",
       "0        1  114709    862.0\n",
       "1        2  113497   8844.0\n",
       "2        3  113228  15602.0\n",
       "3        4  114885  31357.0\n",
       "4        5  113041  11862.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "links.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Estadísticas Descriptivas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    100836.000000\n",
      "mean          3.501557\n",
      "std           1.042529\n",
      "min           0.500000\n",
      "25%           3.000000\n",
      "50%           3.500000\n",
      "75%           4.000000\n",
      "max           5.000000\n",
      "Name: rating, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estadísticas descriptivas de las calificaciones\n",
    "print(ratings['rating'].describe())\n",
    "\n",
    "# Distribución de las calificaciones\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(ratings['rating'], bins=10, kde=True)\n",
    "plt.title('Distribución de Calificaciones')\n",
    "plt.xlabel('Calificación')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Análisis de Géneros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Géneros únicos: ['Adventure' 'Animation' 'Children' 'Comedy' 'Fantasy' 'Romance' 'Drama'\n",
      " 'Action' 'Crime' 'Thriller' 'Horror' 'Mystery' 'Sci-Fi' 'War' 'Musical'\n",
      " 'Documentary' 'IMAX' 'Western' 'Film-Noir' '(no genres listed)']\n",
      "Drama                 4361\n",
      "Comedy                3756\n",
      "Thriller              1894\n",
      "Action                1828\n",
      "Romance               1596\n",
      "Adventure             1263\n",
      "Crime                 1199\n",
      "Sci-Fi                 980\n",
      "Horror                 978\n",
      "Fantasy                779\n",
      "Children               664\n",
      "Animation              611\n",
      "Mystery                573\n",
      "Documentary            440\n",
      "War                    382\n",
      "Musical                334\n",
      "Western                167\n",
      "IMAX                   158\n",
      "Film-Noir               87\n",
      "(no genres listed)      34\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extraer todos los géneros únicos\n",
    "genres = movies['genres'].str.split('|', expand=True).stack().unique()\n",
    "print(f\"Géneros únicos: {genres}\")\n",
    "\n",
    "# Contar la frecuencia de cada género\n",
    "genre_count = movies['genres'].str.get_dummies(sep='|').sum().sort_values(ascending=False)\n",
    "print(genre_count)\n",
    "\n",
    "# Gráfico de barras de la frecuencia de géneros\n",
    "plt.figure(figsize=(14, 8))\n",
    "genre_count.plot(kind='bar')\n",
    "plt.title('Frecuencia de Géneros de Películas')\n",
    "plt.xlabel('Género')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entrenamiento del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos los rangos de valores de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader(rating_scale=(0.5, 5.0))\n",
    "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos los datos de test y de train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset, testset = train_test_split(data, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVD es una técnica de factorización de matrices utilizada en sistemas de recomendación. Es especialmente conocida por su uso en la descomposición de la matriz de calificaciones en matrices más pequeñas y manejables, lo que permite realizar recomendaciones de manera más eficiente.   \n",
    "Durante el entrenamiento, el algoritmo SVD aprende las características latentes de los usuarios y las películas para poder predecir calificaciones para las películas que los usuarios aún no han visto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x756f706b1f10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = SVD()\n",
    "algo.fit(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([0.86011657, 0.86156895, 0.86139586, 0.8691184 , 0.84794148,\n",
       "        0.8929278 , 0.84955437, 0.89138635, 0.86987438, 0.87025896,\n",
       "        0.86248698, 0.86182537, 0.85124115, 0.8771901 , 0.86145424,\n",
       "        0.84900292, 0.84136278, 0.89189038, 0.86570915, 0.84679955,\n",
       "        0.86743976, 0.84273332, 0.86668777, 0.86620264, 0.8697639 ,\n",
       "        0.88335506, 0.87113901, 0.84950232, 0.876416  , 0.86447402,\n",
       "        0.85270173, 0.88335456, 0.84465993, 0.86104587, 0.84679653,\n",
       "        0.84503608, 0.86949668, 0.86544917, 0.88859909, 0.85519285,\n",
       "        0.86097295, 0.86381199, 0.86080354, 0.86861654, 0.87449519,\n",
       "        0.85000092, 0.89246636, 0.89222282, 0.8666823 , 0.87387432]),\n",
       " 'test_mae': array([0.66211443, 0.66068495, 0.66471331, 0.66517528, 0.6519362 ,\n",
       "        0.68693086, 0.65287359, 0.68274596, 0.66578313, 0.66618251,\n",
       "        0.66575036, 0.66195415, 0.65901695, 0.67041635, 0.66208784,\n",
       "        0.65285942, 0.64529344, 0.68238925, 0.65456549, 0.65359839,\n",
       "        0.66305902, 0.64874836, 0.66950638, 0.66735937, 0.66403178,\n",
       "        0.67196813, 0.66555534, 0.64412496, 0.67425432, 0.65998561,\n",
       "        0.66077826, 0.67224079, 0.65434762, 0.65680665, 0.64886921,\n",
       "        0.65023916, 0.67051225, 0.66649543, 0.67788681, 0.66549309,\n",
       "        0.65877027, 0.66152426, 0.6492178 , 0.66624025, 0.6728892 ,\n",
       "        0.66189629, 0.68134289, 0.68273757, 0.66507397, 0.67563815]),\n",
       " 'fit_time': (0.9394981861114502,\n",
       "  0.9625484943389893,\n",
       "  0.9761178493499756,\n",
       "  0.9448435306549072,\n",
       "  0.9958019256591797,\n",
       "  0.9824512004852295,\n",
       "  1.0099432468414307,\n",
       "  0.987401008605957,\n",
       "  0.9525632858276367,\n",
       "  1.0321674346923828,\n",
       "  0.935659646987915,\n",
       "  0.9820985794067383,\n",
       "  0.9694068431854248,\n",
       "  1.0062205791473389,\n",
       "  0.9827730655670166,\n",
       "  0.9939908981323242,\n",
       "  0.9650228023529053,\n",
       "  0.9581294059753418,\n",
       "  0.971837043762207,\n",
       "  0.9863243103027344,\n",
       "  0.9288656711578369,\n",
       "  0.9471478462219238,\n",
       "  0.9574522972106934,\n",
       "  0.9242973327636719,\n",
       "  0.9745113849639893,\n",
       "  0.9445743560791016,\n",
       "  0.9841263294219971,\n",
       "  0.9682316780090332,\n",
       "  0.9430360794067383,\n",
       "  0.9586808681488037,\n",
       "  0.9688103199005127,\n",
       "  0.9553523063659668,\n",
       "  0.9843666553497314,\n",
       "  0.935589075088501,\n",
       "  0.9901812076568604,\n",
       "  0.9740240573883057,\n",
       "  0.9634654521942139,\n",
       "  0.9797744750976562,\n",
       "  0.9701602458953857,\n",
       "  0.952247142791748,\n",
       "  0.9896528720855713,\n",
       "  0.94775390625,\n",
       "  0.9558203220367432,\n",
       "  0.9622485637664795,\n",
       "  0.9501986503601074,\n",
       "  0.9675552845001221,\n",
       "  0.9865055084228516,\n",
       "  0.9299659729003906,\n",
       "  0.9526004791259766,\n",
       "  0.925382137298584),\n",
       " 'test_time': (0.006751537322998047,\n",
       "  0.0072481632232666016,\n",
       "  0.0067827701568603516,\n",
       "  0.006934642791748047,\n",
       "  0.006865501403808594,\n",
       "  0.007015228271484375,\n",
       "  0.007152557373046875,\n",
       "  0.007157087326049805,\n",
       "  0.00710606575012207,\n",
       "  0.007247447967529297,\n",
       "  0.006992816925048828,\n",
       "  0.007760763168334961,\n",
       "  0.0069904327392578125,\n",
       "  0.0072021484375,\n",
       "  0.007169961929321289,\n",
       "  0.007349252700805664,\n",
       "  0.009982585906982422,\n",
       "  0.007106304168701172,\n",
       "  0.007074594497680664,\n",
       "  0.011124610900878906,\n",
       "  0.0068798065185546875,\n",
       "  0.007281303405761719,\n",
       "  0.012482166290283203,\n",
       "  0.0070209503173828125,\n",
       "  0.0212252140045166,\n",
       "  0.007544040679931641,\n",
       "  0.0076751708984375,\n",
       "  0.007100582122802734,\n",
       "  0.006985902786254883,\n",
       "  0.007214546203613281,\n",
       "  0.007112026214599609,\n",
       "  0.00718379020690918,\n",
       "  0.006979227066040039,\n",
       "  0.007130861282348633,\n",
       "  0.007071733474731445,\n",
       "  0.007364511489868164,\n",
       "  0.007519960403442383,\n",
       "  0.0069959163665771484,\n",
       "  0.007208824157714844,\n",
       "  0.007700443267822266,\n",
       "  0.0070037841796875,\n",
       "  0.007196664810180664,\n",
       "  0.006921291351318359,\n",
       "  0.0073969364166259766,\n",
       "  0.0071256160736083984,\n",
       "  0.006888151168823242,\n",
       "  0.007000446319580078,\n",
       "  0.007406473159790039,\n",
       "  0.0068857669830322266,\n",
       "  0.007374286651611328)}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = SVD()\n",
    "cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=50, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.6401\n"
     ]
    }
   ],
   "source": [
    "rmse = accuracy.rmse(algo.test(testset))\n",
    "RMSE: {rmse}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('svd_model.pkl', 'wb') as model_file:\n",
    "    pickle.dump(algo, model_file)"
   ]
  }
 ],
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