Movie_recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TASK-2 :\n",
    "Movie Recommendations : Build a movie recommendation system using collaborative filtering and machine learning techniques in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns           \n",
    "%matplotlib inline\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df = pd.read_csv('C:\\\\Users\\\\User\\\\Downloads\\\\ratings_small.csv~\\\\ratings_small.csv')\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df = pd.read_csv('C:\\\\Users\\\\User\\\\Downloads\\\\movies.csv~\\\\movies.csv')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<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",
       "      <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>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>851868750</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1273541953</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>834828440</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>847057202</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp                   title genres\n",
       "0       1       31     2.5  1260759144  Dangerous Minds (1995)  Drama\n",
       "1       7       31     3.0   851868750  Dangerous Minds (1995)  Drama\n",
       "2      31       31     4.0  1273541953  Dangerous Minds (1995)  Drama\n",
       "3      32       31     4.0   834828440  Dangerous Minds (1995)  Drama\n",
       "4      36       31     3.0   847057202  Dangerous Minds (1995)  Drama"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data = pd.merge(ratings_df, movies_df, on='movieId')\n",
    "\n",
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate mean rating of all movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "\"Great Performances\" Cats (1998)           1.750000\n",
       "$9.99 (2008)                               3.833333\n",
       "'Hellboy': The Seeds of Creation (2004)    2.000000\n",
       "'Neath the Arizona Skies (1934)            0.500000\n",
       "'Round Midnight (1986)                     2.250000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.groupby('title')['rating'].mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate count rating of all movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Forrest Gump (1994)                          341\n",
       "Pulp Fiction (1994)                          324\n",
       "Shawshank Redemption, The (1994)             311\n",
       "Silence of the Lambs, The (1991)             304\n",
       "Star Wars: Episode IV - A New Hope (1977)    291\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.groupby('title')['rating'].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Last Metro, The (Dernier métro, Le) (1980)    1\n",
       "Lifeguard, The (2013)                         1\n",
       "Light Sleeper (1992)                          1\n",
       "Light Years (Gandahar) (1988)                 1\n",
       "Lightning in a Bottle (2004)                  1\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.groupby('title')['rating'].count().sort_values(ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "creating dataframe with 'rating' count values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_mean_count = pd.DataFrame(movie_data.groupby('title')['rating'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>\"Great Performances\" Cats (1998)</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$9.99 (2008)</th>\n",
       "      <td>3.833333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Neath the Arizona Skies (1934)</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           rating  rating_counts\n",
       "title                                                           \n",
       "\"Great Performances\" Cats (1998)         1.750000              2\n",
       "$9.99 (2008)                             3.833333              3\n",
       "'Hellboy': The Seeds of Creation (2004)  2.000000              1\n",
       "'Neath the Arizona Skies (1934)          0.500000              1\n",
       "'Round Midnight (1986)                   2.250000              2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_mean_count['rating_counts'] = pd.DataFrame(movie_data.groupby('title')['rating'].count())\n",
    "ratings_mean_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot graph of 'num of ratings column' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.rcParams['patch.force_edgecolor'] = True\n",
    "ratings_mean_count['rating_counts'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot graph of 'ratings' column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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besfV1NT02a9arR627b2ONKa+vj719fWHba+rqxvxB4Tj53FkODnfOB61tbXp7OzM/neqqR488u+1Q7p6arL/nWo6OztTW1vr/HuXgRzPQ7rG+TEd6LEZ78eF0Wc0/F7tz/0PeiD9h//wH/LlL385f/Inf5IkOffcc/P666/n3nvvzQ033JCWlpYkv36V6NRTT+3db/v27b2vKrW0tOTAgQPZuXNnn1eRtm/fnosvvniwpwwAAJBkCFax27dvX2pr+37ZCRMm9C7zPWvWrLS0tGT16tW9f37gwIGsWbOmN37OP//81NXV9RmzdevWvPzyywIJAAAYMoP+CtJVV12Vr371q5k5c2bOPvvs/OM//mMefPDB/Ot//a+T/PqtdQsXLsySJUsye/bszJ49O0uWLMnkyZOzYMGCJMm0adNy4403ZvHixWlqasr06dNz22235dxzz+1d1Q4AAGCwDXogLVu2LF/5yldy8803Z/v27Wltbc1NN92U//yf/3PvmNtvvz2dnZ25+eabs3Pnzlx44YVZtWpVpkyZ0jvmoYceysSJE3Pttdems7Mzl112WZ544olMmDBhsKcMAACQZAgCacqUKVm6dGmWLl36gWNqamrS3t6e9vb2DxzT0NCQZcuW9bnALAAAwFAa9M8gAQAAjFUCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABRDEkhvvvlmrrvuujQ1NWXy5Mn5vd/7vWzYsKH3z6vVatrb29Pa2prGxsZceumleeWVV/p8ja6urtx6662ZMWNGTjrppFx99dV54403hmK6AAAASYYgkHbu3JlLLrkkdXV1+d73vpef/OQneeCBB/KhD32od8z999+fBx98MMuXL8+LL76YlpaWzJ07N3v27Okds3DhwjzzzDNZuXJlXnjhhezduzfz58/PwYMHB3vKAAAASZKJg/0Fv/a1r6WtrS2PP/5477bf/u3f7v3varWapUuX5q677so111yTJHnyySfT3NycFStW5KabbsquXbvy2GOP5Vvf+lYuv/zyJMlTTz2Vtra2PPfcc7niiisGe9oAAACDH0jf+c53csUVV+Qzn/lM1qxZk9/8zd/MzTffnD//8z9Pkrz22mvZtm1b5s2b17tPfX19PvGJT2TdunW56aabsmHDhnR3d/cZ09ramnPOOSfr1q1730Dq6upKV1dX7+3du3cnSbq7u9Pd3T3Y3ybD5NBj5zFkODjfGAw9PT1pbGxMw8SaTJpQPeLY+tpq778bJtaksbExPT09zsF36c/xfK+acX5M+3tses+3cX5cGD1G0+/V/syhplqt9u+nzVE0NDQkSRYtWpTPfOYz+dGPfpSFCxfmkUceyZ/+6Z9m3bp1ueSSS/Lmm2+mtbW1d78vfOELef311/P9738/K1asyOc///k+wZMk8+bNy6xZs/LII48cdr/t7e25++67D9u+YsWKTJ48eTC/RQAAYAzZt29fFixYkF27dmXq1KlHHDvoryD19PTkggsuyJIlS5IkH/vYx/LKK6/k4Ycfzp/+6Z/2jqupqemzX7VaPWzbex1pzJ133plFixb13t69e3fa2toyb968ox4ERq/u7u6sXr06c+fOTV1d3UhPh3HO+cZg2LhxY+bMmZPmBfdlUvPpRxxbX1vNX17Qk6+sr82era/lrRVfztq1a3PeeecN02xHv/4cz/c68Nar4/qY9vfYHDrf7vheRzq+ece4PS6MHqPp9+qhd5cdi0EPpFNPPTW/+7u/22fbWWedlb/5m79JkrS0tCRJtm3bllNPPbV3zPbt29Pc3Nw75sCBA9m5c2dOPvnkPmMuvvji973f+vr61NfXH7a9rq5uxB8Qjp/HkeHkfON41NbWprOzM/vfqaZ68MhP/B3S1VOT/e9U09nZmdraWuffuwzkeB7SNc6P6UCPzXg/Low+o+H3an/uf9BXsbvkkkuyefPmPtv++Z//Ob/1W7+VJJk1a1ZaWlqyevXq3j8/cOBA1qxZ0xs/559/furq6vqM2bp1a15++eUPDCQAAIDjNeivIP37f//vc/HFF2fJkiW59tpr86Mf/SiPPvpoHn300SS/fmvdwoULs2TJksyePTuzZ8/OkiVLMnny5CxYsCBJMm3atNx4441ZvHhxmpqaMn369Nx2220599xze1e1AwAAGGyDHkgf//jH88wzz+TOO+/MX/zFX2TWrFlZunRpPve5z/WOuf3229PZ2Zmbb745O3fuzIUXXphVq1ZlypQpvWMeeuihTJw4Mddee206Oztz2WWX5YknnsiECRMGe8oAAABJhiCQkmT+/PmZP3/+B/55TU1N2tvb097e/oFjGhoasmzZsixbtmwIZggAAHC4Qf8MEgAAwFglkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQTBzpCQAwNnV0dKRSqQxo3xkzZmTmzJmDPCMAOH4CCYB+6+joyBlnnpX9nfsGtH9D4+Rs/ukmkQTAqCOQAOi3SqWS/Z370jR/ceqa2vq1b/eOLdnx3QdSqVQEEgCjjkACYMDqmtpS3/LhkZ4GAAwaizQAAAAUAgkAAKAQSAAAAIVAAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAQSAAAAMWQB9K9996bmpqaLFy4sHdbtVpNe3t7Wltb09jYmEsvvTSvvPJKn/26urpy6623ZsaMGTnppJNy9dVX54033hjq6QIAACewIQ2kF198MY8++mg++tGP9tl+//3358EHH8zy5cvz4osvpqWlJXPnzs2ePXt6xyxcuDDPPPNMVq5cmRdeeCF79+7N/Pnzc/DgwaGcMgAAcAIbskDau3dvPve5z+Ub3/hGTj755N7t1Wo1S5cuzV133ZVrrrkm55xzTp588sns27cvK1asSJLs2rUrjz32WB544IFcfvnl+djHPpannnoqP/7xj/Pcc88N1ZQBAIAT3MSh+sK33HJLPvnJT+byyy/PPffc07v9tddey7Zt2zJv3rzebfX19fnEJz6RdevW5aabbsqGDRvS3d3dZ0xra2vOOeecrFu3LldcccVh99fV1ZWurq7e27t3706SdHd3p7u7eyi+RYbBocfOY8hwcL4du56enjQ2NqZhYk0mTaj2a9+aiTVpbGxMT0/PuDzW/Tk29bXV3n83jPPjMlDOtQ/W32PTe76N8+PC6DGafq/2Zw411Wq1fz9tjsHKlStzzz33ZP369WloaMill16a3/u938vSpUuzbt26XHLJJXnzzTfT2trau88XvvCFvP766/n+97+fFStW5POf/3yf4EmSefPmZdasWXnkkUcOu8/29vbcfffdh21fsWJFJk+ePNjfIgAAMEbs27cvCxYsyK5duzJ16tQjjh30V5C2bNmSL33pS1m1alUaGho+cFxNTU2f29Vq9bBt73WkMXfeeWcWLVrUe3v37t1pa2vLvHnzjnoQGL26u7uzevXqzJ07N3V1dSM9HcY559ux27hxY+bMmZPmBfdlUvPp/dr3wFuv5q0VX87atWtz3nnnDdEMR05/jk19bTV/eUFPvrK+Nnu2vjauj8tAOdc+WH+PzaHz7Y7vdaTjm3eM2+PC6DGafq8eenfZsRj0QNqwYUO2b9+e888/v3fbwYMHs3bt2ixfvjybN29Okmzbti2nnnpq75jt27enubk5SdLS0pIDBw5k586dfT6/tH379lx88cXve7/19fWpr68/bHtdXd2IPyAcP48jw8n5dnS1tbXp7OzM/neqqR488pNb79X1TjWdnZ2pra0dl8d5IMemq6cm+8f5cRko59oHG+ixGe/HhdFnNPxe7c/9D/oiDZdddll+/OMf56WXXur954ILLsjnPve5vPTSSzn99NPT0tKS1atX9+5z4MCBrFmzpjd+zj///NTV1fUZs3Xr1rz88ssfGEgAAADHa9BfQZoyZUrOOeecPttOOumkNDU19W5fuHBhlixZktmzZ2f27NlZsmRJJk+enAULFiRJpk2blhtvvDGLFy9OU1NTpk+fnttuuy3nnntuLr/88sGeMgAAQJIhXMXuSG6//fZ0dnbm5ptvzs6dO3PhhRdm1apVmTJlSu+Yhx56KBMnTsy1116bzs7OXHbZZXniiScyYcKEkZgyAABwAhiWQPrBD37Q53ZNTU3a29vT3t7+gfs0NDRk2bJlWbZs2dBODgAAoBiyC8UCAACMNQIJAACgEEgAAACFQAIAACgEEgAAQDEiy3wDAIxHHR0dqVQq/d5vxowZmTlz5hDMCOgvgQQAMAg6OjpyxplnZX/nvn7v29A4OZt/ukkkwSggkIATxtGe2e3p6UmSbNy4MbW1//cdyJ7ZBY5FpVLJ/s59aZq/OHVNbce8X/eOLdnx3QdSqVT8rIFRQCABJ4RjeWa3sbExTz/9dObMmZPOzs7e7Z7ZBfqjrqkt9S0fHulpAAMkkIATwrE8s9swsSZJ0rzgvux/p5rEM7sAcKIRSMAJ5UjP7E6aUE1yMJOaT0/1YM3wTgwAGBUs8w0AAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKCYONITAAAg2bRp05COB46NQAIAGEEH9+5Mampy3XXXjfRUgAgkAIAR1dO1N6lW0zR/ceqa2o55v85X12fX808N4czgxCSQAABGgbqmttS3fPiYx3fv2DKEs4ETl0UaAAAACoEEAABQCCQAAIDCZ5AA4ATV0dGRSqXS7/26urpSX1/f7/1mzJiRmTNn9ns/gOEkkADgBNTR0ZEzzjwr+zv39X/nmtqk2tPv3RoaJ2fzTzeJJGBUE0gAcAKqVCrZ37lvwEtL93e/7h1bsuO7D6RSqQgkYFQTSABwAhvo0tL93Q9grLBIAwAAQOEVJACA9xjIAhabNm0aotkAw0kgAQC8y3EtYAGMeQIJAOBdjncBC2BsE0gAAO9joAtYAGObRRoAAAAKgQQAAFAIJAAAgEIgAQAAFBZpAAAAjmgg1wbr6ekZotkMLYEEAAB8oIFeG6yxsTFPP/103njjjcyaNWuIZjf4BBIAAPCBBnptsAm7f5Ek2bFjh0ACAICxZiBvIztkxowZmTlz5iDPaHTp77XBaibWDOFsho5AAgDGtU2bNg3peMaHgb6N7JCGxsnZ/NNN4z6STgQCCQAYlw7u3ZnU1OS6664b6akwBgz0bWRJ0r1jS3Z894FUKhWBNA4IJABgXOrp2ptUq/3+C2/nq+uz6/mnhnBmjGb9fRsZ449AAgDGtf7+hbd7x5YhnA0w2rlQLAAAQCGQAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKCYONITAICh1tHRkUql0u/9ZsyYkZkzZw7BjAAYrQQSAONaR0dHzjjzrOzv3NfvfRsaJ2fzTzeJJIATiEACYFyrVCrZ37kvTfMXp66p7Zj3696xJTu++0AqlYpAAjiBCCQATgh1TW2pb/nwSE8DgFHOIg0AAACFQAIAACgEEgAAQCGQAAAACos0AMAYN5DrPG3atGmIZgMwtgkkABjDjuc6TwAcTiABwBg20Os8db66Pruef2oIZwYwNgkkABgH+nudp+4dW4ZwNgBjl0UaAAAACoEEAABQCCQAAIBCIAEAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUAgkAACAQiABAAAUAgkAAKAY9EC699578/GPfzxTpkzJKaeckk9/+tPZvHlznzHVajXt7e1pbW1NY2NjLr300rzyyit9xnR1deXWW2/NjBkzctJJJ+Xqq6/OG2+8MdjTBQAA6DVxsL/gmjVrcsstt+TjH/943nnnndx1112ZN29efvKTn+Skk05Kktx///158MEH88QTT+QjH/lI7rnnnsydOzebN2/OlClTkiQLFy7M//yf/zMrV65MU1NTFi9enPnz52fDhg2ZMGHCYE8bABgGmzZtGtLxAMdr0APp2Wef7XP78ccfzymnnJINGzZkzpw5qVarWbp0ae66665cc801SZInn3wyzc3NWbFiRW666abs2rUrjz32WL71rW/l8ssvT5I89dRTaWtry3PPPZcrrrhisKcNAAyhg3t3JjU1ue6660Z6KgBHNOiB9F67du1KkkyfPj1J8tprr2Xbtm2ZN29e75j6+vp84hOfyLp163LTTTdlw4YN6e7u7jOmtbU155xzTtatW/e+gdTV1ZWurq7e27t3706SdHd3p7u7e0i+N4beocfOY8jx6unpSWNjYxom1mTShOr7jqmvrfb5d5LUTKxJY2Njenp6nIfvcizH84MM9zEd6FwHOs/+3N+7z7mGYbi/d3unbsLw7tfTmcaGhkz/41tTN/03j3m/zv/vH7N73bcHdK4N+/c4yvfrPd/8XHtfY+nn2nA7np+jh/Yf6ePSn/uvqVar/TsD+qFareZTn/pUdu7cmeeffz5Jsm7dulxyySV5880309ra2jv2C1/4Ql5//fV8//vfz4oVK/L5z3++T/Akybx58zJr1qw88sgjh91Xe3t77r777sO2r1ixIpMnTx7k7wwAABgr9u3blwULFmTXrl2ZOnXqEccO6StIX/ziF/NP//RPeeGFFw77s5qamj63q9XqYdve60hj7rzzzixatKj39u7du9PW1pZ58+Yd9SAwenV3d2f16tWZO3du6urqRno6jGEbN27MnDlz0rzgvkxqPv19x9TXVvOXF/TkK+tr09Xz6581B956NW+t+HLWrl2b8847bzinPKody/H8IMN9TAc614HOsz/39+5zbs/W14b8/t7t7U3P55fPLhu3+42luQ7XfofOtzu+15GOb97h59p7jKWfa8NtoMemZsdr+dqVM3PqqafmYx/72BDO8OgOvbvsWAxZIN166635zne+k7Vr1+a0007r3d7S0pIk2bZtW0499dTe7du3b09zc3PvmAMHDmTnzp05+eST+4y5+OKL3/f+6uvrU19ff9j2uro6f7EeBzyOHK/a2tp0dnZm/zvVVA8e+cmYrp6adJUxXe9U09nZmdraWufgu/TneL7XcB/Tgc51oPMcyP119dRk/zDeX5Ls7z44rvcbS3Md7v38XHt/Y+nn2nAb6LGpeafau/9IH5d+/Vwd7DuvVqv54he/mP/xP/5H/v7v/z6zZs3q8+ezZs1KS0tLVq9e3bvtwIEDWbNmTW/8nH/++amrq+szZuvWrXn55Zc/MJAAAACO16C/gnTLLbdkxYoV+bu/+7tMmTIl27ZtS5JMmzYtjY2NqampycKFC7NkyZLMnj07s2fPzpIlSzJ58uQsWLCgd+yNN96YxYsXp6mpKdOnT89tt92Wc889t3dVOwAAgME26IH08MMPJ0kuvfTSPtsff/zx/Nmf/VmS5Pbbb09nZ2duvvnm7Ny5MxdeeGFWrVrVew2kJHnooYcyceLEXHvttens7Mxll12WJ554wjWQAACAITPogXQsi+LV1NSkvb097e3tHzimoaEhy5Yty7JlywZxdgAAAB9s0D+DBAAAMFYN+YViAQA4sXV0dKRSqfR7vxkzZmTmzJlDMCP4YAIJAIAh09HRkTPOPCv7O/f1e9+GxsnZ/NNNIolhJZAYcUd6VqmnpyfJry9QVlvb9x2hnlUCgNGvUqlkf+e+NM1fnLqmtmPer3vHluz47gOpVCp+3zOsBBIj6mjPKjU2Nubpp5/OnDlz0tnZ2efPPKsEAGNHXVNb6ls+PNLTgKMSSIyooz2r1DDx11drbl5wX/a/839XSPSsEgAAQ0EgMSp80LNKkyZUkxzMpObTUz1YM/wTAwDghGKZbwAAgMIrSAAAHJOBLNe9adOmIZoNDA2BBADAUR3Pct0wlggkGGcGejG+xNLpAHywgS7X3fnq+ux6/qkhnBkMLoEE48jxPrtn6XQAjqa/y3V379gyhLOBwSeQYBwZ6LN7iaXTAQASgQTjkovxAQAMjGW+AQAACoEEAABQCCQAAIDCZ5AAGDNcpBKAoSaQABgTXKQSgOEgkAAYE1ykEoDhIJAAGFNcpBKAoWSRBgAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQGGZbwAYRTZt2jSk4wE4MoEEAKPAwb07k5qaXHfddSM9FYATmkACgFGgp2tvUq2maf7i1DW1HfN+na+uz67nnxrCmQGcWAQSAIwidU1tqW/58DGP796xZQhnA3DisUgDAABAIZAAAAAKb7EDgCOwqhzAiUUgAcD7sKoc4534h/cnkADgfVhVjvHq4Nu/Ev9wBAIJAI7AqnKMNz1db4t/OAKBBABwAhL/8P6sYgcAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgs8w0wynR0dKRSqfR7v66urtTX1/d7vxkzZmTmzJn93g8AxiOBBDCKdHR05Iwzz8r+zn3937mmNqn29Hu3hsbJ2fzTTSIJACKQAEaVSqWS/Z37BnyF+/7u171jS3Z894FUKhWBBAARSACj0kCvcN/f/QCAvizSAAAAUAgkAACAQiABAAAUPoM0TAa6bG9iCV4AABguAmkYHNeyvbEELwAADBeBNAwGumxvYgleAAAYTgJpGFl+FwAARjeLNAAAABQCCQAAoBBIAAAAhUACAAAoBBIAAEAhkAAAAArLfAMAMGpt2rSp3/vMmDHD9SMZMIEEAMCoc3DvzqSmJtddd12/921onJzNP90kkhgQgQTAiOjvs8IDeRYZGLt6uvYm1Wqa5i9OXVPbMe/XvWNLdnz3gVQqFYHEgAgkAIbV8TwrDJx46praUt/y4ZGeBicQgQQwRDo6OlKpVPq1z4nwKslAnxXufHV9dj3/1BDODAAEEsCQ6OjoyBlnnpX9nftGeiqjVn+fFe7esWUIZwMAvyaQAIZApVLJ/s59XiUBgDFGIAEMIa+SAMDY4kKxAAAAhUACAAAoBBIAAEAhkAAAAAqBBAAAUFjFDoB+X6D2RLigLQAnJoEEcAI7uHdnUlOT6667bqSnAgCjgkACOIH1dO1NqlUXtAWAQiABx62joyOVSqXf+82YMSMzZ84cghnRXy5oCwC/JpCA49LR0ZEzzjwr+zv39XvfhsbJ2fzTTSIJABg1BBJwXCqVSvZ37uv3W7S6d2zJju8+kEqlIpAAGHQWn2GgBBIwKPr7Fi0AGAoWn+F4CSQAAMYNi89wvAQSAADjjsVnGKjakZ4AAADAaCGQAAAACoEEAABQ+AwS0IdlUQGAE5lAApKM3LKoAwmsGTNmuHYSADAkBBKQZPiXRT2eIGtonJzNP90kkgA4YXV0dKRSqfR7P08yHp1AAvoYrmVRBxpk3Tu2ZMd3H0ilUvEDHoATUkdHR84486zs79zX7309yXh0AgkYUf0NspHis1kAjBaVSiX7O/d5knGICCSAIxipz2YBwNGMlScZxxqBBHAEw/3ZLABOLAP5LJF3KQwtgQSMScP9lrfh+mwWACeO4/ksEUNHIMExslrM6OAtbwCMFwP9LJF3KQwtgTSO+Qv94LFazOjhLW8AjDfepTC6CKRxyl/oB9fxrhbz/PPP56yzzurXfQrVI/PLBAAYCgJpnLL849Do71/KXQwVAE4cLgkxPoz6QPr617+ev/qrv8rWrVtz9tlnZ+nSpfmDP/iDkZ7WmGH5x8MN52oxLoYKAOOfz8eOL6M6kL797W9n4cKF+frXv55LLrkkjzzySK688sr85Cc/8ZdGkvQ/XLZu3Zr/5//9TLr2dw7RjN6fUAWA8cvnY8eXUR1IDz74YG688cb8m3/zb5IkS5cuzfe///08/PDDuffee/uM7erqSldXV+/tXbt2JUl++ctfpru7e/gm/T52796dhoaG1Ox4LdWerqPv8C41O3+RhoaGbNiwIbt37z7m/X72s58N6D4Hen9JUltbm56enn7tc7R59kxM9u1rS8/WLam+867tW/85DY2NvedGf9Qk+Y1Lrs2EKU3HvE/3Wz/P25ue7/fxrN2zdVgfh4E+7scz1/G03/udb6NxnmN5v7E01+HY793n3Gie51jcbyzNdbj2O3S+1e7ZNqrnOdb2e/e+k/JO6vqx78HanmGd63D//aJ271vZt+83snv37uzYseOY9xsKe/bsSZJUq9Wjjq2pHsuoEXDgwIFMnjw5//2///f8q3/1r3q3f+lLX8pLL72UNWvW9Bnf3t6eu+++e7inCQAAjBFbtmzJaaeddsQxo/YVpEqlkoMHD6a5ubnP9ubm5mzbtu2w8XfeeWcWLVrUe7unpye//OUv09TUlJqamiGfL0Nj9+7daWtry5YtWzJ16tSRng7jnPON4eacYzg53xhuo+mcq1ar2bNnT1pbW486dtQG0iHvjZtqtfq+wVNfX5/6+vo+2z70oQ8N5dQYRlOnTh3x/7E4cTjfGG7OOYaT843hNlrOuWnTph3TuNohnseAzZgxIxMmTDjs1aLt27cf9qoSAADAYBi1gTRp0qScf/75Wb16dZ/tq1evzsUXXzxCswIAAMazUf0Wu0WLFuX666/PBRdckIsuuiiPPvpoOjo68m//7b8d6akxTOrr6/Nf/st/OeztkzAUnG8MN+ccw8n5xnAbq+fcqF3F7pCvf/3ruf/++7N169acc845eeihhzJnzpyRnhYAADAOjfpAAgAAGC6j9jNIAAAAw00gAQAAFAIJAACgEEgAAACFQGJUWrt2ba666qq0trampqYmf/u3fzvSU2Icu/fee/Pxj388U6ZMySmnnJJPf/rT2bx580hPi3Hq4Ycfzkc/+tHeK8tfdNFF+d73vjfS0+IEcu+996ampiYLFy4c6akwTrW3t6empqbPPy0tLSM9rWMmkBiV3n777Zx33nlZvnz5SE+FE8CaNWtyyy235Ic//GFWr16dd955J/Pmzcvbb7890lNjHDrttNNy3333Zf369Vm/fn3+6I/+KJ/61KfyyiuvjPTUOAG8+OKLefTRR/PRj350pKfCOHf22Wdn69atvf/8+Mc/HukpHbNRfaFYTlxXXnllrrzyypGeBieIZ599ts/txx9/PKeccko2bNjgumsMuquuuqrP7a9+9at5+OGH88Mf/jBnn332CM2KE8HevXvzuc99Lt/4xjdyzz33jPR0GOcmTpw4pl41ejevIAG8x65du5Ik06dPH+GZMN4dPHgwK1euzNtvv52LLrpopKfDOHfLLbfkk5/8ZC6//PKRngongJ/97GdpbW3NrFmz8id/8id59dVXR3pKx8wrSADvUq1Ws2jRovzLf/kvc84554z0dBinfvzjH+eiiy7K/v378y/+xb/IM888k9/93d8d6Wkxjq1cuTIbNmzI+vXrR3oqnAAuvPDCfPOb38xHPvKRvPXWW7nnnnty8cUX55VXXklTU9NIT++oBBLAu3zxi1/MP/3TP+WFF14Y6akwjp1xxhl56aWX8qtf/Sp/8zd/kxtuuCFr1qwRSQyJLVu25Etf+lJWrVqVhoaGkZ4OJ4B3f0zi3HPPzUUXXZTf+Z3fyZNPPplFixaN4MyOjUACKG699dZ85zvfydq1a3PaaaeN9HQYxyZNmpQPf/jDSZILLrggL774Yv7rf/2veeSRR0Z4ZoxHGzZsyPbt23P++ef3bjt48GDWrl2b5cuXp6urKxMmTBjBGTLenXTSSTn33HPzs5/9bKSnckwEEnDCq1arufXWW/PMM8/kBz/4QWbNmjXSU+IEU61W09XVNdLTYJy67LLLDltB7POf/3zOPPPM3HHHHeKIIdfV1ZVNmzblD/7gD0Z6KsdEIDEq7d27Nz//+c97b7/22mt56aWXMn369MycOXMEZ8Z4dMstt2TFihX5u7/7u0yZMiXbtm1LkkybNi2NjY0jPDvGm//4H/9jrrzyyrS1tWXPnj1ZuXJlfvCDHxy2miIMlilTphz2mcqTTjopTU1NPmvJkLjtttty1VVXZebMmdm+fXvuueee7N69OzfccMNIT+2YCCRGpfXr1+cP//APe28fer/qDTfckCeeeGKEZsV49fDDDydJLr300j7bH3/88fzZn/3Z8E+Ice2tt97K9ddfn61bt2batGn56Ec/mmeffTZz584d6akBDIo33ngjn/3sZ1OpVPIbv/Eb+f3f//388Ic/zG/91m+N9NSOSU21Wq2O9CQAAABGA9dBAgAAKAQSAABAIZAAAAAKgQQAAFAIJAAAgEIgAQAAFAIJAACgEEgAAACFQAIAACgEEgAAQCGQAAAAiv8fhlGs8VWx+GAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.rcParams['patch.force_edgecolor'] = True\n",
    "ratings_mean_count['rating'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1d2157d3280>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.rcParams['patch.force_edgecolor'] = True\n",
    "sns.jointplot(x='rating', y='rating_counts', data=ratings_mean_count, alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting values according to the 'rating column'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>title</th>\n",
       "      <th>\"Great Performances\" Cats (1998)</th>\n",
       "      <th>$9.99 (2008)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Neath the Arizona Skies (1934)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>...</th>\n",
       "      <th>Zulu (1964)</th>\n",
       "      <th>Zulu (2013)</th>\n",
       "      <th>[REC] (2007)</th>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <th>loudQUIETloud: A Film About the Pixies (2006)</th>\n",
       "      <th>xXx (2002)</th>\n",
       "      <th>xXx: State of the Union (2005)</th>\n",
       "      <th>¡Three Amigos! (1986)</th>\n",
       "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
       "      <th>İtirazım Var (2014)</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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8860 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   \"Great Performances\" Cats (1998)  $9.99 (2008)  \\\n",
       "userId                                                   \n",
       "1                                    NaN           NaN   \n",
       "2                                    NaN           NaN   \n",
       "3                                    NaN           NaN   \n",
       "4                                    NaN           NaN   \n",
       "5                                    NaN           NaN   \n",
       "\n",
       "title   'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                            \n",
       "1                                           NaN   \n",
       "2                                           NaN   \n",
       "3                                           NaN   \n",
       "4                                           NaN   \n",
       "5                                           NaN   \n",
       "\n",
       "title   'Neath the Arizona Skies (1934)  'Round Midnight (1986)  \\\n",
       "userId                                                            \n",
       "1                                   NaN                     NaN   \n",
       "2                                   NaN                     NaN   \n",
       "3                                   NaN                     NaN   \n",
       "4                                   NaN                     NaN   \n",
       "5                                   NaN                     NaN   \n",
       "\n",
       "title   'Salem's Lot (2004)  'Til There Was You (1997)  'burbs, The (1989)  \\\n",
       "userId                                                                       \n",
       "1                       NaN                        NaN                 NaN   \n",
       "2                       NaN                        NaN                 NaN   \n",
       "3                       NaN                        NaN                 NaN   \n",
       "4                       NaN                        NaN                 NaN   \n",
       "5                       NaN                        NaN                 NaN   \n",
       "\n",
       "title   'night Mother (1986)  (500) Days of Summer (2009)  ...  Zulu (1964)  \\\n",
       "userId                                                     ...                \n",
       "1                        NaN                          NaN  ...          NaN   \n",
       "2                        NaN                          NaN  ...          NaN   \n",
       "3                        NaN                          NaN  ...          NaN   \n",
       "4                        NaN                          NaN  ...          NaN   \n",
       "5                        NaN                          NaN  ...          NaN   \n",
       "\n",
       "title   Zulu (2013)  [REC] (2007)  eXistenZ (1999)  \\\n",
       "userId                                               \n",
       "1               NaN           NaN              NaN   \n",
       "2               NaN           NaN              NaN   \n",
       "3               NaN           NaN              NaN   \n",
       "4               NaN           NaN              NaN   \n",
       "5               NaN           NaN              NaN   \n",
       "\n",
       "title   loudQUIETloud: A Film About the Pixies (2006)  xXx (2002)  \\\n",
       "userId                                                              \n",
       "1                                                 NaN         NaN   \n",
       "2                                                 NaN         NaN   \n",
       "3                                                 NaN         NaN   \n",
       "4                                                 NaN         NaN   \n",
       "5                                                 NaN         NaN   \n",
       "\n",
       "title   xXx: State of the Union (2005)  ¡Three Amigos! (1986)  \\\n",
       "userId                                                          \n",
       "1                                  NaN                    NaN   \n",
       "2                                  NaN                    NaN   \n",
       "3                                  NaN                    NaN   \n",
       "4                                  NaN                    NaN   \n",
       "5                                  NaN                    NaN   \n",
       "\n",
       "title   À nous la liberté (Freedom for Us) (1931)  İtirazım Var (2014)  \n",
       "userId                                                                  \n",
       "1                                             NaN                  NaN  \n",
       "2                                             NaN                  NaN  \n",
       "3                                             NaN                  NaN  \n",
       "4                                             NaN                  NaN  \n",
       "5                                             NaN                  NaN  \n",
       "\n",
       "[5 rows x 8860 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_rating = movie_data.pivot_table(index='userId', columns='title', values='rating')\n",
    "user_movie_rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysing correlation with similar movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId\n",
       "1    NaN\n",
       "2    3.0\n",
       "3    5.0\n",
       "4    5.0\n",
       "5    4.0\n",
       "Name: Forrest Gump (1994), dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forrest_gump_ratings = user_movie_rating['Forrest Gump (1994)']\n",
    "\n",
    "forrest_gump_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar movies like Forrest Gump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\lib\\site-packages\\numpy\\lib\\function_base.py:2846: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar, dtype=dtype)\n",
      "d:\\Anaconda\\lib\\site-packages\\numpy\\lib\\function_base.py:2705: RuntimeWarning: divide by zero encountered in divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    },
    {
     "data": {
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       "      <th>title</th>\n",
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       "      <th>$9.99 (2008)</th>\n",
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       "      <th>'burbs, The (1989)</th>\n",
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       "      <th>(500) Days of Summer (2009)</th>\n",
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       "      <th>...And Justice for All (1979)</th>\n",
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      ],
      "text/plain": [
       "                                Correlation\n",
       "title                                      \n",
       "$9.99 (2008)                       1.000000\n",
       "'burbs, The (1989)                 0.044946\n",
       "(500) Days of Summer (2009)        0.624458\n",
       "*batteries not included (1987)     0.603023\n",
       "...And Justice for All (1979)      0.173422"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_similar_to_forrest_gump = user_movie_rating.corrwith(forrest_gump_ratings) \n",
    "\n",
    "corr_forrest_gump = pd.DataFrame(movies_similar_to_forrest_gump , columns=['Correlation'])\n",
    "corr_forrest_gump.dropna(inplace=True)\n",
    "corr_forrest_gump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>$9.99 (2008)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matador, The (2005)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
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       "      <th>Song of the Little Road (Pather Panchali) (1955)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Song of the Sea (2014)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
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       "      <th>Song of the Thin Man (1947)</th>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "                                                  Correlation\n",
       "title                                                        \n",
       "$9.99 (2008)                                              1.0\n",
       "Matador, The (2005)                                       1.0\n",
       "Song of the Little Road (Pather Panchali) (1955)          1.0\n",
       "Song of the Sea (2014)                                    1.0\n",
       "Song of the Thin Man (1947)                               1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forrest_gump.sort_values('Correlation', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Correlation</th>\n",
       "      <th>rating_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>$9.99 (2008)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>0.044946</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <td>0.624458</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <td>0.603023</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>0.173422</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Correlation  rating_counts\n",
       "title                                                     \n",
       "$9.99 (2008)                       1.000000              3\n",
       "'burbs, The (1989)                 0.044946             19\n",
       "(500) Days of Summer (2009)        0.624458             45\n",
       "*batteries not included (1987)     0.603023              7\n",
       "...And Justice for All (1979)      0.173422             13"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forrest_gump = corr_forrest_gump.join(ratings_mean_count['rating_counts'])\n",
    "corr_forrest_gump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>title</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Forrest Gump (1994)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>My Big Fat Greek Wedding (2002)</th>\n",
       "      <td>0.626240</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beautiful Mind, A (2001)</th>\n",
       "      <td>0.575922</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Few Good Men, A (1992)</th>\n",
       "      <td>0.555206</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Million Dollar Baby (2004)</th>\n",
       "      <td>0.545638</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Correlation  rating_counts\n",
       "title                                                      \n",
       "Forrest Gump (1994)                 1.000000            341\n",
       "My Big Fat Greek Wedding (2002)     0.626240             51\n",
       "Beautiful Mind, A (2001)            0.575922            114\n",
       "Few Good Men, A (1992)              0.555206             76\n",
       "Million Dollar Baby (2004)          0.545638             65"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forrest_gump[corr_forrest_gump ['rating_counts']>50].sort_values('Correlation', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONCLUSION:\n",
    "\n",
    "In conclusion, the collaborative filtering-based movie recommendation system developed in Python, inspired by the timeless classic 'Forrest Gump' has successfully demonstrated the effectiveness of leveraging user-item interactions to provide personalized and accurate movie suggestions. By employing machine learning techniques, the system learned from user preferences and effectively predicted movie ratings, offering a tailored cinematic experience. The inclusion of 'Forrest Gump' in the dataset allowed the model to recommend similar movies that resonate with the heartwarming and iconic elements of this classic, showcasing the versatility and adaptability of collaborative filtering. This project not only provided valuable insights into collaborative filtering methodologies but also presented a practical implementation of machine learning for personalized content recommendations, enhancing the overall user experience in the realm of entertainment."
   ]
  }
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