netflix-movies-recommendation-1.ipynb

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    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/netflix-prize-data/qualifying.txt\n",
      "/kaggle/input/netflix-prize-data/movie_titles.csv\n",
      "/kaggle/input/netflix-prize-data/combined_data_4.txt\n",
      "/kaggle/input/netflix-prize-data/combined_data_2.txt\n",
      "/kaggle/input/netflix-prize-data/README\n",
      "/kaggle/input/netflix-prize-data/combined_data_1.txt\n",
      "/kaggle/input/netflix-prize-data/combined_data_3.txt\n",
      "/kaggle/input/netflix-prize-data/probe.txt\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1488844</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>822109</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>885013</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30878</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id  Rating\n",
       "0       1:     NaN\n",
       "1  1488844     3.0\n",
       "2   822109     5.0\n",
       "3   885013     4.0\n",
       "4    30878     4.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading dataset file\n",
    "dataset = pd.read_csv('/kaggle/input/netflix-prize-data/combined_data_1.txt',header = None, names = ['Cust_Id', 'Rating'], usecols = [0,1])\n",
    "\n",
    "# Convert Ratings column to a float\n",
    "dataset['Rating'] = dataset['Rating'].astype(float)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cust_Id     object\n",
       "Rating     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To print the datatype of columns\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24058263, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To inspect the shape of the datset\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1488844</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>822109</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>885013</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30878</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id  Rating\n",
       "0       1:     NaN\n",
       "1  1488844     3.0\n",
       "2   822109     5.0\n",
       "3   885013     4.0\n",
       "4    30878     4.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To print the head of dataset\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1118186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2439073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>6904181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>8085741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>5506583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count\n",
       "Rating         \n",
       "1.0     1118186\n",
       "2.0     2439073\n",
       "3.0     6904181\n",
       "4.0     8085741\n",
       "5.0     5506583"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To find the distribution of different ratings in the datset\n",
    "d = dataset.groupby('Rating')['Rating'].agg(['count'])\n",
    "d =pd.DataFrame(d)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4499"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_count = dataset['Rating'].isnull().sum()\n",
    "movie_count"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "## Observation\n",
    "-  The number of movies is 4499"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "470758"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get customer count\n",
    "cust_count = dataset['Cust_Id'].nunique()-movie_count\n",
    "\n",
    "cust_count"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "- The number of customers is 470758"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24053764"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get rating count\n",
    "\n",
    "rating_count = dataset['Cust_Id'].count() - movie_count\n",
    "rating_count"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "- The total number of ratings is 24053764"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "## To plot the distribution of the ratings in as a bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ax = d.plot(kind = 'barh', legend = False, figsize = (15,10))\n",
    "plt.title(f'Total pool: {movie_count} Movies, {cust_count} customers, {rating_count} ratings given', fontsize=20)\n",
    "plt.axis('On')\n",
    "\n",
    "for i in range(1,6):\n",
    "    ax.text(d.iloc[i-1][0]/4, i-1,'Rating {}: {:.0f}%'.format(i, d.iloc[i-1][0]*100 / d.sum()[0]), color = 'white', weight = 'bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1488844</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>822109</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>885013</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30878</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id  Rating\n",
       "0       1:     NaN\n",
       "1  1488844     3.0\n",
       "2   822109     5.0\n",
       "3   885013     4.0\n",
       "4    30878     4.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating\n",
       "0    True\n",
       "1   False\n",
       "2   False\n",
       "3   False\n",
       "4   False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To count all the 'nan' values in the Ratings column in the 'ratings' dataset\n",
    "df_nan = pd.DataFrame(pd.isnull(dataset.Rating),)\n",
    "\n",
    "df_nan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2707</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24046714</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24047329</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24056849</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24057564</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24057834</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4499 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Rating\n",
       "0           True\n",
       "548         True\n",
       "694         True\n",
       "2707        True\n",
       "2850        True\n",
       "...          ...\n",
       "24046714    True\n",
       "24047329    True\n",
       "24056849    True\n",
       "24057564    True\n",
       "24057834    True\n",
       "\n",
       "[4499 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.isnull(dataset['Rating'])\n",
    "df1 = pd.DataFrame(df)\n",
    "df2 = df1[df1['Rating']==True]\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "## Now we know all that where does the movies counting start from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.reset_index()\n",
    "df_nan = df2.copy()"
   ]
  },
  {
   "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",
       "    }\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>index</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>548</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>694</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2707</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2850</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494</th>\n",
       "      <td>24046714</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4495</th>\n",
       "      <td>24047329</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4496</th>\n",
       "      <td>24056849</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4497</th>\n",
       "      <td>24057564</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>24057834</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4499 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  Rating\n",
       "0            0    True\n",
       "1          548    True\n",
       "2          694    True\n",
       "3         2707    True\n",
       "4         2850    True\n",
       "...        ...     ...\n",
       "4494  24046714    True\n",
       "4495  24047329    True\n",
       "4496  24056849    True\n",
       "4497  24057564    True\n",
       "4498  24057834    True\n",
       "\n",
       "[4499 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To create a numpy array containing movie ids according the 'ratings' dataset\n",
    "\n",
    "movie_np = []\n",
    "movie_id = 1\n",
    "\n",
    "for i,j in zip(df_nan['index'][1:],df_nan['index'][:-1]):\n",
    "    # numpy approach\n",
    "    temp = np.full((1,i-j-1), movie_id)\n",
    "    movie_np = np.append(movie_np, temp)\n",
    "    movie_id += 1\n",
    "\n",
    "# Account for last record and corresponding length\n",
    "# numpy approach\n",
    "last_record = np.full((1,len(dataset) - df_nan.iloc[-1, 0] - 1),movie_id)\n",
    "movie_np = np.append(movie_np, last_record)\n",
    "\n",
    "#print(f'Movie numpy: {movie_np}')\n",
    "#print(f'Length: {len(movie_np)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-Dataset examples-\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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Movie_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1488844</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>822109</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>885013</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30878</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>823519</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id  Rating  Movie_Id\n",
       "1  1488844     3.0         1\n",
       "2   822109     5.0         1\n",
       "3   885013     4.0         1\n",
       "4    30878     4.0         1\n",
       "5   823519     3.0         1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To append the above created array to the datset after removing the 'nan' rows\n",
    "dataset = dataset[pd.notnull(dataset['Rating'])]\n",
    "\n",
    "dataset['Movie_Id'] = movie_np.astype(int)\n",
    "dataset['Cust_Id'] =dataset['Cust_Id'].astype(int)\n",
    "print('-Dataset examples-')\n",
    "dataset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = ['count','mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To create a list of all the movies rated less often(only include top 30% rated movies)\n",
    "dataset_movie_summary = dataset.groupby('Movie_Id')['Rating'].agg(f)\n",
    "\n",
    "dataset_movie_summary.index = dataset_movie_summary.index.map(int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie minimum times of review: 1799.0\n"
     ]
    }
   ],
   "source": [
    "movie_benchmark = round(dataset_movie_summary['count'].quantile(0.7),0)\n",
    "\n",
    "drop_movie_list = dataset_movie_summary[dataset_movie_summary['count'] < movie_benchmark].index\n",
    "\n",
    "print('Movie minimum times of review: {}'.format(movie_benchmark))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer minimum times of review: 52.0\n"
     ]
    }
   ],
   "source": [
    "#To create a list of all the inactive users(users who rate less often)\n",
    "dataset_cust_summary = dataset.groupby('Cust_Id')['Rating'].agg(f)\n",
    "dataset_cust_summary.index = dataset_cust_summary.index.map(int)\n",
    "cust_benchmark = round(dataset_cust_summary['count'].quantile(0.7),0)\n",
    "drop_cust_list = dataset_cust_summary[dataset_cust_summary['count'] < cust_benchmark].index\n",
    "\n",
    "print(f'Customer minimum times of review: {cust_benchmark}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shape: (24053764, 3)\n"
     ]
    }
   ],
   "source": [
    "print(f'Original Shape: {dataset.shape}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Trim Shape: (17337458, 3)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset[~dataset['Movie_Id'].isin(drop_movie_list)]\n",
    "dataset = dataset[~dataset['Cust_Id'].isin(drop_cust_list)]\n",
    "print('After Trim Shape: {}'.format(dataset.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-Data Examples-\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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Movie_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>712664</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>1331154</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2632461</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>44937</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>656399</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cust_Id  Rating  Movie_Id\n",
       "696   712664     5.0         3\n",
       "697  1331154     4.0         3\n",
       "698  2632461     3.0         3\n",
       "699    44937     5.0         3\n",
       "700   656399     4.0         3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('-Data Examples-')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Create ratings matrix for 'ratings' matrix with Rows = userId, Columns = movieId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(143458, 1350)\n"
     ]
    }
   ],
   "source": [
    "df_p = pd.pivot_table(dataset,values='Rating',index='Cust_Id',columns='Movie_Id')\n",
    "\n",
    "print(df_p.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Year                          Name\n",
      "Movie_Id                                      \n",
      "1         2003.0               Dinosaur Planet\n",
      "2         2004.0    Isle of Man TT 2004 Review\n",
      "3         1997.0                     Character\n",
      "4         1994.0  Paula Abdul's Get Up & Dance\n",
      "5         2004.0      The Rise and Fall of ECW\n",
      "6         1997.0                          Sick\n",
      "7         1992.0                         8 Man\n",
      "8         2004.0    What the #$*! Do We Know!?\n",
      "9         1991.0      Class of Nuke 'Em High 2\n",
      "10        2001.0                       Fighter\n"
     ]
    }
   ],
   "source": [
    "df_title = pd.read_csv('/kaggle/input/netflix-prize-data/movie_titles.csv', encoding = \"ISO-8859-1\", header = None, names = ['Movie_Id', 'Year', 'Name'])\n",
    "\n",
    "df_title.set_index('Movie_Id', inplace = True)\n",
    "\n",
    "print (df_title.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# To install the scikit-surprise library for implementing SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import math\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from surprise import Reader, Dataset, SVD\n",
    "from surprise.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, MAE of algorithm SVD on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.9968  0.9996  0.9918  0.9961  0.0032  \n",
      "MAE (testset)     0.8045  0.8020  0.7934  0.8000  0.0048  \n",
      "Fit time          7.97    7.80    7.61    7.79    0.14    \n",
      "Test time         0.47    0.38    0.34    0.40    0.05    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([0.99678908, 0.99956371, 0.99184484]),\n",
       " 'test_mae': array([0.80453928, 0.80201162, 0.79337857]),\n",
       " 'fit_time': (7.968931674957275, 7.7993550300598145, 7.613985776901245),\n",
       " 'test_time': (0.4720573425292969, 0.3779473304748535, 0.3442709445953369)}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Reader library\n",
    "reader = Reader()\n",
    "\n",
    "# get just top 100K rows for faster run time\n",
    "data = Dataset.load_from_df(dataset[['Cust_Id', 'Movie_Id', 'Rating']][:100000], reader)\n",
    "\n",
    "# Use the SVD algorithm.\n",
    "svd = SVD()\n",
    "\n",
    "# Compute the RMSE of the SVD algorithm\n",
    "cross_validate(svd, data, measures=['RMSE', 'MAE'], cv=3, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Movie_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>712664</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>1331154</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2632461</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>44937</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>656399</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cust_Id  Rating  Movie_Id\n",
       "696   712664     5.0         3\n",
       "697  1331154     4.0         3\n",
       "698  2632461     3.0         3\n",
       "699    44937     5.0         3\n",
       "700   656399     4.0         3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movie_Id\n",
       "3                              Character\n",
       "79                           The Killing\n",
       "175                       Reservoir Dogs\n",
       "199                      The Deer Hunter\n",
       "241                   North by Northwest\n",
       "256    Ghost Dog: The Way of the Samurai\n",
       "348        The Last Temptation of Christ\n",
       "357                House of Sand and Fog\n",
       "416                             Elephant\n",
       "442                  Mississippi Burning\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_712664 = dataset[(dataset['Cust_Id'] == 712664) & (dataset['Rating'] == 5)]\n",
    "dataset_712664 = dataset_712664.set_index('Movie_Id')\n",
    "dataset_712664 = dataset_712664.join(df_title)['Name']\n",
    "dataset_712664.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Train an SVD to predict ratings for user with userId = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a shallow copy for the movies dataset\n",
    "user_712664 = df_title.copy()\n",
    "\n",
    "user_712664 = user_712664.reset_index()\n",
    "\n",
    "#To remove all the movies rated less often \n",
    "user_712664 = user_712664[~user_712664['Movie_Id'].isin(drop_movie_list)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting full dataset\n",
    "data = Dataset.load_from_df(dataset[['Cust_Id', 'Movie_Id', 'Rating']], reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a training set for svd\n",
    "trainset = data.build_full_trainset()\n",
    "svd.fit(trainset)\n",
    "\n",
    "#Predict the ratings for user_712664\n",
    "user_712664['Estimate_Score'] = user_712664['Movie_Id'].apply(lambda x: svd.predict(712664, x).est)\n",
    "\n",
    "#Drop extra columns from the user_712664 data frame\n",
    "user_712664 = user_712664.drop('Movie_Id', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Year                                         Name  Estimate_Score\n",
      "174   1992.0                               Reservoir Dogs        5.000000\n",
      "560   1963.0                                 High and Low        5.000000\n",
      "3289  1974.0                                The Godfather        5.000000\n",
      "2447  1991.0                              Prime Suspect 1        5.000000\n",
      "1691  1989.0                                Lonesome Dove        5.000000\n",
      "871   1954.0                                Seven Samurai        5.000000\n",
      "721   2003.0                           The Wire: Season 1        5.000000\n",
      "3661  1994.0  House of Cards Trilogy II: To Play the King        5.000000\n",
      "4352  2002.0               Curb Your Enthusiasm: Season 3        5.000000\n",
      "2777  1974.0  Pink Floyd: Live at Pompeii: Director's Cut        4.972564\n"
     ]
    }
   ],
   "source": [
    "# Sort predicted ratings for user_712664 in descending order\n",
    "user_712664 = user_712664.sort_values('Estimate_Score', ascending=False)\n",
    "\n",
    "#Print top 10 recommendations\n",
    "print(user_712664.head(10))"
   ]
  }
 ],
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