FilmRecommender.ipynb

{
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   "execution_count": 1,
   "id": "08e2beb2-45d5-4661-96a5-26decabcc822",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d40c4dc-5654-430c-a983-068075fdb4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['user_id', 'item_id', 'rating', 'timestamp']\n",
    "df = pd.read_csv('u.data', sep='\\t', names=column_names)"
   ]
  },
  {
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   "execution_count": 3,
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       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
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      "text/plain": [
       "   user_id  item_id  rating  timestamp\n",
       "0        0       50       5  881250949\n",
       "1        0      172       5  881250949\n",
       "2        0      133       1  881250949\n",
       "3      196      242       3  881250949\n",
       "4      186      302       3  891717742"
      ]
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "e9199589-97a3-4eb5-86a1-006be2c91a0e",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>item_id</th>\n",
       "      <th>title</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   item_id              title\n",
       "0        1   Toy Story (1995)\n",
       "1        2   GoldenEye (1995)\n",
       "2        3  Four Rooms (1995)\n",
       "3        4  Get Shorty (1995)\n",
       "4        5     Copycat (1995)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_titles = pd.read_csv(\"Movie_Id_Titles\")\n",
    "movie_titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "254e7e13-742c-4a58-892d-6aaef9b83e46",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
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       "      <td>Star Wars (1977)</td>\n",
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      "text/plain": [
       "   user_id  item_id  rating  timestamp             title\n",
       "0        0       50       5  881250949  Star Wars (1977)\n",
       "1      290       50       5  880473582  Star Wars (1977)\n",
       "2       79       50       4  891271545  Star Wars (1977)\n",
       "3        2       50       5  888552084  Star Wars (1977)\n",
       "4        8       50       5  879362124  Star Wars (1977)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df,movie_titles,on='item_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "940577f8-4044-4cd1-a15d-62c9af513a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "797a7994-be4b-4bdd-82b8-0c8851818a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "They Made Me a Criminal (1939)                5.0\n",
       "Marlene Dietrich: Shadow and Light (1996)     5.0\n",
       "Saint of Fort Washington, The (1993)          5.0\n",
       "Someone Else's America (1995)                 5.0\n",
       "Star Kid (1997)                               5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c2d0e6d-5209-4344-80af-65e972e392ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Star Wars (1977)             584\n",
       "Contact (1997)               509\n",
       "Fargo (1996)                 508\n",
       "Return of the Jedi (1983)    507\n",
       "Liar Liar (1997)             485\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f272579-9235-4bef-b517-bec63381bd36",
   "metadata": {},
   "outputs": [
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       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
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       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
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       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
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       "      <th>12 Angry Men (1957)</th>\n",
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      "text/plain": [
       "                             rating\n",
       "title                              \n",
       "'Til There Was You (1997)  2.333333\n",
       "1-900 (1994)               2.600000\n",
       "101 Dalmatians (1996)      2.908257\n",
       "12 Angry Men (1957)        4.344000\n",
       "187 (1997)                 3.024390"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.DataFrame(df.groupby('title')['rating'].mean())\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b26209d-dd66-4ffa-a647-3dfd2a565412",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
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       "      <th>'Til There Was You (1997)</th>\n",
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       "      <td>9</td>\n",
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       "      <th>101 Dalmatians (1996)</th>\n",
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       "      <td>109</td>\n",
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       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
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      "text/plain": [
       "                             rating  num of ratings\n",
       "title                                              \n",
       "'Til There Was You (1997)  2.333333               9\n",
       "1-900 (1994)               2.600000               5\n",
       "101 Dalmatians (1996)      2.908257             109\n",
       "12 Angry Men (1957)        4.344000             125\n",
       "187 (1997)                 3.024390              41"
      ]
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "ratings['num of ratings'] = pd.DataFrame(df.groupby('title')['rating'].count())\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d04328fd-d111-4318-a96d-da8a322ae30e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Assuming 'ratings' is your DataFrame with the 'num of ratings' column\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.hist(ratings['num of ratings'], bins=70, color='skyblue', edgecolor='black')  # Adjust color and edgecolor as needed\n",
    "\n",
    "# Add labels\n",
    "plt.xlabel('Number of Ratings')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Optionally, add a title\n",
    "plt.title('Distribution of Number of Ratings')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db8f4325-11cb-4618-9ae9-a1a8ba1473b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.hist(ratings['rating'], bins=70, color='skyblue', edgecolor='black')  # Adjust color and edgecolor as needed\n",
    "\n",
    "# Add labels\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Optionally, add a title\n",
    "plt.title('Distribution of Ratings')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3f9bd368-314b-4f44-a5fe-1b2b10d8b559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f991bd4f0d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='rating',y='num of ratings',data=ratings,alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c0dc2a1-0acf-469a-b063-344a45a26fb6",
   "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>title</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <th>187 (1997)</th>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
       "      <th>2001: A Space Odyssey (1968)</th>\n",
       "      <th>3 Ninjas: High Noon At Mega Mountain (1998)</th>\n",
       "      <th>39 Steps, The (1935)</th>\n",
       "      <th>...</th>\n",
       "      <th>Yankee Zulu (1994)</th>\n",
       "      <th>Year of the Horse (1997)</th>\n",
       "      <th>You So Crazy (1994)</th>\n",
       "      <th>Young Frankenstein (1974)</th>\n",
       "      <th>Young Guns (1988)</th>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <th>Young Poisoner's Handbook, The (1995)</th>\n",
       "      <th>Zeus and Roxanne (1997)</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Á köldum klaka (Cold Fever) (1994)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</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>0</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>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</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>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</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>1.0</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>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1664 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title    'Til There Was You (1997)  1-900 (1994)  101 Dalmatians (1996)  \\\n",
       "user_id                                                                   \n",
       "0                              NaN           NaN                    NaN   \n",
       "1                              NaN           NaN                    2.0   \n",
       "2                              NaN           NaN                    NaN   \n",
       "3                              NaN           NaN                    NaN   \n",
       "4                              NaN           NaN                    NaN   \n",
       "\n",
       "title    12 Angry Men (1957)  187 (1997)  2 Days in the Valley (1996)  \\\n",
       "user_id                                                                 \n",
       "0                        NaN         NaN                          NaN   \n",
       "1                        5.0         NaN                          NaN   \n",
       "2                        NaN         NaN                          NaN   \n",
       "3                        NaN         2.0                          NaN   \n",
       "4                        NaN         NaN                          NaN   \n",
       "\n",
       "title    20,000 Leagues Under the Sea (1954)  2001: A Space Odyssey (1968)  \\\n",
       "user_id                                                                      \n",
       "0                                        NaN                           NaN   \n",
       "1                                        3.0                           4.0   \n",
       "2                                        NaN                           NaN   \n",
       "3                                        NaN                           NaN   \n",
       "4                                        NaN                           NaN   \n",
       "\n",
       "title    3 Ninjas: High Noon At Mega Mountain (1998)  39 Steps, The (1935)  \\\n",
       "user_id                                                                      \n",
       "0                                                NaN                   NaN   \n",
       "1                                                NaN                   NaN   \n",
       "2                                                1.0                   NaN   \n",
       "3                                                NaN                   NaN   \n",
       "4                                                NaN                   NaN   \n",
       "\n",
       "title    ...  Yankee Zulu (1994)  Year of the Horse (1997)  \\\n",
       "user_id  ...                                                 \n",
       "0        ...                 NaN                       NaN   \n",
       "1        ...                 NaN                       NaN   \n",
       "2        ...                 NaN                       NaN   \n",
       "3        ...                 NaN                       NaN   \n",
       "4        ...                 NaN                       NaN   \n",
       "\n",
       "title    You So Crazy (1994)  Young Frankenstein (1974)  Young Guns (1988)  \\\n",
       "user_id                                                                      \n",
       "0                        NaN                        NaN                NaN   \n",
       "1                        NaN                        5.0                3.0   \n",
       "2                        NaN                        NaN                NaN   \n",
       "3                        NaN                        NaN                NaN   \n",
       "4                        NaN                        NaN                NaN   \n",
       "\n",
       "title    Young Guns II (1990)  Young Poisoner's Handbook, The (1995)  \\\n",
       "user_id                                                                \n",
       "0                         NaN                                    NaN   \n",
       "1                         NaN                                    NaN   \n",
       "2                         NaN                                    NaN   \n",
       "3                         NaN                                    NaN   \n",
       "4                         NaN                                    NaN   \n",
       "\n",
       "title    Zeus and Roxanne (1997)  unknown  Á köldum klaka (Cold Fever) (1994)  \n",
       "user_id                                                                        \n",
       "0                            NaN      NaN                                 NaN  \n",
       "1                            NaN      4.0                                 NaN  \n",
       "2                            NaN      NaN                                 NaN  \n",
       "3                            NaN      NaN                                 NaN  \n",
       "4                            NaN      NaN                                 NaN  \n",
       "\n",
       "[5 rows x 1664 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviemat = df.pivot_table(index='user_id',columns='title',values='rating')\n",
    "moviemat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da71803a-44ef-44b5-8330-94530e462e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>4.359589</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contact (1997)</th>\n",
       "      <td>3.803536</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fargo (1996)</th>\n",
       "      <td>4.155512</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
       "      <td>4.007890</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liar Liar (1997)</th>\n",
       "      <td>3.156701</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Patient, The (1996)</th>\n",
       "      <td>3.656965</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scream (1996)</th>\n",
       "      <td>3.441423</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <td>3.878319</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air Force One (1997)</th>\n",
       "      <td>3.631090</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Independence Day (ID4) (1996)</th>\n",
       "      <td>3.438228</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 rating  num of ratings\n",
       "title                                                  \n",
       "Star Wars (1977)               4.359589             584\n",
       "Contact (1997)                 3.803536             509\n",
       "Fargo (1996)                   4.155512             508\n",
       "Return of the Jedi (1983)      4.007890             507\n",
       "Liar Liar (1997)               3.156701             485\n",
       "English Patient, The (1996)    3.656965             481\n",
       "Scream (1996)                  3.441423             478\n",
       "Toy Story (1995)               3.878319             452\n",
       "Air Force One (1997)           3.631090             431\n",
       "Independence Day (ID4) (1996)  3.438228             429"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values('num of ratings',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa0a98d3-2776-401a-99dc-ef2c27e62b86",
   "metadata": {},
   "outputs": [
    {
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       "      <th>rating</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
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       "      <th>101 Dalmatians (1996)</th>\n",
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       "      <td>109</td>\n",
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       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
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       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
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      ],
      "text/plain": [
       "                             rating  num of ratings\n",
       "title                                              \n",
       "'Til There Was You (1997)  2.333333               9\n",
       "1-900 (1994)               2.600000               5\n",
       "101 Dalmatians (1996)      2.908257             109\n",
       "12 Angry Men (1957)        4.344000             125\n",
       "187 (1997)                 3.024390              41"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1856836b-e183-4e10-b94d-43b16e90a602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "0    5.0\n",
       "1    5.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    5.0\n",
       "Name: Star Wars (1977), dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starwars_user_ratings = moviemat['Star Wars (1977)']\n",
    "liarliar_user_ratings = moviemat['Liar Liar (1997)']\n",
    "starwars_user_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae0edcc4-8308-461e-9cf3-5f4371dc23ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ceeadu/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/function_base.py:2683: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar, dtype=dtype)\n",
      "/Users/ceeadu/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/function_base.py:2542: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n",
      "/Users/ceeadu/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/function_base.py:2683: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar, dtype=dtype)\n",
      "/Users/ceeadu/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/function_base.py:2542: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    }
   ],
   "source": [
    "similar_to_starwars = moviemat.corrwith(starwars_user_ratings)\n",
    "similar_to_liarliar = moviemat.corrwith(liarliar_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a25011d-d306-4392-a2b1-cbcfb447c599",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Correlation</th>\n",
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       "      <th>title</th>\n",
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      ],
      "text/plain": [
       "                           Correlation\n",
       "title                                 \n",
       "'Til There Was You (1997)     0.872872\n",
       "1-900 (1994)                 -0.645497\n",
       "101 Dalmatians (1996)         0.211132\n",
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      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = pd.DataFrame(similar_to_starwars,columns=['Correlation'])\n",
    "corr_starwars.dropna(inplace=True)\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e58f3183-f60e-4836-ae17-3d1312812412",
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       "      <td>1.0</td>\n",
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       "      <th>Beans of Egypt, Maine, The (1994)</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)</th>\n",
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       "      <th>Outlaw, The (1943)</th>\n",
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      "text/plain": [
       "                                                    Correlation\n",
       "title                                                          \n",
       "Commandments (1997)                                         1.0\n",
       "Cosi (1996)                                                 1.0\n",
       "No Escape (1994)                                            1.0\n",
       "Stripes (1981)                                              1.0\n",
       "Man of the Year (1995)                                      1.0\n",
       "Hollow Reed (1996)                                          1.0\n",
       "Beans of Egypt, Maine, The (1994)                           1.0\n",
       "Good Man in Africa, A (1994)                                1.0\n",
       "Old Lady Who Walked in the Sea, The (Vieille qu...          1.0\n",
       "Outlaw, The (1943)                                          1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "corr_starwars.sort_values('Correlation',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "17dba694-525c-45b6-8a53-ffcedaf6f16e",
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   "outputs": [
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       "    .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>Correlation</th>\n",
       "      <th>num of ratings</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>'Til There Was You (1997)</th>\n",
       "      <td>0.872872</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>-0.645497</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.211132</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.184289</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.027398</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation  num of ratings\n",
       "title                                                 \n",
       "'Til There Was You (1997)     0.872872               9\n",
       "1-900 (1994)                 -0.645497               5\n",
       "101 Dalmatians (1996)         0.211132             109\n",
       "12 Angry Men (1957)           0.184289             125\n",
       "187 (1997)                    0.027398              41"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = corr_starwars.join(ratings['num of ratings'])\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "acb491c2-34e8-48c2-8a7d-68a5041baaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>Correlation</th>\n",
       "      <th>num of ratings</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>Star Wars (1977)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empire Strikes Back, The (1980)</th>\n",
       "      <td>0.748353</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
       "      <td>0.672556</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raiders of the Lost Ark (1981)</th>\n",
       "      <td>0.536117</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin Powers: International Man of Mystery (1997)</th>\n",
       "      <td>0.377433</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation  \\\n",
       "title                                                             \n",
       "Star Wars (1977)                                       1.000000   \n",
       "Empire Strikes Back, The (1980)                        0.748353   \n",
       "Return of the Jedi (1983)                              0.672556   \n",
       "Raiders of the Lost Ark (1981)                         0.536117   \n",
       "Austin Powers: International Man of Mystery (1997)     0.377433   \n",
       "\n",
       "                                                    num of ratings  \n",
       "title                                                               \n",
       "Star Wars (1977)                                               584  \n",
       "Empire Strikes Back, The (1980)                                368  \n",
       "Return of the Jedi (1983)                                      507  \n",
       "Raiders of the Lost Ark (1981)                                 420  \n",
       "Austin Powers: International Man of Mystery (1997)             130  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars[corr_starwars['num of ratings']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "059a5096-81a9-4f1a-ab3a-dbc71de2ecf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>num of ratings</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>Liar Liar (1997)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batman Forever (1995)</th>\n",
       "      <td>0.516968</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mask, The (1994)</th>\n",
       "      <td>0.484650</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Down Periscope (1996)</th>\n",
       "      <td>0.472681</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Con Air (1997)</th>\n",
       "      <td>0.469828</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Correlation  num of ratings\n",
       "title                                             \n",
       "Liar Liar (1997)          1.000000             485\n",
       "Batman Forever (1995)     0.516968             114\n",
       "Mask, The (1994)          0.484650             129\n",
       "Down Periscope (1996)     0.472681             101\n",
       "Con Air (1997)            0.469828             137"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liarliar = pd.DataFrame(similar_to_liarliar,columns=['Correlation'])\n",
    "corr_liarliar.dropna(inplace=True)\n",
    "corr_liarliar = corr_liarliar.join(ratings['num of ratings'])\n",
    "corr_liarliar[corr_liarliar['num of ratings']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec88d8a-1c16-491a-a1c7-43aa49f3b500",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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