simple_recom_movie.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"user_id\", \"item_id\", \"rating\", \"timestamp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"u.data\", sep=\"\\t\", names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_titles = pd.read_csv(\"Movie_Id_Titles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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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       "    <tr>\n",
       "      <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",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, movie_titles, on=\"item_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>Star Wars (1977)</td>\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)"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100003 entries, 0 to 100002\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   user_id    100003 non-null  int64 \n",
      " 1   item_id    100003 non-null  int64 \n",
      " 2   rating     100003 non-null  int64 \n",
      " 3   timestamp  100003 non-null  int64 \n",
      " 4   title      100003 non-null  object\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 4.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"title\")[\"rating\"].mean().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"title\")[\"rating\"].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.DataFrame(df.groupby(\"title\")[\"rating\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
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       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
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       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
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       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
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       "      <th>187 (1997)</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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings[\"num_of_ratings\"] = pd.DataFrame(df.groupby(\"title\")[\"rating\"].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>'Til There Was You (1997)</th>\n",
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       "      <td>9</td>\n",
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       "      <td>2.600000</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
       "      <td>125</td>\n",
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       "      <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"
      ]
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     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 16,
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ratings[\"num_of_ratings\"].hist(bins=70)"
   ]
  },
  {
   "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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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ratings[\"rating\"].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fe56ad6e280>"
      ]
     },
     "execution_count": 18,
     "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=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_matrix = df.pivot_table(index=\"user_id\", columns=\"title\", values=\"rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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></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",
       "  </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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values(\"num_of_ratings\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "starwars_user_ratings = movie_matrix[\"Star Wars (1977)\"]\n",
    "liarliar_user_ratings = movie_matrix[\"Liar Liar (1997)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starwars_user_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "0    NaN\n",
       "1    NaN\n",
       "2    1.0\n",
       "3    2.0\n",
       "4    5.0\n",
       "Name: Liar Liar (1997), dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liarliar_user_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/numpy/lib/function_base.py:2551: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/numpy/lib/function_base.py:2480: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    }
   ],
   "source": [
    "similar_to_starwars = movie_matrix.corrwith(starwars_user_ratings)\n",
    "similar_to_liarliar = movie_matrix.corrwith(liarliar_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_starwars = pd.DataFrame(similar_to_starwars, columns=[\"Correlation\"])\n",
    "corr_starwars.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>title</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>-0.645497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.211132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.184289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.027398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "12 Angry Men (1957)           0.184289\n",
       "187 (1997)                    0.027398"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Commandments (1997)</th>\n",
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       "      <th>Cosi (1996)</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>No Escape (1994)</th>\n",
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       "    <tr>\n",
       "      <th>Stripes (1981)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man of the Year (1995)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hollow Reed (1996)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans of Egypt, Maine, The (1994)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good Man in Africa, A (1994)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlaw, The (1943)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.sort_values(\"Correlation\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th>'Til There Was You (1997)</th>\n",
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       "      <td>9</td>\n",
       "    </tr>\n",
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       "      <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",
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      ],
      "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = corr_starwars.join(ratings[\"num_of_ratings\"])\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "    <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": 32,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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