Recommended Systems.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "coloumn_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=coloumn_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "    <tr>\n",
       "      <th>3</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"
      ]
     },
     "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>item_id</th>\n",
       "      <th>title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "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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       "  <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>881250949</td>\n",
       "      <td>Star Wars (1977)</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Star Wars (1977)</td>\n",
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       "      <th>2</th>\n",
       "      <td>79</td>\n",
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       "      <th>3</th>\n",
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       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>888552084</td>\n",
       "      <td>Star Wars (1977)</td>\n",
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       "      <th>4</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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Marlene Dietrich: Shadow and Light (1996)     5.0\n",
       "Prefontaine (1997)                            5.0\n",
       "Santa with Muscles (1996)                     5.0\n",
       "Star Kid (1997)                               5.0\n",
       "Someone Else's America (1995)                 5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "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": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <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>2.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>3.024390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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']=df.groupby('title')['rating'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "                                             ... \n",
       "Man from Down Under, The (1943)                 1\n",
       "Marlene Dietrich: Shadow and Light (1996)       1\n",
       "Mat' i syn (1997)                               1\n",
       "Mille bolle blu (1993)                          1\n",
       "Á köldum klaka (Cold Fever) (1994)              1\n",
       "Name: num of ratings, Length: 1664, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['num of ratings'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x205e420f4c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "ratings['num of ratings'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x205e2f75d90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " sns.jointplot(x='rating',y='num of ratings',data=ratings,alpha=0.5)"
   ]
  },
  {
   "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></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>880473582</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>891271545</td>\n",
       "      <td>Star Wars (1977)</td>\n",
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       "      <th>3</th>\n",
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       "      <td>50</td>\n",
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       "      <td>888552084</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>879362124</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviemat=df.pivot_table(index='user_id',columns='title',values='rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviemat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>584</td>\n",
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       "    <tr>\n",
       "      <th>Contact (1997)</th>\n",
       "      <td>3.803536</td>\n",
       "      <td>509</td>\n",
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       "    <tr>\n",
       "      <th>Fargo (1996)</th>\n",
       "      <td>4.155512</td>\n",
       "      <td>508</td>\n",
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       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
       "      <td>4.007890</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
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       "      <th>Liar Liar (1997)</th>\n",
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       "      <td>485</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>Great Day in Harlem, A (1994)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Voices, Other Rooms (1997)</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good Morning (1971)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Girls Town (1996)</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Á köldum klaka (Cold Fever) (1994)</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1664 rows × 2 columns</p>\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",
       "...                                      ...             ...\n",
       "Great Day in Harlem, A (1994)       5.000000               1\n",
       "Other Voices, Other Rooms (1997)    3.000000               1\n",
       "Good Morning (1971)                 1.000000               1\n",
       "Girls Town (1996)                   3.000000               1\n",
       "Á köldum klaka (Cold Fever) (1994)  3.000000               1\n",
       "\n",
       "[1664 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values('num of ratings',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "starwars_user_ratings = moviemat['Star Wars (1977)']\n",
    "liarliar_user_ratings = moviemat['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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "similar_to_starwars = moviemat.corrwith(starwars_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\work\\virtual environmnets\\ml\\lib\\site-packages\\numpy\\lib\\function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "d:\\work\\virtual environmnets\\ml\\lib\\site-packages\\numpy\\lib\\function_base.py:2455: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    }
   ],
   "source": [
    "similar_to_liarliar = moviemat.corrwith(liarliar_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_starwars = pd.DataFrame(similar_to_starwars,columns=['Correaltion'])\n",
    "corr_starwars.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>12 Angry Men (1957)</th>\n",
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      "text/plain": [
       "                           Correaltion\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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       "187 (1997)                    0.027398"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>Stripes (1981)</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>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>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>For Ever Mozart (1996)</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frankie Starlight (1995)</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I Like It Like That (1994)</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Dream (1990)</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Theodore Rex (1995)</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1410 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correaltion\n",
       "title                                                          \n",
       "Man of the Year (1995)                                      1.0\n",
       "Hollow Reed (1996)                                          1.0\n",
       "Stripes (1981)                                              1.0\n",
       "Beans of Egypt, Maine, The (1994)                           1.0\n",
       "Old Lady Who Walked in the Sea, The (Vieille qu...          1.0\n",
       "...                                                         ...\n",
       "For Ever Mozart (1996)                                     -1.0\n",
       "Frankie Starlight (1995)                                   -1.0\n",
       "I Like It Like That (1994)                                 -1.0\n",
       "American Dream (1990)                                      -1.0\n",
       "Theodore Rex (1995)                                        -1.0\n",
       "\n",
       "[1410 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.sort_values('Correaltion',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_starwars = corr_starwars.join(ratings['num of ratings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Correaltion</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": [
       "                           Correaltion  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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correaltion</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": [
       "                                                    Correaltion  \\\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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars[corr_starwars['num of ratings']>100].sort_values('Correaltion',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_liarliar = pd.DataFrame(similar_to_liarliar,columns=['Correlation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_liarliar.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_liarliar = corr_liarliar.join(ratings['num of ratings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liarliar[corr_liarliar['num of ratings']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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