SankaraProject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import skcriteria as skc\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import itertools\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('movie_dataset.csv')\n",
    "movies.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv('ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>...</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>director</th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>237000000</td>\n",
       "      <td>Action Adventure Fantasy Science Fiction</td>\n",
       "      <td>19995</td>\n",
       "      <td>culture clash future space war space colony so...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>...</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "      <td>Sam Worthington Zoe Saldana Sigourney Weaver S...</td>\n",
       "      <td>[{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>300000000</td>\n",
       "      <td>Adventure Fantasy Action</td>\n",
       "      <td>285</td>\n",
       "      <td>ocean drug abuse exotic island east india trad...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
       "      <td>...</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "      <td>Johnny Depp Orlando Bloom Keira Knightley Stel...</td>\n",
       "      <td>[{'name': 'Dariusz Wolski', 'gender': 2, 'depa...</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>245000000</td>\n",
       "      <td>Action Adventure Crime</td>\n",
       "      <td>206647</td>\n",
       "      <td>spy based on novel secret agent sequel mi6</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
       "      <td>...</td>\n",
       "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "      <td>Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...</td>\n",
       "      <td>[{'name': 'Thomas Newman', 'gender': 2, 'depar...</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>250000000</td>\n",
       "      <td>Action Crime Drama Thriller</td>\n",
       "      <td>49026</td>\n",
       "      <td>dc comics crime fighter terrorist secret ident...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
       "      <td>...</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "      <td>Christian Bale Michael Caine Gary Oldman Anne ...</td>\n",
       "      <td>[{'name': 'Hans Zimmer', 'gender': 2, 'departm...</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>260000000</td>\n",
       "      <td>Action Adventure Science Fiction</td>\n",
       "      <td>49529</td>\n",
       "      <td>based on novel mars medallion space travel pri...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
       "      <td>...</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "      <td>Taylor Kitsch Lynn Collins Samantha Morton Wil...</td>\n",
       "      <td>[{'name': 'Andrew Stanton', 'gender': 2, 'depa...</td>\n",
       "      <td>Andrew Stanton</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index     budget                                    genres      id  \\\n",
       "0      0  237000000  Action Adventure Fantasy Science Fiction   19995   \n",
       "1      1  300000000                  Adventure Fantasy Action     285   \n",
       "2      2  245000000                    Action Adventure Crime  206647   \n",
       "3      3  250000000               Action Crime Drama Thriller   49026   \n",
       "4      4  260000000          Action Adventure Science Fiction   49529   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  culture clash future space war space colony so...                en   \n",
       "1  ocean drug abuse exotic island east india trad...                en   \n",
       "2         spy based on novel secret agent sequel mi6                en   \n",
       "3  dc comics crime fighter terrorist secret ident...                en   \n",
       "4  based on novel mars medallion space travel pri...                en   \n",
       "\n",
       "                             original_title  \\\n",
       "0                                    Avatar   \n",
       "1  Pirates of the Caribbean: At World's End   \n",
       "2                                   Spectre   \n",
       "3                     The Dark Knight Rises   \n",
       "4                               John Carter   \n",
       "\n",
       "                                            overview  popularity  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3  Following the death of District Attorney Harve...  112.312950   \n",
       "4  John Carter is a war-weary, former military ca...   43.926995   \n",
       "\n",
       "                                production_companies   ...     \\\n",
       "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   ...      \n",
       "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   ...      \n",
       "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   ...      \n",
       "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   ...      \n",
       "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]   ...      \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "1           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "2  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
       "3           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "2                           A Plan No One Escapes   \n",
       "3                                 The Legend Ends   \n",
       "4            Lost in our world, found in another.   \n",
       "\n",
       "                                      title vote_average vote_count  \\\n",
       "0                                    Avatar          7.2      11800   \n",
       "1  Pirates of the Caribbean: At World's End          6.9       4500   \n",
       "2                                   Spectre          6.3       4466   \n",
       "3                     The Dark Knight Rises          7.6       9106   \n",
       "4                               John Carter          6.1       2124   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Sam Worthington Zoe Saldana Sigourney Weaver S...   \n",
       "1  Johnny Depp Orlando Bloom Keira Knightley Stel...   \n",
       "2  Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...   \n",
       "3  Christian Bale Michael Caine Gary Oldman Anne ...   \n",
       "4  Taylor Kitsch Lynn Collins Samantha Morton Wil...   \n",
       "\n",
       "                                                crew           director  \\\n",
       "0  [{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...      James Cameron   \n",
       "1  [{'name': 'Dariusz Wolski', 'gender': 2, 'depa...     Gore Verbinski   \n",
       "2  [{'name': 'Thomas Newman', 'gender': 2, 'depar...         Sam Mendes   \n",
       "3  [{'name': 'Hans Zimmer', 'gender': 2, 'departm...  Christopher Nolan   \n",
       "4  [{'name': 'Andrew Stanton', 'gender': 2, 'depa...     Andrew Stanton   \n",
       "\n",
       "   movieId  \n",
       "0        1  \n",
       "1        2  \n",
       "2        3  \n",
       "3        4  \n",
       "4        5  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.drop(['homepage'], axis = 1,inplace = True)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
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       "<p>5 rows × 4806 columns</p>\n",
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      ],
      "text/plain": [
       "userId   1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "movieId                                                              ...    \n",
       "1         NaN   NaN   NaN   4.0   NaN   NaN   NaN   NaN   NaN   5.0  ...    \n",
       "2         NaN   NaN   NaN   4.0   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN  ...    \n",
       "4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "5         NaN   NaN   NaN   2.0   NaN   NaN   NaN   3.0   NaN   NaN  ...    \n",
       "\n",
       "userId   4797  4798  4799  4800  4801  4802  4803  4804  4805  4806  \n",
       "movieId                                                              \n",
       "1         NaN   4.5   3.5   NaN   NaN   NaN   NaN   NaN   NaN   4.0  \n",
       "2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0  \n",
       "3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "5         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 4806 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset = ratings.pivot(index='movieId',columns='userId', values='rating')\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>4797</th>\n",
       "      <th>4798</th>\n",
       "      <th>4799</th>\n",
       "      <th>4800</th>\n",
       "      <th>4801</th>\n",
       "      <th>4802</th>\n",
       "      <th>4803</th>\n",
       "      <th>4804</th>\n",
       "      <th>4805</th>\n",
       "      <th>4806</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4806 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "movieId                                                              ...    \n",
       "1         0.0   0.0   0.0   4.0   0.0   0.0   0.0   0.0   0.0   5.0  ...    \n",
       "2         0.0   0.0   0.0   4.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    \n",
       "3         0.0   0.0   0.0   0.0   0.0   0.0   0.0   3.0   0.0   0.0  ...    \n",
       "4         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    \n",
       "5         0.0   0.0   0.0   2.0   0.0   0.0   0.0   3.0   0.0   0.0  ...    \n",
       "\n",
       "userId   4797  4798  4799  4800  4801  4802  4803  4804  4805  4806  \n",
       "movieId                                                              \n",
       "1         0.0   4.5   3.5   0.0   0.0   0.0   0.0   0.0   0.0   4.0  \n",
       "2         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   3.0  \n",
       "3         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "5         0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 4806 columns]"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.fillna(0,inplace=True)\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "noOfUsersVoted = ratings.groupby('movieId')['rating'].agg('count')\n",
    "noOfMoviesVoted = ratings.groupby('userId')['rating'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,1,figsize=(16,4))\n",
    "#ratings['rating'].plot(kind='hist')\n",
    "plt.scatter(noOfUsersVoted.index,noOfUsersVoted,color='mediumseagreen')\n",
    "plt.axhline(y=50,color='r')\n",
    "plt.xlabel('MovieId')\n",
    "plt.ylabel('No. Of Users Voted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = final_dataset.loc[noOfUsersVoted[noOfUsersVoted > 50].index,:]\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,1,figsize=(16,4))\n",
    "#ratings['rating'].plot(kind='hist')\n",
    "plt.scatter(noOfMoviesVoted.index,noOfMoviesVoted,color='mediumseagreen')\n",
    "plt.axhline(y=150,color='r')\n",
    "plt.xlabel('UserId')\n",
    "plt.ylabel('No. Of votes by Users')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=final_dataset.loc[:,noOfMoviesVoted[noOfMoviesVoted > 250].index]\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "csr_data = csr_matrix(final_dataset.values)\n",
    "final_dataset.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine', n_jobs=-1, n_neighbors=20)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)\n",
    "knn.fit(csr_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_movie_recommendation(movie_name):\n",
    "    n_movies_to_reccomend = 10\n",
    "    movie_list = movies[movies['title'].str.contains(movie_name)]  \n",
    "    if len(movie_list):        \n",
    "        movie_idx= movie_list.iloc[0]['movieId']\n",
    "        movie_idx = final_dataset[final_dataset['movieId'] == movie_idx].index[0]\n",
    "        distances , indices = knn.kneighbors(csr_data[movie_idx],n_neighbors=n_movies_to_reccomend+1)    \n",
    "        rec_movie_indices = sorted(list(zip(indices.squeeze().tolist(),distances.squeeze().tolist())),key=lambda x: x[1])[:0:-1]\n",
    "        recommend_frame = []\n",
    "        for val in rec_movie_indices:\n",
    "            movie_idx = final_dataset.iloc[val[0]]['movieId']\n",
    "            idx = movies[movies['movieId'] == movie_idx].index\n",
    "            recommend_frame.append({'Title':movies.iloc[idx]['title'].values[0],'Distance':val[1]})\n",
    "        df = pd.DataFrame(recommend_frame,index=range(1,n_movies_to_reccomend+1))\n",
    "        records = df.to_records(index=False)\n",
    "        result = list(records)\n",
    "        return result\n",
    "    else:\n",
    "        return \"No movies found. Please check your input\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.37235984, 'Mickey Blue Eyes'),\n",
       " (0.37014234, 'Alexander'),\n",
       " (0.36770292, 'Assault on Precinct 13'),\n",
       " (0.35888102, 'Little Nicky'),\n",
       " (0.3548525, 'The Expendables 3'),\n",
       " (0.34570065, 'The Great Gatsby'),\n",
       " (0.34349582, 'Transformers: Dark of the Moon'),\n",
       " (0.33354086, 'The Chronicles of Narnia: Prince Caspian'),\n",
       " (0.32627363, 'The Shadow'),\n",
       " (0.31478215, 'The Fugitive')]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_movie_recommendation('Spider-Man 3')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_title_from_index(index):\n",
    "    indexMovie = movies[movies.index == index][\"title\"].values[0]  \n",
    "    return indexMovie\n",
    "\n",
    "def get_index_from_title(title):\n",
    "    return movies[movies.title == title][\"index\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['keywords', 'cast','genres', 'director']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in features:\n",
    "    movies[feature] = movies[feature].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4783 entries, 0 to 4782\n",
      "Data columns (total 25 columns):\n",
      "index                   4783 non-null int64\n",
      "budget                  4783 non-null int64\n",
      "genres                  4783 non-null object\n",
      "id                      4783 non-null int64\n",
      "keywords                4783 non-null object\n",
      "original_language       4783 non-null object\n",
      "original_title          4783 non-null object\n",
      "overview                4780 non-null object\n",
      "popularity              4783 non-null float64\n",
      "production_companies    4783 non-null object\n",
      "production_countries    4783 non-null object\n",
      "release_date            4782 non-null object\n",
      "revenue                 4783 non-null int64\n",
      "runtime                 4781 non-null float64\n",
      "spoken_languages        4783 non-null object\n",
      "status                  4783 non-null object\n",
      "tagline                 3941 non-null object\n",
      "title                   4783 non-null object\n",
      "vote_average            4783 non-null float64\n",
      "vote_count              4783 non-null int64\n",
      "cast                    4783 non-null object\n",
      "crew                    4783 non-null object\n",
      "director                4783 non-null object\n",
      "movieId                 4783 non-null int64\n",
      "combined_features       4783 non-null object\n",
      "dtypes: float64(3), int64(6), object(16)\n",
      "memory usage: 934.3+ KB\n"
     ]
    }
   ],
   "source": [
    "def combine_features(row):\n",
    "\ttry:\n",
    "\t\treturn row['keywords']+ \" \"+row['cast']+ \" \"+row['genres']+ \" \"+row['director']\n",
    "\texcept:\n",
    "\t\tprint(\"Error:\", row)\n",
    "movies[\"combined_features\"] = movies.apply(combine_features, axis=1)\n",
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "count_matrix = cv.fit_transform(movies[\"combined_features\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosineSim = cosine_similarity(count_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_index = get_index_from_title('Spider-Man 3')\n",
    "similar_movies = list(enumerate(cosineSim[movie_index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " (984, 0.03849001794597506),\n",
       " (985, 0.08000000000000002),\n",
       " (986, 0.07698003589195011),\n",
       " (987, 0.223606797749979),\n",
       " (988, 0.03651483716701107),\n",
       " (989, 0.0),\n",
       " (990, 0.0),\n",
       " (991, 0.08340576562282992),\n",
       " (992, 0.0),\n",
       " (993, 0.03922322702763681),\n",
       " (994, 0.0),\n",
       " (995, 0.0),\n",
       " (996, 0.0),\n",
       " (997, 0.08528028654224418),\n",
       " (998, 0.0),\n",
       " (999, 0.043643578047198484),\n",
       " ...]"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.int64'>\n"
     ]
    }
   ],
   "source": [
    "print(type(movie_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " (2328, 0.07698003589195011),\n",
       " ...]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_similar_movies = sorted(similar_movies, key=lambda x:x[1],reverse=True)\n",
    "sorted_similar_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(type(sorted_similar_movies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Spider-Man 3',\n",
       " 'Spider-Man',\n",
       " 'Hancock',\n",
       " 'Oz: The Great and Powerful',\n",
       " 'The Notebook',\n",
       " \"Pirates of the Caribbean: At World's End\",\n",
       " 'Underworld',\n",
       " 'The Specials',\n",
       " 'The Island',\n",
       " 'The Good German',\n",
       " 'Crazy/Beautiful']"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_similar_movies = sorted(similar_movies, key=lambda x:x[1],reverse=True)\n",
    "\n",
    "#print(\"Sorted Similar movies:\", sorted_similar_movies)\n",
    "## Step 8: Print titles of first 50 movies\n",
    "def getMostSimilar(sorted_similar_movies):\n",
    "    i = 0\n",
    "    listMovies = []\n",
    "    for movie in sorted_similar_movies:\n",
    "        sortedMovies = get_title_from_index(movie[0])        \n",
    "        listMovies.append(sortedMovies)\n",
    "        i = i +1\n",
    "        if i>10:\n",
    "            break\n",
    "    \n",
    "    return listMovies\n",
    "getMostSimilar(sorted_similar_movies)      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retMovList(sorted_similar_movies):\n",
    "    sortedMoviesret = getMostSimilar(sorted_similar_movies)\n",
    "    list = []\n",
    "    for movie in sortedMoviesret:        \n",
    "        movie_index = get_index_from_title(movie)\n",
    "        list.append(movie_index)\n",
    "    return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 159, 115, 37, 1559, 1, 2029, 4395, 271, 1523, 2756]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retMovList(sorted_similar_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMoviesforTopsis(sorted_similar_movies):\n",
    "    movieList = retMovList(sorted_similar_movies)    \n",
    "    data = movies.iloc[movieList]\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "re = getMoviesforTopsis(sorted_similar_movies)\n",
    "re = re.iloc[1: , :]\n",
    "data = re.drop(['index', 'genres','id','keywords','original_language', 'original_title','overview','production_companies','production_countries',\n",
    "              'spoken_languages','status','tagline','release_date','title','vote_average','cast','crew','director','movieId','combined_features'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getmovieTitles(sorted_similar_movies):\n",
    "    data = retMovList(sorted_similar_movies)    \n",
    "    altMovies= []     \n",
    "    for movie in data:\n",
    "        mov = get_title_from_index(movie)\n",
    "        altMovies.append(mov)\n",
    "    return altMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Spider-Man 3'"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chk = getmovieTitles(sorted_similar_movies)\n",
    "corAltMovs = chk.pop(0)\n",
    "corAltMovs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Stealth',\n",
       " 'Ratatouille',\n",
       " 'Cars 2',\n",
       " 'Without a Paddle',\n",
       " \"Pirates of the Caribbean: At World's End\",\n",
       " 'Megiddo: The Omega Code 2',\n",
       " 'Snow White: A Deadly Summer',\n",
       " 'Public Enemies',\n",
       " 'Toy Story',\n",
       " 'Faithful']"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = [0.3,0.2,0.2,0.15,0.15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.319482</td>\n",
       "      <td>0.098312</td>\n",
       "      <td>0.056992</td>\n",
       "      <td>0.337853</td>\n",
       "      <td>0.038689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.354980</td>\n",
       "      <td>0.360932</td>\n",
       "      <td>0.462051</td>\n",
       "      <td>0.309931</td>\n",
       "      <td>0.510669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.473307</td>\n",
       "      <td>0.274703</td>\n",
       "      <td>0.414736</td>\n",
       "      <td>0.295970</td>\n",
       "      <td>0.237627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>0.044964</td>\n",
       "      <td>0.047176</td>\n",
       "      <td>0.051582</td>\n",
       "      <td>0.265257</td>\n",
       "      <td>0.025013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.709960</td>\n",
       "      <td>0.764332</td>\n",
       "      <td>0.711904</td>\n",
       "      <td>0.471877</td>\n",
       "      <td>0.525981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004449</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.290386</td>\n",
       "      <td>0.000935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4395</th>\n",
       "      <td>0.002367</td>\n",
       "      <td>0.003591</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237335</td>\n",
       "      <td>0.001286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>0.189323</td>\n",
       "      <td>0.185154</td>\n",
       "      <td>0.158608</td>\n",
       "      <td>0.390904</td>\n",
       "      <td>0.157093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>0.070996</td>\n",
       "      <td>0.404693</td>\n",
       "      <td>0.276727</td>\n",
       "      <td>0.226166</td>\n",
       "      <td>0.615866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2756</th>\n",
       "      <td>0.030765</td>\n",
       "      <td>0.009053</td>\n",
       "      <td>0.007252</td>\n",
       "      <td>0.254088</td>\n",
       "      <td>0.000468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        budget  popularity   revenue   runtime  vote_count\n",
       "159   0.319482    0.098312  0.056992  0.337853    0.038689\n",
       "115   0.354980    0.360932  0.462051  0.309931    0.510669\n",
       "37    0.473307    0.274703  0.414736  0.295970    0.237627\n",
       "1559  0.044964    0.047176  0.051582  0.265257    0.025013\n",
       "1     0.709960    0.764332  0.711904  0.471877    0.525981\n",
       "2029  0.000000    0.004449  0.000000  0.290386    0.000935\n",
       "4395  0.002367    0.003591  0.000000  0.237335    0.001286\n",
       "271   0.189323    0.185154  0.158608  0.390904    0.157093\n",
       "1523  0.070996    0.404693  0.276727  0.226166    0.615866\n",
       "2756  0.030765    0.009053  0.007252  0.254088    0.000468"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Normalisation\n",
    "data_norm = data/np.sqrt(np.power(data,2).sum(axis=0))\n",
    "data_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.095845</td>\n",
       "      <td>0.019662</td>\n",
       "      <td>0.011398</td>\n",
       "      <td>0.050678</td>\n",
       "      <td>0.005803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.106494</td>\n",
       "      <td>0.072186</td>\n",
       "      <td>0.092410</td>\n",
       "      <td>0.046490</td>\n",
       "      <td>0.076600</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.141992</td>\n",
       "      <td>0.054941</td>\n",
       "      <td>0.082947</td>\n",
       "      <td>0.044396</td>\n",
       "      <td>0.035644</td>\n",
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       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>0.013489</td>\n",
       "      <td>0.009435</td>\n",
       "      <td>0.010316</td>\n",
       "      <td>0.039788</td>\n",
       "      <td>0.003752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.212988</td>\n",
       "      <td>0.152866</td>\n",
       "      <td>0.142381</td>\n",
       "      <td>0.070782</td>\n",
       "      <td>0.078897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043558</td>\n",
       "      <td>0.000140</td>\n",
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       "    <tr>\n",
       "      <th>4395</th>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035600</td>\n",
       "      <td>0.000193</td>\n",
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       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.037031</td>\n",
       "      <td>0.031722</td>\n",
       "      <td>0.058636</td>\n",
       "      <td>0.023564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>0.021299</td>\n",
       "      <td>0.080939</td>\n",
       "      <td>0.055345</td>\n",
       "      <td>0.033925</td>\n",
       "      <td>0.092380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2756</th>\n",
       "      <td>0.009229</td>\n",
       "      <td>0.001811</td>\n",
       "      <td>0.001450</td>\n",
       "      <td>0.038113</td>\n",
       "      <td>0.000070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        budget  popularity   revenue   runtime  vote_count\n",
       "159   0.095845    0.019662  0.011398  0.050678    0.005803\n",
       "115   0.106494    0.072186  0.092410  0.046490    0.076600\n",
       "37    0.141992    0.054941  0.082947  0.044396    0.035644\n",
       "1559  0.013489    0.009435  0.010316  0.039788    0.003752\n",
       "1     0.212988    0.152866  0.142381  0.070782    0.078897\n",
       "2029  0.000000    0.000890  0.000000  0.043558    0.000140\n",
       "4395  0.000710    0.000718  0.000000  0.035600    0.000193\n",
       "271   0.056797    0.037031  0.031722  0.058636    0.023564\n",
       "1523  0.021299    0.080939  0.055345  0.033925    0.092380\n",
       "2756  0.009229    0.001811  0.001450  0.038113    0.000070"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_normW = data_norm*w\n",
    "data_normW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget        0.000000\n",
       "popularity    0.000718\n",
       "revenue       0.000000\n",
       "runtime       0.033925\n",
       "vote_count    0.000070\n",
       "dtype: float64"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_normW.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_ideal = data_normW.max()\n",
    "negative_ideal = data_normW.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "SM_P = np.sqrt(np.power(data_normW - positive_ideal,2).sum(axis=1))\n",
    "SM_N = np.sqrt(np.power(data_normW - negative_ideal,2).sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame(SM_N/(SM_N+SM_P))\n",
    "result.columns = ['AltMovie']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AltMovie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.958374</td>\n",
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       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.547448</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.543749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>0.376061</td>\n",
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       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.295963</td>\n",
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       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>0.259015</td>\n",
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       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>0.064537</td>\n",
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       "    <tr>\n",
       "      <th>2756</th>\n",
       "      <td>0.032529</td>\n",
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       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>0.029861</td>\n",
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       "    <tr>\n",
       "      <th>4395</th>\n",
       "      <td>0.005785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      AltMovie\n",
       "1     0.958374\n",
       "115   0.547448\n",
       "37    0.543749\n",
       "1523  0.376061\n",
       "159   0.295963\n",
       "271   0.259015\n",
       "1559  0.064537\n",
       "2756  0.032529\n",
       "2029  0.029861\n",
       "4395  0.005785"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "result.sort_values(['AltMovie'], ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-criteria\n",
      "  Using cached scikit-criteria-0.5.tar.gz (35 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: numpy in c:\\users\\modib\\anaconda3\\lib\\site-packages (from scikit-criteria) (1.19.5)\n",
      "Requirement already satisfied: pandas in c:\\users\\modib\\anaconda3\\lib\\site-packages (from scikit-criteria) (0.23.0)\n",
      "Collecting pyquery\n",
      "  Using cached pyquery-1.4.3-py3-none-any.whl (22 kB)\n",
      "Requirement already satisfied: scipy in c:\\users\\modib\\anaconda3\\lib\\site-packages (from scikit-criteria) (1.1.0)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from scikit-criteria) (2.10)\n",
      "Collecting custom_inherit\n",
      "  Using cached custom_inherit-2.4.0-py3-none-any.whl (15 kB)\n",
      "Requirement already satisfied: seaborn in c:\\users\\modib\\anaconda3\\lib\\site-packages (from scikit-criteria) (0.9.0)\n",
      "Collecting pulp\n",
      "  Using cached PuLP-2.6.0-py3-none-any.whl (14.2 MB)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from jinja2->scikit-criteria) (1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from pandas->scikit-criteria) (2.7.3)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\modib\\anaconda3\\lib\\site-packages (from pandas->scikit-criteria) (2018.4)\n",
      "Collecting cssselect>0.7.9\n",
      "  Using cached cssselect-1.1.0-py2.py3-none-any.whl (16 kB)\n",
      "Requirement already satisfied: lxml>=2.1 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from pyquery->scikit-criteria) (4.2.1)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from seaborn->scikit-criteria) (3.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from matplotlib>=1.4.3->seaborn->scikit-criteria) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from matplotlib>=1.4.3->seaborn->scikit-criteria) (1.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from matplotlib>=1.4.3->seaborn->scikit-criteria) (2.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\modib\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas->scikit-criteria) (1.11.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\modib\\anaconda3\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn->scikit-criteria) (39.1.0)\n",
      "Building wheels for collected packages: scikit-criteria\n",
      "  Building wheel for scikit-criteria (setup.py): started\n",
      "  Building wheel for scikit-criteria (setup.py): finished with status 'done'\n",
      "  Created wheel for scikit-criteria: filename=scikit_criteria-0.5-py3-none-any.whl size=47788 sha256=e52270df9fb2838534554291d61bbc182abc9c23e529f121578a6d7098b425e0\n",
      "  Stored in directory: c:\\users\\modib\\appdata\\local\\pip\\cache\\wheels\\b4\\1e\\09\\b0bb0971d2260f050cb70f792ab7bb0fd2685ff730e57f822b\n",
      "Successfully built scikit-criteria\n",
      "Installing collected packages: cssselect, pyquery, pulp, custom-inherit, scikit-criteria\n",
      "Successfully installed cssselect-1.1.0 custom-inherit-2.4.0 pulp-2.6.0 pyquery-1.4.3 scikit-criteria-0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\modib\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install -U scikit-criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<function max>,\n",
       " <function min>,\n",
       " <function max>,\n",
       " <function min>,\n",
       " <function max>]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = [max,min,max,min,max]\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "alternatives = chk\n",
    "alternatives\n",
    "weights = [1,1,1,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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",
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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>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stealth</th>\n",
       "      <td>135000000</td>\n",
       "      <td>17.889530</td>\n",
       "      <td>76932943</td>\n",
       "      <td>121.0</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratatouille</th>\n",
       "      <td>150000000</td>\n",
       "      <td>65.677399</td>\n",
       "      <td>623722818</td>\n",
       "      <td>111.0</td>\n",
       "      <td>4369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cars 2</th>\n",
       "      <td>200000000</td>\n",
       "      <td>49.986590</td>\n",
       "      <td>559852396</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Without a Paddle</th>\n",
       "      <td>19000000</td>\n",
       "      <td>8.584398</td>\n",
       "      <td>69631118</td>\n",
       "      <td>95.0</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pirates of the Caribbean: At World's End</th>\n",
       "      <td>300000000</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Megiddo: The Omega Code 2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.809588</td>\n",
       "      <td>0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow White: A Deadly Summer</th>\n",
       "      <td>1000000</td>\n",
       "      <td>0.653495</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Enemies</th>\n",
       "      <td>80000000</td>\n",
       "      <td>33.691694</td>\n",
       "      <td>214104620</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy Story</th>\n",
       "      <td>30000000</td>\n",
       "      <td>73.640445</td>\n",
       "      <td>373554033</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Faithful</th>\n",
       "      <td>13000000</td>\n",
       "      <td>1.647335</td>\n",
       "      <td>9789900</td>\n",
       "      <td>91.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             budget  popularity    revenue  \\\n",
       "Stealth                                   135000000   17.889530   76932943   \n",
       "Ratatouille                               150000000   65.677399  623722818   \n",
       "Cars 2                                    200000000   49.986590  559852396   \n",
       "Without a Paddle                           19000000    8.584398   69631118   \n",
       "Pirates of the Caribbean: At World's End  300000000  139.082615  961000000   \n",
       "Megiddo: The Omega Code 2                         0    0.809588          0   \n",
       "Snow White: A Deadly Summer                 1000000    0.653495          0   \n",
       "Public Enemies                             80000000   33.691694  214104620   \n",
       "Toy Story                                  30000000   73.640445  373554033   \n",
       "Faithful                                   13000000    1.647335    9789900   \n",
       "\n",
       "                                          runtime  vote_count  \n",
       "Stealth                                     121.0         331  \n",
       "Ratatouille                                 111.0        4369  \n",
       "Cars 2                                      106.0        2033  \n",
       "Without a Paddle                             95.0         214  \n",
       "Pirates of the Caribbean: At World's End    169.0        4500  \n",
       "Megiddo: The Omega Code 2                   104.0           8  \n",
       "Snow White: A Deadly Summer                  85.0          11  \n",
       "Public Enemies                              140.0        1344  \n",
       "Toy Story                                    81.0        5269  \n",
       "Faithful                                     91.0           4  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2.index=alternatives\n",
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23.0\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def biasGenreRating(genreRatings, scorelimit_1, scorelimit_2):\n",
    "    biased_dataset = genreRatings[((genreRatings['avgAdventureRating'] < scorelimit_1 - 0.2) & (genreRatings['avgActionRating'] > scorelimit_2))\n",
    "                                  | ((genreRatings['avgAdventureRating']> scorelimit_2) & (genreRatings['avgActionRating']< scorelimit_1))]\n",
    "    biased_dataset = pd.concat([biased_dataset[:300], genreRatings[:2]])\n",
    "    biased_dataset = pd.DataFrame(biased_dataset.to_records())\n",
    "    return biased_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the Bias to the dataset\n",
    "biased_dataset = biasGenreRating(genreRatings, 3.2, 2.5)\n",
    "\n",
    "#Print the result number of records and the head of the dataset\n",
    "print(\"Number of records: \", len(biased_dataset))\n",
    "biased_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining the scatterplot drawing function\n",
    "def viewScatterPlot(x_data, y_data, x_label, y_label):\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    plt.xlim(0,5)\n",
    "    plt.ylim(0,5)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.scatter(x_data,y_data, s=30)\n",
    "viewScatterPlot(biased_dataset['avgActionRating'], biased_dataset['avgAdventureRating'], 'Avg Action Rating', 'Avg Adventure rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = biased_dataset[['avgAdventureRating', 'avgActionRating']].values\n",
    "\n",
    "#import Kmeans\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans_1 = KMeans(n_clusters = 2)\n",
    "\n",
    "predictions = kmeans_1.fit_predict(X)\n",
    "def drawClusters(biased_dataset, predictions, cmap='viridis'):\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    plt.xlim(0,5)\n",
    "    plt.ylim(0,5)\n",
    "    ax.set_xlabel('avgAdventureRating')\n",
    "    ax.set_ylabel('avgActionRating')\n",
    "    \n",
    "    clustered = pd.concat([biased_dataset.reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
    "    plt.scatter(clustered['avgAdventureRating'], clustered['avgActionRating'], c=clustered['group'], s=20, cmap=cmap)\n",
    "drawClusters(biased_dataset,predictions)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_2 = KMeans(n_clusters=3)\n",
    "\n",
    "predictions_2 = kmeans_2.fit_predict(X)\n",
    "\n",
    "drawClusters(biased_dataset, predictions_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_3 = KMeans(n_clusters=4)\n",
    "\n",
    "predictions_3 = kmeans_3.fit_predict(X)\n",
    "drawClusters(biased_dataset, predictions_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = biased_dataset[['avgActionRating', 'avgAdventureRating']]\n",
    "\n",
    "possible_k_values = range(2, len(X)+1, 5)\n",
    "\n",
    "def ClusterringErrors(k, data):\n",
    "    kmeans = KMeans(n_clusters=k).fit(data)\n",
    "    predictions = kmeans.predict(data)\n",
    "    #cluster_centers = kmeans.cluster_centers_\n",
    "    # errors = [mean_squared_error(row, cluster_centers[cluster]) for row, cluster in zip(data.values, predictions)]\n",
    "    # return sum(errors)\n",
    "    silhoueteAvg = silhouette_score(data,predictions)\n",
    "    return silhoueteAvg\n",
    "\n",
    "errors_per_k = [ClusterringErrors(k,X) for k in possible_k_values]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,6))\n",
    "plt.plot(possible_k_values, errors_per_k)\n",
    "\n",
    "xticks = np.arange(min(possible_k_values), max(possible_k_values)+1, 5.0)\n",
    "ax.set_xticks(xticks, minor=False)\n",
    "ax.set_xticks(xticks, minor=True)\n",
    "ax.xaxis.grid(True, which='both')\n",
    "yticks = np.arange(round(min(errors_per_k),2), max(errors_per_k), .05)\n",
    "ax.set_yticks(yticks, minor=False)\n",
    "ax.set_yticks(yticks, minor=True)\n",
    "ax.yaxis.grid(True, which='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_4 = KMeans(n_clusters=7)\n",
    "\n",
    "predictions_4 = kmeans_4.fit_predict(X)\n",
    "drawClusters(biased_dataset, predictions_4, cmap='Accent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "biased_dataset_3Genres = getGenreRatings(ratings,movies,['Action','Adventure','Romance'],\n",
    "                                         ['avgActionRating', 'avgAdventureRating','avgRomanceRating'])\n",
    "biased_dataset_3Genres = biasGenreRating(biased_dataset_3Genres, 3.2,2.5).dropna()\n",
    "print(\"Number of records: \", len(biased_dataset_3Genres))\n",
    "biased_dataset_3Genres.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_MoreGenres = biased_dataset_5Genres[['avgActionRating','avgAdventureRating', 'avgRomanceRating']].values\n",
    "kmeans_5 = KMeans(n_clusters=7)\n",
    "predictions_5 = kmeans_5.fit_predict(X_MoreGenres)\n",
    "\n",
    "def drawClusters3d(biased_dataset_5, predictions):\n",
    "    fig = plt.figure(figsize=(10,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    plt.xlim(0, 5)\n",
    "    plt.ylim(0, 5)\n",
    "    ax.set_xlabel('avgAdventureRating')\n",
    "    ax.set_ylabel('avgRomanceRating')\n",
    "    clustered = pd.concat([biased_dataset_3Genres.reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
    "    colors = itertools.cycle(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])\n",
    "    for g in clustered.group.unique():\n",
    "            color = next(colors)\n",
    "            for index, point in clustered[clustered.group == g].iterrows():\n",
    "                if point['avgActionRating'].astype(float) > 3: \n",
    "                    size = 50\n",
    "                else:\n",
    "                    size = 15\n",
    "                plt.scatter(point['avgAdventureRating'], \n",
    "                            point['avgRomanceRating'], \n",
    "                            s=size, \n",
    "                            color=color)\n",
    "    # Plot\n",
    "drawClusters3d(biased_dataset_5Genres, predictions_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "biased_dataset_5Genres = getGenreRatings(ratings,movies,['Action','Adventure','Romance', 'Comedy','Fantasy'],\n",
    "                                         ['avgActionRating', 'avgAdventureRating','avgRomanceRating','avgComedyRating','avgFantasyRating'])\n",
    "biased_dataset_5Genres = biasGenreRating(biased_dataset_5Genres, 3.2,2.5).dropna()\n",
    "print(\"Number of records: \", len(biased_dataset_3Genres))\n",
    "biased_dataset_5Genres.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_MoreGenres = biased_dataset_5Genres[['avgActionRating','avgAdventureRating', 'avgRomanceRating',\n",
    "                                       'avgComedyRating','avgFantasyRating']].values\n",
    "kmeans_5 = KMeans(n_clusters=7)\n",
    "predictions_5 = kmeans_5.predict(X_MoreGenres)\n",
    "\n",
    "def drawClusters3d(biased_dataset_5, predictions):\n",
    "    fig = plt.figure(figsize=(10,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    plt.xlim(0, 5)\n",
    "    plt.ylim(0, 5)\n",
    "    ax.set_xlabel('avgAdventureRating')\n",
    "    ax.set_ylabel('avgRomanceRating')\n",
    "    clustered = pd.concat([biased_dataset_3.reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
    "    colors = itertools.cycle(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])\n",
    "    for g in clustered.group.unique():\n",
    "            color = next(colors)\n",
    "            for index, point in clustered[clustered.group == g].iterrows():\n",
    "                if point['avgActionRating'].astype(float) > 3: \n",
    "                    size = 50\n",
    "                else:\n",
    "                    size = 15\n",
    "                plt.scatter(point['avgAdventureRating'], \n",
    "                            point['avgRomanceRating'], \n",
    "                            s=size, \n",
    "                            color=color)\n",
    "    # Plot\n",
    "drawClusters3d(biased_dataset_5Genres, predictions_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_title = pd.merge(ratings,movies[['movieId','title']],on = 'movieId')\n",
    "user_movieRatings = pd.pivot_table(ratings_title, index='userId', columns='title', values='rating')\n",
    "\n",
    "print('dataset dimensions: ', user_movieRatings.shape)\n",
    "user_movieRatings.iloc[:6, :10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movRatings = pd.merge(ratings, movies, on='movieId')\n",
    "movRatings.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviex = user_movieRatings['10 Things I Hate About You']\n",
    "moviex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_mean_count = pd.DataFrame(movRatings.groupby('title')['rating'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_users_count = pd.DataFrame(user_movieRatings.count(axis='columns').sort_values(ascending=False))\n",
    "ratings_users_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_mean_count['rating_counts'] = pd.DataFrame(user_movieRatings.groupby('title')['title'].count())\n",
    "ratings_mean_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ratings['movieId'].value_counts()[:30].sort_values(ascending=False)\n",
    "movRatings.set_index('userId')\n",
    "movRatings.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_data.groupby('title')['rating'].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMostRatedMovies(user_movieRatings, n_movies):\n",
    "    ratedMovies = user_movieRatings.count().sort_values(ascending=False)[:n_movies]\n",
    "   \n",
    "    return ratedMovies\n",
    "n_movies = 30\n",
    "ratedmovies = getMostRatedMovies(user_movieRatings, n_movies)\n",
    "ratedmovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getUsersWhoRateTheMost(ratedMovies,n_users):\n",
    "    ratedUsers = getMostRatedMovies.count(axis='columns').sort_values(ascending=False)[:n_users]   \n",
    "    print(ratedUsers.head)\n",
    "    return getUsersWhoRateTheMost\n",
    "n_users = 18\n",
    "getUsersWhoRateTheMost(ratedMovies,n_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratedUsers = user_movieRatings.groupby('userId').agg('count')\n",
    "ratedUsers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sortByRatingDensity(user_movieRatings, n_movies, n_users):\n",
    "    mostRatedMovies = getMostRatedMovies(user_movieRatings, n_movies)\n",
    "    mostRatedMovies = getUsersWhoRateTheMost(mostRatedMovies, n_users)\n",
    "    \n",
    "    return mostRatedMovies\n",
    "n_movies = 30\n",
    "n_users = 18\n",
    "mostRatedMoviesUsersSelection = sortByRatingDensity(user_movieRatings, n_movies, n_users)\n",
    "\n",
    "print('dataset dimensions: ', mostRatedMoviesUsersSelection.shape())\n",
    "mostRatedMoviesUsersSelection.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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