K-Means Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from datetime import datetime\n",
    "from pandas import DatetimeIndex, to_datetime\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.metrics import silhouette_score\n",
    "import pickle\n",
    "import sys\n",
    "from sys import exc_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_data = pd.read_csv(\"C:/Users/Godwin/Documents/Workflow/Clustering/movielen/ml-100k/u.data\", names = ['user id', 'item id', 'rating', 'timestamp'], sep = '\\s+')\n",
    "\n",
    "movie_data = pd.read_csv(\"C:/Users/Godwin/Documents/Workflow/Clustering/movielen/ml-100k/u.item\",  names = [\"movie id\", \"movie title\", \"release date\", \"video release date\",\n",
    "              \"IMDb URL\", \"unknown\", \"Action\", \"Adventure\", \"Animation\", \n",
    "              \"Children's\", \"Comedy\", \"Crime\", \"Documentary\", \"Drama\", \"Fantasy\", \n",
    "              \"Film-Noir\", \"Horror\", \"Musical\", \"Mystery\", \"Romance\", \"Sci-Fi\", \n",
    "              \"Thriller\", \"War\", \"Western\"], sep = \"|\")\n",
    "\n",
    "user_info = pd.read_csv(\"C:/Users/Godwin/Documents/Workflow/Clustering/movielen/ml-100k/u.user\", names = [\"user id\", \"age\", \"gender\", \"occupation\", \"zip code\"], sep = '|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
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       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   user id  item id  rating  timestamp\n",
       "0      196      242       3  881250949\n",
       "1      186      302       3  891717742\n",
       "2       22      377       1  878887116\n",
       "3      244       51       2  880606923\n",
       "4      166      346       1  886397596"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing User Data\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie id</th>\n",
       "      <th>movie title</th>\n",
       "      <th>release date</th>\n",
       "      <th>video release date</th>\n",
       "      <th>IMDb URL</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie id        movie title release date  video release date  \\\n",
       "0         1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1         2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2         3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "3         4  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
       "4         5     Copycat (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            IMDb URL  unknown  Action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
       "\n",
       "   Adventure  Animation  Children's  ...  Fantasy  Film-Noir  Horror  Musical  \\\n",
       "0          0          1           1  ...        0          0       0        0   \n",
       "1          1          0           0  ...        0          0       0        0   \n",
       "2          0          0           0  ...        0          0       0        0   \n",
       "3          0          0           0  ...        0          0       0        0   \n",
       "4          0          0           0  ...        0          0       0        0   \n",
       "\n",
       "   Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0        0       0         0    0        0  \n",
       "1        0        0       0         1    0        0  \n",
       "2        0        0       0         1    0        0  \n",
       "3        0        0       0         0    0        0  \n",
       "4        0        0       0         1    0        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing Movie Data\n",
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip code</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user id  age gender  occupation zip code\n",
       "0        1   24      M  technician    85711\n",
       "1        2   53      F       other    94043\n",
       "2        3   23      M      writer    32067\n",
       "3        4   24      M  technician    43537\n",
       "4        5   33      F       other    15213"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing User Info\n",
    "user_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "<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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>1997</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>1998</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>1997</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>1997</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user id  item id  rating  year  month  day\n",
       "0      196      242       3  1997      4   12\n",
       "1      186      302       3  1998      4    4\n",
       "2       22      377       1  1997      7   11\n",
       "3      244       51       2  1997     11   27\n",
       "4      166      346       1  1998      2    2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#User data engineering\n",
    "\n",
    "user_data['timestamp'] = [datetime.utcfromtimestamp(i).strftime('%d/%m/%Y') for i in (user_data['timestamp'])]\n",
    "user_data['timestamp'] = pd.to_datetime(user_data['timestamp'])\n",
    "\n",
    "user_data['year'] = pd.DatetimeIndex(user_data['timestamp']).year\n",
    "user_data['month'] = pd.DatetimeIndex(user_data['timestamp']).month\n",
    "user_data['day'] = pd.DatetimeIndex(user_data['timestamp']).day\n",
    "user_data.drop(['timestamp'], axis = 1, inplace = True)\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user id    0\n",
       "item id    0\n",
       "rating     0\n",
       "year       0\n",
       "month      0\n",
       "day        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "user_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item id</th>\n",
       "      <th>movie title</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item id  movie title  unknown  Action  Adventure  Animation  Children's  \\\n",
       "0        1   Toy Story         0       0          0          1           1   \n",
       "1        2   GoldenEye         0       1          1          0           0   \n",
       "2        3  Four Rooms         0       0          0          0           0   \n",
       "3        4  Get Shorty         0       1          0          0           0   \n",
       "4        5     Copycat         0       0          0          0           0   \n",
       "\n",
       "   Comedy  Crime  Documentary  ...  Fantasy  Film-Noir  Horror  Musical  \\\n",
       "0       1      0            0  ...        0          0       0        0   \n",
       "1       0      0            0  ...        0          0       0        0   \n",
       "2       0      0            0  ...        0          0       0        0   \n",
       "3       1      0            0  ...        0          0       0        0   \n",
       "4       0      1            0  ...        0          0       0        0   \n",
       "\n",
       "   Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0        0       0         0    0        0  \n",
       "1        0        0       0         1    0        0  \n",
       "2        0        0       0         1    0        0  \n",
       "3        0        0       0         0    0        0  \n",
       "4        0        0       0         1    0        0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Movie Data Engineering\n",
    "movie_data.drop(['video release date', 'release date', 'IMDb URL'], axis = 1, inplace = True)\n",
    "\n",
    "movie_data['movie title'] = [i.split('(')[0] for i in movie_data['movie title']]\n",
    "\n",
    "movie_data.rename(columns = {'movie id': 'item id'}, inplace = True)\n",
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item id        0\n",
       "movie title    0\n",
       "unknown        0\n",
       "Action         0\n",
       "Adventure      0\n",
       "Animation      0\n",
       "Children's     0\n",
       "Comedy         0\n",
       "Crime          0\n",
       "Documentary    0\n",
       "Drama          0\n",
       "Fantasy        0\n",
       "Film-Noir      0\n",
       "Horror         0\n",
       "Musical        0\n",
       "Mystery        0\n",
       "Romance        0\n",
       "Sci-Fi         0\n",
       "Thriller       0\n",
       "War            0\n",
       "Western        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "movie_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = user_data.merge(user_info, on = 'user id')\n",
    "data = movie_data.merge(df, on = 'item id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting datapoints where movies were rated by atleast 100 people\n",
    "data = data.groupby(['item id'])\n",
    "data = data.filter(lambda x: x['user id'].count() > 100)\n",
    "\n",
    "# Selecting datapoints where rating is above 4\n",
    "data = data[data['rating'] >= 4]\n",
    "\n",
    "# Selecting the first 200 unique movies\n",
    "item_list = np.unique(data['item id'])[:200]\n",
    "data = data.loc[data['item id'].isin(item_list)]\n",
    "\n",
    "#Selecting the first 500 unique users\n",
    "first_500 = np.unique(data['user id'])[:500]\n",
    "data = data.loc[data['user id'].isin(first_500)]\n",
    "\n",
    "#Distinct Users\n",
    "users = np.unique(data['user id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting Movies a user likes\n",
    "def moviesListForUsers(users, users_data):\n",
    "    # users = a list of users IDs\n",
    "    # users_data = a dataframe of users favourite movies or users watched movies\n",
    "    users_movies_list = []\n",
    "    for user in users:\n",
    "        users_movies_list.append(str(list(users_data[users_data['userId'] == user]['movieId'])).split('[')[1].split(']')[0])\n",
    "    return users_movies_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "data['occupation'] = le.fit_transform(data['occupation'])\n",
    "occupation_mapping = dict(zip(le.classes_, range(len(le.classes_))))\n",
    "\n",
    "data['gender'] = le.fit_transform(data['gender'])\n",
    "gender_mapping = dict(zip(le.classes_, range(len(le.classes_))))\n",
    "\n",
    "#data['movie title'] = le.fit_transform(data['movie title'])\n",
    "#title_mapping = dict(zip(le.classes_, range(len(le.classes_))))\n",
    "data_copy = data.copy()\n",
    "\n",
    "data.drop(['movie title', 'zip code', 'month', 'day','year', 'occupation', 'gender','rating'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25f005ce788>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Elbow method for choosing number of Clusters\n",
    "\n",
    "k = range(2,10)\n",
    "distortion = []\n",
    "distortions = []\n",
    "\n",
    "for i in k:\n",
    "    model = KMeans(n_clusters = i +2, max_iter = 200, random_state= 0)\n",
    "    model.fit(data)\n",
    "    #distortion.append(sum(np.min(cdist(data, model.cluster_centers_, 'euclidean'), axis=1)) / data.shape[0])\n",
    "    distortion.append(model.inertia_)\n",
    "\n",
    "for i in range(len(distortion) -1):\n",
    "    distortions.append(distortion[i] - distortion[i+1])\n",
    "\n",
    "len_differences = len(distortions)\n",
    "X_differences = range(1, len_differences + 1)\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize = (20,7))\n",
    "ax[0].plot(k, distortion)\n",
    "ax[1].plot(X_differences,distortions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Silhouette method for choosing number of clusters\n",
    "\n",
    "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
    "silhouette_avg = []\n",
    "for num_clusters in range_n_clusters:\n",
    "      # initialise kmeans\n",
    "      kmeans = KMeans(n_clusters=num_clusters)\n",
    "      kmeans.fit(data)\n",
    "      cluster_labels = kmeans.labels_\n",
    " \n",
    "      # silhouette score\n",
    "      silhouette_avg.append(silhouette_score(data, cluster_labels))\n",
    "\n",
    "plt.plot(range_n_clusters,silhouette_avg,'bx-')\n",
    "plt.xlabel('Values of K') \n",
    "plt.ylabel('Silhouette score') \n",
    "plt.title('Silhouette analysis For Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters= 4, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "clusters = kmeans.fit_predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assigning Users to Clusters\n",
    "df = {'user id': data['user id'], 'Clusters': clusters}\n",
    "users_cluster = pd.DataFrame(df)\n",
    "#users_cluster.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4569\n",
       "3    3735\n",
       "2    3685\n",
       "0    3523\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_cluster['Clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Returns Movies in a cluster\n",
    "\n",
    "def clustersMovies(users_cluster, users_data):\n",
    "    #User_cluster - Dataframe that consist of user id and their respective cluster\n",
    "    #Users_data - Dataframe that consist of user data\n",
    "\n",
    "    clusters = list(users_cluster['Clusters'])\n",
    "    each_cluster_movies = list()\n",
    "    for i in range(len(np.unique(clusters))):\n",
    "        users_list = list(users_cluster[users_cluster['Clusters'] == i]['user id'])\n",
    "        users_movies_list = list()\n",
    "        for user in users_list:    \n",
    "            users_movies_list.extend(list(users_data[users_data['user id'] == user]['item id']))\n",
    "        users_movies_counts = list()\n",
    "        users_movies_counts.extend([[movie, users_movies_list.count(movie)] for movie in np.unique(users_movies_list)])\n",
    "        each_cluster_movies.append(pd.DataFrame(users_movies_counts, columns=['item id', 'Count']).sort_values(by = ['Count'], ascending = False).reset_index(drop=True))\n",
    "    return each_cluster_movies\n",
    "\n",
    "cluster_movies = clustersMovies(users_cluster, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>item id</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>3475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>174</td>\n",
       "      <td>3223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98</td>\n",
       "      <td>2913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>2908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>2832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item id  Count\n",
       "0       50   3475\n",
       "1      174   3223\n",
       "2       98   2913\n",
       "3      100   2908\n",
       "4       56   2832"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_movies[1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving Clustering Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "class saveLoadFiles:\n",
    "    def save(self, filename, data):\n",
    "        try:\n",
    "            file = open(filename + '.pkl', 'wb')\n",
    "            pickle.dump(data, file)\n",
    "        except:\n",
    "            err = 'Error: {0}, {1}'.format(exc_info()[0], exc_info()[1])\n",
    "            print(err)\n",
    "            file.close()\n",
    "            return [False, err]\n",
    "        else:\n",
    "            file.close()\n",
    "            return [True]\n",
    "    def load(self, filename):\n",
    "        try:\n",
    "            file = open(filename + '.pkl', 'rb')\n",
    "        except:\n",
    "            err = 'Error: {0}, {1}'.format(exc_info()[0], exc_info()[1])\n",
    "            print(err)\n",
    "            file.close()\n",
    "            return [False, err]\n",
    "        else:\n",
    "            data = pickle.load(file)\n",
    "            file.close()\n",
    "            return data\n",
    "    def loadClusterMoviesDataset(self):\n",
    "        return self.load('clusters_movies_dataset')\n",
    "    def saveClusterMoviesDataset(self, data):\n",
    "        return self.save('clusters_movies_dataset', data)\n",
    "    def loadUsersClusters(self):\n",
    "        return self.load('users_clusters')\n",
    "    def saveUsersClusters(self, data):\n",
    "        return self.save('users_clusters', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saveLoadFile = saveLoadFiles()\n",
    "saveLoadFile.saveClusterMoviesDataset(cluster_movies)\n",
    "saveLoadFile.saveUsersClusters(users_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Returns the movies a particular user liked\n",
    "def getMoviesOfUser(user_id, users_data):\n",
    "    return list(users_data[users_data['user id'] == user_id]['item id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recommending Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "class userRequestedFor:\n",
    "    def __init__(self, user_id, users_data):\n",
    "        self.users_data = users_data.copy()\n",
    "        self.user_id = user_id\n",
    "        # Find User Cluster\n",
    "        users_cluster = saveLoadFiles().loadUsersClusters()\n",
    "        self.user_cluster = np.unique(users_cluster[users_cluster['user id'] == self.user_id]['Clusters'])\n",
    "    \n",
    "        # Load User Cluster Movies Dataframe\n",
    "        self.movies_list = saveLoadFiles().loadClusterMoviesDataset()\n",
    "\n",
    "        self.cluster_movies_list = []\n",
    "        for i in self.user_cluster:\n",
    "            self.cluster_movies = self.movies_list[i] # dataframe\n",
    "            self.cluster_movies_list.extend(list(self.cluster_movies['item id'])) # list\n",
    "    def updatedFavouriteMoviesList(self, new_movie_Id):\n",
    "        if new_movie_Id in self.cluster_movies_list:\n",
    "            self.cluster_movies.loc[self.cluster_movies['item id'] == new_movie_Id, 'Count'] += 1\n",
    "        else:\n",
    "            self.cluster_movies = self.cluster_movies.append([{'item id':new_movie_Id, 'Count': 1}], ignore_index=True)\n",
    "        self.cluster_movies.sort_values(by = ['Count'], ascending = False, inplace= True)\n",
    "        self.movies_list[self.user_cluster] = self.cluster_movies\n",
    "        saveLoadFiles().saveClusterMoviesDataset(self.movies_list)\n",
    "\n",
    "    def recommendMostFavouriteMovies(self):\n",
    "        try:\n",
    "            user_movies = getMoviesOfUser(self.user_id, self.users_data)\n",
    "            cluster_movies_list = self.cluster_movies_list.copy()\n",
    "            for user_movie in user_movies:\n",
    "                if user_movie in cluster_movies_list:\n",
    "                    cluster_movies_list.remove(user_movie)\n",
    "            return [True, cluster_movies_list]\n",
    "        except KeyError:\n",
    "            err = \"User history does not exist\"\n",
    "            print(err)\n",
    "            return [False, err]\n",
    "        except:\n",
    "            err = 'Error: {0}, {1}'.format(exc_info()[0], exc_info()[1])\n",
    "            print(err)\n",
    "            return [False, err]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie title:  ['Raiders of the Lost Ark ']\n",
      "Movie title:  ['Silence of the Lambs, The ']\n",
      "Movie title:  ['Fargo ']\n",
      "Movie title:  ['Pulp Fiction ']\n",
      "Movie title:  ['Return of the Jedi ']\n",
      "Movie title:  ['Shawshank Redemption, The ']\n",
      "Movie title:  ['Empire Strikes Back, The ']\n",
      "Movie title:  ['Fugitive, The ']\n",
      "Movie title:  ['Toy Story ']\n",
      "Movie title:  ['Godfather, The ']\n"
     ]
    }
   ],
   "source": [
    "user12Recommendations = userRequestedFor(9, data).recommendMostFavouriteMovies()[1]\n",
    "for movie in user12Recommendations[:10]:\n",
    "    title = np.unique(list(data_copy.loc[data_copy['item id'] == movie]['movie title']))\n",
    "    print('Movie title: ', title)"
   ]
  }
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