Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error,mean_absolute_percentage_error\n",
    "from scipy.spatial.distance import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "views = pd.read_csv('movies_views.csv' ,low_memory=False)\n",
    "ratings = pd.read_csv('movies_ratings.csv',low_memory=False)\n",
    "movies = pd.read_csv('movies_movie.csv',low_memory=False)\n",
    "\n",
    "movies.drop(['rating','portrait','landscape'],inplace=True, axis=1)\n",
    "ratings.drop('rorb_id',inplace=True, axis=1)\n",
    "ratings.rename(columns = {'ron':'m_id'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m_id</th>\n",
       "      <th>m_name</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   m_id                        m_name  release_year\n",
       "0     1                    Toy Story           1995\n",
       "1     2                      Jumanji           1995\n",
       "2     3             Grumpier Old Men           1995\n",
       "3     4            Waiting to Exhale           1995\n",
       "4     5  Father of the Bride Part II           1995"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>rby</th>\n",
       "      <th>m_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating  rby  m_id\n",
       "0       8  1.0   1.0\n",
       "1       8  1.0   3.0\n",
       "2       8  1.0   6.0\n",
       "3      10  1.0  42.0\n",
       "4      10  1.0  45.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>215</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>755</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>339</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>301</td>\n",
       "      <td>49</td>\n",
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      ],
      "text/plain": [
       "   m_id   sum  count\n",
       "0     1  1686    215\n",
       "1     2   755    110\n",
       "2     3   339     52\n",
       "3     4    33      7\n",
       "4     5   301     49"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "views.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centered_rating</th>\n",
       "      <th>rby</th>\n",
       "      <th>m_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centered_rating  rby  m_id\n",
       "0                8  1.0   1.0\n",
       "1                8  1.0   3.0\n",
       "2                8  1.0   6.0\n",
       "3               10  1.0  42.0\n",
       "4               10  1.0  45.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ratings.rename(columns = {'rating':'centered_rating'}, inplace = True)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m_id</th>\n",
       "      <th>m_name</th>\n",
       "      <th>release_year</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>average_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>1686</td>\n",
       "      <td>215</td>\n",
       "      <td>7.841860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>755</td>\n",
       "      <td>110</td>\n",
       "      <td>6.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1995</td>\n",
       "      <td>339</td>\n",
       "      <td>52</td>\n",
       "      <td>6.519231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1995</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>4.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1995</td>\n",
       "      <td>301</td>\n",
       "      <td>49</td>\n",
       "      <td>6.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>8758</td>\n",
       "      <td>Black Butler: Book of the Atlantic</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>8759</td>\n",
       "      <td>No Game No Life: Zero</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>8760</td>\n",
       "      <td>Flint</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8760</th>\n",
       "      <td>8761</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8761</th>\n",
       "      <td>8762</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules</td>\n",
       "      <td>1991</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8762 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      m_id                               m_name  release_year   sum  count  \\\n",
       "0        1                           Toy Story           1995  1686    215   \n",
       "1        2                             Jumanji           1995   755    110   \n",
       "2        3                    Grumpier Old Men           1995   339     52   \n",
       "3        4                   Waiting to Exhale           1995    33      7   \n",
       "4        5         Father of the Bride Part II           1995   301     49   \n",
       "...    ...                                  ...           ...   ...    ...   \n",
       "8757  8758  Black Butler: Book of the Atlantic           2017     8      1   \n",
       "8758  8759               No Game No Life: Zero           2017     7      1   \n",
       "8759  8760                               Flint           2017     7      1   \n",
       "8760  8761        Bungo Stray Dogs: Dead Apple           2018     7      1   \n",
       "8761  8762        Andrew Dice Clay: Dice Rules           1991     8      1   \n",
       "\n",
       "      average_rating  \n",
       "0           7.841860  \n",
       "1           6.863636  \n",
       "2           6.519231  \n",
       "3           4.714286  \n",
       "4           6.142857  \n",
       "...              ...  \n",
       "8757        8.000000  \n",
       "8758        7.000000  \n",
       "8759        7.000000  \n",
       "8760        7.000000  \n",
       "8761        8.000000  \n",
       "\n",
       "[8762 rows x 6 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msc = pd.merge(movies,views,on='m_id')\n",
    "msc['average_rating'] = msc['sum'] / msc['count']\n",
    "msc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>1686</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>755</td>\n",
       "      <td>110</td>\n",
       "      <td>6.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1995</td>\n",
       "      <td>339</td>\n",
       "      <td>52</td>\n",
       "      <td>6.519231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1995</td>\n",
       "      <td>301</td>\n",
       "      <td>49</td>\n",
       "      <td>6.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat</td>\n",
       "      <td>1995</td>\n",
       "      <td>805</td>\n",
       "      <td>102</td>\n",
       "      <td>7.892157</td>\n",
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       "    <tr>\n",
       "      <th>8228</th>\n",
       "      <td>8229</td>\n",
       "      <td>Big Short, The</td>\n",
       "      <td>2015</td>\n",
       "      <td>206</td>\n",
       "      <td>26</td>\n",
       "      <td>7.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8285</th>\n",
       "      <td>8286</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>2016</td>\n",
       "      <td>249</td>\n",
       "      <td>32</td>\n",
       "      <td>7.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8439</th>\n",
       "      <td>8440</td>\n",
       "      <td>Arrival</td>\n",
       "      <td>2016</td>\n",
       "      <td>207</td>\n",
       "      <td>26</td>\n",
       "      <td>7.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8477</th>\n",
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       "      <td>Rogue One: A Star Wars Story</td>\n",
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       "      <td>Logan</td>\n",
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       "      <td>25</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>962 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      m_id                         m_name  release_year   sum  count  \\\n",
       "0        1                     Toy Story           1995  1686    215   \n",
       "1        2                       Jumanji           1995   755    110   \n",
       "2        3              Grumpier Old Men           1995   339     52   \n",
       "4        5   Father of the Bride Part II           1995   301     49   \n",
       "5        6                          Heat           1995   805    102   \n",
       "...    ...                            ...           ...   ...    ...   \n",
       "8228  8229                Big Short, The           2015   206     26   \n",
       "8285  8286                      Zootopia           2016   249     32   \n",
       "8439  8440                       Arrival           2016   207     26   \n",
       "8477  8478  Rogue One: A Star Wars Story           2016   212     27   \n",
       "8505  8506                         Logan           2017   214     25   \n",
       "\n",
       "      average_rating  \n",
       "0           7.841860  \n",
       "1           6.863636  \n",
       "2           6.519231  \n",
       "4           6.142857  \n",
       "5           7.892157  \n",
       "...              ...  \n",
       "8228        7.923077  \n",
       "8285        7.781250  \n",
       "8439        7.961538  \n",
       "8477        7.851852  \n",
       "8505        8.560000  \n",
       "\n",
       "[962 rows x 6 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity_threshold = 25\n",
    "rating_popular_movie= msc.query('count >= @popularity_threshold')\n",
    "rating_popular_movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       m_id      m_name   sum  count  average_rating  centered_rating    rby\n",
       "0         1  Toy Story   1686    215         7.84186                8    1.0\n",
       "1         1  Toy Story   1686    215         7.84186                8    5.0\n",
       "2         1  Toy Story   1686    215         7.84186                9    7.0\n",
       "3         1  Toy Story   1686    215         7.84186                5   15.0\n",
       "4         1  Toy Story   1686    215         7.84186                9   17.0\n",
       "...     ...         ...   ...    ...             ...              ...    ...\n",
       "56306  8506      Logan    214     25         8.56000                8  567.0\n",
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       "56310  8506      Logan    214     25         8.56000               10  610.0\n",
       "\n",
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     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df = pd.merge(rating_popular_movie,ratings,on='m_id')\n",
    "df.drop('release_year', inplace=True, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "m_id   1     2     3     5     6     7     10    11    16    17    ...  7792  \\\n",
       "rby                                                                ...         \n",
       "1.0     8.0   NaN   8.0   NaN   8.0   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
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       "3.0     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "4.0     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "5.0     8.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "608.0   5.0   4.0   4.0   NaN   NaN   NaN   8.0   NaN   9.0   NaN  ...   NaN   \n",
       "609.0   6.0   NaN   NaN   NaN   NaN   NaN   8.0   NaN   NaN   NaN  ...   NaN   \n",
       "610.0  10.0   NaN   NaN   NaN  10.0   NaN   NaN   NaN   9.0   NaN  ...   6.0   \n",
       "611.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "612.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "\n",
       "m_id   7796  7967  7987  8074  8229  8286  8440  8478  8506  \n",
       "rby                                                          \n",
       "1.0     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2.0     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3.0     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4.0     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "5.0     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "608.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "609.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "610.0   NaN   7.0   7.0   9.0   8.0   8.0  10.0   8.0  10.0  \n",
       "611.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "612.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[605 rows x 962 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_matrix = df.pivot(index='rby', columns='m_id', values='centered_rating')\n",
    "user_movie_train_matrix=None\n",
    "user_movie_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cosine_similarity(user1, user2):\n",
    "    common_items = user_movie_matrix.loc[user1].dropna().index.intersection(user_movie_train_matrix.loc[user2].dropna().index)\n",
    "    if len(common_items) == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        user1_ratings = user_movie_train_matrix.loc[user1][common_items]\n",
    "        user2_ratings = user_movie_train_matrix.loc[user2][common_items]\n",
    "        return 1 - cosine(user1_ratings, user2_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_k_nearest_neighbors(user, k):\n",
    "    similarities = []\n",
    "    for other_user in user_movie_train_matrix.index:\n",
    "        if other_user != user:\n",
    "            similarity = compute_cosine_similarity(user, other_user)\n",
    "            similarities.append((other_user, similarity))\n",
    "    similarities.sort(key=lambda x: x[1], reverse=True)\n",
    "    return similarities[:k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_based_recommendation(user, k, rm):\n",
    "    if user not in user_movie_matrix.index:\n",
    "        return \"No recommendations available for this user\"\n",
    "    user_ratings = user_movie_matrix.loc[user].dropna()\n",
    "    if len(user_ratings) == 0:\n",
    "        return \"This user has not rated any movies\"\n",
    "    global user_movie_train_matrix\n",
    "    user_movie_train_matrix = user_movie_matrix.copy()\n",
    "    train_rated_movies = user_movie_train_matrix.loc[user].dropna()\n",
    "    num = int(0.2 * len(train_rated_movies))\n",
    "    np.random.seed(42)\n",
    "    movies_to_remove = np.random.choice(train_rated_movies.index, size=num, replace=False)\n",
    "    \n",
    "    user_movie_train_matrix.loc[user, movies_to_remove] = np.nan\n",
    "    \n",
    "    test_id = movies_to_remove.tolist()\n",
    "    user_df = df[(df['rby'] == user) & (df['m_id'].isin(test_id))]\n",
    "    actual_r = user_df[[\"m_id\", \"centered_rating\"]].reset_index(drop=True)\n",
    "    \n",
    "    neighbors = find_k_nearest_neighbors(user, k)\n",
    "    neighbor_ratings = []\n",
    "    neighbor_similarity_sum =[]\n",
    "    for neighbor in neighbors:\n",
    "        neighbor_user_id = neighbor[0]\n",
    "        neighbor_similarity = neighbor[1]\n",
    "        neighbor_ratings.append(user_movie_train_matrix.loc[neighbor_user_id].fillna(0) * neighbor_similarity)\n",
    "        neighbor_similarity_sum.append(user_movie_train_matrix.loc[neighbor_user_id].notnull() * abs(neighbor_similarity))\n",
    "        \n",
    "    neighbor_ratings_df = pd.concat(neighbor_ratings, axis=1).sum(axis=1)\n",
    "\n",
    "    neighbor_sum_df = pd.concat(neighbor_similarity_sum, axis=1).sum(axis=1)\n",
    "\n",
    "    \n",
    "    zz = pd.DataFrame({'m_id': neighbor_ratings_df.index, 'rating_s': neighbor_ratings_df.values})\n",
    "    xx = pd.DataFrame({'m_id': neighbor_sum_df.index, 'similarity_sum': neighbor_sum_df.values})\n",
    "    xz = pd.merge(zz,xx,on='m_id')\n",
    "    \n",
    "    xz['rating'] = round(xz['rating_s']/xz['similarity_sum'])\n",
    "    xz.drop(['similarity_sum','rating_s'],inplace=True, axis=1)\n",
    "\n",
    "    xz=xz.dropna()\n",
    "    \n",
    "    estimated_r=xz.copy()\n",
    "\n",
    "    merged_ratings = pd.merge(actual_r, estimated_r, on='m_id',  how='inner')\n",
    "    \n",
    "    if merged_ratings.shape[0]>0:\n",
    "        mae = mean_absolute_error(merged_ratings['centered_rating'], merged_ratings['rating'])\n",
    "        mape = mean_absolute_percentage_error(merged_ratings['centered_rating'], merged_ratings['rating'])\n",
    "    else:\n",
    "        mae=\"not available\"\n",
    "        mape=\"not available\"\n",
    "\n",
    "    user_ratings = user_movie_train_matrix.loc[user].dropna()\n",
    "    \n",
    "    unrated_movies = user_movie_matrix.columns.difference(user_ratings.index).tolist()\n",
    "\n",
    "    if len(unrated_movies) == 0:\n",
    "        return \"This user has rated all movies\"\n",
    "    if neighbor_ratings_df[unrated_movies].empty:\n",
    "        return \"There are no unrated movies left to recommend\"\n",
    "    recommended_movie = xz[xz['m_id'].isin(unrated_movies)].sort_values('rating',ascending=False)['m_id'].tolist()\n",
    "    \n",
    "    recommended_movie=recommended_movie[:rm]\n",
    "    tp=len(list(set(recommended_movie).intersection(test_id)))\n",
    "\n",
    "    print(\"no. of nearest neighbour :\",len(neighbors))\n",
    "    print(\"No. of recommended movies :\",len(recommended_movie))\n",
    "    print(\"TP :\",tp)\n",
    "    print(\"no. of Movies watched in test :\",len(test_id))\n",
    "    print(\"Precison :\",tp/len(recommended_movie))\n",
    "    print(\"recall :\",tp/len(test_id))\n",
    "    print(\"MAE :\",mae)\n",
    "    print(\"MAPE :\",mape)\n",
    "    recommended_movie=recommended_movie[:5]\n",
    "    movie_id_to_name = dict(zip(movies['m_id'], df['m_name']))\n",
    "    movie_names = [movie_id_to_name[m_id] for m_id in recommended_movie]\n",
    "    print(\"User-Neighborhood-Based Recommendation:\")\n",
    "    return movie_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of nearest neighbour : 15\n",
      "No. of recommended movies : 60\n",
      "TP : 1\n",
      "no. of Movies watched in test : 8\n",
      "Precison : 0.016666666666666666\n",
      "recall : 0.125\n",
      "MAE : 1.1428571428571428\n",
      "MAPE : 0.14200680272108843\n",
      "User-Neighborhood-Based Recommendation:\n",
      "['Nell ', 'Apollo 13 ', 'Casino ', 'Twelve Monkeys (a.k.a. 12 Monkeys) ', 'Heat ']\n"
     ]
    }
   ],
   "source": [
    "print(user_based_recommendation(123, 15,60))"
   ]
  }
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