Hybrid recommendation algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "egyptian-importance",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "focal-mystery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully connected to database.\n"
     ]
    }
   ],
   "source": [
    "#importing PostgreSQLconnection class from my PostgreSQL_Database_wrapper file and creating an instance of it\n",
    "from PostgreSQL import connectionPostgreSQL\n",
    "db=connectionPostgreSQL(\"DRecommenderSystem_db\",\"postgres\",\"YASH\",\"localhost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "comprehensive-embassy",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Gadhvi (He thought he was Gandhi)</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Yaaram</td>\n",
       "      <td>4.4000000000000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Aur Pyaar Ho Gaya</td>\n",
       "      <td>4.7000000000000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Yahaan</td>\n",
       "      <td>7.4000000000000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>A Question Mark</td>\n",
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      "text/plain": [
       "   userid  movie_id                        movie_title              rating\n",
       "0       1         1  Gadhvi (He thought he was Gandhi)                   7\n",
       "1       3         3                             Yaaram  4.4000000000000004\n",
       "2       5         5                  Aur Pyaar Ho Gaya  4.7000000000000002\n",
       "3       6         6                             Yahaan  7.4000000000000004\n",
       "4       8         8                    A Question Mark  5.5999999999999996"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query=  '''      \n",
    "        SELECT Rating.userId,Rating.Movie_id,Rating.Movie_Title,Rating.Rating\n",
    "        FROM Rating\n",
    "        LEFT JOIN Movies\n",
    "        ON Rating.Movie_id = Movies.Movie_id;\n",
    "        '''\n",
    "data=db.run_query(query)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "85641259",
   "metadata": {},
   "outputs": [],
   "source": [
    "MatrixData=data.drop(['movie_title'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "stock-antenna",
   "metadata": {},
   "outputs": [
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       "userid    1      3      5      6      8      9      10     11     12     \\\n",
       "movie_id                                                                  \n",
       "1           7.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3           NaN    4.4    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
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       "6           NaN    NaN    NaN    7.4    NaN    NaN    NaN    NaN    NaN   \n",
       "8           NaN    NaN    NaN    NaN    5.6    NaN    NaN    NaN    NaN   \n",
       "\n",
       "userid    13     ...  15482  15487  15488  15490  15492  15493  15494  15503  \\\n",
       "movie_id         ...                                                           \n",
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       "6           NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "8           NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "userid    15505  15508  \n",
       "movie_id                \n",
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       "3           NaN    NaN  \n",
       "5           NaN    NaN  \n",
       "6           NaN    NaN  \n",
       "8           NaN    NaN  \n",
       "\n",
       "[5 rows x 5659 columns]"
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     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "\n",
    "MovieUserMatrix = MatrixData.pivot_table(index='movie_id',columns='userid',values='rating')\n",
    "MovieUserMatrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "wanted-running",
   "metadata": {},
   "outputs": [
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       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15494</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15503</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15505</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15508</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5659 rows × 5659 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userid    1      3      5      6      8      9      10     11     12     \\\n",
       "movie_id                                                                  \n",
       "1           7.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "3          -1.0    4.4   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "5          -1.0   -1.0    4.7   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "6          -1.0   -1.0   -1.0    7.4   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "8          -1.0   -1.0   -1.0   -1.0    5.6   -1.0   -1.0   -1.0   -1.0   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "15493      -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "15494      -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "15503      -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "15505      -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "15508      -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "\n",
       "userid    13     ...  15482  15487  15488  15490  15492  15493  15494  15503  \\\n",
       "movie_id         ...                                                           \n",
       "1          -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "3          -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "5          -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "6          -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "8          -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "...         ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "15493      -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0    6.1   -1.0   -1.0   \n",
       "15494      -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0    6.2   -1.0   \n",
       "15503      -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0    5.8   \n",
       "15505      -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "15508      -1.0  ...   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   \n",
       "\n",
       "userid    15505  15508  \n",
       "movie_id                \n",
       "1          -1.0   -1.0  \n",
       "3          -1.0   -1.0  \n",
       "5          -1.0   -1.0  \n",
       "6          -1.0   -1.0  \n",
       "8          -1.0   -1.0  \n",
       "...         ...    ...  \n",
       "15493      -1.0   -1.0  \n",
       "15494      -1.0   -1.0  \n",
       "15503      -1.0   -1.0  \n",
       "15505       4.5   -1.0  \n",
       "15508      -1.0    6.2  \n",
       "\n",
       "[5659 rows x 5659 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MovieUserMatrix_Y=MovieUserMatrix.fillna(-1)\n",
    "MovieUserMatrix_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "going-chess",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>userid</th>\n",
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       "      <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",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>15508</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <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>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5659 rows × 5659 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userid    1      3      5      6      8      9      10     11     12     \\\n",
       "movie_id                                                                  \n",
       "1           1.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3           0.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5           0.0    0.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "6           0.0    0.0    0.0    1.0    0.0    0.0    0.0    0.0    0.0   \n",
       "8           0.0    0.0    0.0    0.0    1.0    0.0    0.0    0.0    0.0   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "15493       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "15494       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "15503       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "15505       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "15508       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "userid    13     ...  15482  15487  15488  15490  15492  15493  15494  15503  \\\n",
       "movie_id         ...                                                           \n",
       "1           0.0  ...    0.0    0.0    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    0.0    0.0   \n",
       "5           0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "6           0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "8           0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "...         ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "15493       0.0  ...    0.0    0.0    0.0    0.0    0.0    1.0    0.0    0.0   \n",
       "15494       0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    1.0    0.0   \n",
       "15503       0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    1.0   \n",
       "15505       0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "15508       0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "userid    15505  15508  \n",
       "movie_id                \n",
       "1           0.0    0.0  \n",
       "3           0.0    0.0  \n",
       "5           0.0    0.0  \n",
       "6           0.0    0.0  \n",
       "8           0.0    0.0  \n",
       "...         ...    ...  \n",
       "15493       0.0    0.0  \n",
       "15494       0.0    0.0  \n",
       "15503       0.0    0.0  \n",
       "15505       1.0    0.0  \n",
       "15508       0.0    1.0  \n",
       "\n",
       "[5659 rows x 5659 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MovieUserMatrix_R=MovieUserMatrix.copy()\n",
    "MovieUserMatrix_R=MovieUserMatrix_R.where(~MovieUserMatrix_R.notna(), 1)\n",
    "MovieUserMatrix_R=MovieUserMatrix_R.fillna(0)\n",
    "MovieUserMatrix_R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "infinite-diesel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sanity check\n",
    "MovieUserMatrix_Y.shape==MovieUserMatrix_R.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "paperback-sheffield",
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting values out of pandas dataframe into numpy-array because my alogorithm accept numpy-array as parameter\n",
    "Y=MovieUserMatrix_Y.values\n",
    "R=MovieUserMatrix_R.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bigger-thriller",
   "metadata": {},
   "source": [
    "# Hybrid recommendation approach "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "separated-auction",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implementation of Collaborative filtering Cost function and gradient, regularized and non regularized\n",
    "def  cofiCostFunc(params, Y, R, num_users, num_movies, num_features, Lambda):\n",
    "    \"\"\"\n",
    "            calculates Collaborative filtering Cost function and gradient both regularized and non regularized\n",
    "            \n",
    "            Parameters\n",
    "            ----------\n",
    "            params: numpy array-like\n",
    "                    initial values for users parameter vectors and movies feature vectors\n",
    "            \n",
    "            Y: numpy array-like\n",
    "                    user-item interaction matrix\n",
    "            R: numpy arra-like\n",
    "                    Its a binary-valued indicator matrix for user-item interaction matrix\n",
    "            num_users: int-like\n",
    "                    total number of users\n",
    "            num_movies: int-like\n",
    "                    total number of movies\n",
    "            Lambda: Float-like\n",
    "                    Regularization parameter\n",
    "                    \n",
    "            Returns\n",
    "            -------\n",
    "            J:Float-like\n",
    "                    Cost\n",
    "            grad:Float-like\n",
    "                    Gradient\n",
    "            reg_J:Float-like\n",
    "                    Regularized Cost\n",
    "            reg_grad:Float-like\n",
    "                    Regularized gradient\n",
    "    \"\"\"\n",
    "    # Unfold the params\n",
    "    X = params[:num_movies*num_features].reshape(num_movies,num_features)\n",
    "    Theta = params[num_movies*num_features:].reshape(num_users,num_features)\n",
    "    \n",
    "    predictions =  np.dot(X,Theta.T)\n",
    "    err = (predictions - Y)\n",
    "    J = 1/2 * np.sum((err**2) * R)# multiplying by R will consider only those values for which value of (i,j)=r(i,j)=1\n",
    "    \n",
    "    #compute regularized cost function\n",
    "    reg_X =  Lambda/2 * np.sum(Theta**2)\n",
    "    reg_Theta = Lambda/2 *np.sum(X**2)\n",
    "    reg_J = J + reg_X + reg_Theta\n",
    "    \n",
    "    # Compute gradient\n",
    "    X_grad = np.dot(err*R,Theta)\n",
    "    Theta_grad = np.dot((err*R).T,X)\n",
    "    grad = np.append(X_grad.flatten(),Theta_grad.flatten()) #flattening calculated gradients of 4*3 dim into 12 real number one dim of vector for optimization algo since optimization algorithms deals with vectos not matrix \n",
    "    \n",
    "    # Compute regularized gradient\n",
    "    reg_X_grad = X_grad + Lambda*X\n",
    "    reg_Theta_grad = Theta_grad + Lambda*Theta\n",
    "    reg_grad = np.append(reg_X_grad.flatten(),reg_Theta_grad.flatten())\n",
    "    #, reg_J, reg_grad\n",
    "    \n",
    "    return J, grad, reg_J, reg_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "false-record",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gradient descent implementation\n",
    "def gradientDescent(initial_parameters,Y,R,num_users,num_movies,num_features,alpha,num_iters,Lambda):\n",
    "    \"\"\"\n",
    "            Optimize X and Theta\n",
    "            \n",
    "            Parameters\n",
    "            ----------\n",
    "            initial_parameters: numpy array-like\n",
    "                    initial values for users parameter vectors and movies feature vectors\n",
    "            \n",
    "            Y: numpy array-like\n",
    "                    user-item interaction matrix\n",
    "            R: numpy arra-like\n",
    "                    Its a binary-valued indicator matrix for user-item interaction matrix\n",
    "            num_users: int-like\n",
    "                    total number of users\n",
    "            num_movies: int-like\n",
    "                    total number of movies\n",
    "            alpha: Float-lik\n",
    "                    learning rate\n",
    "            num_iters: integer-like\n",
    "                    number of iterations of optimization algorithm\n",
    "            Lambda: Float-like\n",
    "                    Regularization parameter\n",
    "                    \n",
    "            Returns\n",
    "            -------\n",
    "            paramsFinal: numpy-array-like\n",
    "                    Learned user parameters and feature vectors for movies\n",
    "            J_history: numpy-array-like\n",
    "                     History of decrease in cost as gradeint descent moves towords global minima\n",
    "    \"\"\"\n",
    "    # unfold the parameters\n",
    "    X = initial_parameters[:num_movies*num_features].reshape(num_movies,num_features)\n",
    "    Theta = initial_parameters[num_movies*num_features:].reshape(num_users,num_features)\n",
    "    \n",
    "    J_history =[]\n",
    "    \n",
    "    for i in range(num_iters):\n",
    "        params = np.append(X.flatten(),Theta.flatten())\n",
    "        cost, grad = cofiCostFunc(params, Y, R, num_users, num_movies, num_features, Lambda)[2:]\n",
    "        \n",
    "        # unfold grad\n",
    "        X_grad = grad[:num_movies*num_features].reshape(num_movies,num_features)\n",
    "        Theta_grad = grad[num_movies*num_features:].reshape(num_users,num_features)\n",
    "        X = X - (alpha * X_grad)\n",
    "        Theta = Theta - (alpha * Theta_grad)\n",
    "        J_history.append(cost)\n",
    "    \n",
    "    paramsFinal = np.append(X.flatten(),Theta.flatten())\n",
    "    return paramsFinal , J_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "speaking-istanbul",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalizeRatings(Y, R):\n",
    "    \"\"\"\n",
    "    normalized Y so that each movie has a rating of 0 on average, and returns the mean rating in Ymean.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    Y: numpy array-like\n",
    "                    user-item interaction matrix\n",
    "    R: numpy arra-like\n",
    "                    Its a binary-valued indicator matrix for user-item interaction matrix\n",
    "    Ynorm:numpy array-like\n",
    "                    Normalize Y\n",
    "    Ymean:numpy array-like\n",
    "                    Mean of all movies\n",
    "    \"\"\"\n",
    "    \n",
    "    m,n = Y.shape[0], Y.shape[1]\n",
    "    Ymean = np.zeros((m,1))\n",
    "    Ynorm = np.zeros((m,n))\n",
    "    \n",
    "    for i in range(m):\n",
    "        Ymean[i] = np.sum(Y[i,:])/np.count_nonzero(R[i,:])\n",
    "        Ynorm[i,R[i,:]==1] = Y[i,R[i,:]==1] - Ymean[i]\n",
    "    return Ynorm, Ymean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "widespread-morris",
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeCost(X, y, theta):\n",
    "    \"\"\"\n",
    "            calculates cost for given value of θ \n",
    "            \n",
    "            Parameters\n",
    "            ----------\n",
    "            X: numpy-array-like\n",
    "               matrix of features for all movies\n",
    "               \n",
    "            y: numpy-array-like\n",
    "               binary vector of web-application user movie ratings (y ∈ R^9742) where 9742 is the number of movies rated by web-app user\n",
    "                    \n",
    "            theta:numpy-array-like\n",
    "               value of θ for web application user\n",
    "               \n",
    "            Returns\n",
    "            -------\n",
    "            j: float-like\n",
    "               cost\n",
    "    \"\"\"\n",
    "    m=y.size\n",
    "    s=np.dot(X,theta)-y\n",
    "    j=(1/(2*m))*(np.dot(np.transpose(s),s))\n",
    "    return j\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "removable-stevens",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CBgradientDescent(X, y, theta, alpha, num_iters):\n",
    "    \"\"\"\n",
    "    Performs gradient descent to learn `theta`. Updates theta by taking `num_iters`\n",
    "    gradient steps with learning rate `alpha`.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X : array_like\n",
    "        matrix of features for all movies\n",
    "    \n",
    "    y : array_like\n",
    "        binary vector of ∈ R^9742 movies where 9742 is the number of movies\n",
    "    \n",
    "    theta : array_like\n",
    "        Initial values for the web-applicaiton user parameters. \n",
    "    \n",
    "    alpha : float\n",
    "        The learning rate.\n",
    "    \n",
    "    num_iters : int\n",
    "        The number of iterations for gradient descent. \n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    theta : array_like\n",
    "        The learned parameters. \n",
    "    \"\"\"\n",
    "    m = float(y.shape[0])\n",
    "    theta = theta.copy()\n",
    "    for i in range(num_iters):\n",
    "        theta=(theta)-(alpha/m)*(np.dot(np.transpose((np.dot(X,theta)-y)),X))\n",
    "    return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "simplified-determination",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(X,my_ratings,moviesdataset):\n",
    "    \"\"\"\n",
    "    Performs prediction.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X : array_like\n",
    "        matrix of features for all movies\n",
    "    \n",
    "    my_ratings : numpy-array-like\n",
    "        Binary vector of web-application user movie ratings (y ∈ R^9742) where 9742 is the number of movies rated by web-app user\n",
    "                    \n",
    "    moviesdataset : pandas dataframe-like\n",
    "        Dataframe contains movieid and title for all 9742 movies\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    sorted_data : array_like\n",
    "        top-N recommendation\n",
    "    \n",
    "    \"\"\"\n",
    "    out_arr = my_ratings[np.nonzero(my_ratings)]\n",
    "    out_arr=out_arr.reshape(-1,1)\n",
    "    idx = np.where(my_ratings)[0]\n",
    "    X_1=[X[x] for x in idx]\n",
    "    X_1=np.array(X_1)\n",
    "    y=out_arr\n",
    "    y=np.reshape(y, -1)\n",
    "    theta =CBgradientDescent(X_1,y,np.zeros((100)),0.001,4000)\n",
    "    #mean=np.reshape(Ymean, -1)\n",
    "    p = X @ theta.T\n",
    "    #p=p+mean\n",
    "    p=np.reshape(p, -1)\n",
    "    predictedData=moviesdataset.copy()\n",
    "    predictedData['Pridiction']=p\n",
    "    sorted_data=predictedData.sort_values(by=['Pridiction'],ascending=False)\n",
    "    return sorted_data[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "above-wedding",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(Yratings,Rratings):\n",
    "    \"\"\"\n",
    "    train and test split\n",
    "    Parameters\n",
    "    ----------\n",
    "    Yratings: numpy array-like\n",
    "                    user-item interaction matrix\n",
    "    Rratings: numpy arra-like\n",
    "                    Its a binary-valued indicator matrix for user-item interaction matrix\n",
    "                    \n",
    "    Returns\n",
    "    -------\n",
    "    Ytrain: numpy array-like\n",
    "                   train user-item interaction matrix\n",
    "    Ytest:  numpy array-like\n",
    "                   test user-item interaction matrix\n",
    "    Rtrain: numpy array-like\n",
    "                   train its a binary-valued indicator matrix for user-item interaction matrix\n",
    "    Rtest:  numpy array-like\n",
    "                   test its a binary-valued indicator matrix for user-item interaction matrix\n",
    "    \n",
    "    \"\"\"\n",
    "    Ytest = np.zeros(Yratings.shape)\n",
    "    Ytrain = Yratings.copy()\n",
    "    Rtest = np.zeros(Rratings.shape)\n",
    "    Rtrain = Rratings.copy()\n",
    "    \n",
    "    for user in range(Yratings.shape[0]):\n",
    "        try:\n",
    "            test_ratings = np.random.choice(Yratings[user, :].nonzero()[0], \n",
    "                                            size=10, \n",
    "                                            replace=False)\n",
    "            Ytrain[user, test_ratings] = 0.\n",
    "            Ytest[user, test_ratings] = Yratings[user, test_ratings]\n",
    "            \n",
    "            Rtrain[user, test_ratings] = 0.\n",
    "            Rtest[user, test_ratings] = Rratings[user, test_ratings]\n",
    "        except ValueError:\n",
    "            test_ratings = np.random.choice(Yratings[user, :].nonzero()[0], \n",
    "                                            size=10, \n",
    "                                            replace=True)\n",
    "            Ytrain[user, test_ratings] = 0.\n",
    "            Ytest[user, test_ratings] = Yratings[user, test_ratings]\n",
    "            \n",
    "            Rtrain[user, test_ratings] = 0.\n",
    "            Rtest[user, test_ratings] = Rratings[user, test_ratings]\n",
    "    # Test and training are truly disjoint\n",
    "    assert(np.all((Ytrain * Ytest) == 0))\n",
    "    assert(np.all((Rtrain * Rtest) == 0))\n",
    "    return Ytrain, Ytest, Rtrain, Rtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "driven-equipment",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Ytrain, Ytest, Rtrain, Rtest = train_test_split(Y,R)\n",
    "num_users = Ytrain.shape[1]\n",
    "num_movies = Ytrain.shape[0]\n",
    "num_features = 100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cd5f5e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X = np.random.randn(num_movies, num_features)\n",
    "Theta = np.random.randn(num_users, num_features)\n",
    "initial_parameters = np.append(X.flatten(),Theta.flatten())\n",
    "Lambda = 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e03ab3b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "paramsFinal,J_history = gradientDescent(initial_parameters,Ytrain,Rtrain,num_users,num_movies,num_features,0.001,50,Lambda)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1da79067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(J_history)\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"$J(\\Theta)$\")\n",
    "plt.title(\"Cost function using Gradient Descent\")\n",
    "X = paramsFinal[:num_movies*num_features].reshape(num_movies,num_features)\n",
    "Theta = paramsFinal[num_movies*num_features:].reshape(num_users,num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ceramic-subdivision",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def get_mse(pred, actual):\n",
    "\n",
    "    pred = pred[actual.nonzero()].flatten()\n",
    "    actual = actual[actual.nonzero()].flatten()\n",
    "    return mean_squared_error(pred, actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "wanted-public",
   "metadata": {},
   "outputs": [],
   "source": [
    "def learningCurve(Ytrain,Rtrain,Ytest,Rtest):\n",
    "    \"\"\"\n",
    "    compute data for learning Curve\n",
    "                    \n",
    "    Parameters\n",
    "    -------\n",
    "    Ytrain: numpy array-like\n",
    "                   train user-item interaction matrix\n",
    "    Ytest:  numpy array-like\n",
    "                   test user-item interaction matrix\n",
    "    Rtrain: numpy array-like\n",
    "                   train its a binary-valued indicator matrix for user-item interaction matrix\n",
    "    Rtest:  numpy array-like\n",
    "                   test its a binary-valued indicator matrix for user-item interaction matrix\n",
    "                   \n",
    "    Returns\n",
    "    -------\n",
    "    \n",
    "    train_size_hist: numpy array-like\n",
    "                   different training set sizes used\n",
    "    train_score_hist: numpy array-like\n",
    "                   scores relative to training size\n",
    "    test_size_hist: numpy array-like\n",
    "                   different test set sizes used\n",
    "    test_score_hist: numpy array-like\n",
    "                   scores relative to training size\n",
    "    \n",
    "    \"\"\"\n",
    "    Y=Ytrain\n",
    "    R=Rtrain\n",
    "    test_Y=Ytest\n",
    "    test_R=Rtest\n",
    "    train_size_hist=[]\n",
    "    train_score_hist=[]\n",
    "    test_size_hist=[]\n",
    "    test_score_hist=[]\n",
    "    size_list=[1,10,20,50,100]\n",
    "    for size in size_list:\n",
    "        Ytrain=Y[:size]\n",
    "        Rtrain=R[:size]\n",
    "        Ytest=test_Y[:size]\n",
    "        Rtest=test_R[:size]\n",
    "        num_users = Ytrain.shape[1]\n",
    "        num_movies = Ytrain.shape[0]\n",
    "        num_features = 10\n",
    "\n",
    "        # Set initial Parameters (Theta,X)\n",
    "        X = np.random.randn(num_movies, num_features)\n",
    "        Theta = np.random.randn(num_users, num_features)\n",
    "        initial_parameters = np.append(X.flatten(),Theta.flatten())\n",
    "        Lambda = 10\n",
    "\n",
    "        # Optimize parameters using Gradient Descent\n",
    "        paramsFinal, J_history = gradientDescent(initial_parameters,Ytrain,Rtrain,num_users,num_movies,num_features,0.001,500,Lambda)\n",
    "        X = paramsFinal[:num_movies*num_features].reshape(num_movies,num_features)\n",
    "        Theta = paramsFinal[num_movies*num_features:].reshape(num_users,num_features)\n",
    "        pred = X @ Theta.T\n",
    "        pred=pred*Rtrain\n",
    "        actual=Ytrain\n",
    "        train_size_hist.append(size)\n",
    "        train_score_hist.append(get_mse(pred, actual))\n",
    "        pred = X @ Theta.T\n",
    "        pred=pred*Rtest\n",
    "        actual=Ytest\n",
    "        test_size_hist.append(size)\n",
    "        test_score_hist.append(get_mse(pred, actual))\n",
    "    return train_size_hist,train_score_hist,test_size_hist,test_score_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dressed-policy",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size_hist,train_score_hist,test_size_hist,test_score_hist=learningCurve(Ytrain,Rtrain,Ytest,Rtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "signal-mathematics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.97, 1.05)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.plot(train_size_hist, train_score_hist, label = 'Training error')\n",
    "plt.plot(train_size_hist, test_score_hist, label = 'Validation error')\n",
    "plt.ylabel('MSE', fontsize = 14)\n",
    "plt.yticks(np.arange(0,2, 0.25))\n",
    "plt.xlabel('Training set size', fontsize = 14)\n",
    "plt.title('Learning curves for a Collaborative Filtering', fontsize = 18, y = 1.03)\n",
    "plt.legend()\n",
    "plt.ylim(0.97,1.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "proved-forth",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashg\\AppData\\Local\\Temp\\ipykernel_22436\\3831420912.py:9: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  moviesdataset[\"movie_title\"] = moviesdataset[\"movie_title\"].str.replace(r\"\\(.*\\)\",\"\")\n"
     ]
    },
    {
     "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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>gadhvi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>yaaram</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>aur pyaar ho gaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>yahaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>a question mark</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id        movie_title\n",
       "0         1            gadhvi \n",
       "1         3             yaaram\n",
       "2         5  aur pyaar ho gaya\n",
       "3         6             yahaan\n",
       "4         8    a question mark"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning and transforming moviesDataset to use it on web-application \n",
    "moviesdataset=data.drop(['userid','rating'],axis=1)\n",
    "moviesdataset=moviesdataset.drop_duplicates()\n",
    "\n",
    "moviesdataset=moviesdataset.sort_values(\"movie_id\")\n",
    "moviesdataset=moviesdataset.reset_index(drop=True)\n",
    "\n",
    "moviesdataset[\"movie_title\"] = moviesdataset[\"movie_title\"].str.lower() \n",
    "moviesdataset[\"movie_title\"] = moviesdataset[\"movie_title\"].str.replace(r\"\\(.*\\)\",\"\")\n",
    "moviesdataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "historical-delivery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New user ratings:\n",
      "\n",
      "Rated 4 for Movie\n",
      "kyon ki...\n",
      "Rated 4 for Movie\n",
      "photograph\n",
      "Rated 1 for Movie\n",
      "sampoorna mahabharat\n",
      "Rated 4 for Movie\n",
      "tarzan's daughter\n",
      "Rated 2 for Movie\n",
      "with luv... tumhaara\n"
     ]
    }
   ],
   "source": [
    "# Initialize my ratings\n",
    "my_ratings = np.zeros((9724,1))\n",
    "\n",
    "# test rating by web-applicaiton user\n",
    "my_ratings[5001] = 4 \n",
    "my_ratings[4001] = 4\n",
    "my_ratings[3001] = 4\n",
    "my_ratings[5501] = 2\n",
    "my_ratings[4501] = 1\n",
    "\n",
    "\n",
    "\n",
    "print(\"New user ratings:\\n\")\n",
    "for i in range(len(my_ratings)):\n",
    "    if my_ratings[i]>0:\n",
    "        print(\"Rated\",int(my_ratings[i]),\"for Movie\")\n",
    "        print((moviesdataset.iloc[i]).movie_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "according-passenger",
   "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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>Pridiction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>7961</td>\n",
       "      <td>kyon ki...</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>13825</td>\n",
       "      <td>tarzan's daughter</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4001</th>\n",
       "      <td>10839</td>\n",
       "      <td>photograph</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3343</th>\n",
       "      <td>8954</td>\n",
       "      <td>masti</td>\n",
       "      <td>3.140809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>4281</td>\n",
       "      <td>dus numbri</td>\n",
       "      <td>3.096112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>7397</td>\n",
       "      <td>kasoor</td>\n",
       "      <td>2.753307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5254</th>\n",
       "      <td>14423</td>\n",
       "      <td>tottaa pataaka item maal</td>\n",
       "      <td>2.718745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>2292</td>\n",
       "      <td>bhoothnath returns</td>\n",
       "      <td>2.506025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>879</td>\n",
       "      <td>always kabhi kabhi</td>\n",
       "      <td>2.501028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4339</th>\n",
       "      <td>11887</td>\n",
       "      <td>revolver rani</td>\n",
       "      <td>2.499519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id               movie_title  Pridiction\n",
       "3001      7961                kyon ki...    4.000000\n",
       "5001     13825         tarzan's daughter    4.000000\n",
       "4001     10839                photograph    4.000000\n",
       "3343      8954                     masti    3.140809\n",
       "1603      4281                dus numbri    3.096112\n",
       "2767      7397                    kasoor    2.753307\n",
       "5254     14423  tottaa pataaka item maal    2.718745\n",
       "881       2292        bhoothnath returns    2.506025\n",
       "376        879        always kabhi kabhi    2.501028\n",
       "4339     11887             revolver rani    2.499519"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top-N-movies\n",
    "prediction(X,my_ratings,moviesdataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "relevant-explorer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Movies_Datase.pkl']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    " \n",
    "# Save the model as a pickle in a file\n",
    "joblib.dump(X, 'Movies_Learned_Features.pkl')\n",
    "joblib.dump(moviesdataset, 'Movies_Datase.pkl')\n",
    "  "
   ]
  }
 ],
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