P35. Unsupervised ML - Recommendation System Data Mining (Movies).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Unsupervised ML - Recommendation System Data Mining (Movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from scipy.spatial.distance import cosine, correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>Toy Story (1995)</td>\n",
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       "    <tr>\n",
       "      <th>8987</th>\n",
       "      <td>7087</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8988</th>\n",
       "      <td>7088</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>8989</th>\n",
       "      <td>7105</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8990</th>\n",
       "      <td>7113</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8991</th>\n",
       "      <td>7117</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8992 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId             movie  rating\n",
       "0          3  Toy Story (1995)     4.0\n",
       "1          6  Toy Story (1995)     5.0\n",
       "2          8  Toy Story (1995)     4.0\n",
       "3         10  Toy Story (1995)     4.0\n",
       "4         11  Toy Story (1995)     4.5\n",
       "...      ...               ...     ...\n",
       "8987    7087  GoldenEye (1995)     3.0\n",
       "8988    7088  GoldenEye (1995)     1.0\n",
       "8989    7105  GoldenEye (1995)     2.0\n",
       "8990    7113  GoldenEye (1995)     3.0\n",
       "8991    7117  GoldenEye (1995)     3.0\n",
       "\n",
       "[8992 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "movies=pd.read_csv('Database/Movie.csv')\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
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       "      <td>Jumanji (1995)</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5204</th>\n",
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       "      <td>Heat (1995)</td>\n",
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       "      <td>7117</td>\n",
       "      <td>Heat (1995)</td>\n",
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       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>7119</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>7120</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>7120</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7163</th>\n",
       "      <td>7120</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>8992 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "      userId                    movie  rating\n",
       "2569       1           Jumanji (1995)     3.5\n",
       "3724       2  Grumpier Old Men (1995)     4.0\n",
       "0          3         Toy Story (1995)     4.0\n",
       "5204       4              Heat (1995)     3.0\n",
       "7444       4         GoldenEye (1995)     4.0\n",
       "...      ...                      ...     ...\n",
       "6463    7117              Heat (1995)     5.0\n",
       "2567    7119         Toy Story (1995)     5.0\n",
       "2568    7120         Toy Story (1995)     4.5\n",
       "3723    7120           Jumanji (1995)     4.0\n",
       "7163    7120           Sabrina (1995)     4.0\n",
       "\n",
       "[8992 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by User IDs\n",
    "movies.sort_values(['userId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "4081"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique users in the dataset\n",
    "len(movies['userId'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique movies in the dataset\n",
    "len(movies['movie'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>movie</th>\n",
       "      <th>Father of the Bride Part II (1995)</th>\n",
       "      <th>GoldenEye (1995)</th>\n",
       "      <th>Grumpier Old Men (1995)</th>\n",
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       "      <th>Sabrina (1995)</th>\n",
       "      <th>Sudden Death (1995)</th>\n",
       "      <th>Tom and Huck (1995)</th>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <th>Waiting to Exhale (1995)</th>\n",
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       "      <td>4.0</td>\n",
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       "      <td>3.5</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4078</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
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       "movie  Father of the Bride Part II (1995)  GoldenEye (1995)  \\\n",
       "0                                     NaN               NaN   \n",
       "1                                     NaN               NaN   \n",
       "2                                     NaN               NaN   \n",
       "3                                     NaN               4.0   \n",
       "4                                     NaN               NaN   \n",
       "...                                   ...               ...   \n",
       "4076                                  4.0               NaN   \n",
       "4077                                  3.5               NaN   \n",
       "4078                                  NaN               3.0   \n",
       "4079                                  NaN               NaN   \n",
       "4080                                  NaN               NaN   \n",
       "\n",
       "movie  Grumpier Old Men (1995)  Heat (1995)  Jumanji (1995)  Sabrina (1995)  \\\n",
       "0                          NaN          NaN             3.5             NaN   \n",
       "1                          4.0          NaN             NaN             NaN   \n",
       "2                          NaN          NaN             NaN             NaN   \n",
       "3                          NaN          3.0             NaN             NaN   \n",
       "4                          NaN          NaN             3.0             NaN   \n",
       "...                        ...          ...             ...             ...   \n",
       "4076                       NaN          NaN             NaN             NaN   \n",
       "4077                       NaN          NaN             NaN             NaN   \n",
       "4078                       4.0          5.0             NaN             3.0   \n",
       "4079                       NaN          NaN             NaN             NaN   \n",
       "4080                       NaN          NaN             4.0             4.0   \n",
       "\n",
       "movie  Sudden Death (1995)  Tom and Huck (1995)  Toy Story (1995)  \\\n",
       "0                      NaN                  NaN               NaN   \n",
       "1                      NaN                  NaN               NaN   \n",
       "2                      NaN                  NaN               4.0   \n",
       "3                      NaN                  NaN               NaN   \n",
       "4                      NaN                  NaN               NaN   \n",
       "...                    ...                  ...               ...   \n",
       "4076                   NaN                  NaN               NaN   \n",
       "4077                   NaN                  NaN               4.0   \n",
       "4078                   1.0                  NaN               4.0   \n",
       "4079                   NaN                  NaN               5.0   \n",
       "4080                   NaN                  NaN               4.5   \n",
       "\n",
       "movie  Waiting to Exhale (1995)  \n",
       "0                           NaN  \n",
       "1                           NaN  \n",
       "2                           NaN  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "...                         ...  \n",
       "4076                        NaN  \n",
       "4077                        NaN  \n",
       "4078                        NaN  \n",
       "4079                        NaN  \n",
       "4080                        NaN  \n",
       "\n",
       "[4081 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting long data into wide data using pivot\n",
    "movies2=movies.pivot_table(index='userId',columns='movie',values='rating').reset_index(drop=True)\n",
    "movies2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>movie</th>\n",
       "      <th>Father of the Bride Part II (1995)</th>\n",
       "      <th>GoldenEye (1995)</th>\n",
       "      <th>Grumpier Old Men (1995)</th>\n",
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       "      <th>Sabrina (1995)</th>\n",
       "      <th>Sudden Death (1995)</th>\n",
       "      <th>Tom and Huck (1995)</th>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <th>Waiting to Exhale (1995)</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>7070</th>\n",
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       "  </tbody>\n",
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       "<p>4081 rows × 10 columns</p>\n",
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       "movie  Father of the Bride Part II (1995)  GoldenEye (1995)  \\\n",
       "3                                     NaN               NaN   \n",
       "6                                     NaN               NaN   \n",
       "8                                     NaN               NaN   \n",
       "10                                    NaN               4.0   \n",
       "11                                    NaN               NaN   \n",
       "...                                   ...               ...   \n",
       "7044                                  4.0               NaN   \n",
       "7070                                  3.5               NaN   \n",
       "7080                                  NaN               3.0   \n",
       "7087                                  NaN               NaN   \n",
       "7105                                  NaN               NaN   \n",
       "\n",
       "movie  Grumpier Old Men (1995)  Heat (1995)  Jumanji (1995)  Sabrina (1995)  \\\n",
       "3                          NaN          NaN             3.5             NaN   \n",
       "6                          4.0          NaN             NaN             NaN   \n",
       "8                          NaN          NaN             NaN             NaN   \n",
       "10                         NaN          3.0             NaN             NaN   \n",
       "11                         NaN          NaN             3.0             NaN   \n",
       "...                        ...          ...             ...             ...   \n",
       "7044                       NaN          NaN             NaN             NaN   \n",
       "7070                       NaN          NaN             NaN             NaN   \n",
       "7080                       4.0          5.0             NaN             3.0   \n",
       "7087                       NaN          NaN             NaN             NaN   \n",
       "7105                       NaN          NaN             4.0             4.0   \n",
       "\n",
       "movie  Sudden Death (1995)  Tom and Huck (1995)  Toy Story (1995)  \\\n",
       "3                      NaN                  NaN               NaN   \n",
       "6                      NaN                  NaN               NaN   \n",
       "8                      NaN                  NaN               4.0   \n",
       "10                     NaN                  NaN               NaN   \n",
       "11                     NaN                  NaN               NaN   \n",
       "...                    ...                  ...               ...   \n",
       "7044                   NaN                  NaN               NaN   \n",
       "7070                   NaN                  NaN               4.0   \n",
       "7080                   1.0                  NaN               4.0   \n",
       "7087                   NaN                  NaN               5.0   \n",
       "7105                   NaN                  NaN               4.5   \n",
       "\n",
       "movie  Waiting to Exhale (1995)  \n",
       "3                           NaN  \n",
       "6                           NaN  \n",
       "8                           NaN  \n",
       "10                          NaN  \n",
       "11                          NaN  \n",
       "...                         ...  \n",
       "7044                        NaN  \n",
       "7070                        NaN  \n",
       "7080                        NaN  \n",
       "7087                        NaN  \n",
       "7105                        NaN  \n",
       "\n",
       "[4081 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing the index values by unique user Ids\n",
    "movies2.index=movies['userId'].unique()\n",
    "movies2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "movie  Father of the Bride Part II (1995)  GoldenEye (1995)  \\\n",
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       "8                                     0.0               0.0   \n",
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       "11                                    0.0               0.0   \n",
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       "\n",
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       "...                        ...          ...             ...             ...   \n",
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       "7070                       0.0          0.0             0.0             0.0   \n",
       "7080                       4.0          5.0             0.0             3.0   \n",
       "7087                       0.0          0.0             0.0             0.0   \n",
       "7105                       0.0          0.0             4.0             4.0   \n",
       "\n",
       "movie  Sudden Death (1995)  Tom and Huck (1995)  Toy Story (1995)  \\\n",
       "3                      0.0                  0.0               0.0   \n",
       "6                      0.0                  0.0               0.0   \n",
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       "...                    ...                  ...               ...   \n",
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       "7087                   0.0                  0.0               5.0   \n",
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       "\n",
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       "6                           0.0  \n",
       "8                           0.0  \n",
       "10                          0.0  \n",
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       "[4081 rows x 10 columns]"
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     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "# Impute those NaNs with 0 values\n",
    "movies2.fillna(0,inplace=True)\n",
    "movies2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.55337157],\n",
       "       [0.        , 1.        , 0.        , ..., 0.45883147, 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 1.        , ..., 0.45883147, 1.        ,\n",
       "        0.62254302],\n",
       "       ...,\n",
       "       [0.        , 0.45883147, 0.45883147, ..., 1.        , 0.45883147,\n",
       "        0.47607054],\n",
       "       [0.        , 0.        , 1.        , ..., 0.45883147, 1.        ,\n",
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       "       [0.55337157, 0.        , 0.62254302, ..., 0.47607054, 0.62254302,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating Cosine Similarity between Users on array data\n",
    "user_sim=1-pairwise_distances(movies2.values,metric='cosine')\n",
    "user_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "<p>4081 rows × 4081 columns</p>\n",
       "</div>"
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       "\n",
       "[4081 rows x 4081 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the results in a dataframe format\n",
    "user_sim2=pd.DataFrame(user_sim)\n",
    "user_sim2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.889532</td>\n",
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       "      <td>0.568212</td>\n",
       "      <td>...</td>\n",
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       "      <th>7087</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.752577</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.622543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7105</th>\n",
       "      <td>0.553372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.622543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.553372</td>\n",
       "      <td>0.765455</td>\n",
       "      <td>0.391293</td>\n",
       "      <td>0.306519</td>\n",
       "      <td>0.622543</td>\n",
       "      <td>0.544201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.553372</td>\n",
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       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4081 rows × 4081 columns</p>\n",
       "</div>"
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       "          3         6         8         10        11        12        13    \\\n",
       "3     1.000000  0.000000  0.000000  0.000000  1.000000  0.000000  0.000000   \n",
       "6     0.000000  1.000000  0.000000  0.000000  0.000000  0.390567  0.707107   \n",
       "8     0.000000  0.000000  1.000000  0.000000  0.000000  0.650945  0.000000   \n",
       "10    0.000000  0.000000  0.000000  1.000000  0.000000  0.000000  0.000000   \n",
       "11    1.000000  0.000000  0.000000  0.000000  1.000000  0.000000  0.000000   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
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       "7080  0.000000  0.458831  0.458831  0.619422  0.000000  0.701884  0.567775   \n",
       "7087  0.000000  0.000000  1.000000  0.000000  0.000000  0.650945  0.000000   \n",
       "7105  0.553372  0.000000  0.622543  0.000000  0.553372  0.765455  0.391293   \n",
       "\n",
       "          14        16        19    ...      6975      6979      6993  \\\n",
       "3     0.000000  0.000000  0.000000  ...  0.000000  0.000000  1.000000   \n",
       "6     0.615457  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
       "8     0.492366  1.000000  0.874157  ...  0.000000  1.000000  0.000000   \n",
       "10    0.615457  0.000000  0.388514  ...  0.800000  0.000000  0.000000   \n",
       "11    0.000000  0.000000  0.000000  ...  0.000000  0.000000  1.000000   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "7044  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
       "7070  0.370543  0.752577  0.657870  ...  0.000000  0.752577  0.000000   \n",
       "7080  0.889532  0.458831  0.568212  ...  0.344124  0.458831  0.000000   \n",
       "7087  0.492366  1.000000  0.874157  ...  0.000000  1.000000  0.000000   \n",
       "7105  0.306519  0.622543  0.544201  ...  0.000000  0.622543  0.553372   \n",
       "\n",
       "          7030      7031      7044      7070      7080      7087      7105  \n",
       "3     0.707107  0.000000  0.000000  0.000000  0.000000  0.000000  0.553372  \n",
       "6     0.000000  0.000000  0.000000  0.000000  0.458831  0.000000  0.000000  \n",
       "8     0.707107  0.000000  0.000000  0.752577  0.458831  1.000000  0.622543  \n",
       "10    0.000000  0.989949  0.000000  0.000000  0.619422  0.000000  0.000000  \n",
       "11    0.707107  0.000000  0.000000  0.000000  0.000000  0.000000  0.553372  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "7044  0.000000  0.000000  1.000000  0.658505  0.000000  0.000000  0.000000  \n",
       "7070  0.532152  0.000000  0.658505  1.000000  0.345306  0.752577  0.468511  \n",
       "7080  0.324443  0.648886  0.000000  0.345306  1.000000  0.458831  0.476071  \n",
       "7087  0.707107  0.000000  0.000000  0.752577  0.458831  1.000000  0.622543  \n",
       "7105  0.831497  0.000000  0.000000  0.468511  0.476071  0.622543  1.000000  \n",
       "\n",
       "[4081 rows x 4081 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the index and column names to user ids \n",
    "user_sim2.index=movies['userId'].unique()\n",
    "user_sim2.columns=movies['userId'].unique()\n",
    "user_sim2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>11</th>\n",
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      ],
      "text/plain": [
       "     3    6    8    10   11\n",
       "3   1.0  0.0  0.0  0.0  1.0\n",
       "6   0.0  1.0  0.0  0.0  0.0\n",
       "8   0.0  0.0  1.0  0.0  0.0\n",
       "10  0.0  0.0  0.0  1.0  0.0\n",
       "11  1.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing first 5 rows and first 5 columns\n",
    "user_sim2.iloc[0:5,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>11</th>\n",
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      ],
      "text/plain": [
       "     3    6    8    10   11\n",
       "3   0.0  0.0  0.0  0.0  1.0\n",
       "6   0.0  0.0  0.0  0.0  0.0\n",
       "8   0.0  0.0  0.0  0.0  0.0\n",
       "10  0.0  0.0  0.0  0.0  0.0\n",
       "11  1.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nullifying diagonal values\n",
    "np.fill_diagonal(user_sim, 0)\n",
    "user_sim2.iloc[0:5,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       11\n",
       "6      168\n",
       "8       16\n",
       "10    4047\n",
       "11       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Similar Users\n",
    "user_sim2.idxmax(axis=1)[0:5]  # (idxmax) Returns index of the maximum element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>userId</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>168</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>6</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>6</td>\n",
       "      <td>Sabrina (1995)</td>\n",
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       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      userId                    movie  rating\n",
       "1          6         Toy Story (1995)     5.0\n",
       "60       168         Toy Story (1995)     4.5\n",
       "3725       6  Grumpier Old Men (1995)     3.0\n",
       "6464       6           Sabrina (1995)     5.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the movies which userId 6 & 168 have watched\n",
    "movies[(movies['userId']==6) | (movies['userId']==168)]   # | means &"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_1=movies[(movies['userId']==6)]\n",
    "user_2=movies[(movies['userId']==168)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1              Toy Story (1995)\n",
       "3725    Grumpier Old Men (1995)\n",
       "6464             Sabrina (1995)\n",
       "Name: movie, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_1['movie']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60    Toy Story (1995)\n",
       "Name: movie, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_2['movie']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>168.0</td>\n",
       "      <td>4.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Sabrina (1995)</td>\n",
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      ],
      "text/plain": [
       "   userId_x                    movie  rating_x  userId_y  rating_y\n",
       "0         6         Toy Story (1995)       5.0     168.0       4.5\n",
       "1         6  Grumpier Old Men (1995)       3.0       NaN       NaN\n",
       "2         6           Sabrina (1995)       5.0       NaN       NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(user_1,user_2,on='movie',how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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