Movie recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# IMPORTING REQUIRED LIBRARIES\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('u.data',sep='\\t',names=['user id','item id','rating','timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user id</th>\n",
       "      <th>item id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user id  item id  rating  timestamp\n",
       "0      196      242       3  881250949\n",
       "1      186      302       3  891717742"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd20652e2e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD8CAYAAABAWd66AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNCHIAE0IMkAT/w/5VrptyEmVzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd2064eac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),cmap='viridis',cbar=False,yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info=pd.read_csv('u.info',names=['value','Info'],sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>943</td>\n",
       "      <td>users</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1682</td>\n",
       "      <td>items</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100000</td>\n",
       "      <td>ratings</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    value     Info\n",
       "0     943    users\n",
       "1    1682    items\n",
       "2  100000  ratings"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "head=['item id','title','release date','video release date','IMDb URL','unknown','Action','Adventure','Animation','Children','Comedy','Crime','Documentary','Drama','Fantasy','Film-Noir','Horror','Musical','Mystery','Romance','Sci-Fi','Thriller','War','Western']\n",
    "items=pd.read_csv('u.item',sep='|',names=head,encoding='latin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item id</th>\n",
       "      <th>title</th>\n",
       "      <th>release date</th>\n",
       "      <th>video release date</th>\n",
       "      <th>IMDb URL</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item id             title release date  video release date  \\\n",
       "0        1  Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1        2  GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            IMDb URL  unknown  Action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "\n",
       "   Adventure  Animation  Children   ...     Fantasy  Film-Noir  Horror  \\\n",
       "0          0          1         1   ...           0          0       0   \n",
       "1          1          0         0   ...           0          0       0   \n",
       "\n",
       "   Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0        0        0       0         0    0        0  \n",
       "1        0        0        0       0         1    0        0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd20678b128>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gz7RXpTB9B8AnJF2Ujv8GxIRUnUUK30C8JmemY81Ok1h9KU20kDwaMdP+i/T7mxGXsVV+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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd20676c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(items.isnull(),cmap='viridis',cbar=False,yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "items.drop(['video release date'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users=pd.read_csv('u.user',sep='|',names=['user id','age','gender','occupation','zip code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user id  age gender  occupation zip code\n",
       "0        1   24      M  technician    85711\n",
       "1        2   53      F       other    94043"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd206c19b00>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD8CAYAAABAWd66AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd206c19470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(users.isnull(),cmap='viridis',cbar=False,yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "occupation=pd.read_csv('u.occupation',names=['List'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user id</th>\n",
       "      <th>item id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user id  item id  rating  timestamp\n",
       "0      196      242       3  881250949\n",
       "1      186      302       3  891717742"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SPLITTING GIVEN DATASET INTO TRAIN AND TEST DATASET\n",
    "\n",
    "from sklearn import cross_validation as cv\n",
    "train,test=cv.train_test_split(data,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user id</th>\n",
       "      <th>item id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user id  item id  rating  timestamp\n",
       "0      196      242       3  881250949\n",
       "1      186      302       3  891717742"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CREATING USER-ITEM MATRIX FROM THE ORIGINAL DATAFRAME\n",
    "\n",
    "trainmatrix=np.zeros((data['user id'].nunique(),data['item id'].nunique()))\n",
    "testmatrix=np.zeros((data['user id'].nunique(),data['item id'].nunique()))\n",
    "\n",
    "for i in train.itertuples():\n",
    "    trainmatrix[i[1]-1][i[2]-1]=i[3]\n",
    "\n",
    "for i in test.itertuples():\n",
    "    testmatrix[i[1]-1][i[2]-1]=i[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.,  3.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 4.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 5.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  5.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  4., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testmatrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MEMORY-BASED COLLABORATIVE FILTERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import pairwise_distances as pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "similar_user=pwd(trainmatrix,metric='cosine')\n",
    "similar_item=pwd(trainmatrix.T,metric='cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.87654547,  0.96949176, ...,  0.8855922 ,\n",
       "         0.89622051,  0.67128039],\n",
       "       [ 0.87654547,  0.        ,  0.91893994, ...,  0.89230411,\n",
       "         0.82949525,  0.92427713],\n",
       "       [ 0.96949176,  0.91893994,  0.        , ...,  0.93525372,\n",
       "         0.9065139 ,  0.96269233],\n",
       "       ..., \n",
       "       [ 0.8855922 ,  0.89230411,  0.93525372, ...,  0.        ,\n",
       "         0.92736547,  0.91886396],\n",
       "       [ 0.89622051,  0.82949525,  0.9065139 , ...,  0.92736547,\n",
       "         0.        ,  0.8550093 ],\n",
       "       [ 0.67128039,  0.92427713,  0.96269233, ...,  0.91886396,\n",
       "         0.8550093 ,  0.        ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here mean_user_rating is calculated in order to Normalise the User Rating.\n",
    "\n",
    "def predict(ratings,similarity,type='user'):\n",
    "    if type=='user':\n",
    "        mean_user_rating=ratings.mean(axis=1)\n",
    "        ratings_diff=(ratings-mean_user_rating[:,np.newaxis])\n",
    "        pred=mean_user_rating[:,np.newaxis]+similarity.dot(ratings_diff)/np.array([np.abs(similarity).sum(axis=1)]).T\n",
    "    else:\n",
    "        pred=ratings.dot(similarity)/np.array([np.abs(similarity).sum(axis=1)])\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "item_prediction=predict(trainmatrix,similar_item,type='item')\n",
    "user_prediction=predict(trainmatrix,similar_user,type='user')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EVALUATION BY CALCULATING ROOT MEAN SQUARED ERROR (RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction=user_prediction[testmatrix.nonzero()].flatten()\n",
    "original=testmatrix[testmatrix.nonzero()].flatten()\n",
    "user_based_error=sqrt(mse(prediction,original))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1580344523911354\n"
     ]
    }
   ],
   "source": [
    "print(user_based_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction=item_prediction[testmatrix.nonzero()].flatten()\n",
    "original=testmatrix[testmatrix.nonzero()].flatten()\n",
    "item_based_error=sqrt(mse(prediction,original))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4625820911814804\n"
     ]
    }
   ],
   "source": [
    "print(item_based_error)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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