DataVisualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "from flask import current_app\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add working directory\n",
    "import os\n",
    "os.chdir(\"C:/Users/Swati Arora/Data Science using Python/Movie Recommender System- Pybots\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Unnamed: 0</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  MovieID  Action  Adventure  Animation  Children's  Comedy  \\\n",
       "0           0        1       0          0          1           1       1   \n",
       "1           1        2       0          1          0           1       0   \n",
       "2           2        3       0          0          0           0       1   \n",
       "3           3        4       0          0          0           0       1   \n",
       "4           4        5       0          0          0           0       1   \n",
       "\n",
       "   Crime  Documentary  Drama  Fantasy  Film-Noir  Horror  Musical  Mystery  \\\n",
       "0      0            0      0        0          0       0        0        0   \n",
       "1      0            0      0        1          0       0        0        0   \n",
       "2      0            0      0        0          0       0        0        0   \n",
       "3      0            0      1        0          0       0        0        0   \n",
       "4      0            0      0        0          0       0        0        0   \n",
       "\n",
       "   Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0       0         0    0        0  \n",
       "1        0       0         0    0        0  \n",
       "2        1       0         0    0        0  \n",
       "3        0       0         0    0        0  \n",
       "4        0       0         0    0        0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_matrix=pd.read_csv(\"genre_matrix.csv\",sep=',', encoding='latin-1')\n",
    "genre_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=genre_matrix[genre_matrix > 0 ].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a=genre_matrix1[genre_matrix1 > 0 ].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b=pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count    20\n",
       "Genre    20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['Genre']=b.index\n",
    "\n",
    "b.columns=['Count','Genre']\n",
    "b.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot')\n",
    "# use the code below to make sure plot is shown in Notebook\n",
    "%matplotlib inline \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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S62jjehewU7v2O4+tn9lZXl5et+9uYUz96GUxjamXZFz96GUxjamXZFz96GUx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\nSZIkSWqNnU+SJEmSJElqzf8HF2ODCU3LUIEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19d1a0d7cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
    "labels=b.Genre\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "plt.bar(xlabel,b.Count,align='center',color='b')\n",
    "plt.title=('Genres')\n",
    "\n",
    "plt.xticks(xlabel, labels)\n",
    "plt.ylabel('Movie Count')\n",
    "\n",
    "\n",
    "plt.suptitle('Genres',fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#df = pd.read_csv(\"ratings.dat\",sep=',')\n",
    "os.chdir(\"C:/Users/Swati Arora/Data Science using Python/Movie Recommender System- Pybots/Data\")\n",
    "df = pd.read_csv('ratings.dat',sep='::', encoding='latin-1', names=['UserID','MovieID','Rating','Timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    348971\n",
       "3    261197\n",
       "5    226310\n",
       "2    107557\n",
       "1     56174\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Rating.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>UserID</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserID  MovieID  Rating  Timestamp\n",
       "0       1     1193       5  978300760\n",
       "1       1      661       3  978302109\n",
       "2       1      914       3  978301968\n",
       "3       1     3408       4  978300275\n",
       "4       1     2355       5  978824291"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x19d1515b4e0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xJicnN+v9ExMT8163r2GfZwBHRMR1wDnAwRHxdz21LUmaoZeef2aeAJwA0PX835yZL++j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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19d1a6db5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot')\n",
    "# use the code below to make sure plot is shown in Notebook\n",
    "%matplotlib inline \n",
    "\n",
    "ax=df.Rating.value_counts().plot(kind='barh',title='Ratings')\n",
    "\n",
    "ax.set_xlabel(\"Count of movies\")\n",
    "ax.set_ylabel(\"Rating\")"
   ]
  },
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