Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Foundation Nanodegree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project: Investigate a TMDb Movie Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters.\n",
    "There are some odd characters in the ‘cast’ column. Don’t worry about cleaning them. You can leave them as is.\n",
    "The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset that I have chosen is the TMDb dataset which contains data on the movies and its ratings. The various trends that are to be explored in the same are going to be:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The popularity of the movies over the years\n",
    "- The revenue made by the movies over the years "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will see the comparison among the following:\n",
    "- Revenue versus Popularity \n",
    "- Revenue versus Votings "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see what are the questions that we will be answering:\n",
    "- What is the runtime of the movies, its revenue, and popularity over the years?\n",
    "- How do the votings affect the revenue of the movie?\n",
    "- Is revenue directly proportional to popularity?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset that we will be investigating in this project will be explanatory and any associations will be tentative as we will be just performing visualizations and basic correlations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three variables that we will be investigating are:\n",
    "- Single variable (1d) explorations that are Popularity, Revenue, Run-time over time \n",
    "- Multi-variable (2d) explorations that are Revenue over time for factors like Votings, Popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv \n",
    "from datetime import datetime \n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling - Presenting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section of the report, we will load the data and check for cleanliness, and then trim and clean the dataset for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10866 non-null int64\n",
      "imdb_id                 10856 non-null object\n",
      "popularity              10866 non-null float64\n",
      "budget                  10866 non-null int64\n",
      "revenue                 10866 non-null int64\n",
      "original_title          10866 non-null object\n",
      "cast                    10790 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10822 non-null object\n",
      "tagline                 8042 non-null object\n",
      "keywords                9373 non-null object\n",
      "overview                10862 non-null object\n",
      "runtime                 10866 non-null int64\n",
      "genres                  10843 non-null object\n",
      "production_companies    9836 non-null object\n",
      "release_date            10866 non-null object\n",
      "vote_count              10866 non-null int64\n",
      "vote_average            10866 non-null float64\n",
      "release_year            10866 non-null int64\n",
      "budget_adj              10866 non-null float64\n",
      "revenue_adj             10866 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Load your data and print out a few lines. Perform operations to inspect data\n",
    "#   types and look for instances of missing or possibly errant data.\n",
    "\n",
    "#loading the data form csv file for analyzing it \n",
    "df = pd.read_csv('tmdb-movies.csv', encoding='ISO-8859-1')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will be deleting all the duplicates that are present in the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how many duplicated records are present\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the duplicated rows  \n",
    "\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10865 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10865 non-null int64\n",
      "imdb_id                 10855 non-null object\n",
      "popularity              10865 non-null float64\n",
      "budget                  10865 non-null int64\n",
      "revenue                 10865 non-null int64\n",
      "original_title          10865 non-null object\n",
      "cast                    10789 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10821 non-null object\n",
      "tagline                 8041 non-null object\n",
      "keywords                9372 non-null object\n",
      "overview                10861 non-null object\n",
      "runtime                 10865 non-null int64\n",
      "genres                  10842 non-null object\n",
      "production_companies    9835 non-null object\n",
      "release_date            10865 non-null object\n",
      "vote_count              10865 non-null int64\n",
      "vote_average            10865 non-null float64\n",
      "release_year            10865 non-null int64\n",
      "budget_adj              10865 non-null float64\n",
      "revenue_adj             10865 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Check the dataset to make sure the duplicates are removed \n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NULL Values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step we will look at the dataset and inspect if it is sufficicnt for our initial reserach. Then depending on the results, we will go furtehr into how are we going to deal with the missing values. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "imdb_id                   10\n",
       "popularity                 0\n",
       "budget                     0\n",
       "revenue                    0\n",
       "original_title             0\n",
       "cast                      76\n",
       "homepage                7929\n",
       "director                  44\n",
       "tagline                 2824\n",
       "keywords                1493\n",
       "overview                   4\n",
       "runtime                    0\n",
       "genres                    23\n",
       "production_companies    1030\n",
       "release_date               0\n",
       "vote_count                 0\n",
       "vote_average               0\n",
       "release_year               0\n",
       "budget_adj                 0\n",
       "revenue_adj                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check if any of the columns have null values \n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now here, after we have seen the structure of our TMBd dataset and deleting the duplicated values, we will clean in this part. \n",
    "As we observed in the above step that few columns have null values. The only relevant thing for us is the imbd_id. We will be removing the null values in this section using dropna process. \n",
    "In the remaining columns that contain the missing values are not the factors that we need for our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10855 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10855 non-null int64\n",
      "imdb_id                 10855 non-null object\n",
      "popularity              10855 non-null float64\n",
      "budget                  10855 non-null int64\n",
      "revenue                 10855 non-null int64\n",
      "original_title          10855 non-null object\n",
      "cast                    10779 non-null object\n",
      "homepage                2934 non-null object\n",
      "director                10815 non-null object\n",
      "tagline                 8038 non-null object\n",
      "keywords                9368 non-null object\n",
      "overview                10852 non-null object\n",
      "runtime                 10855 non-null int64\n",
      "genres                  10834 non-null object\n",
      "production_companies    9830 non-null object\n",
      "release_date            10855 non-null object\n",
      "vote_count              10855 non-null int64\n",
      "vote_average            10855 non-null float64\n",
      "release_year            10855 non-null int64\n",
      "budget_adj              10855 non-null float64\n",
      "revenue_adj             10855 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# To drop the rows taht contain the missing values in imdb_id column\n",
    "\n",
    "df.dropna(subset=['imdb_id'], inplace=True)  \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>tt2908446</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>http://www.thedivergentseries.movie/#insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>One Choice Can Destroy You</td>\n",
       "      <td>...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>3/18/15</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>tt2488496</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>http://www.starwars.com/films/star-wars-episod...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Every generation has a story.</td>\n",
       "      <td>...</td>\n",
       "      <td>Thirty years after defeating the Galactic Empi...</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>Lucasfilm|Truenorth Productions|Bad Robot</td>\n",
       "      <td>12/15/15</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.839999e+08</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>tt2820852</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>http://www.furious7.com/</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Vengeance Hits Home</td>\n",
       "      <td>...</td>\n",
       "      <td>Deckard Shaw seeks revenge against Dominic Tor...</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Universal Pictures|Original Film|Media Rights ...</td>\n",
       "      <td>4/1/15</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.747999e+08</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201   \n",
       "3  140607  tt2488496   11.173104  200000000  2068178225   \n",
       "4  168259  tt2820852    9.335014  190000000  1506249360   \n",
       "\n",
       "                 original_title  \\\n",
       "0                Jurassic World   \n",
       "1            Mad Max: Fury Road   \n",
       "2                     Insurgent   \n",
       "3  Star Wars: The Force Awakens   \n",
       "4                     Furious 7   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "\n",
       "                                            homepage          director  \\\n",
       "0                      http://www.jurassicworld.com/   Colin Trevorrow   \n",
       "1                        http://www.madmaxmovie.com/     George Miller   \n",
       "2     http://www.thedivergentseries.movie/#insurgent  Robert Schwentke   \n",
       "3  http://www.starwars.com/films/star-wars-episod...       J.J. Abrams   \n",
       "4                           http://www.furious7.com/         James Wan   \n",
       "\n",
       "                         tagline      ...       \\\n",
       "0              The park is open.      ...        \n",
       "1             What a Lovely Day.      ...        \n",
       "2     One Choice Can Destroy You      ...        \n",
       "3  Every generation has a story.      ...        \n",
       "4            Vengeance Hits Home      ...        \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "2  Beatrice Prior must confront her inner demons ...     119   \n",
       "3  Thirty years after defeating the Galactic Empi...     136   \n",
       "4  Deckard Shaw seeks revenge against Dominic Tor...     137   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "3   Action|Adventure|Science Fiction|Fantasy   \n",
       "4                      Action|Crime|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...      3/18/15       2480   \n",
       "3          Lucasfilm|Truenorth Productions|Bad Robot     12/15/15       5292   \n",
       "4  Universal Pictures|Original Film|Media Rights ...       4/1/15       2947   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "2           6.3          2015  1.012000e+08  2.716190e+08  \n",
       "3           7.5          2015  1.839999e+08  1.902723e+09  \n",
       "4           7.3          2015  1.747999e+08  1.385749e+09  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To view the dataset \n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will save this new dataset which has no duplicates\n",
    "\n",
    "df.to_csv('data_imdb.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the runtime of the movies, its revenue, and popularity over the years?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this analysis, we have to first group our dataset based on the years with the help of the variable 'release_year'.\n",
    "We will be performing the visualizations using in-built fucntions and histograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10855 entries, 0 to 10854\n",
      "Data columns (total 21 columns):\n",
      "id                      10855 non-null int64\n",
      "imdb_id                 10855 non-null object\n",
      "popularity              10855 non-null float64\n",
      "budget                  10855 non-null int64\n",
      "revenue                 10855 non-null int64\n",
      "original_title          10855 non-null object\n",
      "cast                    10779 non-null object\n",
      "homepage                2934 non-null object\n",
      "director                10815 non-null object\n",
      "tagline                 8038 non-null object\n",
      "keywords                9368 non-null object\n",
      "overview                10852 non-null object\n",
      "runtime                 10855 non-null int64\n",
      "genres                  10834 non-null object\n",
      "production_companies    9830 non-null object\n",
      "release_date            10855 non-null object\n",
      "vote_count              10855 non-null int64\n",
      "vote_average            10855 non-null float64\n",
      "release_year            10855 non-null int64\n",
      "budget_adj              10855 non-null float64\n",
      "revenue_adj             10855 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# First we will import the data from the dataset that we cleaned above\n",
    "\n",
    "df_imdb = pd.read_csv('data_imdb.csv')\n",
    "df_imdb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we will group the input data by release_year variable and then compute the mean values for the numeric columns\n",
    "\n",
    "df_1 = df_imdb.groupby('release_year').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a2358cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To visualize the dataset \n",
    "\n",
    "df_1.hist(figsize=(15,15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Runtime "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will be determining if runtime has had any effect on the popularity of the movies. Then we will be visualizing it with the help of histograms.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A85KsB1YAT01y8qYUKUnafPMF/o5z3LfTXCtW1duqat+qWgIcDnyrql6xwPokSUMyX+D/IMl/mj4zyWuBNe2UJElqw3wDr28CvpDk5dwV8BPADsBhgzZSVauAVZtQnyRpSOYM/Kq6Gnh8kqXAI5rZK6vqW61XJkkaqkGvh/9t4Nst1yJJatGmXg9fkjRmDHxJ6ggDX5I6wsCXpI4w8CWpIwx8SeoIA1+SOsLAl6SOMPAlqSMMfEnqCANfkjrCwJekjjDwJakjDHxJ6ggDX5I6YqDr4W9LlixfOdBy6487dOjtHnXARpYN2P4gBq1xVH0epVHu51G0Kw3CI3xJ6ggDX5I6wsCXpI4w8CWpIwx8SeoIA1+SOsLAl6SOMPAlqSMMfEnqCANfkjrCwJekjjDwJakjDHxJ6ggDX5I6wsCXpI4w8CWpIwx8SeoIA1+SOsLAl6SOaC3wk/xBkm8nWZfkgiRvbKstSdL82vwS843AUVV1bpJdgTVJvl5VF7bYpiRpFq0d4VfVlVV1bjN9E7AO2Ket9iRJc0tVtd9IsgT4DvCIqrpx2n1HAEcALF68+KAVK1awYcMGFi1atKA21l5xw3CKXaAD9tl9oOXWXnEDi3eCq29tuaARGOd+zbX/2vw7HPTvpg2b0q9x0NV+LV26dE1VTQyyrdYDP8ki4Czg2Ko6fa5lJyYmavXq1axatYrJyckFtbNk+cpNL3IzrD/u0IGWW7J8JUcdsJH3r21zFG00xrlfc+2/Nv8OB/27acOm9GscdLVfSQYO/FbP0klyT+DzwCnzhb0kqV1tnqUT4BPAuqr6QFvtSJIG0+YR/hOAVwJPTXJe83NIi+1JkubQ2sBrVf0LkLa2L0laGD9pK0kdYeBLUkcY+JLUEQa+JHWEgS9JHWHgS1JHGPiS1BEGviR1hIEvSR1h4EtSRxj4ktQRBr4kdYSBL0kdYeBLUkcY+JLUEQa+JHWEgS9JHdHaN151xZLlK0ddgjbDXPvvqAM2smyM9u+gf4snPmuXliuZ2bD/V9Yfd+hQt9eGQfu8pfriEb4kdYSBL0kdYeBLUkcY+JLUEQa+JHWEgS9JHWHgS1JHGPiS1BEGviR1hIEvSR1h4EtSRxj4ktQRBr4kdYSBL0kdYeBLUkcY+JLUEQa+JHWEgS9JHWHgS1JHtBr4SZ6V5KIkFydZ3mZbkqS5tRb4SbYDPgo8G3g48GdJHt5We5KkubV5hP/HwMVVdUlV3Q6sAJ7fYnuSpDmkqtrZcPIi4FlV9efN7VcCj62q101b7gjgiObmQ4GLgL2Aa1spbLTs13ixX+Olq/3ar6r2HmRD2w+nnhllhnl3e3apqhOAE35vxWR1VU20Vdio2K/xYr/Gi/2aX5tDOpcDf9B3e1/gVy22J0maQ5uB/wPgIUkemGQH4HDgiy22J0maQ2tDOlW1McnrgK8B2wGfrKoLBlz9hPkXGUv2a7zYr/Fiv+bR2pu2kqSti5+0laSOMPAlqSNGHvhJ3pjk/CQXJHlTM++YJFckOa/5OWTUdQ4iySeTXJPk/L55eyb5epKfNb/v3cxPkg83l534cZJHj67yuS2wX5NJbujbd+8cXeVzm6VfL27+Fn+XZGLa8m9r9tdFSZ655SsezEL6lWRJklv79tfxo6l6frP0671JftL8D30hyR59943z/pqxX5u9v6pqZD/AI4DzgZ3pvYH8DeAhwDHA0aOsbRP782Tg0cD5ffPeAyxvppcDf9dMHwJ8ld7nFR4HnDPq+ofUr0ngy6OueTP6tT+9DwCuAib65j8c+BFwL+CBwM+B7UbdhyH0a0n/clvzzyz9+hNg+2b67/r+Dsd9f83Wr83aX6M+wt8fOLuqbqmqjcBZwGEjrmmTVdV3gN9Mm/184KRm+iTgBX3zP1U9ZwN7JLn/lql0YRbYr7ExU7+qal1VXTTD4s8HVlTVbVX1C+BiepcP2eossF9jY5Z+ndlkB8DZ9D7vA+O/v2br12YZdeCfDzw5yX2S7EzvqHfqw1qva17OfHJquGBMLa6qKwGa3/dt5u8DXNa33OXNvHExW78ADk7yoyRfTfJHoylv6MZ9f83lgUl+mOSsJE8adTGb4T/Se9UM29b+6u8XbMb+GmngV9U6ei9Xvg6cQe8l2EbgY8AfAgcCVwLvH1WNLRro0hNj6Fx61/Z4JPAR4J9GXM+wbKv760rgAVX1KOAtwGeS7DbimhYsyTvoZccpU7NmWGzs9tcM/dqs/TXqI3yq6hNV9eiqejK9lzU/q6qrq+rOqvod8A9spS/FBnT11FBN8/uaZv64X3pixn5V1Y1VtaGZ/gpwzyR7ja7MoRn3/TWjZsjjumZ6Db2x7n8/2qoWJsmrgecAL69moJttYH/N1K/N3V8jD/wk921+PwB4IfDZaWPZh9Eb+hlXXwRe3Uy/Gvi/ffNf1Zyt8zjghqkhkjExY7+S3C9Jmuk/pvc3dt1IKhyuLwKHJ7lXkgfSO7ng+yOuabMl2Tu9764gyYPo9euS0VY1uCTPAv4r8LyquqXvrrHeX7P1a7P311bwDvU/AxfSG855WjPv08Ba4Mf0dtz9R13ngH35LL2XXHfQO8J4LXAf4JvAz5rfezbLht4XxPy86evEKGsfYr9eB1zQ7M+zgcePuv4F9uuwZvo24Grga33Lv6PZXxcBzx51/cPoF/CnffvrXOC5o65/gf26mN5Y/XnNz/HbyP6asV+bu7+8tIIkdcTIh3QkSVuGgS9JHWHgS1JHGPiS1BEGviR1hIGvbVKSO5urCZ6f5Ev9V1HchG1NJnl83+0jk7xqOJVKW46nZWqblGRDVS1qpk8CflpVx27ito4BNlTV+4ZYorTFeYSvLvgezYWzmqP1L0/dkeR/JlnWTK9P8tdJzk2yNsnDkiwBjgTe3LxieFJ639dwdLPOqiQfTPKdJOuSPCbJ6el9T8C7+9p5RZLvN9v4+NSnJaUtycDXNq0J1qfR+8T2IK6tqkfTu4Df0VW1Hjge+GBVHVhV/zzDOrdX71pQx9O7xMRf0vuuh2XNlWD3B14KPKGqDgTuBF6+Of2SNsX2oy5AaslOSc6j94URa+hdkXUQpze/19C7ttMgpp5M1gIXVHNNpCSX0LuA1xOBg4AfNJcZ2om7LqInbTEe4WtbdWtzNL0fsAO9o27oXWq2/+9+x2nr3db8vpPBD4im1vld3/TU7e3pXTfppOYVwoFV9dCqOmbAbUtDY+Brm1ZVNwBvAI5Ock/gUuDhzVUUd6c33DOfm4BdN6OMbwIv6rsy7J5J9tuM7UmbxMDXNq+qfkjv6oKHV9VlwOfoXYn1FOCHA2ziS8BhU2/abkL7FwL/DTgzyY/pDS9tlV9nqW2bp2VKUkd4hC9JHWHgS1JHGPiS1BEGviR1hIEvSR1h4EtSRxj4ktQR/x+ForhT8o9mXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a2606a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1['runtime'].hist(bins=30)\n",
    "plt.xlabel('Runtime')\n",
    "plt.ylabel('Counting')\n",
    "plt.title('Runtime Over the Years');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     56.000000\n",
       "mean     105.714644\n",
       "std        5.508458\n",
       "min       96.179331\n",
       "25%      101.910776\n",
       "50%      105.678699\n",
       "75%      107.593247\n",
       "max      124.343750\n",
       "Name: runtime, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To view the visualization numerically\n",
    "\n",
    "df_1['runtime'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that the runtime is popular in between 106 and 107 minutes \n",
    "- The movies fall in the range of 105 to 107 \n",
    "- The visualization shows that the distribution is right skewed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we want to see how the movie ratings will be deceived over the years. Visually the maximum count will reflect the popularity of the movie based on its ratings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a2aa73c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we will look at the popularity variable in order to determine the ratings \n",
    "\n",
    "df_1['popularity'].hist(bins=38)\n",
    "plt.xlabel('Popularity')\n",
    "plt.ylabel('Countings')\n",
    "plt.title('Popularity Over the Years');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    56.000000\n",
       "mean      0.559693\n",
       "std       0.128434\n",
       "min       0.304112\n",
       "25%       0.469625\n",
       "50%       0.546928\n",
       "75%       0.626934\n",
       "max       1.032126\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To view the visualization numerically\n",
    "\n",
    "df_1['popularity'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that the maximum ratings fall in between 0.44 to 0.62\n",
    "- The disturbution is observed to be skewed to the right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will determining the revenue of the movie that is generated over the years. Using histogram, we will be doing the visualizations and then the maximum counting will tell us about the revenue that is earned by the movies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a2b5b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we will look at the revenue variable over the years in order to determine typical revenues grossed by the movies\n",
    "\n",
    "df_1['revenue'].hist(bins=26)\n",
    "plt.xlabel('Revenue')\n",
    "plt.ylabel('Countings')\n",
    "plt.title('Revenue Over the Years');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.600000e+01\n",
       "mean     3.076766e+07\n",
       "std      1.574209e+07\n",
       "min      1.842102e+06\n",
       "25%      1.815642e+07\n",
       "50%      3.257984e+07\n",
       "75%      4.293171e+07\n",
       "max      5.549569e+07\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To view the visualization numerically\n",
    "\n",
    "df_1['revenue'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that \n",
    "- The distribution is observed to be left skewed \n",
    "- The revenues are seen to be varying widely "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the variables which are associated with the revenue of the movies in the years and how? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps that we will be performing are:\n",
    "\n",
    "- In order to answer the above question, we will be looking at the correlation of the variables from the dataset that is grouped over the years\n",
    "- Here we will be visualizing the relation between two variables \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observations are based on:\n",
    "- Revenue vs Popularity\n",
    "- Revenue vs Votings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.612610</td>\n",
       "      <td>0.100292</td>\n",
       "      <td>0.152558</td>\n",
       "      <td>-0.441775</td>\n",
       "      <td>0.539964</td>\n",
       "      <td>-0.297719</td>\n",
       "      <td>-0.148336</td>\n",
       "      <td>-0.299723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>0.612610</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668215</td>\n",
       "      <td>0.759156</td>\n",
       "      <td>-0.488974</td>\n",
       "      <td>0.849314</td>\n",
       "      <td>-0.574979</td>\n",
       "      <td>0.458952</td>\n",
       "      <td>-0.100506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>0.100292</td>\n",
       "      <td>0.668215</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906124</td>\n",
       "      <td>-0.401485</td>\n",
       "      <td>0.782026</td>\n",
       "      <td>-0.731797</td>\n",
       "      <td>0.891925</td>\n",
       "      <td>-0.158415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>0.152558</td>\n",
       "      <td>0.759156</td>\n",
       "      <td>0.906124</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.466239</td>\n",
       "      <td>0.809243</td>\n",
       "      <td>-0.706442</td>\n",
       "      <td>0.767164</td>\n",
       "      <td>0.074952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>-0.441775</td>\n",
       "      <td>-0.488974</td>\n",
       "      <td>-0.401485</td>\n",
       "      <td>-0.466239</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.612715</td>\n",
       "      <td>0.524770</td>\n",
       "      <td>-0.073168</td>\n",
       "      <td>0.300077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_count</th>\n",
       "      <td>0.539964</td>\n",
       "      <td>0.849314</td>\n",
       "      <td>0.782026</td>\n",
       "      <td>0.809243</td>\n",
       "      <td>-0.612715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.658819</td>\n",
       "      <td>0.505434</td>\n",
       "      <td>-0.215568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_average</th>\n",
       "      <td>-0.297719</td>\n",
       "      <td>-0.574979</td>\n",
       "      <td>-0.731797</td>\n",
       "      <td>-0.706442</td>\n",
       "      <td>0.524770</td>\n",
       "      <td>-0.658819</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.557569</td>\n",
       "      <td>0.377204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget_adj</th>\n",
       "      <td>-0.148336</td>\n",
       "      <td>0.458952</td>\n",
       "      <td>0.891925</td>\n",
       "      <td>0.767164</td>\n",
       "      <td>-0.073168</td>\n",
       "      <td>0.505434</td>\n",
       "      <td>-0.557569</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_adj</th>\n",
       "      <td>-0.299723</td>\n",
       "      <td>-0.100506</td>\n",
       "      <td>-0.158415</td>\n",
       "      <td>0.074952</td>\n",
       "      <td>0.300077</td>\n",
       "      <td>-0.215568</td>\n",
       "      <td>0.377204</td>\n",
       "      <td>0.050086</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id  popularity    budget   revenue   runtime  vote_count  \\\n",
       "id            1.000000    0.612610  0.100292  0.152558 -0.441775    0.539964   \n",
       "popularity    0.612610    1.000000  0.668215  0.759156 -0.488974    0.849314   \n",
       "budget        0.100292    0.668215  1.000000  0.906124 -0.401485    0.782026   \n",
       "revenue       0.152558    0.759156  0.906124  1.000000 -0.466239    0.809243   \n",
       "runtime      -0.441775   -0.488974 -0.401485 -0.466239  1.000000   -0.612715   \n",
       "vote_count    0.539964    0.849314  0.782026  0.809243 -0.612715    1.000000   \n",
       "vote_average -0.297719   -0.574979 -0.731797 -0.706442  0.524770   -0.658819   \n",
       "budget_adj   -0.148336    0.458952  0.891925  0.767164 -0.073168    0.505434   \n",
       "revenue_adj  -0.299723   -0.100506 -0.158415  0.074952  0.300077   -0.215568   \n",
       "\n",
       "              vote_average  budget_adj  revenue_adj  \n",
       "id               -0.297719   -0.148336    -0.299723  \n",
       "popularity       -0.574979    0.458952    -0.100506  \n",
       "budget           -0.731797    0.891925    -0.158415  \n",
       "revenue          -0.706442    0.767164     0.074952  \n",
       "runtime           0.524770   -0.073168     0.300077  \n",
       "vote_count       -0.658819    0.505434    -0.215568  \n",
       "vote_average      1.000000   -0.557569     0.377204  \n",
       "budget_adj       -0.557569    1.000000     0.050086  \n",
       "revenue_adj       0.377204    0.050086     1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we will use correlation to determine the factors which are influencing revenue \n",
    "\n",
    "df_1.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revenue versus Popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a2ec09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will visualize this with the help of a scatterplot of revenue and popularity over the years\n",
    "\n",
    "plt.scatter(x=df_1['revenue'], y=df_1['popularity'])\n",
    "plt.xlabel('Revenue')\n",
    "plt.ylabel('Popularity')\n",
    "plt.title('Revenue vs Popularity Over the Years');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations- Revenue vs Popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above scatterplot, we can see that the popularity of the movies is directly proportional to the revenues. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revenue versus Votings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a2ec0198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will visualize this with the help of a scatterplot of revenue and votings over the years\n",
    "\n",
    "\n",
    "plt.scatter(x=df_1['revenue'], y=df_1['vote_count'])\n",
    "plt.xlabel('Revenue')\n",
    "plt.ylabel('Votings')\n",
    "plt.title('Revenue vs Votings Over the Years');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations- Revenue vs Vote Count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The votings is almost proportional to the revenue of the movies \n",
    "- We can also see that few of the samples have some high voting counts and did not gross in the revenue section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- We worked with runtime of the movies, the popularity and revenue of the same \n",
    "- The analysis was restricted to the numerical values where dollar, rupees, etc was not considered. \n",
    "- If the varying currencies will be taken in consideration then the conversions or normalization needs to be performed\n",
    "- We have looked at other sources like the sales of the mvie tickets, the audio rights sales, sales of the broadcasts, etc. \n",
    "- The voting counts are taken at the face value where we did not look into the nature of the same whether it is good or bad. \n",
    "- The popularity medium is very much unknown which can have an impact on the analysis as limitations and the bias inherent are present in the end values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From our EDA we analyzed that there are different patterns in the revenue, popularity and runtime of the movie over the years\n",
    "- The results are tentative but we observed the runtime to be popular in between 106 minutes and 107 minutes \n",
    "- The ratings were observed to be in the range of 0.44 to 0.62\n",
    "- We also found out that the Popularity of the movies and the revenue have a direct relation between them\n",
    "- The voting counts and the revenue is also observed to be related positively\n",
    "- The relations that we derived are just correlations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  subprocess  import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}