final+tutorial1 (2).ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Nominated Movies: Oscar Prize Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://dl9fvu4r30qs1.cloudfront.net/4b/cf/f2190bd844da9eebb7436d49dd69/oscar-best-picture.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation:\n",
    "As movie lovers, we watch a lot of movies every year. Sometimes we wonder, what is the standard of a good movie? In our understanding, those big movie producers in Hollywood have two general directions when making a film. Either a film can be a commercial success, or it can be an artistic success. As data scientists, we will try to help them determine whether a film can success artistically by predicting its chance of getting an Oscar nominee. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background:\n",
    "We found a dataset with all the Oscar nominated movies from 2006 to 2016. And a dataset with all popular movies in the past 100 years. \n",
    "We only focus on the important awards, which are best motion picture, best actor in a leading role, best actress in a leading role, best director and best writing (includes original screen play and adaptive screen play). Since they are usually highly related with the rating of a movie. A movie with high rating means it’s overall good. And all these five factors are essential to a movie as a whole.\n",
    "The reason we don’t consider other awards is because there is a chance a regular movie wins a technical award like visual effect or music, or one character in that movie has a best supporting award due to his or her own acting. If we take these awards into account, it would lower the accuracy of the model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem:\n",
    "1. Can we use IMDB Rating, Metascore, Box office to predict Oscar Nomination in the future?\n",
    "2. Is our self-designed model performs well to predict the Oscar Nomination?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Collection "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the packages and libraries we will use \n",
    "\n",
    "pandas: used for genrating dataframes and tidying dataset\n",
    "matplotlib: contains useful tools to do data analysis and realize data visualization\n",
    "numpy: for array manipulation \n",
    "re: regular expression library, used for string splitting, matching, etc.\n",
    "sklearn: contains useful tools for machine learning in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import re\n",
    "import math\n",
    "from sklearn import linear_model\n",
    "import sklearn\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tidy the dataset which contains all the information of movies from 2006 to 2016. \n",
    "1. add 'Nomination' columns.\n",
    "2. drop irrelevant columns such as 'Runtime', 'Actors', etc.\n",
    "3. drop year 2016 which will not be used\n",
    "4. print the head of the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Revenue (Millions)</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Nomination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.1</td>\n",
       "      <td>757074</td>\n",
       "      <td>333.13</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prometheus</td>\n",
       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.0</td>\n",
       "      <td>485820</td>\n",
       "      <td>126.46</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bahubali: The Beginning</td>\n",
       "      <td>Action,Adventure,Drama</td>\n",
       "      <td>2015</td>\n",
       "      <td>8.3</td>\n",
       "      <td>76193</td>\n",
       "      <td>6.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>Adventure,Drama,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1047747</td>\n",
       "      <td>187.99</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5- 25- 77</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>2007</td>\n",
       "      <td>7.1</td>\n",
       "      <td>241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Title                     Genre  Year  Rating    Votes  \\\n",
       "0  Guardians of the Galaxy   Action,Adventure,Sci-Fi  2014     8.1   757074   \n",
       "1               Prometheus  Adventure,Mystery,Sci-Fi  2012     7.0   485820   \n",
       "2  Bahubali: The Beginning    Action,Adventure,Drama  2015     8.3    76193   \n",
       "3             Interstellar    Adventure,Drama,Sci-Fi  2014     8.6  1047747   \n",
       "4                5- 25- 77              Comedy,Drama  2007     7.1      241   \n",
       "\n",
       "   Revenue (Millions)  Metascore  Nomination  \n",
       "0              333.13       76.0           0  \n",
       "1              126.46       65.0           0  \n",
       "2                6.50        NaN           0  \n",
       "3              187.99       74.0           0  \n",
       "4                 NaN        NaN           0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#input our data\n",
    "data = pd.read_csv('IMDB-Movie-Data.csv')\n",
    "#add nomination column to keep track of how many nominees a movie gets\n",
    "data['Nomination'] = 0\n",
    "#drop unused columns\n",
    "data = data.drop(['Runtime (Minutes)', 'Actors', 'Director', 'Description', 'Rank'], axis = 1)\n",
    "#since we will exam the nominations from 2006 to 2015, so we drop the rows where year = 2016\n",
    "data = data[data.Year != 2016]\n",
    "data = data.reset_index()\n",
    "data = data.drop(['index'], axis = 1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is another dataset we will use which contains information about nominated movies from 1900s to 2015\n",
    "1. read the data from the excel \n",
    "2. find the row that starts at year 2006, and cut the table to only contains data from 2006 to 2015\n",
    "3. reset the index of the table \n",
    "4. print the table head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ceremony</th>\n",
       "      <th>Award</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Name</th>\n",
       "      <th>Film</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>79</td>\n",
       "      <td>Actor in a Leading Role</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>Blood Diamond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>79</td>\n",
       "      <td>Actor in a Leading Role</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ryan Gosling</td>\n",
       "      <td>Half Nelson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>79</td>\n",
       "      <td>Actor in a Leading Role</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter O'Toole</td>\n",
       "      <td>Venus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>79</td>\n",
       "      <td>Actor in a Leading Role</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Will Smith</td>\n",
       "      <td>The Pursuit of Happyness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>79</td>\n",
       "      <td>Actor in a Leading Role</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Forest Whitaker</td>\n",
       "      <td>The Last King of Scotland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Ceremony                    Award  Winner               Name  \\\n",
       "0  2006        79  Actor in a Leading Role     NaN  Leonardo DiCaprio   \n",
       "1  2006        79  Actor in a Leading Role     NaN       Ryan Gosling   \n",
       "2  2006        79  Actor in a Leading Role     NaN      Peter O'Toole   \n",
       "3  2006        79  Actor in a Leading Role     NaN         Will Smith   \n",
       "4  2006        79  Actor in a Leading Role     1.0    Forest Whitaker   \n",
       "\n",
       "                        Film  \n",
       "0              Blood Diamond  \n",
       "1                Half Nelson  \n",
       "2                      Venus  \n",
       "3   The Pursuit of Happyness  \n",
       "4  The Last King of Scotland  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "awards = pd.read_csv('database.csv')\n",
    "\n",
    "#get the row number which start from year 2006\n",
    "index = 0\n",
    "for i, row in awards.iterrows():\n",
    "    if row['Year'] == '2006':\n",
    "        index = i\n",
    "        break\n",
    "\n",
    "#change the dataframe to include data from 2006-2015\n",
    "awards = awards[index:]\n",
    "awards = awards.reset_index()\n",
    "awards = awards.drop(['index'], axis = 1)\n",
    "awards.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Tidying"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search through the second table and add those nominated movies to the first table in column 'nomination'\n",
    "1. iterate over the first table for each movie Mi, if Mi is contained in the table2, then update the 'nomination' column\n",
    "2. since there are various names for nominations, I use regex to find the first five most important ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i, rowi in data.iterrows():\n",
    "    name = rowi['Title']\n",
    "    \n",
    "    for j, rowj in awards.iterrows():\n",
    "        if rowj['Film'] == name:\n",
    "            \n",
    "            # in order to make our prediction more precise, we will only take \"more valueble\" nominations into account\n",
    "            # so we will take \"writing, actor, actress, directing, best movie\" this five categories\n",
    "            \n",
    "            if (re.match(\"Writing.*\", rowj['Award']) or re.match(\"Actor (in a Leading Role)\",rowj['Award']) \n",
    "                or re.match(\"Actress (in a Leading Role)\",rowj['Award']) or rowj['Award'] == 'Directing' \n",
    "                or rowj['Award'] == 'Best Picture'):\n",
    "                \n",
    "                #increment the number of nominations\n",
    "                cur = rowi['Nomination'] + 1\n",
    "                data.set_value(i, 'Nomination', cur)\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rename the \"Revenue (Millions)\" to Revenue and print the information of the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title         703\n",
       "Genre         703\n",
       "Year          703\n",
       "Rating        703\n",
       "Votes         703\n",
       "Revenue       667\n",
       "Metascore     668\n",
       "Nomination    703\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename(index = str, columns = {'Revenue (Millions)':'Revenue'})\n",
    "data.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many movies do not have the information of Revenue and Metascore.\n",
    "1. For the lost data, we fill the nil with 0.\n",
    "2. Then remove rows whose Revenue and Metascore is 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title         640\n",
       "Genre         640\n",
       "Year          640\n",
       "Rating        640\n",
       "Votes         640\n",
       "Revenue       640\n",
       "Metascore     640\n",
       "Nomination    640\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.fillna(0)\n",
    "data = data[data.Revenue != 0.0]\n",
    "data = data[data.Metascore != 0.0]\n",
    "data.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the subtable that contains data with nomination greater than 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\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>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Nomination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Wolf of Wall Street</td>\n",
       "      <td>Biography,Comedy,Crime</td>\n",
       "      <td>2013</td>\n",
       "      <td>8.2</td>\n",
       "      <td>865134</td>\n",
       "      <td>116.87</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Gone Girl</td>\n",
       "      <td>Crime,Drama,Mystery</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.1</td>\n",
       "      <td>636243</td>\n",
       "      <td>167.74</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>The Help</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2011</td>\n",
       "      <td>8.1</td>\n",
       "      <td>342429</td>\n",
       "      <td>169.71</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>The Martian</td>\n",
       "      <td>Adventure,Drama,Sci-Fi</td>\n",
       "      <td>2015</td>\n",
       "      <td>8.0</td>\n",
       "      <td>556097</td>\n",
       "      <td>228.43</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12 Years a Slave</td>\n",
       "      <td>Biography,Drama,History</td>\n",
       "      <td>2013</td>\n",
       "      <td>8.1</td>\n",
       "      <td>486338</td>\n",
       "      <td>56.67</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Title                    Genre  Year  Rating   Votes  \\\n",
       "18  The Wolf of Wall Street   Biography,Comedy,Crime  2013     8.2  865134   \n",
       "19                Gone Girl      Crime,Drama,Mystery  2014     8.1  636243   \n",
       "25                 The Help                    Drama  2011     8.1  342429   \n",
       "30              The Martian   Adventure,Drama,Sci-Fi  2015     8.0  556097   \n",
       "33         12 Years a Slave  Biography,Drama,History  2013     8.1  486338   \n",
       "\n",
       "    Revenue  Metascore  Nomination  \n",
       "18   116.87       75.0           1  \n",
       "19   167.74       79.0           1  \n",
       "25   169.71       62.0           1  \n",
       "30   228.43       80.0           1  \n",
       "33    56.67       96.0           1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nominate = data[data.Nomination > 0]\n",
    "nominate.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data  Visualization and Data Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I. Nominated vs Overall by Rating, Revenue, Metascore, Votes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Generate the average Revenue, Rating, Votes, Meatscore for 'Nomination' table and overall table\n",
    "2. plot the bar graph for each pair of table \n",
    "\n",
    "Conclusion: It's clear to see that 'Nominated Movie' has a slightly higher average revenue and rating score than the overall dataset. And 'Nominated Movie' has a much higher average Metascore and Votes than the overall dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11738f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174bea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rsP0XqhZnX31vBc4v0/5ItQP0MZ7ZGoCqBb2jpM0BJK0taYvSshxv+6dU3bPb\njiLWlVoSweh9jup2uX3eB7xD0pVUX97DRlnvOcA4SdcBx1B9uYdzNNWe0gKefvveHwOv7ztYTNWd\n1VMOmF3L3/fMPwm8StI1VP3/fxpqZbYfsP2Z8jyJTl8FVildVCcDb7fd1wI6jSpRnjJItYPF9n5J\nV5ft+hgdBxVjTMyg2hHodDqj6B6y/RvbPxpg0kzgf8rffDrwqY5pJwNvYYDvke1eqlbqSWXZi6l2\nHNYFzi5lFwEfHGmsK7vcYiIiouXSIoiIaLkkgoiIlksiiIhouSSCiIiWSyKIiGi5JIKIiJZLIoiI\naLn/D2BJ9APmH7LZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117548208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RMcA6OdncCuwpaXdJrwEmAnMGuU0REa9KHTuMZnuNpL8BrgI2BabZXjTIzXq1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\n6Ny9GFYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aef1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculate the mean of Revenue\n",
    "ave1 = nominate['Revenue'].mean()\n",
    "ave2 = data['Revenue'].mean()\n",
    "\n",
    "#calculate the mean of Rating\n",
    "ave3 = nominate['Rating'].mean()\n",
    "ave4 = data['Rating'].mean()\n",
    "\n",
    "#calculate the mean of Metascore\n",
    "ave5 = nominate['Metascore'].mean()\n",
    "ave6 = data['Metascore'].mean()\n",
    "\n",
    "#calculate the mean of Votes\n",
    "ave7 = nominate['Votes'].mean()\n",
    "ave8 = data['Votes'].mean()\n",
    "\n",
    "#store them in pairs in an array\n",
    "array = [[ave1, ave2],[ave3, ave4], [ave5, ave6],[ave7, ave8]]\n",
    "colors = ['blue','gold', 'red', 'orange']\n",
    "\n",
    "#generate four bar graphs\n",
    "for i in range(0,4):\n",
    "    #x and y\n",
    "    Objects = ('Nomianated Movies', \"All Movies\")\n",
    "    y_pos = np.arange(len(Objects))\n",
    "    plt.bar(y_pos, array[i], alpha=0.6, color = colors[i])\n",
    "    plt.xticks(y_pos, Objects)\n",
    "    if i == 0:\n",
    "        plt.ylabel('Average revenue')\n",
    "        plt.title('Revenue - Nomination')\n",
    "    if i == 1:\n",
    "        plt.ylabel('Average rating')\n",
    "        plt.title('Rating - Nomination')\n",
    "    if i == 2:\n",
    "        plt.ylabel('Average Metascore')\n",
    "        plt.title('Metascore - Nomination')\n",
    "    if i == 3:\n",
    "        plt.ylabel('Average Votes')\n",
    "        plt.title('Votes - Nomination')\n",
    "    plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II. Rating distribution in different Genres by Violin plot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate tables to draw violin plot to analyze the Rating distribution\n",
    "1. drop irrelevant columns\n",
    "2. reset indexs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Nomination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>8.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prometheus</td>\n",
       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
       "      <td>7.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>Adventure,Drama,Sci-Fi</td>\n",
       "      <td>8.6</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>8.1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Title                     Genre  \\\n",
       "0                      Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
       "1                                   Prometheus  Adventure,Mystery,Sci-Fi   \n",
       "2                                 Interstellar    Adventure,Drama,Sci-Fi   \n",
       "3  Pirates of the Caribbean: On Stranger Tides  Action,Adventure,Fantasy   \n",
       "4   Star Wars: Episode VII - The Force Awakens  Action,Adventure,Fantasy   \n",
       "\n",
       "   Rating  Metascore  Nomination  \n",
       "0     8.1       76.0           0  \n",
       "1     7.0       65.0           0  \n",
       "2     8.6       74.0           0  \n",
       "3     6.7       45.0           0  \n",
       "4     8.1       81.0           0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data.drop(['Year','Votes','Revenue'], axis = 1)\n",
    "data2 = data2.reset_index()\n",
    "data2 = data2.drop(['index'], axis = 1)\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column Genre's categories are ambigious since one movie has many genres. So we split one row into multiple rows and count each movie exactly the number of genres. For example, the movie 'Prometheus' has three categories: adventure, mystery, sci-fi. Then I convert this row into three rows, each row has only one genre and append them into the table.\n",
    "1. initialize a new array called data3, store all the new generated rows in the array \n",
    "2. concatenate these rows of array and generate a new dataframe 'data3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>index</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Nomination</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Action</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Prometheus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Prometheus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      Genre  Metascore  Nomination  Rating                    Title\n",
       "0      0     Action       76.0           0     8.1  Guardians of the Galaxy\n",
       "1      0  Adventure       76.0           0     8.1  Guardians of the Galaxy\n",
       "2      0     Sci-Fi       76.0           0     8.1  Guardians of the Galaxy\n",
       "3      0  Adventure       65.0           0     7.0               Prometheus\n",
       "4      0    Mystery       65.0           0     7.0               Prometheus"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = []\n",
    "cnt = 0\n",
    "cnt2 = 0\n",
    "#iterate through the data2 table and split the rows\n",
    "for i, rowi in data2.iterrows():\n",
    "    cate = re.split(',', rowi['Genre'])\n",
    "    length = len(cate)\n",
    "    cnt2 = cnt2 + 1\n",
    "    for j in range(0, length):\n",
    "        row = {'Title':[rowi['Title']], 'Genre':cate[j],'Rating':rowi['Rating'],'Metascore':rowi['Metascore'], 'Nomination':rowi['Nomination']}\n",
    "        row = pd.DataFrame(data=row)\n",
    "        data3.append(row)\n",
    "        cnt = cnt + 1\n",
    "        \n",
    "#concatenate these rows\n",
    "data3 = pd.concat(data3)\n",
    "data3 = data3.reset_index()\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a violin graph to visualize the distribution of Rating for different genres\n",
    "1. import the seaborn package which contains many useful plotting functions\n",
    "2. generate the graph: 'genre' as the x-axis and 'rating' as the y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FeKFgiXGHJUoWnAsDmQWKjKHSAPDxxx9gfHzM62EcCBnnnnJZPsPZowel9bjNa6+9guWl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0vZKlaKZwnSaukaSlnx+ZKBm243daqmLdnyyWcxI6as6GiftSwLV65exW73IHZZ/HAy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\nmKFwVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117374d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "\n",
    "#adjust the figure size \n",
    "a4_dims = (17, 10)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "ax = sns.violinplot(x=\"Genre\", y=\"Rating\", data=data3)\n",
    "sns.plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a violin graph to visualize the distribution of Metascore for different genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CnPkyDnGPQpMNrOKeXNh3u/xDNufzOU5PT6+450AYyaAqNYMEejrEXRBWoUPc3asCXRz0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\nu/DgwQP9OAgCXU2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AcHpavIOuN8HPH4YTJsyp/ULdUpI56MnnuRMXiQuruAN8j32cg75e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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b7074e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a4_dims = (17, 10)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "ax = sns.violinplot(x=\"Genre\", y=\"Metascore\", data=data3)\n",
    "sns.plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III. Number of Nominations in different genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the number of nominations in each Genre: \n",
    "1. use a dictionary dict_genre to store the number of nominations for each genre.\n",
    "2. print the dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Action': 1,\n",
       " 'Adventure': 6,\n",
       " 'Animation': 0,\n",
       " 'Biography': 17,\n",
       " 'Comedy': 6,\n",
       " 'Crime': 5,\n",
       " 'Drama': 45,\n",
       " 'Family': 0,\n",
       " 'Fantasy': 1,\n",
       " 'History': 5,\n",
       " 'Horror': 0,\n",
       " 'Music': 0,\n",
       " 'Musical': 1,\n",
       " 'Mystery': 5,\n",
       " 'Romance': 9,\n",
       " 'Sci-Fi': 2,\n",
       " 'Sport': 2,\n",
       " 'Thriller': 9,\n",
       " 'War': 0,\n",
       " 'Western': 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_genre = {}\n",
    "#find the number of keys(genres)\n",
    "for i, rowi in data3.iterrows():\n",
    "    dict_genre[rowi['Genre']] = 0\n",
    "    \n",
    "#counting\n",
    "for i, rowi in data3.iterrows():\n",
    "    if rowi['Nomination'] != 0:\n",
    "        dict_genre[rowi['Genre']] = dict_genre[rowi['Genre']] + 1\n",
    "        \n",
    "dict_genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a graph that shows the number of nominations in each genre.\n",
    "Conclusion: drama has highest number of nominations, much higher than the rest of the genres. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33XUzatSotG3b9iNlefzxx3PRRRelS5cuadu2bbbbbrvcfPPNmTFjRk455ZTM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\nO1MyLiIiIiIiImJnSsZFRERERERE7Oxfl1H3N7DUFCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11afbc438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf()\n",
    "plt.bar(range(len(dict_genre)), list(dict_genre.values()), align='center')\n",
    "plt.xticks(range(len(dict_genre)), list(dict_genre.keys()))\n",
    "plt.xlabel('Number of Nominations')\n",
    "plt.ylabel('Genre')\n",
    "fig.set_size_inches(17, 9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IV. Relation between Rating and Standardized_Revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aim: find the relation between nomination and revenue, x is 'standardized_revenue' which is computed by (Revenue - average revenue of this year) / sqrt(std of this year's revenue) \n",
    "1. initialize two dictionaries dict_ave and dict_std to calculate each year's mean and std.\n",
    "2. iterate through the data table and calculate the standardized_revenue for each row\n",
    "3. draw scatter plot\n",
    "4. add a regression line to the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#calculate the (Revenue - average of this year) / sqrt(std of this year) \n",
    "dict_ave = {}\n",
    "dict_std = {}\n",
    "\n",
    "for i in range(2006, 2016):\n",
    "    data_cur = data[data.Year == i]\n",
    "    dict_ave[i] = data_cur['Revenue'].mean()\n",
    "    dict_std[i] = data_cur['Revenue'].std()\n",
    "    \n",
    "#add the 'standardized_revenue', 'Average' and 'std' column\n",
    "data['standardized_revenue'] = 0\n",
    "data['Average'] = 0\n",
    "data['Std'] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Nomination</th>\n",
       "      <th>standardized_revenue</th>\n",
       "      <th>Average</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.1</td>\n",
       "      <td>757074</td>\n",
       "      <td>333.13</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prometheus</td>\n",
       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.0</td>\n",
       "      <td>485820</td>\n",
       "      <td>126.46</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>Adventure,Drama,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1047747</td>\n",
       "      <td>187.99</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.7</td>\n",
       "      <td>395025</td>\n",
       "      <td>241.06</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>93</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2015</td>\n",
       "      <td>8.1</td>\n",
       "      <td>661608</td>\n",
       "      <td>936.63</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>80</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Title                     Genre  \\\n",
       "0                      Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
       "1                                   Prometheus  Adventure,Mystery,Sci-Fi   \n",
       "3                                 Interstellar    Adventure,Drama,Sci-Fi   \n",
       "5  Pirates of the Caribbean: On Stranger Tides  Action,Adventure,Fantasy   \n",
       "6   Star Wars: Episode VII - The Force Awakens  Action,Adventure,Fantasy   \n",
       "\n",
       "   Year  Rating    Votes  Revenue  Metascore  Nomination  \\\n",
       "0  2014     8.1   757074   333.13       76.0           0   \n",
       "1  2012     7.0   485820   126.46       65.0           0   \n",
       "3  2014     8.6  1047747   187.99       74.0           0   \n",
       "5  2011     6.7   395025   241.06       45.0           0   \n",
       "6  2015     8.1   661608   936.63       81.0           0   \n",
       "\n",
       "   standardized_revenue  Average  Std  \n",
       "0                    26       85   86  \n",
       "1                     1      110  117  \n",
       "3                    11       85   86  \n",
       "5                    15       93   86  \n",
       "6                    74       80  132  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the standardized_revenue for each row\n",
    "for i, rowi in data.iterrows():\n",
    "    year = int(rowi['Year'])\n",
    "    value = (rowi['Revenue'] - dict_ave[year]) / math.sqrt(dict_std[year])\n",
    "    data.set_value(i, 'standardized_revenue', value)\n",
    "    data.set_value(i, 'Std', dict_std[year])\n",
    "    data.set_value(i, 'Average', dict_ave[year])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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PXC64zZ4j4+jpKnz/ujSDSlJPf2+bsgwu07LMTMtE00nSd6B4Xp6dj7NV6yYi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\ngMIJbgAAAIX7/2C3+2LB/V5nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b142b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#draw the scatter plot\n",
    "fig = plt.gcf()\n",
    "plt.scatter(x = data['standardized_revenue'], y = data['Rating'])\n",
    "\n",
    "#add a regression line\n",
    "arry1 = np.array(data['standardized_revenue'])\n",
    "arry2 = np.array(data['Rating'])\n",
    "m, b = np.polyfit(arry1, arry2, 1)\n",
    "plt.plot(arry1, m*arry1 + b, '-') \n",
    "\n",
    "plt.xlabel('Standardized_revenue')\n",
    "plt.ylabel('Rating')\n",
    "\n",
    "#reset figure size\n",
    "fig.set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Analysis by Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding new columns to build linear regression model later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to find more information about the relationship between the chance of getting nominated and rating, metascore, votes and revenue, we find their average. <br>\n",
    "Here are the average rating and metascore over all data. <br>\n",
    "Because rating and metascore are given by regular audience and professional reviewers. The amount of people review might be influenced by each year's economy, but the average score won't be affected. Therefore we are calculated them as a whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average_rating_all_time = data[\"Rating\"].mean()\n",
    "average_matascore_all_time = data[\"Metascore\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get every year's average revenue, the reason we are calculating this by year is unlike rating, each year's economy might affect the box office. So it's more objective this way. Same thing happen to votes, because economy also affects the money people spending on movie tickets or dvd. Which affect the number of audience and number of votes.\n",
    "\n",
    "We stroe these information in hashmap with year as key and data in a year as value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average_revenue_by_years = data.groupby(\"Year\")\n",
    "average_reveneue_map = {}\n",
    "for name, group in average_revenue_by_years:\n",
    "    average_reveneue_map[name] = group[\"Revenue\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average_votes_by_years = data.groupby(\"Year\")\n",
    "average_votes_map = {}\n",
    "for name, group in average_votes_by_years:\n",
    "    average_votes_map[name] = group[\"Votes\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.866874999999991"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_rating_all_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.696875"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_matascore_all_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2006: 277232.2195121951,\n",
       " 2007: 266530.70454545453,\n",
       " 2008: 266580.1458333333,\n",
       " 2009: 267180.5777777778,\n",
       " 2010: 261082.9298245614,\n",
       " 2011: 259254.73684210525,\n",
       " 2012: 290861.48387096776,\n",
       " 2013: 225531.89285714287,\n",
       " 2014: 211926.8817204301,\n",
       " 2015: 129512.6513761468}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_votes_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2006: 87.25560975609757,\n",
       " 2007: 93.0740909090909,\n",
       " 2008: 97.52541666666669,\n",
       " 2009: 115.742,\n",
       " 2010: 105.08157894736843,\n",
       " 2011: 93.70333333333335,\n",
       " 2012: 110.10306451612907,\n",
       " 2013: 88.08464285714282,\n",
       " 2014: 85.43365591397847,\n",
       " 2015: 80.72559633027525}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_reveneue_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create 4 additional column <br>\n",
    "rating_vs_aveRating is current rating / average rating <br>\n",
    "metascore_vs_aveMeta is current metascore / average metascore <br>\n",
    "revenue_vs_aveRevenue is current revenue / average revenue in current year <br>\n",
    "votes_vs_aveVotes is current votes / average votes in current year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these additional column, we can create two linear model, one is nominated vs four factors (rating, metascore, votes, revenue), another is nominated vs ratio of four factors against average of each factors. Then we can determine which model to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[\"Rating_vs_aveRating\"] = 0.0\n",
    "data[\"Metascore_vs_aveMeta\"] = 0.0\n",
    "data[\"Revenue_vs_aveRevenue\"] = 0.0\n",
    "data[\"Votes_vs_aveVotes\"] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for index, row in data.iterrows():\n",
    "    data.set_value(index, \"Rating_vs_aveRating\", row[\"Rating\"] / average_rating_all_time)\n",
    "    data.set_value(index, \"Metascore_vs_aveMeta\", row[\"Metascore\"] / average_matascore_all_time)\n",
    "    \n",
    "    current_year = row[\"Year\"]\n",
    "    \n",
    "    data.set_value(index, \"Revenue_vs_aveRevenue\", row[\"Revenue\"] / average_reveneue_map[current_year])\n",
    "    data.set_value(index, \"Votes_vs_aveVotes\", row[\"Votes\"] / average_votes_map[current_year])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the new table after adding 4 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Nomination</th>\n",
       "      <th>standardized_revenue</th>\n",
       "      <th>Average</th>\n",
       "      <th>Std</th>\n",
       "      <th>Rating_vs_aveRating</th>\n",
       "      <th>Metascore_vs_aveMeta</th>\n",
       "      <th>Revenue_vs_aveRevenue</th>\n",
       "      <th>Votes_vs_aveVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.1</td>\n",
       "      <td>757074</td>\n",
       "      <td>333.13</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>1.179576</td>\n",
       "      <td>1.273098</td>\n",
       "      <td>3.899283</td>\n",
       "      <td>3.572336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prometheus</td>\n",
       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.0</td>\n",
       "      <td>485820</td>\n",
       "      <td>126.46</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>117</td>\n",
       "      <td>1.019387</td>\n",
       "      <td>1.088834</td>\n",
       "      <td>1.148560</td>\n",
       "      <td>1.670280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>Adventure,Drama,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1047747</td>\n",
       "      <td>187.99</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>1.252389</td>\n",
       "      <td>1.239596</td>\n",
       "      <td>2.200421</td>\n",
       "      <td>4.943908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.7</td>\n",
       "      <td>395025</td>\n",
       "      <td>241.06</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>93</td>\n",
       "      <td>86</td>\n",
       "      <td>0.975699</td>\n",
       "      <td>0.753808</td>\n",
       "      <td>2.572587</td>\n",
       "      <td>1.523694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2015</td>\n",
       "      <td>8.1</td>\n",
       "      <td>661608</td>\n",
       "      <td>936.63</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>80</td>\n",
       "      <td>132</td>\n",
       "      <td>1.179576</td>\n",
       "      <td>1.356855</td>\n",
       "      <td>11.602640</td>\n",
       "      <td>5.108443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Title                     Genre  \\\n",
       "0                      Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
       "1                                   Prometheus  Adventure,Mystery,Sci-Fi   \n",
       "3                                 Interstellar    Adventure,Drama,Sci-Fi   \n",
       "5  Pirates of the Caribbean: On Stranger Tides  Action,Adventure,Fantasy   \n",
       "6   Star Wars: Episode VII - The Force Awakens  Action,Adventure,Fantasy   \n",
       "\n",
       "   Year  Rating    Votes  Revenue  Metascore  Nomination  \\\n",
       "0  2014     8.1   757074   333.13       76.0           0   \n",
       "1  2012     7.0   485820   126.46       65.0           0   \n",
       "3  2014     8.6  1047747   187.99       74.0           0   \n",
       "5  2011     6.7   395025   241.06       45.0           0   \n",
       "6  2015     8.1   661608   936.63       81.0           0   \n",
       "\n",
       "   standardized_revenue  Average  Std  Rating_vs_aveRating  \\\n",
       "0                    26       85   86             1.179576   \n",
       "1                     1      110  117             1.019387   \n",
       "3                    11       85   86             1.252389   \n",
       "5                    15       93   86             0.975699   \n",
       "6                    74       80  132             1.179576   \n",
       "\n",
       "   Metascore_vs_aveMeta  Revenue_vs_aveRevenue  Votes_vs_aveVotes  \n",
       "0              1.273098               3.899283           3.572336  \n",
       "1              1.088834               1.148560           1.670280  \n",
       "3              1.239596               2.200421           4.943908  \n",
       "5              0.753808               2.572587           1.523694  \n",
       "6              1.356855              11.602640           5.108443  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we seperate data into two sets. <br>\n",
    "First set contains data from 2006 to 2013 used as training data. <br>\n",
    "Second set contains data from 2014 to 2015 used as testing data. <br>\n",
    "Amount of training data is about twice the size of testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_data = data.loc[data[\"Year\"] < 2014]\n",
    "testing_data = data.loc[data[\"Year\"] > 2013]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a linear regression on nominee vs combination of rating, metascore, revenue, and votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Nomination</td>    <th>  R-squared:         </th> <td>   0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   13.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Dec 2017</td> <th>  Prob (F-statistic):</th> <td>2.69e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:40:48</td>     <th>  Log-Likelihood:    </th> <td> -6.3304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   438</td>      <th>  AIC:               </th> <td>   22.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   433</td>      <th>  BIC:               </th> <td>   43.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>   -0.2502</td> <td>    0.112</td> <td>   -2.237</td> <td> 0.026</td> <td>   -0.470</td> <td>   -0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rating</th>    <td>    0.0028</td> <td>    0.021</td> <td>    0.135</td> <td> 0.893</td> <td>   -0.039</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Metascore</th> <td>    0.0051</td> <td>    0.001</td> <td>    5.033</td> <td> 0.000</td> <td>    0.003</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Revenue</th>   <td>   -0.0002</td> <td>    0.000</td> <td>   -0.994</td> <td> 0.321</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Votes</th>     <td> 4.251e-08</td> <td> 9.14e-08</td> <td>    0.465</td> <td> 0.642</td> <td>-1.37e-07</td> <td> 2.22e-07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>259.406</td> <th>  Durbin-Watson:     </th> <td>   1.919</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1368.665</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.724</td>  <th>  Prob(JB):          </th> <td>6.28e-298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 9.732</td>  <th>  Cond. No.          </th> <td>3.22e+06</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Nomination   R-squared:                       0.110\n",
       "Model:                            OLS   Adj. R-squared:                  0.102\n",
       "Method:                 Least Squares   F-statistic:                     13.39\n",
       "Date:                Sat, 16 Dec 2017   Prob (F-statistic):           2.69e-10\n",
       "Time:                        00:40:48   Log-Likelihood:                -6.3304\n",
       "No. Observations:                 438   AIC:                             22.66\n",
       "Df Residuals:                     433   BIC:                             43.07\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.2502      0.112     -2.237      0.026      -0.470      -0.030\n",
       "Rating         0.0028      0.021      0.135      0.893      -0.039       0.044\n",
       "Metascore      0.0051      0.001      5.033      0.000       0.003       0.007\n",
       "Revenue       -0.0002      0.000     -0.994      0.321      -0.000       0.000\n",
       "Votes       4.251e-08   9.14e-08      0.465      0.642   -1.37e-07    2.22e-07\n",
       "==============================================================================\n",
       "Omnibus:                      259.406   Durbin-Watson:                   1.919\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1368.665\n",
       "Skew:                           2.724   Prob(JB):                    6.28e-298\n",
       "Kurtosis:                       9.732   Cond. No.                     3.22e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.22e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_var = [\"Rating\", \"Metascore\", \"Revenue\", \"Votes\"]\n",
    "y_var = \"Nomination\"\n",
    "\n",
    "\n",
    "X = [list(row.values) for _, row in training_data[x_var].iterrows()]\n",
    "X_OLS = sm.add_constant(X)\n",
    "Y = training_data[y_var].values\n",
    "\n",
    "\n",
    "model1 = sm.OLS(Y, X_OLS)\n",
    "model1.data.xnames = ['const'] + x_var\n",
    "model1.data.ynames = \"Nomination\"\n",
    "result1 = model1.fit()\n",
    "result1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a linear regression on nominee vs combination of rating against average, metascore against average, revenue against average, and votes against average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Nomination</td>    <th>  R-squared:         </th> <td>   0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   13.50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Dec 2017</td> <th>  Prob (F-statistic):</th> <td>2.23e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:40:48</td>     <th>  Log-Likelihood:    </th> <td> -6.1356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   438</td>      <th>  AIC:               </th> <td>   22.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   433</td>      <th>  BIC:               </th> <td>   42.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                 <td>   -0.2310</td> <td>    0.112</td> <td>   -2.070</td> <td> 0.039</td> <td>   -0.450</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rating_vs_aveRating</th>   <td>   -0.0046</td> <td>    0.144</td> <td>   -0.032</td> <td> 0.975</td> <td>   -0.288</td> <td>    0.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Metascore_vs_aveMeta</th>  <td>    0.3039</td> <td>    0.060</td> <td>    5.031</td> <td> 0.000</td> <td>    0.185</td> <td>    0.423</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Revenue_vs_aveRevenue</th> <td>   -0.0185</td> <td>    0.015</td> <td>   -1.202</td> <td> 0.230</td> <td>   -0.049</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Votes_vs_aveVotes</th>     <td>    0.0196</td> <td>    0.024</td> <td>    0.815</td> <td> 0.415</td> <td>   -0.028</td> <td>    0.067</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>259.376</td> <th>  Durbin-Watson:     </th> <td>   1.921</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1371.118</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.722</td>  <th>  Prob(JB):          </th> <td>1.84e-298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 9.744</td>  <th>  Cond. No.          </th> <td>    37.0</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Nomination   R-squared:                       0.111\n",
       "Model:                            OLS   Adj. R-squared:                  0.103\n",
       "Method:                 Least Squares   F-statistic:                     13.50\n",
       "Date:                Sat, 16 Dec 2017   Prob (F-statistic):           2.23e-10\n",
       "Time:                        00:40:48   Log-Likelihood:                -6.1356\n",
       "No. Observations:                 438   AIC:                             22.27\n",
       "Df Residuals:                     433   BIC:                             42.68\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "const                    -0.2310      0.112     -2.070      0.039      -0.450      -0.012\n",
       "Rating_vs_aveRating      -0.0046      0.144     -0.032      0.975      -0.288       0.279\n",
       "Metascore_vs_aveMeta      0.3039      0.060      5.031      0.000       0.185       0.423\n",
       "Revenue_vs_aveRevenue    -0.0185      0.015     -1.202      0.230      -0.049       0.012\n",
       "Votes_vs_aveVotes         0.0196      0.024      0.815      0.415      -0.028       0.067\n",
       "==============================================================================\n",
       "Omnibus:                      259.376   Durbin-Watson:                   1.921\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1371.118\n",
       "Skew:                           2.722   Prob(JB):                    1.84e-298\n",
       "Kurtosis:                       9.744   Cond. No.                         37.0\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_var = [\"Rating_vs_aveRating\", \"Metascore_vs_aveMeta\", \"Revenue_vs_aveRevenue\", \"Votes_vs_aveVotes\"]\n",
    "y_var = \"Nomination\"\n",
    "\n",
    "\n",
    "X = [list(row.values) for _, row in training_data[x_var].iterrows()]\n",
    "X_OLS = sm.add_constant(X)\n",
    "Y = training_data[y_var].values\n",
    "\n",
    "\n",
    "model2 = sm.OLS(Y, X_OLS)\n",
    "model2.data.xnames = ['const'] + x_var\n",
    "model2.data.ynames = \"Nomination\"\n",
    "result2 = model2.fit()\n",
    "result2.summary()"
   ]
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    "Comparing two regression model, we can tell the first one makes more sense, when we look at the coefficient, consider higher rating and higher metascore increase the chance a film gets nominated. And higher revenue decrease chance of getting nominated because usually commerical movies are not welcomed by the award association. We also discovered votes play small parts of the prediction since usually artistic movies are not super popular."
   ]
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    "## 5. Summary "
   ]
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    "## 6. Resources and Reference\n",
    "This tutorial highlighted several methods of data curation, analysis, and visualization. More information about the libraries or packages we have used in our Oscar Analysis are available from the following links:\n",
    "\n",
    "Pandas: http://pandas.pydata.org/\n",
    "\n",
    "PyPlot: https://matplotlib.org/api/pyplot_api.html\n",
    "\n",
    "Basemap: https://matplotlib.org/basemap/\n",
    "\n",
    "Scikit Image: http://scikit-image.org/\n",
    "\n",
    "Scikit Learn: https://pypi.python.org/pypi/scikit-learn/0.18.1\n",
    "\n",
    "Dataset1: https://www.kaggle.com/sebask/the-oscars-dataset-preparing-for-2017/data\n",
    "\n",
    "Dataset2: https://www.kaggle.com/sebask/the-oscars-dataset-preparing-for-2017/data"
   ]
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