FinalProject_group003.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TXUp1DgW4Wmb"
   },
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V21jCVnC4Wmc"
   },
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mhEy0wos4Wmd"
   },
   "source": [
    "Our project is looking at the correlation between movie genre popularity and GDP. To analyze this, we found datasets relating to our hypothesis, and manipulated them to answer our question. We had to clean the datasets to get data we can work with and visualize. We created graphs to see relationships that relate to our hypothesis, and analyzed them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6iJJ1g394Wmd"
   },
   "source": [
    "# Names\n",
    "\n",
    "- Jason Lee\n",
    "- Laurent Lee\n",
    "- Qingyang Xu\n",
    "- Tianze Zhang\n",
    "- Yue Jiao\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hkw1gC4w4Wme"
   },
   "source": [
    "# Group Members IDs\n",
    "\n",
    "\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "StoCj5ou4Wme"
   },
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V23d4pQL4Wmf"
   },
   "source": [
    "How does GDP per capita correlate to the popularity of certain movie genres in USA? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UvWlknst4Wmf"
   },
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RzeFalvz4Wmg"
   },
   "source": [
    "Movies are not merely just entertainment. They can let us experience what we could not do in reality and rich emotions. Reports have shown that films could evoke emotions within us. It is evident that individuals have different movie genre preferences, and preferences could be affected by a lot of factors. For example, GDP per capita is a measure of economic strength, which is related to people’s personal income. In this project, we aim to investigate the correlation between change in GDP per capita and people’s movie genre preferences. We want unbiased data and visualizations to demonstrate this correlation. \n",
    "\n",
    "References (include links):\n",
    "- 1) https://www.scpr.org/news/2014/12/04/48457/what-watching-movies-can-tell-us-about-how-our-bra/ \n",
    "- 2) https://www.thebalance.com/gdp-per-capita-formula-u-s-compared-to-highest-and-lowest-3305848"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T8OnOp984Wmg"
   },
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QTnMgcef4Wmh"
   },
   "source": [
    "\n",
    "Since GDP per capita is directly related to people’s incomes and lower income could cause people to be depressed, we suspect that when the GDP per capita of a certain time period is lower, people prefer to watch more relaxing movies such as comedies, dramas, and romance, while when the GDP per capita of a certain time period is higher, people prefer to watch more thrilling movies such as horror, action, and adventure. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tWaEFmR84Wmh"
   },
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bbiysT1c4Wmi"
   },
   "source": [
    "### - Dataset Name: movies_metadata.csv\n",
    "- Link to the dataset: https://www.kaggle.com/rounakbanik/the-movies-dataset/home#movies_metadata.csv\n",
    "- Number of observations: 45,376\n",
    "- This dataset was a csv file containing many different movies from the past several decades. Each movie had their genre, a popularity score, and the year they came out.\n",
    "\n",
    "\n",
    "### - Dataset Name: gdp.csv\n",
    "- Link to the dataset: https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG?locations=US\n",
    "- Number of observations: 264\n",
    "- This data set was a csv file that contained the GDP per capita over the years.\n",
    "\n",
    "We will combine the data from these sources into a dataframe. We will then compare the data from these datasets to see how thrilling and relaxing genres perform vs GDP.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xwTJQjD04Wmi"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ofWHm6qCzNbk"
   },
   "source": [
    "Imports used in project:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_tRY2XTA4Wmj"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import bs4\n",
    "from bs4 import BeautifulSoup\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y7Y8VUiv4Wml"
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4UsnZd_d4Wmm"
   },
   "source": [
    "In order to clean our data from the dataset, we represent the data in a pandas dataframe for both our datasets: gdp.csv and movies_metadata.csv. We then drop irrelevant data from the dataframe and rename the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "colab_type": "code",
    "id": "LSfTklpr4Wmn",
    "outputId": "4077f52e-d2de-4614-8c19-5ace5c636a2f"
   },
   "outputs": [],
   "source": [
    "# add change in gdp data\n",
    "data = pd.read_csv('gdp.csv')\n",
    "data.set_index('Country Name', inplace = True)\n",
    "data = data.loc['United States', :]\n",
    "data = data.dropna()\n",
    "data = data.drop('Country Code')\n",
    "data = data.drop('Indicator Code')\n",
    "data = data.drop('Indicator Name')\n",
    "\n",
    "#rename the dataframe\n",
    "gdp = pd.DataFrame(data)\n",
    "gdp = gdp.reset_index()\n",
    "gdp.columns = ['date','GDP']\n",
    "# change the gdp to numeric \n",
    "gdp[\"date\"] = pd.to_numeric(gdp[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EFSD5D8mWgov"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#read in data from .csv\n",
    "df_movies = pd.read_csv('movies_metadata.csv')\n",
    "\n",
    "#drop irrelevant columns\n",
    "df_movies.drop(columns=['homepage', 'id', 'imdb_id', 'original_language', 'overview',\n",
    "                        'spoken_languages', 'status', 'tagline', 'poster_path',\n",
    "                        'production_countries', 'video', 'production_companies',\n",
    "                        'adult', 'belongs_to_collection', 'original_title', 'budget', \n",
    "                        'revenue', 'runtime', 'vote_average', 'vote_count'],\n",
    "                         inplace=True)\n",
    "\n",
    "#rename and move around columns\n",
    "df_movies.columns = ['genres', 'popularity', 'date', 'title']\n",
    "df_movies = df_movies[['title', 'date', 'genres', 'popularity']]\n",
    "#df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8rmd27gRWgo0"
   },
   "outputs": [],
   "source": [
    "#drop rows that do not have data for title or release date\n",
    "df_movies.dropna(subset=['date', 'title'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8LwtuF_Pe9qF"
   },
   "source": [
    "The date column of the dataframe had dates in the format “year-month-date”, but we only needed the year for each movie. We created a method called standardize_date_movie to only get the year from each date and store it back into the dataframe. We also sort the dataframe by the date and drop movies that do not have data for the genre column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qme-81IRWgo2"
   },
   "outputs": [],
   "source": [
    "# Convert all inputs to strings and normalize them\n",
    "def standardize_date_movie(long):\n",
    "    string = str(long)\n",
    "    string = string.strip()\n",
    "    string = string[0:4]\n",
    "    date = int(string)\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qGlOPZSJWgo4"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>genres</th>\n",
       "      <th>popularity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19585</th>\n",
       "      <td>Creature from the Haunted Sea</td>\n",
       "      <td>1961</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 27, 'nam...</td>\n",
       "      <td>0.297889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17874</th>\n",
       "      <td>On the Double</td>\n",
       "      <td>1961</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 35, 'nam...</td>\n",
       "      <td>1.358690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3802</th>\n",
       "      <td>Voyage to the Bottom of the Sea</td>\n",
       "      <td>1961</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 18, '...</td>\n",
       "      <td>3.013584</td>\n",
       "    </tr>\n",
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       "      <th>37369</th>\n",
       "      <td>Nikki, Wild Dog of the North</td>\n",
       "      <td>1961</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 10751...</td>\n",
       "      <td>0.037374</td>\n",
       "    </tr>\n",
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       "      <th>25543</th>\n",
       "      <td>Fire in Castilla (Tactilvision from the Moor o...</td>\n",
       "      <td>1961</td>\n",
       "      <td>[{'id': 99, 'name': 'Documentary'}]</td>\n",
       "      <td>0.337238</td>\n",
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       "                                                   title  date  \\\n",
       "19585                      Creature from the Haunted Sea  1961   \n",
       "17874                                      On the Double  1961   \n",
       "3802                     Voyage to the Bottom of the Sea  1961   \n",
       "37369                       Nikki, Wild Dog of the North  1961   \n",
       "25543  Fire in Castilla (Tactilvision from the Moor o...  1961   \n",
       "\n",
       "                                                  genres  popularity  \n",
       "19585  [{'id': 35, 'name': 'Comedy'}, {'id': 27, 'nam...    0.297889  \n",
       "17874  [{'id': 28, 'name': 'Action'}, {'id': 35, 'nam...    1.358690  \n",
       "3802   [{'id': 12, 'name': 'Adventure'}, {'id': 18, '...    3.013584  \n",
       "37369  [{'id': 12, 'name': 'Adventure'}, {'id': 10751...    0.037374  \n",
       "25543                [{'id': 99, 'name': 'Documentary'}]    0.337238  "
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   ],
   "source": [
    "# Apply standardization and drop any movies out of the date boundary\n",
    "df_movies[\"date\"] = df_movies[\"date\"].apply(standardize_date_movie)\n",
    "df_movies.dropna(inplace=True)\n",
    "\n",
    "#change it to numeric\n",
    "df_movies[\"date\"] = pd.to_numeric(df_movies[\"date\"])\n",
    "\n",
    "# change the popularity to numeric \n",
    "df_movies[\"popularity\"] = pd.to_numeric(df_movies[\"popularity\"])\n",
    "\n",
    "#sort the columns\n",
    "df_movies.sort_values(by=['date'], ascending=True, inplace = True)\n",
    "\n",
    "# take all movies from 1971 to 2017 and get rid of other ones\n",
    "df_movies = df_movies.loc[(df_movies[\"date\"] > 1960) & (df_movies[\"date\"] < 2017)]\n",
    "\n",
    "#drop empty genres\n",
    "df_movies = df_movies[~df_movies['genres'].isin(['[]'])]\n",
    "\n",
    "df_movies.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BkwUyCLufViB"
   },
   "source": [
    "The next step is to separate the movies by genre and store the movies into genre dataframes. The genres we classified at relaxing are Drama, Romance, and Comedy, and the genres we classified as thrilling are action, adventure, and thriller. We made a method called genre_df to separate the movies into their own genre dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Shw9cJe0Wgo-"
   },
   "outputs": [],
   "source": [
    "#funtion to generate new dataframe based on genre\n",
    "def genre_df(string):\n",
    "    df = pd.DataFrame(columns=['title', 'date','genres','popularity'])\n",
    "    for index, row in df_movies.iterrows():\n",
    "        if string in row['genres']:\n",
    "            df.loc[len(df.index)] = df_movies.loc[index]\n",
    "    \n",
    "    #clean up the new dataframe\n",
    "    df.drop('genres', axis=1, inplace=True)\n",
    "    df = df.reset_index()\n",
    "    df = df.drop(['title'], axis = 1)\n",
    "    df = df.groupby(['date']).mean()\n",
    "    df = df.drop('index', axis = 1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U2o1bRBTWgpA"
   },
   "outputs": [],
   "source": [
    "#get the genres into separate genre dataframes\n",
    "drama = genre_df('Drama')\n",
    "romance = genre_df('Romance')\n",
    "comedy = genre_df('Comedy')\n",
    "action = genre_df('Action')\n",
    "thriller = genre_df('Thriller')\n",
    "adventure = genre_df('Adventure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LjKKO_6LgLAt"
   },
   "source": [
    "We then create a merge method to reset the index for each genre dataframe and reformat the gdp in each dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nwSYP7oAjV5O"
   },
   "outputs": [],
   "source": [
    "def merge(df):\n",
    "    df = df.reset_index()\n",
    "    df = df.merge(gdp, how='inner')\n",
    "    return df\n",
    "#apply merge method to each genre \n",
    "drama = merge(drama)\n",
    "romance = merge(romance)\n",
    "comedy = merge(comedy)\n",
    "action = merge(action)\n",
    "thriller = merge(thriller)\n",
    "adventure = merge(adventure)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vcNFa1-2UqTX"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>popularity</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961</td>\n",
       "      <td>2.044215</td>\n",
       "      <td>5.633e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962</td>\n",
       "      <td>2.095774</td>\n",
       "      <td>6.051e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1963</td>\n",
       "      <td>2.227182</td>\n",
       "      <td>6.386e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964</td>\n",
       "      <td>2.245378</td>\n",
       "      <td>6.858e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1965</td>\n",
       "      <td>1.803160</td>\n",
       "      <td>7.437e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date  popularity        GDP\n",
       "0  1961    2.044215  5.633e+11\n",
       "1  1962    2.095774  6.051e+11\n",
       "2  1963    2.227182  6.386e+11\n",
       "3  1964    2.245378  6.858e+11\n",
       "4  1965    1.803160  7.437e+11"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of one the genre drama's dataframe\n",
    "drama.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wXfZWsEcjV5Q"
   },
   "outputs": [],
   "source": [
    "# convert gdp data to float\n",
    "drama['GDP'] = pd.to_numeric(gdp['GDP'])\n",
    "romance['GDP'] = pd.to_numeric(gdp['GDP'])\n",
    "comedy['GDP'] = pd.to_numeric(gdp['GDP'])\n",
    "action['GDP'] = pd.to_numeric(gdp['GDP'])\n",
    "thriller['GDP'] = pd.to_numeric(gdp['GDP'])\n",
    "adventure['GDP'] = pd.to_numeric(gdp['GDP'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "liMpnIF1g0Vq"
   },
   "source": [
    "## Data Visualization\n",
    "Now that we have clean data, we first see how the genre compares to the gdp of the year the movie was released. We do this for every genre by creating a scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RFpoJk_ijV5S"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fad033588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Graph all of them, and save to variable\n",
    "drama_graph = drama.plot.scatter('popularity', 'GDP')\n",
    "romance_graph = romance.plot.scatter('popularity', 'GDP')\n",
    "comedy_graph = comedy.plot.scatter('popularity', 'GDP')\n",
    "action_graph = drama.plot.scatter('popularity', 'GDP')\n",
    "thriller_graph = romance.plot.scatter('popularity', 'GDP')\n",
    "adventure_graph = comedy.plot.scatter('popularity', 'GDP')\n",
    "\n",
    "#Set titles\n",
    "drama_graph.set_title(\"Drama Graph\")\n",
    "romance_graph.set_title(\"Romance Graph\")\n",
    "comedy_graph.set_title(\"Comedy Graph\")\n",
    "action_graph.set_title(\"Action Graph\")\n",
    "thriller_graph.set_title(\"Thriller Graph\")\n",
    "adventure_graph.set_title(\"Adventure Graph\")\n",
    "drama_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XKilveBcUqTl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Adventure Graph')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphing each genre\n",
    "drama_graph = drama.plot(kind='scatter', y='popularity', x='GDP',\n",
    "                         color='red', title = '')\n",
    "romance_graph = romance.plot(kind='scatter', y='popularity', x='GDP',\n",
    "                             color='red')\n",
    "comedy_graph = comedy.plot(kind='scatter', y='popularity', x='GDP',\n",
    "                           color='red')\n",
    "action_graph = action.plot(kind='scatter', y='popularity', x='GDP',\n",
    "                           color='red')\n",
    "thriller_graph = thriller.plot(kind='scatter', y='popularity', x='GDP',\n",
    "                               color='red')\n",
    "adventure_graph = adventure.plot(kind='scatter', y='popularity', x='GDP',\n",
    "                                 color='red')\n",
    "#Setting titles for graphs\n",
    "drama_graph.set_title(\"Drama Graph\")\n",
    "romance_graph.set_title(\"Romance Graph\")\n",
    "comedy_graph.set_title(\"Comedy Graph\")\n",
    "action_graph.set_title(\"Action Graph\")\n",
    "thriller_graph.set_title(\"Thriller Graph\")\n",
    "adventure_graph.set_title(\"Adventure Graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rSaAqDNEiWEP"
   },
   "source": [
    "Now that we have seen how each genre compares with gdp separately, we want to see how each genre compares against eachother side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6YwkMUxAUqTZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Popularity Score')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Remove drama date\n",
    "drama_df = drama\n",
    "drama_df = drama_df.drop(columns='date',axis = 1)\n",
    "drama_df.columns = ['dramaPop','GDP']\n",
    "\n",
    "#Remove romance date\n",
    "romance_df = romance\n",
    "romance_df = romance_df.drop(columns='date',axis = 1)\n",
    "romance_df.columns = ['romancePop','GDP']\n",
    "\n",
    "#Remove comedy date\n",
    "comedy_df = comedy\n",
    "comedy_df = comedy_df.drop(columns='date',axis = 1)\n",
    "comedy_df.columns = ['comedyPop','GDP']\n",
    "\n",
    "#Remove action date\n",
    "action_df = action\n",
    "action_df = action_df.drop(columns='date',axis = 1)\n",
    "action_df.columns = [ 'actionPop','GDP']\n",
    "\n",
    "#Remove thriller date\n",
    "thriller_df = thriller\n",
    "thriller_df = thriller_df.drop(columns='date',axis = 1)\n",
    "thriller_df.columns = ['thrillerPop','GDP']\n",
    "\n",
    "#Remove adventure date\n",
    "adventure_df = adventure\n",
    "adventure_df = adventure_df.drop(columns='date',axis = 1)\n",
    "adventure_df.columns = ['adventurePop','GDP']\n",
    "\n",
    "#Merge all genres into one dataframe\n",
    "df_merged_genres = pd.merge(drama_df,romance_df, how='inner')\n",
    "df_merged_genres = pd.merge(df_merged_genres ,comedy_df, how='inner')\n",
    "df_merged_genres = pd.merge(df_merged_genres ,action_df, how='inner')\n",
    "df_merged_genres = pd.merge(df_merged_genres ,thriller_df, how='inner')\n",
    "df_merged_genres = pd.merge(df_merged_genres ,adventure_df, how='inner')\n",
    "\n",
    "df_merged_genres.plot(x='GDP', y=['dramaPop','romancePop',\n",
    "                                  'comedyPop','actionPop','thrillerPop',\n",
    "                        'adventurePop']).set_ylabel(\"Popularity Score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X3ximULcuoKf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Popularity Score')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#merge relaxing genres and thrilling genres into their own dataframes\n",
    "df_relaxing_merge = pd.concat([drama, comedy, romance])\n",
    "df_thrilling_merge = pd.concat([action, adventure, thriller])\n",
    "\n",
    "#Find average of popularity of thrilling movies and relaxing movies for each year\n",
    "df_avg_thrill = df_thrilling_merge.groupby('date').mean()\n",
    "df_avg_relax = df_relaxing_merge.groupby('date').mean()\n",
    "\n",
    "df_avg_thrill.columns = ['Thrilling Movies Popularity', 'GDP']\n",
    "df_avg_relax.columns = ['Relaxing Movies Popularity', 'GDP']\n",
    "\n",
    "df_thrill_relax_merge = pd.merge(df_avg_thrill, df_avg_relax, how='inner')\n",
    "\n",
    "df_thrill_relax_merge.plot(x='GDP', y=['Thrilling Movies Popularity',\n",
    "                'Relaxing Movies Popularity']).set_ylabel('Popularity Score')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wv4b4KN1vN3Q"
   },
   "source": [
    "To see if there is a correlation between relaxing movies and gdp, we create an OLS using the relaxing movie dataframe, comparing popularity with GDP. We can see from the OLS that there is correlation, with a p-value of 0.00."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "efFmFBC8vBmt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             popularity   R-squared:                       0.471\n",
      "Model:                            OLS   Adj. R-squared:                  0.468\n",
      "Method:                 Least Squares   F-statistic:                     147.8\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           1.01e-24\n",
      "Time:                        21:59:41   Log-Likelihood:                -137.18\n",
      "No. Observations:                 168   AIC:                             278.4\n",
      "Df Residuals:                     166   BIC:                             284.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.4114      0.067     36.241      0.000       2.280       2.543\n",
      "GDP         9.474e-14   7.79e-15     12.155      0.000    7.94e-14     1.1e-13\n",
      "==============================================================================\n",
      "Omnibus:                       10.811   Durbin-Watson:                   0.509\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):                9.414\n",
      "Skew:                           0.500   Prob(JB):                      0.00903\n",
      "Kurtosis:                       2.414   Cond. No.                     1.34e+13\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, 1.34e+13. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "#OLS for relaxing movies\n",
    "f0 = \"popularity ~ GDP\"\n",
    "outcome_1, predictors_1 = patsy.dmatrices(f0, df_relaxing_merge,\n",
    "                                          return_type = \"matrix\")\n",
    "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wK7U_vVRvYNN"
   },
   "source": [
    "We then see if there is a correlation between thirlling movies and gdp, we create another OLS using the thrilling movie dataframe comparing popularity and GDP. We can see from the OLS that there is correlation, with a p-value of 0.00."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rX6IJzpBvB41"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             popularity   R-squared:                       0.547\n",
      "Model:                            OLS   Adj. R-squared:                  0.544\n",
      "Method:                 Least Squares   F-statistic:                     200.4\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           2.41e-30\n",
      "Time:                        21:59:41   Log-Likelihood:                -272.38\n",
      "No. Observations:                 168   AIC:                             548.8\n",
      "Df Residuals:                     166   BIC:                             555.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.8486      0.149     19.144      0.000       2.555       3.142\n",
      "GDP         2.467e-13   1.74e-14     14.156      0.000    2.12e-13    2.81e-13\n",
      "==============================================================================\n",
      "Omnibus:                       65.312   Durbin-Watson:                   0.853\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              270.243\n",
      "Skew:                           1.426   Prob(JB):                     2.08e-59\n",
      "Kurtosis:                       8.521   Cond. No.                     1.34e+13\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, 1.34e+13. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "#OLS for thrilling movies\n",
    "f0 = \"popularity ~ GDP\"\n",
    "outcome_2, predictors_2 = patsy.dmatrices(f0, df_thrilling_merge,\n",
    "                                          return_type = \"matrix\")\n",
    "mod_2 = sm.OLS(outcome_2, predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zRXcaCcFvgXt"
   },
   "source": [
    "As we can see, it seems that thrilling moves are always more popular than relaxing movies. Since we know this, we should compare the relative popularity of thrilling movies and relaxing movies depending on GDP. By relative popularity we mean we should look at the popularity of the genre at the current GDP relative to the average popularity of the genre in general. We calculate this by taking the popularity of the genre at the year divided by the average of the popularity of the genre in general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rBtiqcMVuojQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Popularity Score')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find avg of popularity of thrilling movies and relaxing movies for each year\n",
    "df_avg_thrill = df_thrilling_merge.groupby('date').mean()\n",
    "df_avg_relax = df_relaxing_merge.groupby('date').mean()\n",
    "\n",
    "#Find how popular are the movie genres as a whole\n",
    "df_total_thrill_pop = df_thrilling_merge['popularity'].mean()\n",
    "df_total_relax_pop = df_relaxing_merge['popularity'].mean()\n",
    "\n",
    "# Find the relative popularity of the genres\n",
    "df_relative_thrill_pop = df_avg_thrill\n",
    "df_relative_thrill_pop['popularity'] = (df_avg_thrill['popularity']/\n",
    "                                        df_total_thrill_pop)\n",
    "df_relative_thrill_pop.columns = ['Relative Thrilling Movies Popularity',\n",
    "                                  'GDP']\n",
    "\n",
    "df_relative_relax_pop = df_avg_relax\n",
    "df_relative_relax_pop['popularity'] = (df_avg_relax['popularity']/\n",
    "                                       df_total_relax_pop)\n",
    "df_relative_relax_pop.columns = ['Relative Relaxing Movies Popularity',\n",
    "                                 'GDP']\n",
    "\n",
    "df_relative_thrill_relax = pd.merge(df_relative_thrill_pop, \n",
    "                                    df_relative_relax_pop,how='inner')\n",
    "df_relative_thrill_relax.plot(x='GDP',\n",
    "                              y=['Relative Thrilling Movies Popularity',\n",
    "         'Relative Relaxing Movies Popularity']).set_ylabel('Popularity Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SIZESXaSzjNK"
   },
   "source": [
    "Now that we have compared the relative popularities, we can look at the difference between the realtive popularites. By graphing GDP vs The difference between Thrilling and Relative movie popularites, we can see when relaxing movies become relatively more popular than thrilling movies. Whenever the line is below 0.0 popularity, it means that the relaxing movies are relatively more popular than thrilling movies. As we can see in the graph, this is occurence happens more frequently at low GDPs than in high GDPs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "akBx6v_cuo3I"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Popularity Score Difference')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_compare_relatives = df_relative_thrill_pop\n",
    "\n",
    "df_compare_relatives['Relative Thrilling Movies Popularity'] = \\\n",
    "             (df_relative_thrill_pop['Relative Thrilling Movies Popularity'] - \n",
    "              df_relative_relax_pop['Relative Relaxing Movies Popularity'])\n",
    "\n",
    "df_compare_relatives.columns = \\\n",
    "['Relative difference between Thrilling and Relaxing Popularity', 'GDP']\n",
    "\n",
    "df_compare_relatives.plot(x='GDP', \n",
    "          y='Relative difference between Thrilling and Relaxing Popularity' \\\n",
    "                         ).set_ylabel('Popularity Score Difference')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SadaZrW81Xc0"
   },
   "source": [
    "Now use an OLS regression to test a linear relationship between the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YR4C0kwx1eoO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     popularity_difference   R-squared:                       0.400\n",
      "Model:                               OLS   Adj. R-squared:                  0.389\n",
      "Method:                    Least Squares   F-statistic:                     36.06\n",
      "Date:                   Wed, 12 Jun 2019   Prob (F-statistic):           1.67e-07\n",
      "Time:                           21:59:42   Log-Likelihood:                 23.180\n",
      "No. Observations:                     56   AIC:                            -42.36\n",
      "Df Residuals:                         54   BIC:                            -38.31\n",
      "Df Model:                              1                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.1575      0.034     -4.621      0.000      -0.226      -0.089\n",
      "GDP         2.398e-14   3.99e-15      6.005      0.000     1.6e-14     3.2e-14\n",
      "==============================================================================\n",
      "Omnibus:                        7.742   Durbin-Watson:                   0.914\n",
      "Prob(Omnibus):                  0.021   Jarque-Bera (JB):                7.475\n",
      "Skew:                           0.638   Prob(JB):                       0.0238\n",
      "Kurtosis:                       4.256   Cond. No.                     1.34e+13\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, 1.34e+13. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "df_compare_relatives.columns = ['popularity_difference', 'GDP']\n",
    "\n",
    "f0 = \"popularity_difference ~ GDP\"\n",
    "outcome_3, predictors_3 = patsy.dmatrices(f0, df_compare_relatives,\n",
    "                                          return_type = \"matrix\")\n",
    "mod_3 = sm.OLS(outcome_3, predictors_3)\n",
    "res_3 = mod_3.fit()\n",
    "print(res_3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eHykv1Gv4Wmp"
   },
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dI6U71xDBaCu"
   },
   "source": [
    "We found that there are trends between GDP per capita and popularity of movies. In general, when there is low GDP, the popularity of each genre is low. We noticed that for comedy, drama, and romance, the popularity would increase with GDP until a certain point where they stopped increasing. On average, these “relaxing” genres would grow rapidly until a popularity score of about 4 where they would stay. On the other hand, action and adventure would have a linear relationship with GDP and would keep growing past a popularity score of 4. Thriller, which we thought would act similar to action and adventure movies, turned out to be more similar to the relaxing genres and would cap out at a popularity score of 4.\n",
    "\n",
    "When we compared each genre together on the same graph, we can see that most of the genres have some sort of increase in popularity as gdp goes up. The thrilling movies, adventure, action, and thriller, have a more rapid growth as GDP increases. Adventure movies are an outlier with a sharp increase as gdp increases, but in general the thrilling movies have a higher growth than relaxing movies. The relaxing movies, drama, romance, and comedy, all have a small growth as GDP increases, and they are all relatively the same popularity as GDP increases. \n",
    "\t\n",
    "After grouping the genres into two dataframes, relaxing and thrilling, we were able to see a more direct relationship between relaxing movies and thrilling movies. We see that thrilling movies in general despite gdp, are more popular than relaxing movies. Thrilling movies however, have an increase in popularity as gdp increases, in a linear way. Relaxing movies have a very small increase in popularity as gdp increases, with the graph plateauing as gdp becomes 0.5 * 1e13. After running the test of significance, we found out that both relaxing movies and thrilling movies have a linear correlation with GDP. For both relaxing and thrilling movies, we saw a p-value of 0.00.\n",
    "\n",
    "However since we have seen that thrilling movies will be, in general, more popular than  relaxing movies, we decided to analyze the relative popularity of thrilling and relaxing movies at certain GDPs. We found this by looking at the average popularity of thrilling movies at a certain GDP and dividing this by the average of the popularity of thrilling movies at all GDPs. We did this for relaxing movies as well. This gave us a dataframe of the relative popularity of thrilling and relaxing movies.\n",
    "\n",
    "To further investigate the relative popularities, we graphed the difference between the popularity of thrilling and relaxing movies vs GDP. Whenever this value is below 0, it means that relaxing movies were relatively more popular than thrilling movies at that certain GDP. As we can see in the graph, the line is below 0 more often during low GDPs while the line is above 0 more often in high GDPs. This information is relevant to our hypothesis. While our hypothesis was not completely correct since thrilling movies are more popular in general, this information shows that at low GDPs comedies are relatively more popular than thrilling movies and at high GDPs thrilling movies are relatively more popular than relaxing movies.\n",
    "\n",
    "The OLS regression results showed that there was a linear relationship between the difference in popularity and GDP. The p value was 0.00 meaning it passes the test of significance. Furthermore the intercept was a negative number, meaning at low GDP values, relaxing movies are relatively more popular, but at higher GDP values, thrilling movies are relatively more popular.\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ADw5tJ4p4Wmv"
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   "source": [
    "Our data relies on the GDP per capita and movies throughout the years. To get this data, we found datasets on the web with this information but we must also consider the ethics of these sources. For our movie data, there is not much ethical considerations to consider. The main ethical consideration is if there are any certain types of movies that were purposely left out of the dataset. However, this is most likely not the case as there were many movies that were included in our dataset and the website stated that the dataset was from GroupLens, which is a group that is fairly unbiased and has no reason to purposely exclude movies. This group is also where the popularity comes from, but since this group is fairly unbiased, there is no reason to doubt their popularity choices too much. \n",
    "\n",
    "The GDP per capita data has a lot of ethical considerations to think about. The GDP per capita contains information about the income of American citizens. It is important that the GDP is found without leaking any personal information. However, GDP is just a number that has no links back to personal information, so GDP will not have any personal information associated to it. Furthermore, it is important to make sure that no races are excluded from GDP, but since the government knows that they must be ethical, the GDP that they calculate must also be fair and unbiased. \n",
    "\n",
    "Our study is combining both of these ethical sources and finding the correlation between them so our study will also be ethical. Since our sources have no personally identifiable data, it is impossible for our study to have personally identifiable data. Our analysis is also unbiased since the sources we used were fair and unbiased. Finding a correlation between these two may cause movie producers to wait to release their movie when there is an increase or decrease in GDP per capita. This is not too big of an issue since releasing a movie slightly later will not adversely affect anyone.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CMKZrQ_2EACY"
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   "source": [
    "In this project, we begin with asking “what is the relationship between genre preference and economy performance”. We defined economy performance using  GDP, and we split the possible movie preferences into six different genres. However, we further grouped the movies into thrilling movies and relaxing movies. Thrilling movies included action, adventure, and thrillers, while relaxing movies included romance, dramas, and comedies. After data wrangling, cleaning, and visualization, we found out that thrilling movies are generally more popular than relaxing movies at all GDPs, so we had to analyze the relative popularity of each movie instead. We found the relative popularity by looking at the popularity of the movies at that GDP divided by the popularity of it at all GDPs. By looking at the relative popularities, we found that at lower GDPs relaxing movies are relatively more popular than thrilling movies while at higher GDPs thrilling movies are relatively more popular than relaxing movies.\n",
    "\n",
    "Although we did a lot in this project, there are still some limits. First, a major issue of this project is that correlation does not imply causation, so correlational study like this may not be useful to accurately predict the future. Furthermore, the correlation we found in our analysis is not very strong, and may be due to randomness or third variables: we think this project can be improved through utilizing better indicators for the economy and its effect on individual lives, since GDP per capita is too general and may be affected by other factors besides economy. By doing so, we believe the relation we found will be more robust. There are also confounding variables. A major confounding variable we saw was the fact that GDP always is increasing. This means that instead of only looking at GDP we are actually looking at how movie preferences change over the years. Another confounding variable is the fact that people could just prefer certain genres based on their culture instead of their economical status.\n",
    "\n",
    "Even if this may not be a very influential or mature project, it can still have a positive influence on society. Because our topic of research is relatively new, it may point a direction for future analysis and research. Also, this project may serve as a guidance for movie production, as we know movie preference is correlated with the economy, so movie companies can know what movie they should produce more. Also, since we found correlation even when it seemed like there shouldn't have been, this can inspire other data scientists to investigate anything that interests them, despite whether they believe there will be correlation.\n"
   ]
  }
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