Movie Correlation Project v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09296492",
   "metadata": {},
   "source": [
    "# Portfolio Project - Correlation in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb965a7d",
   "metadata": {},
   "source": [
    "This is based on Alex the Analyst's video on youtube (Data Analyst Portfolio Project | Correlation in Python | Project 4/4).\n",
    "Dataset --> https://www.kaggle.com/datasets/danielgrijalvas/movies?resource=download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af7d3f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (12,8) # Adjust the configuration of the plots we will create\n",
    "\n",
    "# Read in the Data\n",
    "\n",
    "df = pd.read_csv(r'G:\\DATA ANALITYCS\\Portfolio Project\\python correlation project\\movies.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c450c011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>year</th>\n",
       "      <th>released</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>star</th>\n",
       "      <th>country</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shining</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1980</td>\n",
       "      <td>June 13, 1980 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>927000.0</td>\n",
       "      <td>Stanley Kubrick</td>\n",
       "      <td>Stephen King</td>\n",
       "      <td>Jack Nicholson</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>19000000.0</td>\n",
       "      <td>46998772.0</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Blue Lagoon</td>\n",
       "      <td>R</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>1980</td>\n",
       "      <td>July 2, 1980 (United States)</td>\n",
       "      <td>5.8</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>Randal Kleiser</td>\n",
       "      <td>Henry De Vere Stacpoole</td>\n",
       "      <td>Brooke Shields</td>\n",
       "      <td>United States</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>58853106.0</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action</td>\n",
       "      <td>1980</td>\n",
       "      <td>June 20, 1980 (United States)</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>Irvin Kershner</td>\n",
       "      <td>Leigh Brackett</td>\n",
       "      <td>Mark Hamill</td>\n",
       "      <td>United States</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>538375067.0</td>\n",
       "      <td>Lucasfilm</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Airplane!</td>\n",
       "      <td>PG</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1980</td>\n",
       "      <td>July 2, 1980 (United States)</td>\n",
       "      <td>7.7</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>Jim Abrahams</td>\n",
       "      <td>Jim Abrahams</td>\n",
       "      <td>Robert Hays</td>\n",
       "      <td>United States</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>83453539.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caddyshack</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1980</td>\n",
       "      <td>July 25, 1980 (United States)</td>\n",
       "      <td>7.3</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>Harold Ramis</td>\n",
       "      <td>Brian Doyle-Murray</td>\n",
       "      <td>Chevy Chase</td>\n",
       "      <td>United States</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>39846344.0</td>\n",
       "      <td>Orion Pictures</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             name rating      genre  year  \\\n",
       "0                                     The Shining      R      Drama  1980   \n",
       "1                                 The Blue Lagoon      R  Adventure  1980   \n",
       "2  Star Wars: Episode V - The Empire Strikes Back     PG     Action  1980   \n",
       "3                                       Airplane!     PG     Comedy  1980   \n",
       "4                                      Caddyshack      R     Comedy  1980   \n",
       "\n",
       "                        released  score      votes         director  \\\n",
       "0  June 13, 1980 (United States)    8.4   927000.0  Stanley Kubrick   \n",
       "1   July 2, 1980 (United States)    5.8    65000.0   Randal Kleiser   \n",
       "2  June 20, 1980 (United States)    8.7  1200000.0   Irvin Kershner   \n",
       "3   July 2, 1980 (United States)    7.7   221000.0     Jim Abrahams   \n",
       "4  July 25, 1980 (United States)    7.3   108000.0     Harold Ramis   \n",
       "\n",
       "                    writer            star         country      budget  \\\n",
       "0             Stephen King  Jack Nicholson  United Kingdom  19000000.0   \n",
       "1  Henry De Vere Stacpoole  Brooke Shields   United States   4500000.0   \n",
       "2           Leigh Brackett     Mark Hamill   United States  18000000.0   \n",
       "3             Jim Abrahams     Robert Hays   United States   3500000.0   \n",
       "4       Brian Doyle-Murray     Chevy Chase   United States   6000000.0   \n",
       "\n",
       "         gross             company  runtime  \n",
       "0   46998772.0        Warner Bros.    146.0  \n",
       "1   58853106.0   Columbia Pictures    104.0  \n",
       "2  538375067.0           Lucasfilm    124.0  \n",
       "3   83453539.0  Paramount Pictures     88.0  \n",
       "4   39846344.0      Orion Pictures     98.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the Data\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e86b9823",
   "metadata": {},
   "source": [
    "## Handling missing data/ Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b0a4cfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name - 0.0%\n",
      "rating - 0.010041731872717789%\n",
      "genre - 0.0%\n",
      "year - 0.0%\n",
      "released - 0.0002608242044861763%\n",
      "score - 0.0003912363067292645%\n",
      "votes - 0.0003912363067292645%\n",
      "director - 0.0%\n",
      "writer - 0.0003912363067292645%\n",
      "star - 0.00013041210224308815%\n",
      "country - 0.0003912363067292645%\n",
      "budget - 0.2831246739697444%\n",
      "gross - 0.02464788732394366%\n",
      "company - 0.002217005738132499%\n",
      "runtime - 0.0005216484089723526%\n"
     ]
    }
   ],
   "source": [
    "# Missing Data --> This is how Alex Freeberg looks for missing data\n",
    "# using for loop to identify the column with missing data\n",
    "# The're are some missing data at some columns but not much\n",
    "\n",
    "for col in df.columns:\n",
    "    pct_missing = np.mean(df[col].isnull())\n",
    "    print('{} - {}%'.format(col, pct_missing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "22d4487c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7668 entries, 0 to 7667\n",
      "Data columns (total 15 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   name      7668 non-null   object \n",
      " 1   rating    7591 non-null   object \n",
      " 2   genre     7668 non-null   object \n",
      " 3   year      7668 non-null   int64  \n",
      " 4   released  7666 non-null   object \n",
      " 5   score     7665 non-null   float64\n",
      " 6   votes     7665 non-null   float64\n",
      " 7   director  7668 non-null   object \n",
      " 8   writer    7665 non-null   object \n",
      " 9   star      7667 non-null   object \n",
      " 10  country   7665 non-null   object \n",
      " 11  budget    5497 non-null   float64\n",
      " 12  gross     7479 non-null   float64\n",
      " 13  company   7651 non-null   object \n",
      " 14  runtime   7664 non-null   float64\n",
      "dtypes: float64(5), int64(1), object(9)\n",
      "memory usage: 898.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# This is what I usualy do to check missing data/value and also we can check the data types\n",
    "# But, we can't see the percentage :)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e8f0a2",
   "metadata": {},
   "source": [
    "So, because we know that the percentage of missing data at budget column is 28%. I choose to leave it, because the missing data is too large."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ac89c5",
   "metadata": {},
   "source": [
    "## Change Data Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3f791ab",
   "metadata": {},
   "outputs": [
    {
     "ename": "IntCastingNaNError",
     "evalue": "Cannot convert non-finite values (NA or inf) to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIntCastingNaNError\u001b[0m                        Traceback (most recent call last)",
      "Input \u001b[1;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Change Data type --> Budget and gross column from float to int\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbudget\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbudget\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mint64\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgross\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgross\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint64\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5912\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   5905\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m   5906\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m   5907\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[0;32m   5908\u001b[0m     ]\n\u001b[0;32m   5910\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   5911\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 5912\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   5913\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   5915\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py:419\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    418\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mastype\u001b[39m(\u001b[38;5;28mself\u001b[39m: T, dtype, copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m--> 419\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py:304\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[0;32m    302\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    303\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 304\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(b, f)(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    305\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mNotImplementedError\u001b[39;00m):\n\u001b[0;32m    306\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ignore_failures:\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:580\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    562\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    563\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[0;32m    564\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    576\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[0;32m    577\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    578\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[1;32m--> 580\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    582\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m    583\u001b[0m newb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_block(new_values)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1292\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   1289\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m   1291\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1292\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1293\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m   1294\u001b[0m     \u001b[38;5;66;03m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m   1295\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[0;32m   1296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1237\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m   1234\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m   1236\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1237\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1239\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m   1240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1148\u001b[0m, in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m   1145\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot astype a timedelta from [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00marr\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] to [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1147\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloating) \u001b[38;5;129;01mand\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(dtype, np\u001b[38;5;241m.\u001b[39minteger):\n\u001b[1;32m-> 1148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mastype_float_to_int_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1150\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_object_dtype(arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[0;32m   1151\u001b[0m \n\u001b[0;32m   1152\u001b[0m     \u001b[38;5;66;03m# work around NumPy brokenness, #1987\u001b[39;00m\n\u001b[0;32m   1153\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(dtype\u001b[38;5;241m.\u001b[39mtype, np\u001b[38;5;241m.\u001b[39minteger):\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1193\u001b[0m, in \u001b[0;36mastype_float_to_int_nansafe\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m   1189\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1190\u001b[0m \u001b[38;5;124;03mastype with a check preventing converting NaN to an meaningless integer value.\u001b[39;00m\n\u001b[0;32m   1191\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(values)\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m-> 1193\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IntCastingNaNError(\n\u001b[0;32m   1194\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert non-finite values (NA or inf) to integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1195\u001b[0m     )\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "\u001b[1;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
     ]
    }
   ],
   "source": [
    "# Change Data type --> Budget and gross column from float to int\n",
    "\n",
    "df['budget'] = df['budget'].astype('int64')\n",
    "df['gross'] = df['gross'].astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3d1965",
   "metadata": {},
   "source": [
    "Because of this error, we need to drop the missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "75a69d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bc1b745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name        0\n",
       "rating      0\n",
       "genre       0\n",
       "year        0\n",
       "released    0\n",
       "score       0\n",
       "votes       0\n",
       "director    0\n",
       "writer      0\n",
       "star        0\n",
       "country     0\n",
       "budget      0\n",
       "gross       0\n",
       "company     0\n",
       "runtime     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check is there any missing data after we drop it\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99827667",
   "metadata": {},
   "source": [
    "Okay, so there is no more missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "431f6d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we change the data type\n",
    "\n",
    "df['budget'] = df['budget'].astype('int64')\n",
    "df['gross'] = df['gross'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4ceb4ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5421 entries, 0 to 7652\n",
      "Data columns (total 15 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   name      5421 non-null   object \n",
      " 1   rating    5421 non-null   object \n",
      " 2   genre     5421 non-null   object \n",
      " 3   year      5421 non-null   int64  \n",
      " 4   released  5421 non-null   object \n",
      " 5   score     5421 non-null   float64\n",
      " 6   votes     5421 non-null   float64\n",
      " 7   director  5421 non-null   object \n",
      " 8   writer    5421 non-null   object \n",
      " 9   star      5421 non-null   object \n",
      " 10  country   5421 non-null   object \n",
      " 11  budget    5421 non-null   int64  \n",
      " 12  gross     5421 non-null   int64  \n",
      " 13  company   5421 non-null   object \n",
      " 14  runtime   5421 non-null   float64\n",
      "dtypes: float64(3), int64(3), object(9)\n",
      "memory usage: 677.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30394d39",
   "metadata": {},
   "source": [
    "Now, we can see that column budget and gross data type is int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baaee374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to create year_correct column because of the year column and released is different(look at the year on release date)\n",
    "\n",
    "# df['year_correct'] = df['released'].astype(str).str[-20:-15]\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444f5f83",
   "metadata": {},
   "source": [
    "There is a problem in here, because this dataset and the dataset from Alex's video is different(I think because the update). The `released` column is messy in this dataset and not tidy like what I see on Alex's video. So, I decide to not make the `year_correct` column.\n",
    "In the future, maybe I want to cleaning it first with spreadsheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "087d5073",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to sort this data frame by gross column\n",
    "\n",
    "df = df.sort_values(by=['gross'], inplace=False, ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1250f7aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to see the maximum views of rows\n",
    "\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0507484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>year</th>\n",
       "      <th>released</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>star</th>\n",
       "      <th>country</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5445</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2009</td>\n",
       "      <td>December 18, 2009 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Sam Worthington</td>\n",
       "      <td>United States</td>\n",
       "      <td>237000000</td>\n",
       "      <td>2847246203</td>\n",
       "      <td>Twentieth Century Fox</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445</th>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2019</td>\n",
       "      <td>April 26, 2019 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>903000.0</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>Christopher Markus</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>United States</td>\n",
       "      <td>356000000</td>\n",
       "      <td>2797501328</td>\n",
       "      <td>Marvel Studios</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1997</td>\n",
       "      <td>December 19, 1997 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>United States</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2201647264</td>\n",
       "      <td>Twentieth Century Fox</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6663</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2015</td>\n",
       "      <td>December 18, 2015 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>876000.0</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Lawrence Kasdan</td>\n",
       "      <td>Daisy Ridley</td>\n",
       "      <td>United States</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2069521700</td>\n",
       "      <td>Lucasfilm</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7244</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2018</td>\n",
       "      <td>April 27, 2018 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>897000.0</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>Christopher Markus</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>United States</td>\n",
       "      <td>321000000</td>\n",
       "      <td>2048359754</td>\n",
       "      <td>Marvel Studios</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            name rating   genre  year  \\\n",
       "5445                                      Avatar  PG-13  Action  2009   \n",
       "7445                           Avengers: Endgame  PG-13  Action  2019   \n",
       "3045                                     Titanic  PG-13   Drama  1997   \n",
       "6663  Star Wars: Episode VII - The Force Awakens  PG-13  Action  2015   \n",
       "7244                      Avengers: Infinity War  PG-13  Action  2018   \n",
       "\n",
       "                               released  score      votes       director  \\\n",
       "5445  December 18, 2009 (United States)    7.8  1100000.0  James Cameron   \n",
       "7445     April 26, 2019 (United States)    8.4   903000.0  Anthony Russo   \n",
       "3045  December 19, 1997 (United States)    7.8  1100000.0  James Cameron   \n",
       "6663  December 18, 2015 (United States)    7.8   876000.0    J.J. Abrams   \n",
       "7244     April 27, 2018 (United States)    8.4   897000.0  Anthony Russo   \n",
       "\n",
       "                  writer               star        country     budget  \\\n",
       "5445       James Cameron    Sam Worthington  United States  237000000   \n",
       "7445  Christopher Markus  Robert Downey Jr.  United States  356000000   \n",
       "3045       James Cameron  Leonardo DiCaprio  United States  200000000   \n",
       "6663     Lawrence Kasdan       Daisy Ridley  United States  245000000   \n",
       "7244  Christopher Markus  Robert Downey Jr.  United States  321000000   \n",
       "\n",
       "           gross                company  runtime  \n",
       "5445  2847246203  Twentieth Century Fox    162.0  \n",
       "7445  2797501328         Marvel Studios    181.0  \n",
       "3045  2201647264  Twentieth Century Fox    194.0  \n",
       "6663  2069521700              Lucasfilm    138.0  \n",
       "7244  2048359754         Marvel Studios    149.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a201a935",
   "metadata": {},
   "source": [
    "## Handling Duplicated Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8964789",
   "metadata": {},
   "source": [
    "This is what I usually do for handling the duplicated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54dd0670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, we need to know how much is the duplicate data\n",
    "\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f93ce7a4",
   "metadata": {},
   "source": [
    "Result = There is no duplicated data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5288395",
   "metadata": {},
   "source": [
    "## Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e8a62af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot with budget vs gross\n",
    "\n",
    "plt.scatter(x=df['budget'], y=df['gross'])\n",
    "\n",
    "plt.title('Budget vs Gross Earnings')\n",
    "\n",
    "plt.xlabel('Gross Earnings')\n",
    "\n",
    "plt.ylabel('Budget for Film')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8704052",
   "metadata": {},
   "source": [
    "## Regression Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da386178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='budget', ylabel='gross'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Budget vs Gross using seaborn\n",
    "\n",
    "sns.regplot(x='budget', y='gross', data=df, scatter_kws={\"color\":\"red\"}, line_kws={\"color\":\"blue\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75eb399",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c159fd",
   "metadata": {},
   "source": [
    "Let's start looking at correlation. There is some correlation method --> pearson, kendall, and spearman.\n",
    "We can look the correlation with each method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "849bd0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056386</td>\n",
       "      <td>0.206021</td>\n",
       "      <td>0.327722</td>\n",
       "      <td>0.274321</td>\n",
       "      <td>0.075077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.056386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.474256</td>\n",
       "      <td>0.072001</td>\n",
       "      <td>0.222556</td>\n",
       "      <td>0.414068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>votes</th>\n",
       "      <td>0.206021</td>\n",
       "      <td>0.474256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.439675</td>\n",
       "      <td>0.614751</td>\n",
       "      <td>0.352303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>0.327722</td>\n",
       "      <td>0.072001</td>\n",
       "      <td>0.439675</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.740247</td>\n",
       "      <td>0.318695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross</th>\n",
       "      <td>0.274321</td>\n",
       "      <td>0.222556</td>\n",
       "      <td>0.614751</td>\n",
       "      <td>0.740247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.275796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>0.075077</td>\n",
       "      <td>0.414068</td>\n",
       "      <td>0.352303</td>\n",
       "      <td>0.318695</td>\n",
       "      <td>0.275796</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year     score     votes    budget     gross   runtime\n",
       "year     1.000000  0.056386  0.206021  0.327722  0.274321  0.075077\n",
       "score    0.056386  1.000000  0.474256  0.072001  0.222556  0.414068\n",
       "votes    0.206021  0.474256  1.000000  0.439675  0.614751  0.352303\n",
       "budget   0.327722  0.072001  0.439675  1.000000  0.740247  0.318695\n",
       "gross    0.274321  0.222556  0.614751  0.740247  1.000000  0.275796\n",
       "runtime  0.075077  0.414068  0.352303  0.318695  0.275796  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson method\n",
    "\n",
    "df.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "526ee9bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039389</td>\n",
       "      <td>0.296512</td>\n",
       "      <td>0.220833</td>\n",
       "      <td>0.239539</td>\n",
       "      <td>0.064824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.039389</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.350185</td>\n",
       "      <td>-0.006406</td>\n",
       "      <td>0.124943</td>\n",
       "      <td>0.292254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>votes</th>\n",
       "      <td>0.296512</td>\n",
       "      <td>0.350185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.346274</td>\n",
       "      <td>0.553625</td>\n",
       "      <td>0.205344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>0.220833</td>\n",
       "      <td>-0.006406</td>\n",
       "      <td>0.346274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.512057</td>\n",
       "      <td>0.231278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross</th>\n",
       "      <td>0.239539</td>\n",
       "      <td>0.124943</td>\n",
       "      <td>0.553625</td>\n",
       "      <td>0.512057</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.176979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>0.064824</td>\n",
       "      <td>0.292254</td>\n",
       "      <td>0.205344</td>\n",
       "      <td>0.231278</td>\n",
       "      <td>0.176979</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year     score     votes    budget     gross   runtime\n",
       "year     1.000000  0.039389  0.296512  0.220833  0.239539  0.064824\n",
       "score    0.039389  1.000000  0.350185 -0.006406  0.124943  0.292254\n",
       "votes    0.296512  0.350185  1.000000  0.346274  0.553625  0.205344\n",
       "budget   0.220833 -0.006406  0.346274  1.000000  0.512057  0.231278\n",
       "gross    0.239539  0.124943  0.553625  0.512057  1.000000  0.176979\n",
       "runtime  0.064824  0.292254  0.205344  0.231278  0.176979  1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kendall method\n",
    "\n",
    "df.corr(method='kendall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "831f5730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057741</td>\n",
       "      <td>0.427623</td>\n",
       "      <td>0.312886</td>\n",
       "      <td>0.351045</td>\n",
       "      <td>0.095444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.057741</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495409</td>\n",
       "      <td>-0.009971</td>\n",
       "      <td>0.183192</td>\n",
       "      <td>0.412155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>votes</th>\n",
       "      <td>0.427623</td>\n",
       "      <td>0.495409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.493461</td>\n",
       "      <td>0.745793</td>\n",
       "      <td>0.300621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>0.312886</td>\n",
       "      <td>-0.009971</td>\n",
       "      <td>0.493461</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692958</td>\n",
       "      <td>0.330794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross</th>\n",
       "      <td>0.351045</td>\n",
       "      <td>0.183192</td>\n",
       "      <td>0.745793</td>\n",
       "      <td>0.692958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.257400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>0.095444</td>\n",
       "      <td>0.412155</td>\n",
       "      <td>0.300621</td>\n",
       "      <td>0.330794</td>\n",
       "      <td>0.257400</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year     score     votes    budget     gross   runtime\n",
       "year     1.000000  0.057741  0.427623  0.312886  0.351045  0.095444\n",
       "score    0.057741  1.000000  0.495409 -0.009971  0.183192  0.412155\n",
       "votes    0.427623  0.495409  1.000000  0.493461  0.745793  0.300621\n",
       "budget   0.312886 -0.009971  0.493461  1.000000  0.692958  0.330794\n",
       "gross    0.351045  0.183192  0.745793  0.692958  1.000000  0.257400\n",
       "runtime  0.095444  0.412155  0.300621  0.330794  0.257400  1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spearman method\n",
    "\n",
    "df.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b29ab5",
   "metadata": {},
   "source": [
    "Conclusion = High correlation between budget and gross."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43f52f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.corr(method='pearson')\n",
    "\n",
    "sns.heatmap(correlation_matrix, annot=True)\n",
    "\n",
    "plt.title('Correlation Matrix for Numeric Features')\n",
    "\n",
    "plt.xlabel('Movie Features')\n",
    "\n",
    "plt.ylabel('Movie Features')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec551a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>year</th>\n",
       "      <th>released</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>star</th>\n",
       "      <th>country</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5445</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2009</td>\n",
       "      <td>December 18, 2009 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Sam Worthington</td>\n",
       "      <td>United States</td>\n",
       "      <td>237000000</td>\n",
       "      <td>2847246203</td>\n",
       "      <td>Twentieth Century Fox</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445</th>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2019</td>\n",
       "      <td>April 26, 2019 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>903000.0</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>Christopher Markus</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>United States</td>\n",
       "      <td>356000000</td>\n",
       "      <td>2797501328</td>\n",
       "      <td>Marvel Studios</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1997</td>\n",
       "      <td>December 19, 1997 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>United States</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2201647264</td>\n",
       "      <td>Twentieth Century Fox</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6663</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2015</td>\n",
       "      <td>December 18, 2015 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>876000.0</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Lawrence Kasdan</td>\n",
       "      <td>Daisy Ridley</td>\n",
       "      <td>United States</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2069521700</td>\n",
       "      <td>Lucasfilm</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7244</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2018</td>\n",
       "      <td>April 27, 2018 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>897000.0</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>Christopher Markus</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>United States</td>\n",
       "      <td>321000000</td>\n",
       "      <td>2048359754</td>\n",
       "      <td>Marvel Studios</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            name rating   genre  year  \\\n",
       "5445                                      Avatar  PG-13  Action  2009   \n",
       "7445                           Avengers: Endgame  PG-13  Action  2019   \n",
       "3045                                     Titanic  PG-13   Drama  1997   \n",
       "6663  Star Wars: Episode VII - The Force Awakens  PG-13  Action  2015   \n",
       "7244                      Avengers: Infinity War  PG-13  Action  2018   \n",
       "\n",
       "                               released  score      votes       director  \\\n",
       "5445  December 18, 2009 (United States)    7.8  1100000.0  James Cameron   \n",
       "7445     April 26, 2019 (United States)    8.4   903000.0  Anthony Russo   \n",
       "3045  December 19, 1997 (United States)    7.8  1100000.0  James Cameron   \n",
       "6663  December 18, 2015 (United States)    7.8   876000.0    J.J. Abrams   \n",
       "7244     April 27, 2018 (United States)    8.4   897000.0  Anthony Russo   \n",
       "\n",
       "                  writer               star        country     budget  \\\n",
       "5445       James Cameron    Sam Worthington  United States  237000000   \n",
       "7445  Christopher Markus  Robert Downey Jr.  United States  356000000   \n",
       "3045       James Cameron  Leonardo DiCaprio  United States  200000000   \n",
       "6663     Lawrence Kasdan       Daisy Ridley  United States  245000000   \n",
       "7244  Christopher Markus  Robert Downey Jr.  United States  321000000   \n",
       "\n",
       "           gross                company  runtime  \n",
       "5445  2847246203  Twentieth Century Fox    162.0  \n",
       "7445  2797501328         Marvel Studios    181.0  \n",
       "3045  2201647264  Twentieth Century Fox    194.0  \n",
       "6663  2069521700              Lucasfilm    138.0  \n",
       "7244  2048359754         Marvel Studios    149.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at company\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85c844f",
   "metadata": {},
   "source": [
    "The `company` column is not numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a649ec1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>year</th>\n",
       "      <th>released</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>star</th>\n",
       "      <th>country</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5445</th>\n",
       "      <td>386</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>527</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>785</td>\n",
       "      <td>1263</td>\n",
       "      <td>1534</td>\n",
       "      <td>47</td>\n",
       "      <td>237000000</td>\n",
       "      <td>2847246203</td>\n",
       "      <td>1382</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445</th>\n",
       "      <td>388</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>137</td>\n",
       "      <td>8.4</td>\n",
       "      <td>903000.0</td>\n",
       "      <td>105</td>\n",
       "      <td>513</td>\n",
       "      <td>1470</td>\n",
       "      <td>47</td>\n",
       "      <td>356000000</td>\n",
       "      <td>2797501328</td>\n",
       "      <td>983</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>4909</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1997</td>\n",
       "      <td>534</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>785</td>\n",
       "      <td>1263</td>\n",
       "      <td>1073</td>\n",
       "      <td>47</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2201647264</td>\n",
       "      <td>1382</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6663</th>\n",
       "      <td>3643</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>529</td>\n",
       "      <td>7.8</td>\n",
       "      <td>876000.0</td>\n",
       "      <td>768</td>\n",
       "      <td>1806</td>\n",
       "      <td>356</td>\n",
       "      <td>47</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2069521700</td>\n",
       "      <td>945</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7244</th>\n",
       "      <td>389</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>145</td>\n",
       "      <td>8.4</td>\n",
       "      <td>897000.0</td>\n",
       "      <td>105</td>\n",
       "      <td>513</td>\n",
       "      <td>1470</td>\n",
       "      <td>47</td>\n",
       "      <td>321000000</td>\n",
       "      <td>2048359754</td>\n",
       "      <td>983</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  rating  genre  year  released  score      votes  director  writer  \\\n",
       "5445   386       5      0  2009       527    7.8  1100000.0       785    1263   \n",
       "7445   388       5      0  2019       137    8.4   903000.0       105     513   \n",
       "3045  4909       5      6  1997       534    7.8  1100000.0       785    1263   \n",
       "6663  3643       5      0  2015       529    7.8   876000.0       768    1806   \n",
       "7244   389       5      0  2018       145    8.4   897000.0       105     513   \n",
       "\n",
       "      star  country     budget       gross  company  runtime  \n",
       "5445  1534       47  237000000  2847246203     1382    162.0  \n",
       "7445  1470       47  356000000  2797501328      983    181.0  \n",
       "3045  1073       47  200000000  2201647264     1382    194.0  \n",
       "6663   356       47  245000000  2069521700      945    138.0  \n",
       "7244  1470       47  321000000  2048359754      983    149.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numerized = df\n",
    "\n",
    "for col_name in df_numerized.columns:\n",
    "    if(df_numerized[col_name]).dtype == 'object':\n",
    "        df_numerized[col_name] = df_numerized[col_name].astype('category')\n",
    "        df_numerized[col_name] = df_numerized[col_name].cat.codes\n",
    "        \n",
    "df_numerized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12a6a7df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>year</th>\n",
       "      <th>released</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>star</th>\n",
       "      <th>country</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5445</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2009</td>\n",
       "      <td>December 18, 2009 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Sam Worthington</td>\n",
       "      <td>United States</td>\n",
       "      <td>237000000</td>\n",
       "      <td>2847246203</td>\n",
       "      <td>Twentieth Century Fox</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445</th>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2019</td>\n",
       "      <td>April 26, 2019 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>903000.0</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>Christopher Markus</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>United States</td>\n",
       "      <td>356000000</td>\n",
       "      <td>2797501328</td>\n",
       "      <td>Marvel Studios</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1997</td>\n",
       "      <td>December 19, 1997 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>United States</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2201647264</td>\n",
       "      <td>Twentieth Century Fox</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6663</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2015</td>\n",
       "      <td>December 18, 2015 (United States)</td>\n",
       "      <td>7.8</td>\n",
       "      <td>876000.0</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Lawrence Kasdan</td>\n",
       "      <td>Daisy Ridley</td>\n",
       "      <td>United States</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2069521700</td>\n",
       "      <td>Lucasfilm</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7244</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>2018</td>\n",
       "      <td>April 27, 2018 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>897000.0</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>Christopher Markus</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>United States</td>\n",
       "      <td>321000000</td>\n",
       "      <td>2048359754</td>\n",
       "      <td>Marvel Studios</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            name rating   genre  year  \\\n",
       "5445                                      Avatar  PG-13  Action  2009   \n",
       "7445                           Avengers: Endgame  PG-13  Action  2019   \n",
       "3045                                     Titanic  PG-13   Drama  1997   \n",
       "6663  Star Wars: Episode VII - The Force Awakens  PG-13  Action  2015   \n",
       "7244                      Avengers: Infinity War  PG-13  Action  2018   \n",
       "\n",
       "                               released  score      votes       director  \\\n",
       "5445  December 18, 2009 (United States)    7.8  1100000.0  James Cameron   \n",
       "7445     April 26, 2019 (United States)    8.4   903000.0  Anthony Russo   \n",
       "3045  December 19, 1997 (United States)    7.8  1100000.0  James Cameron   \n",
       "6663  December 18, 2015 (United States)    7.8   876000.0    J.J. Abrams   \n",
       "7244     April 27, 2018 (United States)    8.4   897000.0  Anthony Russo   \n",
       "\n",
       "                  writer               star        country     budget  \\\n",
       "5445       James Cameron    Sam Worthington  United States  237000000   \n",
       "7445  Christopher Markus  Robert Downey Jr.  United States  356000000   \n",
       "3045       James Cameron  Leonardo DiCaprio  United States  200000000   \n",
       "6663     Lawrence Kasdan       Daisy Ridley  United States  245000000   \n",
       "7244  Christopher Markus  Robert Downey Jr.  United States  321000000   \n",
       "\n",
       "           gross                company  runtime  \n",
       "5445  2847246203  Twentieth Century Fox    162.0  \n",
       "7445  2797501328         Marvel Studios    181.0  \n",
       "3045  2201647264  Twentieth Century Fox    194.0  \n",
       "6663  2069521700              Lucasfilm    138.0  \n",
       "7244  2048359754         Marvel Studios    149.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "42f61e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df_numerized.corr(method='pearson')\n",
    "\n",
    "sns.heatmap(correlation_matrix, annot=True)\n",
    "\n",
    "plt.title('Correlation Matrix for Numeric Features')\n",
    "\n",
    "plt.xlabel('Movie Features')\n",
    "\n",
    "plt.ylabel('Movie Features')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8ca2a291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name      name        1.000000\n",
       "          rating     -0.029234\n",
       "          genre       0.010996\n",
       "          year        0.025542\n",
       "          released   -0.006152\n",
       "          score       0.014450\n",
       "          votes       0.012615\n",
       "          director    0.015246\n",
       "          writer      0.012880\n",
       "          star       -0.006882\n",
       "          country    -0.025490\n",
       "          budget      0.023392\n",
       "          gross       0.005639\n",
       "          company     0.021697\n",
       "          runtime     0.010850\n",
       "rating    name       -0.029234\n",
       "          rating      1.000000\n",
       "          genre       0.147796\n",
       "          year        0.019499\n",
       "          released    0.018083\n",
       "          score       0.065983\n",
       "          votes       0.006031\n",
       "          director    0.014656\n",
       "          writer     -0.003149\n",
       "          star        0.009196\n",
       "          country     0.008230\n",
       "          budget     -0.203946\n",
       "          gross      -0.181906\n",
       "          company    -0.092357\n",
       "          runtime     0.140792\n",
       "genre     name        0.010996\n",
       "          rating      0.147796\n",
       "          genre       1.000000\n",
       "          year       -0.073167\n",
       "          released    0.022142\n",
       "          score       0.035106\n",
       "          votes      -0.135990\n",
       "          director   -0.008553\n",
       "          writer      0.017578\n",
       "          star        0.003341\n",
       "          country    -0.009164\n",
       "          budget     -0.368523\n",
       "          gross      -0.244101\n",
       "          company    -0.071334\n",
       "          runtime    -0.059237\n",
       "year      name        0.025542\n",
       "          rating      0.019499\n",
       "          genre      -0.073167\n",
       "          year        1.000000\n",
       "          released   -0.001740\n",
       "          score       0.056386\n",
       "          votes       0.206021\n",
       "          director   -0.038354\n",
       "          writer     -0.025908\n",
       "          star       -0.032157\n",
       "          country    -0.066748\n",
       "          budget      0.327722\n",
       "          gross       0.274321\n",
       "          company    -0.014333\n",
       "          runtime     0.075077\n",
       "released  name       -0.006152\n",
       "          rating      0.018083\n",
       "          genre       0.022142\n",
       "          year       -0.001740\n",
       "          released    1.000000\n",
       "          score       0.045874\n",
       "          votes       0.028833\n",
       "          director    0.002308\n",
       "          writer      0.008072\n",
       "          star        0.015706\n",
       "          country    -0.017228\n",
       "          budget      0.019952\n",
       "          gross       0.008501\n",
       "          company    -0.002407\n",
       "          runtime     0.008975\n",
       "score     name        0.014450\n",
       "          rating      0.065983\n",
       "          genre       0.035106\n",
       "          year        0.056386\n",
       "          released    0.045874\n",
       "          score       1.000000\n",
       "          votes       0.474256\n",
       "          director    0.005413\n",
       "          writer      0.012843\n",
       "          star        0.007296\n",
       "          country    -0.043051\n",
       "          budget      0.072001\n",
       "          gross       0.222556\n",
       "          company     0.020656\n",
       "          runtime     0.414068\n",
       "votes     name        0.012615\n",
       "          rating      0.006031\n",
       "          genre      -0.135990\n",
       "          year        0.206021\n",
       "          released    0.028833\n",
       "          score       0.474256\n",
       "          votes       1.000000\n",
       "          director   -0.010376\n",
       "          writer     -0.005316\n",
       "          star       -0.017638\n",
       "          country     0.041551\n",
       "          budget      0.439675\n",
       "          gross       0.614751\n",
       "          company     0.118470\n",
       "          runtime     0.352303\n",
       "director  name        0.015246\n",
       "          rating      0.014656\n",
       "          genre      -0.008553\n",
       "          year       -0.038354\n",
       "          released    0.002308\n",
       "          score       0.005413\n",
       "          votes      -0.010376\n",
       "          director    1.000000\n",
       "          writer      0.261735\n",
       "          star        0.036593\n",
       "          country     0.011133\n",
       "          budget     -0.009662\n",
       "          gross      -0.029560\n",
       "          company    -0.008223\n",
       "          runtime     0.017433\n",
       "writer    name        0.012880\n",
       "          rating     -0.003149\n",
       "          genre       0.017578\n",
       "          year       -0.025908\n",
       "          released    0.008072\n",
       "          score       0.012843\n",
       "          votes      -0.005316\n",
       "          director    0.261735\n",
       "          writer      1.000000\n",
       "          star        0.018520\n",
       "          country     0.022488\n",
       "          budget     -0.039466\n",
       "          gross      -0.035885\n",
       "          company    -0.003697\n",
       "          runtime    -0.017561\n",
       "star      name       -0.006882\n",
       "          rating      0.009196\n",
       "          genre       0.003341\n",
       "          year       -0.032157\n",
       "          released    0.015706\n",
       "          score       0.007296\n",
       "          votes      -0.017638\n",
       "          director    0.036593\n",
       "          writer      0.018520\n",
       "          star        1.000000\n",
       "          country    -0.009990\n",
       "          budget     -0.021473\n",
       "          gross      -0.000004\n",
       "          company     0.014082\n",
       "          runtime     0.010108\n",
       "country   name       -0.025490\n",
       "          rating      0.008230\n",
       "          genre      -0.009164\n",
       "          year       -0.066748\n",
       "          released   -0.017228\n",
       "          score      -0.043051\n",
       "          votes       0.041551\n",
       "          director    0.011133\n",
       "          writer      0.022488\n",
       "          star       -0.009990\n",
       "          country     1.000000\n",
       "          budget      0.052977\n",
       "          gross       0.060078\n",
       "          company     0.048569\n",
       "          runtime    -0.034477\n",
       "budget    name        0.023392\n",
       "          rating     -0.203946\n",
       "          genre      -0.368523\n",
       "          year        0.327722\n",
       "          released    0.019952\n",
       "          score       0.072001\n",
       "          votes       0.439675\n",
       "          director   -0.009662\n",
       "          writer     -0.039466\n",
       "          star       -0.021473\n",
       "          country     0.052977\n",
       "          budget      1.000000\n",
       "          gross       0.740247\n",
       "          company     0.170235\n",
       "          runtime     0.318695\n",
       "gross     name        0.005639\n",
       "          rating     -0.181906\n",
       "          genre      -0.244101\n",
       "          year        0.274321\n",
       "          released    0.008501\n",
       "          score       0.222556\n",
       "          votes       0.614751\n",
       "          director   -0.029560\n",
       "          writer     -0.035885\n",
       "          star       -0.000004\n",
       "          country     0.060078\n",
       "          budget      0.740247\n",
       "          gross       1.000000\n",
       "          company     0.149553\n",
       "          runtime     0.275796\n",
       "company   name        0.021697\n",
       "          rating     -0.092357\n",
       "          genre      -0.071334\n",
       "          year       -0.014333\n",
       "          released   -0.002407\n",
       "          score       0.020656\n",
       "          votes       0.118470\n",
       "          director   -0.008223\n",
       "          writer     -0.003697\n",
       "          star        0.014082\n",
       "          country     0.048569\n",
       "          budget      0.170235\n",
       "          gross       0.149553\n",
       "          company     1.000000\n",
       "          runtime     0.037585\n",
       "runtime   name        0.010850\n",
       "          rating      0.140792\n",
       "          genre      -0.059237\n",
       "          year        0.075077\n",
       "          released    0.008975\n",
       "          score       0.414068\n",
       "          votes       0.352303\n",
       "          director    0.017433\n",
       "          writer     -0.017561\n",
       "          star        0.010108\n",
       "          country    -0.034477\n",
       "          budget      0.318695\n",
       "          gross       0.275796\n",
       "          company     0.037585\n",
       "          runtime     1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is to make it easier to read the correlation\n",
    "\n",
    "corr_mat = df_numerized.corr()\n",
    "\n",
    "corr_pairs = corr_mat.unstack()\n",
    "\n",
    "corr_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72495dbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget    genre      -0.368523\n",
       "genre     budget     -0.368523\n",
       "          gross      -0.244101\n",
       "gross     genre      -0.244101\n",
       "budget    rating     -0.203946\n",
       "rating    budget     -0.203946\n",
       "          gross      -0.181906\n",
       "gross     rating     -0.181906\n",
       "genre     votes      -0.135990\n",
       "votes     genre      -0.135990\n",
       "company   rating     -0.092357\n",
       "rating    company    -0.092357\n",
       "genre     year       -0.073167\n",
       "year      genre      -0.073167\n",
       "genre     company    -0.071334\n",
       "company   genre      -0.071334\n",
       "year      country    -0.066748\n",
       "country   year       -0.066748\n",
       "genre     runtime    -0.059237\n",
       "runtime   genre      -0.059237\n",
       "country   score      -0.043051\n",
       "score     country    -0.043051\n",
       "budget    writer     -0.039466\n",
       "writer    budget     -0.039466\n",
       "director  year       -0.038354\n",
       "year      director   -0.038354\n",
       "writer    gross      -0.035885\n",
       "gross     writer     -0.035885\n",
       "country   runtime    -0.034477\n",
       "runtime   country    -0.034477\n",
       "star      year       -0.032157\n",
       "year      star       -0.032157\n",
       "director  gross      -0.029560\n",
       "gross     director   -0.029560\n",
       "name      rating     -0.029234\n",
       "rating    name       -0.029234\n",
       "year      writer     -0.025908\n",
       "writer    year       -0.025908\n",
       "country   name       -0.025490\n",
       "name      country    -0.025490\n",
       "budget    star       -0.021473\n",
       "star      budget     -0.021473\n",
       "votes     star       -0.017638\n",
       "star      votes      -0.017638\n",
       "writer    runtime    -0.017561\n",
       "runtime   writer     -0.017561\n",
       "country   released   -0.017228\n",
       "released  country    -0.017228\n",
       "company   year       -0.014333\n",
       "year      company    -0.014333\n",
       "director  votes      -0.010376\n",
       "votes     director   -0.010376\n",
       "country   star       -0.009990\n",
       "star      country    -0.009990\n",
       "budget    director   -0.009662\n",
       "director  budget     -0.009662\n",
       "country   genre      -0.009164\n",
       "genre     country    -0.009164\n",
       "          director   -0.008553\n",
       "director  genre      -0.008553\n",
       "          company    -0.008223\n",
       "company   director   -0.008223\n",
       "star      name       -0.006882\n",
       "name      star       -0.006882\n",
       "released  name       -0.006152\n",
       "name      released   -0.006152\n",
       "votes     writer     -0.005316\n",
       "writer    votes      -0.005316\n",
       "          company    -0.003697\n",
       "company   writer     -0.003697\n",
       "writer    rating     -0.003149\n",
       "rating    writer     -0.003149\n",
       "company   released   -0.002407\n",
       "released  company    -0.002407\n",
       "          year       -0.001740\n",
       "year      released   -0.001740\n",
       "gross     star       -0.000004\n",
       "star      gross      -0.000004\n",
       "director  released    0.002308\n",
       "released  director    0.002308\n",
       "star      genre       0.003341\n",
       "genre     star        0.003341\n",
       "score     director    0.005413\n",
       "director  score       0.005413\n",
       "name      gross       0.005639\n",
       "gross     name        0.005639\n",
       "rating    votes       0.006031\n",
       "votes     rating      0.006031\n",
       "score     star        0.007296\n",
       "star      score       0.007296\n",
       "released  writer      0.008072\n",
       "writer    released    0.008072\n",
       "rating    country     0.008230\n",
       "country   rating      0.008230\n",
       "gross     released    0.008501\n",
       "released  gross       0.008501\n",
       "          runtime     0.008975\n",
       "runtime   released    0.008975\n",
       "star      rating      0.009196\n",
       "rating    star        0.009196\n",
       "star      runtime     0.010108\n",
       "runtime   star        0.010108\n",
       "          name        0.010850\n",
       "name      runtime     0.010850\n",
       "          genre       0.010996\n",
       "genre     name        0.010996\n",
       "director  country     0.011133\n",
       "country   director    0.011133\n",
       "name      votes       0.012615\n",
       "votes     name        0.012615\n",
       "score     writer      0.012843\n",
       "writer    score       0.012843\n",
       "name      writer      0.012880\n",
       "writer    name        0.012880\n",
       "company   star        0.014082\n",
       "star      company     0.014082\n",
       "score     name        0.014450\n",
       "name      score       0.014450\n",
       "director  rating      0.014656\n",
       "rating    director    0.014656\n",
       "director  name        0.015246\n",
       "name      director    0.015246\n",
       "released  star        0.015706\n",
       "star      released    0.015706\n",
       "director  runtime     0.017433\n",
       "runtime   director    0.017433\n",
       "genre     writer      0.017578\n",
       "writer    genre       0.017578\n",
       "released  rating      0.018083\n",
       "rating    released    0.018083\n",
       "star      writer      0.018520\n",
       "writer    star        0.018520\n",
       "rating    year        0.019499\n",
       "year      rating      0.019499\n",
       "budget    released    0.019952\n",
       "released  budget      0.019952\n",
       "score     company     0.020656\n",
       "company   score       0.020656\n",
       "          name        0.021697\n",
       "name      company     0.021697\n",
       "genre     released    0.022142\n",
       "released  genre       0.022142\n",
       "writer    country     0.022488\n",
       "country   writer      0.022488\n",
       "name      budget      0.023392\n",
       "budget    name        0.023392\n",
       "year      name        0.025542\n",
       "name      year        0.025542\n",
       "votes     released    0.028833\n",
       "released  votes       0.028833\n",
       "score     genre       0.035106\n",
       "genre     score       0.035106\n",
       "director  star        0.036593\n",
       "star      director    0.036593\n",
       "runtime   company     0.037585\n",
       "company   runtime     0.037585\n",
       "votes     country     0.041551\n",
       "country   votes       0.041551\n",
       "score     released    0.045874\n",
       "released  score       0.045874\n",
       "company   country     0.048569\n",
       "country   company     0.048569\n",
       "budget    country     0.052977\n",
       "country   budget      0.052977\n",
       "year      score       0.056386\n",
       "score     year        0.056386\n",
       "gross     country     0.060078\n",
       "country   gross       0.060078\n",
       "rating    score       0.065983\n",
       "score     rating      0.065983\n",
       "          budget      0.072001\n",
       "budget    score       0.072001\n",
       "runtime   year        0.075077\n",
       "year      runtime     0.075077\n",
       "company   votes       0.118470\n",
       "votes     company     0.118470\n",
       "rating    runtime     0.140792\n",
       "runtime   rating      0.140792\n",
       "rating    genre       0.147796\n",
       "genre     rating      0.147796\n",
       "gross     company     0.149553\n",
       "company   gross       0.149553\n",
       "          budget      0.170235\n",
       "budget    company     0.170235\n",
       "votes     year        0.206021\n",
       "year      votes       0.206021\n",
       "score     gross       0.222556\n",
       "gross     score       0.222556\n",
       "writer    director    0.261735\n",
       "director  writer      0.261735\n",
       "gross     year        0.274321\n",
       "year      gross       0.274321\n",
       "gross     runtime     0.275796\n",
       "runtime   gross       0.275796\n",
       "          budget      0.318695\n",
       "budget    runtime     0.318695\n",
       "year      budget      0.327722\n",
       "budget    year        0.327722\n",
       "votes     runtime     0.352303\n",
       "runtime   votes       0.352303\n",
       "          score       0.414068\n",
       "score     runtime     0.414068\n",
       "votes     budget      0.439675\n",
       "budget    votes       0.439675\n",
       "votes     score       0.474256\n",
       "score     votes       0.474256\n",
       "votes     gross       0.614751\n",
       "gross     votes       0.614751\n",
       "          budget      0.740247\n",
       "budget    gross       0.740247\n",
       "name      name        1.000000\n",
       "director  director    1.000000\n",
       "gross     gross       1.000000\n",
       "budget    budget      1.000000\n",
       "country   country     1.000000\n",
       "star      star        1.000000\n",
       "writer    writer      1.000000\n",
       "votes     votes       1.000000\n",
       "score     score       1.000000\n",
       "released  released    1.000000\n",
       "year      year        1.000000\n",
       "genre     genre       1.000000\n",
       "rating    rating      1.000000\n",
       "company   company     1.000000\n",
       "runtime   runtime     1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_pairs = corr_pairs.sort_values()\n",
    "\n",
    "sorted_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "96c71a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "votes     gross       0.614751\n",
       "gross     votes       0.614751\n",
       "          budget      0.740247\n",
       "budget    gross       0.740247\n",
       "name      name        1.000000\n",
       "director  director    1.000000\n",
       "gross     gross       1.000000\n",
       "budget    budget      1.000000\n",
       "country   country     1.000000\n",
       "star      star        1.000000\n",
       "writer    writer      1.000000\n",
       "votes     votes       1.000000\n",
       "score     score       1.000000\n",
       "released  released    1.000000\n",
       "year      year        1.000000\n",
       "genre     genre       1.000000\n",
       "rating    rating      1.000000\n",
       "company   company     1.000000\n",
       "runtime   runtime     1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_corr = sorted_pairs[(sorted_pairs) > 0.5]\n",
    "\n",
    "high_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ba48b7",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "1. Votes and budget have the highest correlation to gross earnings\n",
    "2. Company has a low correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "015212f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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