president_polls_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictive Analysis\n",
    "\n",
    "Analyst = Kofi Anane Poku\n",
    "\n",
    "Linkedin Profile : linkedin.com/in/kofi-poku-anane\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TABLE OF CONTENT\n",
    "\n",
    "Link to Download Dataset\n",
    "\n",
    "Link : https://github.com/kananep/datasets/blob/main/president_polls.csv\n",
    "\n",
    "Link : https://drive.google.com/file/d/1UP0bwupiTVDdJV3207ks1wdGj5S8em2B/view?usp=drive_link"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project consist of the analysis of presidential polls for 2024 American Election to be held on November 5th 2024 . The dataset was sampled to predict the most likely to win the election (The Top 4 candidates)\n",
    "\n",
    "* Data Preparation was done to clean the data\n",
    "\n",
    "The Predictive Analysis Section used \n",
    "\n",
    "1 . Linear Regression with explaination of the output result\n",
    "\n",
    "2 . Bar plot illustrating the Top 4 Candidates by percentages values\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function warnings.filterwarnings(action, message='', category=<class 'Warning'>, module='', lineno=0, append=False)>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>poll_id</th>\n",
       "      <th>pollster_id</th>\n",
       "      <th>pollster</th>\n",
       "      <th>sponsor_ids</th>\n",
       "      <th>sponsors</th>\n",
       "      <th>display_name</th>\n",
       "      <th>pollster_rating_id</th>\n",
       "      <th>pollster_rating_name</th>\n",
       "      <th>fte_grade</th>\n",
       "      <th>methodology</th>\n",
       "      <th>...</th>\n",
       "      <th>election_date</th>\n",
       "      <th>stage</th>\n",
       "      <th>nationwide_batch</th>\n",
       "      <th>ranked_choice_reallocated</th>\n",
       "      <th>ranked_choice_round</th>\n",
       "      <th>party</th>\n",
       "      <th>answer</th>\n",
       "      <th>candidate_id</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84924</td>\n",
       "      <td>1102</td>\n",
       "      <td>Emerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>88</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>A-</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DEM</td>\n",
       "      <td>Biden</td>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84924</td>\n",
       "      <td>1102</td>\n",
       "      <td>Emerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>88</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>A-</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REP</td>\n",
       "      <td>Trump</td>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>47.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84924</td>\n",
       "      <td>1102</td>\n",
       "      <td>Emerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>88</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>A-</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DEM</td>\n",
       "      <td>Biden</td>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84924</td>\n",
       "      <td>1102</td>\n",
       "      <td>Emerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>88</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>A-</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REP</td>\n",
       "      <td>Trump</td>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>43.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84924</td>\n",
       "      <td>1102</td>\n",
       "      <td>Emerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>88</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>A-</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IND</td>\n",
       "      <td>Kennedy</td>\n",
       "      <td>31042</td>\n",
       "      <td>Robert F. Kennedy</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4696</th>\n",
       "      <td>74812</td>\n",
       "      <td>241</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>71</td>\n",
       "      <td>Reuters</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>154</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>B-</td>\n",
       "      <td>Probability Panel</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REP</td>\n",
       "      <td>DeSantis</td>\n",
       "      <td>16646</td>\n",
       "      <td>Ron DeSantis</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4697</th>\n",
       "      <td>74812</td>\n",
       "      <td>241</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>71</td>\n",
       "      <td>Reuters</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>154</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>B-</td>\n",
       "      <td>Probability Panel</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DEM</td>\n",
       "      <td>Biden</td>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4698</th>\n",
       "      <td>74812</td>\n",
       "      <td>241</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>71</td>\n",
       "      <td>Reuters</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>154</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>B-</td>\n",
       "      <td>Probability Panel</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REP</td>\n",
       "      <td>Haley</td>\n",
       "      <td>16640</td>\n",
       "      <td>Nikki Haley</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4699</th>\n",
       "      <td>74681</td>\n",
       "      <td>1674</td>\n",
       "      <td>PEM Management Corporation</td>\n",
       "      <td>1629</td>\n",
       "      <td>John Bolton Super PAC</td>\n",
       "      <td>PEM Management Corporation</td>\n",
       "      <td>675</td>\n",
       "      <td>PEM Management Corporation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DEM</td>\n",
       "      <td>Biden</td>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4700</th>\n",
       "      <td>74681</td>\n",
       "      <td>1674</td>\n",
       "      <td>PEM Management Corporation</td>\n",
       "      <td>1629</td>\n",
       "      <td>John Bolton Super PAC</td>\n",
       "      <td>PEM Management Corporation</td>\n",
       "      <td>675</td>\n",
       "      <td>PEM Management Corporation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>...</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>general</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REP</td>\n",
       "      <td>Trump</td>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4701 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      poll_id  pollster_id                    pollster sponsor_ids  \\\n",
       "0       84924         1102                     Emerson         NaN   \n",
       "1       84924         1102                     Emerson         NaN   \n",
       "2       84924         1102                     Emerson         NaN   \n",
       "3       84924         1102                     Emerson         NaN   \n",
       "4       84924         1102                     Emerson         NaN   \n",
       "...       ...          ...                         ...         ...   \n",
       "4696    74812          241                       Ipsos          71   \n",
       "4697    74812          241                       Ipsos          71   \n",
       "4698    74812          241                       Ipsos          71   \n",
       "4699    74681         1674  PEM Management Corporation        1629   \n",
       "4700    74681         1674  PEM Management Corporation        1629   \n",
       "\n",
       "                   sponsors                display_name  pollster_rating_id  \\\n",
       "0                       NaN             Emerson College                  88   \n",
       "1                       NaN             Emerson College                  88   \n",
       "2                       NaN             Emerson College                  88   \n",
       "3                       NaN             Emerson College                  88   \n",
       "4                       NaN             Emerson College                  88   \n",
       "...                     ...                         ...                 ...   \n",
       "4696                Reuters                       Ipsos                 154   \n",
       "4697                Reuters                       Ipsos                 154   \n",
       "4698                Reuters                       Ipsos                 154   \n",
       "4699  John Bolton Super PAC  PEM Management Corporation                 675   \n",
       "4700  John Bolton Super PAC  PEM Management Corporation                 675   \n",
       "\n",
       "            pollster_rating_name fte_grade             methodology  ...  \\\n",
       "0                Emerson College        A-  IVR/Online Panel/Email  ...   \n",
       "1                Emerson College        A-  IVR/Online Panel/Email  ...   \n",
       "2                Emerson College        A-  IVR/Online Panel/Email  ...   \n",
       "3                Emerson College        A-  IVR/Online Panel/Email  ...   \n",
       "4                Emerson College        A-  IVR/Online Panel/Email  ...   \n",
       "...                          ...       ...                     ...  ...   \n",
       "4696                       Ipsos        B-       Probability Panel  ...   \n",
       "4697                       Ipsos        B-       Probability Panel  ...   \n",
       "4698                       Ipsos        B-       Probability Panel  ...   \n",
       "4699  PEM Management Corporation       NaN              Live Phone  ...   \n",
       "4700  PEM Management Corporation       NaN              Live Phone  ...   \n",
       "\n",
       "      election_date    stage nationwide_batch ranked_choice_reallocated  \\\n",
       "0           11/5/24  general            False                     False   \n",
       "1           11/5/24  general            False                     False   \n",
       "2           11/5/24  general            False                     False   \n",
       "3           11/5/24  general            False                     False   \n",
       "4           11/5/24  general            False                     False   \n",
       "...             ...      ...              ...                       ...   \n",
       "4696        11/5/24  general            False                     False   \n",
       "4697        11/5/24  general            False                     False   \n",
       "4698        11/5/24  general            False                     False   \n",
       "4699        11/5/24  general            False                     False   \n",
       "4700        11/5/24  general            False                     False   \n",
       "\n",
       "      ranked_choice_round party    answer  candidate_id     candidate_name  \\\n",
       "0                     NaN   DEM     Biden         19368          Joe Biden   \n",
       "1                     NaN   REP     Trump         16651       Donald Trump   \n",
       "2                     NaN   DEM     Biden         19368          Joe Biden   \n",
       "3                     NaN   REP     Trump         16651       Donald Trump   \n",
       "4                     NaN   IND   Kennedy         31042  Robert F. Kennedy   \n",
       "...                   ...   ...       ...           ...                ...   \n",
       "4696                  NaN   REP  DeSantis         16646       Ron DeSantis   \n",
       "4697                  NaN   DEM     Biden         19368          Joe Biden   \n",
       "4698                  NaN   REP     Haley         16640        Nikki Haley   \n",
       "4699                  NaN   DEM     Biden         19368          Joe Biden   \n",
       "4700                  NaN   REP     Trump         16651       Donald Trump   \n",
       "\n",
       "       pct  \n",
       "0     43.2  \n",
       "1     47.4  \n",
       "2     36.7  \n",
       "3     43.4  \n",
       "4      6.5  \n",
       "...    ...  \n",
       "4696  25.0  \n",
       "4697  44.0  \n",
       "4698  19.0  \n",
       "4699  46.4  \n",
       "4700  42.2  \n",
       "\n",
       "[4701 rows x 44 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president = pd.read_csv('president_polls.csv')\n",
    "president"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poll_id                         0\n",
       "pollster_id                     0\n",
       "pollster                        0\n",
       "sponsor_ids                  2957\n",
       "sponsors                     2957\n",
       "display_name                    0\n",
       "pollster_rating_id              0\n",
       "pollster_rating_name            0\n",
       "fte_grade                     178\n",
       "methodology                    97\n",
       "transparency_score           3218\n",
       "state                        3380\n",
       "start_date                      0\n",
       "end_date                        0\n",
       "sponsor_candidate_id         4689\n",
       "sponsor_candidate            4689\n",
       "sponsor_candidate_party      4689\n",
       "question_id                     0\n",
       "sample_size                    18\n",
       "population                      0\n",
       "subpopulation                4701\n",
       "population_full                 0\n",
       "tracking                     3917\n",
       "created_at                      0\n",
       "notes                        4649\n",
       "url                             2\n",
       "source                       4500\n",
       "internal                     4691\n",
       "partisan                     4461\n",
       "race_id                         0\n",
       "cycle                           0\n",
       "office_type                     0\n",
       "seat_number                     0\n",
       "seat_name                    4701\n",
       "election_date                   0\n",
       "stage                           0\n",
       "nationwide_batch                0\n",
       "ranked_choice_reallocated       0\n",
       "ranked_choice_round          4701\n",
       "party                           0\n",
       "answer                          0\n",
       "candidate_id                    0\n",
       "candidate_name                  0\n",
       "pct                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "president.fillna(0 , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['poll_id', 'pollster_id', 'pollster', 'sponsor_ids', 'sponsors',\n",
       "       'display_name', 'pollster_rating_id', 'pollster_rating_name',\n",
       "       'fte_grade', 'methodology', 'transparency_score', 'state', 'start_date',\n",
       "       'end_date', 'sponsor_candidate_id', 'sponsor_candidate',\n",
       "       'sponsor_candidate_party', 'question_id', 'sample_size', 'population',\n",
       "       'subpopulation', 'population_full', 'tracking', 'created_at', 'notes',\n",
       "       'url', 'source', 'internal', 'partisan', 'race_id', 'cycle',\n",
       "       'office_type', 'seat_number', 'seat_name', 'election_date', 'stage',\n",
       "       'nationwide_batch', 'ranked_choice_reallocated', 'ranked_choice_round',\n",
       "       'party', 'answer', 'candidate_id', 'candidate_name', 'pct'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>candidate_id</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>pct</th>\n",
       "      <th>party</th>\n",
       "      <th>election_date</th>\n",
       "      <th>race_id</th>\n",
       "      <th>partisan</th>\n",
       "      <th>methodology</th>\n",
       "      <th>fte_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>43.2</td>\n",
       "      <td>DEM</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>47.4</td>\n",
       "      <td>REP</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>36.7</td>\n",
       "      <td>DEM</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>43.4</td>\n",
       "      <td>REP</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31042</td>\n",
       "      <td>Robert F. Kennedy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>IND</td>\n",
       "      <td>11/5/24</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   candidate_id     candidate_name   pct party election_date  race_id  \\\n",
       "0         19368          Joe Biden  43.2   DEM       11/5/24     8914   \n",
       "1         16651       Donald Trump  47.4   REP       11/5/24     8914   \n",
       "2         19368          Joe Biden  36.7   DEM       11/5/24     8914   \n",
       "3         16651       Donald Trump  43.4   REP       11/5/24     8914   \n",
       "4         31042  Robert F. Kennedy   6.5   IND       11/5/24     8914   \n",
       "\n",
       "  partisan             methodology fte_grade  \n",
       "0        0  IVR/Online Panel/Email        A-  \n",
       "1        0  IVR/Online Panel/Email        A-  \n",
       "2        0  IVR/Online Panel/Email        A-  \n",
       "3        0  IVR/Online Panel/Email        A-  \n",
       "4        0  IVR/Online Panel/Email        A-  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president_new = president[['candidate_id','candidate_name','pct' ,'party' ,'election_date','race_id','partisan','methodology','fte_grade',]]\n",
    "president_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>candidate_id</th>\n",
       "      <th>pct</th>\n",
       "      <th>race_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4701.000000</td>\n",
       "      <td>4701.000000</td>\n",
       "      <td>4701.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18689.582217</td>\n",
       "      <td>40.181306</td>\n",
       "      <td>8890.099766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3391.162062</td>\n",
       "      <td>8.982626</td>\n",
       "      <td>45.692064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16638.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>8749.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16651.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>8889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16661.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>8914.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19368.000000</td>\n",
       "      <td>44.200000</td>\n",
       "      <td>8914.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31116.000000</td>\n",
       "      <td>67.600000</td>\n",
       "      <td>8914.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       candidate_id          pct      race_id\n",
       "count   4701.000000  4701.000000  4701.000000\n",
       "mean   18689.582217    40.181306  8890.099766\n",
       "std     3391.162062     8.982626    45.692064\n",
       "min    16638.000000     0.700000  8749.000000\n",
       "25%    16651.000000    38.000000  8889.000000\n",
       "50%    16661.000000    42.000000  8914.000000\n",
       "75%    19368.000000    44.200000  8914.000000\n",
       "max    31116.000000    67.600000  8914.000000"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president_new.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "candidate_id        int64\n",
       "candidate_name     object\n",
       "pct               float64\n",
       "party              object\n",
       "election_date      object\n",
       "race_id             int64\n",
       "partisan           object\n",
       "methodology        object\n",
       "fte_grade          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president_new.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11/5/24\n",
       "1       11/5/24\n",
       "2       11/5/24\n",
       "3       11/5/24\n",
       "4       11/5/24\n",
       "         ...   \n",
       "4696    11/5/24\n",
       "4697    11/5/24\n",
       "4698    11/5/24\n",
       "4699    11/5/24\n",
       "4700    11/5/24\n",
       "Name: election_date, Length: 4701, dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president_new['election_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\toy\\AppData\\Local\\Temp\\ipykernel_4052\\3117949099.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  president_new['election_date'] = pd.to_datetime(president_new['election_date'] , format= \"%m/%d/%y\")\n"
     ]
    }
   ],
   "source": [
    "president_new['election_date'] = pd.to_datetime(president_new['election_date'] , format= \"%m/%d/%y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>candidate_id</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>pct</th>\n",
       "      <th>party</th>\n",
       "      <th>election_date</th>\n",
       "      <th>race_id</th>\n",
       "      <th>partisan</th>\n",
       "      <th>methodology</th>\n",
       "      <th>fte_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>43.2</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>47.4</td>\n",
       "      <td>REP</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>36.7</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>43.4</td>\n",
       "      <td>REP</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31042</td>\n",
       "      <td>Robert F. Kennedy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>IND</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   candidate_id     candidate_name   pct party election_date  race_id  \\\n",
       "0         19368          Joe Biden  43.2   DEM    2024-11-05     8914   \n",
       "1         16651       Donald Trump  47.4   REP    2024-11-05     8914   \n",
       "2         19368          Joe Biden  36.7   DEM    2024-11-05     8914   \n",
       "3         16651       Donald Trump  43.4   REP    2024-11-05     8914   \n",
       "4         31042  Robert F. Kennedy   6.5   IND    2024-11-05     8914   \n",
       "\n",
       "  partisan             methodology fte_grade  \n",
       "0        0  IVR/Online Panel/Email        A-  \n",
       "1        0  IVR/Online Panel/Email        A-  \n",
       "2        0  IVR/Online Panel/Email        A-  \n",
       "3        0  IVR/Online Panel/Email        A-  \n",
       "4        0  IVR/Online Panel/Email        A-  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "president_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'candidate_id'}>,\n",
       "        <Axes: title={'center': 'pct'}>],\n",
       "       [<Axes: title={'center': 'election_date'}>,\n",
       "        <Axes: title={'center': 'race_id'}>]], dtype=object)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "president_new.hist(figsize= (15 , 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "president_new_correlation = president_new[['candidate_id','pct','race_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(president_new_correlation.corr(),\n",
    "            annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm \n",
    "y = president_new['candidate_id']\n",
    "x = president_new['pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>const</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>47.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>43.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const   pct\n",
       "0    1.0  43.2\n",
       "1    1.0  47.4\n",
       "2    1.0  36.7\n",
       "3    1.0  43.4\n",
       "4    1.0   6.5"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = sm.add_constant(x)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           candidate_id   R-squared:                       0.488\n",
      "Model:                            OLS   Adj. R-squared:                  0.488\n",
      "Method:                 Least Squares   F-statistic:                     4475.\n",
      "Date:                Wed, 22 May 2024   Prob (F-statistic):               0.00\n",
      "Time:                        13:02:28   Log-Likelihood:                -43311.\n",
      "No. Observations:                4701   AIC:                         8.663e+04\n",
      "Df Residuals:                    4699   BIC:                         8.664e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       2.928e+04    162.282    180.455      0.000     2.9e+04    2.96e+04\n",
      "pct         -263.6762      3.941    -66.898      0.000    -271.403    -255.949\n",
      "==============================================================================\n",
      "Omnibus:                      973.083   Durbin-Watson:                   2.446\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3787.123\n",
      "Skew:                           0.982   Prob(JB):                         0.00\n",
      "Kurtosis:                       6.934   Cond. No.                         189.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression model\n",
    "model1 = sm.OLS(endog = y, exog = x).fit()\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain the regression result . The coefficient of pct is -263.6762 meaning as pct decrease by 1 unit the dependent variable candidate_id decreases by -263.6762 .\n",
    "\n",
    "To predict the most likely to win the 2024 American election is based on the pct estimate , as the pct decrease by 1 unit , the probability of candidate_id losing the election is decrease by -263.6762"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'candidate id')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = president_new['pct']\n",
    "plt.plot(x, y, 'o')\n",
    "b, a = np.polyfit(x, y, 1)\n",
    "plt.plot(x, b * x + a)\n",
    "plt.xlabel(\"pct\")\n",
    "plt.ylabel(\"candidate id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph explain the regression result above in a visual way , as the pct decreases by 1 unit from 70 to lowest ,  the candidate_id decreases by -263.6762 meaning the probability of the candidate winning the election decreases ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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>candidate_id</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>pct</th>\n",
       "      <th>party</th>\n",
       "      <th>election_date</th>\n",
       "      <th>race_id</th>\n",
       "      <th>partisan</th>\n",
       "      <th>methodology</th>\n",
       "      <th>fte_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>67.60</td>\n",
       "      <td>REP</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8911</td>\n",
       "      <td>0</td>\n",
       "      <td>Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>67.40</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8762</td>\n",
       "      <td>0</td>\n",
       "      <td>Online Panel</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>63.00</td>\n",
       "      <td>REP</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8866</td>\n",
       "      <td>0</td>\n",
       "      <td>Online Panel</td>\n",
       "      <td>A/B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>61.00</td>\n",
       "      <td>REP</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8886</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>60.00</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8762</td>\n",
       "      <td>0</td>\n",
       "      <td>Probability Panel</td>\n",
       "      <td>A/B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>50.30</td>\n",
       "      <td>REP</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8863</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Text-to-Web</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3792</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>50.20</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8869</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Text-to-Web/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1047</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>50.20</td>\n",
       "      <td>REP</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8826</td>\n",
       "      <td>0</td>\n",
       "      <td>IVR/Online Panel/Email</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4096</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>50.20</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8765</td>\n",
       "      <td>REP</td>\n",
       "      <td>Live Phone/Text-to-Web</td>\n",
       "      <td>C/D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4368</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>50.13</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>8914</td>\n",
       "      <td>0</td>\n",
       "      <td>Online Panel</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      candidate_id candidate_name    pct party election_date  race_id  \\\n",
       "1075         16651   Donald Trump  67.60   REP    2024-11-05     8911   \n",
       "1106         19368      Joe Biden  67.40   DEM    2024-11-05     8762   \n",
       "3917         16651   Donald Trump  63.00   REP    2024-11-05     8866   \n",
       "889          16651   Donald Trump  61.00   REP    2024-11-05     8886   \n",
       "816          19368      Joe Biden  60.00   DEM    2024-11-05     8762   \n",
       "...            ...            ...    ...   ...           ...      ...   \n",
       "3649         16651   Donald Trump  50.30   REP    2024-11-05     8863   \n",
       "3792         19368      Joe Biden  50.20   DEM    2024-11-05     8869   \n",
       "1047         16651   Donald Trump  50.20   REP    2024-11-05     8826   \n",
       "4096         19368      Joe Biden  50.20   DEM    2024-11-05     8765   \n",
       "4368         19368      Joe Biden  50.13   DEM    2024-11-05     8914   \n",
       "\n",
       "     partisan                   methodology fte_grade  \n",
       "1075        0            Online Panel/Email        A-  \n",
       "1106        0                  Online Panel         0  \n",
       "3917        0                  Online Panel       A/B  \n",
       "889         0                             0         C  \n",
       "816         0             Probability Panel       A/B  \n",
       "...       ...                           ...       ...  \n",
       "3649        0  IVR/Online Panel/Text-to-Web        A-  \n",
       "3792        0         IVR/Text-to-Web/Email        A-  \n",
       "1047        0        IVR/Online Panel/Email        A-  \n",
       "4096      REP        Live Phone/Text-to-Web       C/D  \n",
       "4368        0                  Online Panel        B+  \n",
       "\n",
       "[212 rows x 9 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "election_prediction = president_new[president['pct'] > 50].sort_values(by = 'pct' , ascending = False)\n",
    "election_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>candidate_id</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>67.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>67.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4216</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>59.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3959</th>\n",
       "      <td>20699</td>\n",
       "      <td>Liz Cheney</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3961</th>\n",
       "      <td>20699</td>\n",
       "      <td>Liz Cheney</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3629</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>56.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>56.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>56.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>16640</td>\n",
       "      <td>Nikki Haley</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3627</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>19368</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>55.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3731</th>\n",
       "      <td>16651</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      candidate_id candidate_name   pct\n",
       "1075         16651   Donald Trump  67.6\n",
       "1106         19368      Joe Biden  67.4\n",
       "3917         16651   Donald Trump  63.0\n",
       "889          16651   Donald Trump  61.0\n",
       "4216         19368      Joe Biden  60.0\n",
       "816          19368      Joe Biden  60.0\n",
       "1051         16651   Donald Trump  59.3\n",
       "3068         19368      Joe Biden  59.0\n",
       "887          16651   Donald Trump  59.0\n",
       "3679         16651   Donald Trump  58.7\n",
       "3694         19368      Joe Biden  58.0\n",
       "2305         19368      Joe Biden  58.0\n",
       "3959         20699     Liz Cheney  58.0\n",
       "3929         16651   Donald Trump  58.0\n",
       "3961         20699     Liz Cheney  58.0\n",
       "1108         19368      Joe Biden  57.6\n",
       "2328         16651   Donald Trump  57.0\n",
       "1968         19368      Joe Biden  57.0\n",
       "3629         16651   Donald Trump  57.0\n",
       "1450         19368      Joe Biden  57.0\n",
       "3066         19368      Joe Biden  57.0\n",
       "1538         16651   Donald Trump  57.0\n",
       "1065         16651   Donald Trump  56.6\n",
       "1145         19368      Joe Biden  56.2\n",
       "1143         19368      Joe Biden  56.1\n",
       "361          16640    Nikki Haley  56.0\n",
       "3627         16651   Donald Trump  56.0\n",
       "3072         19368      Joe Biden  56.0\n",
       "1147         19368      Joe Biden  55.9\n",
       "3731         16651   Donald Trump  55.4"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "election_prediction = president_new[['candidate_id' ,'candidate_name', 'pct']].sort_values(by='pct' , ascending = False)[:30]\n",
    "election_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure( figsize= (20,8))\n",
    "sns.barplot(data=election_prediction , x= 'pct' , y = 'candidate_name')\n",
    "plt.title(\"Probability of a Candidate Winning a Election\", fontsize = 20)\n",
    "plt.grid(axis='y', linestyle='--',alpha=0.6)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shows the Top 4 candidates likely to win the 2024 American Election. Donald Trump has the highest estimate rate of winning the 2024 election by probability, followed by Joe Biden ,Liz Cheney and Nikki Haley"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONCLUSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model predicts American 2024 election who is likely to win the race by the pct estimates. Linear regression was used for the prediction and the bar plot for illustration "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}