Political Campaign Contributions WA 2023.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "efd67c0e",
   "metadata": {},
   "source": [
    "# Exploritory Data Analyisis of Washington State Political Donors\n",
    "\n",
    "Link to the Dataset:\n",
    "https://catalog.data.gov/dataset/contributions-to-candidates-and-political-committees\n",
    "\n",
    "The dataset contains 6,136,766 rows (instances) of political donations made to candidates and state initiates, in the years spanning 2007-2026.\n",
    "\n",
    "While the dataset oringally had thirty-five columns, only three 'filer_name', 'amount', and 'election_year' where germane to the anaylsis due to, relevance and sparsity. \n",
    "\n",
    "The analysis explores the budgetary flow of donation money from Washington State PACs durring the timeframe.\n",
    "Four predictive models where used on the data in an attempt to make a workable forecast model with good accuracy.\n",
    "\n",
    "The models where not accurate and the outputs indicated that no linear trend exists in the data. Given this dataset, there is no indication of donation money increasing or decreasing over time. \n",
    "\n",
    "Null Hypothesis H0: \n",
    "The distribution of political donations is random.\n",
    "\n",
    "Alternative Hypothesis H1: \n",
    "The distribution of political donations is not random.\n",
    "\n",
    "Research Questions:\n",
    "1.\tHow many unique political donors?\n",
    "\n",
    "2.\tWho are the top 30 names of the most frequent donars?\n",
    "\n",
    "3.\tWhich donors have contributed the largest sums of money throughout the years?\n",
    "\n",
    "4.\tWho is the top donor of all time? \n",
    "\n",
    "5.\tGiven the 4 models, Nueral Net, GradientBooster, Polynomial Regression, and LOESS, can a accurate predictive model be constructed from the data?  \n",
    "\n",
    "This study explores the distribution of political donations in Washington State using a dataset from www.Data.gov. The null hypothesis posits that the distribution is random, while the alternative hypothesis suggests otherwise. Four predictive models, including Neural Net, Gradient Booster, Polynomial Regression, and LOESS, are employed to forecast donation patterns. The dataset is cleaned, and statistical tools such as KDE plots and Shapiro-Wilk tests are used for analysis. However, none of the models provide accurate predictions, leading to acceptance of the null hypothesis. The study also identifies top donors and observes trends in donation frequency. While predictive accuracy is lacking, the exploratory analysis provides valuable insights for political campaign strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73030d64",
   "metadata": {},
   "source": [
    "# Step 1: Data Import and Inspection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e18a32a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-14 16:45:03.927713: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "#-- Importing all nessesary liberaries --#\n",
    "\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from scipy import stats\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed065f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Importing the raw data set and creating a pandas data frame named 'df_raw' --#\n",
    "df_raw = pd.read_csv('/Users/michaelsegaline/Desktop/Contributions_to_Candidates_and_Political_Committees.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81b5b334",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>report_number</th>\n",
       "      <th>origin</th>\n",
       "      <th>committee_id</th>\n",
       "      <th>filer_id</th>\n",
       "      <th>type</th>\n",
       "      <th>filer_name</th>\n",
       "      <th>office</th>\n",
       "      <th>legislative_district</th>\n",
       "      <th>position</th>\n",
       "      <th>...</th>\n",
       "      <th>contributor_address</th>\n",
       "      <th>contributor_city</th>\n",
       "      <th>contributor_state</th>\n",
       "      <th>contributor_zip</th>\n",
       "      <th>contributor_occupation</th>\n",
       "      <th>contributor_employer_name</th>\n",
       "      <th>contributor_employer_city</th>\n",
       "      <th>contributor_employer_state</th>\n",
       "      <th>url</th>\n",
       "      <th>contributor_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17656709</td>\n",
       "      <td>110170428</td>\n",
       "      <td>C3</td>\n",
       "      <td>31937</td>\n",
       "      <td>FANTT--969</td>\n",
       "      <td>Candidate</td>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>CITY COUNCIL MEMBER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City Council Member Position 3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://apollo.pdc.wa.gov/public/registrations...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17656708</td>\n",
       "      <td>110170428</td>\n",
       "      <td>C3</td>\n",
       "      <td>31937</td>\n",
       "      <td>FANTT--969</td>\n",
       "      <td>Candidate</td>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>CITY COUNCIL MEMBER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City Council Member Position 3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://apollo.pdc.wa.gov/public/registrations...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17656707</td>\n",
       "      <td>110170428</td>\n",
       "      <td>C3</td>\n",
       "      <td>31937</td>\n",
       "      <td>FANTT--969</td>\n",
       "      <td>Candidate</td>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>CITY COUNCIL MEMBER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City Council Member Position 3</td>\n",
       "      <td>...</td>\n",
       "      <td>1613 41st St</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>WA</td>\n",
       "      <td>98221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://apollo.pdc.wa.gov/public/registrations...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17656706</td>\n",
       "      <td>110170428</td>\n",
       "      <td>C3</td>\n",
       "      <td>31937</td>\n",
       "      <td>FANTT--969</td>\n",
       "      <td>Candidate</td>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>CITY COUNCIL MEMBER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City Council Member Position 3</td>\n",
       "      <td>...</td>\n",
       "      <td>618 St Mary’s Place</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>WA</td>\n",
       "      <td>98221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://apollo.pdc.wa.gov/public/registrations...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17656705</td>\n",
       "      <td>110170428</td>\n",
       "      <td>C3</td>\n",
       "      <td>31937</td>\n",
       "      <td>FANTT--969</td>\n",
       "      <td>Candidate</td>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>CITY COUNCIL MEMBER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City Council Member Position 3</td>\n",
       "      <td>...</td>\n",
       "      <td>1514 7th St</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>WA</td>\n",
       "      <td>98221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://apollo.pdc.wa.gov/public/registrations...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  report_number origin  committee_id    filer_id       type  \\\n",
       "0  17656709      110170428     C3         31937  FANTT--969  Candidate   \n",
       "1  17656708      110170428     C3         31937  FANTT--969  Candidate   \n",
       "2  17656707      110170428     C3         31937  FANTT--969  Candidate   \n",
       "3  17656706      110170428     C3         31937  FANTT--969  Candidate   \n",
       "4  17656705      110170428     C3         31937  FANTT--969  Candidate   \n",
       "\n",
       "                                  filer_name               office  \\\n",
       "0  Thomas John Fantini (Thomas \"TJ\" Fantini)  CITY COUNCIL MEMBER   \n",
       "1  Thomas John Fantini (Thomas \"TJ\" Fantini)  CITY COUNCIL MEMBER   \n",
       "2  Thomas John Fantini (Thomas \"TJ\" Fantini)  CITY COUNCIL MEMBER   \n",
       "3  Thomas John Fantini (Thomas \"TJ\" Fantini)  CITY COUNCIL MEMBER   \n",
       "4  Thomas John Fantini (Thomas \"TJ\" Fantini)  CITY COUNCIL MEMBER   \n",
       "\n",
       "   legislative_district                        position  ...  \\\n",
       "0                   NaN  City Council Member Position 3  ...   \n",
       "1                   NaN  City Council Member Position 3  ...   \n",
       "2                   NaN  City Council Member Position 3  ...   \n",
       "3                   NaN  City Council Member Position 3  ...   \n",
       "4                   NaN  City Council Member Position 3  ...   \n",
       "\n",
       "   contributor_address contributor_city contributor_state contributor_zip  \\\n",
       "0                  NaN              NaN               NaN             NaN   \n",
       "1                  NaN              NaN               NaN             NaN   \n",
       "2         1613 41st St        Anacortes                WA           98221   \n",
       "3  618 St Mary’s Place        Anacortes                WA           98221   \n",
       "4          1514 7th St        Anacortes                WA           98221   \n",
       "\n",
       "  contributor_occupation contributor_employer_name  contributor_employer_city  \\\n",
       "0                    NaN                       NaN                        NaN   \n",
       "1                    NaN                       NaN                        NaN   \n",
       "2                    NaN                       NaN                        NaN   \n",
       "3                    NaN                       NaN                        NaN   \n",
       "4                    NaN                       NaN                        NaN   \n",
       "\n",
       "   contributor_employer_state  \\\n",
       "0                         NaN   \n",
       "1                         NaN   \n",
       "2                         NaN   \n",
       "3                         NaN   \n",
       "4                         NaN   \n",
       "\n",
       "                                                 url contributor_location  \n",
       "0  https://apollo.pdc.wa.gov/public/registrations...                  NaN  \n",
       "1  https://apollo.pdc.wa.gov/public/registrations...                  NaN  \n",
       "2  https://apollo.pdc.wa.gov/public/registrations...                  NaN  \n",
       "3  https://apollo.pdc.wa.gov/public/registrations...                  NaN  \n",
       "4  https://apollo.pdc.wa.gov/public/registrations...                  NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-- Inspecting the Dataset --#\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4de2f0f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'report_number', 'origin', 'committee_id', 'filer_id', 'type',\n",
       "       'filer_name', 'office', 'legislative_district', 'position', 'party',\n",
       "       'ballot_number', 'for_or_against', 'jurisdiction',\n",
       "       'jurisdiction_county', 'jurisdiction_type', 'election_year', 'amount',\n",
       "       'cash_or_in_kind', 'receipt_date', 'description', 'memo',\n",
       "       'primary_general', 'code', 'contributor_category', 'contributor_name',\n",
       "       'contributor_address', 'contributor_city', 'contributor_state',\n",
       "       'contributor_zip', 'contributor_occupation',\n",
       "       'contributor_employer_name', 'contributor_employer_city',\n",
       "       'contributor_employer_state', 'url', 'contributor_location'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-- Exploring the columns in the dataframe --#\n",
    "df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e7b9a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6136766, 36)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-- Inspecting the size of the dataframe, it has 6,136,766 instances of political donations to canidates --#\n",
    "df_raw.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11751e66",
   "metadata": {},
   "source": [
    "# Step 2 Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0dac98a8",
   "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>filer_name</th>\n",
       "      <th>election_year</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>103.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>51.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>103.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  filer_name  election_year  amount\n",
       "0  Thomas John Fantini (Thomas \"TJ\" Fantini)           2023   50.00\n",
       "1  Thomas John Fantini (Thomas \"TJ\" Fantini)           2023  100.00\n",
       "2  Thomas John Fantini (Thomas \"TJ\" Fantini)           2023  103.48\n",
       "3  Thomas John Fantini (Thomas \"TJ\" Fantini)           2023   51.99\n",
       "4  Thomas John Fantini (Thomas \"TJ\" Fantini)           2023  103.48"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-- Creating a new dataframe with the relevant variables --# \n",
    "#-- Now selecting the relevant varaibles --#\n",
    "columns_to_keep = ['filer_name', 'election_year', 'amount' ]\n",
    "\n",
    "df = df_raw[columns_to_keep]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d50d2648",
   "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>filer_name</th>\n",
       "      <th>election_year</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>103.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>51.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thomas John Fantini (Thomas \"TJ\" Fantini)</td>\n",
       "      <td>2023</td>\n",
       "      <td>103.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136761</th>\n",
       "      <td>Paul Graves (PAUL GRAVES)</td>\n",
       "      <td>2018</td>\n",
       "      <td>200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136762</th>\n",
       "      <td>Paul Graves (PAUL GRAVES)</td>\n",
       "      <td>2018</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136763</th>\n",
       "      <td>Paul Graves (PAUL GRAVES)</td>\n",
       "      <td>2018</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136764</th>\n",
       "      <td>Paul Graves (PAUL GRAVES)</td>\n",
       "      <td>2018</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136765</th>\n",
       "      <td>Paul Graves (PAUL GRAVES)</td>\n",
       "      <td>2018</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6136766 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        filer_name  election_year  amount\n",
       "0        Thomas John Fantini (Thomas \"TJ\" Fantini)           2023   50.00\n",
       "1        Thomas John Fantini (Thomas \"TJ\" Fantini)           2023  100.00\n",
       "2        Thomas John Fantini (Thomas \"TJ\" Fantini)           2023  103.48\n",
       "3        Thomas John Fantini (Thomas \"TJ\" Fantini)           2023   51.99\n",
       "4        Thomas John Fantini (Thomas \"TJ\" Fantini)           2023  103.48\n",
       "...                                            ...            ...     ...\n",
       "6136761                  Paul Graves (PAUL GRAVES)           2018  200.00\n",
       "6136762                  Paul Graves (PAUL GRAVES)           2018  100.00\n",
       "6136763                  Paul Graves (PAUL GRAVES)           2018  100.00\n",
       "6136764                  Paul Graves (PAUL GRAVES)           2018   50.00\n",
       "6136765                  Paul Graves (PAUL GRAVES)           2018  100.00\n",
       "\n",
       "[6136766 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-- Now dropping all the rows with empty values to produce a clean dataframe --#\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "852c797c",
   "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>election_year</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.136766e+06</td>\n",
       "      <td>6.136766e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.015464e+03</td>\n",
       "      <td>2.862966e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.814356e+00</td>\n",
       "      <td>9.981094e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.007000e+03</td>\n",
       "      <td>-2.500000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.012000e+03</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.020000e+03</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.026000e+03</td>\n",
       "      <td>8.929810e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       election_year        amount\n",
       "count   6.136766e+06  6.136766e+06\n",
       "mean    2.015464e+03  2.862966e+02\n",
       "std     4.814356e+00  9.981094e+03\n",
       "min     2.007000e+03 -2.500000e+06\n",
       "25%     2.012000e+03  1.000000e+01\n",
       "50%     2.016000e+03  3.500000e+01\n",
       "75%     2.020000e+03  1.000000e+02\n",
       "max     2.026000e+03  8.929810e+06"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-- Getting descriptive statistics --#\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce00252",
   "metadata": {},
   "source": [
    "The descriptive statistics indicate that the mean donation amount is $286 per year, per donor. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0352f724",
   "metadata": {},
   "source": [
    "# Step 3 Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7db6020",
   "metadata": {},
   "source": [
    "Univartiate Exploration \n",
    "\n",
    "The following two graphs indicate that more feature engineering must be conducted with the 'amount' column. The number of big donar outliers have skewed the data, so that instances of donations near the '0' mark are viewed as negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5959bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='amount', ylabel='Density'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Visualizing the distrobution of \"amount\" of money contrubuted via Kernel Density Estimate Plot (kde) --#\n",
    "\n",
    "sns.kdeplot(df.amount, shade = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e5f056d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Exploring the density of 'Amount' and 'Frequency' in a histogram plot --#\n",
    "plt.hist(df['amount'], bins=20)  \n",
    "plt.xlabel(\"Amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Distribution of Amounts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f271f9",
   "metadata": {},
   "source": [
    "While the dataset indicated that some people did get donations refunded to them,the negative numbers wouldnt be enough to skewed the distrobution to be on the 'negative' side of '0'. Therefore it is the outliers that skewed small amounts to viewed as negative on the curve. Therefore the variable 'amount', must be converted to the absolute value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67be79f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Create a new DataFrame with positive values --#\n",
    "df['amount'] = df['amount'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46e57a74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['amount'], bins=20)  \n",
    "plt.xlabel(\"Amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Distribution of Amounts\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54dd72e5",
   "metadata": {},
   "source": [
    "Now all the data in the amount column is positive. Yeilding a mean of $291 donated per donor per year. Naturally the average Washingtonian is not donating that much; This is the result of the Political Action Committies (PACs), whom are donating large sums and skewing the distrobution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11361c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.136766e+06\n",
       "mean     2.917273e+02\n",
       "std      9.980937e+03\n",
       "min      1.000000e-02\n",
       "25%      1.000000e+01\n",
       "50%      3.500000e+01\n",
       "75%      1.000000e+02\n",
       "max      8.929810e+06\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "afe97714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='election_year', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Visualizing the frequency distrobution of \"election_year\" --#\n",
    "\n",
    "\n",
    "sns.kdeplot(df.election_year, shade = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5332a788",
   "metadata": {},
   "source": [
    "The above graph shows the frquenceny of donations per year (not a dollar amount)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a48c146",
   "metadata": {},
   "source": [
    "Bivariate Exploration = Exploring 2 variables at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "20a6e9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Barplot of the indicating the amount of money donated per year --#\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.barplot(data = df, x=\"election_year\", y= 'amount')\n",
    "plt.title('Bar Plot - total donations over the years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effe4e85",
   "metadata": {},
   "source": [
    "Notice that 2010 is the highest year for donations. Notice that presidential election years have spikes of doanations, with the year prior to a presidential election, yeilding the least. Aside from the outliering years of  2010 adn 2018, the peak-years are consistantly around $400 Million. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28819bb",
   "metadata": {},
   "source": [
    "# Now inspecting the names of political donors or PACs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "950ad1b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique donors: 9629\n"
     ]
    }
   ],
   "source": [
    "#-- Count the number of unique donor names --#\n",
    "unique_name_count = df.filer_name.nunique()\n",
    "\n",
    "#-- Print the count of unique donor names --#\n",
    "print(\"Number of unique donors:\", unique_name_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5246dd17",
   "metadata": {},
   "source": [
    "# Of those 9629 names, below the top 30 with the highest frequency to donations (not dollar amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "992e9ee9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WASHINGTON EDUCATION ASSOCIATION POLITICAL ACTION COMMITTEE                              503791\n",
      "FIREFIGHTERS ACTION SUPPORT TEAM (FAST PAC)                                              291133\n",
      "WASHINGTON REALTORS POLITICAL ACTION COMMITTEE                                           249200\n",
      "International Brotherhood of Electrical Workers Local 77 Joe Murphy PAC                  110875\n",
      "WA ST TROOPERS PAC                                                                       105451\n",
      "WASHINGTON STATE ASSOCIATION FOR JUSTICE JUSTICE FOR ALL                                  89659\n",
      "ROSSI DINO J                                                                              84714\n",
      "Jay R Inslee (JAY INSLEE)                                                                 82728\n",
      "NUCOR PAC OF WA                                                                           75914\n",
      "SEATTLE FIRE FIGHTERS PAC                                                                 73267\n",
      "MCKENNA ROBERT M (ROBERT MCKENNA)                                                         72525\n",
      "GREGOIRE CHRISTINE O                                                                      72352\n",
      "Jay R Inslee                                                                              69473\n",
      "FARMERS EMPLOYEES AND AGENTS PAC                                                          68176\n",
      "CIT FOR SPOKANE SCHOOLS                                                                   67773\n",
      "WASHINGTON STATE DENTAL POLITICAL ACTION COMMITTEE                                        61522\n",
      "PLUMBERS & STEAMFITTERS LOCAL 598 PAC                                                     51604\n",
      "REPUBLICAN STATE LEADERSHIP COMMITTEE - WASHINGTON PAC                                    49264\n",
      "ACTIVE IN DEMOCRACY TACOMA FIREFIGHTERS UNION                                             48262\n",
      "Washington State Democratic Committee - NonExempt                                         44069\n",
      "AWSP-WASHINGTON SCHOOL PRINCIPALS LEGISLATIVE EFFECTIVENESS ASSOCIATION (AWSP-WSPLEA)     39133\n",
      "Spokane Firefighters Union PAC                                                            37566\n",
      "AMERICAN FEDERATION OF TEACHERS WASHINGTON COMMITTEE ON POLITICAL EDUCAT                  37451\n",
      "Robert W. Ferguson (Robert Ferguson)                                                      36149\n",
      "WASHINGTON HOSPITALITY ASSOCIATION PAC                                                    33205\n",
      "Loren D. Culp (Loren Culp)                                                                30854\n",
      "Kent f\\Firefighters, IAFF Local 1747 Political Committee                                  29767\n",
      "SEATTLE POLICE OFFICERS GUILD POLITICAL ACTION COMMITTEE (SPOG PAC)                       28683\n",
      "UNITED FOOD AND COMMERCIAL WORKERS LOCAL 1439 ABC                                         27891\n",
      "WA UNITED FOR MARRIAGE                                                                    27615\n",
      "Name: filer_name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#-- Use the 'value_counts()' function to count the occurrences of each name --#\n",
    "name_counts = df['filer_name'].value_counts()\n",
    "\n",
    "#-- Get the top 30 most frequent names --#\n",
    "top_30_names = name_counts.head(30)\n",
    "\n",
    "#-- Print the top 30 names and their counts --#\n",
    "print(top_30_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69fd3b5",
   "metadata": {},
   "source": [
    "# Below is the top 20 donors of total sum of dollars: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0199d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filer_name\n",
      "NO ON 1631 (SPONSORED BY WESTERN STATES PETROLEUM ASSOCIATION)    31670041.80\n",
      "YES ON 1183 COALITION                                             26181709.08\n",
      "WASHINGTON EDUCATION ASSOCIATION POLITICAL ACTION COMMITTEE       26174091.04\n",
      "NEW DIRECTION PAC                                                 25350380.71\n",
      "Washington State Democratic Committee - NonExempt                 24321241.75\n",
      "NO ON 522                                                         22915729.93\n",
      "Washington State Democratic Committee - Exempt                    22896252.47\n",
      "YES! TO AFFORDABLE GROCERIES, (SEE EMAIL FOR REST OF NAME)        22442233.51\n",
      "HOUSE DEMOCRATIC CAMPAIGN COMMITTEE (HDCC)                        21767655.20\n",
      "PROTECT OUR COMMUNITIES                                           21250779.95\n",
      "The Leadership Council                                            19966355.45\n",
      "HARRY TRUMAN FUND                                                 18378859.76\n",
      "WASHINGTON REALTORS POLITICAL ACTION COMMITTEE                    18204819.81\n",
      "Jay R Inslee (JAY INSLEE)                                         18103450.67\n",
      "STOP THE FOOD & BEVERAGE TAX HIKES                                17681978.63\n",
      "CLEAN AIR CLEAN ENERGY WASHINGTON                                 16420054.09\n",
      "GROCERY MANUFACTURERS ASSOCIATION AGAINST I-522                   15168072.26\n",
      "MCKENNA ROBERT M (ROBERT MCKENNA)                                 14593794.72\n",
      "WA ST REPUB PARTY NON EXEMPT                                      14573999.53\n",
      "WA ST REPUB PARTY EXEMPT                                          14277642.28\n",
      "Name: amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#-- Group by 'Name' and calculate the sum of 'amount' for each name --#\n",
    "name_amount_totals = df.groupby('filer_name')['amount'].sum()\n",
    "\n",
    "#-- Get the top 20 names and their total 'amount' donated --#\n",
    "top_20_names = name_amount_totals.nlargest(20)\n",
    "\n",
    "#-- Print the top 20 names and their total 'amount' donated --#\n",
    "print(top_20_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e906f37",
   "metadata": {},
   "source": [
    "# Below is the top 5 donors  of overall donations over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33332eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Filter data for the top 5 names --#\n",
    "top_names = df['filer_name'].value_counts().head(5).index\n",
    "filtered_df = df[df['filer_name'].isin(top_names)]\n",
    "\n",
    "#-- Group and aggregate data --#\n",
    "agg_df = filtered_df.groupby(['election_year', 'filer_name'])['amount'].sum().unstack()\n",
    "\n",
    "#-- Create the time series plot --#\n",
    "agg_df.plot(marker='o')\n",
    "\n",
    "#-- Customize the plot --#\n",
    "plt.title(\"Top 5 Names: Amount Donated Over Time\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Total Amount Donated\")\n",
    "plt.legend(title=\"Name\")\n",
    "\n",
    "#-- Show the plot --#\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccde4b9",
   "metadata": {},
   "source": [
    "The above graph indicates that the Washington Education Asociation Poitical Action Committee (WEP) and Washington Realators Political Action Committee (RAP) are the most frequent doners to politcal initatives or canditates; Meanwhile, Firefighters Action Support Team is in a distant 3rd place. \n",
    "\n",
    "It appears that 2015, the biggest outlier doner(s) began to have a competition. Notice that from 2015 on, both WEP and RAP's outlier doners, have a very similar distrobution; WEP always able to keep the lead as the Washington Realtors Political Action Commitee donates in an attempt to match. \n",
    "\n",
    "The number of donations to canidates is niether increasing nor decreasing over time, as verified with the following models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b8ce8723",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Now Dropping the outliers (WEP, RAP) to increase --#\n",
    "\n",
    "# Get the top 2 names\n",
    "top_names = df['filer_name'].value_counts().head(2).index\n",
    "\n",
    "# Filter out rows with 'filer_name' in the top 2\n",
    "df_filtered = df[~df['filer_name'].isin(top_names)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50764e94",
   "metadata": {},
   "source": [
    "# Testing for Normality with the Shapiro-Wilk test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e7b2f59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample does not look Gaussian (not normally distributed)\n"
     ]
    }
   ],
   "source": [
    "#-- Perform the Shapiro-Wilk test --#\n",
    "statistic, p_value = stats.shapiro(df.amount)\n",
    "\n",
    "#-- Check the p-value to determine if the data is normally distributed --#\n",
    "alpha = 0.05  # Significance level\n",
    "if p_value > alpha:\n",
    "    print(\"Sample looks Gaussian (normally distributed)\")\n",
    "else:\n",
    "    print(\"Sample does not look Gaussian (not normally distributed)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60291531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='amount', ylabel='Density'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df.amount, shade = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb475db7",
   "metadata": {},
   "source": [
    "The distrobution of the 'amount' variable is not Guasian or Normal, so the assumptions of parametric models are not met. Therefore other predictive non-parametric models will be explored."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1043e55f",
   "metadata": {},
   "source": [
    "# Now training a Neural Net to predict the amount per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "31986cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Spliting data into a training and testing set --#\n",
    "X = df[['election_year']]  #-- Features (independent variable)--#\n",
    "y = df['amount']    #-- Target variable (dependent variable) --#\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8ef463ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "153420/153420 [==============================] - 208s 1ms/step - loss: 113745304.0000 - val_loss: 42907308.0000\n",
      "Epoch 2/10\n",
      "153420/153420 [==============================] - 205s 1ms/step - loss: 113746000.0000 - val_loss: 42906052.0000\n",
      "Epoch 3/10\n",
      "153420/153420 [==============================] - 204s 1ms/step - loss: 113747576.0000 - val_loss: 42912248.0000\n",
      "Epoch 4/10\n",
      "153420/153420 [==============================] - 202s 1ms/step - loss: 113745832.0000 - val_loss: 42907072.0000\n",
      "Epoch 5/10\n",
      "153420/153420 [==============================] - 203s 1ms/step - loss: 113746632.0000 - val_loss: 42907916.0000\n",
      "Epoch 6/10\n",
      "153420/153420 [==============================] - 210s 1ms/step - loss: 113750224.0000 - val_loss: 42907132.0000\n",
      "Epoch 7/10\n",
      "153420/153420 [==============================] - 208s 1ms/step - loss: 113739032.0000 - val_loss: 42910540.0000\n",
      "Epoch 8/10\n",
      "153420/153420 [==============================] - 201s 1ms/step - loss: 113750296.0000 - val_loss: 42906028.0000\n",
      "Epoch 9/10\n",
      "153420/153420 [==============================] - 206s 1ms/step - loss: 113744928.0000 - val_loss: 42916084.0000\n",
      "Epoch 10/10\n",
      "153420/153420 [==============================] - 208s 1ms/step - loss: 113749272.0000 - val_loss: 42909140.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fea9a8caaf0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#--Creating an early stopping monitor for computation / time saving --#\n",
    "\n",
    "early_stopping_monitor = tf.keras.callbacks.EarlyStopping(patience =2)\n",
    "\n",
    "# Build a simple feedforward neural network\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Input(shape=(X_train.shape[1],)),   # Input layer\n",
    "    keras.layers.Dense(64, activation='relu'),       # Hidden layer with 64 neurons and ReLU activation\n",
    "    keras.layers.Dense(32, activation='relu'),       # Hidden layer with 32 neurons and ReLU activation\n",
    "    keras.layers.Dense(1, activation='linear')       # Output layer with linear activation for regression\n",
    "])\n",
    "\n",
    "#-- Compile the model --#\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')  # Use mean squared error for regression tasks\n",
    "\n",
    "#-- Train the model --#\n",
    "\n",
    "#-- After 10 epochs, there is not a very big reduction in model loss --#\n",
    "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5be915a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38355/38355 [==============================] - 36s 945us/step - loss: 42909140.0000\n",
      "Mean Squared Error: 42909140.0\n"
     ]
    }
   ],
   "source": [
    "#-- Evaluate the model --#\n",
    "mse = model.evaluate(X_test, y_test)\n",
    "print(f\"Mean Squared Error: {mse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e11800be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 19ms/step\n",
      "Year: 2026, Predicted Amount: 233.08114624023438\n",
      "Year: 2027, Predicted Amount: 233.19549560546875\n",
      "Year: 2028, Predicted Amount: 233.30984497070312\n",
      "Year: 2029, Predicted Amount: 233.4241943359375\n",
      "Year: 2030, Predicted Amount: 233.53851318359375\n",
      "Year: 2031, Predicted Amount: 233.65286254882812\n",
      "Year: 2032, Predicted Amount: 233.7672119140625\n",
      "Year: 2033, Predicted Amount: 233.88156127929688\n",
      "Year: 2034, Predicted Amount: 233.99591064453125\n",
      "Year: 2035, Predicted Amount: 234.11024475097656\n"
     ]
    }
   ],
   "source": [
    "#-- Create a list of the next 10 years (or adjust as needed) --#\n",
    "next_years = np.arange(2026, 2036).reshape(-1, 1)\n",
    "\n",
    "#-- Use your trained model to make predictions --#\n",
    "predictions = model.predict(next_years)\n",
    "\n",
    "#-- Print the predictions --#\n",
    "for year, prediction in zip(next_years, predictions):\n",
    "    print(f\"Year: {year[0]}, Predicted Amount: {prediction[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2ec983",
   "metadata": {},
   "source": [
    "The neural net yeilded a Mean Squared Error score of 42909140 after 10 epochs. When testing other model wieghts, it would be too computationally expensive to run say 100 epochs to increase the model accuracy. The running more epochs wont nessesary improve the model score either. Lastly the model predictions for the next 10 years is trending around the $233 mark. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7058a065",
   "metadata": {},
   "source": [
    "# Now using ensemble methods "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "21e6d5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Create a Gradient Boosting Regressor --#\n",
    "regressor = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "38f1c5d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 42907193.856706366\n",
      "R-squared (R2): 3.7794255936196564e-05\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Calculate MSE --#\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "\n",
    "#-- Calculate R-squared (R2) --#\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"R-squared (R2): {r2}\")\n",
    "\n",
    "#-- Visualize the predicted values --#\n",
    "plt.scatter(X_test, y_test, color='gray', label='Actual')\n",
    "plt.scatter(X_test, y_pred, color='red', label='Predicted')\n",
    "plt.legend()\n",
    "plt.xlabel('X (Feature)')\n",
    "plt.ylabel('Y (Target)')\n",
    "plt.title('Actual vs. Predicted Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea49fd4",
   "metadata": {},
   "source": [
    "Gradientbooster is an ensmble method, meaning, it runs numerous small decision trees at the same time. Its a strong non-parametric model that can handle dirty and non-parametric data.\n",
    "\n",
    "The Mean Squared Error (MSE) is a metric used to measure the average squared difference between the predicted values and the actual values in a regression problem. It's calculated by taking the average of the squares of the differences between the predicted and actual values for each data point.\n",
    "\n",
    "MSE is a crucial tool to assess the accuracy of a regression model, where a lower MSE indicates that the model is closer to the actual values.\n",
    "\n",
    "With a MSE of 42,907,193 does not indicate that the model is nowhere near accurate and idenitical to the Neural Net's MSE score. \n",
    "\n",
    "Additionally the R2 or Coefficient of determination should be as close to '1' as possible to indicate an accurate score. An R2 score of 0.0000377 is not an accurate model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a358b8",
   "metadata": {},
   "source": [
    "# Now running Polynomial Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0fea9ada",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 99618388.74829671\n",
      "R-squared (R2): 7.048061900216673e-06\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#-- Create polynomial features of degree 2 --#\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "X_poly = poly.fit_transform(X)\n",
    "\n",
    "#-- Create and fit a linear regression model --#\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_poly, y)\n",
    "\n",
    "#-- Make predictions on the same data for visualization --#\n",
    "y_pred = regressor.predict(X_poly)\n",
    "\n",
    "#-- Calculate model evaluation scores --#\n",
    "mse = mean_squared_error(y, y_pred)  # Mean Squared Error\n",
    "r2 = r2_score(y, y_pred)  # R-squared\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"R-squared (R2): {r2}\")\n",
    "\n",
    "#-- Visualization: Plot the original data points and the regression line --#\n",
    "plt.scatter(X, y, color='blue', label='Actual Data')\n",
    "plt.plot(X, y_pred, color='red', label='Polynomial Regression')\n",
    "\n",
    "plt.xlabel('Amount (Standardized)')\n",
    "plt.ylabel('Target Variable')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491186e0",
   "metadata": {},
   "source": [
    "With a MSE of 99,618,388 does not indicate that the model is nowhere near accurate and idenitical to the Neural Net's MSE score. Mooreover,An R2 score of 0.000006 is not an accurate model. \n",
    "\n",
    "In the above timeline, 2018 is the most dense year with political donations. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61a0879",
   "metadata": {},
   "source": [
    "# Now Running LOESS Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71b68fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- The Variables have to be converted to a numpy array --#\n",
    "\n",
    "X = df['election_year'].values\n",
    "y = df['amount'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6edc2bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "lowess = sm.nonparametric.lowess(y, X, frac=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc3d1b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Extract Smooth Values --#\n",
    "smoothed_x = lowess[:, 0]\n",
    "smoothed_y = lowess[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb6a6c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Plot the Values --#\n",
    "plt.scatter(X, y, label='Data', alpha=0.5)\n",
    "plt.plot(smoothed_x, smoothed_y, 'r', label='LOESS')\n",
    "plt.legend()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Non-Parametric Regression (LOESS)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b013ae2c",
   "metadata": {},
   "source": [
    "With LOESS there's no single score, like R-squared in linear regression, for LOESS smoothing because it's not a traditional regression method but rather a data visualization tool.\n",
    "\n",
    "The above visualization indicates the line of best fit, \"red line\", is directly on the '0' mark; Showcasing that there is no appearent trend in the data on direction or the other. While the distrobution of the residuals matchs the previous visulizations."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b2d9e2f4",
   "metadata": {},
   "source": [
    "# In Final Anlysis: \n",
    "\n",
    "1.\tHow many unique political donors?\n",
    "\n",
    "\n",
    "\n",
    "According to the data there are 9629 unique political donors to include political action committees.\n",
    "\n",
    "2.\tWho are the Top 30 names of most frequent donars?\n",
    "\n",
    "\n",
    "\n",
    "The output was rendered in the above visualization.\n",
    "\n",
    "3.\tWhich donors have contributed the largest sums of money throughout the years?\n",
    "\n",
    "\n",
    "\n",
    "The above output was renedered with the top 5 PACS: Washington Education Asociation Poitical Action Committee (WEP) and Washington Realtors Political Action Committee (RAP) are the most frequent doners to politcal initatives or canditates; Meanwhile, Firefighters Action Support Team is in a distant 3rd place.\n",
    "\n",
    "4.\tWho is the top donor of all time? \n",
    "\n",
    "\n",
    "\n",
    "Washington Education Asociation Poitical Action Committee (WEP) \n",
    "\n",
    "5.\tGiven the 4 models, Nueral Net, GradientBooster, Polynomial Regression, and LOESS, can a accurate predictive model be constructed from the data? \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "No,we accept the Null Hypothesis H0:, that the frequency distribution of political donations is random and does not demonstrate any patterns. \n",
    "\n",
    "If the alternative hypothesis were true, given the dataset size, one of the four predictive models would have yielded more accurate results.\n",
    "\n",
    "The data was visually left skewed towards the ‘0’ mark and tested negative for being Guassian or parametric in the Shapiro-Wilk test. \n",
    "\n",
    "The neural net had an accuracy of 42,909,140; after experimenting with different model weights. The mean squared error should be near 0, 42 million is completely unacceptable. \n",
    "\n",
    "The mean square error of the gradient booster ensemble method was similar at 42,907,000 and an Coefficient of Determination score of 0.0000377. The coefficient should be near 1, instead, it pairs with the inaccurate MSE score. \n",
    "\n",
    "Thirdly the polynomial regression, didn’t provide a good score either, with an MSE score of 99 million and a bad R2 score to match at 0.000007. \n",
    "\n",
    "Lastly, the LOESS test / visualization indications that the line of best fit, through the residuals, is at the ‘0’ mark; Reconfirming a poor predictive model. \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}