DS-3000 Final Project (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Columns, Removing Null Values, and Filtering Target Values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\peter\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (34,36,38,44,46,59,61) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent Approved: 84.44%\n",
      "Percent Denied:   15.56%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "\n",
    "\n",
    "df = pd.read_csv(r'C:\\School\\Summer 1 2021\\DS 3000\\DS3000-10\\DS\\hmda_2017_ma_all-records_labels.csv')\n",
    "dropped_columns = ['as_of_year', 'respondent_id', 'agency_name', 'agency_abbr', 'agency_code',\n",
    "                  'loan_type', 'property_type', 'loan_purpose', 'owner_occupancy', 'preapproval_name',\n",
    "                  'preapproval', 'action_taken_name', 'msamd_name', 'msamd', 'state_name',\n",
    "                  'state_abbr', 'state_code', 'county_code', 'census_tract_number', 'applicant_ethnicity',\n",
    "                  'co_applicant_ethnicity', 'applicant_race_1', 'applicant_race_name_2', 'applicant_race_2',\n",
    "                   'applicant_race_name_3', 'applicant_race_3', 'applicant_race_name_4', 'applicant_race_4',\n",
    "                  'applicant_race_name_5', 'applicant_race_5', 'co_applicant_race_1', 'co_applicant_race_name_2',\n",
    "                  'co_applicant_race_2', 'co_applicant_race_name_3', 'co_applicant_race_3', 'co_applicant_race_name_4',\n",
    "                  'co_applicant_race_4', 'co_applicant_race_name_5', 'co_applicant_race_5', 'applicant_sex',\n",
    "                  'co_applicant_sex', 'purchaser_type', 'denial_reason_name_1', 'denial_reason_1', 'denial_reason_name_2',\n",
    "                  'denial_reason_2', 'denial_reason_name_3', 'denial_reason_3', 'rate_spread', 'hoepa_status',\n",
    "                  'lien_status', 'edit_status_name', 'edit_status', 'sequence_number', 'application_date_indicator',\n",
    "                  'purchaser_type_name', 'co_applicant_sex_name', 'co_applicant_race_name_1' ] \n",
    "\n",
    "\n",
    "df = df.drop(columns=dropped_columns)\n",
    "df = df.replace('Information not provided by applicant in mail, Internet, or telephone application', np.NaN)\n",
    "df = df.dropna()\n",
    "df = df.loc[(df['action_taken'] == 1) | (df['action_taken'] == 3)]\n",
    "\n",
    "features_count = df['action_taken'].value_counts()\n",
    "features_count\n",
    "approved = features_count[1] \n",
    "denied = features_count[3] \n",
    "percent_approved = round((approved / (denied + approved)) * 100, 2)\n",
    "percent_denied = round((denied / (denied + approved)) * 100, 2)\n",
    "print(f'Percent Approved: {percent_approved}%\\nPercent Denied:   {percent_denied}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering Using One-Hot Encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "\n",
    "features = df.drop(columns=['action_taken'])\n",
    "\n",
    "ohe_cols = ['loan_type_name', 'property_type_name', 'loan_purpose_name', 'owner_occupancy_name',\n",
    "           'county_name','applicant_ethnicity_name', 'co_applicant_ethnicity_name', 'applicant_race_name_1', \n",
    "           'applicant_sex_name', 'hoepa_status_name', 'lien_status_name']\n",
    "\n",
    "ohe_features = features[ohe_cols]\n",
    "not_ohe_features_df = features.drop(columns=ohe_features)\n",
    "\n",
    "encoder = OneHotEncoder(sparse = False)\n",
    "encoded_df = encoder.fit_transform(ohe_features)\n",
    "encoder.get_feature_names()\n",
    "features_df = pd.DataFrame(encoded_df, columns = encoder.get_feature_names())\n",
    "ohe_features = pd.concat([features_df.reset_index(drop=True), not_ohe_features_df.reset_index(drop=True)], axis=1)\n",
    "target = df['action_taken']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering Using SVM Iterative Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9986770034549591\n",
      "0.7676011831028642\n",
      "0.998264093705233\n",
      "0.7640114265490305\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(ohe_features, target, random_state=3000)\n",
    "\n",
    "select = RFE(DecisionTreeClassifier(), n_features_to_select = 10)\n",
    "select.fit(X_train, y_train)\n",
    "\n",
    "X_train_selected = select.transform(X_train)\n",
    "X_test_selected = select.transform(X_test)\n",
    "\n",
    "model = DecisionTreeClassifier().fit(X=X_train, y=y_train)\n",
    "\n",
    "print(model.score(X_train, y_train))\n",
    "print(model.score(X_test, y_test))\n",
    "\n",
    "model = DecisionTreeClassifier().fit(X=X_train_selected, y=y_train)\n",
    "print(model.score(X_train_selected, y_train))\n",
    "print(model.score(X_test_selected, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Final DataFrame and Sending it to a CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action_taken</th>\n",
       "      <th>x2_Home purchase</th>\n",
       "      <th>x8_Male</th>\n",
       "      <th>loan_amount_000s</th>\n",
       "      <th>applicant_income_000s</th>\n",
       "      <th>population</th>\n",
       "      <th>minority_population</th>\n",
       "      <th>hud_median_family_income</th>\n",
       "      <th>tract_to_msamd_income</th>\n",
       "      <th>number_of_owner_occupied_units</th>\n",
       "      <th>number_of_1_to_4_family_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>550</td>\n",
       "      <td>179.0</td>\n",
       "      <td>4755.0</td>\n",
       "      <td>27.440001</td>\n",
       "      <td>104800.0</td>\n",
       "      <td>133.389999</td>\n",
       "      <td>1352.0</td>\n",
       "      <td>1949.0</td>\n",
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       "      <td>288</td>\n",
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       "      <td>3.190000</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>66.0</td>\n",
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       "      <td>11.760000</td>\n",
       "      <td>94300.0</td>\n",
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       "      <td>1793.0</td>\n",
       "      <td>2039.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243</td>\n",
       "      <td>57.0</td>\n",
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       "      <td>1087.0</td>\n",
       "      <td>1047.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>420</td>\n",
       "      <td>199.0</td>\n",
       "      <td>5521.0</td>\n",
       "      <td>2.230000</td>\n",
       "      <td>94300.0</td>\n",
       "      <td>152.429993</td>\n",
       "      <td>1721.0</td>\n",
       "      <td>2128.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158222</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2076.0</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>107.160004</td>\n",
       "      <td>711.0</td>\n",
       "      <td>979.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158223</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2076.0</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>107.160004</td>\n",
       "      <td>711.0</td>\n",
       "      <td>979.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158224</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>87.0</td>\n",
       "      <td>7314.0</td>\n",
       "      <td>41.110001</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>89.230003</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>2470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158225</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>24.150000</td>\n",
       "      <td>66600.0</td>\n",
       "      <td>52.750000</td>\n",
       "      <td>179.0</td>\n",
       "      <td>664.0</td>\n",
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       "    <tr>\n",
       "      <th>158226</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6836.0</td>\n",
       "      <td>9.260000</td>\n",
       "      <td>74500.0</td>\n",
       "      <td>102.290001</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158227 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        action_taken  x2_Home purchase  x8_Male  loan_amount_000s  \\\n",
       "0                  1               1.0      1.0               550   \n",
       "1                  3               0.0      1.0               288   \n",
       "2                  3               0.0      0.0               200   \n",
       "3                  3               0.0      0.0               243   \n",
       "4                  1               0.0      1.0               420   \n",
       "...              ...               ...      ...               ...   \n",
       "158222             3               1.0      1.0                60   \n",
       "158223             3               1.0      1.0                35   \n",
       "158224             3               0.0      0.0                10   \n",
       "158225             3               0.0      1.0                 5   \n",
       "158226             3               1.0      0.0                71   \n",
       "\n",
       "        applicant_income_000s  population  minority_population  \\\n",
       "0                       179.0      4755.0            27.440001   \n",
       "1                        75.0      6609.0             3.190000   \n",
       "2                        66.0      6139.0            11.760000   \n",
       "3                        57.0      5232.0            61.240002   \n",
       "4                       199.0      5521.0             2.230000   \n",
       "...                       ...         ...                  ...   \n",
       "158222                   78.0      2076.0             2.940000   \n",
       "158223                   28.0      2076.0             2.940000   \n",
       "158224                   87.0      7314.0            41.110001   \n",
       "158225                   29.0      1913.0            24.150000   \n",
       "158226                   69.0      6836.0             9.260000   \n",
       "\n",
       "        hud_median_family_income  tract_to_msamd_income  \\\n",
       "0                       104800.0             133.389999   \n",
       "1                        94300.0             128.529999   \n",
       "2                        94300.0             146.729996   \n",
       "3                        94300.0              56.450001   \n",
       "4                        94300.0             152.429993   \n",
       "...                          ...                    ...   \n",
       "158222                   69000.0             107.160004   \n",
       "158223                   69000.0             107.160004   \n",
       "158224                   84000.0              89.230003   \n",
       "158225                   66600.0              52.750000   \n",
       "158226                   74500.0             102.290001   \n",
       "\n",
       "        number_of_owner_occupied_units  number_of_1_to_4_family_units  \n",
       "0                               1352.0                         1949.0  \n",
       "1                               2038.0                         2474.0  \n",
       "2                               1793.0                         2039.0  \n",
       "3                               1087.0                         1047.0  \n",
       "4                               1721.0                         2128.0  \n",
       "...                                ...                            ...  \n",
       "158222                           711.0                          979.0  \n",
       "158223                           711.0                          979.0  \n",
       "158224                          1316.0                         2470.0  \n",
       "158225                           179.0                          664.0  \n",
       "158226                          2185.0                         2555.0  \n",
       "\n",
       "[158227 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = pd.Series(select.get_support())\n",
    "cols = ohe_features.columns\n",
    "filtered_cols = cols[mask]\n",
    "filtered_features = ohe_features[filtered_cols]\n",
    "final_df = pd.concat([target.reset_index(drop=True), filtered_features.reset_index(drop=True)], axis=1)\n",
    "final_df.to_csv(r'C:\\School\\Summer 1 2021\\DS 3000\\DS3000-10\\DS\\final_data.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the CSV from GitHub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action_taken</th>\n",
       "      <th>x2_Home purchase</th>\n",
       "      <th>x8_Male</th>\n",
       "      <th>loan_amount_000s</th>\n",
       "      <th>applicant_income_000s</th>\n",
       "      <th>population</th>\n",
       "      <th>minority_population</th>\n",
       "      <th>hud_median_family_income</th>\n",
       "      <th>tract_to_msamd_income</th>\n",
       "      <th>number_of_owner_occupied_units</th>\n",
       "      <th>number_of_1_to_4_family_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>550</td>\n",
       "      <td>179.0</td>\n",
       "      <td>4755.0</td>\n",
       "      <td>27.440001</td>\n",
       "      <td>104800.0</td>\n",
       "      <td>133.389999</td>\n",
       "      <td>1352.0</td>\n",
       "      <td>1949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>288</td>\n",
       "      <td>75.0</td>\n",
       "      <td>6609.0</td>\n",
       "      <td>3.190000</td>\n",
       "      <td>94300.0</td>\n",
       "      <td>128.529999</td>\n",
       "      <td>2038.0</td>\n",
       "      <td>2474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>66.0</td>\n",
       "      <td>6139.0</td>\n",
       "      <td>11.760000</td>\n",
       "      <td>94300.0</td>\n",
       "      <td>146.729996</td>\n",
       "      <td>1793.0</td>\n",
       "      <td>2039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>61.240002</td>\n",
       "      <td>94300.0</td>\n",
       "      <td>56.450001</td>\n",
       "      <td>1087.0</td>\n",
       "      <td>1047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>420</td>\n",
       "      <td>199.0</td>\n",
       "      <td>5521.0</td>\n",
       "      <td>2.230000</td>\n",
       "      <td>94300.0</td>\n",
       "      <td>152.429993</td>\n",
       "      <td>1721.0</td>\n",
       "      <td>2128.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158222</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2076.0</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>107.160004</td>\n",
       "      <td>711.0</td>\n",
       "      <td>979.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158223</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2076.0</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>107.160004</td>\n",
       "      <td>711.0</td>\n",
       "      <td>979.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158224</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>87.0</td>\n",
       "      <td>7314.0</td>\n",
       "      <td>41.110001</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>89.230003</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>2470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158225</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>24.150000</td>\n",
       "      <td>66600.0</td>\n",
       "      <td>52.750000</td>\n",
       "      <td>179.0</td>\n",
       "      <td>664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158226</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6836.0</td>\n",
       "      <td>9.260000</td>\n",
       "      <td>74500.0</td>\n",
       "      <td>102.290001</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>2555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158227 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        action_taken  x2_Home purchase  x8_Male  loan_amount_000s  \\\n",
       "0                  1               1.0      1.0               550   \n",
       "1                  3               0.0      1.0               288   \n",
       "2                  3               0.0      0.0               200   \n",
       "3                  3               0.0      0.0               243   \n",
       "4                  1               0.0      1.0               420   \n",
       "...              ...               ...      ...               ...   \n",
       "158222             3               1.0      1.0                60   \n",
       "158223             3               1.0      1.0                35   \n",
       "158224             3               0.0      0.0                10   \n",
       "158225             3               0.0      1.0                 5   \n",
       "158226             3               1.0      0.0                71   \n",
       "\n",
       "        applicant_income_000s  population  minority_population  \\\n",
       "0                       179.0      4755.0            27.440001   \n",
       "1                        75.0      6609.0             3.190000   \n",
       "2                        66.0      6139.0            11.760000   \n",
       "3                        57.0      5232.0            61.240002   \n",
       "4                       199.0      5521.0             2.230000   \n",
       "...                       ...         ...                  ...   \n",
       "158222                   78.0      2076.0             2.940000   \n",
       "158223                   28.0      2076.0             2.940000   \n",
       "158224                   87.0      7314.0            41.110001   \n",
       "158225                   29.0      1913.0            24.150000   \n",
       "158226                   69.0      6836.0             9.260000   \n",
       "\n",
       "        hud_median_family_income  tract_to_msamd_income  \\\n",
       "0                       104800.0             133.389999   \n",
       "1                        94300.0             128.529999   \n",
       "2                        94300.0             146.729996   \n",
       "3                        94300.0              56.450001   \n",
       "4                        94300.0             152.429993   \n",
       "...                          ...                    ...   \n",
       "158222                   69000.0             107.160004   \n",
       "158223                   69000.0             107.160004   \n",
       "158224                   84000.0              89.230003   \n",
       "158225                   66600.0              52.750000   \n",
       "158226                   74500.0             102.290001   \n",
       "\n",
       "        number_of_owner_occupied_units  number_of_1_to_4_family_units  \n",
       "0                               1352.0                         1949.0  \n",
       "1                               2038.0                         2474.0  \n",
       "2                               1793.0                         2039.0  \n",
       "3                               1087.0                         1047.0  \n",
       "4                               1721.0                         2128.0  \n",
       "...                                ...                            ...  \n",
       "158222                           711.0                          979.0  \n",
       "158223                           711.0                          979.0  \n",
       "158224                          1316.0                         2470.0  \n",
       "158225                           179.0                          664.0  \n",
       "158226                          2185.0                         2555.0  \n",
       "\n",
       "[158227 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.read_csv(r'https://raw.githubusercontent.com/peterballaro/DS3000-10/main/final_data.csv')\n",
    "final_df"
   ]
  }
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