data_visualization_EDA_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e9cbda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8097170e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',None)\n",
    "sns.set(style='darkgrid',palette='deep',font_scale=1.1,rc={'figure.figsize':[20,10]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a06f92b",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey = pd.read_csv('csvs/survey_responses_training.csv')\n",
    "voter_info = pd.read_csv('csvs/voter_information_training.csv')\n",
    "voter_hist = pd.read_csv('csvs/voting_history_training.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "a04418e5",
   "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>myv_van_id</th>\n",
       "      <th>contact_type_name</th>\n",
       "      <th>datetime_canvassed</th>\n",
       "      <th>master_survey_question_name</th>\n",
       "      <th>master_survey_response_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3727336</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-31 00:00:00 UTC</td>\n",
       "      <td>**MSQ2016 Ticket</td>\n",
       "      <td>1 - Strong Support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3596564</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-21 00:00:00 UTC</td>\n",
       "      <td>**MSQ2016 Ticket</td>\n",
       "      <td>1 - Strong Support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3460754</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-11 00:00:00 UTC</td>\n",
       "      <td>**16cccRecVBMApp</td>\n",
       "      <td>No, Not Received</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3489299</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2018-07-28 00:00:00 UTC</td>\n",
       "      <td>MSQ2018: VOL DCCC</td>\n",
       "      <td>2= No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4186850</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2018-06-16 00:00:00 UTC</td>\n",
       "      <td>MSQ2018:CONG ID DCCC</td>\n",
       "      <td>5 - Support GOP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   myv_van_id contact_type_name       datetime_canvassed  \\\n",
       "0     3727336             Phone  2016-10-31 00:00:00 UTC   \n",
       "1     3596564             Phone  2016-10-21 00:00:00 UTC   \n",
       "2     3460754             Phone  2016-10-11 00:00:00 UTC   \n",
       "3     3489299             Phone  2018-07-28 00:00:00 UTC   \n",
       "4     4186850             Phone  2018-06-16 00:00:00 UTC   \n",
       "\n",
       "  master_survey_question_name master_survey_response_name  \n",
       "0            **MSQ2016 Ticket          1 - Strong Support  \n",
       "1            **MSQ2016 Ticket          1 - Strong Support  \n",
       "2            **16cccRecVBMApp            No, Not Received  \n",
       "3           MSQ2018: VOL DCCC                       2= No  \n",
       "4        MSQ2018:CONG ID DCCC             5 - Support GOP  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "0c1d5a3c",
   "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>myv_van_id</th>\n",
       "      <th>age_combined</th>\n",
       "      <th>gender_combined</th>\n",
       "      <th>ethnicity_combined</th>\n",
       "      <th>zip</th>\n",
       "      <th>registration_date</th>\n",
       "      <th>van_precinct_name</th>\n",
       "      <th>state_house_district_latest</th>\n",
       "      <th>state_senate_district_latest</th>\n",
       "      <th>us_cong_district_latest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37835355</td>\n",
       "      <td>24</td>\n",
       "      <td>U</td>\n",
       "      <td>B</td>\n",
       "      <td>75228</td>\n",
       "      <td>2015-11-05</td>\n",
       "      <td>1065</td>\n",
       "      <td>100</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40915631</td>\n",
       "      <td>26</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75150</td>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>1300</td>\n",
       "      <td>107</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40802481</td>\n",
       "      <td>47</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75043</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>1722</td>\n",
       "      <td>113</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38944227</td>\n",
       "      <td>56</td>\n",
       "      <td>U</td>\n",
       "      <td>B</td>\n",
       "      <td>75041</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>1729</td>\n",
       "      <td>113</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4055165</td>\n",
       "      <td>62</td>\n",
       "      <td>U</td>\n",
       "      <td>B</td>\n",
       "      <td>75081</td>\n",
       "      <td>2000-11-02</td>\n",
       "      <td>2514</td>\n",
       "      <td>102</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   myv_van_id  age_combined gender_combined ethnicity_combined    zip  \\\n",
       "0    37835355            24               U                  B  75228   \n",
       "1    40915631            26               U                NaN  75150   \n",
       "2    40802481            47               U                NaN  75043   \n",
       "3    38944227            56               U                  B  75041   \n",
       "4     4055165            62               U                  B  75081   \n",
       "\n",
       "  registration_date  van_precinct_name  state_house_district_latest  \\\n",
       "0        2015-11-05               1065                          100   \n",
       "1        2018-11-06               1300                          107   \n",
       "2        2018-10-20               1722                          113   \n",
       "3        2016-11-08               1729                          113   \n",
       "4        2000-11-02               2514                          102   \n",
       "\n",
       "   state_senate_district_latest  us_cong_district_latest  \n",
       "0                            23                       30  \n",
       "1                             2                        5  \n",
       "2                             2                       32  \n",
       "3                             2                       32  \n",
       "4                             8                       32  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voter_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1d00e3de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>myv_van_id</th>\n",
       "      <th>party_id</th>\n",
       "      <th>election_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4244191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3719754</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3576807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4096169</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3475084</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-05-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  myv_van_id  party_id election_date\n",
       "0           0     4244191       1.0    2016-05-24\n",
       "1           1     3719754       2.0    2016-05-24\n",
       "2           2     3576807       1.0    2016-05-24\n",
       "3           3     4096169       1.0    2016-05-24\n",
       "4           4     3475084       2.0    2016-05-24"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voter_hist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "893f0e1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7261\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    27940\n",
       "1     5490\n",
       "Name: voted, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(voter_hist['myv_van_id'].unique()))\n",
    "\n",
    "voter_hist['voted'] = np.where(voter_hist['election_date'] == '2018-11-06',1,0)\n",
    "voter_hist['voted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "c889b940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Data Types \n",
      "\n",
      "1. survey d-types:\n",
      " myv_van_id                      int64\n",
      "contact_type_name              object\n",
      "datetime_canvassed             object\n",
      "master_survey_question_name    object\n",
      "master_survey_response_name    object\n",
      "dtype: object\n",
      "\n",
      "\n",
      "2. voter_information d-types:\n",
      " myv_van_id                       int64\n",
      "age_combined                     int64\n",
      "gender_combined                 object\n",
      "ethnicity_combined              object\n",
      "zip                              int64\n",
      "registration_date               object\n",
      "van_precinct_name                int64\n",
      "state_house_district_latest      int64\n",
      "state_senate_district_latest     int64\n",
      "us_cong_district_latest          int64\n",
      "dtype: object\n",
      "\n",
      "\n",
      "3. voter_history d-types:\n",
      " Unnamed: 0         int64\n",
      "myv_van_id         int64\n",
      "party_id         float64\n",
      "election_date     object\n",
      "voted              int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('\\n\\n Data Types \\n')\n",
    "print(f'1. survey d-types:\\n {survey.dtypes}\\n\\n')\n",
    "print(f'2. voter_information d-types:\\n {voter_info.dtypes}\\n\\n')\n",
    "print(f'3. voter_history d-types:\\n {voter_hist.dtypes}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe3626e",
   "metadata": {},
   "source": [
    "# an agenda; a forethought:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8293d89",
   "metadata": {},
   "source": [
    "#### 1A. Join voter_info and survey_respones, keep voter_history separate. \n",
    "    - lets use these 2 merged df's to generate some numeric variables.\n",
    "    - this is looking entirely categorical so far. joining all 3 will make isolating target variable hard.\n",
    "\n",
    "#### 1B. Check for missing_values \n",
    "    - probably not many because this is VAN.\n",
    "\n",
    "#### 2. \"master_survey_respone_name\" --> try to group it / make useful\n",
    "    - lot of messiness here / redundancy\n",
    "\n",
    "#### 3. feature-engineering = i want to see a numerical abs(date_registered - election day 2018\n",
    "    - get an average amount of days from OG registration to Election Day 2018.\n",
    "    - this at least will correlate to the target variable (did they vote?) \n",
    "    - and their voting history (% of elections voted in)\n",
    "    - could be cool\n",
    "\n",
    "\n",
    "#### 4. see relationships with seaborn\n",
    "     - let's use groupby with sns.heatmap / pivot_table to check.\n",
    "     - to see how locations play in with gender, party, etc. later on...\n",
    "     - precints, districts, etc. will have redundancy. \n",
    "     - what is a good balance to distribute as a categorical variable but not be too sparse?\n",
    "     - maybe ethnicity aint useful\n",
    "\n",
    "\n",
    "#### 5. one-hot-encode categoricals (this is why we are trying to limit features AMAP)\n",
    "\n",
    "#### 6. Quick n dirty Random Forest to verify important features\n",
    "\n",
    "#### 7. GridsearchCV for best params across Logreg, GradboostDT, RandoForest \n",
    "    - why? \n",
    "    - logreg is an important baseline. we are doing basic classification. always good to start small. \n",
    "    - Gradboostdt is very robust against over-fitting, and is a great \"out of the box\" classifier\n",
    "    - random forest, tho higher computational cost, like gradboost, does deliver us feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3267c3",
   "metadata": {},
   "source": [
    "## 1A. merge survey_responses.csv + voter_information.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d1a8996d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>myv_van_id</th>\n",
       "      <th>contact_type_name</th>\n",
       "      <th>datetime_canvassed</th>\n",
       "      <th>master_survey_question_name</th>\n",
       "      <th>master_survey_response_name</th>\n",
       "      <th>age_combined</th>\n",
       "      <th>gender_combined</th>\n",
       "      <th>ethnicity_combined</th>\n",
       "      <th>zip</th>\n",
       "      <th>registration_date</th>\n",
       "      <th>van_precinct_name</th>\n",
       "      <th>state_house_district_latest</th>\n",
       "      <th>state_senate_district_latest</th>\n",
       "      <th>us_cong_district_latest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3727336</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-31 00:00:00 UTC</td>\n",
       "      <td>**MSQ2016 Ticket</td>\n",
       "      <td>1 - Strong Support</td>\n",
       "      <td>68</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>75224</td>\n",
       "      <td>1992-09-20</td>\n",
       "      <td>4046</td>\n",
       "      <td>111</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3596564</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-21 00:00:00 UTC</td>\n",
       "      <td>**MSQ2016 Ticket</td>\n",
       "      <td>1 - Strong Support</td>\n",
       "      <td>93</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>75228</td>\n",
       "      <td>1993-05-20</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3596564</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-21 00:00:00 UTC</td>\n",
       "      <td>MSQ2016 POTUS</td>\n",
       "      <td>1 - Support Dem</td>\n",
       "      <td>93</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>75228</td>\n",
       "      <td>1993-05-20</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3460754</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-11 00:00:00 UTC</td>\n",
       "      <td>**16cccRecVBMApp</td>\n",
       "      <td>No, Not Received</td>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75228</td>\n",
       "      <td>1988-11-08</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3460754</td>\n",
       "      <td>SMS Text</td>\n",
       "      <td>2018-11-06 12:34:00 UTC</td>\n",
       "      <td>2018MSQ: Erly Vt Cmt</td>\n",
       "      <td>Election Day</td>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75228</td>\n",
       "      <td>1988-11-08</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   myv_van_id contact_type_name       datetime_canvassed  \\\n",
       "0     3727336             Phone  2016-10-31 00:00:00 UTC   \n",
       "1     3596564             Phone  2016-10-21 00:00:00 UTC   \n",
       "2     3596564             Phone  2016-10-21 00:00:00 UTC   \n",
       "3     3460754             Phone  2016-10-11 00:00:00 UTC   \n",
       "4     3460754          SMS Text  2018-11-06 12:34:00 UTC   \n",
       "\n",
       "  master_survey_question_name master_survey_response_name  age_combined  \\\n",
       "0            **MSQ2016 Ticket          1 - Strong Support            68   \n",
       "1            **MSQ2016 Ticket          1 - Strong Support            93   \n",
       "2               MSQ2016 POTUS             1 - Support Dem            93   \n",
       "3            **16cccRecVBMApp            No, Not Received            67   \n",
       "4        2018MSQ: Erly Vt Cmt                Election Day            67   \n",
       "\n",
       "  gender_combined ethnicity_combined    zip registration_date  \\\n",
       "0               F                  W  75224        1992-09-20   \n",
       "1               F                  H  75228        1993-05-20   \n",
       "2               F                  H  75228        1993-05-20   \n",
       "3               M                  W  75228        1988-11-08   \n",
       "4               M                  W  75228        1988-11-08   \n",
       "\n",
       "   van_precinct_name  state_house_district_latest  \\\n",
       "0               4046                          111   \n",
       "1               1058                          100   \n",
       "2               1058                          100   \n",
       "3               1058                          100   \n",
       "4               1058                          100   \n",
       "\n",
       "   state_senate_district_latest  us_cong_district_latest  \n",
       "0                            23                       33  \n",
       "1                             2                        5  \n",
       "2                             2                        5  \n",
       "3                             2                        5  \n",
       "4                             2                        5  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(survey,voter_info,how='outer',on='myv_van_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7f02246a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16782, 14)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "f4bac5c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.us_cong_district_latest.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "a2e2f7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.state_house_district_latest.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "eca6fedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.zip.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "ebe7ee8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### just checking..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c0131da",
   "metadata": {},
   "source": [
    "## 1B. missing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "94f1fbac",
   "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>missing_val</th>\n",
       "      <th>miss_val_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>master_survey_response_name</th>\n",
       "      <td>7649</td>\n",
       "      <td>45.578596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact_type_name</th>\n",
       "      <td>6594</td>\n",
       "      <td>39.292099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime_canvassed</th>\n",
       "      <td>6594</td>\n",
       "      <td>39.292099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>master_survey_question_name</th>\n",
       "      <td>6594</td>\n",
       "      <td>39.292099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity_combined</th>\n",
       "      <td>811</td>\n",
       "      <td>4.832559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             missing_val  miss_val_%\n",
       "master_survey_response_name         7649   45.578596\n",
       "contact_type_name                   6594   39.292099\n",
       "datetime_canvassed                  6594   39.292099\n",
       "master_survey_question_name         6594   39.292099\n",
       "ethnicity_combined                   811    4.832559"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val = df.isnull().sum()\n",
    "miss_val_percent = 100* df.isnull().sum() / len(df)\n",
    "missing_df = pd.concat([missing_val,miss_val_percent],axis=1)\n",
    "missing_df.columns = ['missing_val','miss_val_%']\n",
    "missing_df = missing_df[missing_df.iloc[:,0] != 0].sort_values('missing_val',ascending=False)\n",
    "missing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea0b92d",
   "metadata": {},
   "source": [
    "#### Kind of a bummer. \n",
    "- Really wanted to use \"master_survey_response_name\", but 45% missing values is a bit steep. \n",
    "- i'll perform the cleaning on it that i wanted and see about adding it in. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547b1cce",
   "metadata": {},
   "source": [
    "## 2. Survey_Responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "aaf0109e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['**MSQ2016 Ticket', '**16cccRecVBMApp', 'MSQ2018: VOL DCCC',\n",
       "       'MSQ2018:CONG ID DCCC', '2018MSQ: Erly Vt Cmt',\n",
       "       'MSQ2018: Pri-ID DCCC', 'MSQ2016 Vote Plan',\n",
       "       '2018MSQ: D Ticket ID', 'MSQ2016 State House',\n",
       "       '15 MSQ: Generic ID', 'MSQ: Support Beto', '**SubVBMBallot',\n",
       "       '2018MSQ: CandidNomin', 'MSQ2016 POTUS', '2014MSQ: Ticket',\n",
       "       'MSQ2016 Party ID', 'MSQ2016 Volunteer', 'MSQ2016 Commissioner',\n",
       "       'MSQ2016 Yard Sign', 'MSQ2018: VOTE PLAN', '17-18 Volunteer Ask',\n",
       "       'MSQ2018: PET DCCC', '2014MSQ: Downballot'], dtype=object)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey['master_survey_question_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "f4651e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#useless"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "80271e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1 - Strong Support', 'No, Not Received', '2= No',\n",
       "       '5 - Support GOP', 'Mail', '2-Undecided', 'Early Vote',\n",
       "       'T- Ticket Splitter', '1 - Strong Democrat', 'i- Independent',\n",
       "       '1= Yes', '5 - Strong Cruz', 'Not Voting', '2 - Lean Democrat',\n",
       "       '2 - Lean Dem', '4 - Lean GOP', 'No, In Person',\n",
       "       'd- 1—Splitter moreDs', 'Voting in Person', '5 - Strong Against',\n",
       "       '4-No Support Rep', 'Yes, Received', '3-No Other Dem',\n",
       "       'T - Ticket Splitter', 'Ticket Splitter', '3 - Neither/Unsure',\n",
       "       'Maybe/Depends', '5-Strong Republican', 'Yes - Requested',\n",
       "       'No Will Not', nan, 'No', 'Yes', '1-Yes', 'Maybe', '3- Undecided',\n",
       "       'Election Day', '3 - Undecided', 'Already Voted', 'Yes, Plan Made',\n",
       "       '1 - Support Dem', '2- Lean Democrat', '1- Strong Democrat',\n",
       "       '4- Lean Republican', '5- Strong Republican',\n",
       "       'P- OthrPrimaryCntact'], dtype=object)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey['master_survey_response_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "f328d4a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#useful?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e701b2db",
   "metadata": {},
   "source": [
    "#### Group responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0011f73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def survey_response(x):\n",
    "    \n",
    "    repub = {'republican':['5','4','rep']}\n",
    "    dem = {'democrat':['1','dem','lean democrat']}\n",
    "    undec = {'undecided':['ticket','3','undecided','maybe']}\n",
    "    no = {'not_voting':['no']}\n",
    "    responses = [repub,dem,undec,no]\n",
    "    \n",
    "    for response in responses:\n",
    "        for k,v in response.items():\n",
    "            for z in v:\n",
    "                if str(z) in str(x).lower():\n",
    "                    return k\n",
    "\n",
    "df['master_survey_response_name'] = df['master_survey_response_name'].apply(survey_response)            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "8e0193aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "democrat      5518\n",
       "undecided      897\n",
       "republican     742\n",
       "not_voting     262\n",
       "Name: master_survey_response_name, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['master_survey_response_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "618aac01",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='master_survey_question_name',axis=1,inplace=True)\n",
    "\n",
    "# rename it for easier use.\n",
    "df = df.rename(columns={'master_survey_response_name':'survey'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1056bed",
   "metadata": {},
   "source": [
    "## 3. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df87f8fa",
   "metadata": {},
   "source": [
    "#### on abs(Dates)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "536c4c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.6    533\n",
       "2.0     479\n",
       "0.0     419\n",
       "10.0    335\n",
       "6.0     325\n",
       "       ... \n",
       "29.2      1\n",
       "33.2      1\n",
       "37.7      1\n",
       "39.3      1\n",
       "28.4      1\n",
       "Name: vote_since_register, Length: 411, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['eday'] = '2018-11-06'\n",
    "\n",
    "df['registration_date'] = pd.to_datetime(df['registration_date']).dt.date\n",
    "df['eday'] = pd.to_datetime(df['eday']).dt.date\n",
    "\n",
    "\n",
    "# get days since, normalize by years. note: same day registration = 0\n",
    "df['vote_since_register'] = (df['eday'] - df['registration_date'])\n",
    "df['vote_since_register'] = df['vote_since_register'].apply(lambda x: round(int(x.days) / 365,1))\n",
    "\n",
    "# check distribution\n",
    "df['vote_since_register'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b24249",
   "metadata": {},
   "source": [
    "#### on % Of Elections Voted in..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "0590b62a",
   "metadata": {},
   "outputs": [],
   "source": [
    "elecs = len(voter_hist['election_date'].unique())\n",
    "gb = voter_hist.groupby('myv_van_id')['election_date']\n",
    "voter_gb_van_id = (gb.count()).apply(lambda x: round(100* x/elecs,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e551dad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2a3a53f5",
   "metadata": {},
   "source": [
    "####  lock-in our 'Voted' Target variable while we're here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "6c63510f",
   "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</th>\n",
       "      <th>voted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myv_van_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>34.78</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7509</th>\n",
       "      <td>43.48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10993</th>\n",
       "      <td>21.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22643</th>\n",
       "      <td>4.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25108</th>\n",
       "      <td>52.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            % election  voted\n",
       "myv_van_id                   \n",
       "1905             34.78      1\n",
       "7509             43.48      1\n",
       "10993            21.74      1\n",
       "22643             4.35      0\n",
       "25108            52.17      1"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_gb_elections = pd.DataFrame(voter_gb_van_id).reset_index().rename(columns={'election_date':'% election'})\n",
    "df2 = pd.merge(history_gb_elections,voter_hist,how='inner',on='myv_van_id')\n",
    "df2 = df2.drop(columns=['Unnamed: 0','party_id','election_date'])\n",
    "df3 = pd.pivot_table(data=df2,index='myv_van_id')\n",
    "df3['voted'] = np.where(df3['voted'] > 0,1,0)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b66d9455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7261"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfcd090",
   "metadata": {},
   "source": [
    "## Re-Cap: we have:\n",
    "    - engineered abs(registration - e-day 2018) feature\n",
    "    - engineered % of elections voted in by Van_id\n",
    "    - added a 1 for 'voted' and 0 for those that didnt' vote in 2018 election, and removed redundant Van_ids\n",
    "    - grouped some survey responses and made another column with 1/0 for them sounding like a voter at door/phone\n",
    "## What's Left? \n",
    "    - find a location metric to use that won't be too sparse but also not too boring.\n",
    "    - merge df + df3 on inner Van_id, so that we have only one unique Van_id, but all our features for them\n",
    "    - Features:\n",
    "        1. Gender                     - Categorical (tri-variate; ok for sparse matrix)\n",
    "        2. Age                        - Numeric\n",
    "        3. Voted?                     - (Target)             \n",
    "        4. % Of Elections voted in    - Numeric\n",
    "        5. abs(registration-eday)     - Numeric\n",
    "        6. Congressional_district     - Categorical (six-variate; ok for sparse matrix)\n",
    "        7. Ethnicity                  - Categorical (pent-variate; pending removal of NaN)\n",
    "        8. Survey                     - this will be tough on inner merge... i wonder how it will look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "552115ad",
   "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",
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       "    }\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>myv_van_id</th>\n",
       "      <th>contact_type_name</th>\n",
       "      <th>datetime_canvassed</th>\n",
       "      <th>survey</th>\n",
       "      <th>age_combined</th>\n",
       "      <th>gender_combined</th>\n",
       "      <th>ethnicity_combined</th>\n",
       "      <th>zip</th>\n",
       "      <th>registration_date</th>\n",
       "      <th>van_precinct_name</th>\n",
       "      <th>state_house_district_latest</th>\n",
       "      <th>state_senate_district_latest</th>\n",
       "      <th>us_cong_district_latest</th>\n",
       "      <th>eday</th>\n",
       "      <th>vote_since_register</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3727336</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-31 00:00:00 UTC</td>\n",
       "      <td>democrat</td>\n",
       "      <td>68</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>75224</td>\n",
       "      <td>1992-09-20</td>\n",
       "      <td>4046</td>\n",
       "      <td>111</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3596564</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-21 00:00:00 UTC</td>\n",
       "      <td>democrat</td>\n",
       "      <td>93</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>75228</td>\n",
       "      <td>1993-05-20</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3596564</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-21 00:00:00 UTC</td>\n",
       "      <td>democrat</td>\n",
       "      <td>93</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>75228</td>\n",
       "      <td>1993-05-20</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3460754</td>\n",
       "      <td>Phone</td>\n",
       "      <td>2016-10-11 00:00:00 UTC</td>\n",
       "      <td>not_voting</td>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75228</td>\n",
       "      <td>1988-11-08</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3460754</td>\n",
       "      <td>SMS Text</td>\n",
       "      <td>2018-11-06 12:34:00 UTC</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75228</td>\n",
       "      <td>1988-11-08</td>\n",
       "      <td>1058</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   myv_van_id contact_type_name       datetime_canvassed      survey  \\\n",
       "0     3727336             Phone  2016-10-31 00:00:00 UTC    democrat   \n",
       "1     3596564             Phone  2016-10-21 00:00:00 UTC    democrat   \n",
       "2     3596564             Phone  2016-10-21 00:00:00 UTC    democrat   \n",
       "3     3460754             Phone  2016-10-11 00:00:00 UTC  not_voting   \n",
       "4     3460754          SMS Text  2018-11-06 12:34:00 UTC        None   \n",
       "\n",
       "   age_combined gender_combined ethnicity_combined    zip registration_date  \\\n",
       "0            68               F                  W  75224        1992-09-20   \n",
       "1            93               F                  H  75228        1993-05-20   \n",
       "2            93               F                  H  75228        1993-05-20   \n",
       "3            67               M                  W  75228        1988-11-08   \n",
       "4            67               M                  W  75228        1988-11-08   \n",
       "\n",
       "   van_precinct_name  state_house_district_latest  \\\n",
       "0               4046                          111   \n",
       "1               1058                          100   \n",
       "2               1058                          100   \n",
       "3               1058                          100   \n",
       "4               1058                          100   \n",
       "\n",
       "   state_senate_district_latest  us_cong_district_latest        eday  \\\n",
       "0                            23                       33  2018-11-06   \n",
       "1                             2                        5  2018-11-06   \n",
       "2                             2                        5  2018-11-06   \n",
       "3                             2                        5  2018-11-06   \n",
       "4                             2                        5  2018-11-06   \n",
       "\n",
       "   vote_since_register  \n",
       "0                 26.1  \n",
       "1                 25.5  \n",
       "2                 25.5  \n",
       "3                 30.0  \n",
       "4                 30.0  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "61161c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['datetime_canvassed','contact_type_name','registration_date','van_precinct_name','eday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "874e5127",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_renames = {'age_combined':'age',\n",
    "             'gender_combined':'gender',\n",
    "             'ethnicity_combined':'ethnicity',\n",
    "             'state_house_district_latest':'state_house',\n",
    "             'state_senate_district_latest':'state_senate',\n",
    "             'vote_since_register':'yrs_reg',\n",
    "             'us_cong_district_latest':'CD'}\n",
    "df = df.rename(columns=col_renames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "25863ee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>myv_van_id</th>\n",
       "      <th>survey</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>zip</th>\n",
       "      <th>state_house</th>\n",
       "      <th>state_senate</th>\n",
       "      <th>CD</th>\n",
       "      <th>yrs_reg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3727336</td>\n",
       "      <td>democrat</td>\n",
       "      <td>68</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>75224</td>\n",
       "      <td>111</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3596564</td>\n",
       "      <td>democrat</td>\n",
       "      <td>93</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>75228</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3596564</td>\n",
       "      <td>democrat</td>\n",
       "      <td>93</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>75228</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3460754</td>\n",
       "      <td>not_voting</td>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75228</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3460754</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75228</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   myv_van_id      survey  age gender ethnicity    zip  state_house  \\\n",
       "0     3727336    democrat   68      F         W  75224          111   \n",
       "1     3596564    democrat   93      F         H  75228          100   \n",
       "2     3596564    democrat   93      F         H  75228          100   \n",
       "3     3460754  not_voting   67      M         W  75228          100   \n",
       "4     3460754        None   67      M         W  75228          100   \n",
       "\n",
       "   state_senate  CD  yrs_reg  \n",
       "0            23  33     26.1  \n",
       "1             2   5     25.5  \n",
       "2             2   5     25.5  \n",
       "3             2   5     30.0  \n",
       "4             2   5     30.0  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "ab0996d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% election</th>\n",
       "      <th>voted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myv_van_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>34.78</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7509</th>\n",
       "      <td>43.48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10993</th>\n",
       "      <td>21.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22643</th>\n",
       "      <td>4.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25108</th>\n",
       "      <td>52.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            % election  voted\n",
       "myv_van_id                   \n",
       "1905             34.78      1\n",
       "7509             43.48      1\n",
       "10993            21.74      1\n",
       "22643             4.35      0\n",
       "25108            52.17      1"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "723a0e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = pd.merge(df3,df,on='myv_van_id',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "69a6ea72",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = training.drop_duplicates(subset='myv_van_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "ad311a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = training[training['gender'].isin(['M','F'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "625e7846",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "95d3c8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "democrat      814\n",
       "republican    348\n",
       "undecided     330\n",
       "not_voting    152\n",
       "Name: survey, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.survey.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cef0808",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "6a1702fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>myv_van_id</th>\n",
       "      <th>% election</th>\n",
       "      <th>voted</th>\n",
       "      <th>survey</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>zip</th>\n",
       "      <th>state_house</th>\n",
       "      <th>state_senate</th>\n",
       "      <th>CD</th>\n",
       "      <th>yrs_reg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1905</td>\n",
       "      <td>34.78</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>57</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>75048</td>\n",
       "      <td>112</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7509</td>\n",
       "      <td>43.48</td>\n",
       "      <td>1</td>\n",
       "      <td>republican</td>\n",
       "      <td>59</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75204</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10993</td>\n",
       "      <td>21.74</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>48</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>75227</td>\n",
       "      <td>110</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22643</td>\n",
       "      <td>4.35</td>\n",
       "      <td>0</td>\n",
       "      <td>republican</td>\n",
       "      <td>65</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>75220</td>\n",
       "      <td>114</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25108</td>\n",
       "      <td>52.17</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>86</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>75089</td>\n",
       "      <td>112</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   myv_van_id  % election  voted      survey  age gender ethnicity    zip  \\\n",
       "0        1905       34.78      1        None   57      F         W  75048   \n",
       "1        7509       43.48      1  republican   59      M         W  75204   \n",
       "3       10993       21.74      1        None   48      M         B  75227   \n",
       "4       22643        4.35      0  republican   65      M         W  75220   \n",
       "5       25108       52.17      1        None   86      F         W  75089   \n",
       "\n",
       "   state_house  state_senate  CD  yrs_reg  \n",
       "0          112             8  32     10.0  \n",
       "1          108            16  32     11.3  \n",
       "3          110            23  30      6.0  \n",
       "4          114            16  24      2.6  \n",
       "5          112            16  32      1.0  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613b66a9",
   "metadata": {},
   "source": [
    "### The majority of voters is < 10 years registered, almost perfect linear relationship to age and yrs_reg... not super interesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "acd0ea3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = training.age,y = training.yrs_reg);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14671f96",
   "metadata": {},
   "source": [
    "### Adding in the \"hue\" feature of the sns.lmplot, i see a lot of nested results. this shows me the geometry of the results might be worth playing with. I transformd the numeric data by log and sqrt to see if the visual changed. it didn't really. just wanted to check!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "c4446cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 554.435x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='age',y='yrs_reg',hue='voted',data=training,fit_reg=False,scatter_kws={'alpha':0.9,'s':10},height=7,aspect=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c061621b",
   "metadata": {},
   "source": [
    "#### Keep it simple with categorical hists and bars. Our numeric aren't that profound, yet they might be in our Classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f95586",
   "metadata": {},
   "source": [
    "### Women voted ~.25 more times than men on eday 11-06-18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f1464a55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='voted',col='gender',kind='count',data=training);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee475e05",
   "metadata": {},
   "source": [
    "### Some good variance here in ethnicity. could really help our our classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2bd349eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training.groupby('ethnicity')['voted'].sum().plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b1afd8",
   "metadata": {},
   "source": [
    "### Besides CD 26 which offers almost no results, this heatmap shows that ethnicity and CD together provide some interesting information. There is variety and different 'temps' across CDs, across ethnicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "153912fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = pd.pivot_table(data=training,index='CD',columns='ethnicity',values='% election')\n",
    "sns.heatmap(result,annot=True,cmap='RdYlGn');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "91022501",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ethnicity</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>H</th>\n",
       "      <th>W</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.746522</td>\n",
       "      <td>16.905824</td>\n",
       "      <td>12.875667</td>\n",
       "      <td>23.917249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10.622911</td>\n",
       "      <td>14.230505</td>\n",
       "      <td>13.859687</td>\n",
       "      <td>22.775904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>13.975357</td>\n",
       "      <td>22.112755</td>\n",
       "      <td>11.714941</td>\n",
       "      <td>23.917208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>12.073495</td>\n",
       "      <td>17.136261</td>\n",
       "      <td>13.261041</td>\n",
       "      <td>24.339898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8.076429</td>\n",
       "      <td>18.841429</td>\n",
       "      <td>13.389801</td>\n",
       "      <td>21.382802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ethnicity          A          B          H          W\n",
       "CD                                                   \n",
       "5          14.746522  16.905824  12.875667  23.917249\n",
       "24         10.622911  14.230505  13.859687  22.775904\n",
       "26               NaN        NaN        NaN   4.350000\n",
       "30         13.975357  22.112755  11.714941  23.917208\n",
       "32         12.073495  17.136261  13.261041  24.339898\n",
       "33          8.076429  18.841429  13.389801  21.382802"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a8fed5",
   "metadata": {},
   "source": [
    "# 5. One-hot-encoding of Categoricals. make some choices quickly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "cae16c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = training.drop(['survey','zip','state_house','state_senate','voted_response'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d1b3e0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>myv_van_id</th>\n",
       "      <th>% election</th>\n",
       "      <th>voted</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>CD</th>\n",
       "      <th>yrs_reg</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>21.74</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>4.35</td>\n",
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       "      <td>24</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25108</td>\n",
       "      <td>52.17</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   myv_van_id  % election  voted  age gender ethnicity  CD  yrs_reg\n",
       "0        1905       34.78      1   57      F         W  32     10.0\n",
       "1        7509       43.48      1   59      M         W  32     11.3\n",
       "3       10993       21.74      1   48      M         B  30      6.0\n",
       "4       22643        4.35      0   65      M         W  24      2.6\n",
       "5       25108       52.17      1   86      F         W  32      1.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ff76a8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_encoded = pd.get_dummies(training,columns=['CD','gender','ethnicity','CD']).set_index('myv_van_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f76a9313",
   "metadata": {},
   "source": [
    "# 6. Random Forest Quick n Dirty to see Feature_Importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f030abaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = training_encoded.voted.dropna(how='all')\n",
    "X = training_encoded.drop(columns='voted').dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3b5bdd83",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=47)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "cffb27ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8013253119213384"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth=2, n_estimators=1000, random_state=47)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred_prob = clf.predict_proba(X_test)[:,1]\n",
    "roc_auc_score(y_test,y_pred_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b252a8b4",
   "metadata": {},
   "source": [
    "## Feature_Importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "9da09248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = pd.DataFrame({'feature':X_train.columns,'importance':clf.feature_importances_})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "importances.plot.barh();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7184c56",
   "metadata": {},
   "source": [
    "# 7. GridSearchCV, train Gradient Boost Decision Tree & Logistic Regression\n",
    "    - logr = (really good baseline, simple tuning, perfect for classification of this kind.)\n",
    "    - gbdt = (best out of the box, has worked well for me in the past, prevents overfitting)\n",
    "    - i chose not to go with a kernelized (polynomial, rbf) SVM because the geometry of my numerical data wasnt         super crazy. i checked it with log/sqrt transformations too. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "5269c61c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for LogisticRegression() the best paramaters are {'C': 4, 'max_iter': 1000, 'penalty': 'l2'}\n",
      "\n",
      "for LogisticRegression() the best auc_score is 0.8495956707756891\n",
      "for GradientBoostingClassifier() the best paramaters are {'learning_rate': 0.01, 'max_depth': 2, 'n_estimators': 1000}\n",
      "\n",
      "for GradientBoostingClassifier() the best auc_score is 0.8747556306778737\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "param_grid_log = {'C':[1.5,2.5,3.5,4],'max_iter':[1000,10000],'penalty':['l2']}\n",
    "param_grid_gb = {'n_estimators':[1000,5000,10000],'learning_rate':[.01,.1,.5],'max_depth':[2,3,4]}\n",
    "#param_grid_rf = {'n_estimators':[1000,5000,10000],'max_depth':[2,3,4,5]}\n",
    "\n",
    "logr = LogisticRegression()\n",
    "gb = GradientBoostingClassifier()\n",
    "# rf = RandomForestClassifier()\n",
    "\n",
    "param_grids = [param_grid_log,param_grid_gb]\n",
    "clfs = [logr,gb]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=42)\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "X_train_scaled = min_max_scaler.fit_transform(X_train)\n",
    "X_test_scaled = min_max_scaler.transform(X_test)\n",
    "\n",
    "for clf, param_grid in zip(clfs,param_grids):\n",
    "    grid_clf_auc = GridSearchCV(clf, param_grid = param_grid, scoring = 'roc_auc' )\n",
    "    grid_clf_auc.fit(X_train_scaled,y_train)\n",
    "    print(f'for {clf} the best paramaters are {grid_clf_auc.best_params_}\\n')\n",
    "    print(f'for {clf} the best auc_score is {grid_clf_auc.best_score_}')\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d61d4b",
   "metadata": {},
   "source": [
    "#### If i had more time, i'd dive into the features, removing cloudy categoricals such as CD and see what other numericals i could create. the good news: the feature engineering i did paid off. those were my most important features according to the quick n' dirty Random Forest. \n",
    "- GBDT = .874 AUC\n",
    "- logreg = .849 AUC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5638fd0",
   "metadata": {},
   "source": [
    "#### ideal parameters for GBDT = {'learning_rate': 0.01, 'max_depth': 2, 'n_estimators': 1000}\n",
    "I will use GBDT due to its robustness to over-fitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a2b1ede",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba2698e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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