Running Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import spatial\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>District</th>\n",
       "      <th>Party</th>\n",
       "      <th>Room</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>Email Address</th>\n",
       "      <th>Link</th>\n",
       "      <th>Name</th>\n",
       "      <th>County</th>\n",
       "      <th>% Dem</th>\n",
       "      <th>% Rep</th>\n",
       "      <th>Weeks in Office</th>\n",
       "      <th>No. Enviro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>James</td>\n",
       "      <td>Arciero</td>\n",
       "      <td>2nd Middlesex</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>277</td>\n",
       "      <td>(617) 722-2012</td>\n",
       "      <td>James.Arciero@mahouse.gov</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>James Arciero</td>\n",
       "      <td>Middlesex County</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>572</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Ashe</td>\n",
       "      <td>2nd Hampden</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>236</td>\n",
       "      <td>(617) 722-2430</td>\n",
       "      <td>Brian.Ashe@mahouse.gov</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>Brian Ashe</td>\n",
       "      <td>Hampden County</td>\n",
       "      <td>0.550959</td>\n",
       "      <td>0.390720</td>\n",
       "      <td>572</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Bruce</td>\n",
       "      <td>Ayers</td>\n",
       "      <td>1st Norfolk</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>167</td>\n",
       "      <td>(617) 722-2230</td>\n",
       "      <td>Bruce.Ayers@mahouse.gov</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>Bruce Ayers</td>\n",
       "      <td>Norfolk County</td>\n",
       "      <td>0.612467</td>\n",
       "      <td>0.333084</td>\n",
       "      <td>1092</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Ruth</td>\n",
       "      <td>Balser</td>\n",
       "      <td>12th Middlesex</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>167</td>\n",
       "      <td>(617) 722-2810</td>\n",
       "      <td>Ruth.Balser@mahouse.gov</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>Ruth Balser</td>\n",
       "      <td>Middlesex County</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>1092</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Christine</td>\n",
       "      <td>Barber</td>\n",
       "      <td>34th Middlesex</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>254</td>\n",
       "      <td>(617) 722-2220</td>\n",
       "      <td>Christine.Barber@mahouse.gov</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>Christine Barber</td>\n",
       "      <td>Middlesex County</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 First Name Last Name        District     Party  \\\n",
       "0           0             0      James   Arciero   2nd Middlesex  Democrat   \n",
       "1           1             1      Brian      Ashe     2nd Hampden  Democrat   \n",
       "2           2             2      Bruce     Ayers     1st Norfolk  Democrat   \n",
       "3           3             3       Ruth    Balser  12th Middlesex  Democrat   \n",
       "4           4             4  Christine    Barber  34th Middlesex  Democrat   \n",
       "\n",
       "  Room    Phone Number                 Email Address  \\\n",
       "0  277  (617) 722-2012     James.Arciero@mahouse.gov   \n",
       "1  236  (617) 722-2430        Brian.Ashe@mahouse.gov   \n",
       "2  167  (617) 722-2230       Bruce.Ayers@mahouse.gov   \n",
       "3  167  (617) 722-2810       Ruth.Balser@mahouse.gov   \n",
       "4  254  (617) 722-2220  Christine.Barber@mahouse.gov   \n",
       "\n",
       "                                                Link              Name  \\\n",
       "0  https://malegislature.gov/Legislators/Profile/...     James Arciero   \n",
       "1  https://malegislature.gov/Legislators/Profile/...        Brian Ashe   \n",
       "2  https://malegislature.gov/Legislators/Profile/...       Bruce Ayers   \n",
       "3  https://malegislature.gov/Legislators/Profile/...       Ruth Balser   \n",
       "4  https://malegislature.gov/Legislators/Profile/...  Christine Barber   \n",
       "\n",
       "             County     % Dem     % Rep  Weeks in Office  No. Enviro  \n",
       "0  Middlesex County  0.663227  0.281705              572           5  \n",
       "1    Hampden County  0.550959  0.390720              572           1  \n",
       "2    Norfolk County  0.612467  0.333084             1092          14  \n",
       "3  Middlesex County  0.663227  0.281705             1092           6  \n",
       "4  Middlesex County  0.663227  0.281705              288           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reps = pd.read_excel('data.xlsx', sheet_name='Reps Profiling')\n",
    "reps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Link</th>\n",
       "      <th>H2836</th>\n",
       "      <th>H2007</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>James Arciero</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Brian Ashe</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Bruce Ayers</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ruth Balser</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Christine Barber</td>\n",
       "      <td>https://malegislature.gov/Legislators/Profile/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0              Name  \\\n",
       "0           0     James Arciero   \n",
       "1           1        Brian Ashe   \n",
       "2           2       Bruce Ayers   \n",
       "3           3       Ruth Balser   \n",
       "4           4  Christine Barber   \n",
       "\n",
       "                                                Link  H2836  H2007  \n",
       "0  https://malegislature.gov/Legislators/Profile/...      1      0  \n",
       "1  https://malegislature.gov/Legislators/Profile/...      0      0  \n",
       "2  https://malegislature.gov/Legislators/Profile/...      1      0  \n",
       "3  https://malegislature.gov/Legislators/Profile/...      1      0  \n",
       "4  https://malegislature.gov/Legislators/Profile/...      1      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bills = pd.read_excel('data.xlsx', sheet_name=\"Bill Filtering\")\n",
    "bills.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression using Rep features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Clean and label with H2836**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Party</th>\n",
       "      <th>Name</th>\n",
       "      <th>County</th>\n",
       "      <th>% Dem</th>\n",
       "      <th>% Rep</th>\n",
       "      <th>Weeks in Office</th>\n",
       "      <th>No. Enviro</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>James Arciero</td>\n",
       "      <td>Middlesex County</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>572</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Brian Ashe</td>\n",
       "      <td>Hampden County</td>\n",
       "      <td>0.550959</td>\n",
       "      <td>0.390720</td>\n",
       "      <td>572</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Bruce Ayers</td>\n",
       "      <td>Norfolk County</td>\n",
       "      <td>0.612467</td>\n",
       "      <td>0.333084</td>\n",
       "      <td>1092</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Ruth Balser</td>\n",
       "      <td>Middlesex County</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>1092</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Christine Barber</td>\n",
       "      <td>Middlesex County</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1     Party              Name            County     % Dem  \\\n",
       "0             0  Democrat     James Arciero  Middlesex County  0.663227   \n",
       "1             1  Democrat        Brian Ashe    Hampden County  0.550959   \n",
       "2             2  Democrat       Bruce Ayers    Norfolk County  0.612467   \n",
       "3             3  Democrat       Ruth Balser  Middlesex County  0.663227   \n",
       "4             4  Democrat  Christine Barber  Middlesex County  0.663227   \n",
       "\n",
       "      % Rep  Weeks in Office  No. Enviro  \n",
       "0  0.281705              572           5  \n",
       "1  0.390720              572           1  \n",
       "2  0.333084             1092          14  \n",
       "3  0.281705             1092           6  \n",
       "4  0.281705              288           0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('data.xlsx', sheet_name='Reps Profiling')\n",
    "df.drop(['Unnamed: 0', 'First Name', 'Last Name', 'District', 'Room', 'Phone Number', 'Email Address', 'Link'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Turn categorical labels into numbers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat      127\n",
       "Republican     31\n",
       "Unenrolled      1\n",
       "Name: Party, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Middlesex County     37\n",
       "Suffolk County       19\n",
       "Essex County         18\n",
       "Worcester County     18\n",
       "Norfolk County       15\n",
       "Bristol County       14\n",
       "Plymouth County      12\n",
       "Hampden County       11\n",
       "Barnstable County     5\n",
       "Berkshire County      4\n",
       "Hampshire County      3\n",
       "Franklin County       2\n",
       "Dukes County          1\n",
       "Name: County, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['County'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Party  County\n",
      "0        0       8\n",
      "1        0       6\n",
      "2        0       9\n",
      "3        0       8\n",
      "4        0       8\n",
      "..     ...     ...\n",
      "154      1       0\n",
      "155      1       5\n",
      "156      0       6\n",
      "157      1       4\n",
      "158      0      12\n",
      "\n",
      "[159 rows x 2 columns]\n"
     ]
    },
    {
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       "      <th>14</th>\n",
       "      <th>% Dem</th>\n",
       "      <th>% Rep</th>\n",
       "      <th>Weeks in Office</th>\n",
       "      <th>No. Enviro</th>\n",
       "      <th>Label</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.550959</td>\n",
       "      <td>0.390720</td>\n",
       "      <td>572</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.612467</td>\n",
       "      <td>0.333084</td>\n",
       "      <td>1092</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>1092</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.539938</td>\n",
       "      <td>0.405843</td>\n",
       "      <td>288</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.641509</td>\n",
       "      <td>0.271856</td>\n",
       "      <td>288</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.550959</td>\n",
       "      <td>0.390720</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.584960</td>\n",
       "      <td>0.359986</td>\n",
       "      <td>468</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>0.412466</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9   10   11   12   13  \\\n",
       "0    1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "1    1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2    1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "3    1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "4    1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "154  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "155  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "156  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       "157  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "158  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "      14     % Dem     % Rep  Weeks in Office  No. Enviro  Label  \n",
       "0    0.0  0.663227  0.281705              572           5      1  \n",
       "1    0.0  0.550959  0.390720              572           1      0  \n",
       "2    0.0  0.612467  0.333084             1092          14      1  \n",
       "3    0.0  0.663227  0.281705             1092           6      1  \n",
       "4    0.0  0.663227  0.281705              288           0      1  \n",
       "..   ...       ...       ...              ...         ...    ...  \n",
       "154  0.0  0.539938  0.405843              288           2      0  \n",
       "155  0.0  0.641509  0.271856              288           2      0  \n",
       "156  0.0  0.550959  0.390720              156           0      1  \n",
       "157  0.0  0.584960  0.359986              468           0      0  \n",
       "158  1.0  0.517429  0.412466              392           0      0  \n",
       "\n",
       "[159 rows x 20 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cleanDataset(df):\n",
    "    #Update wrong cell\n",
    "    df['Party'][155] = 'Republican'\n",
    "    \n",
    "    #Implement the One-Hot Encoding\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    num_df = df[[\"Party\", 'County']].apply(le.fit_transform)\n",
    "    print(num_df)\n",
    "    enc = preprocessing.OneHotEncoder()\n",
    "    enc.fit(num_df)\n",
    "    \n",
    "    onehotlabels = enc.transform(num_df).toarray()\n",
    "    clean_df = pd.DataFrame(onehotlabels)\n",
    "    clean_df[['% Dem', '% Rep', 'Weeks in Office', 'No. Enviro']] = df[['% Dem', '% Rep', 'Weeks in Office', 'No. Enviro']]\n",
    "    clean_df['Label'] = bills['H2836']\n",
    "\n",
    "    return clean_df\n",
    "\n",
    "clean = cleanDataset(df)\n",
    "clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(clean, test_size=0.4, random_state=3)\n",
    "y_labels_train = train[\"Label\"]\n",
    "X_class_train = train.drop(\"Label\", axis=1)\n",
    "y_labels_test = test[\"Label\"]\n",
    "X_class_test = test.drop(\"Label\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>14</th>\n",
       "      <th>% Dem</th>\n",
       "      <th>% Rep</th>\n",
       "      <th>Weeks in Office</th>\n",
       "      <th>No. Enviro</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.539938</td>\n",
       "      <td>0.405843</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.663227</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>572</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.662853</td>\n",
       "      <td>0.268302</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.550959</td>\n",
       "      <td>0.390720</td>\n",
       "      <td>1404</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.584960</td>\n",
       "      <td>0.359986</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9   10   11   12   13  \\\n",
       "45   1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "35   1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "134  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "50   1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "76   0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "21   0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "0    1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "131  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "152  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       "106  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "      14     % Dem     % Rep  Weeks in Office  No. Enviro  Label  \n",
       "45   0.0  0.729229  0.216372              156          13      1  \n",
       "35   0.0  0.663227  0.281705              988           0      1  \n",
       "134  0.0  0.518633  0.425797              468          16      0  \n",
       "50   0.0  0.612467  0.333084             1508           4      1  \n",
       "76   0.0  0.663227  0.281705             1300          10      0  \n",
       "..   ...       ...       ...              ...         ...    ...  \n",
       "21   0.0  0.539938  0.405843              156           0      0  \n",
       "0    0.0  0.663227  0.281705              572           5      1  \n",
       "131  0.0  0.662853  0.268302               80           0      1  \n",
       "152  0.0  0.550959  0.390720             1404           0      1  \n",
       "106  0.0  0.584960  0.359986              392           0      1  \n",
       "\n",
       "[103 rows x 20 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.fit(X_class_train, y_labels_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicts = log.predict(X_class_test)\n",
    "\n",
    "pred_prob = log.predict_proba(X_class_test)\n",
    "ones_prob = [x[1] for x in pred_prob]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_prob[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones_prob[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame({\"log\":predicts, \"prob_one\":ones_prob, \"actual\":y_labels_test})\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "y = clean[\"Label\"]\n",
    "X = clean.drop(\"Label\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_log = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvs = cross_val_score(new_log, X, y, cv=5)\n",
    "cvs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_test = len(results.index)\n",
    "len_train = len(X_class_train.index)\n",
    "num_correct = len(results[results['log']==results['actual']].index)\n",
    "num_correct/len_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Putting into Excel with all Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log = LogisticRegression()\n",
    "log.fit(X_class_train, y_labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicts = log.predict(X) # run on entire dataset to get all scores\n",
    "pred_prob = log.predict_proba(X)\n",
    "ones_prob = [x[1] for x in pred_prob]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones_prob[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame({\"log\":predicts, \"prob_one\":ones_prob, \"actual\":y})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(LogisticRegression(), X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(cross_val_score(LogisticRegression(), X, y, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_all = len(results.index)\n",
    "num_correct = len(results[results['log']==results['actual']].index)\n",
    "num_correct/len_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results['prob_one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reps = pd.read_excel('data.xlsx', sheet_name='Reps Profiling')\n",
    "reps['Score'] = pd.Series(results['prob_one'])\n",
    "reps['Label'] = bills['H2836']\n",
    "reps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('Final.xlsx') as writer:\n",
    "    reps.to_excel(writer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Into Json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_name = pd.read_excel(\"Final.xlsx\")\n",
    "by_score = pd.read_excel(\"Final.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_name.index = df['Name']\n",
    "by_score.index = df['Score']\n",
    "\n",
    "\n",
    "by_name.drop(['ID', 'Room', 'County','Weeks in Office'], axis=1, inplace=True)\n",
    "by_score.drop(['ID', 'Room', 'County','Weeks in Office'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jprint(obj):\n",
    "    text = json.dumps(obj, sort_keys=True, indent=4)\n",
    "    print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = by_name.to_dict(orient='index')\n",
    "jprint(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"byName.json\", \"w\") as write_file:\n",
    "    # dump takes in data, then place to put it\n",
    "    json.dump(name, write_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json # py library\n",
    "file = open('byName.json', 'r')\n",
    "dic = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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