Practice-Projec-3_G3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project No.3\n",
    "### Cameron Cumberland\n",
    "#### 2019 March"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Third part of project, final practice before the Great Leap into the Final project. The project was not finished, for which I apologize. I was able to attempt Logistic Regression, but unable to get it to work, and now we are onto our final. \n",
    "Thank you for the class! And here we go towards our final...\n",
    "</i>  /gulp/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GOAL: To place a given house into a Ward based on features such as price, sq.footage, and other factors.\n",
    "\n",
    "USING: Linear and Logistic Regression techniques learned in Lessons 7, 8, and 9. \n",
    "\n",
    "NOTE: Provided as-is by the author."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import Panda and Numpy and other libraries for use\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline \n",
    "#to view our charts inline, not a separate window\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression #as LogReg\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn import tree \n",
    "#for our secondary regression test, seeing if \n",
    "#tree or LogReg give vastly different results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>BATHRM</th>\n",
       "      <th>HF_BATHRM</th>\n",
       "      <th>HEAT</th>\n",
       "      <th>AC</th>\n",
       "      <th>NUM_UNITS</th>\n",
       "      <th>ROOMS</th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>AYB</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ASSESSMENT_NBHD</th>\n",
       "      <th>ASSESSMENT_SUBNBHD</th>\n",
       "      <th>CENSUS_TRACT</th>\n",
       "      <th>CENSUS_BLOCK</th>\n",
       "      <th>WARD</th>\n",
       "      <th>SQUARE</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>QUADRANT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Warm Cool</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040832</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>0152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Warm Cool</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040764</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>0152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Hot Water Rad</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040678</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>0152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Hot Water Rad</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040629</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>0152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Warm Cool</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.039361</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>0152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  BATHRM  HF_BATHRM           HEAT AC  NUM_UNITS  ROOMS  BEDRM  \\\n",
       "0           0       4          0      Warm Cool  Y        2.0      8      4   \n",
       "1           1       3          1      Warm Cool  Y        2.0     11      5   \n",
       "2           2       3          1  Hot Water Rad  Y        2.0      9      5   \n",
       "3           3       3          1  Hot Water Rad  Y        2.0      8      5   \n",
       "4           4       2          1      Warm Cool  Y        1.0     11      3   \n",
       "\n",
       "      AYB  YR_RMDL   ...     LONGITUDE  ASSESSMENT_NBHD ASSESSMENT_SUBNBHD  \\\n",
       "0  1910.0   1988.0   ...    -77.040832       Old City 2   040 D Old City 2   \n",
       "1  1898.0   2007.0   ...    -77.040764       Old City 2   040 D Old City 2   \n",
       "2  1910.0   2009.0   ...    -77.040678       Old City 2   040 D Old City 2   \n",
       "3  1900.0   2003.0   ...    -77.040629       Old City 2   040 D Old City 2   \n",
       "4  1913.0   2012.0   ...    -77.039361       Old City 2   040 D Old City 2   \n",
       "\n",
       "   CENSUS_TRACT CENSUS_BLOCK    WARD  SQUARE          X          Y QUADRANT  \n",
       "0        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "1        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "2        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "3        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "4        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining the location for data and attempting to read it with panda\n",
    "location = \"datasets/DC_Properties.csv\"\n",
    "dc_df = pd.read_csv(location, low_memory=False)\n",
    "dc_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158957, 49)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'BATHRM', 'HF_BATHRM', 'HEAT', 'AC', 'NUM_UNITS', 'ROOMS',\n",
       "       'BEDRM', 'AYB', 'YR_RMDL', 'EYB', 'STORIES', 'SALEDATE', 'PRICE',\n",
       "       'QUALIFIED', 'SALE_NUM', 'GBA', 'BLDG_NUM', 'STYLE', 'STRUCT', 'GRADE',\n",
       "       'CNDTN', 'EXTWALL', 'ROOF', 'INTWALL', 'KITCHENS', 'FIREPLACES',\n",
       "       'USECODE', 'LANDAREA', 'GIS_LAST_MOD_DTTM', 'SOURCE', 'CMPLX_NUM',\n",
       "       'LIVING_GBA', 'FULLADDRESS', 'CITY', 'STATE', 'ZIPCODE', 'NATIONALGRID',\n",
       "       'LATITUDE', 'LONGITUDE', 'ASSESSMENT_NBHD', 'ASSESSMENT_SUBNBHD',\n",
       "       'CENSUS_TRACT', 'CENSUS_BLOCK', 'WARD', 'SQUARE', 'X', 'Y', 'QUADRANT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>CNDTN</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1095000.0</td>\n",
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       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Good</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2100000.0</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1602000.0</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>13</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BEDRM      PRICE  ZIPCODE  USECODE    WARD  YR_RMDL      CNDTN\n",
       "0      4  1095000.0  20009.0       24  Ward 2   1988.0       Good\n",
       "1      5        NaN  20009.0       24  Ward 2   2007.0       Good\n",
       "2      5  2100000.0  20009.0       24  Ward 2   2009.0  Very Good\n",
       "3      5  1602000.0  20009.0       24  Ward 2   2003.0       Good\n",
       "4      3        NaN  20009.0       13  Ward 2   2012.0       Good"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a data frame with price-relevant data \n",
    "#(bedrooms as proxy for house size)\n",
    "\n",
    "\n",
    "test_df = pd.DataFrame(data=dc_df, columns=['BEDRM','PRICE','ZIPCODE','USECODE','WARD','YR_RMDL','CNDTN'])\n",
    "\n",
    "test_df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BEDRM          0\n",
       "PRICE      60741\n",
       "ZIPCODE        1\n",
       "USECODE        0\n",
       "WARD           1\n",
       "YR_RMDL    78029\n",
       "CNDTN      52261\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking for empty values, I will be replacing them\n",
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1    5.738363e+05\n",
       "Ward 2    1.098570e+06\n",
       "Ward 3    3.077716e+06\n",
       "Ward 4    4.899224e+05\n",
       "Ward 5    3.952432e+05\n",
       "Ward 6    5.268684e+05\n",
       "Ward 7    2.339222e+05\n",
       "Ward 8    2.715224e+05\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting ward by median price\n",
    "test_df['PRICE'].groupby(test_df['WARD']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>CNDTN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Good</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>13</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>11</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Fair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BEDRM  PRICE  ZIPCODE  USECODE    WARD  YR_RMDL    CNDTN\n",
       "1       5    NaN  20009.0       24  Ward 2   2007.0     Good\n",
       "4       3    NaN  20009.0       13  Ward 2   2012.0     Good\n",
       "6       2    NaN  20009.0       24  Ward 2   1988.0  Average\n",
       "9       2    NaN  20009.0       11  Ward 2   1979.0  Average\n",
       "10      5    NaN  20009.0       24  Ward 2   1950.0     Fair"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss_price = test_df.loc[test_df['PRICE'].isnull()]\n",
    "miss_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mispr = list(miss_price.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>CNDTN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1.098570e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.098570e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>13</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1.098570e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1.098570e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>11</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>1.098570e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Fair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BEDRM         PRICE  ZIPCODE  USECODE    WARD  YR_RMDL    CNDTN\n",
       "1       5  1.098570e+06  20009.0       24  Ward 2   2007.0     Good\n",
       "4       3  1.098570e+06  20009.0       13  Ward 2   2012.0     Good\n",
       "6       2  1.098570e+06  20009.0       24  Ward 2   1988.0  Average\n",
       "9       2  1.098570e+06  20009.0       11  Ward 2   1979.0  Average\n",
       "10      5  1.098570e+06  20009.0       24  Ward 2   1950.0     Fair"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making a list with replaced --mean--missing PRICE\n",
    "test_df['PRICE'].fillna(test_df.groupby(['WARD'])['PRICE'].transform('mean'), inplace=True)\n",
    "test_df.iloc[mispr].head()       \n",
    "#iloc to see if our replacement worked...and it DOES!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BEDRM          0\n",
       "PRICE          1\n",
       "ZIPCODE        1\n",
       "USECODE        0\n",
       "WARD           1\n",
       "YR_RMDL    78029\n",
       "CNDTN      52261\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isnull().sum()\n",
    "#one price is still NaN...maybe we can remove that one!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>CNDTN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104610</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BEDRM  PRICE  ZIPCODE  USECODE WARD  YR_RMDL    CNDTN\n",
       "104610      4    NaN      NaN       23  NaN      NaN  Average"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stillgone = test_df.loc[test_df['PRICE'].isnull()]\n",
    "stillgone.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convoluted method to get rid of stillgone! What an annoying row.\n",
    "test_df = test_df[np.isfinite(test_df['PRICE'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BEDRM          0\n",
       "PRICE          0\n",
       "ZIPCODE        0\n",
       "USECODE        0\n",
       "WARD           0\n",
       "YR_RMDL    78028\n",
       "CNDTN      52261\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>CNDTN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1.095000e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1.098570e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2.100000e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1.602000e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.098570e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>13</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BEDRM         PRICE  ZIPCODE  USECODE    WARD  YR_RMDL      CNDTN\n",
       "0      4  1.095000e+06  20009.0       24  Ward 2   1988.0       Good\n",
       "1      5  1.098570e+06  20009.0       24  Ward 2   2007.0       Good\n",
       "2      5  2.100000e+06  20009.0       24  Ward 2   2009.0  Very Good\n",
       "3      5  1.602000e+06  20009.0       24  Ward 2   2003.0       Good\n",
       "4      3  1.098570e+06  20009.0       13  Ward 2   2012.0       Good"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b2f6a62e8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b36a91a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#how many homes from which wards are present in the data\n",
    "#Ward 8 is the lowest, Ward 6 and 3 (the largest)\n",
    "sns.countplot(x='WARD',data=dc_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b30a55320>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hjsQ5O289p/X22JeWhDASZ62/1vOr4Sfpb953niUWY4wxjorsvGKJxRhjwk6ET95bYjHG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\nd1k5sWlH6sSkpdFY7qmT0Lc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kJtCnRxxFdW7O75fJj1dt9auzLL+UWQOy2FhWxYy+Gawu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\nMDgQC6gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b30a4fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#trying to make a new dataframe to look at WARD and PRICE more rigorously\n",
    "#here we go:\n",
    "\n",
    "wardpr = dc_df[['WARD','PRICE','YR_RMDL','BEDRM','LONGITUDE','LATITUDE']]\n",
    "\n",
    "warheat = wardpr.corr()\n",
    "\n",
    "sns.heatmap(warheat, vmin=-1, annot=True, linewidths=1.5, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>So, the data I tried running my regression on wasn't very successful. I may try to play around with it tonight (Thursday), but that depends on how my packing to move comes along (eep).\n",
    "</i>\n",
    "<p>It would appear that LogReg is not happy running a regression on PRICE that is not grouped into either a binary or into bins.\n",
    "I would have to fit the data into bins, and that takes more time finding stuff to do that...oof.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>CNDTN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Good</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2.100000e+06</td>\n",
       "      <td>20009.0</td>\n",
       "      <td>24</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
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       "      <td>Ward 2</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Good</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.098570e+06</td>\n",
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       "      <td>13</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BEDRM         PRICE  ZIPCODE  USECODE    WARD  YR_RMDL      CNDTN\n",
       "0      4  1.095000e+06  20009.0       24  Ward 2   1988.0       Good\n",
       "1      5  1.098570e+06  20009.0       24  Ward 2   2007.0       Good\n",
       "2      5  2.100000e+06  20009.0       24  Ward 2   2009.0  Very Good\n",
       "3      5  1.602000e+06  20009.0       24  Ward 2   2003.0       Good\n",
       "4      3  1.098570e+06  20009.0       13  Ward 2   2012.0       Good"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#just trying to remember what we are looking at! Less scrolling\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>BEDRM</th>\n",
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       "      <th>ZIPCODE</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>YR_RMDL</th>\n",
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       "      <th>CNDTN_Average</th>\n",
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       "      <th>CNDTN_Poor</th>\n",
       "      <th>CNDTN_Very Good</th>\n",
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      "text/plain": [
       "   BEDRM         PRICE  ZIPCODE  USECODE  YR_RMDL  WARD_Ward 1  WARD_Ward 2  \\\n",
       "0      4  1.095000e+06  20009.0       24   1988.0            0            1   \n",
       "1      5  1.098570e+06  20009.0       24   2007.0            0            1   \n",
       "2      5  2.100000e+06  20009.0       24   2009.0            0            1   \n",
       "3      5  1.602000e+06  20009.0       24   2003.0            0            1   \n",
       "4      3  1.098570e+06  20009.0       13   2012.0            0            1   \n",
       "\n",
       "   WARD_Ward 3  WARD_Ward 4  WARD_Ward 5  WARD_Ward 6  WARD_Ward 7  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   WARD_Ward 8  CNDTN_Average  CNDTN_Default  CNDTN_Excellent  CNDTN_Fair  \\\n",
       "0            0              0              0                0           0   \n",
       "1            0              0              0                0           0   \n",
       "2            0              0              0                0           0   \n",
       "3            0              0              0                0           0   \n",
       "4            0              0              0                0           0   \n",
       "\n",
       "   CNDTN_Good  CNDTN_Poor  CNDTN_Very Good  \n",
       "0           1           0                0  \n",
       "1           1           0                0  \n",
       "2           0           0                1  \n",
       "3           1           0                0  \n",
       "4           1           0                0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using dummies to 'numericalize' WARD and CNDTN data\n",
    "#this will allow our machine algorithm to make use of\n",
    "#the qualitative data in CNDTN and the geographical\n",
    "#data in WARD\n",
    "primo = pd.get_dummies(data=test_df, columns=['WARD','CNDTN'])\n",
    "primo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158956, 20)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#following steps from Module 8 to attempt Logistic Regression\n",
    "primo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#extracting target value\n",
    "#copying the target value column\n",
    "y = primo['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#copy of PRIMO without the 'PRICE' column\n",
    "X = primo.drop(['PRICE','ZIPCODE','USECODE','YR_RMDL'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#setting up the training...let's start with 75% training, 25% test\n",
    "#and hope this works\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=32)\n",
    "#seed values, test : outcome\n",
    "#324123524, .25 :\n",
    "#\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: 'continuous'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-bf464193cacf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#building LogReg model, already imported as\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mLogReg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mLogReg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1215\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype,\n\u001b[0;32m   1216\u001b[0m                          order=\"C\")\n\u001b[1;32m-> 1217\u001b[1;33m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1218\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1219\u001b[0m         \u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m    170\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[0;32m    171\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[1;32m--> 172\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Unknown label type: %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Unknown label type: 'continuous'"
     ]
    }
   ],
   "source": [
    "#building LogReg model, already imported as \n",
    "LogReg = LogisticRegression()\n",
    "LogReg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy check\n",
    "LogReg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictive value\n",
    "y_pred = LogReg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making our confusion matrix, here we go\n",
    "cm = pd.DataFrame(\n",
    "    confusion_matric(y_test, y_pred),\n",
    "    columns=['Predicted Price','NP Price'],\n",
    "    index=['True Price','True NP Price']\n",
    ")\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LogReg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Ward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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