walkthrough_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the notebook by importing necessary modules\n",
    "import acquire\n",
    "import prepare\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, explained_variance_score\n",
    "from scipy import stats\n",
    "from math import sqrt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Acquire "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28073, 11)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= acquire.get_zillow_data()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28073 entries, 0 to 28072\n",
      "Data columns (total 11 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Unnamed: 0                    28073 non-null  int64  \n",
      " 1   parcelid                      28073 non-null  int64  \n",
      " 2   fips                          28073 non-null  float64\n",
      " 3   latitude                      28073 non-null  float64\n",
      " 4   longitude                     28073 non-null  float64\n",
      " 5   lotsizesquarefeet             27876 non-null  float64\n",
      " 6   calculatedfinishedsquarefeet  28070 non-null  float64\n",
      " 7   bedroomcnt                    28073 non-null  float64\n",
      " 8   bathroomcnt                   28073 non-null  float64\n",
      " 9   taxamount                     28072 non-null  float64\n",
      " 10  taxvaluedollarcnt             28072 non-null  float64\n",
      "dtypes: float64(9), int64(2)\n",
      "memory usage: 2.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>parcelid</th>\n",
       "      <th>fips</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lotsizesquarefeet</th>\n",
       "      <th>calculatedfinishedsquarefeet</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28073.00000</td>\n",
       "      <td>2.807300e+04</td>\n",
       "      <td>28073.000000</td>\n",
       "      <td>2.807300e+04</td>\n",
       "      <td>2.807300e+04</td>\n",
       "      <td>2.787600e+04</td>\n",
       "      <td>28070.000000</td>\n",
       "      <td>28073.000000</td>\n",
       "      <td>28073.000000</td>\n",
       "      <td>28072.000000</td>\n",
       "      <td>2.807200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14036.00000</td>\n",
       "      <td>1.303448e+07</td>\n",
       "      <td>6049.496349</td>\n",
       "      <td>3.402023e+07</td>\n",
       "      <td>-1.181959e+08</td>\n",
       "      <td>1.110263e+04</td>\n",
       "      <td>1935.358283</td>\n",
       "      <td>3.317814</td>\n",
       "      <td>2.322196</td>\n",
       "      <td>6487.964122</td>\n",
       "      <td>5.332710e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8104.12139</td>\n",
       "      <td>3.634770e+06</td>\n",
       "      <td>21.285999</td>\n",
       "      <td>2.750596e+05</td>\n",
       "      <td>3.603042e+05</td>\n",
       "      <td>6.937799e+04</td>\n",
       "      <td>995.141621</td>\n",
       "      <td>0.928716</td>\n",
       "      <td>1.011392</td>\n",
       "      <td>8166.165051</td>\n",
       "      <td>7.036372e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.071186e+07</td>\n",
       "      <td>6037.000000</td>\n",
       "      <td>3.334085e+07</td>\n",
       "      <td>-1.194484e+08</td>\n",
       "      <td>2.360000e+02</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.260000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7018.00000</td>\n",
       "      <td>1.150992e+07</td>\n",
       "      <td>6037.000000</td>\n",
       "      <td>3.382229e+07</td>\n",
       "      <td>-1.184054e+08</td>\n",
       "      <td>5.591000e+03</td>\n",
       "      <td>1279.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2705.097500</td>\n",
       "      <td>1.986365e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14036.00000</td>\n",
       "      <td>1.259418e+07</td>\n",
       "      <td>6037.000000</td>\n",
       "      <td>3.402298e+07</td>\n",
       "      <td>-1.181536e+08</td>\n",
       "      <td>6.851000e+03</td>\n",
       "      <td>1675.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4734.315000</td>\n",
       "      <td>3.822645e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21054.00000</td>\n",
       "      <td>1.414833e+07</td>\n",
       "      <td>6059.000000</td>\n",
       "      <td>3.418804e+07</td>\n",
       "      <td>-1.179270e+08</td>\n",
       "      <td>8.800000e+03</td>\n",
       "      <td>2326.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7516.247500</td>\n",
       "      <td>6.316750e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28072.00000</td>\n",
       "      <td>1.676392e+08</td>\n",
       "      <td>6111.000000</td>\n",
       "      <td>3.481877e+07</td>\n",
       "      <td>-1.175554e+08</td>\n",
       "      <td>6.971010e+06</td>\n",
       "      <td>17245.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>276797.830000</td>\n",
       "      <td>2.385837e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0      parcelid          fips      latitude     longitude  \\\n",
       "count  28073.00000  2.807300e+04  28073.000000  2.807300e+04  2.807300e+04   \n",
       "mean   14036.00000  1.303448e+07   6049.496349  3.402023e+07 -1.181959e+08   \n",
       "std     8104.12139  3.634770e+06     21.285999  2.750596e+05  3.603042e+05   \n",
       "min        0.00000  1.071186e+07   6037.000000  3.334085e+07 -1.194484e+08   \n",
       "25%     7018.00000  1.150992e+07   6037.000000  3.382229e+07 -1.184054e+08   \n",
       "50%    14036.00000  1.259418e+07   6037.000000  3.402298e+07 -1.181536e+08   \n",
       "75%    21054.00000  1.414833e+07   6059.000000  3.418804e+07 -1.179270e+08   \n",
       "max    28072.00000  1.676392e+08   6111.000000  3.481877e+07 -1.175554e+08   \n",
       "\n",
       "       lotsizesquarefeet  calculatedfinishedsquarefeet    bedroomcnt  \\\n",
       "count       2.787600e+04                  28070.000000  28073.000000   \n",
       "mean        1.110263e+04                   1935.358283      3.317814   \n",
       "std         6.937799e+04                    995.141621      0.928716   \n",
       "min         2.360000e+02                    300.000000      1.000000   \n",
       "25%         5.591000e+03                   1279.000000      3.000000   \n",
       "50%         6.851000e+03                   1675.000000      3.000000   \n",
       "75%         8.800000e+03                   2326.000000      4.000000   \n",
       "max         6.971010e+06                  17245.000000     12.000000   \n",
       "\n",
       "        bathroomcnt      taxamount  taxvaluedollarcnt  \n",
       "count  28073.000000   28072.000000       2.807200e+04  \n",
       "mean       2.322196    6487.964122       5.332710e+05  \n",
       "std        1.011392    8166.165051       7.036372e+05  \n",
       "min        1.000000      51.260000       1.000000e+03  \n",
       "25%        2.000000    2705.097500       1.986365e+05  \n",
       "50%        2.000000    4734.315000       3.822645e+05  \n",
       "75%        3.000000    7516.247500       6.316750e+05  \n",
       "max       11.000000  276797.830000       2.385837e+07  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Originally I got 77614 rows and 10 columns. I went back and edited my sql query to narrow features more closely.\n",
    "After editing my query to have bedrooms/bathrooms > 0, I got 28703 rows and 9 columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make reading the FIPS easier, I assigned the county names to each FIPS code and then appended the results onto the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>parcelid</th>\n",
       "      <th>fips</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lotsizesquarefeet</th>\n",
       "      <th>calculatedfinishedsquarefeet</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>11721753</td>\n",
       "      <td>6037.0</td>\n",
       "      <td>34033180.0</td>\n",
       "      <td>-118355396.0</td>\n",
       "      <td>5672.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2627.48</td>\n",
       "      <td>205123.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  parcelid    fips    latitude    longitude  lotsizesquarefeet  \\\n",
       "0           0  11721753  6037.0  34033180.0 -118355396.0             5672.0   \n",
       "\n",
       "   calculatedfinishedsquarefeet  bedroomcnt  bathroomcnt  taxamount  \\\n",
       "0                        1316.0         3.0          2.0    2627.48   \n",
       "\n",
       "   taxvaluedollarcnt              county  \n",
       "0           205123.0  los_angeles_county  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_name = []\n",
    "for row in df['fips']:\n",
    "        if row == 6037.0:    \n",
    "            county_name.append('los_angeles_county')\n",
    "        elif row == 6059.0:   \n",
    "            county_name.append('orange_county')\n",
    "        elif row == 6111.0:  \n",
    "            county_name.append('ventura_county')\n",
    "        else:           \n",
    "            county_name.append('no_county')\n",
    "df['county']= county_name\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My first iteration of the dataframe had lots of nans/nulls. I handled them originally by filling them with the mode (most commonly occuring value) for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bedroomcnt'].fillna(df['bedroomcnt'].mode()[0], inplace=True)\n",
    "df['bathroomcnt'].fillna(df['bathroomcnt'].mode()[0], inplace=True)\n",
    "df['calculatedfinishedsquarefeet'].fillna(df['calculatedfinishedsquarefeet'].mode()[0], inplace=True)\n",
    "df['fips'].fillna(df['fips'].mode()[0], inplace=True)\n",
    "df['taxamount'].fillna(df['taxamount'].mode()[0], inplace=True)\n",
    "df['taxvaluedollarcnt'].fillna(df['taxvaluedollarcnt'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of Tax Value')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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kSZLUv/iP3ZIkSZIkSZIkSWo7t2eUJEmSJEmSJElS65k0kyRJkiRJkiRJUuuZNJMkSZIkSZIkSVLrmTSTJEmSJEmSJElS65k0kyRJkiRJkiRJUuuZNJMkSZIkSZIkSVLrmTSTJEmSJEmSJElS65k0kyRJkiRJkiRJUuv9B0zLqClGGbQyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x1440 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 20))\n",
    "\n",
    "plt.suptitle(\"Plotting the distributions of the numerical columns\")\n",
    "# row, columns, index\n",
    "plt.subplot(3, 3, 1)\n",
    "plt.hist(df.parcelid, align='left', edgecolor='darkblue', alpha=0.5, color='palevioletred')\n",
    "plt.title('Parcel ID')\n",
    "plt.xlabel(\"Parcel ID\")\n",
    "plt.ylabel(\"Count of Parcel ID\")\n",
    "\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "plt.hist(df.bedroomcnt, align='left', edgecolor='darkblue', alpha=0.5, color='palevioletred')\n",
    "plt.title('Bedroom Count')\n",
    "plt.xlabel(\"Number of Bedrooms\")\n",
    "plt.ylabel(\"Count of Number of Bedrooms\")\n",
    "\n",
    "\n",
    "plt.subplot(3, 3, 3)\n",
    "plt.hist(df.bathroomcnt, align='left', edgecolor='darkblue', alpha=0.5, color='palevioletred')\n",
    "plt.title('Bathroom Count')\n",
    "plt.xlabel(\"Number of Bathrooms\")\n",
    "plt.ylabel(\"Count of number of bathrooms\")\n",
    "\n",
    "\n",
    "plt.subplot(3, 3, 4)\n",
    "plt.hist(df.calculatedfinishedsquarefeet, align='left', edgecolor='darkblue', alpha=0.5, color='palevioletred')\n",
    "plt.title('Square Footage')\n",
    "plt.xlabel(\"Square Footage\")\n",
    "plt.ylabel(\"Count of Square Footage\")\n",
    "\n",
    "\n",
    "plt.subplot(3, 3, 5)\n",
    "plt.hist(df.fips, align='left', edgecolor='darkblue', alpha=0.5, color='palevioletred')\n",
    "plt.title('Federal Information Processing System (FIPS)')\n",
    "plt.xlabel(\"FIPS\")\n",
    "plt.ylabel(\"Count of Clients in each Tenure\")\n",
    "\n",
    "\n",
    "plt.subplot(3, 3, 6)\n",
    "plt.hist(df.taxamount, align='left', edgecolor='darkblue', alpha=0.5, color='palevioletred')\n",
    "plt.title('Tax Amount')\n",
    "plt.xlabel(\"Tax Amount\")\n",
    "plt.ylabel(\"Count of Tax Amount\")\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.hist(df.taxvaluedollarcnt, align='left', edgecolor='darkblue', alpha=0.5, color='palevioletred')\n",
    "plt.title('Tax Value')\n",
    "plt.xlabel(\"Tax Value\")\n",
    "plt.ylabel(\"Count of Tax Value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the prepare.py created, the following functions are defined:\n",
    "<ul>\n",
    "    <li> clean_zillow- Takes in the full dataframe from acquire.py, handles nans, renames columns and appends necessary data.\n",
    "    <li> prep_zillow_data- Takes in the clean zillow data and splits into train, validate, test.\n",
    "    <li> prep_split_zillow_data- This function takes in the clean data, drops the tax amount for modeling purposes and then splits the data into X/Y Train, Validate/Test.\n",
    "    <li> remove_outliers_zillow- This function takes in the cleaned zillow data and handles outliers as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28073 entries, 0 to 28072\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   fips            28073 non-null  float64\n",
      " 1   lot_size        28073 non-null  float64\n",
      " 2   square_feet     28073 non-null  float64\n",
      " 3   bedroom_count   28073 non-null  float64\n",
      " 4   bathroom_count  28073 non-null  float64\n",
      " 5   tax_amount      28073 non-null  float64\n",
      " 6   tax_value       28073 non-null  float64\n",
      " 7   county          28073 non-null  object \n",
      "dtypes: float64(7), object(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df=prepare.clean_zillow()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>fips</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>bedroom_count</th>\n",
       "      <th>bathroom_count</th>\n",
       "      <th>tax_amount</th>\n",
       "      <th>tax_value</th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16223</th>\n",
       "      <td>6037.0</td>\n",
       "      <td>5270.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1055.89</td>\n",
       "      <td>54624.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>6111.0</td>\n",
       "      <td>5548.0</td>\n",
       "      <td>2115.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7213.02</td>\n",
       "      <td>638000.0</td>\n",
       "      <td>ventura_county</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>6037.0</td>\n",
       "      <td>8559.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1536.38</td>\n",
       "      <td>89105.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27470</th>\n",
       "      <td>6037.0</td>\n",
       "      <td>7009.0</td>\n",
       "      <td>1697.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4506.43</td>\n",
       "      <td>219000.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13093</th>\n",
       "      <td>6111.0</td>\n",
       "      <td>4446.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9707.20</td>\n",
       "      <td>779236.0</td>\n",
       "      <td>ventura_county</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fips  lot_size  square_feet  bedroom_count  bathroom_count  \\\n",
       "16223  6037.0    5270.0       1192.0            3.0             2.0   \n",
       "428    6111.0    5548.0       2115.0            3.0             3.0   \n",
       "453    6037.0    8559.0       1790.0            3.0             2.0   \n",
       "27470  6037.0    7009.0       1697.0            3.0             2.0   \n",
       "13093  6111.0    4446.0       2041.0            3.0             2.5   \n",
       "\n",
       "       tax_amount  tax_value              county  \n",
       "16223     1055.89    54624.0  los_angeles_county  \n",
       "428       7213.02   638000.0      ventura_county  \n",
       "453       1536.38    89105.0  los_angeles_county  \n",
       "27470     4506.43   219000.0  los_angeles_county  \n",
       "13093     9707.20   779236.0      ventura_county  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1019)\n",
    "train, validate, test = prepare.prep_zillow_data()\n",
    "train.shape, validate.shape, test.shape\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Testing:\n",
    "### 1st Hypothesis Test:\n",
    "$H_0$: Homes have the same mean tax value in each county.\n",
    "\n",
    "$H_a$: Homes in Los Angeles have a higher mean tax value than in Ventura or Orange Counties.\n",
    "\n",
    "alpha ($\\alpha$): 1 - confidence level (95% confidence level -> $\\alpha = .05$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = train.shape[0]     # number of observations\n",
    "degf = n - 2        # degrees of freedom: the # of values in the final calculation of a statistic that are free to vary.\n",
    "conf_interval = .95 # desired confidence interval\n",
    "α = 1 - conf_interval\n",
    "null_hypothesis = \"homes have the same mean tax value in each county.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = train[train.county == 'los_angeles_county'].tax_value\n",
    "x2 = train[train.county != 'los_angeles_county'].tax_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t, p= stats.ttest_ind(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject the null hypothesis that homes have the same mean tax value in each county.\n"
     ]
    }
   ],
   "source": [
    "if p > α:\n",
    "    print(\"We fail to reject the null hypothesis. The null hypothesis is that\", null_hypothesis)\n",
    "else:\n",
    "    print(\"We reject the null hypothesis that\", null_hypothesis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2nd Hypothesis Test:\n",
    "𝐻0 : Number of bathrooms have no correlation with tax value.\n",
    "\n",
    "𝐻𝑎 : Homes with more bathrooms are correlated with higher tax values.\n",
    "\n",
    "alpha ( 𝛼 ): 1 - confidence level (95% confidence level ->  𝛼=.05 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = train.shape[0]     # number of observations\n",
    "degf = n - 2        # degrees of freedom: the # of values in the final calculation of a statistic that are free to vary.\n",
    "conf_interval = .95 # desired confidence interval\n",
    "α = 1 - conf_interval\n",
    "null_hypothesis='number of bathrooms have no correlation with tax value.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train.bathroom_count\n",
    "y = train.tax_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.514021012939429, 0.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr, p = stats.pearsonr(x, y)\n",
    "corr, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject the null hypothesis that number of bathrooms have no correlation with tax value.\n"
     ]
    }
   ],
   "source": [
    "if p >  α:\n",
    "    print(\"We fail to reject the null hypothesis. The null hypothesis is that\", null_hypothesis)\n",
    "else:\n",
    "    print(\"We reject the null hypothesis that\", null_hypothesis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3rd Hypothesis Test:\n",
    "𝐻0 : Number of bedrooms have no correlation with tax value.\n",
    "\n",
    "𝐻𝑎 : Homes with more bedrooms are correlated with higher tax values.\n",
    "\n",
    "alpha ( 𝛼 ): 1 - confidence level (95% confidence level ->  𝛼=.05 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = df.shape[0]     # number of observations\n",
    "degf = n - 2        # degrees of freedom: the # of values in the final calculation of a statistic that are free to vary.\n",
    "conf_interval = .95 # desired confidence interval\n",
    "α = 1 - conf_interval\n",
    "null_hypothesis='number of bedrooms have no correlation with tax value.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = df.bedroom_count\n",
    "y2 = df.tax_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2807020979909201, 0.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr, p = stats.pearsonr(x2, y2)\n",
    "corr, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject the null hypothesis that number of bedrooms have no correlation with tax value.\n"
     ]
    }
   ],
   "source": [
    "if p >  α:\n",
    "    print(\"We fail to reject the null hypothesis. The null hypothesis is that\", null_hypothesis)\n",
    "else:\n",
    "    print(\"We reject the null hypothesis that\", null_hypothesis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4th Hypothesis Test:\n",
    "𝐻0 : Square footage has no correlation with tax value.\n",
    "\n",
    "𝐻𝑎 : Homes with a higher square footage are correlated with higher tax values.\n",
    "\n",
    "alpha ( 𝛼 ): 1 - confidence level (95% confidence level ->  𝛼=.05 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = df.shape[0]     # number of observations\n",
    "degf = n - 2        # degrees of freedom: the # of values in the final calculation of a statistic that are free to vary.\n",
    "conf_interval = .95 # desired confidence interval\n",
    "α = 1 - conf_interval\n",
    "null_hypothesis=\"square footage has no correlation with tax value.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = df.square_feet\n",
    "y3 = df.tax_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.611763274652983, 0.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr, p = stats.pearsonr(x3, y3)\n",
    "corr, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fail to reject the null hypothesis. The null hypothesis is that square footage has no correlation with tax value.\n"
     ]
    }
   ],
   "source": [
    "if p >  α:\n",
    "    print(\"We fail to reject the null hypothesis. The null hypothesis is that\", null_hypothesis)\n",
    "elif t < 0 :\n",
    "    print(\"We fail to reject the null hypothesis. The null hypothesis is that\", null_hypothesis)\n",
    "else:\n",
    "    print(\"We reject the null hypothesis that\", null_hypothesis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "#evaluating tax value assessment to number of bedrooms\n",
    "sns.jointplot( \"bathroom_count\", \"tax_value\",data=train, kind='reg', height=10, color= 'ForestGreen');\n",
    "plt.xlabel('Number of Bedrooms')\n",
    "plt.ylabel('Estimated Property Value by Tax Assessment (in hundred thousands)')\n",
    "plt.title ('Number of Bedrooms and Estimated Property Value by Tax Assessment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Estimated Property Value by Tax Assessment \\n (in hundred thousands)')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 20))\n",
    "#evaluating tax value assessment to number of bedrooms\n",
    "sns.regplot(data= train, x='bedroom_count', y= 'tax_value')\n",
    "plt.title(\"Correlation between Number of Bedrooms and Estimated Tax Property Value\")\n",
    "plt.xlabel('Number of Bedrooms')\n",
    "plt.ylabel('Estimated Property Value by Tax Assessment \\n (in hundred thousands)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(),vmin=-1, vmax=1, annot=True)\n",
    "plt.title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression, LassoLars, TweedieRegressor\n",
    "import sklearn.preprocessing\n",
    "from sklearn.feature_selection import SelectKBest, f_regression\n",
    "from sklearn.metrics import explained_variance_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((15720, 5), (15720, 1))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_validate, X_test, y_train, y_validate, y_test = prepare.prep_split_zillow_data()\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler, X_train_scaled, X_validate_scaled, X_test_scaled= prepare.Robust_Scaler(X_train, X_validate, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select K Best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_kbest(x, y, k):\n",
    "    \n",
    "    # parameters: f_regression stats test, give me k features\n",
    "    f_selector = SelectKBest(f_regression, k=k)\n",
    "    \n",
    "    # find the top k X's correlated with y\n",
    "    f_selector.fit(X_train_scaled, y_train)\n",
    "    \n",
    "    # boolean mask of whether the column was selected or not. \n",
    "    feature_mask = f_selector.get_support()\n",
    "    \n",
    "    f_feature = X_train_scaled.iloc[:,feature_mask].columns.tolist()\n",
    "    \n",
    "    return print(f'The two best predictors, according to k best are: {f_feature}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The two best predictors, according to k best are: ['square_feet', 'bedroom_count', 'bathroom_count'].\n"
     ]
    }
   ],
   "source": [
    "select_kbest(X_train_scaled, 'tax_value', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the ML algorithm\n",
    "lm = LinearRegression()\n",
    "\n",
    "# create the rfe object, indicating the ML object (lm) and the number of features I want to end up with. \n",
    "rfe = RFE(lm, 3)\n",
    "\n",
    "# fit the data using RFE\n",
    "rfe.fit(X_train_scaled,y_train)  \n",
    "\n",
    "# get the mask of the columns selected\n",
    "feature_mask = rfe.support_\n",
    "\n",
    "# get list of the column names. \n",
    "rfe_feature = X_train_scaled.iloc[:,feature_mask].columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['square_feet', 'bedroom_count', 'bathroom_count']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((15720, 3), (6738, 3), (5615, 3), (15720, 1), (6738, 1), (5615, 1))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2, X_validate2, X_test2, y_train2, y_validate2, y_test2= prepare.model_zillow()\n",
    "X_train2.shape, X_validate2.shape, X_test2.shape, y_train2.shape, y_validate2.shape, y_test2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>square_feet</th>\n",
       "      <th>bedroom_count</th>\n",
       "      <th>bathroom_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16223</th>\n",
       "      <td>1192.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2115.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27470</th>\n",
       "      <td>1697.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13093</th>\n",
       "      <td>2041.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       square_feet  bedroom_count  bathroom_count\n",
       "16223       1192.0            3.0             2.0\n",
       "428         2115.0            3.0             3.0\n",
       "453         1790.0            3.0             2.0\n",
       "27470       1697.0            3.0             2.0\n",
       "13093       2041.0            3.0             2.5"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>tax_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16223</th>\n",
       "      <td>54624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>638000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>89105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27470</th>\n",
       "      <td>219000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13093</th>\n",
       "      <td>779236.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tax_value\n",
       "16223    54624.0\n",
       "428     638000.0\n",
       "453      89105.0\n",
       "27470   219000.0\n",
       "13093   779236.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       15720.000000\n",
       "mean       531681.202990\n",
       "std        695379.524373\n",
       "min         10504.000000\n",
       "25%        198997.000000\n",
       "50%        382798.500000\n",
       "75%        632048.250000\n",
       "max      23858374.000000\n",
       "Name: tax_value, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tax_value.describe().apply(lambda x: format(x, 'f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(pandas.core.frame.DataFrame, pandas.core.frame.DataFrame)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_train2), type(y_validate2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE using Mean\n",
      "Train/In-Sample:  695357.41 \n",
      "Validate/Out-of-Sample:  764590.6\n",
      "RMSE using Median\n",
      "Train/In-Sample:  711117.42 \n",
      "Validate/Out-of-Sample:  780705.18\n"
     ]
    }
   ],
   "source": [
    "# 1. Predict tax_value_mean\n",
    "tax_pred_mean = y_train2.tax_value.mean()\n",
    "y_train2['tax_pred_mean'] = tax_pred_mean\n",
    "y_validate2['tax_pred_mean'] = tax_pred_mean\n",
    "\n",
    "# 2. compute tax_pred_median\n",
    "tax_pred_median = y_train2.tax_value.median()\n",
    "y_train2['tax_pred_median'] = tax_pred_median\n",
    "y_validate2['tax_pred_median'] = tax_pred_median\n",
    "\n",
    "# 3. RMSE of tax_pred_mean\n",
    "rmse_train = mean_squared_error(y_train2.tax_value, y_train2.tax_pred_mean) ** .5\n",
    "rmse_validate = mean_squared_error(y_validate2.tax_value, y_validate2.tax_pred_mean) ** (1/2)\n",
    "\n",
    "print(\"RMSE using Mean\\nTrain/In-Sample: \", round(rmse_train, 2), \n",
    "      \"\\nValidate/Out-of-Sample: \", round(rmse_validate, 2))\n",
    "\n",
    "# 4. RMSE of tax_pred_median\n",
    "rmse_train = mean_squared_error(y_train2.tax_value, y_train2.tax_pred_median) ** .5\n",
    "rmse_validate = mean_squared_error(y_validate2.tax_value, y_validate2.tax_pred_median) ** .5\n",
    "print(\"RMSE using Median\\nTrain/In-Sample: \", round(rmse_train, 2), \n",
    "      \"\\nValidate/Out-of-Sample: \", round(rmse_validate, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric_df = pd.DataFrame(data=[{\n",
    "    'model': 'mean_baseline', \n",
    "    'RMSE_validate': rmse_validate,\n",
    "    'r^2_validate': explained_variance_score(y_validate2.tax_value, y_validate2.tax_pred_mean)}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>model</th>\n",
       "      <th>RMSE_validate</th>\n",
       "      <th>r^2_validate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean_baseline</td>\n",
       "      <td>780705.183041</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  RMSE_validate  r^2_validate\n",
       "0  mean_baseline  780705.183041  2.220446e-16"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for OLS using LinearRegression\n",
      "Training/In-Sample:  550049.853919652 \n",
      "Validation/Out-of-Sample:  583697.2826204126\n"
     ]
    }
   ],
   "source": [
    "# create the model object\n",
    "# \n",
    "# make the thing\n",
    "# \n",
    "lm = LinearRegression(normalize=True)\n",
    "\n",
    "# fit the model to our training data. We must specify the column in y_train, \n",
    "# since we have converted it to a dataframe from a series! \n",
    "# \n",
    "# fit the thing\n",
    "# \n",
    "lm.fit(X_train2, y_train2.tax_value)\n",
    "\n",
    "# predict train\n",
    "# \n",
    "# use the thing!\n",
    "# \n",
    "y_train2['tax_pred_lm'] = lm.predict(X_train2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_train = mean_squared_error(y_train2.tax_value, y_train2.tax_pred_lm) ** (1/2)\n",
    "\n",
    "# predict validate\n",
    "y_validate2['tax_pred_lm'] = lm.predict(X_validate2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_validate = mean_squared_error(y_validate2.tax_value, y_validate2.tax_pred_lm) ** (1/2)\n",
    "\n",
    "print(\"RMSE for OLS using LinearRegression\\nTraining/In-Sample: \", rmse_train, \n",
    "      \"\\nValidation/Out-of-Sample: \", rmse_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>model</th>\n",
       "      <th>RMSE_validate</th>\n",
       "      <th>r^2_validate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean_baseline</td>\n",
       "      <td>780705.183041</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OLS Regressor</td>\n",
       "      <td>583697.282620</td>\n",
       "      <td>4.173118e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  RMSE_validate  r^2_validate\n",
       "0  mean_baseline  780705.183041  2.220446e-16\n",
       "1  OLS Regressor  583697.282620  4.173118e-01"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df = metric_df.append({\n",
    "    'model': 'OLS Regressor', \n",
    "    'RMSE_validate': rmse_validate,\n",
    "    'r^2_validate': explained_variance_score(y_validate2.tax_value, y_validate2.tax_pred_lm)}, ignore_index=True)\n",
    "metric_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lasso Lars Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Lasso + Lars\n",
      "Training/In-Sample:  550049.9406948847 \n",
      "Validation/Out-of-Sample:  583716.5827724609\n"
     ]
    }
   ],
   "source": [
    "# create the model object\n",
    "lars = LassoLars(alpha=1)\n",
    "\n",
    "# fit the model to our training data. We must specify the column in y_train, \n",
    "# since we have converted it to a dataframe from a series!\n",
    "lars.fit(X_train2, y_train2.tax_value)\n",
    "\n",
    "# predict train\n",
    "y_train2['tax_pred_lars'] = lars.predict(X_train2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_train = mean_squared_error(y_train2.tax_value, y_train2.tax_pred_lars) ** (1/2)\n",
    "\n",
    "# predict validate\n",
    "y_validate2['tax_pred_lars'] = lars.predict(X_validate2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_validate = mean_squared_error(y_validate2.tax_value, y_validate2.tax_pred_lars) ** (1/2)\n",
    "\n",
    "print(\"RMSE for Lasso + Lars\\nTraining/In-Sample: \", rmse_train, \n",
    "      \"\\nValidation/Out-of-Sample: \", rmse_validate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>model</th>\n",
       "      <th>RMSE_validate</th>\n",
       "      <th>r^2_validate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean_baseline</td>\n",
       "      <td>780705.183041</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OLS Regressor</td>\n",
       "      <td>583697.282620</td>\n",
       "      <td>4.173118e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso Lars</td>\n",
       "      <td>583716.582772</td>\n",
       "      <td>4.172732e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  RMSE_validate  r^2_validate\n",
       "0  mean_baseline  780705.183041  2.220446e-16\n",
       "1  OLS Regressor  583697.282620  4.173118e-01\n",
       "2     Lasso Lars  583716.582772  4.172732e-01"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df = metric_df.append({\n",
    "    'model': 'Lasso Lars', \n",
    "    'RMSE_validate': rmse_validate,\n",
    "    'r^2_validate': explained_variance_score(y_validate2.tax_value, y_validate2.tax_pred_lars)}, ignore_index=True)\n",
    "metric_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tweedie Regressor (GLM) Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for GLM using Tweedie, power=1 & alpha=0\n",
      "Training/In-Sample:  695357.406352167 \n",
      "Validation/Out-of-Sample:  695357.406352167\n"
     ]
    }
   ],
   "source": [
    "# create the model object\n",
    "glm = TweedieRegressor(power=1, alpha=0)\n",
    "\n",
    "\n",
    "# fit the model to our training data. We must specify the column in y_train, \n",
    "# since we have converted it to a dataframe from a series! \n",
    "glm.fit(X_train2, y_train2.tax_value)\n",
    "\n",
    "# predict train\n",
    "y_train2['tax_pred_glm'] = glm.predict(X_train2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_train = mean_squared_error(y_train2.tax_value, y_train2.tax_pred_glm) ** (1/2)\n",
    "\n",
    "# predict validate\n",
    "y_validate2['tax_pred_glm'] = glm.predict(X_validate2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_validate = mean_squared_error(y_train2.tax_value, y_train2.tax_pred_glm) ** (1/2)\n",
    "\n",
    "print(\"RMSE for GLM using Tweedie, power=1 & alpha=0\\nTraining/In-Sample: \", rmse_train, \n",
    "      \"\\nValidation/Out-of-Sample: \", rmse_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric_df = metric_df.append({\n",
    "    'model': 'GLM (Poisson)', \n",
    "    'RMSE_validate': rmse_validate,\n",
    "    'r^2_validate': explained_variance_score(y_validate2.tax_value, y_validate2.tax_pred_glm)}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>model</th>\n",
       "      <th>RMSE_validate</th>\n",
       "      <th>r^2_validate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean_baseline</td>\n",
       "      <td>780705.183041</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OLS Regressor</td>\n",
       "      <td>583697.282620</td>\n",
       "      <td>4.173118e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso Lars</td>\n",
       "      <td>583716.582772</td>\n",
       "      <td>4.172732e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GLM (Poisson)</td>\n",
       "      <td>695357.406352</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  RMSE_validate  r^2_validate\n",
       "0  mean_baseline  780705.183041  2.220446e-16\n",
       "1  OLS Regressor  583697.282620  4.173118e-01\n",
       "2     Lasso Lars  583716.582772  4.172732e-01\n",
       "3  GLM (Poisson)  695357.406352  2.220446e-16"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "# make the polynomial features to get a new set of features\n",
    "pf = PolynomialFeatures(degree=2)\n",
    "\n",
    "# fit and transform X_train_scaled\n",
    "X_train_degree2 = pf.fit_transform(X_train)\n",
    "\n",
    "# transform X_validate_scaled & X_test_scaled\n",
    "X_validate_degree2 = pf.transform(X_validate)\n",
    "X_test_degree2 =  pf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Polynomial Model, degrees=2\n",
      "Training/In-Sample:  550049.853919652 \n",
      "Validation/Out-of-Sample:  340702517738.45386\n"
     ]
    }
   ],
   "source": [
    "# create the model object\n",
    "lm2 = LinearRegression(normalize=True)\n",
    "\n",
    "# fit the model to our training data. We must specify the column in y_train, \n",
    "# since we have converted it to a dataframe from a series! \n",
    "lm2.fit(X_train2, y_train2.tax_value)\n",
    "\n",
    "# predict train\n",
    "y_train2['tax_pred_lm2'] = lm2.predict(X_train2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_train = mean_squared_error(y_train2.tax_value, y_train2.tax_pred_lm2) ** (1/2)\n",
    "\n",
    "# predict validate\n",
    "y_validate2['tax_pred_lm2'] = lm2.predict(X_validate2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_validate = mean_squared_error(y_validate2.tax_value, y_validate2.tax_pred_lm2)\n",
    "\n",
    "print(\"RMSE for Polynomial Model, degrees=2\\nTraining/In-Sample: \", rmse_train, \n",
    "      \"\\nValidation/Out-of-Sample: \", rmse_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>model</th>\n",
       "      <th>RMSE_validate</th>\n",
       "      <th>r^2_validate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean_baseline</td>\n",
       "      <td>7.807052e+05</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OLS Regressor</td>\n",
       "      <td>5.836973e+05</td>\n",
       "      <td>4.173118e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso Lars</td>\n",
       "      <td>5.837166e+05</td>\n",
       "      <td>4.172732e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GLM (Poisson)</td>\n",
       "      <td>6.953574e+05</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quadratic Model</td>\n",
       "      <td>3.407025e+11</td>\n",
       "      <td>4.173118e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Quadratic Model</td>\n",
       "      <td>3.407025e+11</td>\n",
       "      <td>4.173118e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             model  RMSE_validate  r^2_validate\n",
       "0    mean_baseline   7.807052e+05  2.220446e-16\n",
       "1    OLS Regressor   5.836973e+05  4.173118e-01\n",
       "2       Lasso Lars   5.837166e+05  4.172732e-01\n",
       "3    GLM (Poisson)   6.953574e+05  2.220446e-16\n",
       "4  Quadratic Model   3.407025e+11  4.173118e-01\n",
       "5  Quadratic Model   3.407025e+11  4.173118e-01"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df = metric_df.append({\n",
    "    'model': 'Quadratic Model', \n",
    "    'RMSE_validate': rmse_validate,\n",
    "    'r^2_validate': explained_variance_score(y_validate2.tax_value, y_validate2.tax_pred_lm2)}, ignore_index=True)\n",
    "metric_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qB5Jsz+isWQAA+KYD7nP1M1khaVVVO4Yrt1JV357kJ5P8xRqbfk+SH62qbVX10CRPTXJzRlePnV5VD62qyugKr5szmo1gR1U9bdjP0VX1xPXGCAAAy0aCC1g6VfX2jJJVP1BV+6rqvCQ/m+S8qvpUkhuTnD22yrlJLm9ztgIAcLB/W1Wfrqrrk/x4khclSVV9T1VdOdQ5NskHhzrXZnQPrvcO9f5VVe1LcnyS66vqkiTp7puTvC/J9Uk+luSS7r5huHfXO5N8IsmnM/q//eLu/u9Jnpfkt4cx7XUZXdm1aowAALDM3IMLAAAAAACAheIKLgAAAAAAABaKBBcAAAAAAAALRYILAAAAAACAhSLBBQAAAAAAwEKR4AIAAAAAAGChSHABAAAAAACwUCS4AAAAAAAAWCgSXAAAAAAAACyU/x+9PIu3xGq1uwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "# row, columns, index\n",
    "plt.subplot(2, 5, 1)\n",
    "plt.hist(y_validate2.tax_value, color='cornflowerblue')\n",
    "plt.title('Actual Tax Values')\n",
    "plt.subplot(2, 5, 2)\n",
    "plt.hist(y_validate2.tax_pred_lm, color='palevioletred')\n",
    "plt.title('Model: LinearRegression')\n",
    "plt.subplot(2, 5, 3)\n",
    "plt.hist(y_validate2.tax_pred_glm, color='lightcoral')\n",
    "plt.title('Model: TweedieRegressor')\n",
    "plt.subplot(2, 5, 4)\n",
    "plt.hist(y_validate2.tax_pred_lm2, color='tomato')\n",
    "plt.title('Model 2nd degree Polynomial')\n",
    "plt.subplot(2, 5, 5)\n",
    "plt.hist(y_validate2.tax_pred_lars, color='salmon')\n",
    "plt.title('Model Lasso Lars')\n",
    "\n",
    "\n",
    "plt.suptitle(\"Comparing the Distribution of Actual Tax Values to Distributions of Predicted Tax Values for the Top Models\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Takeaway: The tweedie regressor model performs closest to the baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for GLM Model using LinearRegression\n",
      "Out-of-Sample Performance:  648058.7413728263\n"
     ]
    }
   ],
   "source": [
    "y_test = pd.DataFrame(y_test2)\n",
    "\n",
    "# predict on test\n",
    "y_test2['tax_pred_glm'] = glm.predict(X_test2)\n",
    "\n",
    "# evaluate: rmse\n",
    "rmse_test = mean_squared_error(y_test2.tax_value, y_test2.tax_pred_glm) ** (1/2)\n",
    "\n",
    "print(\"RMSE for GLM Model using LinearRegression\\nOut-of-Sample Performance: \", rmse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_validate.head()\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "plt.axhline(label=\"No Error\")\n",
    "\n",
    "plt.scatter(y_validate2.tax_value, y_validate2.tax_pred_mean - y_validate2.tax_value , \n",
    "            alpha=.5, s=100, label=\"Baseline: LinearRegression\")\n",
    "\n",
    "plt.scatter(y_validate2.tax_value, y_validate.tax_pred_lm2 - y_validate2.tax_value , \n",
    "            alpha=.5, s=100, label=\"Model: PolynomialRegressor\")\n",
    "\n",
    "plt.scatter(y_validate2.tax_value, y_validate.tax_pred_glm - y_validate2.tax_value, \n",
    "            alpha=.6, s=100, label=\"Model: TweedieRegressor\")\n",
    "\n",
    "plt.scatter(y_validate2.tax_value, y_validate.tax_pred_lars - y_validate2.tax_value , \n",
    "            alpha=.7, s=100, label=\"Model: LassoLars\")\n",
    "\n",
    "plt.scatter(y_validate2.tax_value, y_validate.tax_pred_lm - y_validate2.tax_value , \n",
    "            alpha=.3, s=100, label=\"Model: LinearRegression\")\n",
    "\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"Actual tax assessed value\")\n",
    "plt.ylabel(\"Predicted tax assessed value - Actual tax assessed value\")\n",
    "plt.title(\"Size of errors change as the actual value changes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of Tax Values')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(y_test.tax_value, color='cornflowerblue')\n",
    "plt.title('Actual Tax Values')\n",
    "plt.xlabel(\"Tax Values\")\n",
    "plt.ylabel(\"Count of Tax Values\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(y_test.tax_pred_glm, color='palevioletred')\n",
    "plt.title('Predicted Tax Values')\n",
    "plt.xlabel(\"Tax Values\")\n",
    "plt.ylabel(\"Count of Tax Values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overall Tax Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>fips</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>bedroom_count</th>\n",
       "      <th>bathroom_count</th>\n",
       "      <th>tax_amount</th>\n",
       "      <th>tax_value</th>\n",
       "      <th>county</th>\n",
       "      <th>tax_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6037.0</td>\n",
       "      <td>5672.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2627.48</td>\n",
       "      <td>205123.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6037.0</td>\n",
       "      <td>8284.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2319.90</td>\n",
       "      <td>136104.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6037.0</td>\n",
       "      <td>6707.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>543.69</td>\n",
       "      <td>35606.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6059.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>2541.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9819.72</td>\n",
       "      <td>880456.0</td>\n",
       "      <td>orange_county</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6037.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1399.27</td>\n",
       "      <td>107110.0</td>\n",
       "      <td>los_angeles_county</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     fips  lot_size  square_feet  bedroom_count  bathroom_count  tax_amount  \\\n",
       "0  6037.0    5672.0       1316.0            3.0             2.0     2627.48   \n",
       "1  6037.0    8284.0       1458.0            3.0             2.0     2319.90   \n",
       "2  6037.0    6707.0       1421.0            2.0             1.0      543.69   \n",
       "3  6059.0    4975.0       2541.0            4.0             3.0     9819.72   \n",
       "4  6037.0    5000.0       1491.0            3.0             2.0     1399.27   \n",
       "\n",
       "   tax_value              county  tax_rate  \n",
       "0   205123.0  los_angeles_county      1.28  \n",
       "1   136104.0  los_angeles_county      1.70  \n",
       "2    35606.0  los_angeles_county      1.53  \n",
       "3   880456.0       orange_county      1.12  \n",
       "4   107110.0  los_angeles_county      1.31  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tax_rate'] = round(((df.tax_amount / df.tax_value) * 100),2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = df.groupby(df.county)\n",
    "la_county= grouped.get_group(\"los_angeles_county\")\n",
    "v_county= grouped.get_group(\"ventura_county\")\n",
    "o_county= grouped.get_group(\"orange_county\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of Tax Rate')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.subplot(2, 3, 1)\n",
    "sns.distplot(la_county.tax_rate, color=\"deepskyblue\")\n",
    "plt.title('Los Angeles County')\n",
    "plt.xlabel(\"Tax Rate\")\n",
    "plt.ylabel(\"Count of Tax Rate\")\n",
    "\n",
    "plt.subplot(2, 3, 2)\n",
    "sns.distplot(v_county.tax_rate, color='dodgerblue')\n",
    "plt.title('Ventura County')\n",
    "plt.xlabel(\"Tax Rate\")\n",
    "plt.ylabel(\"Count of Tax Rate\")\n",
    "plt.subplot(2, 3, 3)\n",
    "sns.distplot(o_county.tax_rate, color=\"DarkTurquoise\")\n",
    "plt.title('Orange County')\n",
    "plt.xlabel(\"Tax Rate\")\n",
    "plt.ylabel(\"Count of Tax Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.408764811088739, 1.182332923329231, 1.2073068974425338)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_county.tax_rate.mean(), v_county.tax_rate.mean(), o_county.tax_rate.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.26, 1.12, 1.15)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_county.tax_rate.median(), v_county.tax_rate.median(), o_county.tax_rate.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('full_zillow.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion and Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best 3 predictors for tax value are number of bedrooms, bathrooms and square footage according to KBest and RFE.<br>\n",
    "The best model for the data is the Tweedie Regressor which performed within 0.89 of the mean baseline. The worst performing model is the polynomial which performed within 4.41 of the baseline.<br>\n",
    "Although the Tweedie Regressor performs the best, it is still well below the baseline and the original data set has over 1.3 million rows while the cleaned data frame has about 30,000 it is not an wide scale reprentation of each county."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If more data and time were available, investigating number of stories, presence of hoa, and the combination of bedrooms and bathrooms would be good to investigate as features for predicting tax value."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}