analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "%run -i \"functions/isolate.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that I have validated the model, I will use it to demostrate some of it's capabilities by predicting home values across the entire dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv('data/independent_vars.csv')\n",
    "y = pd.read_csv('data/dependent_var.csv')\n",
    "X.drop('Unnamed: 0', axis = 1, inplace = True)\n",
    "y.drop('Unnamed: 0', axis = 1, inplace = True)\n",
    "\n",
    "linreg = LinearRegression()\n",
    "model = linreg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_pred_price = model.predict(X)\n",
    "pred_price = scaled_pred_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/clean_housing.csv')\n",
    "\n",
    "#Reverse the scaling of the price used in the model.\n",
    "data['pred_price'] = (pred_price*185546)+451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.set_context('notebook')\n",
    "plt.title('Actual Price versus Predicted Price')\n",
    "sns.scatterplot(x='pred_price', y='price', data = data)\n",
    "sns.lineplot(x='pred_price', y='pred_price', data = data, \n",
    "             color = 'black', label = 'Predicted Price')\n",
    "plt.legend()\n",
    "plt.xlabel('Predicted Price')\n",
    "plt.ylabel('Actual Price')\n",
    "plt.yticks(ticks=(200000, 400000, 600000, 800000, 1000000), \n",
    "           labels= ('$200k', '$400k', \n",
    "                    '$600k', '$800k', '$1mil'))\n",
    "plt.xticks(ticks=(200000, 400000, 600000, 800000, 1000000), \n",
    "           labels= ('$200k', '$400k', \n",
    "                    '$600k', '$800k', '$1mil'))\n",
    "plt.savefig('figures/figure1.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin showcasing the capabilities of this model, I will isolate the impact of each target independent variable by imputing the mean of each column for the remaining values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "Impact of month on housing prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_month = isolate('month_sold', 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>sqft_living15</th>\n",
       "      <th>long * Seattle^3</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>lat^3</th>\n",
       "      <th>dist_to_Seattle</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>lat^4</th>\n",
       "      <th>grade</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>lat^2</th>\n",
       "      <th>Renovated</th>\n",
       "      <th>sqft_living * Kent^3</th>\n",
       "      <th>view</th>\n",
       "      <th>view^3</th>\n",
       "      <th>long^3</th>\n",
       "      <th>long * Seattle^2</th>\n",
       "      <th>grade^3</th>\n",
       "      <th>sqft_living * Kent^2</th>\n",
       "      <th>lat * Bellevue^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-9.012326e-17</td>\n",
       "      <td>-0.320939</td>\n",
       "      <td>7.332756e-18</td>\n",
       "      <td>-0.453705</td>\n",
       "      <td>-4.475958e-15</td>\n",
       "      <td>3.885352e-12</td>\n",
       "      <td>-2.050265e-12</td>\n",
       "      <td>2.213594</td>\n",
       "      <td>-7.841393e-15</td>\n",
       "      <td>2.435718e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.027366</td>\n",
       "      <td>0.066826</td>\n",
       "      <td>1.297567e-15</td>\n",
       "      <td>4.609993</td>\n",
       "      <td>0.773581</td>\n",
       "      <td>0.311422</td>\n",
       "      <td>0.394302</td>\n",
       "      <td>0.102978</td>\n",
       "      <td>0.177068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-9.012326e-17</td>\n",
       "      <td>-0.320939</td>\n",
       "      <td>7.332756e-18</td>\n",
       "      <td>-0.453705</td>\n",
       "      <td>-4.475958e-15</td>\n",
       "      <td>3.885352e-12</td>\n",
       "      <td>-2.050265e-12</td>\n",
       "      <td>2.213594</td>\n",
       "      <td>-7.841393e-15</td>\n",
       "      <td>2.435718e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.027366</td>\n",
       "      <td>0.066826</td>\n",
       "      <td>1.297567e-15</td>\n",
       "      <td>4.609993</td>\n",
       "      <td>0.773581</td>\n",
       "      <td>0.311422</td>\n",
       "      <td>0.394302</td>\n",
       "      <td>0.102978</td>\n",
       "      <td>0.177068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-9.012326e-17</td>\n",
       "      <td>-0.320939</td>\n",
       "      <td>7.332756e-18</td>\n",
       "      <td>-0.453705</td>\n",
       "      <td>-4.475958e-15</td>\n",
       "      <td>3.885352e-12</td>\n",
       "      <td>-2.050265e-12</td>\n",
       "      <td>2.213594</td>\n",
       "      <td>-7.841393e-15</td>\n",
       "      <td>2.435718e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.027366</td>\n",
       "      <td>0.066826</td>\n",
       "      <td>1.297567e-15</td>\n",
       "      <td>4.609993</td>\n",
       "      <td>0.773581</td>\n",
       "      <td>0.311422</td>\n",
       "      <td>0.394302</td>\n",
       "      <td>0.102978</td>\n",
       "      <td>0.177068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-9.012326e-17</td>\n",
       "      <td>-0.320939</td>\n",
       "      <td>7.332756e-18</td>\n",
       "      <td>-0.453705</td>\n",
       "      <td>-4.475958e-15</td>\n",
       "      <td>3.885352e-12</td>\n",
       "      <td>-2.050265e-12</td>\n",
       "      <td>2.213594</td>\n",
       "      <td>-7.841393e-15</td>\n",
       "      <td>2.435718e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.027366</td>\n",
       "      <td>0.066826</td>\n",
       "      <td>1.297567e-15</td>\n",
       "      <td>4.609993</td>\n",
       "      <td>0.773581</td>\n",
       "      <td>0.311422</td>\n",
       "      <td>0.394302</td>\n",
       "      <td>0.102978</td>\n",
       "      <td>0.177068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-9.012326e-17</td>\n",
       "      <td>-0.320939</td>\n",
       "      <td>7.332756e-18</td>\n",
       "      <td>-0.453705</td>\n",
       "      <td>-4.475958e-15</td>\n",
       "      <td>3.885352e-12</td>\n",
       "      <td>-2.050265e-12</td>\n",
       "      <td>2.213594</td>\n",
       "      <td>-7.841393e-15</td>\n",
       "      <td>2.435718e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.027366</td>\n",
       "      <td>0.066826</td>\n",
       "      <td>1.297567e-15</td>\n",
       "      <td>4.609993</td>\n",
       "      <td>0.773581</td>\n",
       "      <td>0.311422</td>\n",
       "      <td>0.394302</td>\n",
       "      <td>0.102978</td>\n",
       "      <td>0.177068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sqft_living15  long * Seattle^3   sqft_living     lat^3  dist_to_Seattle  \\\n",
       "0  -9.012326e-17         -0.320939  7.332756e-18 -0.453705    -4.475958e-15   \n",
       "1  -9.012326e-17         -0.320939  7.332756e-18 -0.453705    -4.475958e-15   \n",
       "2  -9.012326e-17         -0.320939  7.332756e-18 -0.453705    -4.475958e-15   \n",
       "3  -9.012326e-17         -0.320939  7.332756e-18 -0.453705    -4.475958e-15   \n",
       "4  -9.012326e-17         -0.320939  7.332756e-18 -0.453705    -4.475958e-15   \n",
       "\n",
       "           long           lat     lat^4         grade           age  ...  \\\n",
       "0  3.885352e-12 -2.050265e-12  2.213594 -7.841393e-15  2.435718e-15  ...   \n",
       "1  3.885352e-12 -2.050265e-12  2.213594 -7.841393e-15  2.435718e-15  ...   \n",
       "2  3.885352e-12 -2.050265e-12  2.213594 -7.841393e-15  2.435718e-15  ...   \n",
       "3  3.885352e-12 -2.050265e-12  2.213594 -7.841393e-15  2.435718e-15  ...   \n",
       "4  3.885352e-12 -2.050265e-12  2.213594 -7.841393e-15  2.435718e-15  ...   \n",
       "\n",
       "      lat^2  Renovated  sqft_living * Kent^3          view    view^3  \\\n",
       "0  0.999939   0.027366              0.066826  1.297567e-15  4.609993   \n",
       "1  0.999939   0.027366              0.066826  1.297567e-15  4.609993   \n",
       "2  0.999939   0.027366              0.066826  1.297567e-15  4.609993   \n",
       "3  0.999939   0.027366              0.066826  1.297567e-15  4.609993   \n",
       "4  0.999939   0.027366              0.066826  1.297567e-15  4.609993   \n",
       "\n",
       "     long^3  long * Seattle^2   grade^3  sqft_living * Kent^2  \\\n",
       "0  0.773581          0.311422  0.394302              0.102978   \n",
       "1  0.773581          0.311422  0.394302              0.102978   \n",
       "2  0.773581          0.311422  0.394302              0.102978   \n",
       "3  0.773581          0.311422  0.394302              0.102978   \n",
       "4  0.773581          0.311422  0.394302              0.102978   \n",
       "\n",
       "   lat * Bellevue^2  \n",
       "0          0.177068  \n",
       "1          0.177068  \n",
       "2          0.177068  \n",
       "3          0.177068  \n",
       "4          0.177068  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_month.loc[:, 'month_sold^3'] = iso_month['month_sold']**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_sold</th>\n",
       "      <th>month_sold^3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-1.788865</td>\n",
       "      <td>-5.724438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.468173</td>\n",
       "      <td>-3.164692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.147480</td>\n",
       "      <td>-1.510899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.826788</td>\n",
       "      <td>-0.565174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.506095</td>\n",
       "      <td>-0.129627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-0.185403</td>\n",
       "      <td>-0.006373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.135290</td>\n",
       "      <td>0.002476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.455983</td>\n",
       "      <td>0.094808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.776675</td>\n",
       "      <td>0.468509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.097368</td>\n",
       "      <td>1.321467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.418060</td>\n",
       "      <td>2.851570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.738753</td>\n",
       "      <td>5.256703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month_sold  month_sold^3\n",
       "0    -1.788865     -5.724438\n",
       "1    -1.468173     -3.164692\n",
       "2    -1.147480     -1.510899\n",
       "3    -0.826788     -0.565174\n",
       "4    -0.506095     -0.129627\n",
       "5    -0.185403     -0.006373\n",
       "6     0.135290      0.002476\n",
       "7     0.455983      0.094808\n",
       "8     0.776675      0.468509\n",
       "9     1.097368      1.321467\n",
       "10    1.418060      2.851570\n",
       "11    1.738753      5.256703"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso_month[['month_sold', 'month_sold^3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(iso_month)\n",
    "pred_price = (y_pred*185546)+451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Month_data = pd.DataFrame(['Jan', 'Feb', 'Mar', 'Apr',\n",
    "                           'May', 'Jun', 'Jul', 'Aug',\n",
    "                           'Sep', 'Oct', 'Nov', 'Dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Month_data['Predicted Price'] = pred_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Month_data['Average Price'] = 451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>0</th>\n",
       "      <th>Predicted Price</th>\n",
       "      <th>Average Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>450989.761894</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Feb</td>\n",
       "      <td>460019.192369</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Mar</td>\n",
       "      <td>463684.463570</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Apr</td>\n",
       "      <td>463157.267269</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>May</td>\n",
       "      <td>459609.295238</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Jun</td>\n",
       "      <td>454212.239249</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Jul</td>\n",
       "      <td>448137.791073</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Aug</td>\n",
       "      <td>442557.642482</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Sep</td>\n",
       "      <td>438643.485248</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Oct</td>\n",
       "      <td>437567.011143</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Nov</td>\n",
       "      <td>440499.911938</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Dec</td>\n",
       "      <td>448613.879406</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  Predicted Price  Average Price\n",
       "0   Jan    450989.761894         451089\n",
       "1   Feb    460019.192369         451089\n",
       "2   Mar    463684.463570         451089\n",
       "3   Apr    463157.267269         451089\n",
       "4   May    459609.295238         451089\n",
       "5   Jun    454212.239249         451089\n",
       "6   Jul    448137.791073         451089\n",
       "7   Aug    442557.642482         451089\n",
       "8   Sep    438643.485248         451089\n",
       "9   Oct    437567.011143         451089\n",
       "10  Nov    440499.911938         451089\n",
       "11  Dec    448613.879406         451089"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=0, y='Predicted Price', data=Month_data, \n",
    "             sort = False, label='Predicted Price', \n",
    "             linewidth=4)\n",
    "sns.lineplot(x=0, y='Average Price', data=Month_data, \n",
    "             label='Average Price', linewidth=4)\n",
    "plt.title('Isolated Impact of Month on Predicted Price')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.yticks(ticks=(440000, 445000, 450000, 455000, 460000), \n",
    "                  labels= ('$440k', '$445k', '$450k', \n",
    "                  '$455k', '$460k'))\n",
    "plt.savefig('figures/figure2.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12595.463569863583"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(Month_data['Predicted Price'].max()\n",
    " - Month_data['Average Price'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-13521.988856994198"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(Month_data['Predicted Price'].min()\n",
    " - Month_data['Average Price'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "Impact of Renovation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "iso_ren = isolate('Renovated', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(iso_ren)\n",
    "pred_price = (y_pred*185546)+451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ren_data = pd.DataFrame(['Not', 'Renovated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ren_data['Predicted Price'] = pred_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ren_data['Average Price'] = 451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=0, y='Predicted Price', data=ren_data)\n",
    "sns.lineplot(x=0, y='Average Price', data=ren_data, \n",
    "             label='Average Price')\n",
    "plt.xlabel('Renovation Status')\n",
    "plt.ylabel('')\n",
    "plt.yticks(ticks=(440000, 455000, 470000), \n",
    "           labels=('$440k', '$455k', '$470k'))\n",
    "plt.title('''Isolated Impact of Renovation Status \n",
    "on Predicted Price''')\n",
    "plt.ylim(bottom = 440000, top = 472000)\n",
    "plt.savefig('figures/figure4.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18457.11066763877"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ren_data['Predicted Price'].max()\n",
    " - ren_data['Average Price'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-434.74395568459295"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ren_data['Predicted Price'].min()\n",
    " - ren_data['Average Price'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "Impact of square footage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16261.000000\n",
       "mean      1843.576902\n",
       "std        671.424738\n",
       "min        370.000000\n",
       "25%       1340.000000\n",
       "50%       1750.000000\n",
       "75%       2260.000000\n",
       "max       4940.000000\n",
       "Name: sqft_living, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['sqft_living'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_size = isolate('sqft_living', 4570)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_size.loc[:, 'sqft_living * Seattle'] = iso_size['sqft_living']*iso_size['Seattle']\n",
    "iso_size.loc[:, 'sqft_living * Seattle^3'] = (iso_size['sqft_living']*iso_size['Seattle'])**3\n",
    "iso_size.loc[:, 'sqft_living^3'] = iso_size['sqft_living']**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(iso_size)\n",
    "pred_price = (y_pred*185546)+451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_data = pd.DataFrame(range(370, 4940))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_data['Predicted Price'] = pred_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_data['Average Price'] = 451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=0, y='Predicted Price', \n",
    "             data=size_data, label = 'Predicted Price', \n",
    "             linewidth=4)\n",
    "sns.lineplot(x=0, y='Average Price', \n",
    "             data=size_data, label = 'Average Price', \n",
    "             linewidth=4)\n",
    "plt.xlabel('Square Footage')\n",
    "plt.ylabel('')\n",
    "plt.yticks(ticks=(300000, 400000, 500000, 600000, 700000), \n",
    "           labels=('$300k', '$400k', '$500k', \n",
    "                   '$600k', '$700k'))\n",
    "plt.xticks(ticks=(1000, 2000, 3000, 4000, 5000), \n",
    "           labels=('1000 sqft', '2000 sqft', '3000 sqft', \n",
    "                   '4000 sqft', '5000 sqft'))\n",
    "plt.title('''Isolated Impact of Square Footage \n",
    "on Predicted Price''')\n",
    "plt.savefig('figures/figure5.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1630    476001.985445\n",
       "Name: Predicted Price, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_data[size_data[0]==2000]['Predicted Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1730    489001.960285\n",
       "Name: Predicted Price, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_data[size_data[0]==2100]['Predicted Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-12913.130000000005"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "475858.24 - 488771.37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4352    4722\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_data[size_data['Predicted Price']\n",
    "          == size_data['Predicted Price'].max()][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "What Kind of Additions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.50    4149\n",
       "1.00    3399\n",
       "1.75    2409\n",
       "2.00    1568\n",
       "2.25    1540\n",
       "1.50    1239\n",
       "2.75     778\n",
       "3.00     447\n",
       "3.50     337\n",
       "3.25     276\n",
       "0.75      42\n",
       "3.75      38\n",
       "4.00      28\n",
       "1.25       7\n",
       "0.50       4\n",
       "Name: bathrooms, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['bathrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_beds = isolate('bathrooms', 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008614     7841\n",
       "0.727945     4864\n",
       "5.522646     2437\n",
       "21.447931     961\n",
       "58.138489     158\n",
       "Name: bedrooms^4, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['bedrooms^4'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7841\n",
       "4    4864\n",
       "2    2437\n",
       "5     961\n",
       "1     158\n",
       "Name: bedrooms, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['bedrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_bed = iso_beds.copy()\n",
    "one_bed['bedrooms^4'] = 58.138489 \n",
    "two_bed = iso_beds.copy()\n",
    "two_bed['bedrooms^4'] = 5.522646\n",
    "three_bed = iso_beds.copy()\n",
    "three_bed['bedrooms^4'] = .008614\n",
    "four_bed = iso_beds.copy()\n",
    "four_bed['bedrooms^4'] = .727945\n",
    "five_bed = iso_beds.copy()\n",
    "five_bed['bedrooms^4'] = 21.447931\n",
    "\n",
    "beds = [one_bed, two_bed, three_bed, four_bed, five_bed]\n",
    "rooms = pd.DataFrame()\n",
    "for i in beds:\n",
    "    rooms = rooms.append(i)\n",
    "    \n",
    "y_pred = model.predict(rooms)\n",
    "pred_price = (y_pred*185546)+451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "room_data = pd.DataFrame(np.linspace(.5, 4, 8))\n",
    "\n",
    "one_bed = room_data.copy()\n",
    "one_bed['bedrooms'] = 1 \n",
    "two_bed = room_data.copy()\n",
    "two_bed['bedrooms'] = 2\n",
    "three_bed = room_data.copy()\n",
    "three_bed['bedrooms'] = 3\n",
    "four_bed = room_data.copy()\n",
    "four_bed['bedrooms'] = 4\n",
    "five_bed = room_data.copy()\n",
    "five_bed['bedrooms'] = 5\n",
    "\n",
    "beds = [one_bed, two_bed, three_bed, four_bed, five_bed]\n",
    "rooms = pd.DataFrame()\n",
    "for i in beds:\n",
    "    rooms = rooms.append(i)\n",
    "    \n",
    "rooms['Predicted Price'] = pred_price\n",
    "rooms['Average Price'] = 451089\n",
    "rooms['Number of Bathrooms']= rooms[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540.425x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='bedrooms', y='Predicted Price', \n",
    "            hue='Number of Bathrooms', \n",
    "            data = rooms, legend_out=True, kind='point',\n",
    "            height=3, aspect=2, palette='deep')\n",
    "plt.title('Predicted Price by Number of Rooms')\n",
    "plt.xlabel('Number of Bedrooms')\n",
    "plt.ylabel('Predicted Price')\n",
    "plt.yticks(ticks=(400000, 420000, 440000, 460000, 480000), \n",
    "           labels=('$400k', '$420k', '$440k', \n",
    "                   '$460k', '$480k'))\n",
    "plt.ylim(bottom=400000, top = 485000)\n",
    "plt.savefig('figures/figure6.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "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>0</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>Predicted Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Number of Bathrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>414936.238871</td>\n",
       "      <td>451089</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>421615.532192</td>\n",
       "      <td>451089</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>428294.825514</td>\n",
       "      <td>451089</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>434974.118836</td>\n",
       "      <td>451089</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>441653.412157</td>\n",
       "      <td>451089</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>448332.705479</td>\n",
       "      <td>451089</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>455011.998801</td>\n",
       "      <td>451089</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>461691.292123</td>\n",
       "      <td>451089</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>444058.817900</td>\n",
       "      <td>451089</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  bedrooms  Predicted Price  Average Price  Number of Bathrooms\n",
       "0  0.5         1    414936.238871         451089                  0.5\n",
       "1  1.0         1    421615.532192         451089                  1.0\n",
       "2  1.5         1    428294.825514         451089                  1.5\n",
       "3  2.0         1    434974.118836         451089                  2.0\n",
       "4  2.5         1    441653.412157         451089                  2.5\n",
       "5  3.0         1    448332.705479         451089                  3.0\n",
       "6  3.5         1    455011.998801         451089                  3.5\n",
       "7  4.0         1    461691.292123         451089                  4.0\n",
       "0  0.5         2    444058.817900         451089                  0.5"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rooms[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29478"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "399972-429450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6781"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "399972-406753"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "Best time to invest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16261.000000\n",
       "mean        44.345797\n",
       "std         30.288348\n",
       "min         -1.000000\n",
       "25%         16.000000\n",
       "50%         44.000000\n",
       "75%         65.000000\n",
       "max        115.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_age = isolate('age', 116)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_age.loc[:, 'age^2'] = iso_age['age']**2\n",
    "iso_age.loc[:, 'age^3'] = iso_age['age']**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(iso_age)\n",
    "pred_price = (y_pred*185546)+451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_data = pd.DataFrame(range(0,116))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_data['Predicted Price'] = pred_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_data['Average Price'] = 451089"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>0</th>\n",
       "      <th>Predicted Price</th>\n",
       "      <th>Average Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>474009.156526</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>470915.975698</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>467983.460380</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>465208.839598</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>462589.342383</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>496885.982287</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>494709.892209</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>492386.889807</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>489914.204112</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>487289.064152</td>\n",
       "      <td>451089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0  Predicted Price  Average Price\n",
       "0      0    474009.156526         451089\n",
       "1      1    470915.975698         451089\n",
       "2      2    467983.460380         451089\n",
       "3      3    465208.839598         451089\n",
       "4      4    462589.342383         451089\n",
       "..   ...              ...            ...\n",
       "111  111    496885.982287         451089\n",
       "112  112    494709.892209         451089\n",
       "113  113    492386.889807         451089\n",
       "114  114    489914.204112         451089\n",
       "115  115    487289.064152         451089\n",
       "\n",
       "[116 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=0, y='Predicted Price', data=age_data, \n",
    "             linewidth=4, label='Predicted Price')\n",
    "sns.lineplot(x=0, y='Average Price', data=age_data, \n",
    "             linewidth=4, label='Average Price')\n",
    "plt.title('Isolated Impact of Age on Predicted Price')\n",
    "plt.yticks(ticks=(420000, 440000, 460000, 480000, 500000), \n",
    "           labels=('$420k', '$440k', '$460k', \n",
    "                   '$480k', '$500k'))\n",
    "plt.xlabel('Age of House')\n",
    "plt.ylabel('')\n",
    "plt.savefig('figures/figure7.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25    25\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low = age_data['Predicted Price'].min()\n",
    "age_data[age_data['Predicted Price']==low][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93    93\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high = age_data['Predicted Price'].max()\n",
    "age_data[age_data['Predicted Price']==high][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}