real_estate_lineer_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fundamental steps \n",
    "\n",
    "- read and set data types\n",
    "- cleaning\n",
    "    - look numerical and categorical features, (missing, outliers)\n",
    "        - categorical features (dummy vs as-is)\n",
    "- exploration\n",
    "    - relations numerical vs target feature w/ categorical ones\n",
    "- regression\n",
    "    - x,y separation\n",
    "    - standardization (feature scaling)\n",
    "    - do (get results)\n",
    "    - validate\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>price</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "      <th>view</th>\n",
       "      <th>view_map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>234314.144</td>\n",
       "      <td>643.09</td>\n",
       "      <td>2015</td>\n",
       "      <td>No sea view</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>228581.528</td>\n",
       "      <td>656.22</td>\n",
       "      <td>2009</td>\n",
       "      <td>No sea view</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>281626.336</td>\n",
       "      <td>487.29</td>\n",
       "      <td>2018</td>\n",
       "      <td>Sea view</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>401255.608</td>\n",
       "      <td>1504.75</td>\n",
       "      <td>2015</td>\n",
       "      <td>No sea view</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458674.256</td>\n",
       "      <td>1275.46</td>\n",
       "      <td>2009</td>\n",
       "      <td>Sea view</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price     size  year         view  view_map\n",
       "0  234314.144   643.09  2015  No sea view         0\n",
       "1  228581.528   656.22  2009  No sea view         0\n",
       "2  281626.336   487.29  2018     Sea view         1\n",
       "3  401255.608  1504.75  2015  No sea view         0\n",
       "4  458674.256  1275.46  2009     Sea view         1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('dataset/real_estate.csv')\n",
    "\n",
    "data['view_map'] = data['view'].map({'No sea view':0, 'Sea view':1}) \n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# discovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>price</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "      <th>view_map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>292289.470160</td>\n",
       "      <td>853.024200</td>\n",
       "      <td>2012.600000</td>\n",
       "      <td>0.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>77051.727525</td>\n",
       "      <td>297.941951</td>\n",
       "      <td>4.729021</td>\n",
       "      <td>0.502418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>154282.128000</td>\n",
       "      <td>479.750000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>234280.148000</td>\n",
       "      <td>643.330000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>280590.716000</td>\n",
       "      <td>696.405000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>335723.696000</td>\n",
       "      <td>1029.322500</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500681.128000</td>\n",
       "      <td>1842.510000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price         size         year    view_map\n",
       "count     100.000000   100.000000   100.000000  100.000000\n",
       "mean   292289.470160   853.024200  2012.600000    0.490000\n",
       "std     77051.727525   297.941951     4.729021    0.502418\n",
       "min    154282.128000   479.750000  2006.000000    0.000000\n",
       "25%    234280.148000   643.330000  2009.000000    0.000000\n",
       "50%    280590.716000   696.405000  2015.000000    0.000000\n",
       "75%    335723.696000  1029.322500  2018.000000    1.000000\n",
       "max    500681.128000  1842.510000  2018.000000    1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n",
    "\n",
    "# %49 is no sea view, not bad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = sns.scatterplot(x='size', y='price', hue='view', palette='Set2', data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x='year', y='price', hue='view', size='size', palette='Set2', data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.regplot(x=\"size\", y=\"price\", data=data);\n",
    "sns.lmplot(x=\"size\", y=\"price\", col='view', data=data);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 3), (100,))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need OLS w/ constant **for lineer regression**\n",
    "y = data['price']\n",
    "x = data[['size', 'year','view_map']]\n",
    "\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### standardization - _feature scaling_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.70816415,  0.51006137, -0.98019606],\n",
       "       [-0.66387316, -0.76509206, -0.98019606],\n",
       "       [-1.23371919,  1.14763808,  1.02020406]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled = scaler.transform(x)\n",
    "\n",
    "x_scaled[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# do regression w/ default settings, copy_X, fit_intercept, n_jobs, normalize\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "reg.fit(x_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Features</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bias</td>\n",
       "      <td>292289.470160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>size</td>\n",
       "      <td>66117.388569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>year</td>\n",
       "      <td>12793.514096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>view</td>\n",
       "      <td>28357.336730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Features        Weights\n",
       "0     Bias  292289.470160\n",
       "1     size   66117.388569\n",
       "2     year   12793.514096\n",
       "3     view   28357.336730"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get regression results\n",
    "\n",
    "summary = pd.DataFrame(data=[['Bias', reg.intercept_], \n",
    "                             ['size', reg.coef_[0]], \n",
    "                             ['year', reg.coef_[1]],\n",
    "                             ['view', reg.coef_[2]],\n",
    "                            ],\n",
    "                      columns=['Features', 'Weights'])\n",
    "\n",
    "summary\n",
    "\n",
    "# print('r-squared:', reg.score(x_scaled, y),\n",
    "#       '\\nadjusted r-squared:', 1- (1-reg.score(x_scaled, y)) * (x_scaled.shape[0]-1) / (x_scaled.shape[0]-x_scaled.shape[1]-1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# validations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>size</th>\n",
       "      <th>year</th>\n",
       "      <th>view</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>750</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>750</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>750</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size  year  view\n",
       "0   750  2009     0\n",
       "1   750  2009     1\n",
       "2   750  2010     0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test_data = pd.DataFrame(data=[[750,2009,0],\n",
    "                               [750,2009,1],\n",
    "                               [750,2010,0]], \n",
    "                         columns=['size', 'year', 'view'])\n",
    "\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>size</th>\n",
       "      <th>year</th>\n",
       "      <th>view</th>\n",
       "      <th>predicted_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>750</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>231727.850292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>750</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>288453.870090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>750</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>234446.799181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size  year  view  predicted_price\n",
       "0   750  2009     0    231727.850292\n",
       "1   750  2009     1    288453.870090\n",
       "2   750  2010     0    234446.799181"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['predicted_price'] = reg.predict(scaler.transform(test_data))\n",
    "\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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