Linear Regression/Linear Regression on AmesHousing Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 82 columns):\n",
      "Order              1460 non-null int64\n",
      "PID                1460 non-null int64\n",
      "MS SubClass        1460 non-null int64\n",
      "MS Zoning          1460 non-null object\n",
      "Lot Frontage       1211 non-null float64\n",
      "Lot Area           1460 non-null int64\n",
      "Street             1460 non-null object\n",
      "Alley              109 non-null object\n",
      "Lot Shape          1460 non-null object\n",
      "Land Contour       1460 non-null object\n",
      "Utilities          1460 non-null object\n",
      "Lot Config         1460 non-null object\n",
      "Land Slope         1460 non-null object\n",
      "Neighborhood       1460 non-null object\n",
      "Condition 1        1460 non-null object\n",
      "Condition 2        1460 non-null object\n",
      "Bldg Type          1460 non-null object\n",
      "House Style        1460 non-null object\n",
      "Overall Qual       1460 non-null int64\n",
      "Overall Cond       1460 non-null int64\n",
      "Year Built         1460 non-null int64\n",
      "Year Remod/Add     1460 non-null int64\n",
      "Roof Style         1460 non-null object\n",
      "Roof Matl          1460 non-null object\n",
      "Exterior 1st       1460 non-null object\n",
      "Exterior 2nd       1460 non-null object\n",
      "Mas Vnr Type       1449 non-null object\n",
      "Mas Vnr Area       1449 non-null float64\n",
      "Exter Qual         1460 non-null object\n",
      "Exter Cond         1460 non-null object\n",
      "Foundation         1460 non-null object\n",
      "Bsmt Qual          1420 non-null object\n",
      "Bsmt Cond          1420 non-null object\n",
      "Bsmt Exposure      1419 non-null object\n",
      "BsmtFin Type 1     1420 non-null object\n",
      "BsmtFin SF 1       1459 non-null float64\n",
      "BsmtFin Type 2     1419 non-null object\n",
      "BsmtFin SF 2       1459 non-null float64\n",
      "Bsmt Unf SF        1459 non-null float64\n",
      "Total Bsmt SF      1459 non-null float64\n",
      "Heating            1460 non-null object\n",
      "Heating QC         1460 non-null object\n",
      "Central Air        1460 non-null object\n",
      "Electrical         1460 non-null object\n",
      "1st Flr SF         1460 non-null int64\n",
      "2nd Flr SF         1460 non-null int64\n",
      "Low Qual Fin SF    1460 non-null int64\n",
      "Gr Liv Area        1460 non-null int64\n",
      "Bsmt Full Bath     1459 non-null float64\n",
      "Bsmt Half Bath     1459 non-null float64\n",
      "Full Bath          1460 non-null int64\n",
      "Half Bath          1460 non-null int64\n",
      "Bedroom AbvGr      1460 non-null int64\n",
      "Kitchen AbvGr      1460 non-null int64\n",
      "Kitchen Qual       1460 non-null object\n",
      "TotRms AbvGrd      1460 non-null int64\n",
      "Functional         1460 non-null object\n",
      "Fireplaces         1460 non-null int64\n",
      "Fireplace Qu       743 non-null object\n",
      "Garage Type        1386 non-null object\n",
      "Garage Yr Blt      1385 non-null float64\n",
      "Garage Finish      1385 non-null object\n",
      "Garage Cars        1460 non-null float64\n",
      "Garage Area        1460 non-null float64\n",
      "Garage Qual        1385 non-null object\n",
      "Garage Cond        1385 non-null object\n",
      "Paved Drive        1460 non-null object\n",
      "Wood Deck SF       1460 non-null int64\n",
      "Open Porch SF      1460 non-null int64\n",
      "Enclosed Porch     1460 non-null int64\n",
      "3Ssn Porch         1460 non-null int64\n",
      "Screen Porch       1460 non-null int64\n",
      "Pool Area          1460 non-null int64\n",
      "Pool QC            1 non-null object\n",
      "Fence              297 non-null object\n",
      "Misc Feature       60 non-null object\n",
      "Misc Val           1460 non-null int64\n",
      "Mo Sold            1460 non-null int64\n",
      "Yr Sold            1460 non-null int64\n",
      "Sale Type          1460 non-null object\n",
      "Sale Condition     1460 non-null object\n",
      "SalePrice          1460 non-null int64\n",
      "dtypes: float64(11), int64(28), object(43)\n",
      "memory usage: 935.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(r'C:\\Users\\My PC\\Desktop\\AmesHousing.txt',delimiter='\\t')\n",
    "train=data[0:1460]\n",
    "test=data[1460:]\n",
    "print(train.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
       "0      1  526301100           20        RL         141.0     31770   Pave   \n",
       "1      2  526350040           20        RH          80.0     11622   Pave   \n",
       "2      3  526351010           20        RL          81.0     14267   Pave   \n",
       "3      4  526353030           20        RL          93.0     11160   Pave   \n",
       "4      5  527105010           60        RL          74.0     13830   Pave   \n",
       "\n",
       "  Alley Lot Shape Land Contour  ... Pool Area Pool QC  Fence Misc Feature  \\\n",
       "0   NaN       IR1          Lvl  ...         0     NaN    NaN          NaN   \n",
       "1   NaN       Reg          Lvl  ...         0     NaN  MnPrv          NaN   \n",
       "2   NaN       IR1          Lvl  ...         0     NaN    NaN         Gar2   \n",
       "3   NaN       Reg          Lvl  ...         0     NaN    NaN          NaN   \n",
       "4   NaN       IR1          Lvl  ...         0     NaN  MnPrv          NaN   \n",
       "\n",
       "  Misc Val Mo Sold Yr Sold Sale Type  Sale Condition  SalePrice  \n",
       "0        0       5    2010       WD           Normal     215000  \n",
       "1        0       6    2010       WD           Normal     105000  \n",
       "2    12500       6    2010       WD           Normal     172000  \n",
       "3        0       4    2010       WD           Normal     244000  \n",
       "4        0       3    2010       WD           Normal     189900  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(7,15))\n",
    "ax1=fig.add_subplot(3,1,1)\n",
    "ax2=fig.add_subplot(3,1,2)\n",
    "ax3=fig.add_subplot(3,1,3)\n",
    "\n",
    "train.plot(x=\"Garage Area\", y=\"SalePrice\", ax=ax1, kind=\"scatter\")\n",
    "train.plot(x=\"Gr Liv Area\", y=\"SalePrice\", ax=ax2, kind=\"scatter\")\n",
    "train.plot(x=\"Overall Cond\", y=\"SalePrice\", ax=ax3, kind=\"scatter\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[116.86624683]\n",
      "5366.821710056043\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(train[['Gr Liv Area']], train['SalePrice'])\n",
    "print(lr.coef_)\n",
    "print(lr.intercept_)\n",
    "\n",
    "a0 = lr.intercept_\n",
    "a1 = lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56034.362001412796\n",
      "57088.25161263909\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(train[['Gr Liv Area']], train['SalePrice'])\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "train_predictions = lr.predict(train[['Gr Liv Area']])\n",
    "test_predictions = lr.predict(test[['Gr Liv Area']])\n",
    "\n",
    "train_mse = mean_squared_error(train_predictions, train['SalePrice'])\n",
    "test_mse = mean_squared_error(test_predictions, test['SalePrice'])\n",
    "\n",
    "train_rmse = np.sqrt(train_mse)\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "\n",
    "print(train_rmse)\n",
    "print(test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56032.39801525867\n",
      "57066.90779448559\n"
     ]
    }
   ],
   "source": [
    "cols = ['Overall Cond', 'Gr Liv Area']\n",
    "lr.fit(train[cols], train['SalePrice'])\n",
    "train_predictions = lr.predict(train[cols])\n",
    "test_predictions = lr.predict(test[cols])\n",
    "\n",
    "train_rmse_2 = np.sqrt(mean_squared_error(train_predictions, train['SalePrice']))\n",
    "test_rmse_2 = np.sqrt(mean_squared_error(test_predictions, test['SalePrice']))\n",
    "\n",
    "print(train_rmse_2)\n",
    "print(test_rmse_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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