asher_ols_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "import sklearn.metrics as metrics\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./kaggle_data/train.csv')\n",
    "df = df[df.SalePrice < 700000] # Remove some outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are the manually picked columns that are all categorical\n",
    "total_categorical_cols = ['MSZoning', 'Street', 'Alley', 'LandContour', \n",
    "                    'LotConfig', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', \n",
    "                    'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', \n",
    "                    'Foundation', 'Heating', 'CentralAir', 'PavedDrive',\n",
    "                    'MiscFeature', 'SaleType', 'SaleCondition']\n",
    "\n",
    "# These columns will have to be manually processed\n",
    "total_rating_cols = ['LotShape', 'Utilities', 'LandSlope', 'ExterQual', 'ExterCond', \n",
    "                   'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
    "                  'HeatingQC', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu',\n",
    "                    'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence']\n",
    "\n",
    "#########\n",
    "# Below will be the columns actually used in the model\n",
    "#########\n",
    "#category_cols = ['Neighborhood', 'SaleType', 'HouseStyle', 'LotConfig']\n",
    "category_cols = ['HouseStyle', 'SaleType', 'SaleCondition']\n",
    "\n",
    "rating_cols = ['LotShape', 'LandSlope', 'ExterQual', 'ExterCond', 'BsmtQual', \n",
    "               'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', \n",
    "               'HeatingQC', 'Electrical', 'KitchenQual', 'Functional',\n",
    "              'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence']\n",
    "\n",
    "continuous_cols = ['MSSubClass', 'LotFrontage', 'OverallQual', 'OverallCond', \n",
    "                   'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', \n",
    "                   'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', \n",
    "                   'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', \n",
    "                   'GarageYrBlt', 'GarageCars', 'PoolArea', 'MoSold', 'YrSold']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take rating columns and convert to numerical scale\n",
    "rating_df = df.copy()[rating_cols]\n",
    "\n",
    "replace_dict = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1,\n",
    "               'Reg': 1, 'IR1': 2, 'IR2': 3, 'IR3': 4,\n",
    "               'AllPub': 4, 'NoSewr': 3, 'NoSeWa': 2, 'ELO': 1,\n",
    "               'Gtl': 1, 'Mod': 2, 'Sev': 3,\n",
    "               'NA': 0,\n",
    "               'Av': 3, 'Mn': 2, 'No': 1,\n",
    "               'GLQ': 6, 'ALQ': 5, 'BLQ': 4, 'Rec': 3, 'LwQ': 2, 'Unf': 1,\n",
    "               'SBrkr': 5, 'FuseA': 4, 'FuseF': 3, 'FuseP': 2, 'Mix': 1,\n",
    "               'Typ': 8, 'Min1': 7, 'Min2': 6, 'Mod': 5, 'Maj1': 4, 'Maj2': 3, 'Sev': 2, 'Sal': 1,\n",
    "               '2Types': 6, 'Attchd': 5, 'Basment': 4, 'BuiltIn': 3, 'CarPort': 2, 'Detchd': 1,\n",
    "               'Fin': 3, 'RFn': 2, 'Unf': 1,\n",
    "               'GdPrv': 4, 'MnPrv': 3, 'GdWo': 2, 'MnWw': 1}\n",
    "\n",
    "rating_df.replace(replace_dict, inplace=True)\n",
    "\n",
    "na_to_zero_cols = ['LotShape', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
    "                  'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageCond', 'GarageQual',\n",
    "                   'PoolQC', 'Fence', 'Electrical']\n",
    "\n",
    "na_to_zero_cols = [col for col in na_to_zero_cols if col in rating_df]\n",
    "\n",
    "rating_df[na_to_zero_cols] = rating_df.loc[:, na_to_zero_cols].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select categorical columns and get dummy column values (different approach from what we did before with category type)\n",
    "# Preferably pick columns that are not like rating columns (Bad, Okay, Great). We should handle those separately\n",
    "dummy_df = pd.get_dummies(df[category_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num rows after filtering out nans: 1125\n"
     ]
    }
   ],
   "source": [
    "YVar = df[['SalePrice']]\n",
    "XVar = pd.concat([df[continuous_cols], dummy_df, rating_df], axis=1)\n",
    "\n",
    "# Filter out nan values\n",
    "YVar = YVar[XVar.notna().all(axis='columns')]\n",
    "XVar = XVar[XVar.notna().all(axis='columns')]\n",
    "\n",
    "print('Num rows after filtering out nans: ' + str(len(XVar)))\n",
    "\n",
    "XVar = XVar.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data 80-20 so we can test how well the model works\n",
    "msk = np.random.rand(len(XVar)) < 0.8\n",
    "\n",
    "train_x = XVar[msk]\n",
    "train_y = YVar[msk]\n",
    "\n",
    "test_x = XVar[~msk]\n",
    "test_y = YVar[~msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2:  0.8381\n",
      "RMSE:  30906.0377\n"
     ]
    }
   ],
   "source": [
    "# Run linear regression with sklearn\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(train_x, train_y)\n",
    "\n",
    "# Make predictions using the testing set, then convert to pandas series\n",
    "saleprice_preds = regr.predict(test_x)\n",
    "saleprice_preds = pd.Series([x for lst in saleprice_preds for x in lst])\n",
    "\n",
    "# Print out coefficient information (coefficient of determination is perfect if it is 1)\n",
    "#print('Coefficients: \\n', regr.coef_)\n",
    "mse = metrics.mean_squared_error(test_y, saleprice_preds) \n",
    "r2 = metrics.r2_score(test_y, saleprice_preds)\n",
    "\n",
    "print('r2: ', round(r2,4))\n",
    "print('RMSE: ', round(np.sqrt(mse),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_price</th>\n",
       "      <th>predicted_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>307000</td>\n",
       "      <td>267164.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>129500</td>\n",
       "      <td>112135.0</td>\n",
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       "      <td>159000</td>\n",
       "      <td>159353.0</td>\n",
       "    </tr>\n",
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       "      <td>3</td>\n",
       "      <td>306000</td>\n",
       "      <td>275309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>207500</td>\n",
       "      <td>175619.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>238</td>\n",
       "      <td>140000</td>\n",
       "      <td>143114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>64500</td>\n",
       "      <td>68041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>120500</td>\n",
       "      <td>104428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>241</td>\n",
       "      <td>179600</td>\n",
       "      <td>204785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>242</td>\n",
       "      <td>287090</td>\n",
       "      <td>273363.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual_price  predicted_price\n",
       "0          307000         267164.0\n",
       "1          129500         112135.0\n",
       "2          159000         159353.0\n",
       "3          306000         275309.0\n",
       "4          207500         175619.0\n",
       "..            ...              ...\n",
       "238        140000         143114.0\n",
       "239         64500          68041.0\n",
       "240        120500         104428.0\n",
       "241        179600         204785.0\n",
       "242        287090         273363.0\n",
       "\n",
       "[243 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct the results dataframe\n",
    "actual_price = test_y.reset_index()['SalePrice']\n",
    "pred_price = saleprice_preds.round()\n",
    "\n",
    "res = pd.DataFrame({'actual_price': actual_price, 'predicted_price': pred_price})\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>actual_price</th>\n",
       "      <th>predicted_price</th>\n",
       "      <th>residual</th>\n",
       "      <th>percent_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>307000</td>\n",
       "      <td>267164.0</td>\n",
       "      <td>39836.0</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>129500</td>\n",
       "      <td>112135.0</td>\n",
       "      <td>17365.0</td>\n",
       "      <td>13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>159000</td>\n",
       "      <td>159353.0</td>\n",
       "      <td>-353.0</td>\n",
       "      <td>-0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>306000</td>\n",
       "      <td>275309.0</td>\n",
       "      <td>30691.0</td>\n",
       "      <td>10.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>207500</td>\n",
       "      <td>175619.0</td>\n",
       "      <td>31881.0</td>\n",
       "      <td>15.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>238</td>\n",
       "      <td>140000</td>\n",
       "      <td>143114.0</td>\n",
       "      <td>-3114.0</td>\n",
       "      <td>-2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>64500</td>\n",
       "      <td>68041.0</td>\n",
       "      <td>-3541.0</td>\n",
       "      <td>-5.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>120500</td>\n",
       "      <td>104428.0</td>\n",
       "      <td>16072.0</td>\n",
       "      <td>13.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>241</td>\n",
       "      <td>179600</td>\n",
       "      <td>204785.0</td>\n",
       "      <td>-25185.0</td>\n",
       "      <td>-14.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>242</td>\n",
       "      <td>287090</td>\n",
       "      <td>273363.0</td>\n",
       "      <td>13727.0</td>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual_price  predicted_price  residual  percent_diff\n",
       "0          307000         267164.0   39836.0         12.98\n",
       "1          129500         112135.0   17365.0         13.41\n",
       "2          159000         159353.0    -353.0         -0.22\n",
       "3          306000         275309.0   30691.0         10.03\n",
       "4          207500         175619.0   31881.0         15.36\n",
       "..            ...              ...       ...           ...\n",
       "238        140000         143114.0   -3114.0         -2.22\n",
       "239         64500          68041.0   -3541.0         -5.49\n",
       "240        120500         104428.0   16072.0         13.34\n",
       "241        179600         204785.0  -25185.0        -14.02\n",
       "242        287090         273363.0   13727.0          4.78\n",
       "\n",
       "[243 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res['residual'] = res.actual_price - res.predicted_price\n",
    "res['percent_diff'] = (res['residual'] / res.actual_price * 100).round(decimals=2)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average percent_difference: 13.12\n"
     ]
    }
   ],
   "source": [
    "print('Average percent_difference: %.2f' % (res.percent_diff.abs().mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19687d22e48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicted price is actually really close to actual, but not when actual price is very very high\n",
    "#res.plot(kind='line', y=['actual_price', 'predicted_price'])\n",
    "#plt.plot(res.predicted_price, 'r--', res.actual_price)\n",
    "#plt.show()\n",
    "\n",
    "#sns.lineplot(data=res[['actual_price', 'predicted_price']])\n",
    "sns_plot = sns.lineplot(data=res.actual_price, linewidth=2.5, label='Actual Price')\n",
    "sns_plot = sns.lineplot(data=res.predicted_price, color='maroon', linewidth=2.5, label='Predicted Price')\n",
    "sns_plot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-200000, 300000)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "res2 = res[res.residual < 150000]\n",
    "res2.plot(kind='line', y='residual')\n",
    "plt.ylim(-200000, 300000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4 64-bit ('base': conda)",
   "language": "python",
   "name": "python37464bitbaseconda536c566210f0426bb8cfea15c7ee20a0"
  },
  "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
}