regression models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>...</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>Full Bath</th>\n",
       "      <th>Half Bath</th>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>6</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>112.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1656</td>\n",
       "      <td>...</td>\n",
       "      <td>1656</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>210</td>\n",
       "      <td>62</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>5</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>896</td>\n",
       "      <td>...</td>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>108.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>1329</td>\n",
       "      <td>...</td>\n",
       "      <td>1329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>7</td>\n",
       "      <td>1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>2110</td>\n",
       "      <td>...</td>\n",
       "      <td>2110</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>928</td>\n",
       "      <td>...</td>\n",
       "      <td>1629</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lot Frontage  Lot Area  Overall Qual  Year Built  Year Remod/Add  \\\n",
       "0         141.0     31770             6        1960            1960   \n",
       "1          80.0     11622             5        1961            1961   \n",
       "2          81.0     14267             6        1958            1958   \n",
       "3          93.0     11160             7        1968            1968   \n",
       "4          74.0     13830             5        1997            1998   \n",
       "\n",
       "   Mas Vnr Area  BsmtFin SF 1  Bsmt Unf SF  Total Bsmt SF  1st Flr SF  \\\n",
       "0         112.0         639.0        441.0         1080.0        1656   \n",
       "1           0.0         468.0        270.0          882.0         896   \n",
       "2         108.0         923.0        406.0         1329.0        1329   \n",
       "3           0.0        1065.0       1045.0         2110.0        2110   \n",
       "4           0.0         791.0        137.0          928.0         928   \n",
       "\n",
       "     ...      Gr Liv Area  Full Bath  Half Bath  TotRms AbvGrd  Fireplaces  \\\n",
       "0    ...             1656          1          0              7           2   \n",
       "1    ...              896          1          0              5           0   \n",
       "2    ...             1329          1          1              6           0   \n",
       "3    ...             2110          2          1              8           2   \n",
       "4    ...             1629          2          1              6           1   \n",
       "\n",
       "   Garage Cars  Garage Area  Wood Deck SF  Open Porch SF  SalePrice  \n",
       "0          2.0        528.0           210             62     215000  \n",
       "1          1.0        730.0           140              0     105000  \n",
       "2          1.0        312.0           393             36     172000  \n",
       "3          2.0        522.0             0              0     244000  \n",
       "4          2.0        482.0           212             34     189900  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import csv\n",
    "datafile = \"Resources/IowaCleanData.csv\"\n",
    "df = pd.read_csv(datafile)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lot Frontage', 'Lot Area', 'Overall Qual', 'Year Built',\n",
       "       'Year Remod/Add', 'Mas Vnr Area', 'BsmtFin SF 1', 'Bsmt Unf SF',\n",
       "       'Total Bsmt SF', '1st Flr SF', '2nd Flr SF', 'Gr Liv Area', 'Full Bath',\n",
       "       'Half Bath', 'TotRms AbvGrd', 'Fireplaces', 'Garage Cars',\n",
       "       'Garage Area', 'Wood Deck SF', 'Open Porch SF', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2197, 20), (733, 20), (2197,), (733,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split into train/test\n",
    "X = df[['Lot Frontage', 'Lot Area', 'Overall Qual', 'Year Built','Year Remod/Add', 'Mas Vnr Area', 'BsmtFin SF 1', 'Bsmt Unf SF','Total Bsmt SF', '1st Flr SF', '2nd Flr SF', 'Gr Liv Area', 'Full Bath','Half Bath', 'TotRms AbvGrd', 'Fireplaces', 'Garage Cars','Garage Area', 'Wood Deck SF', 'Open Porch SF']]\n",
    "y = df['SalePrice']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1225220757.8794043, R2: 0.8319330212616827\n"
     ]
    }
   ],
   "source": [
    "# Multiple Linear Regression model\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "MSE = mean_squared_error(y_test, predictions)\n",
    "\n",
    "r2 = r2_score(y_test, predictions)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "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": [
    "# Multiple Linear Regression model\n",
    "\n",
    "# Plot Residuals\n",
    "\n",
    "plt.scatter(predictions, predictions - y_test)\n",
    "\n",
    "plt.hlines(y=0, xmin=predictions.min(), xmax=predictions.max())\n",
    "\n",
    "plt.savefig('Images/Multiple Linear Regression')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(model.predict(X_train), model.predict(X_train) - y_train, c=\"blue\", label=\"Training Data\")\n",
    "\n",
    "# plt.scatter(model.predict(X_test), model.predict(X_test) - y_test, c=\"orange\", label=\"Testing Data\")\n",
    "\n",
    "# plt.legend()\n",
    "\n",
    "# plt.hlines(y=0, xmin=y.min(), xmax=y.max())\n",
    "\n",
    "# plt.title(\"Residual Plot\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1225219302.7043579, R2: 0.8319332208721383\n"
     ]
    }
   ],
   "source": [
    "# Ridge model\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge = Ridge(alpha=.01).fit(X_train, y_train)\n",
    "\n",
    "predictions = ridge.predict(X_test)\n",
    "\n",
    "MSE = mean_squared_error(y_test, predictions)\n",
    "\n",
    "r2 = ridge.score(X_test, y_test)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ridge model\n",
    "\n",
    "# Plot Residuals\n",
    "\n",
    "plt.scatter(predictions, predictions - y_test)\n",
    "\n",
    "plt.hlines(y=0, xmin=predictions.min(), xmax=predictions.max())\n",
    "\n",
    "plt.savefig('Images/Ridge')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1225222762.3354769, R2: 0.8319327463048033\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kamrk\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# LASSO model\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=Y)\n",
    "\n",
    "lasso = Lasso(alpha=.01).fit(X_train, y_train)\n",
    "\n",
    "predictions = lasso.predict(X_test)\n",
    "\n",
    "MSE = mean_squared_error(y_test, predictions)\n",
    "\n",
    "r2 = lasso.score(X_test, y_test)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LASSO model\n",
    "\n",
    "# Plot Residuals\n",
    "\n",
    "plt.scatter(predictions, predictions - y_test)\n",
    "\n",
    "plt.hlines(y=0, xmin=predictions.min(), xmax=predictions.max())\n",
    "\n",
    "plt.savefig('Images/LASSO')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1223744529.6377602, R2: 0.8321355196432239\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kamrk\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# ElasticNet model\n",
    "\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "elasticnet = ElasticNet(alpha=.01).fit(X_train, y_train)\n",
    "\n",
    "predictions = elasticnet.predict(X_test)\n",
    "\n",
    "MSE = mean_squared_error(y_test, predictions)\n",
    "\n",
    "r2 = elasticnet.score(X_test, y_test)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ElasticNet model\n",
    "\n",
    "# Plot Residuals\n",
    "\n",
    "plt.scatter(predictions, predictions - y_test)\n",
    "\n",
    "plt.hlines(y=0, xmin=predictions.min(), xmax=predictions.max())\n",
    "\n",
    "plt.savefig('Images/ElasticNet')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}