Prediction Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.transforms as mtransforms\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, explained_variance_score\n",
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/data_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# one hot\n",
    "data = pd.get_dummies(data, prefix = [\"HEAT\", \"AC\", \"STYLE\", \"STRUCT\", \"GRADE\", \"CNDTN\", \"EXTWALL\", \"INTWALL\", \"ROOF\", \"ASSESSMENT_SUBNBHD\"], columns = [\"HEAT\", \"AC\", \"STYLE\", \"STRUCT\", \"GRADE\", \"CNDTN\", \"EXTWALL\", \"INTWALL\", \"ROOF\", \"ASSESSMENT_SUBNBHD\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split\n",
    "x_data = data.drop([\"PRICE\"], axis=1)\n",
    "y_data = data[\"PRICE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normalization\n",
    "x_data = x_data.astype(float)\n",
    "x_data = x_data.apply(lambda x: (x - x.min()) / (x.max() - x.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# train & test\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: -2.94405252651e+17\n",
      "Explained variance score: -2.94366684355e+17\n",
      "Mean absolute error: 2.8243062655e+12\n",
      "Mean absolute percentage error: 731247768.56\n",
      "Mean squared error: 6.08889711451e+28\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1758b652390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linreg = LinearRegression(n_jobs = -1)\n",
    "linreg.fit(x_train, y_train)\n",
    "\n",
    "y_pred_lin = linreg.predict(x_test)\n",
    "\n",
    "lin_score = linreg.score(x_test, y_test)\n",
    "lin_msqe = mean_squared_error(y_test, y_pred_lin)\n",
    "\n",
    "print(\"R2 Score: \" + str(r2_score(y_test, y_pred_lin)))\n",
    "print(\"Explained variance score: \" + str(explained_variance_score(y_test, y_pred_lin)))\n",
    "print(\"Mean absolute error: \" + str(mean_absolute_error(y_test, y_pred_lin)))\n",
    "print(\"Mean absolute percentage error: \" + str(mean_absolute_percentage_error(y_test, y_pred_lin)))\n",
    "print(\"Mean squared error: \" + str(mean_squared_error(y_test, y_pred_lin)))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, y_pred_lin, c='black', marker=\"x\")\n",
    "ax.plot([0, y_test.max()], [0, y_test.max()], \"r--\", lw=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.707268632106\n",
      "Explained variance score: 0.707275262306\n",
      "Mean absolute error: 153931.634174\n",
      "Mean absolute percentage error: 43.7666957143\n",
      "Mean squared error: 60542777863.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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jFp8NiAzhWieD8m3DvgVVDhYT6UZJhvsBIeH+vYhtDwFuBl5Tb79Z1NzzMnIU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\nQAp3EZEO9P8BUYcAk8HkhuUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1758b775128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lassoreg = Lasso()\n",
    "lassoreg.fit(x_train, y_train)\n",
    "\n",
    "y_pred_lasso = lassoreg.predict(x_test)\n",
    "\n",
    "lasso_score = lassoreg.score(x_test, y_test)\n",
    "lasso_msqe = mean_squared_error(y_test, y_pred_lasso)\n",
    "\n",
    "print(\"R2 Score: \" + str(r2_score(y_test, y_pred_lasso)))\n",
    "print(\"Explained variance score: \" + str(explained_variance_score(y_test, y_pred_lasso)))\n",
    "print(\"Mean absolute error: \" + str(mean_absolute_error(y_test, y_pred_lasso)))\n",
    "print(\"Mean absolute percentage error: \" + str(mean_absolute_percentage_error(y_test, y_pred_lasso)))\n",
    "print(\"Mean squared error: \" + str(mean_squared_error(y_test, y_pred_lasso)))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, y_pred_lasso, c='black')\n",
    "ax.plot([0, y_test.max()], [0, y_test.max()], \"r--\", lw=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logreg = LogisticRegression(n_jobs=-1, solver=\"sag\")\n",
    "logreg.fit(x_train, y_train)\n",
    "\n",
    "y_pred_log = logreg.predict(x_test)\n",
    "\n",
    "lin_score = linreg.score(x_test, y_test)\n",
    "lin_msqe = mean_squared_error(y_test, y_pred_log)\n",
    "\n",
    "print(\"R2 Score: \" + str(r2_score(y_test, y_pred_log)))\n",
    "print(\"Explained variance score: \" + str(explained_variance_score(y_test, y_pred_log)))\n",
    "print(\"Mean absolute error: \" + str(mean_absolute_error(y_test, y_pred_log)))\n",
    "print(\"Mean absolute percentage error: \" + str(mean_absolute_percentage_error(y_test, y_pred_log)))\n",
    "print(\"Mean squared error: \" + str(mean_squared_error(y_test, y_pred_log)))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, y_pred_log, c='black')\n",
    "ax.plot([0, y_test.max()], [0, y_test.max()], \"r--\", lw=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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