{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Dataset & Quick Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "from sklearn.datasets import load_boston\n",
    "\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 15, 15\n",
    "# 讀取資料\n",
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "target = raw_df.values[1::2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data.data.shape)  # data ==> Features\n",
    "print(data.target.shape)  # target ==> Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Features V.S. Y\n",
    "Can you explain the relation between other features with house prices?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- CRIM     per capita crime rate by town\n",
    "- ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "- INDUS    proportion of non-retail business acres per town\n",
    "- CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
    "- NOX      nitric oxides concentration (parts per 10 million)\n",
    "- RM       average number of rooms per dwelling\n",
    "- AGE      proportion of owner-occupied units built prior to 1940\n",
    "- DIS      weighted distances to five Boston employment centres\n",
    "- RAD      index of accessibility to radial highways\n",
    "- TAX      full-value property-tax rate per $10,000\n",
    "- PTRATIO  pupil-teacher ratio by town\n",
    "- B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
    "- LSTAT    % lower status of the population\n",
    "\"\"\"\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Home Prices: SVR\n",
    "在沒有做任何new feature生成的情況下，是否可以讓SVR的performance接近linear regression?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import needed models in scikit-learn by yourself\n",
    "# and fit a linear model using training dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.25,\n",
    "                                                    random_state=42,\n",
    "                                                    shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Prediciton with SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation \n",
    "利用Root mean square或 mean absolute error來評估結果，看看是否有辦法做的比\n",
    "linear regression好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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