features_weights

{
 "metadata": {
  "name": "features_and_weights"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# import some libraries \n\nfrom sklearn import linear_model\nimport numpy as np",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 198
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# enter the features and prices of the houses on the market\n\nhouse_one_features = np.array([2, 1.5, 1]) # 2 bedroom, 1.5 bathroom, 1 floor house\nhouse_one_price = np.array([430000]) # $430,000 house\n\nhouse_two_features = np.array([4, 3, 2]) # 4 bedroom, 3 bathroom, 2 floor house \nhouse_two_price = np.array([980000]) # $980,000 house\n\nhouse_three_features = np.array([3, 2, 1.5]) # 3 bedroom, 2 bathroom, 1.5 floor house\nhouse_three_price = np.array([660000]) # $660,000 house\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# put all the features together, and all the prices together\n# the features are lined up with their corresponding house price\n\nhouse_features = np.array([house_one_features, house_two_features, house_three_features])\nhouse_prices = np.array([house_one_price, house_two_price, house_three_price])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# calculate the weights based on the features and prices\n\nmodel = linear_model.LinearRegression(fit_intercept=False) # set up a model\nmodel.fit(house_features, house_prices) # calculate the weights. this single line does all the math to learn how to do the prediction\nprint 'Weights: ', model.coef_",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Weights:  [[  54400.  228000.   27200.]]\n"
      }
     ],
     "prompt_number": 201
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# enter the features for the house whose price we want to predict\n\nhouse_four_features = np.array([3, 2, 1]) # 3 bedroom, 2 bathroom, 1 floor house",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# use the weights that we just calculated to predict the price\n\npredicted_price = model.predict(house_four_features) # predict the price\nprint 'Predicted price of house: ', predicted_price",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Predicted price of house:  [ 646400.]\n"
      }
     ],
     "prompt_number": 203
    }
   ],
   "metadata": {}
  }
 ]
}