Luxury-Watch-Valuation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "import re\n",
    "import ast\n",
    "import seaborn as sns\n",
    "from pandas import Categorical\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "from __future__ import division\n",
    "\n",
    "pd.options.display.max_columns = 999\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dftest = pd.read_csv('/home/adrienporter/McNulty/proj3-mcnulty/\\\n",
    "porter_adrien/watches/watches/watchbot_output/watchbot4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def unique_functions(df_columns):\n",
    "    d = defaultdict(int)\n",
    "    unique = set()\n",
    "    for i in df_columns:\n",
    "        separate = i.split(',')\n",
    "        for j in separate:\n",
    "            unique.add(j.strip())\n",
    "    return unique\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "columns = unique_functions(dftest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f = open('/home/adrienporter/McNulty/proj3-mcnulty/porter_adrien/watches/watches/logs/scrapy_output3.txt', 'r')\n",
    "log = f.read()\n",
    "log_data = re.findall('{.+}', log)\n",
    "dict_list = []\n",
    "for i in log_data:\n",
    "    dict_list.append(ast.literal_eval(i))\n",
    "# dict_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clean_dicts = []\n",
    "\n",
    "for watch_dict in dict_list[1:]:\n",
    "    for k, v in watch_dict.items():\n",
    "        if k == v:\n",
    "            for i in k.split(','):\n",
    "                watch_dict[i.strip()] = 1\n",
    "            watch_dict.pop(k, None)\n",
    "    clean_dicts.append(watch_dict)\n",
    "# clean_dicts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(clean_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>4 Year Calendar</th>\n",
       "      <th>Alarm</th>\n",
       "      <th>Annual calendar</th>\n",
       "      <th>Availability</th>\n",
       "      <th>Base Caliber</th>\n",
       "      <th>Blue Steel Hands</th>\n",
       "      <th>Bracelet color</th>\n",
       "      <th>Bracelet length</th>\n",
       "      <th>Bracelet material</th>\n",
       "      <th>Bracelet thickness</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Buckle width</th>\n",
       "      <th>Case diameter</th>\n",
       "      <th>Case material</th>\n",
       "      <th>Center Seconds</th>\n",
       "      <th>Chiming clock</th>\n",
       "      <th>Chronograph</th>\n",
       "      <th>Chronometer</th>\n",
       "      <th>Clasp</th>\n",
       "      <th>Clasp material</th>\n",
       "      <th>Code</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Crown Left</th>\n",
       "      <th>Date</th>\n",
       "      <th>Dial</th>\n",
       "      <th>Dial numerals</th>\n",
       "      <th>Display Back</th>\n",
       "      <th>Double Chronograph</th>\n",
       "      <th>Equation of time</th>\n",
       "      <th>Flyback</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>GMT</th>\n",
       "      <th>Gemstone</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Genevian Seal</th>\n",
       "      <th>Glass</th>\n",
       "      <th>Guilloche Dial</th>\n",
       "      <th>Guilloche Dial (handwork)</th>\n",
       "      <th>Helium Valve</th>\n",
       "      <th>Jumping hour</th>\n",
       "      <th>Limited Edition</th>\n",
       "      <th>Location</th>\n",
       "      <th>Luminescent Hands</th>\n",
       "      <th>Luminescent Numerals</th>\n",
       "      <th>Luminous indexes</th>\n",
       "      <th>Material bezel</th>\n",
       "      <th>Minute Repeater</th>\n",
       "      <th>Month</th>\n",
       "      <th>Moonphase</th>\n",
       "      <th>Movement</th>\n",
       "      <th>Movement/Caliber</th>\n",
       "      <th>Number of jewels</th>\n",
       "      <th>Only Original Parts</th>\n",
       "      <th>PVD/DLC coating</th>\n",
       "      <th>Panorama-Date</th>\n",
       "      <th>Perpetual Calendar</th>\n",
       "      <th>Power Reserve Display</th>\n",
       "      <th>Power reserve</th>\n",
       "      <th>Price</th>\n",
       "      <th>Quick Set</th>\n",
       "      <th>Ref. No.</th>\n",
       "      <th>Repeater</th>\n",
       "      <th>Rotating Bezel</th>\n",
       "      <th>Screw-Down Crown</th>\n",
       "      <th>Screw-Down Push-Buttons</th>\n",
       "      <th>Skeletonized</th>\n",
       "      <th>Small Seconds</th>\n",
       "      <th>Tachymeter</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>Tourbillon</th>\n",
       "      <th>Waterproof</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Year</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Available now</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White Gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rolex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29 mm</td>\n",
       "      <td>White gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fold clasp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15312</td>\n",
       "      <td>2 (fine)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ladies' watch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States, New York, New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Automatic Self Wind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$12,999 [Negotiable]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.chrono24.com/rolex/datejust-80319-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>With papers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Available now</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Silver</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rolex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41 mm</td>\n",
       "      <td>White gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fold clasp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ROLE310959_1</td>\n",
       "      <td>1 (mint)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Men's watch/Unisex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States, Pennsylvania, Philadelphia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$29,500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10 ATM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.chrono24.com/rolex/day-date-ii-2182...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Available now</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold/Steel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold/Steel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rolex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36 mm</td>\n",
       "      <td>Steel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fold clasp</td>\n",
       "      <td>Steel</td>\n",
       "      <td>RX0105171X</td>\n",
       "      <td>1 (mint)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Silver</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Men's watch/Unisex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sapphire Glass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States, New York, New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yellow gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$6,199 [Negotiable]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001</td>\n",
       "      <td>http://www.chrono24.com/rolex/mens-rolex-datej...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>With papers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Available now</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rolex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26 x 32.6 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fold clasp, hidden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RLX6811</td>\n",
       "      <td>1 (mint)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mother of pearl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ladies' watch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sapphire Glass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States, Texas, Austin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Rolex 2235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$15,900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11 mm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002</td>\n",
       "      <td>http://www.chrono24.com/rolex/--ladies-preside...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>With box</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Available now</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rolex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31 mm</td>\n",
       "      <td>Steel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W11364</td>\n",
       "      <td>1 (mint)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Silver</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sapphire Glass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States, California, Newport Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$4,125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30 ATM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.chrono24.com/rolex/datejust-mid-siz...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                4 Year Calendar  Alarm  Annual calendar   Availability  \\\n",
       "0          NaN              NaN    NaN              NaN  Available now   \n",
       "1  With papers              NaN    NaN              NaN  Available now   \n",
       "2          NaN              NaN    NaN              NaN  Available now   \n",
       "3  With papers              NaN    NaN              NaN  Available now   \n",
       "4     With box              NaN    NaN              NaN  Available now   \n",
       "\n",
       "  Base Caliber  Blue Steel Hands Bracelet color Bracelet length  \\\n",
       "0          NaN               NaN           Gold             NaN   \n",
       "1          NaN               NaN         Silver             NaN   \n",
       "2          NaN               NaN     Gold/Steel             NaN   \n",
       "3          NaN               NaN           Gold             NaN   \n",
       "4          NaN               NaN            NaN             NaN   \n",
       "\n",
       "  Bracelet material Bracelet thickness  Brand Buckle width Case diameter  \\\n",
       "0        White Gold                NaN  Rolex          NaN         29 mm   \n",
       "1               NaN                NaN  Rolex          NaN         41 mm   \n",
       "2        Gold/Steel                NaN  Rolex          NaN         36 mm   \n",
       "3               NaN                NaN  Rolex          NaN  26 x 32.6 mm   \n",
       "4               NaN                NaN  Rolex          NaN         31 mm   \n",
       "\n",
       "  Case material  Center Seconds  Chiming clock  Chronograph  Chronometer  \\\n",
       "0    White gold             NaN            NaN          NaN          NaN   \n",
       "1    White gold             NaN            NaN          NaN          NaN   \n",
       "2         Steel             NaN            NaN          NaN          NaN   \n",
       "3           NaN             NaN            NaN          NaN          NaN   \n",
       "4         Steel             NaN            NaN          NaN          NaN   \n",
       "\n",
       "                Clasp Clasp material          Code Condition  Crown Left  \\\n",
       "0          Fold clasp            NaN         15312  2 (fine)         NaN   \n",
       "1          Fold clasp            NaN  ROLE310959_1  1 (mint)         NaN   \n",
       "2          Fold clasp          Steel    RX0105171X  1 (mint)         NaN   \n",
       "3  Fold clasp, hidden            NaN       RLX6811  1 (mint)         NaN   \n",
       "4                 NaN            NaN        W11364  1 (mint)         NaN   \n",
       "\n",
       "   Date             Dial Dial numerals  Display Back  Double Chronograph  \\\n",
       "0   NaN              NaN           NaN           NaN                 NaN   \n",
       "1   NaN            Black           NaN           NaN                 NaN   \n",
       "2   NaN           Silver           NaN           NaN                 NaN   \n",
       "3   NaN  Mother of pearl           NaN           NaN                 NaN   \n",
       "4   NaN           Silver           NaN           NaN                 NaN   \n",
       "\n",
       "   Equation of time  Flyback Frequency  GMT  Gemstone              Gender  \\\n",
       "0               NaN      NaN       NaN  NaN       NaN       Ladies' watch   \n",
       "1               NaN      NaN       NaN  NaN       NaN  Men's watch/Unisex   \n",
       "2               NaN      NaN       NaN  NaN       NaN  Men's watch/Unisex   \n",
       "3               NaN      NaN       NaN  NaN       NaN       Ladies' watch   \n",
       "4               NaN      NaN       NaN  NaN       NaN                 NaN   \n",
       "\n",
       "   Genevian Seal           Glass  Guilloche Dial  Guilloche Dial (handwork)  \\\n",
       "0            NaN             NaN             NaN                        NaN   \n",
       "1            NaN             NaN             NaN                        NaN   \n",
       "2            NaN  Sapphire Glass             NaN                        NaN   \n",
       "3            NaN  Sapphire Glass             NaN                        NaN   \n",
       "4            NaN  Sapphire Glass             NaN                        NaN   \n",
       "\n",
       "   Helium Valve  Jumping hour  Limited Edition  \\\n",
       "0           NaN           NaN              NaN   \n",
       "1           NaN           NaN              NaN   \n",
       "2           NaN           NaN              NaN   \n",
       "3           NaN           NaN              NaN   \n",
       "4           NaN           NaN              NaN   \n",
       "\n",
       "                                    Location  Luminescent Hands  \\\n",
       "0          United States, New York, New York                NaN   \n",
       "1  United States, Pennsylvania, Philadelphia                NaN   \n",
       "2          United States, New York, New York                NaN   \n",
       "3               United States, Texas, Austin                NaN   \n",
       "4   United States, California, Newport Beach                NaN   \n",
       "\n",
       "   Luminescent Numerals  Luminous indexes Material bezel  Minute Repeater  \\\n",
       "0                   NaN               NaN            NaN              NaN   \n",
       "1                   NaN               NaN            NaN              NaN   \n",
       "2                   NaN               NaN    Yellow gold              NaN   \n",
       "3                   NaN               NaN            NaN              NaN   \n",
       "4                   NaN               NaN            NaN              NaN   \n",
       "\n",
       "   Month  Moonphase   Movement     Movement/Caliber Number of jewels  \\\n",
       "0    NaN        NaN  Automatic  Automatic Self Wind              NaN   \n",
       "1    NaN        NaN  Automatic                  NaN              NaN   \n",
       "2    NaN        NaN  Automatic                  NaN              NaN   \n",
       "3    NaN        NaN  Automatic           Rolex 2235              NaN   \n",
       "4    NaN        NaN  Automatic                  NaN              NaN   \n",
       "\n",
       "   Only Original Parts  PVD/DLC coating  Panorama-Date  Perpetual Calendar  \\\n",
       "0                  NaN              NaN            NaN                 NaN   \n",
       "1                  NaN              NaN            NaN                 NaN   \n",
       "2                  NaN              NaN            NaN                 NaN   \n",
       "3                  NaN              NaN            NaN                 NaN   \n",
       "4                  NaN              NaN            NaN                 NaN   \n",
       "\n",
       "   Power Reserve Display Power reserve                 Price  Quick Set  \\\n",
       "0                    NaN           NaN  $12,999 [Negotiable]        NaN   \n",
       "1                    NaN           NaN               $29,500        NaN   \n",
       "2                    NaN           NaN   $6,199 [Negotiable]        NaN   \n",
       "3                    NaN           NaN               $15,900        NaN   \n",
       "4                    NaN           NaN                $4,125        NaN   \n",
       "\n",
       "  Ref. No.  Repeater  Rotating Bezel  Screw-Down Crown  \\\n",
       "0    80319       NaN             NaN               NaN   \n",
       "1   218239       NaN             NaN               NaN   \n",
       "2    16263       NaN             NaN               NaN   \n",
       "3   179368       NaN             NaN               NaN   \n",
       "4      NaN       NaN             NaN               NaN   \n",
       "\n",
       "   Screw-Down Push-Buttons  Skeletonized  Small Seconds  Tachymeter Thickness  \\\n",
       "0                      NaN           NaN            NaN         NaN     10 mm   \n",
       "1                      NaN           NaN            NaN         NaN       NaN   \n",
       "2                      NaN           NaN            NaN         NaN       NaN   \n",
       "3                      NaN           NaN            NaN         NaN     11 mm   \n",
       "4                      NaN           NaN            NaN         NaN       NaN   \n",
       "\n",
       "   Tourbillon Waterproof  Weekday  Year  \\\n",
       "0         NaN        NaN      NaN   NaN   \n",
       "1         NaN     10 ATM      NaN   NaN   \n",
       "2         NaN        NaN      NaN  2001   \n",
       "3         NaN        NaN      NaN  2002   \n",
       "4         NaN     30 ATM      NaN   NaN   \n",
       "\n",
       "                                                 url  \n",
       "0  http://www.chrono24.com/rolex/datejust-80319-1...  \n",
       "1  http://www.chrono24.com/rolex/day-date-ii-2182...  \n",
       "2  http://www.chrono24.com/rolex/mens-rolex-datej...  \n",
       "3  http://www.chrono24.com/rolex/--ladies-preside...  \n",
       "4  http://www.chrono24.com/rolex/datejust-mid-siz...  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Case Diameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "def clean_case_d(s):\n",
    "    try:\n",
    "        x = re.findall(r'^[0-9.,]+', s)\n",
    "        x = re.sub(',', '.', x[0])\n",
    "        if int(x[0]) > 60:\n",
    "            return None\n",
    "        else:\n",
    "            return x\n",
    "    except:\n",
    "        return None\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Case diameter'] = df['Case diameter'].apply(clean_case_d)\n",
    "df['Case diameter'] = pd.to_numeric(df['Case diameter'], errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Clean Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def clean_price(s):\n",
    "    try:\n",
    "        x = re.findall(r'[0-9,]+', s)\n",
    "        x = re.sub(',', '', x[0])\n",
    "        return x\n",
    "    except:\n",
    "        return None\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['Price'] = df['Price'].apply(clean_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['Price'] = pd.to_numeric(df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def clean_year(s):\n",
    "    try:\n",
    "        x = re.findall(r'([0-9]{4})', s)\n",
    "        if int(x[0]) > 2018 or int(x[0])< 1700:\n",
    "            return None\n",
    "        else:\n",
    "            return x[0]\n",
    "    except:\n",
    "        return None\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['Year'] = df['Year'].apply(clean_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df[df['Frequency'] == '28.800 Hz']\n",
    "# df['Frequency'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_freq(s):\n",
    "    try:\n",
    "        x = re.findall(r'([0-9]{4})', s)\n",
    "        if int(x[0]) > 2018 or int(x[0])< 1700:\n",
    "            return None\n",
    "        else:\n",
    "            return x[0]\n",
    "    except:\n",
    "        return None\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Power Reserve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_pr(s):\n",
    "    try:\n",
    "        x = re.findall(r'[0-9]+', s)\n",
    "        if int(x[0]) > 2000:\n",
    "            return None\n",
    "        else:\n",
    "            return x[0]\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Power reserve'] = df['Power reserve'].apply(clean_pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Part 1: Predict Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# rearrange columns\n",
    "cols = df.columns.tolist()\n",
    "cols = cols[11:] + cols[:11]\n",
    "# cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df[cols]\n",
    "# df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Buckle width</th>\n",
       "      <th>Case diameter</th>\n",
       "      <th>Case material</th>\n",
       "      <th>Center Seconds</th>\n",
       "      <th>Chiming clock</th>\n",
       "      <th>Chronograph</th>\n",
       "      <th>Chronometer</th>\n",
       "      <th>Clasp</th>\n",
       "      <th>Clasp material</th>\n",
       "      <th>Code</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Crown Left</th>\n",
       "      <th>Date</th>\n",
       "      <th>Dial</th>\n",
       "      <th>Dial numerals</th>\n",
       "      <th>Display Back</th>\n",
       "      <th>Double Chronograph</th>\n",
       "      <th>Equation of time</th>\n",
       "      <th>Flyback</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>GMT</th>\n",
       "      <th>Gemstone</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Genevian Seal</th>\n",
       "      <th>Glass</th>\n",
       "      <th>Guilloche Dial</th>\n",
       "      <th>Guilloche Dial (handwork)</th>\n",
       "      <th>Helium Valve</th>\n",
       "      <th>Jumping hour</th>\n",
       "      <th>Limited Edition</th>\n",
       "      <th>Location</th>\n",
       "      <th>Luminescent Hands</th>\n",
       "      <th>Luminescent Numerals</th>\n",
       "      <th>Luminous indexes</th>\n",
       "      <th>Material bezel</th>\n",
       "      <th>Minute Repeater</th>\n",
       "      <th>Month</th>\n",
       "      <th>Moonphase</th>\n",
       "      <th>Movement</th>\n",
       "      <th>Movement/Caliber</th>\n",
       "      <th>Number of jewels</th>\n",
       "      <th>Only Original Parts</th>\n",
       "      <th>PVD/DLC coating</th>\n",
       "      <th>Panorama-Date</th>\n",
       "      <th>Perpetual Calendar</th>\n",
       "      <th>Power Reserve Display</th>\n",
       "      <th>Power reserve</th>\n",
       "      <th>Price</th>\n",
       "      <th>Quick Set</th>\n",
       "      <th>Ref. No.</th>\n",
       "      <th>Repeater</th>\n",
       "      <th>Rotating Bezel</th>\n",
       "      <th>Screw-Down Crown</th>\n",
       "      <th>Screw-Down Push-Buttons</th>\n",
       "      <th>Skeletonized</th>\n",
       "      <th>Small Seconds</th>\n",
       "      <th>Tachymeter</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>Tourbillon</th>\n",
       "      <th>Waterproof</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Year</th>\n",
       "      <th>url</th>\n",
       "      <th></th>\n",
       "      <th>4 Year Calendar</th>\n",
       "      <th>Alarm</th>\n",
       "      <th>Annual calendar</th>\n",
       "      <th>Availability</th>\n",
       "      <th>Base Caliber</th>\n",
       "      <th>Blue Steel Hands</th>\n",
       "      <th>Bracelet color</th>\n",
       "      <th>Bracelet length</th>\n",
       "      <th>Bracelet material</th>\n",
       "      <th>Bracelet thickness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22454</th>\n",
       "      <td>Patek Philippe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>White gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Buckle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6002G-001 bnib</td>\n",
       "      <td>0 (unworn)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Roman numerals</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Men's watch/Unisex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States, California, Newport Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White gold</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6002G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>1950000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6002G-001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010</td>\n",
       "      <td>http://www.chrono24.com/patekphilippe/6002g-00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ready to ship in 3-5 days</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leather</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Brand Buckle width  Case diameter Case material  \\\n",
       "22454  Patek Philippe          NaN           33.0    White gold   \n",
       "\n",
       "       Center Seconds  Chiming clock  Chronograph  Chronometer   Clasp  \\\n",
       "22454             NaN            NaN          NaN          NaN  Buckle   \n",
       "\n",
       "      Clasp material            Code   Condition  Crown Left  Date  Dial  \\\n",
       "22454            NaN  6002G-001 bnib  0 (unworn)         NaN   1.0  Blue   \n",
       "\n",
       "        Dial numerals  Display Back  Double Chronograph  Equation of time  \\\n",
       "22454  Roman numerals           NaN                 NaN               NaN   \n",
       "\n",
       "       Flyback Frequency  GMT  Gemstone              Gender  Genevian Seal  \\\n",
       "22454      NaN       NaN  NaN       NaN  Men's watch/Unisex            NaN   \n",
       "\n",
       "      Glass  Guilloche Dial  Guilloche Dial (handwork)  Helium Valve  \\\n",
       "22454   NaN             NaN                        NaN           NaN   \n",
       "\n",
       "       Jumping hour  Limited Edition  \\\n",
       "22454           NaN              NaN   \n",
       "\n",
       "                                       Location  Luminescent Hands  \\\n",
       "22454  United States, California, Newport Beach                NaN   \n",
       "\n",
       "       Luminescent Numerals  Luminous indexes Material bezel  Minute Repeater  \\\n",
       "22454                   NaN               NaN     White gold              NaN   \n",
       "\n",
       "       Month  Moonphase   Movement Movement/Caliber Number of jewels  \\\n",
       "22454    NaN        NaN  Automatic            6002G              NaN   \n",
       "\n",
       "       Only Original Parts  PVD/DLC coating  Panorama-Date  \\\n",
       "22454                  NaN              NaN            NaN   \n",
       "\n",
       "       Perpetual Calendar  Power Reserve Display Power reserve      Price  \\\n",
       "22454                 1.0                    NaN          None  1950000.0   \n",
       "\n",
       "       Quick Set   Ref. No.  Repeater  Rotating Bezel  Screw-Down Crown  \\\n",
       "22454        NaN  6002G-001       NaN             NaN               NaN   \n",
       "\n",
       "       Screw-Down Push-Buttons  Skeletonized  Small Seconds  Tachymeter  \\\n",
       "22454                      NaN           NaN            NaN         NaN   \n",
       "\n",
       "      Thickness  Tourbillon Waterproof  Weekday  Year  \\\n",
       "22454       NaN         NaN        NaN      NaN  2010   \n",
       "\n",
       "                                                     url       \\\n",
       "22454  http://www.chrono24.com/patekphilippe/6002g-00...  NaN   \n",
       "\n",
       "       4 Year Calendar  Alarm  Annual calendar               Availability  \\\n",
       "22454              NaN    NaN              NaN  Ready to ship in 3-5 days   \n",
       "\n",
       "      Base Caliber  Blue Steel Hands Bracelet color Bracelet length  \\\n",
       "22454          NaN               NaN            NaN             NaN   \n",
       "\n",
       "      Bracelet material Bracelet thickness  \n",
       "22454           Leather                NaN  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.Price > 1.900000e+06]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# drop unnecessary columns\n",
    "df2 = df2.drop(['Code', 'Movement/Caliber', 'url', 'Availability', 'Location', 'Buckle width',\\\n",
    "               '', 'Number of jewels', 'Ref. No.', 'Thickness', 'Base Caliber', 'Bracelet length',\\\n",
    "                'Bracelet thickness', 'Frequency'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vars_to_dummy = ['Case material', 'Clasp', 'Clasp material', 'Condition', 'Dial', 'Dial numerals', 'Gender', 'Glass',\\\n",
    "           'Material bezel', 'Movement', 'Waterproof', 'Bracelet color', 'Bracelet material', 'Brand']\n",
    "\n",
    "continuous_vars = ['Year', 'Price', 'Case diameter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in df2.columns:\n",
    "    if i not in vars_to_dummy + continuous_vars:\n",
    "        df2[str(i)] = df2[str(i)].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df2.dropna(subset=['Price', 'Case diameter', 'Case material'])\n",
    "df2 = df2.drop(['Clasp', 'Clasp material', 'Condition', 'Dial', 'Dial numerals', \\\n",
    "                        'Gender', 'Glass', 'Material bezel', 'Movement', 'Movement', 'Waterproof', 'Year',\\\n",
    "                        'Bracelet color', 'Bracelet material'], axis=1)\n",
    "df2['log Case diameter'] = np.log(df2['Case diameter'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = pd.get_dummies(df2, columns=['Brand', 'Case material'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f1b56a15510>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L5pHJMIfanZx4pI5YIk2FXrsu7d9o4tZeEISSjnWVrpbvCyY42ulEo1ERDCcxGTS01ZVz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\nJHoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b56a15810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(np.log(df2[['Price', 'Case diameter']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price                        1.000000\n",
       "Minute Repeater              0.236550\n",
       "Case material_Platinum       0.225693\n",
       "Case material_White gold     0.189077\n",
       "Tourbillon                   0.184595\n",
       "Perpetual Calendar           0.164214\n",
       "Brand_Patek Philippe         0.146139\n",
       "Moonphase                    0.130154\n",
       "Month                        0.109179\n",
       "Skeletonized                 0.105841\n",
       "Case material_Pink gold      0.102516\n",
       "4 Year Calendar              0.091691\n",
       "Power Reserve Display        0.071326\n",
       "Small Seconds                0.067143\n",
       "Jumping hour                 0.066418\n",
       "log Case diameter            0.056777\n",
       "Case diameter                0.048964\n",
       "Display Back                 0.042593\n",
       "Limited Edition              0.031443\n",
       "Chiming clock                0.028896\n",
       "Annual calendar              0.025488\n",
       "Case material_Carbon         0.017312\n",
       "Case material_Titanium       0.014054\n",
       "Center Seconds               0.008717\n",
       "Weekday                      0.008236\n",
       "Equation of time             0.007092\n",
       "Guilloche Dial (handwork)    0.006793\n",
       "Genevian Seal                0.006478\n",
       "Chronograph                  0.005494\n",
       "Flyback                      0.004760\n",
       "                               ...   \n",
       "PVD/DLC coating             -0.003686\n",
       "Case material_Plastic       -0.003892\n",
       "Panorama-Date               -0.004341\n",
       "Case material_Silver        -0.004736\n",
       "Case material_Yellow gold   -0.005744\n",
       "Luminescent Numerals        -0.006092\n",
       "Double Chronograph          -0.007590\n",
       "Date                        -0.008234\n",
       "Screw-Down Push-Buttons     -0.013440\n",
       "Helium Valve                -0.015267\n",
       "Only Original Parts         -0.018968\n",
       "Luminescent Hands           -0.019120\n",
       "Luminous indexes            -0.021279\n",
       "Quick Set                   -0.023660\n",
       "Brand_IWC                   -0.024854\n",
       "Alarm                       -0.025722\n",
       "Brand_Hublot                -0.028113\n",
       "Case material_Ceramic       -0.032483\n",
       "Tachymeter                  -0.033173\n",
       "GMT                         -0.035317\n",
       "Brand_Jaeger-LeCoultre      -0.036523\n",
       "Brand_Panerai               -0.045777\n",
       "Brand_Omega                 -0.047016\n",
       "Case material_Gold/Steel    -0.056755\n",
       "Chronometer                 -0.073393\n",
       "Brand_Rolex                 -0.073853\n",
       "Rotating Bezel              -0.087718\n",
       "Brand_TAG Heuer             -0.093582\n",
       "Brand_Breitling             -0.116217\n",
       "Case material_Steel         -0.217825\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.corr()['Price'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# l = df2.columns\n",
    "# for i, k in enumerate(l):\n",
    "#     print i, k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = df2.drop('Price', axis=1)\n",
    "y = df2['Price']\n",
    "y_log = np.log(df2['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_log, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xEY+4g81yLpqa49XfgYhELKFwIMIvCBFkON4zA0f5/kalw2VRSb7PyHTwTCInw0yX1f+L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\nQUgwRPgFQRASDBF+QRCEBOP/AySYXXrzb0kKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b5186ef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "lr = LinearRegression()\n",
    "# lr.fit(X, y)\n",
    "predicted = cross_val_predict(lr, X, y_log, cv=10)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_log, predicted)\n",
    "ax.plot([np.log(y.min()), np.log(y.max())], [np.log(y.min()), np.log(y.max())], 'k--', lw=4)\n",
    "ax.set_xlabel('Measured')\n",
    "ax.set_ylabel('Predicted')\n",
    "plt.show()\n",
    "# lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.72459208708\n"
     ]
    },
    {
     "data": {
      "image/png": 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ocETOHEqG8s79TeC6s/v/U+PTHJEIVzpRaiDpl5H+gDqdHnbuT/4SCEPF2dHK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b50ba7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import metrics\n",
    "\n",
    "rcv = RidgeCV(cv=10)\n",
    "k_fold = KFold(5)\n",
    "rcv.fit(X_train, y_train)\n",
    "print rcv.score(X_test, y_test)\n",
    "\n",
    "# for k, (train, test) in enumerate(k_fold.split(X, y)):\n",
    "#     rcv.fit(X.iloc[train], y.iloc[train])\n",
    "#     print(\"[fold {0}] alpha: {1:.5f}, score: {2:.5f}\".\n",
    "#           format(k, rcv.alpha_, rcv.score(X.iloc[test], y.iloc[test])))\n",
    "\n",
    "predicted = cross_val_predict(rcv, X, y_log, cv=10)\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_log, predicted)\n",
    "ax.plot([np.log(y.min()), np.log(y.max())], [np.log(y.min()), np.log(y.max())], 'k--', lw=4)\n",
    "ax.set_xlabel('Measured')\n",
    "ax.set_ylabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TFgS2npac/kWsWQUUz1lKWnZhPIo66JRNDmyOqW+2n1TQt6QaOWdW8CDd0ubk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\nEviFECLJSOAXQogk8/8Bbgnv/YIVliIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b529442d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "lcv = LassoCV(cv=10)\n",
    "lcv.fit(X_train, y_train)\n",
    "lcv.score(X_test, y_test)\n",
    "\n",
    "predicted = cross_val_predict(lcv, X, y_log, cv=10)\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_log, predicted)\n",
    "ax.plot([np.log(y.min()), np.log(y.max())], [np.log(y.min()), np.log(y.max())], 'k--', lw=4)\n",
    "ax.set_xlabel('Measured')\n",
    "ax.set_ylabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b50fb6350>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4k5PH+3Ku5Tasdat/ev0YGebfn16pu0/me2Lpas49Mw9XX48BdwMXAUcjYi1A9fVYN+eQ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b5093d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "y_log.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zer65Lgw2NU3ljoFk7fulilf775H9XYu+3uwcfM8nm6+QiBnM5C32zu27uNqM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\nrq3oA141C7jVpueWqpPdCgGiWFpLAdi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Z241KGTj+l7/wNH/wp5fpDlLWtyKUtpVI15FU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KXkmgDtg5NlPNfmhaemuL7cIk0zYq1zN33ya9JMLE+Me9RKDlmWMTtRxA8SwjjB78YcOlBiZqJI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b501e2850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = rcv.predict(X)\n",
    "residuals = y_log - predicted\n",
    "\n",
    "df_plot = pd.DataFrame({'predicted' : predicted, 'residuals' : residuals}) \n",
    "\n",
    "with sns.axes_style('white'):\n",
    "    plot=df_plot.plot(kind='scatter',\n",
    "                  x='predicted',y='residuals',alpha=0.5,figsize=(10,6))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TPZRSbI81+ib70s0COhy4DtjX3bQMuE5Vn83AuQcDnyS8/hQ4KNUH+vfvTSi0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\nAMYYk6f+Hx3AsaVTuEfcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b531b1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(df_plot['residuals'], dist=\"norm\", plot=plt)\n",
    "plt.title(\"Normal Q-Q plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Model Part 2: Classify Over/Under-valued"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create over/undervalued label\n",
    "\n",
    "def valuation(x):\n",
    "    n = [0 if v > 0 else 1 for v in x]\n",
    "    return n\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2['Overvalued'] = valuation(y_log - predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4294\n",
       "0    3504\n",
       "Name: Overvalued, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Overvalued'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = df2.drop(['Overvalued', 'Price', 'Case diameter'], axis=1)\n",
    "y = df2['Overvalued']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.56 (+/- 0.04)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model, decomposition, datasets\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "logistic = linear_model.LogisticRegressionCV(cv=10)\n",
    "logistic.fit(X_train, y_train)\n",
    "coefs = logistic.coef_\n",
    "\n",
    "scores = cross_val_score(logistic, X_test, y_test, cv=10, n_jobs = 1)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "skf = StratifiedKFold(n_splits=3)\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "#     print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "from scipy import interp\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "def ROC_StratKfold_cv(classifier, X, y):\n",
    "    # Run classifier with cross-validation and plot ROC curves\n",
    "    skf = StratifiedKFold(n_splits=6)\n",
    "    scores_train = []\n",
    "    scores_test = []\n",
    "\n",
    "    mean_tpr = 0.0\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "    colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])\n",
    "    lw = 2\n",
    "\n",
    "    i = 0\n",
    "    \n",
    "#     plt.figure(figsize=(15,10))\n",
    "    \n",
    "    for (train, test), color in zip(skf.split(X, y), colors):\n",
    "        probas_ = classifier.fit(X.iloc[train], y.iloc[train]).predict_proba(X.iloc[test])\n",
    "        train_acc = classifier.score(X.iloc[train], y.iloc[train])\n",
    "        test_acc = classifier.score(X.iloc[test], y.iloc[test])\n",
    "        scores_train.append(train_acc) \n",
    "        scores_test.append(test_acc)\n",
    "        \n",
    "        # Compute ROC curve and area the curve\n",
    "        fpr, tpr, thresholds = roc_curve(y.iloc[test], probas_[:, 1])\n",
    "        mean_tpr += interp(mean_fpr, fpr, tpr)\n",
    "        mean_tpr[0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr, lw=lw, color=color,\n",
    "                 label='ROC fold %d (area = %0.2f)' % (i, roc_auc))\n",
    "\n",
    "        i += 1\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',\n",
    "             label='Luck')\n",
    "\n",
    "    mean_tpr /= skf.get_n_splits(X, y)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "\n",
    "    plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',\n",
    "             label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)\n",
    "\n",
    "\n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Training Data Accuracy: %0.2f\" %(np.mean(scores_train)))\n",
    "    print(\"Test Data Accuracy:     %0.2f\" %(np.mean(scores_test)))\n",
    "    \n",
    "    y_true = y.iloc[test]\n",
    "    y_pred = classifier.predict(X.iloc[test])\n",
    "\n",
    "\n",
    "    conf = confusion_matrix(y_true, y_pred)\n",
    "    print(conf)\n",
    "\n",
    "    print ('\\n')\n",
    "    print (\"Precision:              %0.2f\" %(conf[0, 0] / (conf[0, 0] + conf[1, 0])))\n",
    "    print (\"Recall:                 %0.2f\"% (conf[0, 0] / (conf[0, 0] + conf[0, 1])))\n",
    "    \n",
    "    cm=confusion_matrix(y_true, y_pred, labels=None)\n",
    "    \n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Generate a confusion matrix plot: \n",
    "\n",
    "def plot_confusion_matrix(cm,title='Confusion matrix', cmap=plt.cm.Reds):\n",
    "    plt.imshow(cm, interpolation='nearest',cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dGEibLrFsZTTTz3g4w4p3zfxzYgnlc2bcrcOGDSbeftsNTUvrxiKy0kDQwNfV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BFw1L5D50WsWbTyO2P34VK+BUKBl7CGmVLt5SuBkfYwFSJS3i4fflmq+h7WtYiceSaqAbF2EogA\n2l8mtv8DBopIXXubASLSqrBtGmWHOSMwrlWjgVUichr4FEgqoE0I8L49XOPAugvmE1VNE5FewFR7\nh1oOa8z8Hk83rqrHRGQQsFxEnEACkDPbVz+gn4ikYw0njS7gI74AnhSRXVjDR0/n+3yXiMQAE4G2\nbssXikh1YL19VuIEZmFVEy3MM8AWEZmKdY0iRkSGAfuADW7tYoC3RaQf1kTvS0XkeeBTu6/lsM7I\nvvdgm0YZYKqPGoZh+DkzNGQYhuHnTCIwDMPwcyYRGIZh+DmTCAzDMPycSQSGYRh+ziQCwzAMP2cS\ngWEYhp/7Dzv4sN92pVe5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b56524510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Accuracy: 0.55\n",
      "Test Data Accuracy:     0.55\n",
      "[[  0 584]\n",
      " [  0 715]]\n",
      "\n",
      "\n",
      "Precision:              nan\n",
      "Recall:                 0.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/adrienporter/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:70: RuntimeWarning: invalid value encountered in long_scalars\n"
     ]
    },
    {
     "data": {
      "image/png": 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MepuXg2pCRBwPXAaMA66WNG/Y+VOAc4ESaab/MyQ9lM+tZNjs/2OXczPrFl0Wkzs390We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\n+S/I290TFkLF5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b54efa550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logCV_clf = linear_model.LogisticRegressionCV(cv=10)\n",
    "ROC_StratKfold_cv(logCV_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LIVImqTDRCoUC0FcIrTO7JO8SbpeQRO0kO7E34zli2cgpNgMG6pZvyaxfp5In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\ng8Fg8HJMIDAYDAYv539NEJDGG8ivRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b55c5c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Accuracy: 0.58\n",
      "Test Data Accuracy:     0.50\n",
      "[[187 397]\n",
      " [260 455]]\n",
      "\n",
      "\n",
      "Precision:              0.42\n",
      "Recall:                 0.32\n"
     ]
    },
    {
     "data": {
      "image/png": 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V0l8xOUE5IgZcBfoetyNBOSIGW5ovIiKqJEE5IqI6UlOOiKiSBOWIiOpITTki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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b51108dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import cross_validation\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "log_clf = LogisticRegression()\n",
    "ROC_StratKfold_cv(log_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ez83nVsASSFGEICt4VoAGIIRoA+Q95FOhUFzR+IHfrSaeD7ez35R75flAhxur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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b56291690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Accuracy: 0.46\n",
      "Test Data Accuracy:     0.46\n",
      "[[296 288]\n",
      " [391 324]]\n",
      "\n",
      "\n",
      "Precision:              0.43\n",
      "Recall:                 0.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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LN0+w/R3AO0ZZ/yCwT6fiioh+0tanL7YHnisT8hbA4cDfAQuBE4Hzyn+/Wu6y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\nJClHRFRIknJERIUkKUdEVEiSckREhfx/t4gvheAlweAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b55c24510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import naive_bayes\n",
    "\n",
    "NB_clf = naive_bayes.GaussianNB()\n",
    "ROC_StratKfold_cv(NB_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# NB_clf = naive_bayes.BernoulliNB()\n",
    "# ROC_StratKfold_cv(NB_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# NB_clf = naive_bayes.MultinomialNB()\n",
    "# ROC_StratKfold_cv(NB_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vICLCQ5cuLrp2ddG8uRvzFVpeFOU0gYEvYrX+jqJoDAYG5wr3eIqQnv46mZmP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8SS5HUDMHnyBN5800+JQC7F4oXAZIKVqzQb12ol/2bmlk/of6gHP136Ee9N/7D8zl5MOtA1hcmH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\nKCFQKBQKC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b51fd44d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Accuracy: 0.86\n",
      "Test Data Accuracy:     0.59\n",
      "[[281 303]\n",
      " [258 457]]\n",
      "\n",
      "\n",
      "Precision:              0.52\n",
      "Recall:                 0.48\n"
     ]
    },
    {
     "data": {
      "image/png": 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wa0kLJN0CrAGutP0NYCfbq0uWHwM7le2ZBsDNKEE5IkZbj4Oy7Q22l1CNj1gq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\nkf8fkCdybH5o+eQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b555dc050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r1 = RandomForestClassifier()\n",
    "ROC_StratKfold_cv(r1, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# l1 = LinearSVC()\n",
    "# ROC_StratKfold_cv(l1, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# s1 = SVC()\n",
    "# ROC_StratKfold_cv(s1, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# d1 = DecisionTreeClassifier()\n",
    "# ROC_StratKfold_cv(d1, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r1.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "importances = r1.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in r1.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(X.shape[1]):\n",
    "    print(\"%d. %s (%f)\" % (f + 1, X.columns[indices[f]], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X.shape[1]), X.columns[indices], rotation='vertical')\n",
    "plt.xlim([-1, 20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X.columns[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "importances"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}