artAuctionAjay

gistfile1.txt

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Art Auction - What Will The Hammer Price Be?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Authors:  Bhavya Agarwal, Farhan Ali, Premadurga Kolli and Xiufeng Yang  **\n",
      "\n",
      "**Modified By: Ajay Lakshminarayanarao**\n",
      "\n",
      "**Guided by: Professor Steven Skiena**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**[Introduction](#intro)**\n",
      "\n",
      "**[Data Cleaning & Extraction](#clean)**\n",
      "\n",
      "**[Baseline Model](#baseline)**\n",
      "\n",
      "**[Advanced Models](#advanced)**\n",
      "\n",
      "**[Evaluation, Results & Comparison](#evaluation)**\n",
      "\n",
      "**[Final Model & Prediction](#prediction)**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"intro\"></a>\n",
      "#Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook contains code for machine learning model built to predict auction prices of paintings sold at auction houses.\n",
      "Machine learning algorithms are:\n",
      "\n",
      "- Baseline Model \n",
      "- Linear Regression Model:\n",
      "    * All features Normalized\n",
      "    * Log of Power law like variables taken\n",
      "    * 80:20 data split for training and validation respectively\n",
      "- Decision Tree Regression Model:\n",
      "    * All features Normalized\n",
      "    * Log of Power law like variables taken\n",
      "    * 80:20 data split for training and validation respectively\n",
      "- k-NN Regression Model:\n",
      "    * All features Normalized\n",
      "    * Log of Power law like variables taken\n",
      "    * 80:20 data split for training and validation respectively\n",
      "    * optimal k value -5\n",
      "    * distance - uniform (was the best)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Importing libraries used in this notebook\n",
      "import csv\n",
      "import numpy as np\n",
      "from sklearn import datasets, linear_model\n",
      "import pylab as pl\n",
      "import csv\n",
      "from string import rfind,find\n",
      "from economics import Inflation\n",
      "import datetime\n",
      "from sklearn.linear_model import ElasticNet\n",
      "from sklearn.metrics import r2_score\n",
      "import cv2\n",
      "import pandas as pd\n",
      "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
      "from sklearn.neighbors import KNeighborsRegressor\n",
      "from numpy import genfromtxt\n",
      "from sklearn.svm import SVR\n",
      "from sklearn import preprocessing\n",
      "import numpy as np\n",
      "from sklearn import datasets, linear_model,cross_validation\n",
      "from sklearn.metrics import r2_score\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"clean\"></a>\n",
      "# Data Cleaning & Extraction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = {}\n",
      "i = 0\n",
      "mediums = []\n",
      "names = []\n",
      "titles = []\n",
      "inflation = Inflation()\n",
      "costly_paintings = 0\n",
      "with open('new_art.csv', 'rb') as csvfile:\n",
      "    datareader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n",
      "    for row in datareader:\n",
      "        if len(row) == 0 :\n",
      "            break;\n",
      "        if row[0] != 'actual_price':\n",
      "            height = float(row[7].split('x')[0])\n",
      "            width = float(row[7].split('x')[1].split('i')[0])\n",
      "        if row[0] != 'Unsold' and row[0] != 'actual_price' and row[5] != 'N/A' and row[7].count('x') !=2 and height != 0 and width != 0:\n",
      "            \n",
      "            price = row[0][:-4]\n",
      "            price = price.replace(',', '')\n",
      "            row[0] = int(price)\n",
      "            year = int(row[6].split('-')[2])\n",
      "            if year == 2014:\n",
      "                row[0] = row[0]\n",
      "            elif year == 2013:\n",
      "                row[0] = row[0]*1.015\n",
      "            else:\n",
      "                row[0] = inflation.inflate(row[0], datetime.date(2012,1,1), datetime.date(year,1,1), 'United States')*1.015*1.015;\n",
      "            \n",
      "            if row[0] > 10000:\n",
      "                costly_paintings = costly_paintings+1\n",
      "                row[2] = row[2][:-2]\n",
      "                row[2] = row[2].encode('ascii',errors='ignore')\n",
      "                if row[1] == 'N/A':\n",
      "                    row[1] = 0\n",
      "                else:\n",
      "                    row[1] = 2014 - int(row[1])\n",
      "                row[5] = 2014 - int(row[5])    \n",
      "                row[4] = row[4].lower()\n",
      "                mediums.append(row[4])\n",
      "                index = rfind(row[6], \"-\")\n",
      "                \n",
      "                row[7] = height\n",
      "                row.append(year)\n",
      "                row.append(width)\n",
      "                row.append(height*width)\n",
      "                row.append(height/width)\n",
      "                titles.append(row[3])\n",
      "                names.append(row[2])\n",
      "                del row[6]\n",
      "                del row[3]\n",
      "                x[i] = row\n",
      "                i = i+1\n",
      "                \n",
      "mediums = set(mediums)\n",
      "names = set(names)\n",
      "artist_freq = dict()\n",
      "artist_avg = dict()\n",
      "\n",
      "for val in names:\n",
      "    artist_freq[val] = 0\n",
      "    artist_avg[val] = 0\n",
      "    \n",
      "medium_dict = dict();\n",
      "i = 1\n",
      "for val in mediums:\n",
      "    medium_dict[val] = i\n",
      "    i = i+1\n",
      "\n",
      "for row in x:\n",
      "    x[row][3] = medium_dict[x[row][3]]\n",
      "    artist_avg[x[row][2]] = artist_avg[x[row][2]] + x[row][0]\n",
      "    artist_freq[x[row][2]] = artist_freq[x[row][2]] + 1\n",
      "\n",
      "import operator\n",
      "\n",
      "key = max(artist_avg.iteritems(), key=operator.itemgetter(1))[0]\n",
      "print key\n",
      "print artist_avg\n",
      "print artist_freq\n",
      "print medium_dict\n",
      "\n",
      "for row in x:\n",
      "    x[row][2] = artist_avg[x[row][2]]/artist_freq[x[row][2]]\n",
      "\n",
      "input_data = np.empty([len(x),len(x[0])])\n",
      "for row in x:\n",
      "    input_data[row] = np.array(x[row])\n",
      "\n",
      "np.savetxt(\"input_costly2.csv\", input_data, delimiter=\",\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Amedeo,Modigliani\n",
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'Carl,Rudolph,Krafft': 8, 'Zoe,Mozert': 2, 'John,Charles,Morris': 1, 'Ali,Mokawas': 1, 'Alexander,Rabine': 5, 'Gerard,Malanga': 1, 'Gijsbrecht,Leytens': 1, 'Sidney,Harold,Meteyard': 3, 'In-ting,Ran': 3, 'Giovanni,Battista,Merano': 2, 'Frederick,De,Moucheron': 30, 'Josep,Amat,Pag\\\\xe8s': 2, 'Shiryu,Morita': 4, 'Manfredo,Massironi': 3, 'Captain,Frederick,Marryat': 1, 'Marian,Ellis,Rowan': 7, 'Jo,Koster': 3, 'Aristide,Joseph,Bonaventure,Maillol': 66, 'Jac,Leirner': 4, 'Helen,Marten': 1, 'Louis,Fran\\\\xe3\\\\xa7ois,Gerard,Van,Der,Puyl': 1, 'Michele,Rocca,,Called,Michele,Da,Parma': 2, 'Robert,Ryman': 55, 'Lamy': 1, 'Sirak,Melkonian': 10, 'Arthur,Wesley,Dow': 9, 'Conrad,Meyer': 2, 'Raoul,Millais': 11, 'L\\\\xe1szl\\\\xf3,B\\\\xe1r\\\\xf3,Medny\\\\xe1nszky,(ladislav,Med\\\\u0148ansk\\\\xfd)': 63, 'Aleksei,Vasilevich,Ganzen': 2, 'Maria,Fromowicz,Nassau': 1, 'K.,Laxma,Goud': 30, 'Thierry,Poncelet': 1, 'Ugo,Mulas': 2, 'Larry,Poons': 47, 'Marc,Riboud': 4, 'Frantisek,Maly': 2, 'Crist\\\\xf3bal,Rojas': 1, 'Tim,Lokiec': 2, 'Maxime,Emile,Louis,Maufra': 116, 'Lyndell,Brown,&,Charles,Green': 1, 'Franz,Russ': 1, 'George,Francis,Miles': 1, 'Jean,Moral': 2, 'Johann,Georg,Muller': 58, 'Achille,Etna,Michallon': 8, 'Guglielmo,Micheli': 3, 'Rudolf,M\\\\xfcller': 2, 'Pierre-auguste,Renoir,And,Richard,Guino': 11, 'Kathleen,Moir,Morris': 32, 'Jules,Emile,Elis\\\\xe9e,Maclet': 28, 'Francis,John,Mccomas': 6, 'Wladimir,Jegorowitsch,Makowskij': 1, 'Donald,L,Malick': 1, 'Sheng,Mao': 3, 'Alasdair,Gray': 5, 'Edwin,Megargee': 1, 'Alexis,Joseph,Mazerolle': 2, 'Mark,Richard,Myers': 2, 'Daniel,Mytens': 2, 'Satish,Gujral': 36, 'Antoinette,Mad-jarova': 1, 'Syed,Haider,Raza': 423, 'Pietro,Gabrini': 8, 'Barbara,Brooks,Morgan': 5, 'Steve,Mcqueen': 1, 'Hugo,Mund': 1, 'Niccola,Musso': 1, 'Juan,Mu\\\\xf1oz': 19, 'Robert,Ernst,Marx': 1, 'Carl,Krall': 1, 'Frank,Mccarthy': 100, 'Anton,Von,Maron': 10, 'Maryan': 1, 'Gabriele,Munter': 183, 'Horatio,Lengo,Y,Martinez': 1, 'Alexander,Rodchenko': 97, 'Romolo,Romani': 1, 'Nicolas,Bernard,L\\\\xe9pici\\\\xe9': 8, 'Annie,Rose,Laing': 1, 'Dolph,Menzel': 2, 'Tsuruo,Matsugaki': 1, 'Jaroslaw,Modzelewski': 1, 'Louis,H.,Grimshaw': 16, 'Giuseppe,Gambarini': 3, 'Carlo,Magini': 4, 'Mary,Ellen,Mark': 1, 'Ian,Mckeever': 2, 'Hans,Fredrik,Gude': 52, 'Max,L\\\\xe9on,Moreau': 10, 'Rudi,Mantofani': 45, 'Wartan,Mahokian': 1, 'Stefan,Luchian': 12, 'Gustave,Maincent': 1, 'Zhou,Li': 2, 'John,Francis,Murphy': 15, 'Antonio,Marasco': 16, 'Gerhard,Grauss': 1, 'Elinor,Milchan': 3, 'George,Mayer,Marton': 1, 'Jan,Mortel': 17, 'Albert,Pierre,Marquet': 336, 'Theodor,Matthei': 2, 'Graca,Morais': 1, 'Sergeij,Iwanowitsch,Kalmykow': 2, 'Pupuk,Daru,Purnomo': 3, 'Louis,Van,Lint(school)': 1, 'Maria,Sibylla,Merian': 8, 'Jules,Moigniez': 3, 'Rafael,Moreno': 2, 'Charles,Laval': 2, 'Count,Amadeo,Preziosi': 42, 'Clemente,Pujol,De,Gustavino': 4, 'Derrick,Greaves': 11, 'Edward,Lear': 222, 'Magnus,Plessen': 1, 'Wang,Qingsong': 75, 'Tyeb,Mehta,&,Ram,Kumar': 1, 'Edward,Penny': 3, 'William,Marlow': 13, 'Anna,Mary,Robertson,Moses': 99, 'Koo,Mei': 2, 'Tatsuo,Miyajima': 5, 'Richard,Robjent': 2, 'Vasily,Aleksandrovich,Minyaev': 2, 'Jaspar,Geerards': 1, 'Evert,Moll': 5, 'A.,Marshall': 1, 'Jan,M\\\\xfcller': 1, 'Eduard,Von,Gr\\\\xfctzner': 77, 'Emmanuel,Guiragossian': 3, 'Anne,Katrine,Dolven': 2, 'Paul,Eugene,Mengin': 1, 'Adolf,Langhard': 1, 'Pavel,Kotlarevsky': 3, 'Giovanni,Venanzi,Di,Pesaro': 1, 'Konrad,Magi': 3, 'Marcus,Larsson': 6, 'Herta,M\\\\xfcller': 1, 'Alan,Green': 3, 'Giovanni,Battista,Lombardi': 2, 'Frans,Van,Cuyck,Van,Myerhop': 1, 'Jan,Matulka': 12, 'Jervis,Mcentee': 30, 'Nikola,Michailov': 1, 'Moses,Maimon': 1, 'Jean,Baptiste,Martin': 3, 'Wilhelm,Roegge': 1, 'Henry,Moore': 355, 'Hap,Grieshsaber': 2, 'Meuris': 1, 'Frank,Mckelvey': 126, 'Nikolai,Antonovitch,Maksimenko': 1, 'David,Mach': 6, 'Giovanni,Battista,Ghisi': 1, 'Otto,Friedrich,Wilhelm,Modersohn': 120, 'Emilie,Mediz-pelikan': 5, 'Giuseppe,Magni': 9, 'Charles,Balthezar,Saint,Memin': 1, 'William,Julien,Emile,Edouard,Laparra': 1, 'Roberto,Matta': 8, 'Jozef,Mehoffer': 3, 'John,Meyer': 14, 'Maria,Marevna': 3, 'Achille,Lauge': 5, 'Edward,Kemeys': 1, 'Colin,Mccahon': 17, 'Aneh,Mohammad': 1, 'Gr\\\\xe9goire,Michonze': 3, 'James,J.,Mcauliffe': 2, 'Francesco,Menzio': 5, 'Louis,Adrien,Masreliez': 1, 'Paul,Reiffenstein': 1, 'Adolf,Ernst,Meissner': 2, 'Constantine,Popov': 1, 'Eso,Peluzzi': 3, 'Giuseppe,Manfredi': 1, 'Hans,J\\\\xf6rg,Glattfelder': 3, 'Andrea,(il,Chiaveghino),Mainardi': 2, 'Wilhelm,Marc': 1, 'Adolfo,Methfessel': 5, 'Anton,Romako': 23, 'Lygia,Pape': 1, 'Eugene,Galien,Laloue': 30, 'Henri,Charles,Guerard': 1, 'Jason,Rhoades': 5, 'Heroman,Van,Der,Mijn': 1, 'Joaqu\\\\xedn,Vaquero,Palacios': 4, 'Giorgia,Madiai': 1, 'Charles,H.,Poingdestre': 1, 'Italo,Mus': 7, 'Wafaa,Mezouar': 2, 'Hans,Meyboden': 1, 'Anton,Mirou': 9, 'Helmut,Andreas,Peter,Grieshaber': 18, 'Helmuth,Macke': 2, 'Robert,S.,Moskowitz': 2, 'Czeslaw,Mystkowski': 1, 'Henri,Mege': 3, 'Agostino,Masucci': 5, 'Ulvi,Liegi': 21, 'Oksana,Mas': 2, 'C\\\\xe4sar,Metz': 1, 'Chen,Guangming': 1, 'Ludwig,Meixner': 4, 'William,Tylee,Ranney': 3, 'Pierre,Roy': 23, 'Reena,Saini,Kallat': 7, 'Dao,Min': 1, 'Arthur,Percy': 1, 'Catharina,Peeters': 1, 'Nicolaes,Molenaer': 10, 'Imro,Weiner,Kra': 10, 'Dong,Gao': 4, 'Fakhir,Mohammed': 1, 'Alfonso,X.,Pe\\\\xf1a': 4, 'J\\\\xf8rn,Larsen': 1, 'Ingolvur,Av,Reyni': 1, 'Mund,Hug\\\\xf3n\\\\xe9,D\\\\xf6m\\\\xf6t\\\\xf6r,Gizella': 2, 'Joseph,M.,Glasco': 1, 'Heinrich,Lihl': 3, 'Mouna,Reideiz': 1, 'Godfrey,Clive,Miller': 14, 'Marguerite,Stuber,Pearson': 4, 'Heinrich,Marr': 1, 'Johannes,Maes': 1, 'Tom,Mcewan': 3, 'Mandi': 1, 'Tony,Lewis': 1, 'Elias,Martin': 3, 'Gilbert,William,Gaul': 17, 'Howard,Morgan': 2, 'J.,Murday': 1, 'Jean,Grandjean': 1, 'Kathryn,Woodman,Leighton': 4, 'Ib,Geertsen': 1, 'Riccardo,Meacci': 1, 'George,Grard': 1, 'Carl,Mark\\\\xf2': 1, 'Andr\\\\xe9,Masson': 288, 'Amedeo,Modigliani': 179, 'Umberto,Mastroianni': 1, 'Auguste,Leveque': 1, 'Domenico,Morelli': 5, 'Augusto,Murer': 2, 'Lucy,Yukenbarri,Napanangka': 1, 'Joaquim,Torrents,Llado': 1, 'Jean,Baptiste,Perroneau': 5, 'Lofty,Narbardayal,Nadjamerrek': 7, 'Jean,Mischeling': 1, 'Charles,Rodius': 1, 'Miloud,Labied': 18, 'Francois,Henri,Mulard': 1, 'Konstantin,Egorovich,Makovsky': 133, 'Jacob,Gillberg': 1, 'Luc,Didier': 1, 'Clayton,Pinkerton': 1, 'Francois(franz),Gailliard': 13, 'Ludek,(ludwig),Marold': 4, 'Ohara,Koson': 1, 'Germain,Ribot': 1, 'Ivan,Myasoedov': 1, 'Nikiforos,Lytras': 17, 'Helmar,Lerski': 1, 'John,Phillip': 3, 'Sarah,Raphael': 2, 'Carl,Nys': 1, 'Luis,Marsans': 1, 'Manuel,Gonzales,Mendez': 1, 'Wilfried,Moser': 7, 'Nikolai,Egorovich,Makovskij': 3, 'Stanislav,Podhr\\\\xe1zsky': 1, 'Yu,Ming': 8, 'Emanuel,Aage,Petersen': 3, 'Aleksander,Mroczkowski': 1, 'Napoleone,(luigi),Grady': 1, 'Michael,Michaeledes': 1, 'Moriz,Melzer': 6, 'Aleksander,Gierymski': 6, 'Don,Mcilvane': 1, 'Gerardo,Murillo': 11, 'Cyr-jean-aime,(st-cyr),Girier': 1, 'Itzhak,Danziger': 1, 'Joseph,Mcwilliams': 1, 'Gerhard,Marcks': 2, 'Erina,Matsui': 1, 'Samuel,Finley,Breeze,Morse': 2, 'Joseph,K\\\\xf6lschbach': 4, 'Constant,Montald': 15, 'Fernand,Khnopff': 40, 'Hans,K\\\\xf6rnig': 3, 'Dieter,Roth,And,Bj\\\\xf6rn,Roth': 1, 'Rubaldo,Merello': 1, 'Wilhelm,Alexander,Meyerheim': 5, 'Paolo,Maggis': 1, 'Mario,Guido,Dal,Monte': 1, 'Alfred,James,Munnings': 273, 'Fortunino,Matania': 10, 'Kenneth,Martin': 11, 'Carl,Friedrich,Moritz,M\\\\xfcller': 3, 'Robert,Malaval': 10, 'Tracey,Moffatt': 34, 'Jose,Justo,Montiel': 1, 'Dick,Nguleingulei,Murramurra': 1, 'Karl,Mediz': 3, 'Giovanni,March': 1, 'Pierre,Nilouss': 11, 'Kasimir,Malewitsch': 5, 'Axel,Nilsson': 6, 'Zeng,Mi': 8, 'Giuseppe,Migneco': 42, 'William,(sir),Mactaggart': 24, 'Yannis,Gaitis': 112, 'Antonio,Ermolao,Paoletti': 26, 'Marc,Mendelson': 1, 'Charles-achille,D`hardiviller': 1, 'Martin,Ramirez': 6, 'Charles,Willson,Peale': 14}\n",
        "{'copper': 56, 'sanguine': 99, 'ceramic': 3, 'paper': 4, 'crayon': 5, 'orotone': 6, 'etching': 7, 'aquatint': 8, 'fiberglass': 9, 'stainless steel': 10, 'color lithograph': 11, 'wood': 12, 'steel': 80, 'serigraph': 14, 'alabaster': 15, 'pigment': 16, 'watercolor': 17, 'spray paint': 18, 'pyroxylin': 19, 'silver': 20, 'albumen print': 21, 'aluminum': 22, 'resin': 28, 'iodine-silver print': 24, 'assemblage': 25, 'color woodcut': 26, 'collage': 27, 'c-print': 23, 'felt tip pen': 29, 'pastel': 30, 'canvas': 31, 'dye transfer print': 32, 'lead': 33, 'pencil': 91, 'mezzotint': 34, 'pen': 36, 'ink': 94, 'print': 37, 'linoleum': 38, 'lacquer': 39, 'heliograph': 40, 'porcelain': 41, 'gypsum': 42, 'vinyl': 43, 'offset': 44, 'cardboard': 45, 'bronze': 46, 'panel': 47, 'engraving': 48, 'stone': 49, 'bodycolor': 50, 'cibachrome': 51, 'lithograph': 52, 'color screenprint': 53, 'stencil': 54, 'mixed media': 55, 'golden': 1, 'terracotta': 57, 'woodcut': 58, 'wash': 59, 'photogram': 60, 'brush': 61, 'marker': 62, 'polymer paint': 63, 'unknown': 64, 'platinum print': 65, 'plastic': 66, 'pochoir': 67, 'wax': 68, 'silkscreen': 69, 'feather': 70, 'charcoal': 71, 'poster': 73, 'mixed technique': 74, 'gouache': 75, 'tempera': 76, 'color coupler print': 77, 'collotype': 78, 'decollage': 79, 'platinum palladium print': 35, 'photograph': 81, 'graphic': 82, 'casein': 83, 'chalk': 84, 'graphite': 85, 'reproduction': 86, 'silhoutte': 87, 'sculpture': 88, 'marble': 89, 'photolithography': 90, 'plate': 13, 'sepia': 72, 'fabric linen flag w/polyester': 93, 'ballpoint pen': 92, 'gelatin silver print': 95, 'monotype': 96, 'tapestry': 97, 'drypoint': 98, 'polaroid': 2, 'distemper': 100, 'rubber': 101, 'granite': 102, 'acrylic': 103, 'cotton': 104, 'photogravure': 105, 'plaster': 106, 'color offset': 107, 'oil': 108, 'screenprint': 109, 'linocut': 110, 'photoengraving': 111, 'kaolin': 112, 'leaf': 113, 'brass': 114, 'chromolithograph': 115, 'enamel': 116, 'glass': 117, 'metal': 118, 'coal': 119, 'painting': 120, 'drawing': 121}\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"baseline\"></a>\n",
      "#Baseline Model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "initial_data = []\n",
      "i = 0\n",
      "with open('baseline_data.csv', 'rb') as csvfile:\n",
      "    datareader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n",
      "    \n",
      "    for row in datareader:\n",
      "        if(i > 0):\n",
      "            lower = int(row[3].replace(\",\",\"\"))\n",
      "            upper = int(row[4].replace(\",\",\"\"))\n",
      "            actual = int(row[5])\n",
      "            if(row[2] == \"GBP\"):\n",
      "                lower = lower*1.6\n",
      "                upper = upper*1.6\n",
      "                actual = actual*1.6\n",
      "            initial_data.append([lower,upper,actual])\n",
      "        i = i+1\n",
      "\n",
      "data = np.array(initial_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "less_than_estimate = 0\n",
      "more_than_estimate = 0\n",
      "in_estimate = 0\n",
      "error = np.zeros((len(data),1))\n",
      "for i in range(0,len(data)):\n",
      "    error[i] = (((data[i,2] - (data[i,2] + data[i,1])/2)/data[i,2])*100)\n",
      "    if(data[i,2] > data[i,1]):\n",
      "        more_than_estimate+=1\n",
      "    elif(data[i,2] < data[i,0]):\n",
      "        less_than_estimate +=1\n",
      "    else:\n",
      "        in_estimate +=1\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "\n",
      "index = np.array([1,2,3])\n",
      "result = [less_than_estimate,in_estimate,more_than_estimate]\n",
      "rects1 = plt.bar(index, result)\n",
      "bar_width = 0.35\n",
      "\n",
      "\n",
      "top = 30;\n",
      "error_count = np.zeros((2*top,1))\n",
      "for i in range(-top,top-1):\n",
      "    error_count[i+top] = len(np.where((error<i) & (error>i-1))[0])\n",
      "    \n",
      "error_count[top-1] = len(np.where(error>top-2)[0])                        \n",
      "\n",
      "index = np.arange(2*top)\n",
      "index = index - top\n",
      "rects1 = plt.bar(index, error_count)\n",
      "bar_width = 0.35\n",
      "\n",
      "plt.xlabel('Variation from estimate')\n",
      "plt.ylabel('Number of Art pieces')\n",
      "plt.title('Number of pieces based on variation from estimate')\n",
      "plt.legend()\n",
      "plt.show()\n",
      "\n",
      "#x = np.arange(0,len(data))\n",
      "#plt.scatter(x, error)\n",
      "#plt.show()\n",
      "#plt.xlabel('Variation from estimate')\n",
      "#plt.ylabel('Number of Art pieces')\n",
      "#plt.title('Number of pieces based on variation from estimate')\n",
      "#plt.xticks(index + bar_width, ('Less', 'In Range', 'More'))\n",
      "#plt.legend()\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/pymodules/python2.7/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n",
        "  warnings.warn(\"No labeled objects found. \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lsbxYkKvG/tbVASYrjeXlqEG6m+hcA2r5+bMizQ3SnW2cLfWbDhw4uOBAPHDg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\nDmiIAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa767964c>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr = linear_model.LinearRegression()\n",
      "split_point = 4*len(data)/5\n",
      "features = data[0:split_point,0:2]\n",
      "value = data[0:split_point,2]\n",
      "regr.fit(features,value)\n",
      "\n",
      "test = data[split_point:len(data), 0:2]\n",
      "val = data[split_point:len(data), 2]\n",
      "predictions = np.zeros(len(test))\n",
      "diff = np.zeros(len(test))\n",
      "for i in range(0,len(test)):\n",
      "    predictions[i] = regr.predict(test[i])\n",
      "    if val[i] != 0:\n",
      "        diff[i] = ((predictions[i] - val[i])/val[i])*100\n",
      "    #print val[i], predictions[i],i+split_point\n",
      "    \n",
      "top = 500;\n",
      "error_count = np.zeros((2*top+1,1))\n",
      "for i in range(0,top-1):\n",
      "    error_count[i+top] = len(np.where((diff<i) & (diff>i-1))[0])\n",
      "\n",
      "for i in range(-top,0):\n",
      "    error_count[i+top] = len(np.where((diff>i) & (diff<i+1))[0])\n",
      "    \n",
      "error_count[2*top] = len(np.where(diff>top-2)[0])                        \n",
      "\n",
      "index = np.arange(-top,top+1)\n",
      "rects1 = plt.bar(index, error_count)\n",
      "bar_width = 0.35\n",
      "\n",
      "plt.xlabel('Variation from estimate')\n",
      "plt.ylabel('Number of Art pieces')\n",
      "plt.title('Number of pieces based on variation from estimate')\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UHEREJELBQUREIhQcREQkQsFBREQiFBxERCRCwUFERCIUHEREJCLt4HAEsABY\nCJxbZJmr3fy5wH4pp0dERBJIMzj0BX6KBYi9gBOBPUPLTAB2BXYDTgOuSzE9DSuXy9U7Calq5vw1\nc95A+WtlaQaHscAiYCmwAbgNODq0zETgJjc8CxgKbJ9imhpSsx+gzZy/Zs4bKH+tLM3gMBJY5o2/\n6KZ1t8yoFNMkIiIJpBkcuhIul6lwPRERSUn4xFxN44BOrM0B4HxgE/BDb5nrgRxW5QTWeD0eWBna\n1iJgl5TSKSLSrBZj7boNpR+WsA5gADCH+Abp6W54HPBErRInIiL1cyTwV+zK/3w37XT3F/ipmz8X\n+GhNUyciIiIiIr3bOVj7RLs37XzsYbkFwOHe9I8B89y8n9QqgRW6BCshzQEeAkZ785ohf/8JPI/l\n8ffA1t68ZsjfccBzwPtES7nNkL+wJA+xNrIbsPbLed60duAB4G/A/djt84Fi32GjGg3MwI7JZ4Gz\n3PRmymOB0cAfgSXkg8Ne2Am1P9aGsYh8Y/qT2DMVYO0XQQN4I9rKGz4T+Lkbbpb8HUb+DrgfuD9o\nnvztAeyO/SD94NAs+fP1xfLRgeUrrs2w0R2E9brgB4crgP/rhs+l9DHa6N0L7QDs64aHYFX4e1Kl\nPDZi5n9EPmOBo4Fp2MN0S7FM7Q8Mx064T7rlbgb+pSaprMwab3gI8Jobbpb8PYCV+MAeagyeWWmW\n/C3ArsbCmiV/viQPsTa6h4E3QtP8B29vIv99xH2HY2lsK7CTPcBarNQ+kirlsdGCw9HYg3B/CU0f\n4aYHggfqwtOXE33QrtF8H3gBmARc7qY1U/4Cp5K/E60Z8+drxvwleYi1N9qe/K3yK8n3yFDsO+wt\nOrBS0iyqlMd+VU9i9x7AikNhF2L1YX49WJrPYaSlWP4uAO7G8nkhcB5wFXBK7ZJWFd3lDyx/7wG3\n1ipRVZQkf62gFR5G7aJ0PnvLZzAE+B1wNoW1E9CDPNYjOBxWZPqHgJ2wxkywKomnseL5cgobb0dh\nUW85hd1tjHLT6qlY/sJuJX9l3Uz5m4Q9v3KoN62Z8henN+UvqXCeRlN41dlbrcSC/wqs2u8VNz3u\nO+wN31V/LDDcAtzppjVbHiPiGqQHYAFkMflSxSwsgGRo/Aa/3bzhM7EvFJonf0dgd04MC01vlvwF\nZmB3IQWaLX+Q7CHW3qCDaIN0cOfVeUQba+O+w0aVwdqxfhya3kx5jPV3Cm9lvQBrQFkA/LM3PbhV\ncBH2boglVxtmAAAEL0lEQVRG9lssrXOwaL+dN68Z8rcQ+Acw2/1d681rhvx9DquHfwe7KrvXm9cM\n+QuLe4i1N5kGvIRVcS7DqnDbgQeJv82z2HfYqA7EbgCZQ/43dwTNlUcRERERERERERERERERERER\nERERERGRavlfol0Cf5PCZx+68x8UPmkd5yvYU6CB/6Y6D2sNwO4Vn4111d0ovgls4Y3/AWirwnY/\ngj3XICKSqq9h/en7Hsce3EkiaaeQ4aeVq2Uc1s9SnHp2WLkE+EAK250EXJPCdkVECrRjfbwE/Xd1\nYE9PA1wHPIW9oKTTW2cp9qj/08DxwFTg827exVjX1/OAKW7asVhHYwuAZ4BBQI58sDgR6+F3Hvku\nBMC6N74Ue7L0cQqfVMeNLwTedNvdOSZtpbZ9hcvbA1iQmYl1VfBZ4n3H5W2u93kMxkoFc9w+voB1\nubLe7fcht9xS7LPucJ/DjdhTzb/CSm6PYk/KftwtPxZ4zOXrUez9FAOwnoJfIV9SGowF91lu2YlF\n0i4iUra7yZ9UzsNOmgDbuP99sSv/D7nxJcC3vfVvBI4JrQPWn8xn3HD4xTvB+AgsGH3A7ech8u8j\n2AQc5YZ/iPUiGzaewl5Y/bR1t+2gW4LfY90X9AU+jJ14ww4nH+z6uH0e5PL9M2+54EVRfp9j/ngH\n1k//3lh/OX8GfuGWmQjc4W2nrxv+NNalC1j1nN+9x2XAF93wUCzgbBmTfmlSjfY+B2ku04AT3PDx\nbjwYfhq7It0b6xAs8Osi2/oU8AR21fyp0DrhzsMy2JVyDngde63nr4CD3fz3sKtyXDo6YvYX1yFZ\nkLaPY0Go2Lbvc8Pz3HLvYyWJuP0c7v5mu7R8ENjVrXsYVio5kGhXzHGWYB0fdrn/D7rp/r6Hku/j\n60fkP8cMhXk+HAvos10eBlLYo6c0uXp02S2t4y6sx8j9sKvO2VhvkOcA/wS8hZUOBnnrvB2znUHA\n/8Oqi5YDk0PrxPVJH56W8aZt8KZvIvnvIEhbF4Un0lLbfi/Bfi6nsJQQ2A8r4VyKlU4u6SZ96xPs\n+xK3rc8BO2IBtJhjsOo1aUEqOUia1mJXnTeSf/FPG3aSXY29oSrJHTJBIHgde7GJf/fQGqJ363Rh\ndfjjyVf9nIDV/VfDU1Xc9n3YW/MGu/GRwLbYHVjvYqWSK7FAAfH5LUcb1lMpFL5oajWF7zi/j/wL\n6/H2Ly1CJQdJ2zSs7v0LbnwuVoJYgHWj/EiCbbyJ3aL6LNZV9ixv3lTgemAdcIA3fQVWLTIDu7K/\nh3wbgl+qKPamrPB0f/jlhNuO21fYA9itt4+78TXAl7Gqpf/Ervo3AP/m5v8M+CNWggrf5ptk31dg\n7xW+CKtaC6bPIF+NdBlWwrgKq8brg3Whr0ZpERERERERERERERERERERERERERERERERERERqb//\nDzY/bSUz0hkAAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa766eeac>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_features = data[0:len(data), 0:2];\n",
      "\n",
      "means = np.zeros((len(data),1))\n",
      "for i in range(0,len(data)):\n",
      "    means[i][0] = (data[i][0] + data[i][0])/2\n",
      "#means = np.mean(total_features,axis=1)\n",
      "total_values = data[0:len(data), 2];\n",
      "\n",
      "split_point = 4*len(total_values)/5\n",
      "features = means[0:split_point]\n",
      "value = total_values[0:split_point]\n",
      "\n",
      "regr2 = linear_model.LinearRegression()\n",
      "regr2.fit(features,value)\n",
      "\n",
      "test = means[split_point:len(data)]\n",
      "val = total_values[split_point:len(data)]\n",
      "\n",
      "\n",
      "diff = np.zeros((len(test),1))\n",
      "predictions = np.zeros((len(test),1))\n",
      "\n",
      "for i in range(0,len(test)):\n",
      "    predictions[i] = regr2.predict(test[i])\n",
      "    if val[i] != 0:\n",
      "        diff[i] = ((predictions[i] - val[i])/val[i])*100\n",
      "\n",
      "top = 500;\n",
      "error_count = np.zeros((2*top+1,1))\n",
      "for i in range(0,top-1):\n",
      "    error_count[i+top] = len(np.where((diff<i) & (diff>i-1))[0])\n",
      "\n",
      "for i in range(-top,0):\n",
      "    error_count[i+top] = len(np.where((diff>i) & (diff<i+1))[0])\n",
      "    \n",
      "error_count[2*top] = len(np.where(diff>top-2)[0])                        \n",
      "\n",
      "index = np.arange(-top,top+1)\n",
      "rects1 = plt.bar(index, error_count)\n",
      "bar_width = 0.35\n",
      "\n",
      "plt.xlabel('Variation from estimate')\n",
      "plt.ylabel('Number of Art pieces')\n",
      "plt.title('Number of pieces based on variation from estimate')\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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B4u639DPfJzOu1bb0mioPkfaSu4HfEZ/3jcmwWcBVwB+BK4nb1XvF2UT75rzM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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa79cb5cc>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"advanced\"></a>\n",
      "#Advanced Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Linear Regression Model\n",
      "- All features Normalized\n",
      "- Log of Power law like variables taken\n",
      "- 80:20 data split for training and validation respectively"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def stats(y_true,y_pred):\n",
      "    print 'Mean absolute Error :-', mean_absolute_error(val, y_pred)\n",
      "    print 'Mean squared Error :-', mean_squared_error(y_true, y_pred)\n",
      "    print 'R2 score :-', r2_score(y_true, y_pred)\n",
      "\n",
      "knn_data = pd.read_csv('data_without_dates.csv')\n",
      "\n",
      "split_point = 28000\n",
      "\n",
      "train = knn_data[0:split_point]\n",
      "features = train[train.columns[0]]\n",
      "value = train[train.columns[1]]\n",
      "testo = knn_data[split_point:len(knn_data)]\n",
      "test = testo[testo.columns[0]]\n",
      "val = testo[testo.columns[1]]\n",
      "\n",
      "neigh = linear_model.LinearRegression(normalize=False)\n",
      "#neigh = KNeighborsRegressor(n_neighbors=5,weights='uniform',p=1,algorithm='auto')\n",
      "neigh.fit(features[:,np.newaxis],value)\n",
      "#neigh.fit(features, value) \n",
      "prediction = neigh.predict(test[:,np.newaxis])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Decision Tree Regression Model\n",
      "- All features Normalized\n",
      "- Log of Power law like variables taken\n",
      "- 80:20 data split for training and validation respectively"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def stats(y_true,y_pred):\n",
      "    print 'Mean absolute Error :-', mean_absolute_error(val, y_pred)\n",
      "    print 'Mean squared Error :-', mean_squared_error(y_true, y_pred)\n",
      "    print 'R2 score :-', r2_score(y_true, y_pred)\n",
      "    \n",
      "knn_data = pd.read_csv('data_without_dates.csv')\n",
      "split_point = 28000\n",
      "train = knn_data[0:split_point]\n",
      "features = train[train.columns[0]]\n",
      "value = train[train.columns[1]]\n",
      "testo = knn_data[split_point:len(knn_data)]\n",
      "test = testo[testo.columns[0]]\n",
      "val = testo[testo.columns[1]]\n",
      "\n",
      "from sklearn import tree\n",
      "clf = tree.DecisionTreeRegressor()\n",
      "clf = clf.fit(features[:,np.newaxis],value)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##k-NN Regression Model\n",
      "- All features Normalized\n",
      "- Log of Power law like variables taken\n",
      "- 80:20 data split for training and validation respectively\n",
      "- optimal k value -5\n",
      "- distance - uniform (was the best)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def stats(y_true,y_pred):\n",
      "    \n",
      "    from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score\n",
      "    print 'Mean absolute Error :-', mean_absolute_error(val, y_pred)\n",
      "    print 'Mean squared Error :-', mean_squared_error(y_true, y_pred)\n",
      "    print 'R2 score :-', r2_score(y_true, y_pred)\n",
      "\n",
      "knn_data = pd.read_csv('data_without_dates.csv')\n",
      "\n",
      "split_point = 28000\n",
      "\n",
      "train = knn_data[0:split_point]\n",
      "features = train[train.columns[0]]\n",
      "value = train[train.columns[1]]\n",
      "testo = knn_data[split_point:len(knn_data)]\n",
      "test = testo[testo.columns[0]]\n",
      "val = testo[testo.columns[1]]\n",
      "\n",
      "from sklearn import tree\n",
      "clf = KNeighborsRegressor(n_neighbors=5,weights='uniform',p=1,algorithm='auto')\n",
      "clf = clf.fit(features[:,np.newaxis],value)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"evaluation\"></a>\n",
      "#Evaluation, Results and Comparison"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "neigh = linear_model.LinearRegression(normalize=False)\n",
      "#neigh = KNeighborsRegressor(n_neighbors=5,weights='uniform',p=1,algorithm='auto')\n",
      "neigh.fit(features[:,np.newaxis],value)\n",
      "#neigh.fit(features, value) \n",
      "prediction = neigh.predict(test[:,np.newaxis])\n",
      "\n",
      "print \"EVALUATION STATISTICS FOR Linear REGRESSION MODEL :\"\n",
      "print \"====================================================\"\n",
      "print(\"Residual sum of squares :- %.2f\"\n",
      "      % np.mean((prediction - val) ** 2))\n",
      "print('Variance score :- %.2f' % neigh.score(test[:,np.newaxis], val))\n",
      "stats(val,neigh.predict(test[:,np.newaxis]))\n",
      "predictions = np.zeros(len(test))\n",
      "diff = np.zeros(len(test))\n",
      "for i in range(0,len(val)):\n",
      "    predictions[i] = neigh.predict(test[:,np.newaxis][i])\n",
      "    predictions[i] = np.power(10,predictions[i])\n",
      "    val[:,np.newaxis][i] = np.power(10,val[:,np.newaxis][i])\n",
      "    diff[:,np.newaxis][i] = ((predictions[i] - val[:,np.newaxis][i])/val[:,np.newaxis][i])*100\n",
      "    \n",
      "print \"Mean of Relative Error % :-\",np.average(abs(diff))\n",
      "print \"Median of Relative Error % :-\",np.median(abs(diff))\n",
      "print \"\\n\"\n",
      "\n",
      "print \"REALTIVE ERROR DISTRIBUTION PLOT FOR Linear REGRESSION MODEL :\"\n",
      "print \"================================================================\"\n",
      "\n",
      "top = 1500;\n",
      "error_count = np.zeros((2*top+1,1))\n",
      "for i in range(0,top-1):\n",
      "    error_count[i+top] = len(np.where((diff<i) & (diff>i-1))[0])\n",
      "\n",
      "for i in range(-top,0):\n",
      "    error_count[i+top] = len(np.where((diff>i) & (diff<i+1))[0])\n",
      "    \n",
      "error_count[2*top] = len(np.where(diff>top-2)[0])                        \n",
      "\n",
      "index = np.arange(-top,top+1)\n",
      "rects1 = plt.bar(index, error_count)\n",
      "bar_width = 0.35\n",
      "plt.xlabel('Relative Error Percentages',fontsize=20)\n",
      "plt.ylabel('Number of Art pieces',fontsize=20)\n",
      "plt.title('Number of pieces based on variation from estimate',fontsize=20)\n",
      "plt.legend()\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "EVALUATION STATISTICS FOR Linear REGRESSION MODEL :\n",
        "====================================================\n",
        "Residual sum of squares :- 0.05\n",
        "Variance score :- 0.75\n",
        "Mean absolute Error :- 0.167945412452\n",
        "Mean squared Error :- 0.0515668001224\n",
        "R2 score :- 0.74827485988\n",
        "Mean of Relative Error % :-"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 35.4583603172\n",
        "Median of Relative Error % :- 31.1460165393\n",
        "\n",
        "\n",
        "REALTIVE ERROR DISTRIBUTION PLOT FOR Linear REGRESSION MODEL :\n",
        "================================================================\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ej7VgO3sCTwEboGKOBxLzB8PPMPH+HgqFAoVCoQXJa5exwMpOJ8LMRplisUix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\nMzMzMzPLhv8PhfMM3BVwMV8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa7a2adac>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "knn_data = pd.read_csv('data_without_dates.csv')\n",
      "split_point = 28000\n",
      "train = knn_data[0:split_point]\n",
      "features = train[train.columns[0]]\n",
      "value = train[train.columns[1]]\n",
      "testo = knn_data[split_point:len(knn_data)]\n",
      "test = testo[testo.columns[0]]\n",
      "val = testo[testo.columns[1]]\n",
      "clf = tree.DecisionTreeRegressor()\n",
      "clf = clf.fit(features[:,np.newaxis],value)\n",
      "predictions = np.zeros(len(test[:,np.newaxis]))\n",
      "diff = np.zeros(len(test[:,np.newaxis]))\n",
      "for i in range(0,len(val)):\n",
      "    predictions[i] = clf.predict(test[:,np.newaxis][i])\n",
      "    predictions[i] = np.power(10,predictions[i])\n",
      "    val[:,np.newaxis][i] = np.power(10,val[:,np.newaxis][i])\n",
      "    diff[:,np.newaxis][i] = ((predictions[i] - val[:,np.newaxis][i])/val[:,np.newaxis][i])*100\n",
      "    \n",
      "print \"Decision trees:\\n\"    \n",
      "print \"Mean of Relative Error % :-\",np.average(abs(diff))\n",
      "print \"Median of Relative Error % :-\",np.median(abs(diff))\n",
      "print \"\\n\"\n",
      "\n",
      "print \"REALTIVE ERROR DISTRIBUTION PLOT FOR DECISION TREE REGRESSION MODEL :\"\n",
      "print \"================================================================\"\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "top = 1500;\n",
      "error_count = np.zeros((2*top+1,1))\n",
      "for i in range(0,top-1):\n",
      "    error_count[i+top] = len(np.where((diff<i) & (diff>i-1))[0])\n",
      "\n",
      "for i in range(-top,0):\n",
      "    error_count[i+top] = len(np.where((diff>i) & (diff<i+1))[0])\n",
      "    \n",
      "error_count[2*top] = len(np.where(diff>top-2)[0])                        \n",
      "\n",
      "index = np.arange(-top,top+1)\n",
      "rects1 = plt.bar(index, error_count)\n",
      "bar_width = 0.35\n",
      "plt.xlabel('Relative Error Percentages',fontsize=20)\n",
      "plt.ylabel('Number of Art pieces',fontsize=20)\n",
      "plt.title('Number of pieces based on variation from estimate',fontsize=20)\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Decision trees:\n",
        "\n",
        "Mean of Relative Error % :- 31.8358704334\n",
        "Median of Relative Error % :- 28.2889042863\n",
        "\n",
        "\n",
        "REALTIVE ERROR DISTRIBUTION PLOT FOR DECISION TREE REGRESSION MODEL :\n",
        "================================================================\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HGzWzxvFoo9mQtxyK3tjfp6Fcgn81YTv7AE8DG6JijocS8/vDzyDxnIBCoUCh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\nzEYIV8oc6CjgIeA7KNo9Edi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Yahl6kO+DynCPTEnPtWGZq9DN82VUoayarOzZqCLb12P/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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa768238c>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "knn_data = pd.read_csv('data_without_dates.csv')\n",
      "split_point = 28000\n",
      "train = knn_data[0:split_point]\n",
      "features = train[train.columns[0]]\n",
      "value = train[train.columns[1]]\n",
      "testo = knn_data[split_point:len(knn_data)]\n",
      "test = testo[testo.columns[0]]\n",
      "val = testo[testo.columns[1]]\n",
      "clf = KNeighborsRegressor(n_neighbors=5,weights='uniform',p=1,algorithm='auto')\n",
      "clf = clf.fit(features[:,np.newaxis],value)\n",
      "\n",
      "\n",
      "predictions = np.zeros(len(test[:,np.newaxis]))\n",
      "diff = np.zeros(len(test[:,np.newaxis]))\n",
      "for i in range(0,len(val)):\n",
      "    predictions[i] = clf.predict(test[:,np.newaxis][i])\n",
      "    predictions[i] = np.power(10,predictions[i])\n",
      "    val[:,np.newaxis][i] = np.power(10,val[:,np.newaxis][i])\n",
      "    diff[:,np.newaxis][i] = ((predictions[i] - val[:,np.newaxis][i])/val[:,np.newaxis][i])*100\n",
      "    \n",
      "print \"KNN:\\n\"    \n",
      "print \"Mean of Relative Error % :-\",np.average(abs(diff))\n",
      "print \"Median of Relative Error % :-\",np.median(abs(diff))\n",
      "print \"\\n\"\n",
      "\n",
      "print \"REALTIVE ERROR DISTRIBUTION PLOT FOR k-NN REGRESSION MODEL :\"\n",
      "print \"================================================================\"\n",
      "\n",
      "top = 1500;\n",
      "error_count = np.zeros((2*top+1,1))\n",
      "for i in range(0,top-1):\n",
      "    error_count[i+top] = len(np.where((diff<i) & (diff>i-1))[0])\n",
      "\n",
      "for i in range(-top,0):\n",
      "    error_count[i+top] = len(np.where((diff>i) & (diff<i+1))[0])\n",
      "    \n",
      "error_count[2*top] = len(np.where(diff>top-2)[0])                        \n",
      "\n",
      "index = np.arange(-top,top+1)\n",
      "rects1 = plt.bar(index, error_count)\n",
      "bar_width = 0.35\n",
      "plt.xlabel('Relative Error Percentages',fontsize=20)\n",
      "plt.ylabel('Number of Art pieces',fontsize=20)\n",
      "plt.title('Number of pieces based on variation from estimate',fontsize=20)\n",
      "plt.legend()\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "KNN:\n",
        "\n",
        "Mean of Relative Error % :- 34.2712743025\n",
        "Median of Relative Error % :- 28.2330582656\n",
        "\n",
        "\n",
        "REALTIVE ERROR DISTRIBUTION PLOT FOR k-NN REGRESSION MODEL :\n",
        "================================================================\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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knQgREUmIIrp0aeMcCuNPf0fHIGBFn+kiIo2kHIp0yEOlzFoMwIYwl4ZSjoWI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\nRERERERERERERERERERERNLh/wMaCwJWY+8ODAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa4f7e98c>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"prediction\"></a>\n",
      "#Final Model & Prediction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def stats(y_true,y_pred):\n",
      "\n",
      "    print 'Mean absolute Error :-', mean_absolute_error(val, y_pred)\n",
      "    print 'Mean squared Error :-', mean_squared_error(y_true, y_pred)\n",
      "    print 'R2 score :-', r2_score(y_true, y_pred)\n",
      "\n",
      "knn_data = pd.read_csv('data_without_dates.csv')\n",
      "\n",
      "split_point = 28000\n",
      "\n",
      "train = knn_data[0:split_point]\n",
      "features = train[train.columns[0]]\n",
      "value = train[train.columns[1]]\n",
      "testo = knn_data[split_point:len(knn_data)]\n",
      "test = testo[testo.columns[0]]\n",
      "val = testo[testo.columns[1]]\n",
      "\n",
      "neigh = tree.DecisionTreeRegressor()\n",
      "#neigh = KNeighborsRegressor(n_neighbors=5,weights='uniform',p=1,algorithm='auto')\n",
      "neigh.fit(features[:,np.newaxis],value)\n",
      "#neigh.fit(features, value) \n",
      "prediction = neigh.predict(test[:,np.newaxis])\n",
      "print \"EVALUATION STATISTICS FOR DECISION TREE REGRESSION MODEL :\"\n",
      "print \"====================================================\"\n",
      "print(\"Residual sum of squares :- %.2f\"\n",
      "      % np.mean((prediction - val) ** 2))\n",
      "print('Variance score :- %.2f' % neigh.score(test[:,np.newaxis], val))\n",
      "stats(val,neigh.predict(test[:,np.newaxis]))\n",
      "predictions = np.zeros(len(test))\n",
      "diff = np.zeros(len(test))\n",
      "for i in range(0,len(test)):\n",
      "    predictions[i] = clf.predict(test[:,np.newaxis][i])\n",
      "    predictions[i] = np.power(10,predictions[i])\n",
      "    #val[i] = np.power(val[i],10)\n",
      "    #diff[i] = ((predictions[i] - val[i])/val[i])*100\n",
      "#print \"Decision trees:\\n\"    \n",
      "#print \"Mean of Relative Error % :-\",np.average(abs(diff))\n",
      "#print \"Median of Relative Error % :-\",np.median(abs(diff))\n",
      "#print \"\\n\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "EVALUATION STATISTICS FOR DECISION TREE REGRESSION MODEL :\n",
        "====================================================\n",
        "Residual sum of squares :- 0.04\n",
        "Variance score :- 0.80\n",
        "Mean absolute Error :- 0.150322672252\n",
        "Mean squared Error :- 0.0412383194184\n",
        "R2 score :- 0.798693700031\n"
       ]
      }
     ],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}