karthick_playing_with_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import xgboost\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "matplotlib.interactive(True)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
    "from sklearn import preprocessing\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** Run the program combine_two_parts.py and use the dataframe clean_data **\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ramya/anaconda3/lib/python3.5/site-packages/pandas/core/indexing.py:141: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/media/ramya/D/Data_Science_Retreat/Jose_Minicompetition/DSR_minicompetition/clean_team_data.py:111: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  df.drop(\"Person.ID\", axis=1, inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns in the first part is  1614\n",
      "Number of columns in the second part is  150\n",
      "Number of columns in the complete & cleaned data sets  is  1764\n"
     ]
    }
   ],
   "source": [
    "from combine_two_parts import clean_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = clean_data[\"Grant.Status\"]\n",
    "X = clean_data.drop([\"Grant.Status\", \"Grant.Application.ID\"], axis = 1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators = 500, max_depth = 30, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6095, 2613)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=30, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=2,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=500, n_jobs=1, oob_score=True, random_state=1,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78556193601312552"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = model.oob_score_\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79946421737466511"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = model.score(X_test, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2613, 1762)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.50864502,  0.49135498],\n",
       "       [ 0.69402722,  0.30597278],\n",
       "       [ 0.735635  ,  0.264365  ],\n",
       "       ..., \n",
       "       [ 0.89076992,  0.10923008],\n",
       "       [ 0.67087707,  0.32912293],\n",
       "       [ 0.5702932 ,  0.4297068 ]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.49135498,  0.30597278,  0.264365  , ...,  0.10923008,\n",
       "        0.32912293,  0.4297068 ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.09226826e-01,   5.07340735e-05,   5.15176897e-05, ...,\n",
       "         2.10232550e-04,   0.00000000e+00,   1.10653959e-02])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219 1762\n"
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       " ('RFCD_280211.0', 0.00015550549518306061),\n",
       " ('S_205A', 0.00015524432879451659),\n",
       " ('RFCD_360105.0', 0.00015460336665801158),\n",
       " ('SEO_670306.0', 0.00015453955768916124),\n",
       " ('RFCD_291404.0', 0.00015392175068735191),\n",
       " ('RFCD_310199.0', 0.00015361923364309632),\n",
       " ('Dept.No._2518', 0.00015313928244613926),\n",
       " ('SEO_750805.0', 0.00015301028461401619),\n",
       " ('RFCD_420306.0', 0.00015149213817793481),\n",
       " ('RFCD_321399.0', 0.00015084518830532141),\n",
       " ('S_105A', 0.00015045042528871269),\n",
       " ('SEO_630501.0', 0.00014958490102603297),\n",
       " ('RFCD_270306.0', 0.00014934085620990888),\n",
       " ('RFCD_280205.0', 0.00014837894358467652),\n",
       " ('RFCD_230105.0', 0.00014784727236994742),\n",
       " ('RFCD_270404.0', 0.00014745092188110206),\n",
       " ('RFCD_291104.0', 0.00014641159382548286),\n",
       " ('S_56D', 0.00014612370875159158),\n",
       " ('SEO_630104.0', 0.00014588521959208811),\n",
       " ('RFCD_250402.0', 0.00014483826010684126),\n",
       " ('RFCD_250204.0', 0.00014422994540932559),\n",
       " ('SEO_620399.0', 0.00014339677404558401),\n",
       " ('SEO_730304.0', 0.00014325115332730471),\n",
       " ('RFCD_410402.0', 0.00014302097867738444),\n",
       " ('SEO_730302.0', 0.00014025800340934332),\n",
       " ('RFCD_321212.0', 0.00013948493009450851),\n",
       " ('Dept.No._4363', 0.00013904351339620846),\n",
       " ('RFCD_321201.0', 0.00013885367021859957),\n",
       " ('RFCD_320403.0', 0.00013882490775577532),\n",
       " ('RFCD_300802.0', 0.00013759030863448878),\n",
       " ('SEO_750801.0', 0.00013728513650159411),\n",
       " ('RFCD_280102.0', 0.00013727031156449804),\n",
       " ('S_87C', 0.00013712612525304207),\n",
       " ('RFCD_321007.0', 0.00013623007421435397),\n",
       " ('SEO_729901.0', 0.00013581750990358004),\n",
       " ('RFCD_379902.0', 0.00013568723241694762),\n",
       " ('RFCD_260501.0', 0.00013565116379489397),\n",
       " ('S_113A', 0.00013510032203571849),\n",
       " ('RFCD_380102.0', 0.00013454724212005946),\n",
       " ('SEO_770403.0', 0.00013256131719499529),\n",
       " ('Dept.No._498', 0.00013247956877299226),\n",
       " ('RFCD_390305.0', 0.0001318179543110175),\n",
       " ('SEO_770103.0', 0.00013133954583096483),\n",
       " ('RFCD_329902.0', 0.00013103501155654364),\n",
       " ('SEO_740401.0', 0.00013056008649502446),\n",
       " ('RFCD_300506.0', 0.00013020808550424798),\n",
       " ('RFCD_230117.0', 0.00013017560972105384),\n",
       " ('RFCD_321213.0', 0.00012925147966741146),\n",
       " ('RFCD_330302.0', 0.00012782826655562368),\n",
       " ('SEO_680199.0', 0.00012764305018396734),\n",
       " ('RFCD_270799.0', 0.0001275216447663391),\n",
       " ('RFCD_440104.0', 0.00012684518856560068),\n",
       " ('SEO_751003.0', 0.00012575633002913855),\n",
       " ('SEO_749904.0', 0.00012521332852272779),\n",
       " ('RFCD_230118.0', 0.00012419573718597062),\n",
       " ('SEO_751004.0', 0.00012412684701675926),\n",
       " ('RFCD_300399.0', 0.00012276010686667173),\n",
       " ('S_222B', 0.00012235738637879855),\n",
       " ('Faculty.No._10', 0.00012226663393282222),\n",
       " ('RFCD_270704.0', 0.00012131915268142665),\n",
       " ('Role_HONVISIT', 0.00012131596081339385),\n",
       " ('RFCD_380206.0', 0.00012056329977783167),\n",
       " ('SEO_700101.0', 0.00011919027909596693),\n",
       " ('SEO_750401.0', 0.00011889052024502375),\n",
       " ('Dept.No._3708', 0.000118506500356548),\n",
       " ('RFCD_250104.0', 0.00011850547108265089),\n",
       " ('Dept.No._2848', 0.00011814707656693096),\n",
       " ('RFCD_310104.0', 0.00011778757064702803),\n",
       " ('S_143C', 0.000117741855604193),\n",
       " ('S_215C', 0.0001174638286303537),\n",
       " ('S_3C', 0.00011738002471530408),\n",
       " ('RFCD_270301.0', 0.00011502285888024269),\n",
       " ('S_196D', 0.00011399824509327478),\n",
       " ('RFCD_370402.0', 0.00011353978425443895),\n",
       " ('RFCD_330106.0', 0.00011227401669984627),\n",
       " ('SEO_620105.0', 0.00011211108148176755),\n",
       " ('RFCD_291405.0', 0.00011099959118141411),\n",
       " ('RFCD_260601.0', 0.00011020731368423342),\n",
       " ('RFCD_270403.0', 0.00010971022043335932),\n",
       " ('SEO_620301.0', 0.00010923409638557127),\n",
       " ('RFCD_230199.0', 0.00010867386107833465),\n",
       " ('RFCD_310202.0', 0.00010851715492817517),\n",
       " ('RFCD_430203.0', 0.00010653599952923322),\n",
       " ('RFCD_270805.0', 0.00010630661573454436),\n",
       " ('RFCD_380201.0', 0.00010626982518840148),\n",
       " ('RFCD_280304.0', 0.0001047470558820924),\n",
       " ('RFCD_321401.0', 0.00010411394565940038),\n",
       " ('RFCD_320599.0', 0.00010402307508579585),\n",
       " ('RFCD_330206.0', 0.00010366631876965703),\n",
       " ('RFCD_420399.0', 0.00010328133480073836),\n",
       " ('SEO_660205.0', 0.00010215649841929252),\n",
       " ('RFCD_370299.0', 0.00010192332215903909),\n",
       " ('SEO_751002.0', 0.00010184096660101494),\n",
       " ('RFCD_250502.0', 0.00010178405019719539),\n",
       " ('RFCD_279901.0', 0.00010163573436030637),\n",
       " ('S_266B', 0.00010018711712161992),\n",
       " ('RFCD_330101.0', 9.9552230600813305e-05),\n",
       " ('RFCD_279999.0', 9.9512667081865514e-05),\n",
       " ('SEO_760203.0', 9.9367981169363041e-05),\n",
       " ('RFCD_300102.0', 9.9280687401889884e-05),\n",
       " ('S_288D', 9.8754206934763511e-05),\n",
       " ('SEO_680299.0', 9.8509698888047385e-05),\n",
       " ('RFCD_321001.0', 9.8111458630390958e-05),\n",
       " ('S_111C', 9.6871076295949161e-05),\n",
       " ('SEO_770903.0', 9.5925074831281458e-05),\n",
       " ('SEO_770504.0', 9.5539124652702895e-05),\n",
       " ('RFCD_370107.0', 9.5512030272111955e-05),\n",
       " ('Dept.No._1798', 9.5428289434454629e-05),\n",
       " ('RFCD_330303.0', 9.539912568553804e-05),\n",
       " ('RFCD_259902.0', 9.4993400893355679e-05),\n",
       " ('RFCD_330108.0', 9.4927114755862576e-05),\n",
       " ('SEO_680302.0', 9.4897095558075853e-05),\n",
       " ('RFCD_320703.0', 9.427899526913598e-05),\n",
       " ('SEO_750703.0', 9.3994087583915294e-05),\n",
       " ('RFCD_280199.0', 9.3972195967285396e-05),\n",
       " ('Dept.No._4153', 9.3894773713375343e-05),\n",
       " ('RFCD_250501.0', 9.366575552965554e-05),\n",
       " ('RFCD_370199.0', 9.3426511864756753e-05),\n",
       " ('RFCD_330301.0', 9.1861100279460429e-05),\n",
       " ('SEO_670503.0', 9.1674919368952106e-05),\n",
       " ('RFCD_280104.0', 9.1668091194970904e-05),\n",
       " ('Dept.No._548', 9.1588348680934906e-05),\n",
       " ('SEO_769999.0', 9.1298768281494635e-05),\n",
       " ('SEO_750803.0', 9.0877411781848722e-05),\n",
       " ('SEO_730000.0', 9.0773866607025784e-05),\n",
       " ('SEO_659999.0', 9.0656939680939581e-05),\n",
       " ('SEO_680404.0', 8.9634996622166199e-05),\n",
       " ('SEO_779902.0', 8.9407013917336541e-05),\n",
       " ('RFCD_420303.0', 8.8740466524137376e-05),\n",
       " ('RFCD_250105.0', 8.8162880663504958e-05),\n",
       " ('SEO_670199.0', 8.7870969937025849e-05),\n",
       " ('RFCD_291503.0', 8.7575780428982711e-05),\n",
       " ('S_52D', 8.7541146679854455e-05),\n",
       " ('RFCD_360201.0', 8.7292996642772709e-05),\n",
       " ('SEO_770602.0', 8.6995010992942535e-05),\n",
       " ('S_211C', 8.6862782931723572e-05),\n",
       " ('RFCD_300899.0', 8.6680794979554127e-05),\n",
       " ('SEO_620504.0', 8.6656882498602872e-05),\n",
       " ('RFCD_240203.0', 8.6452377612969097e-05),\n",
       " ('RFCD_300103.0', 8.6377727106488439e-05),\n",
       " ('S_172D', 8.6004641529181271e-05),\n",
       " ('Dept.No._4193', 8.5469100492520099e-05),\n",
       " ('S_12D', 8.489802717420375e-05),\n",
       " ('S_223C', 8.4576376355325537e-05),\n",
       " ('SEO_730117.0', 8.42138462084224e-05),\n",
       " ('Dept.No._2773', 8.3542169586134314e-05),\n",
       " ('S_228D', 8.3017804573721051e-05),\n",
       " ('Dept.No._2878', 8.2805879990042428e-05),\n",
       " ('SEO_750309.0', 8.2718102213427788e-05),\n",
       " ('SEO_730220.0', 8.2576907036614617e-05),\n",
       " ('RFCD_330305.0', 8.2334872668261223e-05),\n",
       " ('RFCD_350303.0', 8.229977368363687e-05),\n",
       " ('S_84D', 8.2159266477064153e-05),\n",
       " ('RFCD_270804.0', 8.0968070458038195e-05),\n",
       " ('RFCD_440205.0', 8.0172784996228479e-05),\n",
       " ('RFCD_300199.0', 7.980040569194464e-05),\n",
       " ('RFCD_270503.0', 7.9286758139462585e-05),\n",
       " ('SEO_750604.0', 7.85374866380749e-05),\n",
       " ('SEO_770605.0', 7.8471772388552722e-05),\n",
       " ('S_260D', 7.8005991936606797e-05),\n",
       " ('SEO_751099.0', 7.7847725763148244e-05),\n",
       " ('RFCD_319999.0', 7.721335903073767e-05),\n",
       " ('S_83C', 7.6894592854430667e-05),\n",
       " ('RFCD_390404.0', 7.6812718105998042e-05),\n",
       " ('RFCD_320801.0', 7.6807821905670285e-05),\n",
       " ('RFCD_330307.0', 7.6783012094497211e-05),\n",
       " ('SEO_671201.0', 7.620678925347774e-05),\n",
       " ('RFCD_250605.0', 7.4737907347626422e-05),\n",
       " ('SEO_671306.0', 7.4537186148754175e-05),\n",
       " ('RFCD_290801.0', 7.4366215386575897e-05),\n",
       " ('SEO_750304.0', 7.4276797444174108e-05),\n",
       " ('RFCD_390114.0', 7.4243535057479307e-05),\n",
       " ('RFCD_350302.0', 7.4171884273053373e-05),\n",
       " ('SEO_660402.0', 7.4128847800252587e-05),\n",
       " ('RFCD_370201.0', 7.3878941267263093e-05),\n",
       " ('SEO_680403.0', 7.3503896871631125e-05),\n",
       " ('RFCD_410102.0', 7.3440308648072352e-05),\n",
       " ('RFCD_429999.0', 7.3139740053596819e-05),\n",
       " ('RFCD_420304.0', 7.2088044194073956e-05),\n",
       " ('RFCD_370106.0', 7.1774154869884028e-05),\n",
       " ('RFCD_390303.0', 7.1486685712636276e-05),\n",
       " ('S_316D', 7.1355327005559794e-05),\n",
       " ('SEO_671399.0', 7.1163409524273552e-05),\n",
       " ('SEO_670901.0', 7.0934625625093353e-05),\n",
       " ('SEO_720402.0', 7.0928260252193381e-05),\n",
       " ('RFCD_320804.0', 7.0317615065479963e-05),\n",
       " ('RFCD_320303.0', 7.0265135689229579e-05),\n",
       " ('S_210B', 6.9998767186588444e-05),\n",
       " ('RFCD_390108.0', 6.9986059300912871e-05),\n",
       " ('RFCD_250203.0', 6.9586822035411821e-05),\n",
       " ('S_86B', 6.8786729830197662e-05),\n",
       " ('S_68D', 6.8604662981751552e-05),\n",
       " ('S_234B', 6.8551913555420211e-05),\n",
       " ('RFCD_300602.0', 6.8416280725928889e-05),\n",
       " ('S_164D', 6.8189401933594786e-05),\n",
       " ('Dept.No._118', 6.7665763783696551e-05),\n",
       " ('RFCD_270101.0', 6.7267714739056871e-05),\n",
       " ('RFCD_291899.0', 6.7038489246052882e-05),\n",
       " ('SEO_770805.0', 6.6405336905649536e-05),\n",
       " ('S_78B', 6.6396864621931105e-05),\n",
       " ('RFCD_291706.0', 6.6155856519447469e-05),\n",
       " ('S_309A', 6.5973608198661681e-05),\n",
       " ('Dept.No._2738', 6.5657236681056886e-05),\n",
       " ('SEO_671305.0', 6.5473669737059161e-05),\n",
       " ('S_112D', 6.4946299467739843e-05),\n",
       " ('SEO_680201.0', 6.4795735217245624e-05),\n",
       " ('SEO_630102.0', 6.4155859054176425e-05),\n",
       " ('SEO_671299.0', 6.3414907158002254e-05),\n",
       " ('S_242B', 6.336454975975245e-05),\n",
       " ('SEO_730200.0', 6.3101414478253833e-05),\n",
       " ('SEO_630302.0', 6.3044567571974379e-05),\n",
       " ('RFCD_340401.0', 6.2782248283571013e-05),\n",
       " ('SEO_680202.0', 6.2178525262535574e-05),\n",
       " ('RFCD_321018.0', 6.1846504540699995e-05),\n",
       " ('RFCD_260105.0', 6.124316478439595e-05),\n",
       " ('SEO_660403.0', 6.1045743235746896e-05),\n",
       " ('RFCD_360101.0', 6.0941845790624439e-05),\n",
       " ('RFCD_270305.0', 6.0840900917644828e-05),\n",
       " ('SEO_630101.0', 6.0805585509720221e-05),\n",
       " ('S_73A', 6.0681270904558596e-05),\n",
       " ('RFCD_440110.0', 6.0557687889602264e-05),\n",
       " ('SEO_720401.0', 6.0378052163053272e-05),\n",
       " ('G_40C', 6.0045134204628251e-05),\n",
       " ('SEO_750499.0', 6.0014871047584753e-05),\n",
       " ('RFCD_249902.0', 5.9588641037616184e-05),\n",
       " ('S_213A', 5.9496009479970778e-05),\n",
       " ('RFCD_390113.0', 5.8724390026430168e-05),\n",
       " ('SEO_671707.0', 5.8426273554963322e-05),\n",
       " ('SEO_770199.0', 5.8279719520826508e-05),\n",
       " ('S_28D', 5.8132193349630975e-05),\n",
       " ('RFCD_370301.0', 5.803274071434605e-05),\n",
       " ('RFCD_379999.0', 5.8027685353052689e-05),\n",
       " ('SEO_770804.0', 5.7854479710305115e-05),\n",
       " ('RFCD_300299.0', 5.7811863726662444e-05),\n",
       " ('RFCD_370603.0', 5.7545012843067097e-05),\n",
       " ('RFCD_420202.0', 5.7335660621166209e-05),\n",
       " ('RFCD_350299.0', 5.7107045104669668e-05),\n",
       " ('RFCD_370499.0', 5.6696479859188911e-05),\n",
       " ('RFCD_370204.0', 5.6074776460001193e-05),\n",
       " ('SEO_750699.0', 5.6008967937224848e-05),\n",
       " ('S_135C', 5.5854843944473805e-05),\n",
       " ('Faculty.No._40', 5.5735074665175634e-05),\n",
       " ('SEO_720205.0', 5.544390568198294e-05),\n",
       " ('SEO_690199.0', 5.5256292967356437e-05),\n",
       " ('S_15C', 5.525597494047049e-05),\n",
       " ('SEO_750204.0', 5.50804305002587e-05),\n",
       " ('RFCD_410302.0', 5.4299276294056308e-05),\n",
       " ('RFCD_321102.0', 5.4120364374394329e-05),\n",
       " ('RFCD_250107.0', 5.4111817735524779e-05),\n",
       " ('RFCD_321403.0', 5.379716209537491e-05),\n",
       " ('RFCD_350107.0', 5.3481046182504698e-05),\n",
       " ('Dept.No._68', 5.3385339296095219e-05),\n",
       " ('RFCD_250303.0', 5.3294433264679235e-05),\n",
       " ('S_132D', 5.2512439555624105e-05),\n",
       " ('RFCD_270803.0', 5.24818830235624e-05),\n",
       " ('RFCD_270604.0', 5.225696741125017e-05),\n",
       " ('RFCD_370105.0', 5.210023052277452e-05),\n",
       " ('SEO_770705.0', 5.1914496507086826e-05),\n",
       " ('RFCD_300303.0', 5.1804110163869184e-05),\n",
       " ('RFCD_240202.0', 5.1540154276448697e-05),\n",
       " ('S_101A', 5.1517689695801042e-05),\n",
       " ('RFCD_290102.0', 5.1477937096417908e-05),\n",
       " ('RFCD_260301.0', 5.1308522227853915e-05),\n",
       " ('S_100D', 5.0734073529259293e-05),\n",
       " ('RFCD_370101.0', 5.0327059663060673e-05),\n",
       " ('RFCD_290401.0', 5.0208585534161722e-05),\n",
       " ('RFCD_380101.0', 5.0134099978797002e-05),\n",
       " ('SEO_750899.0', 4.9419863890227484e-05),\n",
       " ('RFCD_300501.0', 4.9417218411815731e-05),\n",
       " ('S_279C', 4.8986512995950761e-05),\n",
       " ('S_280D', 4.76477041246407e-05),\n",
       " ('SEO_750104.0', 4.75380356363243e-05),\n",
       " ('RFCD_360104.0', 4.7496510137865707e-05),\n",
       " ('RFCD_300510.0', 4.7212870517794786e-05),\n",
       " ('RFCD_321217.0', 4.7113912355584773e-05),\n",
       " ('SEO_720201.0', 4.706441975718813e-05),\n",
       " ('RFCD_270502.0', 4.701120417258101e-05),\n",
       " ('SEO_620503.0', 4.6427949824555373e-05),\n",
       " ('RFCD_260599.0', 4.5711408286778121e-05),\n",
       " ('SEO_700399.0', 4.5568975523295791e-05),\n",
       " ('RFCD_260108.0', 4.5105971907730317e-05),\n",
       " ('RFCD_300903.0', 4.505915328041238e-05),\n",
       " ('S_247C', 4.4316973745111542e-05),\n",
       " ('RFCD_290899.0', 4.413163191983255e-05),\n",
       " ('SEO_630502.0', 4.3640847384044236e-05),\n",
       " ('RFCD_300499.0', 4.3548619011376951e-05),\n",
       " ('S_180D', 4.3112810611975327e-05),\n",
       " ('S_324D', 4.3097466790699461e-05),\n",
       " ('RFCD_280405.0', 4.2834295291341445e-05),\n",
       " ('S_13A', 4.2501463945773345e-05),\n",
       " ('SEO_750605.0', 4.2297885465914009e-05),\n",
       " ('S_26B', 4.2211152271277103e-05),\n",
       " ('RFCD_250199.0', 4.2065885872705891e-05),\n",
       " ('S_282B', 4.2008472353443741e-05),\n",
       " ('S_95C', 4.1714354925114114e-05),\n",
       " ('S_133A', 4.1588476333349597e-05),\n",
       " ('RFCD_440105.0', 4.1540899225679099e-05),\n",
       " ('SEO_770406.0', 4.0922820671271966e-05),\n",
       " ('SEO_671709.0', 4.0742564742462054e-05),\n",
       " ('RFCD_440207.0', 4.0340221730537173e-05),\n",
       " ('SEO_770405.0', 4.0299565816960303e-05),\n",
       " ('Dept.No._4388', 4.0190370999114716e-05),\n",
       " ('RFCD_300599.0', 4.0136861380594419e-05),\n",
       " ('RFCD_280506.0', 3.9751800792140246e-05),\n",
       " ('SEO_660504.0', 3.9601376707371672e-05),\n",
       " ('RFCD_300203.0', 3.9384039086766011e-05),\n",
       " ('RFCD_259999.0', 3.9337027680782891e-05),\n",
       " ('RFCD_320901.0', 3.9201686635395615e-05),\n",
       " ('RFCD_291704.0', 3.9175781310843006e-05),\n",
       " ('SEO_760102.0', 3.8775505334196519e-05),\n",
       " ('RFCD_260399.0', 3.8720809676616662e-05),\n",
       " ('RFCD_380399.0', 3.8158913716847108e-05),\n",
       " ('RFCD_300606.0', 3.7416920586263465e-05),\n",
       " ('SEO_670799.0', 3.6955216529327022e-05),\n",
       " ('SEO_680402.0', 3.6692771154912286e-05),\n",
       " ('RFCD_380207.0', 3.6674907895735955e-05),\n",
       " ('RFCD_230101.0', 3.6368664844072443e-05),\n",
       " ('RFCD_240502.0', 3.6161491487565525e-05),\n",
       " ('SEO_660502.0', 3.6135344660616737e-05),\n",
       " ('SEO_620209.0', 3.5876876480901814e-05),\n",
       " ('RFCD_379901.0', 3.5509055345067222e-05),\n",
       " ('S_170B', 3.5413460034416744e-05),\n",
       " ('S_229A', 3.5102167999024126e-05),\n",
       " ('SEO_671206.0', 3.4959595140790899e-05),\n",
       " ('RFCD_320203.0', 3.4299553784690215e-05),\n",
       " ('RFCD_291401.0', 3.4240627887303874e-05),\n",
       " ('SEO_720202.0', 3.4055290295005568e-05),\n",
       " ('RFCD_350204.0', 3.3819416506682601e-05),\n",
       " ('RFCD_430199.0', 3.3777618109600645e-05),\n",
       " ('RFCD_420305.0', 3.3665392288744868e-05),\n",
       " ('SEO_749903.0', 3.3614036528923326e-05),\n",
       " ('RFCD_300201.0', 3.308388501184216e-05),\n",
       " ('RFCD_280401.0', 3.2531606064769657e-05),\n",
       " ('SEO_770704.0', 3.2444314840762256e-05),\n",
       " ('SEO_770402.0', 3.2440137120463605e-05),\n",
       " ('RFCD_370601.0', 3.2139549057634751e-05),\n",
       " ('SEO_670399.0', 3.1896790861213229e-05),\n",
       " ('S_274B', 3.1878775270596217e-05),\n",
       " ('RFCD_270709.0', 3.1721189662505939e-05),\n",
       " ('RFCD_340299.0', 3.1637716447656093e-05),\n",
       " ('SEO_750399.0', 3.1611135009934973e-05),\n",
       " ('SEO_750799.0', 3.1379344892868665e-05),\n",
       " ('Dept.No._2788', 3.1237092952885087e-05),\n",
       " ('S_38B', 3.1131395752430729e-05),\n",
       " ('RFCD_420217.0', 3.1059353372168646e-05),\n",
       " ('SEO_671099.0', 3.0763119377609689e-05),\n",
       " ('RFCD_370403.0', 3.0732950831396247e-05),\n",
       " ('SEO_680203.0', 3.0566916387886416e-05),\n",
       " ('SEO_770305.0', 3.0122407286308287e-05),\n",
       " ('RFCD_420302.0', 3.0052433012522055e-05),\n",
       " ('SEO_670499.0', 3.0047754799390767e-05),\n",
       " ('S_275C', 2.9962424593290295e-05),\n",
       " ('SEO_740501.0', 2.9955294034107152e-05),\n",
       " ('RFCD_300605.0', 2.9876251209570991e-05),\n",
       " ('RFCD_300403.0', 2.9588305279546503e-05),\n",
       " ('RFCD_300804.0', 2.9086359782281305e-05),\n",
       " ('RFCD_250404.0', 2.8912766536314603e-05),\n",
       " ('SEO_660501.0', 2.8787438821426205e-05),\n",
       " ('RFCD_310201.0', 2.8724810525052492e-05),\n",
       " ('G_30E', 2.8567952273052079e-05),\n",
       " ('SEO_760104.0', 2.8448254417907771e-05),\n",
       " ('RFCD_310106.0', 2.832999623747083e-05),\n",
       " ('RFCD_330201.0', 2.8176777936127323e-05),\n",
       " ('RFCD_280105.0', 2.8155188017917046e-05),\n",
       " ('RFCD_430107.0', 2.7916611135022015e-05),\n",
       " ('RFCD_291402.0', 2.7784895615070066e-05),\n",
       " ('SEO_620502.0', 2.7627991747663917e-05),\n",
       " ('RFCD_320304.0', 2.7626314977407698e-05),\n",
       " ('SEO_690401.0', 2.7386659439162742e-05),\n",
       " ('RFCD_360202.0', 2.7290588226981489e-05),\n",
       " ('SEO_770902.0', 2.7096212703947811e-05),\n",
       " ('RFCD_291799.0', 2.6836735629701799e-05),\n",
       " ('RFCD_300805.0', 2.6781679557774036e-05),\n",
       " ('RFCD_240199.0', 2.6621191270098963e-05),\n",
       " ('RFCD_290201.0', 2.6565426413110822e-05),\n",
       " ('SEO_620104.0', 2.6541096075429071e-05),\n",
       " ('SEO_771002.0', 2.637989678455161e-05),\n",
       " ('RFCD_400101.0', 2.6307832692508561e-05),\n",
       " ('RFCD_370103.0', 2.6306761640529022e-05),\n",
       " ('RFCD_321030.0', 2.5988373230873385e-05),\n",
       " ('Dept.No._3748', 2.5859774293992144e-05),\n",
       " ('SEO_750299.0', 2.5556369238306987e-05),\n",
       " ('RFCD_321000.0', 2.5535878581459472e-05),\n",
       " ('RFCD_370203.0', 2.5513474956268614e-05),\n",
       " ('SEO_630301.0', 2.5491859679936332e-05),\n",
       " ('S_198B', 2.5252017495769669e-05),\n",
       " ('RFCD_330203.0', 2.5139382146686261e-05),\n",
       " ('RFCD_260102.0', 2.4214746828679484e-05),\n",
       " ('Dept.No._28', 2.4068762661967167e-05),\n",
       " ('RFCD_321103.0', 2.4055918556916083e-05),\n",
       " ('RFCD_430111.0', 2.404509171724485e-05),\n",
       " ('S_241A', 2.3918496553089001e-05),\n",
       " ('SEO_770201.0', 2.3896715633585397e-05),\n",
       " ('RFCD_290702.0', 2.3738717039036706e-05),\n",
       " ('RFCD_321215.0', 2.3705236064602098e-05),\n",
       " ('RFCD_430104.0', 2.3651506280823283e-05),\n",
       " ('SEO_779907.0', 2.362611060249433e-05),\n",
       " ('S_227C', 2.3528939087800753e-05),\n",
       " ('S_286B', 2.3515839183035471e-05),\n",
       " ('RFCD_370501.0', 2.322402326019797e-05),\n",
       " ('SEO_630403.0', 2.3099412931015041e-05),\n",
       " ('SEO_710502.0', 2.2984805238045539e-05),\n",
       " ('S_268D', 2.2889800269405665e-05),\n",
       " ('SEO_710601.0', 2.2673155382091076e-05),\n",
       " ('SEO_770505.0', 2.2646367114833026e-05),\n",
       " ('RFCD_250102.0', 2.2611517339413751e-05),\n",
       " ('SEO_670903.0', 2.2591150375253937e-05),\n",
       " ('S_141A', 2.1771113380212783e-05),\n",
       " ('SEO_660104.0', 2.1608332795674408e-05),\n",
       " ('RFCD_340213.0', 2.1493036749770323e-05),\n",
       " ('S_103C', 2.129671312518379e-05),\n",
       " ('RFCD_270207.0', 2.1284806038095652e-05),\n",
       " ('SEO_671704.0', 2.1247880992448334e-05),\n",
       " ('SEO_690201.0', 2.1155846470254038e-05),\n",
       " ('SEO_670706.0', 2.1100261489602382e-05),\n",
       " ('S_203C', 2.1084463192268727e-05),\n",
       " ('SEO_710603.0', 2.0949523124373767e-05),\n",
       " ('RFCD_350203.0', 2.0884991911554472e-05),\n",
       " ('SEO_669999.0', 2.0818004461089189e-05),\n",
       " ('RFCD_300104.0', 2.0774217139064668e-05),\n",
       " ('Dept.No._603', 2.0483988057199657e-05),\n",
       " ('RFCD_340205.0', 2.0316744034651435e-05),\n",
       " ('RFCD_340405.0', 2.0296142680837777e-05),\n",
       " ('RFCD_291005.0', 2.0056002947371394e-05),\n",
       " ('S_232D', 2.0040950340809141e-05),\n",
       " ('RFCD_360199.0', 1.9972194264509838e-05),\n",
       " ('RFCD_280212.0', 1.9752628518795996e-05),\n",
       " ('SEO_750302.0', 1.9363940970799269e-05),\n",
       " ('RFCD_390117.0', 1.9007070294276984e-05),\n",
       " ('RFCD_240303.0', 1.887125224099906e-05),\n",
       " ('RFCD_280101.0', 1.8709615207268741e-05),\n",
       " ('SEO_671705.0', 1.8613241335867404e-05),\n",
       " ('SEO_620101.0', 1.8430729211517956e-05),\n",
       " ('SEO_680401.0', 1.8319386820498093e-05),\n",
       " ('RFCD_290901.0', 1.8184222994236914e-05),\n",
       " ('RFCD_230112.0', 1.8176131549609642e-05),\n",
       " ('RFCD_390202.0', 1.7867373063745728e-05),\n",
       " ('SEO_760201.0', 1.7761785857971957e-05),\n",
       " ('RFCD_321209.0', 1.775044067898252e-05),\n",
       " ('RFCD_290805.0', 1.7633866547967211e-05),\n",
       " ('RFCD_299999.0', 1.7490524089347614e-05),\n",
       " ('S_16D', 1.7458051223844307e-05),\n",
       " ('RFCD_260699.0', 1.7437685833012366e-05),\n",
       " ('SEO_720599.0', 1.742695569292185e-05),\n",
       " ('RFCD_390302.0', 1.738425913460659e-05),\n",
       " ('S_144D', 1.7296679878814534e-05),\n",
       " ('RFCD_310102.0', 1.7269215434007944e-05),\n",
       " ('SEO_770202.0', 1.7089298149018432e-05),\n",
       " ('RFCD_270209.0', 1.6921095468086052e-05),\n",
       " ('SEO_770404.0', 1.6642461852031592e-05),\n",
       " ('SEO_620108.0', 1.6554756326569886e-05),\n",
       " ('RFCD_291099.0', 1.638529105515708e-05),\n",
       " ('SEO_630402.0', 1.6322979964411197e-05),\n",
       " ('S_27C', 1.62779144520532e-05),\n",
       " ('SEO_690301.0', 1.6232043078490133e-05),\n",
       " ('RFCD_380304.0', 1.6012929889892716e-05),\n",
       " ('Dept.No._2838', 1.5975176892131624e-05),\n",
       " ('RFCD_380299.0', 1.5908327710483761e-05),\n",
       " ('RFCD_430112.0', 1.5660581994787926e-05),\n",
       " ('RFCD_270801.0', 1.5655414364910255e-05),\n",
       " ('RFCD_440103.0', 1.5473657283466003e-05),\n",
       " ('RFCD_250407.0', 1.5284505727865098e-05),\n",
       " ('RFCD_390499.0', 1.5247875104928334e-05),\n",
       " ('SEO_720404.0', 1.5085019263209585e-05),\n",
       " ('RFCD_270307.0', 1.500298932060718e-05),\n",
       " ('RFCD_410202.0', 1.4997758570232737e-05),\n",
       " ('RFCD_389999.0', 1.4988854173874828e-05),\n",
       " ('SEO_770506.0', 1.4848695357633545e-05),\n",
       " ('RFCD_280505.0', 1.4495291239078916e-05),\n",
       " ('RFCD_320302.0', 1.4458082962441141e-05),\n",
       " ('RFCD_349999.0', 1.4363110804280388e-05),\n",
       " ('RFCD_360103.0', 1.4343442189892866e-05),\n",
       " ('SEO_750102.0', 1.4308283861340101e-05),\n",
       " ('RFCD_300902.0', 1.4207598220891682e-05),\n",
       " ('RFCD_280201.0', 1.4159842976562008e-05),\n",
       " ('S_347C', 1.4112828249895314e-05),\n",
       " ('RFCD_239901.0', 1.388850910699632e-05),\n",
       " ('RFCD_290502.0', 1.3695082554519414e-05),\n",
       " ('Dept.No._2948', 1.3593966726351292e-05),\n",
       " ('RFCD_320307.0', 1.3519089221505337e-05),\n",
       " ('S_150B', 1.3366373377626718e-05),\n",
       " ('SEO_751006.0', 1.3312206626212848e-05),\n",
       " ('SEO_720302.0', 1.2923186569339098e-05),\n",
       " ('RFCD_321002.0', 1.2677520204969556e-05),\n",
       " ('RFCD_290903.0', 1.2632154363262072e-05),\n",
       " ('SEO_780106.0', 1.2555164912854264e-05),\n",
       " ('RFCD_280208.0', 1.2503298118076819e-05),\n",
       " ('SEO_690101.0', 1.2367946025765404e-05),\n",
       " ('RFCD_420307.0', 1.2300224856882421e-05),\n",
       " ('SEO_670704.0', 1.2276743005808427e-05),\n",
       " ('SEO_610101.0', 1.2089524215895319e-05),\n",
       " ('RFCD_260112.0', 1.1990946657791048e-05),\n",
       " ('RFCD_420199.0', 1.1988786477380753e-05),\n",
       " ('RFCD_270705.0', 1.1675308449395177e-05),\n",
       " ('SEO_630202.0', 1.1578543092183572e-05),\n",
       " ('RFCD_329900.0', 1.1496802715061023e-05),\n",
       " ('Dept.No._4323', 1.1483524427415626e-05),\n",
       " ('RFCD_230111.0', 1.1468174416784292e-05),\n",
       " ('S_221A', 1.1327282782609659e-05),\n",
       " ('SEO_779906.0', 1.1274042708786566e-05),\n",
       " ...]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = X.columns\n",
    "importances = model.feature_importances_\n",
    "important_names = feature_names[importances > np.mean(importances)]\n",
    "print(len(important_names), len(feature_names))\n",
    "my_dict = dict(zip(feature_names, importances))\n",
    "sorted(my_dict.items(), key = lambda x: x[1], reverse= True)\n",
    "#y = np.sort(importances)\n",
    "#print(y)\n",
    "#for i in important_names: print(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#confusion_matrix(y_test, clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth is : 10\n",
      "Train score :  0.699753896637\n",
      "Test Score :  0.702640642939\n",
      "Depth is : 20\n",
      "Train score :  0.764068908942\n",
      "Test Score :  0.76846536548\n",
      "Depth is : 30\n",
      "Train score :  0.785561936013\n",
      "Test Score :  0.799464217375\n",
      "Depth is : 40\n",
      "Train score :  0.793929450369\n",
      "Test Score :  0.807500956755\n",
      "Depth is : 50\n",
      "Train score :  0.797046759639\n",
      "Test Score :  0.811327975507\n",
      "Depth is : 60\n",
      "Train score :  0.801640689089\n",
      "Test Score :  0.814772292384\n",
      "Depth is : 70\n",
      "Train score :  0.799671862182\n",
      "Test Score :  0.812093379258\n"
     ]
    }
   ],
   "source": [
    "depth = [10, 20, 30, 40, 50, 60, 70]\n",
    "for dep in depth:\n",
    "    model = RandomForestClassifier(n_estimators = 500, max_depth = dep, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    train_score = model.oob_score_\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    print(\"Depth is :\", dep)\n",
    "    print(\"Train score : \", train_score)\n",
    "    print(\"Test Score : \", test_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth is : 80\n",
      "Train score :  0.799507793273\n",
      "Test Score :  0.812093379258\n",
      "Depth is : 90\n",
      "Train score :  0.8\n",
      "Test Score :  0.812858783008\n",
      "Depth is : 100\n",
      "Train score :  0.799507793273\n",
      "Test Score :  0.812476081133\n",
      "Depth is : 120\n",
      "Train score :  0.799507793273\n",
      "Test Score :  0.812476081133\n",
      "Depth is : 130\n",
      "Train score :  0.799507793273\n",
      "Test Score :  0.812476081133\n",
      "Depth is : 140\n",
      "Train score :  0.799507793273\n",
      "Test Score :  0.812476081133\n"
     ]
    }
   ],
   "source": [
    "depth = [80, 90, 100, 120, 130, 140]\n",
    "for dep in depth:\n",
    "    model = RandomForestClassifier(n_estimators = 500, max_depth = dep, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    train_score = model.oob_score_\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    print(\"Depth is :\", dep)\n",
    "    print(\"Train score : \", train_score)\n",
    "    print(\"Test Score : \", test_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth is : 2\n",
      "Train score :  0.797046759639\n",
      "Test Score :  0.811327975507\n",
      "Depth is : 3\n",
      "Train score :  0.797046759639\n",
      "Test Score :  0.811327975507\n",
      "Depth is : 4\n",
      "Train score :  0.797046759639\n",
      "Test Score :  0.811327975507\n",
      "Depth is : 6\n",
      "Train score :  0.802953240361\n",
      "Test Score :  0.810562571757\n",
      "Depth is : 8\n",
      "Train score :  0.802296964725\n",
      "Test Score :  0.80864906238\n",
      "Depth is : 10\n",
      "Train score :  0.799507793273\n",
      "Test Score :  0.809797168006\n"
     ]
    }
   ],
   "source": [
    "mini = [2, 3, 4, 6, 8, 10]\n",
    "for dep in mini:\n",
    "    model = RandomForestClassifier(n_estimators = 500, max_depth = 50, \n",
    "                               min_samples_split = dep, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    train_score = model.oob_score_\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    print(\"Depth is :\", dep)\n",
    "    print(\"Train score : \", train_score)\n",
    "    print(\"Test Score : \", test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth is : 100\n",
      "Train score :  0.791468416735\n",
      "Test Score :  0.810562571757\n",
      "Depth is : 200\n",
      "Train score :  0.794257588187\n",
      "Test Score :  0.812093379258\n",
      "Depth is : 300\n",
      "Train score :  0.798031173093\n",
      "Test Score :  0.809414466131\n",
      "Depth is : 400\n",
      "Train score :  0.799179655455\n",
      "Test Score :  0.810562571757\n",
      "Depth is : 500\n",
      "Train score :  0.797046759639\n",
      "Test Score :  0.811327975507\n"
     ]
    }
   ],
   "source": [
    "mini = [100, 200, 300, 400, 500]\n",
    "for dep in mini:\n",
    "    model = RandomForestClassifier(n_estimators = dep, max_depth = 50, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    train_score = model.oob_score_\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    print(\"Depth is :\", dep)\n",
    "    print(\"Train score : \", train_score)\n",
    "    print(\"Test Score : \", test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth is : 30\n",
      "Train score :  0.777850697293\n",
      "Test Score :  0.79984691925\n",
      "Depth is : 40\n",
      "Train score :  0.781788351107\n",
      "Test Score :  0.79984691925\n",
      "Depth is : 50\n",
      "Train score :  0.787038556194\n",
      "Test Score :  0.803673938002\n",
      "Depth is : 60\n",
      "Train score :  0.786382280558\n",
      "Test Score :  0.809031764256\n",
      "Depth is : 70\n",
      "Train score :  0.789991796555\n",
      "Test Score :  0.808266360505\n",
      "Depth is : 80\n",
      "Train score :  0.787366694011\n",
      "Test Score :  0.810945273632\n",
      "Depth is : 90\n",
      "Train score :  0.789663658737\n",
      "Test Score :  0.80788365863\n",
      "Depth is : 100\n",
      "Train score :  0.791468416735\n",
      "Test Score :  0.810562571757\n"
     ]
    }
   ],
   "source": [
    "mini = [30, 40, 50, 60, 70, 80, 90, 100]\n",
    "for dep in mini:\n",
    "    model = RandomForestClassifier(n_estimators = dep, max_depth = 50, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    train_score = model.oob_score_\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    print(\"Depth is :\", dep)\n",
    "    print(\"Train score : \", train_score)\n",
    "    print(\"Test Score : \", test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth is : 100\n",
      "Train score :  0.791468416735\n",
      "Test Score :  0.810562571757\n",
      "Depth is : 120\n",
      "Train score :  0.796390484003\n",
      "Test Score :  0.812093379258\n",
      "Depth is : 130\n",
      "Train score :  0.79376538146\n",
      "Test Score :  0.811710677382\n",
      "Depth is : 140\n",
      "Train score :  0.793929450369\n",
      "Test Score :  0.809031764256\n",
      "Depth is : 150\n",
      "Train score :  0.794749794914\n",
      "Test Score :  0.811327975507\n",
      "Depth is : 160\n",
      "Train score :  0.794913863823\n",
      "Test Score :  0.809414466131\n"
     ]
    }
   ],
   "source": [
    "mini = [100, 120, 130, 140, 150, 160]\n",
    "for dep in mini:\n",
    "    model = RandomForestClassifier(n_estimators = dep, max_depth = 50, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    train_score = model.oob_score_\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    print(\"Depth is :\", dep)\n",
    "    print(\"Train score : \", train_score)\n",
    "    print(\"Test Score : \", test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check with different features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = [\n",
    "        #\"Neural Net\", \n",
    "        #\"Logistic Regression\", \n",
    "        #\"Nearest Neighbors\", \n",
    "        #\"Linear SVM\", \n",
    "        #\"RBF SVM\", \n",
    "        #\"Gaussian Process\",\n",
    "        \"Decision Tree\", \n",
    "        \"Random Forest\", \n",
    "        #\"AdaBoost\",\n",
    "        \"Gradient Boosting Classifier\"\n",
    "        #\"Naive Bayes\", \n",
    "        #\"QDA\"\n",
    "        ]\n",
    "\n",
    "classifiers = [\n",
    "        #MLPClassifier(alpha=0.0001),\n",
    "        #LogisticRegression(C=1e-10, penalty='l2', tol=1e-6),\n",
    "        #KNeighborsClassifier(3),\n",
    "        #SVC(kernel=\"linear\", C=0.025),\n",
    "        #SVC(gamma=2, C=1),\n",
    "        #GaussianProcessClassifier(1.0 * RBF(1.0), warm_start=True),\n",
    "        DecisionTreeClassifier(max_depth=5),\n",
    "        RandomForestClassifier(n_estimators = 500, max_depth = 50, \n",
    "                               min_samples_split = 4, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1\n",
    "                             ),\n",
    "        #AdaBoostClassifier(n_estimators = 150, algorithm = \"SAMME\"),\n",
    "        GradientBoostingClassifier(n_estimators= 500, max_depth = 50)\n",
    "        #GaussianNB(),\n",
    "        #QuadraticDiscriminantAnalysis()\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "ROC AUC: 0.81\n"
     ]
    },
    {
     "data": {
      "image/png": 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P8HOLg1aTkoXgY7BfF8yAM4NWk/26YCKRsEOVBta0aVPuv//+sMNoUEpUREQaAb9lM3w8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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dae5e8208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "ROC AUC: 0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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B5J1zt9Tk+FJDmzbCXh0xDfQEUxGpWCQS4dZbbw07hki5km341hx4guCZPobg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\n9+7Ne++9x6FDh2jfvn0we3T66acH651wwglRh5I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5uAtgBX5mV1ebj3SV6HNzmCrxt6UQ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\nY4wxxiSt/wfqNaIZfYB8+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dae607ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier\n",
      "ROC AUC: 0.89\n"
     ]
    },
    {
     "data": {
      "image/png": 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g7vhXdQwwtgG5JEW89/Dx+/iZbwIQuXECpll+yKlEJFOVlJRwzTXXMHPmTPVF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3da4b301d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms = {}\n",
    "n_neg = float(y_test.value_counts()[0])\n",
    "n_pos = float(y_test.value_counts()[1])\n",
    "for name, clf in zip(names, classifiers):\n",
    "    print(name)\n",
    "    clf.fit(X_train, y_train)\n",
    "    #cm = confusion_matrix(y_test, clf.predict(X_test))\n",
    "    #cms[name] = cm\n",
    "    #print(\"True positives: {}\".format(cm[1,1] / n_pos))\n",
    "    #print(\"False negatives: {}\".format(cm[1,0] / n_pos))\n",
    "    #print(\"True negatives: {}\".format(cm[0,0] / n_neg))\n",
    "    #print(\"True positives: {}\".format(cm[0,1] / n_neg))\n",
    "    #print(\"\")\n",
    "    fpr, tpr, _ = roc_curve(y_test, clf.predict_proba(X_test)[:,1])\n",
    "    \n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    print(\"ROC AUC: %0.2f\" % roc_auc)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-90-9e76bae278ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m                                \u001b[0mmin_samples_split\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_leaf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                                random_state = 1)\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0;31m#train_score = model.train_score_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mtest_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramya/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m   1026\u001b[0m         \u001b[0;31m# fit the boosting stages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m         n_stages = self._fit_stages(X, y, y_pred, sample_weight, random_state,\n\u001b[0;32m-> 1028\u001b[0;31m                                     begin_at_stage, monitor, X_idx_sorted)\n\u001b[0m\u001b[1;32m   1029\u001b[0m         \u001b[0;31m# change shape of arrays after fit (early-stopping or additional ests)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_stages\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramya/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stages\u001b[0;34m(self, X, y, y_pred, sample_weight, random_state, begin_at_stage, monitor, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1081\u001b[0m             y_pred = self._fit_stage(i, X, y, y_pred, sample_weight,\n\u001b[1;32m   1082\u001b[0m                                      \u001b[0msample_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1083\u001b[0;31m                                      X_csc, X_csr)\n\u001b[0m\u001b[1;32m   1084\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1085\u001b[0m             \u001b[0;31m# track deviance (= loss)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramya/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stage\u001b[0;34m(self, i, X, y, y_pred, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc, X_csr)\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m                 tree.fit(X, residual, sample_weight=sample_weight,\n\u001b[0;32m--> 787\u001b[0;31m                          check_input=False, X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m             \u001b[0;31m# update tree leaves\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramya/anaconda3/lib/python3.5/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1027\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1030\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramya/anaconda3/lib/python3.5/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    348\u001b[0m                                            self.min_impurity_split)\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "depth = [10, 30, 50,70]\n",
    "for dep in depth:\n",
    "    model = GradientBoostingClassifier(n_estimators = 500, max_depth = dep, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    #train_score = model.train_score_\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    print(\"Depth is :\", dep)\n",
    "    print(\"Train score : \", train_score)\n",
    "    print(\"Test Score : \", test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1762"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "SEO_750505.0\n",
      "SEO_750599.0\n",
      "SEO_750601.0\n",
      "SEO_750602.0\n",
      "SEO_750603.0\n",
      "SEO_750604.0\n",
      "SEO_750605.0\n",
      "SEO_750699.0\n",
      "SEO_750701.0\n",
      "SEO_750702.0\n",
      "SEO_750703.0\n",
      "SEO_750704.0\n",
      "SEO_750799.0\n",
      "SEO_750801.0\n",
      "SEO_750802.0\n",
      "SEO_750803.0\n",
      "SEO_750804.0\n",
      "SEO_750805.0\n",
      "SEO_750899.0\n",
      "SEO_750900.0\n",
      "SEO_750901.0\n",
      "SEO_750902.0\n",
      "SEO_751001.0\n",
      "SEO_751002.0\n",
      "SEO_751003.0\n",
      "SEO_751004.0\n",
      "SEO_751005.0\n",
      "SEO_751006.0\n",
      "SEO_751099.0\n",
      "SEO_759900.0\n",
      "SEO_759999.0\n",
      "SEO_760000.0\n",
      "SEO_760101.0\n",
      "SEO_760102.0\n",
      "SEO_760103.0\n",
      "SEO_760104.0\n",
      "SEO_760199.0\n",
      "SEO_760201.0\n",
      "SEO_760202.0\n",
      "SEO_760203.0\n",
      "SEO_760299.0\n",
      "SEO_769999.0\n",
      "SEO_770000.0\n",
      "SEO_770101.0\n",
      "SEO_770102.0\n",
      "SEO_770103.0\n",
      "SEO_770199.0\n",
      "SEO_770201.0\n",
      "SEO_770202.0\n",
      "SEO_770302.0\n",
      "SEO_770303.0\n",
      "SEO_770304.0\n",
      "SEO_770305.0\n",
      "SEO_770306.0\n",
      "SEO_770307.0\n",
      "SEO_770399.0\n",
      "SEO_770402.0\n",
      "SEO_770403.0\n",
      "SEO_770404.0\n",
      "SEO_770405.0\n",
      "SEO_770406.0\n",
      "SEO_770407.0\n",
      "SEO_770408.0\n",
      "SEO_770409.0\n",
      "SEO_770501.0\n",
      "SEO_770502.0\n",
      "SEO_770503.0\n",
      "SEO_770504.0\n",
      "SEO_770505.0\n",
      "SEO_770506.0\n",
      "SEO_770507.0\n",
      "SEO_770599.0\n",
      "SEO_770602.0\n",
      "SEO_770603.0\n",
      "SEO_770604.0\n",
      "SEO_770605.0\n",
      "SEO_770606.0\n",
      "SEO_770701.0\n",
      "SEO_770702.0\n",
      "SEO_770703.0\n",
      "SEO_770704.0\n",
      "SEO_770705.0\n",
      "SEO_770706.0\n",
      "SEO_770707.0\n",
      "SEO_770799.0\n",
      "SEO_770801.0\n",
      "SEO_770802.0\n",
      "SEO_770803.0\n",
      "SEO_770804.0\n",
      "SEO_770805.0\n",
      "SEO_770806.0\n",
      "SEO_770807.0\n",
      "SEO_770899.0\n",
      "SEO_770902.0\n",
      "SEO_770903.0\n",
      "SEO_770904.0\n",
      "SEO_770905.0\n",
      "SEO_770906.0\n",
      "SEO_770999.0\n",
      "SEO_771002.0\n",
      "SEO_771003.0\n",
      "SEO_771006.0\n",
      "SEO_771007.0\n",
      "SEO_771102.0\n",
      "SEO_771103.0\n",
      "SEO_771199.0\n",
      "SEO_779902.0\n",
      "SEO_779903.0\n",
      "SEO_779904.0\n",
      "SEO_779905.0\n",
      "SEO_779906.0\n",
      "SEO_779907.0\n",
      "SEO_779999.0\n",
      "SEO_780101.0\n",
      "SEO_780102.0\n",
      "SEO_780103.0\n",
      "SEO_780104.0\n",
      "SEO_780105.0\n",
      "SEO_780106.0\n",
      "SEO_780107.0\n",
      "SEO_780108.0\n",
      "SEO_780199.0\n",
      "SEO_999999.0\n",
      "Role_CHIEF_INVESTIGATOR\n",
      "Role_DELEGATED_RESEARCHER\n",
      "Role_EXT_CHIEF_INVESTIGATOR\n",
      "Role_PRINCIPAL_SUPERVISOR\n",
      "Role_STUD_CHIEF_INVESTIGATOR\n",
      "Role_HONVISIT\n",
      "Role_STUDRES\n",
      "Role_EXTERNAL_ADVISOR\n",
      "Country.of.Birth_Asia Pacific\n",
      "Country.of.Birth_Australia\n",
      "Country.of.Birth_Western Europe\n",
      "Country.of.Birth_Great Britain\n",
      "Country.of.Birth_North America\n",
      "Country.of.Birth_New Zealand\n",
      "Country.of.Birth_Eastern Europe\n",
      "Country.of.Birth_The Americas\n",
      "Country.of.Birth_South Africa\n",
      "Country.of.Birth_Middle East and Africa\n",
      "Home.Language_Other\n",
      "Home.Language_English\n",
      "Dept.No._3073\n",
      "Dept.No._2538\n",
      "Dept.No._2923\n",
      "Dept.No._2498\n",
      "Dept.No._2548\n",
      "Dept.No._2678\n",
      "Dept.No._2153\n",
      "Dept.No._2533\n",
      "Dept.No._3028\n",
      "Dept.No._2563\n",
      "Dept.No._3123\n",
      "Dept.No._2653\n",
      "Dept.No._3098\n",
      "Dept.No._2713\n",
      "Dept.No._1998\n",
      "Dept.No._2163\n",
      "Dept.No._2883\n",
      "Dept.No._2523\n",
      "Dept.No._1258\n",
      "Dept.No._2558\n",
      "Dept.No._1038\n",
      "Dept.No._2768\n",
      "Dept.No._2053\n",
      "Dept.No._2553\n",
      "Dept.No._2578\n",
      "Dept.No._1623\n",
      "Dept.No._653\n",
      "Dept.No._2603\n",
      "Dept.No._2298\n",
      "Dept.No._2813\n",
      "Dept.No._3268\n",
      "Dept.No._2103\n",
      "Dept.No._2828\n",
      "Dept.No._3258\n",
      "Dept.No._2778\n",
      "Dept.No._3198\n",
      "Dept.No._2893\n",
      "Dept.No._3048\n",
      "Dept.No._3508\n",
      "Dept.No._2668\n",
      "Dept.No._1033\n",
      "Dept.No._1098\n",
      "Dept.No._2763\n",
      "Dept.No._828\n",
      "Dept.No._803\n",
      "Dept.No._2253\n",
      "Dept.No._1578\n",
      "Dept.No._2728\n",
      "Dept.No._1678\n",
      "Dept.No._2973\n",
      "Dept.No._593\n",
      "Dept.No._548\n",
      "Dept.No._2568\n",
      "Dept.No._498\n",
      "Dept.No._3658\n",
      "Dept.No._3273\n",
      "Dept.No._3708\n",
      "Dept.No._1428\n",
      "Dept.No._2853\n",
      "Dept.No._2628\n",
      "Dept.No._68\n",
      "Dept.No._603\n",
      "Dept.No._3943\n",
      "Dept.No._528\n",
      "Dept.No._188\n",
      "Dept.No._4188\n",
      "Dept.No._1528\n",
      "Dept.No._2178\n",
      "Dept.No._998\n",
      "Dept.No._978\n",
      "Dept.No._2773\n",
      "Dept.No._2543\n",
      "Dept.No._1798\n",
      "Dept.No._1663\n",
      "Dept.No._2518\n",
      "Dept.No._1013\n",
      "Dept.No._2878\n",
      "Dept.No._2848\n",
      "Dept.No._148\n",
      "Dept.No._203\n",
      "Dept.No._4323\n",
      "Dept.No._2948\n",
      "Dept.No._4163\n",
      "Dept.No._4193\n",
      "Dept.No._4153\n",
      "Dept.No._2738\n",
      "Dept.No._248\n",
      "Dept.No._3748\n",
      "Dept.No._28\n",
      "Dept.No._458\n",
      "Dept.No._3248\n",
      "Dept.No._4388\n",
      "Dept.No._2983\n",
      "Dept.No._4363\n",
      "Dept.No._4063\n",
      "Dept.No._3703\n",
      "Dept.No._953\n",
      "Dept.No._4243\n",
      "Dept.No._118\n",
      "Dept.No._2788\n",
      "Dept.No._2838\n",
      "Dept.No._163\n",
      "Dept.No._3773\n",
      "Dept.No._1498\n",
      "Dept.No._168\n",
      "Dept.No._4203\n",
      "Dept.No._1253\n",
      "Dept.No._3713\n",
      "Dept.No._4418\n",
      "Dept.No._2933\n",
      "Faculty.No._31\n",
      "Faculty.No._25\n",
      "Faculty.No._19\n",
      "Faculty.No._34\n",
      "Faculty.No._1\n",
      "Faculty.No._13\n",
      "Faculty.No._7\n",
      "Faculty.No._46\n",
      "Faculty.No._4\n",
      "Faculty.No._22\n",
      "Faculty.No._10\n",
      "Faculty.No._64\n",
      "Faculty.No._79\n",
      "Faculty.No._118\n",
      "Faculty.No._58\n",
      "Faculty.No._82\n",
      "Faculty.No._40\n",
      "Faculty.No._187\n",
      "With.PHD_Yes \n",
      "No. team members\n"
     ]
    }
   ],
   "source": [
    "for col in X_train.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Work with start_date removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ids    1557\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_indices = pd.read_csv(\"./data/training2_ids.csv\")\n",
    "train_indices.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ids    518\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_indices = pd.read_csv(\"./data/testing_ids.csv\")\n",
    "test_indices.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>8669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>8672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>8673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>8674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>8676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>8677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>8679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>8681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>8683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>8684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>8685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>8686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>8687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>8688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>8689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>8690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>8692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>8693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>8694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>8696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>8697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>8698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>8699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>8702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>8703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>8704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>8705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>8708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1557 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ids\n",
       "0     6634\n",
       "1     6635\n",
       "2     6636\n",
       "3     6637\n",
       "4     6638\n",
       "5     6639\n",
       "6     6640\n",
       "7     6642\n",
       "8     6643\n",
       "9     6644\n",
       "10    6645\n",
       "11    6646\n",
       "12    6648\n",
       "13    6651\n",
       "14    6652\n",
       "15    6653\n",
       "16    6654\n",
       "17    6656\n",
       "18    6657\n",
       "19    6658\n",
       "20    6660\n",
       "21    6661\n",
       "22    6663\n",
       "23    6664\n",
       "24    6665\n",
       "25    6666\n",
       "26    6668\n",
       "27    6669\n",
       "28    6670\n",
       "29    6672\n",
       "...    ...\n",
       "1527  8669\n",
       "1528  8670\n",
       "1529  8672\n",
       "1530  8673\n",
       "1531  8674\n",
       "1532  8676\n",
       "1533  8677\n",
       "1534  8679\n",
       "1535  8681\n",
       "1536  8683\n",
       "1537  8684\n",
       "1538  8685\n",
       "1539  8686\n",
       "1540  8687\n",
       "1541  8688\n",
       "1542  8689\n",
       "1543  8690\n",
       "1544  8692\n",
       "1545  8693\n",
       "1546  8694\n",
       "1547  8696\n",
       "1548  8697\n",
       "1549  8698\n",
       "1550  8699\n",
       "1551  8700\n",
       "1552  8702\n",
       "1553  8703\n",
       "1554  8704\n",
       "1555  8705\n",
       "1556  8708\n",
       "\n",
       "[1557 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_indices#, test_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_indices = np.r_[train_indices] - 1\n",
    "test_indices = np.r_[test_indices] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "valid_data = X.loc[list(train_indices.flatten())]\n",
    "test_data = X.loc[list(test_indices.flatten())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators = 150, max_depth = 15, \n",
    "                               min_samples_split = 2, min_samples_leaf = 2,\n",
    "                               oob_score = True, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-26-e1997885ff9b>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-26-e1997885ff9b>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for i\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "for i \n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_test, clf.predict_proba(X_test)[:,1])\n",
    "    \n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    print(\"ROC AUC: %0.2f\" % roc_auc)"
   ]
  }
 ],
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