ml-bank-marketing-solution (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "672e100a-7a76-45b1-9fd0-9fa6228c4ded",
    "_uuid": "4f78858453bf17ce1431e91a3f9d487e25f28604"
   },
   "source": [
    "##  Predictive Analysis on Bank Marketing Dataset :\n",
    "\n",
    "### Bank Marketing Dataset contains both type variables 'Categorical' and 'Numerical'.\n",
    "\n",
    "### Categorical Variable :\n",
    "\n",
    "    * Marital - (Married , Single , Divorced)\",\n",
    "    * Job - (Management,BlueCollar,Technician,entrepreneur,retired,admin.,services,selfemployed,housemaid,student,unemployed,unknown)\n",
    "    * Contact - (Telephone,Cellular,Unknown)\n",
    "    * Education - (Primary,Secondary,Tertiary,Unknown)\n",
    "    * Month - (Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec)\n",
    "    * Poutcome - (Success,Failure,Other,Unknown)\n",
    "    * Housing - (Yes/No)\n",
    "    * Loan - (Yes/No)\n",
    "    * deposit - (Yes/No)\n",
    "    * Default - (Yes/No)\n",
    "    \n",
    "### Numerical Variable:\n",
    "    *  Age\n",
    "    * Balance\n",
    "    * Day\n",
    "    * Duration\n",
    "    * Campaign\n",
    "    * Pdays\n",
    "    * Previous\n",
    "   \n",
    " \n",
    "       \n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "f8f46eb2-1162-4c82-8303-f68118fbc273",
    "_uuid": "233b2785a3cfc5d1a25ab5fad467bc6b5892ecaa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\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 LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics as m\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "data = pd.read_csv('../input/bank.csv',sep=',',header='infer')\n",
    "data = data.drop(['day','poutcome'],axis=1)\n",
    "\n",
    "def binaryType_(data):\n",
    "    \n",
    "    data.deposit.replace(('yes', 'no'), (1, 0), inplace=True)\n",
    "    data.default.replace(('yes','no'),(1,0),inplace=True)\n",
    "    data.housing.replace(('yes','no'),(1,0),inplace=True)\n",
    "    data.loan.replace(('yes','no'),(1,0),inplace=True)\n",
    "    #data.marital.replace(('married','single','divorced'),(1,2,3),inplace=True)\n",
    "    data.contact.replace(('telephone','cellular','unknown'),(1,2,3),inplace=True)\n",
    "    data.month.replace(('jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'),(1,2,3,4,5,6,7,8,9,10,11,12),inplace=True)\n",
    "    #data.education.replace(('primary','secondary','tertiary','unknown'),(1,2,3,4),inplace=True)\n",
    "    \n",
    "    return data\n",
    "\n",
    "data = binaryType_(data)\n",
    "\n",
    "# for i in range(len(data.marital.unique())):\n",
    "#     data[\"marital_\"+str(data.marital.unique()[i])] = (data.marital == data.marital.unique()[i]).astype(int)\n",
    "\n",
    "# for j in range(len(data.job.unique())):\n",
    "#     data[\"job_\"+str(data.job.unique()[j])] = (data.job == data.job.unique()[j]).astype(int)\n",
    "\n",
    "# for k in range(len(data.contact.unique())):\n",
    "#     data[\"contact_\"+str(data.contact.unique()[k])] = (data.contact == data.contact.unique()[k]).astype(int)\n",
    "\n",
    "# for l in range(len(data.education.unique())):\n",
    "#     data['education_'+str(data.education.unique()[l])] = (data.education == data.education.unique()[l]).astype(int)\n",
    "\n",
    "# for n in range(len(data.month.unique())):\n",
    "#     data['month_'+str(data.month.unique()[n])] = (data.month == data.month.unique()[n]).astype(int)\n",
    "\n",
    "\n",
    "#print(data.is_success.value_counts())\n",
    "#print(data.describe())\n",
    "#print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e203529b-4042-4360-b481-269f3658a15c",
    "_uuid": "aec62468ca431c5c4de8d38ee6fccd657a3e52a4"
   },
   "source": [
    "### Outlier :\n",
    "           Data_point > (Q3 * 1.5) is said to be outlier where Q3 is 75% Quantile !\n",
    "           \n",
    "### Age:\n",
    "       * Average age of the people in the dataset is ~41 with std of 10.61\n",
    "       * Min. age is 18\n",
    "       * Max. age is 95\n",
    "       * quantile 75%(percentile) refers that 75 percentage of the people have 49 or less  age.\n",
    "       * As 95 is max, there is great chance that its a outlier \"49*(3/2) = 73.5\". So anything greater than 73.5 is outlier.\n",
    "\n",
    "### Balance:\n",
    "       * Average balance of the people in the dataset is (approx)1528.53 with std of 3255.41, as standard deviation is quite huge it means that balance is wide spread across the dataset.\n",
    "       * Min. balance is -6847\n",
    "       * Max. balance is 81204\n",
    "       * quantile 75%(percentile) refers that 75 percentage of the people have 1708 or less balance.\n",
    "       * while comparing with 75% quantile, 81204 is very huge and its a outlier data point.\n",
    "\n",
    "### Duration:\n",
    "       * Average duration of the people speaking in the dataset is (approx)371 with std of 347, as standard deviation is quite huge it means that duration is wide spread across the dataset.\n",
    "       * Min. duration is 2\n",
    "       * Max. duration is 3881\n",
    "       * quantile 75%(percentile) refers that 75 percentage of the people spoke for 496 seconds or less.\n",
    "       * while comparing with 75% quantile, 3881 is a outlier data point.\n",
    "\n",
    "### Pdays:\n",
    "        * Average no. of days passed after the client was contacted from previous campaign in the dataset is (approx)51.33 with std of 108.75.\n",
    "       * Min. pdays is -1\n",
    "       * Max. pdays is 854\n",
    "       * quantile 75%(percentile),for 75% of records it is 20.75 days, which means the Client was frequently contacted.\n",
    "\n",
    "### Campaign:\n",
    "       * Average no. of contacts performed during the current campaign for a client in the dataset is (approx)2.50 with std of 2.72.\n",
    "       * Min. balance is 1\n",
    "       * Max. balance is 63\n",
    "       * quantile 75%(percentile),for 75% of records, 3 times the client has been contacted in the current campaign for a client.\n",
    "       * while comparing with 75% quantile,63 is a outlier data point.\n",
    "\n",
    "### Previous:\n",
    "       * Average no. of contacts performed before this campaign for a client in the dataset is (approx)0.83 with std of 2.29.\n",
    "       * Min. balance is 0.\n",
    "       * Max. balance is 58\n",
    "       * quantile 75%(percentile),for 75% of records, 1 times the client has been contacted before this campaign.\n",
    "       * while comparing with 75% quantile,58 is a outlier data point.\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "4693c408-41b1-4c3d-80d0-6c2ad9be08c0",
    "_uuid": "6875970f49baa65a11a31da0a70dd74ef9992d28"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe837109a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist((data.duration),bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "359f6269-0079-40c7-b193-70a8517b80dc",
    "_uuid": "7dccba559d986a86e2b56a4b3ce2d629a36b80b8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vSe4NJru/Se4NJru/Se4NJru/Se7tZJb6GMDDwPok65K8AdgM7FziHiRJLPEWQFUdS3IT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\ndL2cuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819ec1e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.age,bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "bb5fa4ac-fec8-422a-8166-59346c38e989",
    "_uuid": "b1f311f6a0f71512037d0cd7599845e1f42cef23"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sS/LZJB9r81uTHGjb9uF2YwNJzmzzc2359NB7XNfqDye5ZKi+Jo+pJBuS3JHki+14eY3H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\nDABJ6pQBIEmdMgAkqVP/B1BCIKKzB8N7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819bcdef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.balance,bins=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7262839d-88c0-4eca-b6cd-c7ad2bcef9c1",
    "_uuid": "e617c459f28d6bc61a37706d641a4ca5511dcb16"
   },
   "source": [
    "**Above, All the Histogram suggest that data is skewed towards left i.e. existence of skewness brings us to a point that we need to sample the data efficiently while classifiying the train_data and test_data !**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "0b476dfb-3ec5-45d0-9d05-8a46fcf12aa1",
    "_uuid": "1810e0d7a9a6c5603f39afa104b7c66d3f12ea3a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sSPJ0krEktwy6H0mDc7SQYniR1KoAk2Qe8AXgWuBi4IYkFw+2K+n0ct55581q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\nzjHASJKkzvn/f9zevzc5RAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819b689b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(9, 6))\n",
    "ax1 = fig.add_subplot(211)\n",
    "bp1 = ax1.boxplot(data.balance,0,'')\n",
    "ax2 = fig.add_subplot(212)\n",
    "bp2 = ax2.boxplot(data.balance,0,'gD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "92d927d4-22b5-4904-9c53-158ddf335942",
    "_uuid": "af27b25d4b0bba5ea1817be17d9dbc4cfe6c3f35"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u+SCrgX8hyQ6A7vHUWgdX1YGqGlTVYH5+fhIzS5LWMDLoVfUd4NtJru5W7QK+ATwKLHbr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\nFC6j6nwNgiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819b11a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(6, 6))\n",
    "ax = fig.add_subplot(211)\n",
    "bp = ax.boxplot(data.age,0,'')\n",
    "ax = fig.add_subplot(212)\n",
    "bp = ax.boxplot(data.age,0,'gD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "a71b6d9c-cf80-4826-8a95-3b78d819f5a7",
    "_uuid": "4703036a0a6d38198b0ecb2a563402b1f03b3e96"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819969d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(9, 6))\n",
    "ax1 = fig.add_subplot(211)\n",
    "bp1 = ax1.boxplot(data.duration,0,'')\n",
    "ax2 = fig.add_subplot(212)\n",
    "bp2 = ax2.boxplot(data.duration,0,'gD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "205531b3-abd0-4405-8b79-0fb2d547b4c9",
    "_uuid": "fd46cd3a3b7548efdb92c23ac0ba58da525f2a13"
   },
   "source": [
    "\n",
    "Above boxplot suggest how the data is spread across the dataset\n",
    "** Most of the data is lying above the 3rd quantile by multiplication factor of 1.5 i.e. by theortical aspect the data points are outlier for most of the data points.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "b7ee63ae-c411-4673-a680-f85290e092b0",
    "_uuid": "f072f0854b7f4d31fc81829ff08b059e9366b02c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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trbWNVXVlVa2e7gEBAADmsnmDLGqt3Znkzkn7PrObte/Y97EAAABIBvxxbQAAAGaGaAMA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\n/w9ogmtstwkduwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819d2d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.housing, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "ce2d3971-3ab4-4148-af64-0cbb49a954ee",
    "_uuid": "0ecc8fa863b33882c6869e5d10241d55766e39aa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bknyhtbaxqj5ZVWtme0AAAIBRtmCQRa2125LcNm3fx3ez9k37PhYAAADJgD+uDQAAwNwQ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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819cf0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.default, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "f4989fc2-d0d3-4731-8fc1-be59005bec34",
    "_uuid": "c2604819d788dd05e7684e47f551322619535e72"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6JHe01tZW1bVVtWJvDwgAADDO5gyyqLV2T5J7Zuz73E7WvmfPxwIAACAZ8Mu1AQAAGA3R\nBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA\n0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHR\nBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA\n0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHRBgAA0DHR\nBgAA0DHRBgAA0DHRBgAA0DHRBgAA0LGBoq2qzq+qDVW1saqumeX136mqn1TVY1X17ao6ZvijAgAA\njJ9dRltVHZTkxiQXJFmS5LKqWjJj2Q+TLGutnZzkj5P8/rAHBQAAGEeDXGk7M8nG1tqm1trWJKuS\nXDR9QWvtvtba/53a/EGSieGOCQAAMJ4Gibajkjw9bXtyat/OfCLJn+3JUAAAAGw3Z4A1Ncu+NuvC\nqn+aZFmSd+/k9ZVJVibJ/PnzBxwRAABgfA1ypW0yydHTtieSPDtzUVWdm+TfJFnRWnthtgO11m5u\nrS1rrS2bN2/e7swLAAAwVgaJtoeTLKqqhVV1SJJLk6yevqCqTk3yX7I92H4+/DEBAADG0y6jrbX2\nUpKrk9ybZF2SO1pra6vq2qpaMbXsi0kOS3JnVT1aVat3cjgAAABeg0E+05bW2j1J7pmx73PTfj93\nyHMBAACQAb9cGwAAgNEQbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0T\nbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAA\nAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0T\nbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAA\nAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0TbQAAAB0bKNqq6vyq2lBVG6vqmllef31VfXXq9Qer\nasGwBwUAABhHu4y2qjooyY1JLkiyJMllVbVkxrJPJPlFa+24JF9K8u+HPSgAAMA4GuRK25lJNrbW\nNrXWtiZZleSiGWsuSvJfp37/4yS/VVU1vDEBAADG0yDRdlSSp6dtT07tm3VNa+2lJL9McsQwBgQA\nABhncwZYM9sVs7Yba1JVK5OsnNr8u6raMMD5gdkdmeSvRz0EO+d2A2AMeC/qmPeh/cIxgywaJNom\nkxw9bXsiybM7WTNZVXOSvDHJ38w8UGvt5iQ3DzIY8Oqqak1rbdmo5wBgfHkvgn1jkNsjH06yqKoW\nVtUhSS5NsnrGmtVJ/tnU7xcn+U5r7deutAEAAPDa7PJKW2vtpaq6Osm9SQ5K8oettbVVdW2SNa21\n1UluSfJHVbUx26+wXbo3hwYAABgX5YIY7J+qauXULccAMBLei2DfEG0AAAAdG+QzbQAAAIyIaAMA\nAOiYaAMAAOjYIN/TBnSgqt6R5KIkR2X7l9c/m2R1a23dSAcDAGCvcqUN9gNV9a+SrEpSSR7K9u9P\nrCS3V9U1o5wNAKrq46OeAQ5knh4J+4GqejzJCa21F2fsPyTJ2tbaotFMBgBJVT3VWps/6jngQOX2\nSNg/vJzk7Un+asb+t029BgB7VVU9trOXkrx1X84C40a0wf7hnyf5dlU9keTpqX3zkxyX5OqRTQXA\nOHlrkvOS/GLG/krywL4fB8aHaIP9QGvtG1V1fJIzs/1BJJVkMsnDrbVtIx0OgHFxd5LDWmuPznyh\nqr6778eB8eEzbQAAAB3z9EgAAICOiTYAAICOiTYADjhV9XejngEAhkW0AQAAdEy0AXDAqu2+WFV/\nWVU/rqpLpvYfVlXfrqr/ObX/oqn9C6pqXVX9QVWtrapvVtUbRvtfADDuRBsAB7IPJlma5JQk5yb5\nYlW9LcmWJB9orZ2W5Jwk/6GqaupvFiW5sbV2QpK/TfKhfT82APx/og2AA9k/TnJ7a21ba+3/JPle\nkjOy/bsO/11VPZbkv2f79x++depvfjrte6geSbJg344MAK/ky7UBOJDVTvZ/LMm8JKe31l6sqp8l\nmTv12gvT1m1L4vZIAEbKlTYADmR/nuSSqjqoquYlOTvJQ0nemOTnU8F2TpJjRjkkALwaV9oAOJB9\nLclZSX6UpCX5l621/11VtyX5elWtSfJokvUjnBEAXlW11kY9AwAAADvh9kgAAICOiTYAAICOiTYA\nAICOiTYAAICOiTYAAICOiTYAAICOiTYAAICOiTYAAICO/T8MPy5mailwyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819d609b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.loan, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "eae22e0a-8666-4ddb-bbef-ecb0c71bab44",
    "_uuid": "ee757ecd3e034a3c63a60c680419a1682293acbf"
   },
   "source": [
    "By looking at the bar graph, we can observe that Feature vs Label the data is wide spread i.e. we cannot predict completely based on feature alone.\n",
    "**Feature Engineering**\n",
    "   * First, We can convert the duration from Seconds to Minutes and then making it as categorical feature.\n",
    "   * Converting the age of the person into categorical feature by segregating the age as Adult , Middle Aged and old.\n",
    "   * Similarly we can converting the continous feature value into discrete feature value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "c4317dc9-50a2-4725-8e36-9a7e1fe52762",
    "_uuid": "1c78ff6d361c3203f5541b82fa730d64f78a75a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
      "       'loan', 'contact', 'month', 'duration', 'campaign', 'pdays', 'previous',\n",
      "       'deposit', 'Adult', 'Middle_Aged', 'old', 'primary', 'secondary',\n",
      "       'tertiary', 'unknown', 'Neg_Balance', 'No_Balance', 'Pos_Balance',\n",
      "       'Not_Contacted', 'Contacted', 't_min', 't_e_min', 'e_min',\n",
      "       'pdays_not_contacted', 'months_passed', 'married', 'singles',\n",
      "       'divorced'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#data['duration'] = data['duration']/60\n",
    "def age_(data):\n",
    "    \n",
    "    data['Adult'] = 0\n",
    "    data['Middle_Aged'] = 0\n",
    "    data['old'] = 0    \n",
    "    data.loc[(data['age'] <= 35) & (data['age'] >= 18),'Adult'] = 1\n",
    "    data.loc[(data['age'] <= 60) & (data['age'] >= 36),'Middle_Aged'] = 1\n",
    "    #data.loc[(data['age'] <= 60) & (data['age'] >= 46),'Elderly'] = 1\n",
    "    data.loc[data['age'] >=61,'old'] = 1\n",
    "    \n",
    "    return data\n",
    "\n",
    "def campaign_(data):\n",
    "    \n",
    "    \n",
    "    data.loc[data['campaign'] == 1,'campaign'] = 1\n",
    "    data.loc[(data['campaign'] >= 2) & (data['campaign'] <= 3),'campaign'] = 2\n",
    "    data.loc[data['campaign'] >= 4,'campaign'] = 3\n",
    "    \n",
    "    return data\n",
    "\n",
    "def duration_(data):\n",
    "    \n",
    "    data['t_min'] = 0\n",
    "    data['t_e_min'] = 0\n",
    "    data['e_min']=0\n",
    "    data.loc[data['duration'] <= 5,'t_min'] = 1\n",
    "    data.loc[(data['duration'] > 5) & (data['duration'] <= 10),'t_e_min'] = 1\n",
    "    data.loc[data['duration'] > 10,'e_min'] = 1\n",
    "    \n",
    "    return data\n",
    "\n",
    "def pdays_(data):\n",
    "    data['pdays_not_contacted'] = 0\n",
    "    data['months_passed'] = 0\n",
    "    data.loc[data['pdays'] == -1 ,'pdays_not_contacted'] = 1\n",
    "    data['months_passed'] = data['pdays']/30\n",
    "    data.loc[(data['months_passed'] >= 0) & (data['months_passed'] <=2) ,'months_passed'] = 1\n",
    "    data.loc[(data['months_passed'] > 2) & (data['months_passed'] <=6),'months_passed'] = 2\n",
    "    data.loc[data['months_passed'] > 6 ,'months_passed'] = 3\n",
    "    \n",
    "    return data\n",
    "\n",
    "def previous_(data):\n",
    "    \n",
    "    data['Not_Contacted'] = 0\n",
    "    data['Contacted'] = 0\n",
    "    data.loc[data['previous'] == 0 ,'Not_Contacted'] = 1\n",
    "    data.loc[(data['previous'] >= 1) & (data['pdays'] <=99) ,'Contacted'] = 1\n",
    "    data.loc[data['previous'] >= 100,'Contacted'] = 2\n",
    "    \n",
    "    return data\n",
    "\n",
    "def balance_(data):\n",
    "    data['Neg_Balance'] = 0\n",
    "    data['No_Balance'] = 0\n",
    "    data['Pos_Balance'] = 0\n",
    "    \n",
    "    data.loc[~data['balance']<0,'Neg_Balance'] = 1\n",
    "    data.loc[data['balance'] == 0,'No_Balance'] = 1\n",
    "    data.loc[(data['balance'] >= 1) & (data['balance'] <= 100),'Pos_Balance'] = 1\n",
    "    data.loc[(data['balance'] >= 101) & (data['balance'] <= 500),'Pos_Balance'] = 2\n",
    "    data.loc[(data['balance'] >= 501) & (data['balance'] <= 2000),'Pos_Balance'] = 3\n",
    "    data.loc[(data['balance'] >= 2001) & (data['balance'] <= 10000),'Pos_Balance'] = 4\n",
    "    data.loc[data['balance'] >= 10001,'Pos_Balance'] = 5\n",
    "    \n",
    "    return data\n",
    "\n",
    "def job_(data):\n",
    "    \n",
    "    data.loc[data['job'] == \"management\",'job'] = 1\n",
    "    data.loc[data['job'] == \"technician\",'job'] = 2\n",
    "    data.loc[data['job'] == \"entrepreneur\",'job'] = 3\n",
    "    data.loc[data['job'] == \"blue-collar\",'job'] = 4\n",
    "    data.loc[data['job'] == \"retired\",'job'] = 5\n",
    "    data.loc[data['job'] == \"admin.\",'job'] = 6\n",
    "    data.loc[data['job'] == \"services\",'job'] = 7\n",
    "    data.loc[data['job'] == \"self-employed\",'job'] = 8\n",
    "    data.loc[data['job'] == \"unemployed\",'job'] = 9\n",
    "    data.loc[data['job'] == \"student\",'job'] = 10\n",
    "    data.loc[data['job'] == \"housemaid\",'job'] = 11\n",
    "    data.loc[data['job'] == \"unknown\",'job'] = 12\n",
    "    \n",
    "    return data\n",
    "\n",
    "def marital_(data):\n",
    "    \n",
    "    data['married'] = 0\n",
    "    data['singles'] = 0\n",
    "    data['divorced'] = 0\n",
    "    data.loc[data['marital'] == 'married','married'] = 1\n",
    "    data.loc[data['marital'] == 'singles','singles'] = 1\n",
    "    data.loc[data['marital'] == 'divorced','divorced'] = 1\n",
    "    \n",
    "    return data\n",
    "\n",
    "def education_(data):\n",
    "    \n",
    "    data['primary'] = 0\n",
    "    data['secondary'] = 0\n",
    "    data['tertiary'] = 0\n",
    "    data['unknown'] = 0\n",
    "    data.loc[data['education'] == 'primary','primary'] = 1\n",
    "    data.loc[data['education'] == 'secondary','secondary'] = 1\n",
    "    data.loc[data['education'] == 'tertiary','tertiary'] = 1\n",
    "    data.loc[data['education'] == 'unknown','unknown'] = 1    \n",
    "    \n",
    "    return data\n",
    "\n",
    "data = campaign_(data)\n",
    "data = age_(data)\n",
    "data = education_(data)\n",
    "data = balance_(data)\n",
    "data = job_(data)\n",
    "data = previous_(data)\n",
    "data = duration_(data)\n",
    "data = pdays_(data)\n",
    "data = marital_(data)\n",
    "print(data.columns)\n",
    "\n",
    "# print(data.balance.value_counts())\n",
    "# print(data.duration.value_counts())\n",
    "# print(data.pdays.value_counts())\n",
    "# print(data.campaign.value_counts())\n",
    "# print(data.age.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "250cde86-90e6-4244-a639-5c7b58bf1b45",
    "_uuid": "dd01da80bfbef6275856f76f4933b303d748492c"
   },
   "source": [
    "**Plotting bar chart :**\n",
    "\n",
    "**data.Adult vs data.deposit :**\n",
    "    \n",
    "    The data is spread equally opting for term deposit or not.\n",
    "\n",
    "**data.Middle_Aged vs data.deposit :**\n",
    "   \n",
    "    The data is points out that people opt less for term deposit.\n",
    "\n",
    "**data.old vs data.deposit :**\n",
    "    \n",
    "    The data is points out that people opt more for term deposit as it covers people who are retired.\n",
    "\n",
    "**data.t_min vs data.deposit :**\n",
    "    \n",
    "    The data point brings out the fact that if th client is less interested in enrolling for term deposit, he/she is ready to invest less time on call with the agent.\n",
    "\n",
    "    Note : t_min - Five minutes or less\n",
    "\n",
    "**data.t_e_min vs data.deposit :**\n",
    "    \n",
    "    The data points brings out the fact that if th client is interested in enrolling for term deposit, he/she is ready to investing minimum of 5 to 10 minute time on call with the agent.\n",
    "\n",
    "    Note : t_e_min - greater than Five minutes or more\n",
    "\n",
    "**data.e_min vs data.deposit :**\n",
    "    \n",
    "    The data points suggest that if th client is very much interested in enrolling for term deposit, he/she is ready to investing more than 10 minute of time on call with the agent.\n",
    "\n",
    "    Note : e_min - greater than ten minutes or more\n",
    "\n",
    "**data.pdays_not_contacted vs data.deposit :**\n",
    "   \n",
    "    The data points refers to the client who were not contacted in the previous campaign.And it looks like the people are contaced in current campaign are not contacted previously.\n",
    "\n",
    "**data.months_passed vs data.deposit :**\n",
    "   \n",
    "    The data points refers to the months passed after the client has been contacted before the current campaign.\n",
    "\n",
    "**data.Contacted vs data.deposit :**\n",
    "    \n",
    "    The data points refers to the no. of contact for a client has been contacted before this campaign.Fewer no. of contacts are more likely to enroll for term deposit\n",
    "\n",
    "**data.not_Contacted vs data.deposit :**\n",
    "   \n",
    "    The data points refers that no contact is made for a client before this campaign. Not contacted Clients are less likely to enroll for term deposit\n",
    "\n",
    "**data.Pos_Balance vs data.deposit :**\n",
    "    \n",
    "    Here, We can clearly see as the balance in the account increases the no. of client enrolling for the term deposit is more and more.\n",
    "\n",
    "**data.No_Balance vs data.deposit :**\n",
    "    \n",
    "    Here, We can see as the balance in the account is zero, the no. of client enrolling for the term deposit are less.\n",
    "\n",
    "**data.Neg_Balance vs data.deposit :**\n",
    "   \n",
    "    We can infer that as the balance in the account is -ve, the no. of client enrolling for the term deposit are very less and feature come in place while classifying such data points\n",
    "\n",
    "**data.campaign vs data.deposit :**\n",
    "    \n",
    "    The data points refers that no. of contact made to a client in this campaign. If a client is contacted once or twice are more likely to enroll than clients who are contacted more than 3 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "37d07388-6010-4d1f-85c5-d05b8b57da34",
    "_uuid": "e9c6ddf8f3cff75d281ec18f5d23805e15b41d99"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fbK09UlWfqqrVsz0gAADAKJs3yKLW2l1J7pqy7xP7WPvWAx8LAACAZMAv1wYAAGBuiDYA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819c96a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.Adult, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "c25819de-cf71-4383-b2ee-0d0b8284eaae",
    "_uuid": "d26e50f1d115bac4a780cd5832a26e8249edf4bf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ub63dXVXnVdXSbT0gAADAbDZnmEWttWuSXDNl32c3svadWz8WAAAAyZBfrg0AAMDMEG0A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819b34240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.Middle_Aged, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "46cf231c-8d31-427a-8cd0-6032ff54088e",
    "_uuid": "f610a18f38ad600a3cf2103bd2357988de58b64a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9PSAAAMA4mzPIotbavUnunbHvsztY++49HwsAAIBkwC/XBgAAYDREGwAAQMdEGwAAQMdE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819ea22b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "draw_data = pd.crosstab(data.old, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "6a61b052-e6b6-43b5-9852-87b4beacb6f7",
    "_uuid": "01a06f78a018e93cfd4f18e3005df6201a5f6191"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAA42zeIItaa3cmuXPGuc/uZO17X/tYAAAAJAN+uTYAAACjIdoAAAA6JtoAAAA6JtoA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\nAADQMdEGAADQMdEGAADQsf8Hi5o9KCKeCYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819e3af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.t_min, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "720a31aa-a79f-4a44-87fc-33cc34046d87",
    "_uuid": "7a77e2cf012291abbafe13733d62d330aa63b488"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2gAAAAHPZvEEWtdZuTXLrlH2f2sXat7z4sQAAAEgG/HJtAAAARkO0AQAAdEy0AQAAdEy0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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8198dbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.t_e_min, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "98178876-4e69-4996-aaa4-9440d287470c",
    "_uuid": "b509ebdeefabb1ca2400a7078870733d62a40ee6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vqr09IAAAwHy2YJBFrbWbktw0Zd+n97D21Bc+FgAAAMmAD9cGAABgNEQbAABAx0QbAABA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe837109c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.e_min, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "36d365c0-4ceb-4eb8-8391-f6f6cea67601",
    "_uuid": "7364d285b13704582904ad95db5bde787eadab78"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mqr5RVYfOfqkAAADzz05DW1Xtk+SsJK9IcmiSk2YIZZ/r7sO7e0WSjyb5xKxXCgAAMA+N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\nMGBCGwAAwIAJbQAAAAP2/wENMXMBJUx8MgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe818a04400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.pdays_not_contacted, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "a5dd0f14-b7f1-4b40-bac6-8157507352ee",
    "_uuid": "76a188f4aa0fe161f37657421c26b8fbbe3f3fae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GjIb74wAA/DQzbQAAACOmtAEAAIyY0gYAADBiShsAAMCIKW0AAAAjprQBAACMmNIGAAAw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe818a21828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.months_passed, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(int), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "9526e69c-2f76-49b1-8752-1793cd199252",
    "_uuid": "7e8891a70bedae8790b3dcc765ea74745959ca1a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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de7r7gSSbk5w3x7r3JHl/kvsXcT4AAICxNkq0HZ3knhnbk9P7HlZVpyQ5prs/vYizAQAA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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe81988ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "draw_data = pd.crosstab(data.Contacted, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "eb0e5982-ad1a-497d-ab7c-17f8a4d3bfe0",
    "_uuid": "f3148fb5bcc2efb7ddc4a4098c6b24f8481d4489"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qL1fVkfPfKgAAwOSZM7RV1QFJ1id5VZIjk5w+Syi7pLuP7u5VSX4nyX+Y904BAAAm0Dg7\nbccn2drd27r7/iQbkpwyvaC7/27a8CeS9Py1CAAAMLkWjVFzSJI7p42nkvzszKKqeleSX0tyUJJ/\nNNtCVbU2ydokOeywwx5prwAAABNnnJ22mmXux3bSunt9d/9Mkt9I8tuzLdTdF3b36u5evWTJkkfW\nKQAAwAQaJ7RNJTl02nhpkrsfpn5Dkl96NE0BAACwyzihbVOSFVW1vKoOSnJako3TC6pqxbTha5Lc\nPn8tAgAATK45v9PW3Tuq6uwkVyU5IMnHu/umqjo/yebu3pjk7Kp6eZIfJvlOkl/Zm00DAABMinEe\nRJLuviLJFTPmzp32+Zx57gsAAICM+XJtAAAAFobQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYA\nADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAA\nwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAA\nAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAzYWKGtqk6uqluramtVrZvl+K9V1ZaqurGq\nPl9Vh89/qwAAAJNnztBWVQckWZ/kVUmOTHJ6VR05o+yrSVZ39zFJPp3kd+a7UQAAgEk0zk7b8Um2\ndve27r4/yYYkp0wv6O4vdvffj4Z/mWTp/LYJAAAwmcYJbYckuXPaeGo0tztvT/K52Q5U1dqq2lxV\nm7dv3z5+lwAAABNqnNBWs8z1rIVVv5xkdZLfne14d1/Y3au7e/WSJUvG7xIAAGBCLRqjZirJodPG\nS5PcPbOoql6e5F8neUl3/2B+2gMAAJhs4+y0bUqyoqqWV9VBSU5LsnF6QVW9IMkfJFnT3X8z/20C\nAABMpjlDW3fvSHJ2kquS3JzkU919U1WdX1VrRmW/m+RJSS6rqhuqauNulgMAAOARGOf2yHT3FUmu\nmDF37rTPL5/nvgAAAMiYL9cGAABgYQhtAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEA\nAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAA\nMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADA\ngAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAM2VmirqpOr6taq2lpV62Y5fmJV/VVV7aiq\nN8x/mwAAAJNpztBWVQckWZ/kVUmOTHJ6VR05o+zbSc5Icsl8NwgAADDJFo1Rc3ySrd29LUmqakOS\nU5JsebCgu781OvbAXugRAABgYo1ze+QhSe6cNp4azT1iVbW2qjZX1ebt27fvyRIAAAATZZzQVrPM\n9Z5crLsv7O7V3b16yZIle7IEAADARBkntE0lOXTaeGmSu/dOOwAAAEw3TmjblGRFVS2vqoOSnJZk\n495tCwAAgGSM0NbdO5KcneSqJDcn+VR331RV51fVmiSpqhdW1VSSNyb5g6q6aW82DQAAMCnGeXpk\nuvuKJFfMmDt32udN2XXbJAAAAPNorJdrAwAAsDCENgAAgAET2gAAAAZMaAMAABgwoQ0AAGDAhDYA\nAIABE9oAAAAGTGgDAAAYMKENAABgwIQ2AACAARPaAAAABkxoAwAAGDChDQAAYMCENgAAgAET2gAA\nAAZMaAMAABgwoQ0AAGDAhDYAAIABE9oAAAAGTGgDAAAYMKENAABgwIQ2AACAARPaAAAABkxoAwAA\nGDChDQAAYMCENgAAgAET2gAAAAZMaAMAABgwoQ0AAGDAxgptVXVyVd1aVVurat0sxx9fVZ8cHb+u\nqpbNd6MAAACTaM7QVlUHJFmf5FVJjkxyelUdOaPs7Um+093PSvKRJP9+vhsFAACYROPstB2fZGt3\nb+vu+5NsSHLKjJpTknxi9PnTSX6hqmr+2gQAAJhM44S2Q5LcOW08NZqbtaa7dyT5bpKnzkeDAAAA\nk2zRGDWz7Zj1HtSkqtYmWTsa/t+qunWM6wOz+LPkaUnuWeg+2L06d6E7ANi7/C0aNn+H9guHj1M0\nTmibSnLotPHSJHfvpmaqqhYl+ckkfztzoe6+MMmF4zQGPLyq2tzdqxe6DwAml79FsG+Mc3vkpiQr\nqmp5VR2U5LQkG2fUbEzyK6PPb0jyhe7+sZ02AAAAHpk5d9q6e0dVnZ3kqiQHJPl4d99UVecn2dzd\nG5N8LMkfV9XW7NphO21vNg0AADApyoYY7J+qau3olmMAWBD+FsG+IbQBAAAM2DjfaQMAAGCBCG0A\nAAADJrQBAAAM2DjvaQMGoKqek+SUJIdk18vr706ysbtvXtDGAADYq+y0wX6gqn4jyYYkleQr2fX+\nxEpyaVWtW8jeAKCqzlzoHuCxzNMjYT9QVbclOaq7fzhj/qAkN3X3ioXpDACSqvp2dx+20H3AY5Xb\nI2H/8ECSZyb56xnzzxgdA4C9qqpu3N2hJD+9L3uBSSO0wf7hXyT5fFXdnuTO0dxhSZ6V5OwF6wqA\nSfLTSU5K8p0Z85Xk2n3fDkwOoQ32A919ZVU9O8nx2fUgkkoylWRTd+9c0OYAmBSXJ3lSd98w80BV\nXb3v24HJ4TttAAAAA+bpkQAAAAMmtAEAAAyY0AYAADBgQhsA+0xVdVX93rTxr1fVeXOc80tVdeQY\na/96Vd1SVd+oqq9V1Vv3sMeXVtWL9+Tc0fm/tQfnnFFVH93TawLw2Ca0AbAv/SDJ66rqaY/gnF9K\n8rChraremeQVSY7v7uclOTG7nrK6J16aZI9DW5JHHNoA4OEIbQDsSzuSXJjkV2ceqKrDq+rzVXXj\n6N/DRjtea5L8blXdUFU/s5t1fyvJWd39d0nS3d/t7k+M1v2FqvpqVX29qj5eVY8fzX+rqt5fVX81\nOvacqlqW5J1JfnV0vROq6h9X1XWjNf68qn56dP6TquoPR+feWFWvr6oPJXnC6NyLR3W/XFVfGc39\nQVUdMJo/s6puq6ovJfn5+foPBuCxR2gDYF9bn+QtVfWTM+Y/muSPuvuYJBcn+c/dfW2SjUne292r\nuvuOmYtV1cFJDt7NscVJLkpyancfnV3vJ/3n00ru6e5jk/x+kl/v7m8luSDJR0bXuybJl5P8XHe/\nIMmGJP9qdO77kny3u48e9fyF7l6X5Pujc99SVc9NcmqSn+/uVUl2jn73ZyR5f3aFtVdkjp1EACab\n0AbAPjXaDfujJO+ZcehFSS4Zff7jJP9wzCUrye5eOroyyTe7+7bR+BPZdevkg/776N/rkyzbzRpL\nk1xVVV9P8t4kR43mX55dATRJ0t3fmeXcX0hyXJJNVXXDaHxEkp9NcnV3b+/u+5N8cre/HQATT2gD\nYCH8xyRvT/ITD1OzuyD2o0W7QuD/q6ojZjk81/fafjD6d2d27cLN5r8k+ehop+6fJVk8be25eqwk\nnxjtvK3q7pXdfd6Drc9xLgAkEdoAWADd/bdJPpVdwe1B1yY5bfT5Ldl1W2KSfC/JwXMs+e+SrK+q\nf5AkVfUPqmptkluSLKuqZ43q/kmSL82x1szr/WSSu0aff2Xa/J8lOfvBQVX91OjjD6vqwNHnzyd5\nQ1U9fVTzlKo6PMl1SV5aVU8d1b5xjp4AmGBCGwAL5feSTH+K5HuSnFlVN2ZXuDpnNL8hyXtHDwLZ\n3YNIfj/JF7PrNsRvZFcw+/vuvi/JmUkuG93e+EB2fWft4Xw2yWsffBBJkvNG51+T5J5pdf82yU89\n+IqBJC8bzV+Y5Maquri7tyT57SR/Nvq9/meSZ3T3/x6t+xdJ/jzJX83REwATrLrdnQEAADBUdtoA\nAAAGbHdfugaAwamq9fnxd5r9p+7+w4XoBwD2BbdHAgAADJjbIwEAAAZMaAMAABgwoQ0AAGDAhDYA\nAIAB+//XAdQmMtvyJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819d52a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.Not_Contacted, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "b475f336-42b5-40bd-a90d-c1c29e6c3ee3",
    "_uuid": "1e5b8ecd93fbe549e390b151c5edf89035a18db8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uHeUPAPYkpQ2Aeefaa6/Ni170ouy///5JkjVr1uT+++/PF77whbzkJS/ZMe/HP/7xjtcv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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8189fd7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.Pos_Balance, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "848c43bb-982b-4a39-a881-5cc93caed6bf",
    "_uuid": "c916f969da9488e51af3cf7659f08e9c2761b583"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819913f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.No_Balance, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "5df161ae-d229-40da-8b7b-9d96e618fb12",
    "_uuid": "bef75034cad19e8739e016d1d4fb8f7b36f140a0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AMGAjRVtVnV1Vd1fV1qpaPcXx91fVt6vqzqr6alUdP/OjAgAAzD8LpltQVQcmWZPkjUnG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\n9v8Bnk2yLn5tOTMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819dfb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.Neg_Balance, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "415d01a5-34a7-465e-a573-89f13ac74cf2",
    "_uuid": "385299948fdf3fa78c0b18a5a208cdc5fadbd308"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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quizJptbahiQXV9WZSZ5M8oMk757PoQEAAMbFIDciSWvt5iQ3z9h36bTHHxzyXAAAAGTA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe818c43d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.campaign, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "7915db4d-7a79-49c5-b23d-231e85c90302",
    "_uuid": "6ef46419b0ac6daa10b65fd88a742e24544e6201"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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snN+YAAAAS9Mkpe3RSW6Ys7xpdt09eV2SL29vQ1WdWFUbq2rjzTffPHlKAACAJWqS0lbb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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8199695c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.marital, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "2075eca7-fc7f-49aa-8638-d075f204fd74",
    "_uuid": "dde763a918b201a1077d57c13ab3f4bd47ee4f68"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mmghmx/oFyxdOJQU7hDNtbKWq3p+5g+VZo1XHJblXdz97eqlgdlTVG5Psn+S9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\nKW0AAAADprQBAAAMmNIGAAAwYP8//Zm4x3NL600AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819c0e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = pd.crosstab(data.education, data.deposit)\n",
    "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4d356905-e5a9-4633-adb6-44117faf0bea",
    "_uuid": "555e57cb8f14b6e8717341f422ef05af4b460ebb"
   },
   "source": [
    "**Classifiers :**\n",
    "Based on the values of different parameters we can conclude to the following classifiers for Binary Classification.\n",
    "\n",
    "*  Gradient Boosting\n",
    "*  AdaBoosting\n",
    "*  Logistics Regression\n",
    "*  Random Forest Classifier\n",
    "*  Linear Discriminant Analysis\n",
    "*  K Nearest Neighbour\n",
    "\n",
    "\n",
    "And performance metric using precision and recall calculation along with roc_auc_score & accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "20e60fcf-4888-455a-a18e-01e60edf37be",
    "_uuid": "193ca773b61c9315cc18167ad72a3d18dad7e43c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifiers = {'Gradient Boosting Classifier':GradientBoostingClassifier(),'Adaptive Boosting Classifier':AdaBoostClassifier(),'Linear Discriminant Analysis':LinearDiscriminantAnalysis(),'Logistic Regression':LogisticRegression(),'Random Forest Classifier': RandomForestClassifier(),'K Nearest Neighbour':KNeighborsClassifier(8)}#'Decision Tree Classifier':DecisionTreeClassifier(),'Gaussian Naive Bayes Classifier':GaussianNB(),'Support Vector Classifier':SVC(probability=True),}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "425ee1bf-a073-4a46-85ca-73b4c6d0b22f",
    "_uuid": "0fb9a1f3e1ed1365754de21bc2347457b22500ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['job', 'default', 'housing', 'loan', 'contact', 'month', 'campaign',\n",
      "       'Adult', 'Middle_Aged', 'old', 'primary', 'secondary', 'tertiary',\n",
      "       'unknown', 'Neg_Balance', 'No_Balance', 'Pos_Balance', 'Not_Contacted',\n",
      "       'Contacted', 't_min', 't_e_min', 'e_min', 'pdays_not_contacted',\n",
      "       'months_passed', 'married', 'singles', 'divorced'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "data_y = pd.DataFrame(data['deposit'])\n",
    "data_X = data.drop(['deposit','balance','previous','pdays','age','duration','education','marital'],axis=1)\n",
    "print(data_X.columns)\n",
    "log_cols = [\"Classifier\", \"Accuracy\",\"Precision Score\",\"Recall Score\",\"F1-Score\",\"roc-auc_Score\"]\n",
    "#metrics_cols = []\n",
    "log = pd.DataFrame(columns=log_cols)\n",
    "#metric = pd.DataFrame(columns=metrics_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "edb49a0e-093e-45ad-8bc8-df179ad109b1",
    "_uuid": "1c9ed2a8d6acd5b27d187f15d95ba3e54fe2135c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Classifier  Accuracy  Precision Score  Recall Score  \\\n",
      "0  Gradient Boosting Classifier  0.720555         0.724479      0.715833   \n",
      "0  Gradient Boosting Classifier  0.722794         0.726876      0.718055   \n",
      "0  Adaptive Boosting Classifier  0.704433         0.704962      0.701173   \n",
      "0  Adaptive Boosting Classifier  0.703986         0.704727      0.700559   \n",
      "0  Linear Discriminant Analysis  0.675325         0.675440      0.671772   \n",
      "0  Linear Discriminant Analysis  0.681146         0.681719      0.677351   \n",
      "0           Logistic Regression  0.674429         0.674377      0.671015   \n",
      "0           Logistic Regression  0.683386         0.683928      0.679667   \n",
      "0      Random Forest Classifier  0.674429         0.673754      0.671815   \n",
      "0      Random Forest Classifier  0.667264         0.666422      0.664771   \n",
      "0           K Nearest Neighbour  0.682938         0.691008      0.675947   \n",
      "0           K Nearest Neighbour  0.671742         0.680795      0.664133   \n",
      "\n",
      "   F1-Score  roc-auc_Score  \n",
      "0  0.715928       0.724479  \n",
      "0  0.718171       0.726876  \n",
      "0  0.701510       0.704962  \n",
      "0  0.700867       0.704727  \n",
      "0  0.671843       0.675440  \n",
      "0  0.677389       0.681719  \n",
      "0  0.671120       0.674377  \n",
      "0  0.679748       0.683928  \n",
      "0  0.672055       0.673754  \n",
      "0  0.664989       0.666422  \n",
      "0  0.673635       0.691008  \n",
      "0  0.660747       0.680795  \n"
     ]
    },
    {
     "data": {
      "image/png": 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2dxa3jYiI6F89JgSS7gDau1tve8MlHtEgJGkt4FxgFeAR4CO2/y5pbeAiSu/J\nL4HP2h4haQxwhe2xkjYAzqM8nbEMpWflq8DakmYAV1EmaHbUHwYcD7yHcu7Ptn1aD+HdRPleiI5Y\nN6E8IjoCeBTYx/Y/JW0KfJfSw3M9sF3d3z7ADsCKlGGgd0n6HPAhYAXgJ7aPlvQK4EfAGvV4v2p7\niqTjgB2BecCVtg+TdAzlKZQTJI0DvgOsBNwL7Gv7cUnXALcAW1MSpP1sX9f0HyUiYhD580X30jZ7\nLgB7n/tCB2lrayuTJ08eqLAWSm89BP8PWBV4sFP5WsA/+iSiwel04ELbF0jaFzgV2Bk4hfJVzj+U\ndEA32x5Q61xU52EMA44ExtoeB1ATiA77A68H3mp7Xn2zZE/eC1xe21mO8njoTrYfkbQb8DVgX0pS\nsr/tG+tFvNHmwIa2H5P0bkpvw2ZACzBV0laUZOgftneo+1q5xrYLsJ7tdkmjuojvQuBTtq+VdCxw\nNHBIXbes7c0kbV/Lt+28saT96zmhrf25Xk5FRMTAaJs9l7bH2gAwHuBoFk1vCcHJwBcahgwAkLRK\nXff+vgpskNmckhwBfA+Y3FC+c/38A+CELra9CfiipDWAy2z/RVIX1Z63LfCdjq572491U++bdYLn\na4G31bJ1gbHAVXUfw4B/1gv1SNs3NsT6voa2rmrYz7vrzx/q8ghKgnAdcIKk4ym9GddJWhZ4DjhH\n0s+BKxoDlLQyMMr2tbXoAuCShiqX1d/TgTFdHaTtsyhDIrSu29ptb1VExEBaYdTyz38ePXz0859b\nW1sHIpxF0ltCMMb2zM6Ftqd1uqtd2jR9YbL9A0m3ULrlfy3po8Dfetikpcn2P0e5oB5MudBuUre9\n0/bmjRUlvaqXtp7utP9v2D6zc6U6HLE98A1JV9o+VtJmwDbA7pRJp+9qIvYObfX3fJp/4iUiYtBZ\nZ+Laz38+fsKJAxjJouvtKYMVe1g3fEkGMsjdSLngAUykjMED3MwLT1vs3nkjAElvAP5m+1RgKrAh\nMIfu3xZ5JXBAvfumpyED2wsowxbLSHoPcA+wiqTN67bLSdrA9uOUF1R19CR0GWv1a2BfSSNqG5L0\nWkmrA8/Y/j6lJ2TjWmdl27+gDAOM6xTfE8DjkrasRXsB1xIREYNObwnBrZI+1rlQ0n6Ubt6Xo5Uk\nzWr4+SzlLvwjkmZSLmqfrnUPAT4r6ffAasATXbS3G/DHOoFwPcpchH8DN0j6o6TOL4k6B/g7MFPS\n7cCePQWhns1oAAATuklEQVRbX0v9P8DhtudSnlY4vm47A3h7rbofcJakmyi9AF3Fiu0rKUMKN9VJ\npZdSkpe3AL+vx/HFus+RwBX1vFwLfKaLJidRhjdmUhKGY3s6noiIGBgt7e3d905LWhX4CTCXFxKA\n8ZQZ87vUlx4ttSStBDxbJ9TtDuxhe6eBjqsrkkbYfqp+PhJYzfane9lsUGldt7V90pkTBzqMiIge\nDbYhg5aWlunt7e3je6vX47it7X8Bb6/Puo+txT+3/dslEOPLwSbA6ZJagNmU2fyD1Q6SPk/5mz8A\n7DOw4URExGDS7BcTXQ1c3cexDDn1ufmNBjqOZtieAkwZ6DgiImJwavrlRhEREfHylYQgIiIikhBE\nREREEoKIiIggCUFERESQhCAiIiJIQhARERHkhTIxhKwxco1B9w1gEREvF+khiIiIiCQEERERkYQg\nIiIiSEIQERERJCGIiIgIkhBEREQEeewwhpBZc2ZxxDWHDnQYEREv8XJ4JDo9BBEREZGEICIiIpIQ\nREREBEkIIiIigiQEERERQRKCiIiIIAlBREREkIQgIiIiSEIQERERJCGIiIgIhsBXF0uaD9xBifU+\nYC/bs5dAu2OAK2yPXdy2OrV7DPAx4JFa9CvbRy7JfTTsaxywuu1fdLN+M+AEYFWgHbgeOBj4EDDe\n9ieXUBy/APa0PVvSwcCBwG3AFGB928ctif1ERETfGfQJAfCs7XEAki4ADgK+NrAh9epk2ycs7EaS\nhtmevxCbjAPGAy9JCCStClwC7G77JkktwAeAkQsbV29sb9+w+AlgO9v31eWpzbYjaVnb85ZocBER\n0ZShkBA0ugnYEEDSCOCnwKuA5YCjbP+03vn/knI3/HbAwE62n5W0CXAu8ExdT21rReB/KRfXecBn\nbV8taR9gZ2AYMBY4EVge2AtoA7a3/VgzgUvahnK3vixwK3Cg7TZJ99eY3g2cLulW4AxglRrnx2zf\nLemDwNHAfOAJYFvgWGC4pC2Ab9ie0rDLg4ALbN8EYLsduLTG0hjX+4Gj6nH9G5ho+1+S3gmcUqu1\nA1sBIyh3/a+sx3Gg7evqMYwH/gd4AzBV0rnA49SeCEmrAN8BXlfbPMT2DbVHZXVgDPAosGcz5zMi\nYqD9+aJ7aZs9F4C9z90bgNbWViZPnjyQYS2yITOHQNIwYBteuON8DtjF9sbA1sCJ9S4Y4E3AGbY3\nAGZT7owBzgMOtr15p+YPArD9FmAP4IKaJEBJBPYENqP0TDxj+62U5GTvbsL9jKQZ9ec9ta3zgd3q\nPpaldKt3eM72FrYvBs4CPmV7E+Aw4Nu1zpeB99jeCNjR9txaNsX2uE7JQEfc07uJr9H1wNvqMV0M\nHF7LDwMOqr0zWwLP1vPw61q2ETCjsSHbBwD/ALa2fXKn/ZxC6TnZlPL3OKdh3SaUpO0lyYCk/SVN\nkzStbc5zTRxORET/aJs9l7bH2mh7rA3b2Oahhx4a6LAW2VDoIRguaQblDnI6cFUtbwG+LmkrYAEg\nylg5wH22Oy5W04ExklYGRtm+tpZ/D9iuft4COA2g3o0/AKxT111tew4wR9ITwM9q+R3U3oouvGjI\nQNJGNaY/16KOoY9v1eUptd4ISq/GJQ138SvU3zcA50v6EXBZN/tdFGsAUyStRukl6OjqvwE4SdJF\nwGW2Z9Xei3MlLQdc3nCOm7EtsH7Dcb1SUsfwxVTbz3a1ke2zKEkSreu2ti/MgUVE9KUVRi3//OfR\nw0cDpYdgqBoKCcGztsfVC/oVlAvpqcBESrf6Jrb/U7utO+7q2xq2nw8MpyQQ3V1QWrop79zWgobl\nBTR//npqH+Dp+nsZYHbHnIlGtg+Q9F/ADsCMOqGwJ3dS7rx/2ku904CTbE+VNAE4pu7vOEk/B7YH\nbpa0re3f1QRsB+B7kr5p+8Je2u+wDLB55wt/TRCe7nKLiIhBbJ2Jaz//+fgJJw5gJEvGkBkysP0E\nZYb8YfUOdWXg4ZoMbA2s1cv2s4En6ng7lISiw+86liWtQxnnvmcJhn83pZfijXV5L+DazpVsPwnc\nV+cLIKml9i4gaW3bt9j+MmWsfU1gDt1PEjwdmFSTCGobH5bUOX1dmTLPAmBSQ921bd9h+3hgGrCe\npLUo5/xs4LvAxs2fAq4Enn+qoYmEJiIi+tGQSQgAbP8BuB3YHbgIGC9pGuVifncTTXwEOEPSTZQx\n8Q7fBoZJuoPSfb+P7bauGljEuJ+r+76k7mMBZYJdVyYC+0m6nXKXv1Mt/6akOyT9kZLA3A5cTemG\nnyFpt077/BflPJ0g6R5Jf6LMBXiy0/6OqXFdR0k0Ohwi6Y81jmcpEzUnUHon/kCZB3AKzTuY8vea\nKeku4ICF2DYiIvpYS3t7hmVjaGhdt7V90pkTe68YEdHPBvOQQUtLy/T29vbxvdUbUj0EERER0TeS\nEEREREQSgoiIiEhCEBERESQhiIiICJIQREREBEkIIiIigiQEERERQRKCiIiIYGi83CgCgDVGrjGo\nvw0sImIoSw9BREREJCGIiIiIJAQRERFBEoKIiIggCUFERESQhCAiIiLIY4cxhMyaM4sjrjl0oMOI\niOjRUH08Oj0EERERkYQgIiIikhBEREQESQgiIiKCJAQRERFBEoKIiIggCUFERESQhCAiIiJIQhAR\nEREkIYiIiAiSEERERARLaUIg6amGz9tL+ouk13Wqs4+kBZI2bCj7o6Qx/Rhqx37HSNqzh3Xtkj7V\nUHa6pH16afMASXv3UmcfSad3s+6prsojImJoWioTgg6StgFOA95r++9dVJkFfLEP9ruwL5UaA3SZ\nEFQPA5+WtHyzDdr+ju0LFzKOPrUI5yUiIpaQpfZ/wJK2BM4Gtrd9bzfVrgC2krSu7Xs6bf9u4CvA\nCsC9wEdsPyXpy8D7geHAjcDHbbdLuqYuvwOYKulC4DtAR8/EIbZvkPRO4JRa1g5sBRwHvFnSDOAC\n2yd3ivMR4AZgUj2mxjjXBs4AVgGeAT5m+25JxwBP2T5B0qbAd4GngeuB7WyPrU2sLulXwNrAT2wf\n3tD2icDWwOPA7rYfkTSuHtdK9bzsa/vxevyH2Z4maTQwzfaY2pOxA7Ai8ArgXd38LSIiBqU/X3Qv\nbbPnPr+897kvdL62trYyefLkgQhroS2tPQQrAD8FdrZ9dw/1FgCTgS80FtYL2lHAtrY3BqYBn62r\nT7e9ab2gDgfe17DpKNvvtH0i5aJ/su1NgQ8A59Q6hwEH2R4HbAk8CxwJXGd7XBfJQIfjgEMlDetU\nfhbwKdub1La/3cW25wEH2N4cmN9p3ThgN+AtwG6S1qzlrwBuq8d/LXB0Lb8QOML2hsAdDeU92RyY\nZPslyYCk/SVNkzStbc5zTTQVEdG/2mbPpe2xtud/bD//89BDDw10eE1bWnsI/kO5W98P+HQvdX8A\nfFHS6xvK3gasD9wgCWB54Ka6bmtJh1PukF8N3An8rK6b0tDGtsD6dXuAV0oaSbnTP0nSRcBltmc1\n1OmW7fsk/Z6GoQVJI4C3A5c0tLFC43aSRgEjbd/YcLyNScz/2X6i1r0LWAt4kJIsdRzP94HLJK1M\nSXqureUXAJf0GjxcZfuxbo7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819d577f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "rs = StratifiedShuffleSplit(n_splits=2, test_size=0.2,random_state=0)\n",
    "rs.get_n_splits(data_X,data_y)\n",
    "for Name,classify in classifiers.items():\n",
    "    for train_index, test_index in rs.split(data_X,data_y):\n",
    "        #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "        X,X_test = data_X.iloc[train_index], data_X.iloc[test_index]\n",
    "        y,y_test = data_y.iloc[train_index], data_y.iloc[test_index]\n",
    "        # Scaling of Features \n",
    "#         from sklearn.preprocessing import StandardScaler\n",
    "#         sc_X = StandardScaler()\n",
    "#         X = sc_X.fit_transform(X)\n",
    "#         X_test = sc_X.transform(X_test)\n",
    "        cls = classify\n",
    "        cls =cls.fit(X,y)\n",
    "        y_out = cls.predict(X_test)\n",
    "        accuracy = m.accuracy_score(y_test,y_out)\n",
    "        precision = m.precision_score(y_test,y_out,average='macro')\n",
    "        recall = m.recall_score(y_test,y_out,average='macro')\n",
    "        roc_auc = roc_auc_score(y_out,y_test)\n",
    "        f1_score = m.f1_score(y_test,y_out,average='macro')\n",
    "        log_entry = pd.DataFrame([[Name,accuracy,precision,recall,f1_score,roc_auc]], columns=log_cols)\n",
    "        #metric_entry = pd.DataFrame([[precision,recall,f1_score,roc_auc]], columns=metrics_cols)\n",
    "        log = log.append(log_entry)\n",
    "        #metric = metric.append(metric_entry)\n",
    "        \n",
    "print(log)\n",
    "plt.xlabel('Accuracy')\n",
    "plt.title('Classifier Accuracy')\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"g\")  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "b2a9423c-a0f9-4b47-99a6-53eab2a0f254",
    "_uuid": "176cc5b52cee541d49011abac2b75e62c18bdc65"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o3D1lZi+5e0/2fWmRiIg0E3HHKD4wszMSrURERDJS3DGKHwLXmtkaYBvB6aeUu5+aVGEi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819d9df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(log['Recall Score'], log['Precision Score'], color='navy',\n",
    "         label='Precision-Recall')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall')\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "6d0d8b6b-0aa1-46aa-a48b-287f3266a624",
    "_uuid": "d4ff18b59d4850d4d6745876636f87f2830fe1e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Classifier  Accuracy  Precision Score  Recall Score  \\\n",
      "0  Gradient Boosting Classifier  0.720555         0.724479      0.715833   \n",
      "0  Gradient Boosting Classifier  0.722794         0.726876      0.718055   \n",
      "0  Adaptive Boosting Classifier  0.704433         0.704962      0.701173   \n",
      "0  Adaptive Boosting Classifier  0.703986         0.704727      0.700559   \n",
      "0  Linear Discriminant Analysis  0.675325         0.675440      0.671772   \n",
      "0  Linear Discriminant Analysis  0.681146         0.681719      0.677351   \n",
      "0           Logistic Regression  0.674429         0.674377      0.671015   \n",
      "0           Logistic Regression  0.683386         0.683928      0.679667   \n",
      "0      Random Forest Classifier  0.674429         0.673754      0.671815   \n",
      "0      Random Forest Classifier  0.667264         0.666422      0.664771   \n",
      "0           K Nearest Neighbour  0.682938         0.691008      0.675947   \n",
      "0           K Nearest Neighbour  0.671742         0.680795      0.664133   \n",
      "0  Gradient Boosting Classifier  0.711599         0.714116      0.703560   \n",
      "0  Adaptive Boosting Classifier  0.698164         0.696441      0.693898   \n",
      "0  Linear Discriminant Analysis  0.674429         0.672363      0.669548   \n",
      "0           Logistic Regression  0.675325         0.673302      0.670378   \n",
      "0      Random Forest Classifier  0.647559         0.644852      0.643223   \n",
      "0           K Nearest Neighbour  0.683386         0.689397      0.672132   \n",
      "\n",
      "   F1-Score  roc-auc_Score  \n",
      "0  0.715928       0.724479  \n",
      "0  0.718171       0.726876  \n",
      "0  0.701510       0.704962  \n",
      "0  0.700867       0.704727  \n",
      "0  0.671843       0.675440  \n",
      "0  0.677389       0.681719  \n",
      "0  0.671120       0.674377  \n",
      "0  0.679748       0.683928  \n",
      "0  0.672055       0.673754  \n",
      "0  0.664989       0.666422  \n",
      "0  0.673635       0.691008  \n",
      "0  0.660747       0.680795  \n",
      "0  0.704292       0.714116  \n",
      "0  0.694547       0.696441  \n",
      "0  0.670093       0.672363  \n",
      "0  0.670932       0.673302  \n",
      "0  0.643578       0.644852  \n",
      "0  0.670837       0.689397  \n"
     ]
    },
    {
     "data": {
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padP5jXlBkrSfpD36O46IiGhes08ZPFQ/vgD8T9+F8/pg+x/Ajj1UGQl8Gvhe\nk/W78kXbl0jaBDgDWHVeYm0kaVHbs+e3Hdunz28bERHRWj0mBJLuAtq7W2977QUe0QAkaWXgbGA5\n4HHg47b/LmkV4AJK78mVwBdsD5c0BrjC9lhJawLnUJ7OWITSs/J1YBVJ04CrKRM0O+oPBY4FNqec\n+zNtn9JDeDdTvheiI9b1KI+IDgeeAPay/U9J6wM/oPTw3ABsUfe3F7AVsCRlGOgDkr4IfBRYAviZ\n7SMlLQ1cBKxUj/frtidLOgbYFpgNXGX7EElHUZ5COU7SOOB0YCngfmBv209Juha4FdiEkiDtY/v6\npv8oEREDyJ8vuJ+2GbMA2OPsVzpIR40axaRJk/orrLnSWw/B/wOWBx7uVL4y8I8+iWhgOhU43/Z5\nkvYGTga2B06ifJXzTyTt1822+9U6F9R5GEOBw4GxtscB1ASiw77A24B3255d3yzZkw8Bl9V2FqM8\nHrqd7ccl7QR8E9ibkpTsa/umehFvtCGwtu0nJX2Q0tuwATAEmCJpY0oy9A/bW9V9LVNj2wFY3Xa7\npJFdxHc+8Fnb10k6GjgSOKiuW9T2BpK2rOWbdd5Y0r71nLD0skv1cioiIvpH24xZtD3ZBoBxP0cz\nb3pLCE4EvtwwZACApOXqum36KrABZkNKcgTwQ2BSQ/n29fOPgeO62PZm4CuSVgIutf0XSV1Ue9lm\nwOkdXfe2n+ym3nfqBM+3AO+pZe8ExgJX130MBf5ZL9QjbN/UEOvWDW1d3bCfD9afP9Tl4ZQE4Xrg\nOEnHUnozrpe0KPAicJakXwBXNAYoaRlgpO3ratF5wMUNVS6tv28HxnR1kLbPoAyJsNaEsd32VkVE\n9KclRi7+8udlhy378udRo0b1RzjzpLeEYIzt6Z0LbU/tdFe7sGn6wmT7x5JupXTL/1rSJ4C/9bDJ\nkCbb/yLlgnog5UK7Xt32btsbNlaU9MZe2nqu0/6/bfv7nSvV4YgtgW9Lusr20ZI2ADYFdqZMOv1A\nE7F3aKu/59D8Ey8REQPOarut8vLnYyce34+RzLvenjJYsod1wxZkIAPcTZQLHsBulDF4gFt45WmL\nnTtvBCDp7cDfbJ8MTAHWBmbS/dsirwL2q3ff9DRkYPslyrDFIpI2B+4DlpO0Yd12MUlr2n6K8oKq\njp6ELmOtfg3sLWl4bUOS3iJpReB52z+i9ISsW+ssY/uXlGGAcZ3iexp4StJGtWh34DoiImLA6S0h\nuE3SJztNbLxUAAAT60lEQVQXStqH0s37erSUpEcafr5AuQv/uKTplIva52rdg4AvSPo9sALwdBft\n7QT8sU4gXJ0yF+HfwI2S/iip80uizgL+DkyXdCewa0/B1tdSfwM41PYsytMKx9ZtpwHvrVX3Ac6Q\ndDOlF6CrWLF9FWVI4eY6qfQSSvKyFvD7ehxfqfscAVxRz8t1wOe7aHJPyvDGdErCcHRPxxMREf1j\nSHt7973TkpYHfgbM4pUEYDxlxvwO9aVHCy1JSwEv1Al1OwO72N6uv+PqiqThtp+tnw8HVrD9uV42\nG1DWmjC2fctvbN7fYURE9GigDRlIut32+N7q9Thua/tfwHvrs+5ja/EvbP92AcT4erAecKqkIcAM\nymz+gWorSV+i/M0fAvbq33AiImIgafaLia4BrunjWAad+tz8Ov0dRzNsTwYm93ccERExMDX9cqOI\niIh4/UpCEBEREUkIIiIiIglBREREkIQgIiIiSEIQERERJCGIiIgI8kKZGERWGjF6wH0DWETE60V6\nCCIiIiIJQURERCQhiIiICJIQREREBEkIIiIigiQEERERQR47jEHkkZkPc9i1B/d3GBERr8tHoNND\nEBEREUkIIiIiIglBREREkIQgIiIiSEIQERERJCGIiIgIkhBEREQESQgiIiKCJAQRERFBEoKIiIhg\nEHx1saQ5wF2UWB8Adrc9YwG0Owa4wvbY+W2rU7tHAZ8EHq9Fv7J9+ILcR8O+xgEr2v5lN+s3AI4D\nlgfagRuAA4GPAuNtf2YBxfFLYFfbMyQdCOwP3AFMBtawfcyC2E9ERPSdAZ8QAC/YHgcg6TzgAOCb\n/RtSr060fdzcbiRpqO05c7HJOGA88JqEQNLywMXAzrZvljQE+DAwYm7j6o3tLRsWPw1sYfuBujyl\n2XYkLWp79gINLiIimjIYEoJGNwNrA0gaDlwOvBFYDDjC9uX1zv9Kyt3wewED29l+QdJ6wNnA83U9\nta0lgf+lXFxnA1+wfY2kvYDtgaHAWOB4YHFgd6AN2NL2k80ELmlTyt36osBtwP622yQ9WGP6IHCq\npNuA04DlapyftH2vpI8ARwJzgKeBzYCjgWGSJgDftj25YZcHAOfZvhnAdjtwSY2lMa5tgCPqcf0b\n2M32vyS9HzipVmsHNgaGU+7631CPY3/b19djGA98A3g7MEXS2cBT1J4IScsBpwNvrW0eZPvG2qOy\nIjAGeALYtZnzGRHRl/58wf20zZjV7fo9zt6jy/JRo0YxadKkvgqrTw2aOQSShgKb8sod54vADrbX\nBTYBjq93wQCrAqfZXhOYQbkzBjgHOND2hp2aPwDA9lrALsB5NUmAkgjsCmxA6Zl43va7KclJ1/8i\n4POSptWfzWtb5wI71X0sSulW7/Ci7Qm2LwTOAD5rez3gEOB7tc7XgM1trwNsa3tWLZtse1ynZKAj\n7tu7ia/RDcB76jFdCBxayw8BDqi9MxsBL9Tz8Otatg4wrbEh2/sB/wA2sX1ip/2cROk5WZ/y9zir\nYd16lKTtNcmApH0lTZU0tW1mWxOHExEx/9pmzKLtybZuf2x3+fPoo4/2d+jzbDD0EAyTNI1yB3k7\ncHUtHwJ8S9LGwEuAKGPlAA/Y7rhY3Q6MkbQMMNL2dbX8h8AW9fME4BSAejf+ELBaXXeN7ZnATElP\nAz+v5XdReyu68KohA0nr1Jj+XIs6hj6+W5cn13rDKb0aFzfcxS9Rf98InCvpIuDSbvY7L1YCJkta\ngdJL0NHVfyNwgqQLgEttP1J7L86WtBhwWcM5bsZmwBoNx/UGSR3DF1Nsv9DVRrbPoCRJrDVhbPvc\nHFhExLxaYuTiPa5fdtiyXZaPGjWqL8JpicGQELxge1y9oF9BuZCeDOxG6VZfz/Z/ard1x119463k\nHGAYJYHo7oIypJvyzm291LD8Es2fv57aB3iu/l4EmNExZ6KR7f0k/RewFTCtTijsyd2UO+/Le6l3\nCnCC7SmSJgJH1f0dI+kXwJbALZI2s/27moBtBfxQ0ndsn99L+x0WATbsfOGvCcJzXW4REdFPVttt\nlR7XHzvx+BZF0jqDZsjA9tOUGfKH1DvUZYDHajKwCbByL9vPAJ6u4+1QEooOv+tYlrQaZZz7vgUY\n/r2UXop31OXdges6V7L9DPBAnS+ApCG1dwFJq9i+1fbXKGPto4GZdD9J8FRgz5pEUNv4mKTO6esy\nlHkWAHs21F3F9l22jwWmAqtLWplyzs8EfgCs2/wp4Crg5acamkhoIiKihQZNQgBg+w/AncDOwAXA\neElTKRfze5to4uPAaZJupoyJd/geMFTSXZTu+71sL7ABa9sv1n1fXPfxEmWCXVd2A/aRdCflLn+7\nWv4dSXdJ+iMlgbkTuIbSDT9N0k6d9vkvynk6TtJ9kv5EmQvwTKf9HVXjup6SaHQ4SNIfaxwvUCZq\nTqT0TvyBMg/gJJp3IOXvNV3SPcB+c7FtRET0sSHt7RmWjcFhrQlj27f8xub9HUZExKAaMpB0u+3x\nvdUbVD0EERER0TeSEEREREQSgoiIiEhCEBERESQhiIiICJIQREREBEkIIiIigiQEERERQRKCiIiI\nYHC83CgCgJVGjB5U3w4WETGYpIcgIiIikhBEREREEoKIiIggCUFERESQhCAiIiJIQhARERHkscMY\nRB6Z+TCHXXtwf4cREdGjwfp4dHoIIiIiIglBREREJCGIiIgIkhBEREQESQgiIiKCJAQRERFBEoKI\niIggCUFERESQhCAiIiJIQhAREREkIYiIiAgW0oRA0rMNn7eU9BdJb+1UZy9JL0lau6Hsj5LGtDDU\njv2OkbRrD+vaJX22oexUSXv10uZ+kvbopc5ekk7tZt2zXZVHRMTgtFAmBB0kbQqcAnzI9t+7qPII\n8JU+2O/cvlRqDNBlQlA9BnxO0uLNNmj7dNvnz2UcfWoezktERCwgC+3/gCVtBJwJbGn7/m6qXQFs\nLOmdtu/rtP0Hgf8BlgDuBz5u+1lJXwO2AYYBNwGfst0u6dq6/D5giqTzgdOBjp6Jg2zfKOn9wEm1\nrB3YGDgGeJekacB5tk/sFOfjwI3AnvWYGuNcBTgNWA54Hvik7XslHQU8a/s4SesDPwCeA24AtrA9\ntjaxoqRfAasAP7N9aEPbxwObAE8BO9t+XNK4elxL1fOyt+2n6vEfYnuqpGWBqbbH1J6MrYAlgaWB\nD3Tzt4iIGJD+fMH9tM2Y9fLyHme/0vk6atQoJk2a1B9hzbWFtYdgCeByYHvb9/ZQ7yVgEvDlxsJ6\nQTsC2Mz2usBU4At19am2168X1GHA1g2bjrT9ftvHUy76J9peH/gwcFatcwhwgO1xwEbAC8DhwPW2\nx3WRDHQ4BjhY0tBO5WcAn7W9Xm37e11sew6wn+0NgTmd1o0DdgLWAnaSNLqWLw3cUY//OuDIWn4+\ncJjttYG7Gsp7siGwp+3XJAOS9pU0VdLUtpltTTQVEdFabTNm0fZk28s/tl/+efTRR/s7vKYtrD0E\n/6Hcre8DfK6Xuj8GviLpbQ1l7wHWAG6UBLA4cHNdt4mkQyl3yG8C7gZ+XtdNbmhjM2CNuj3AGySN\noNzpnyDpAuBS24801OmW7Qck/Z6GoQVJw4H3Ahc3tLFE43aSRgIjbN/UcLyNScz/2X661r0HWBl4\nmJIsdRzPj4BLJS1DSXquq+XnARf3GjxcbfvJbo7rDEpSw1oTxrY30VZEREstMfLVo7XLDlv25c+j\nRo1qdTjzbGFNCF4CPgr8RtKXbX+ru4q2Z9eu8cMaiodQLmK7NNaVtCTlDny87Ydrt/ySDVWea/i8\nCLCh7Rc67fIYSb8AtgRukbTZXBzXt4BLgN817GNG7W3ozpBe2my8LZ9D9/9mertYz+aVHqklO617\njoiIQWq13VZ51fKxE4/vp0jmz8I6ZIDt5yl3wrtJ2qeX6udS7uiXq8u3AO+T9A4ASUtJWo1XLnRP\n1LvzHXto8yrgMx0LdewdSavYvsv2sZShiNWBmcCIJo7pXuCeelzYfgZ4QNJHattDJK3TaZungJmS\n3lOLdu5tP9UivHJ8uwI31J6Ep+r8DIDdKcMJAA8C69XPPZ2XiIjoBwttQgBQu6k/BBwhabse6s0C\nTgbeUpcfB/YCfiJpOiVBWN32DMqkvruAy4Dbetj9gcB4SdNrV/x+tfyg+njjnZT5A1cC04HZku6U\n9PleDuubwEoNy7sB+9T27ga6Os59gDMk3UzpMXi6l31AuatfU9LtlImAR9fyPYHv1PMyrqH8OGB/\nSTcBy3ZuLCIi+teQ9vYMyy7sJA23/Wz9fDiwgu3e5la03FoTxrZv+Y3N+zuMiIgeDbQhA0m32x7f\nW72FdQ5BvNpWkr5E+ffwEKX3IyIiFiJJCALbk3n1ExAREbGQWajnEERERESRhCAiIiKSEEREREQS\ngoiIiCAJQURERJCEICIiIshjhzGIrDRi9ID7wo+IiNeL9BBEREREEoKIiIhIQhARERHk5UYxiEia\nCdzX33HMhWWBJ/o7iLmQePtW4u1bgy1eaF3MK9terrdKmVQYg8l9zbyxa6CQNDXx9p3E27cSb98b\naDFnyCAiIiKSEEREREQSghhczujvAOZS4u1bibdvJd6+N6BizqTCiIiISA9BREREJCGIiIgI8thh\nDDCSPgScBAwFzrJ9TKf1SwDnA+sB/wZ2sv1gq+PsFFNvMW8MfBdYG9jZ9iWtj/JV8fQW7xeATwCz\ngceBvW0/1PJAX4mnt3j3Aw4A5gDPAvvavqflgb4ST4/xNtTbEbgYWN/21BaG2DmO3s7vXsB3ANei\nU22f1dIgXx1Pr+dX0keBo4B24E7bu7Y0yFfH0tv5PRHYpC4uBbzF9sjWRlmkhyAGDElDgdOALYA1\ngF0krdGp2j7AU7bfAZwIHNvaKF+tyZj/DuwF/Li10b1Wk/H+ARhve23gEmBSa6N8RZPx/tj2WrbH\nUWI9ocVhvqzJeJE0AjgQuLW1Eb4mjqbiBSbbHld/+jMZ6DVeSasCXwLeZ3tN4KCWB/pKLL3Ga/vz\nHecWOAW4tPWRFkkIYiDZAPir7b/ZngVcCGzXqc52wHn18yXAppKGtDDGznqN2faDtqcDL/VHgJ00\nE+81tp+vi7cAK7U4xkbNxPtMw+LSlLvC/tLMv2GAr1OSlxdbGVwXmo13oGgm3k8Cp9l+CsD2Yy2O\nsdHcnt9dgJ+0JLIuJCGIgUTAww3Lj9SyLuvYng08Dby5JdF1rZmYB5K5jXcf4Mo+jahnTcUr6QBJ\n91Musge2KLau9BqvpHcDo21f0crAutHsv4cPS5ou6RJJo1sTWpeaiXc1YDVJN0q6pXbZ95em/3uT\ntDLwNuC3LYirS0kIYiDp6k6/891eM3VaaaDF05um45X0MWA8Zfy4vzQVr+3TbK8CHAYc0edRda/H\neCUtQhnqOrhlEfWsmfP7c2BMHUL6Da/00PWHZuJdFFgVmEi54z5LUr+MyTN3/3/YGbjE9pw+jKdH\nSQhiIHkEaLz7WAn4R3d1JC0KLAM82ZLoutZMzANJU/FK2gz4CrCt7bYWxdaVuT2/FwLb92lEPest\n3hHAWOBaSQ8C7wGmSOqv77Pv9fza/nfDv4EzKRN6+0uz/4+43PZ/bD9AeSHaqi2Kr7O5+fe7M/04\nXAB5yiAGltuAVSW9jTKjeWeg8+zgKcCewM3AjsBvbffnHXkzMQ8kvcZbu7S/D3yon8dfobl4V7X9\nl7q4FfAX+k+P8dp+mvKGOwAkXQsc0o9PGTRzflew/c+6uC3wp9aG+CrN/Pd2GaVn4FxJy1KGEP7W\n0ihf0dT/HyS9E3gj5f9r/SY9BDFg1DkBnwF+TfmfzkW275Z0tKRta7UfAG+W9FfgC8Dh/RNt0UzM\nktaX9AjwEeD7ku4eyPFShgiGAxdLmiZpSj+F22y8n5F0t6RplH8Te/ZTuM3GO2A0Ge+B9fzeSZmf\nsVf/RNt0vL8G/i3pHuAa4Iu2/z2A44WSwFzYzzc3+eriiIiISA9BREREkIQgIiIiSEIQERERJCGI\niIgIkhBEREQESQgiYiEjaQdJ7ZJW7+9YIgaSJAQRsbDZBbiB8iUxfaK+5S5iUMn3EETEQkPScMpX\n2W4CTLG9ei0/FNid8kbKK20fLukdwOnAcsAcyhdLjaZ8s+DWdbtTgam2z61fRXw28EHgVMrXFO8L\nLA78Fdjd9vOSlq/tvr2GtT/l9bhP2D6ptvtN4F+2T+7L8xHRKD0EEbEw2R74le0/A09KWlfSFrX8\nv2yvQ3ljIsAFlNforgO8F/hnly2+2ou2J9i+ELjU9vp1+z9R3hwJcDJwXS1fF7ib8g2ce8LLL0Da\nue4/omXyLoOIWJjsAny3fr6wLi8CnGP7eQDbT0oaAcj2z2rZiwBSr2+2ntzweaykbwAjKV8F/eta\n/gFgj9ruHMorvJ+W9O/6HonlgT/019ftxsIrCUFELBQkvZlyMR4rqR0YSnkV7U9p7jXbALN5dc/q\nkp3WP9fw+Vxge9t3StqL8jrenpxFeU/AKMrQQ0RLZcggIhYWOwLn217Z9hjbo4EHKK/P3lvSUgCS\n3mT7GeARSdvXsiXq+oeANeryMsCmPexvBPBPSYsBuzWU/x9l3gCShkp6Qy3/GfAhYH1e6U2IaJkk\nBBGxsNiFctFt9FNgRcprtafWNyYeUtftTnnT33TgJmCU7YeBi4DplDH+P/Swv68CtwJXA/c2lH8O\n2ETSXcDtwJoAtmdR3s53UR1KiGipPGUQETEA1MmEdwAfsf2X/o4nFj7pIYiI6GeS1qA8mvh/SQai\nv6SHICIiItJDEBEREUkIIiIigiQEERERQRKCiIiIIAlBREREAP8fcHsindzO9TAAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe819d57e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "rs = ShuffleSplit(n_splits=1, test_size=0.2,random_state=0)\n",
    "rs.get_n_splits(data_X,data_y)\n",
    "for Name,classify in classifiers.items():\n",
    "    for train_index, test_index in rs.split(data_X,data_y):\n",
    "        #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "        X,X_test = data_X.iloc[train_index], data_X.iloc[test_index]\n",
    "        y,y_test = data_y.iloc[train_index], data_y.iloc[test_index]\n",
    "        cls = classify\n",
    "        cls =cls.fit(X,y)\n",
    "        y_out = cls.predict(X_test)\n",
    "        accuracy = accuracy_score(y_test,y_out)\n",
    "        precision = m.precision_score(y_test,y_out,average='macro')\n",
    "        recall = m.recall_score(y_test,y_out,average='macro')\n",
    "        f1_score = m.f1_score(y_test,y_out,average='macro')\n",
    "        roc_auc = roc_auc_score(y_out,y_test)\n",
    "        log_entry = pd.DataFrame([[Name,accuracy,precision,recall,f1_score,roc_auc]], columns=log_cols)\n",
    "        #metric_entry = pd.DataFrame([[precision,recall,f1_score,roc_auc]], columns=metrics_cols)\n",
    "        log = log.append(log_entry)\n",
    "        #metric = metric.append(metric_entry)\n",
    "    \n",
    "print(log)\n",
    "plt.xlabel('Accuracy')\n",
    "plt.title('Classifier Accuracy')\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"g\")  \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}