AutoML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credit card fraud detection\n",
    "In this project we will attempt to fit several machine learning models on skewed data. The data is highly imbalanced.\n",
    "We would like to invesitgate which of the methods below work best with unbalanced data.\n",
    "* Auto Machine Learning\n",
    "* Logistic Regression\n",
    "* SVMs\n",
    "* Decision trees\n",
    "* anomaly detection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nanaakwasiabayieboateng/anaconda/lib/python3.5/site-packages/odo/backends/pandas.py:102: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n",
      "You can access NaTType as type(pandas.NaT)\n",
      "  @convert.register((pd.Timestamp, pd.Timedelta), (pd.tslib.NaTType, type(None)))\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "import scipy as sci\n",
    "import matplotlib as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels\n",
    "import bokeh\n",
    "import sympy\n",
    "import requests\n",
    "import blaze\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "#from auto_ml import Predictor\n",
    "#from auto_ml.utils_models import load_ml_model\n",
    "\n",
    "# suppress warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "#import warnings\n",
    "\n",
    "#def fxn():\n",
    "#    warnings.warn(\"deprecated\", DeprecationWarning)\n",
    "\n",
    "#with warnings.catch_warnings():\n",
    "#    warnings.simplefilter(\"ignore\")\n",
    "#    fxn()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
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       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
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       "      <td>no</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
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       "      <td>no</td>\n",
       "      <td>2</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
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      ],
      "text/plain": [
       "              job  marital  education default  balance housing loan  contact  \\\n",
       "age                                                                            \n",
       "58     management  married   tertiary      no     2143     yes   no  unknown   \n",
       "44     technician   single  secondary      no       29     yes   no  unknown   \n",
       "33   entrepreneur  married  secondary      no        2     yes  yes  unknown   \n",
       "47    blue-collar  married    unknown      no     1506     yes   no  unknown   \n",
       "33        unknown   single    unknown      no        1      no   no  unknown   \n",
       "\n",
       "     day month  duration  campaign  pdays  previous poutcome   y  \n",
       "age                                                               \n",
       "58     5   may       261         1     -1         0  unknown  no  \n",
       "44     5   may       151         1     -1         0  unknown  no  \n",
       "33     5   may        76         1     -1         0  unknown  no  \n",
       "47     5   may        92         1     -1         0  unknown  no  \n",
       "33     5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creditcard=pd.read_csv(\"/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/H20/creditcard.csv\")\n",
    "creditcard.head()\n",
    "\n",
    "hrdata=pd.read_excel(\"/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/WA_Fn-UseC_-HR-Employee-Attrition.xlsx\")\n",
    "bankdata2=pd.DataFrame.from_csv(\"/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/Anomalydetection/bank/bank-full.csv\", sep=';')\n",
    "\n",
    "bankdata2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "           ...                 V21           V22           V23           V24  \\\n",
       "count      ...        2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean       ...        1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std        ...        7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min        ...       -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%        ...       -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%        ...       -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%        ...        1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max        ...        2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creditcard.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "           ...           RelationshipSatisfaction StandardHours  \\\n",
       "0          ...                                  1            80   \n",
       "1          ...                                  4            80   \n",
       "2          ...                                  2            80   \n",
       "3          ...                                  3            80   \n",
       "4          ...                                  4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hrdata.head()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>...</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
       "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
       "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
       "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
       "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
       "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
       "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
       "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
       "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
       "std        602.024335                 1.093082    20.329428        0.711561   \n",
       "min          1.000000                 1.000000    30.000000        1.000000   \n",
       "25%        491.250000                 2.000000    48.000000        2.000000   \n",
       "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
       "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
       "max       2068.000000                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel          ...           RelationshipSatisfaction  \\\n",
       "count  1470.000000          ...                        1470.000000   \n",
       "mean      2.063946          ...                           2.712245   \n",
       "std       1.106940          ...                           1.081209   \n",
       "min       1.000000          ...                           1.000000   \n",
       "25%       1.000000          ...                           2.000000   \n",
       "50%       2.000000          ...                           3.000000   \n",
       "75%       3.000000          ...                           4.000000   \n",
       "max       5.000000          ...                           4.000000   \n",
       "\n",
       "       StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
       "count         1470.0       1470.000000        1470.000000   \n",
       "mean            80.0          0.793878          11.279592   \n",
       "std              0.0          0.852077           7.780782   \n",
       "min             80.0          0.000000           0.000000   \n",
       "25%             80.0          0.000000           6.000000   \n",
       "50%             80.0          1.000000          10.000000   \n",
       "75%             80.0          1.000000          15.000000   \n",
       "max             80.0          3.000000          40.000000   \n",
       "\n",
       "       TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
       "count            1470.000000      1470.000000     1470.000000   \n",
       "mean                2.799320         2.761224        7.008163   \n",
       "std                 1.289271         0.706476        6.126525   \n",
       "min                 0.000000         1.000000        0.000000   \n",
       "25%                 2.000000         2.000000        3.000000   \n",
       "50%                 3.000000         3.000000        5.000000   \n",
       "75%                 3.000000         3.000000        9.000000   \n",
       "max                 6.000000         4.000000       40.000000   \n",
       "\n",
       "       YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count         1470.000000              1470.000000           1470.000000  \n",
       "mean             4.229252                 2.187755              4.123129  \n",
       "std              3.623137                 3.222430              3.568136  \n",
       "min              0.000000                 0.000000              0.000000  \n",
       "25%              2.000000                 0.000000              2.000000  \n",
       "50%              3.000000                 1.000000              3.000000  \n",
       "75%              7.000000                 3.000000              7.000000  \n",
       "max             18.000000                15.000000             17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hrdata.describe()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     1233\n",
       "Yes     237\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hrdata['Attrition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SOy4ipgLLgKDoyeeAbdijvUTEkcA64GPADuzPXiLiCYpdywFeAr6GPdojIr4C/ClwCMUp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\nJGEYSJIwDCRJGAaSJAwDSRLw/wniXS/LIgauAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11eab3b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib inline\n",
    "hrdata['Age'].plot(kind=\"hist\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1297ef160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(hrdata['DailyRate'])\n",
    "# suppresses message\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uZ7GPPvpojj766K7/5syZ0zXzXnvtlbvuuitDhw7NxIkTc9VVV2Xu3LkZOXJkpkyZ0nW/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1298227b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.boxplot(x=hrdata['DistanceFromHome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hrdata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%matplotlib inline\n",
    "creditcard['Class'].plot(kind=\"hist\")\n",
    "plt.title(\"Fraud class histogram\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=creditcard['Class']\n",
    "sns.distplot(x, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_classes = pd.value_counts(creditcard['Class'], sort = True).sort_index()\n",
    "count_classes.plot(kind = 'bar')\n",
    "plt.title(\"Fraud class histogram\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Split dataframe into training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(hrdata, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1029, 35)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(441, 35)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alternatively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#split dataframe into train and test, 70% and 30% respectively\n",
    "\n",
    "index = np.random.rand(len(hrdata)) < 0.7\n",
    "\n",
    "train2 = hrdata[index]\n",
    "\n",
    "test = hrdata[~index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1003, 35)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train3, validate, test3 = np.split(hrdata.sample(frac=1), [int(.6*len(hrdata)), int(.8*len(hrdata))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(882, 35)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train3.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(294, 35)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validate.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(294, 35)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### create labels from split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_label=train['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_label=test['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train=train.loc[:, train.columns != 'Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test=test.loc[:, test.columns != 'Attrition']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alternatively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.drop('Attrition', axis=1).head()\n",
    "\n",
    "\n",
    "X = train.drop('Attrition', axis=1)\n",
    "y = train.drop.loc[:, 'Attrition'] \n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Autosklearn\n",
    "auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "from sklearn.cross_validation import train_test_split  \n",
    "from tpot import TPOTClassifier  \n",
    " \n",
    "    \n",
    "pipeline_optimizer = TPOTClassifier()\n",
    "    \n",
    "\n",
    "#hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\t', compression='gzip')  \n",
    "  \n",
    "X = hrdata.drop('class', axis=1).values  \n",
    "y = hrdata.loc[:, 'class'].values  \n",
    "  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,  \n",
    "                                                    train_size=0.75,  \n",
    "                                                    test_size=0.25)  \n",
    "  \n",
    "my_tpot = TPOTClassifier(generations=10)  \n",
    "my_tpot.fit(X_train, y_train)  \n",
    "  \n",
    "print(my_tpot.score(X_test, y_test)) \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#X = dataset.drop('class', axis=1)\n",
    "#Y = dataset['class'] \n",
    "#seed =148\n",
    "#validation_size = 0.20\n",
    "\n",
    "#X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state=seed)\n",
    "\n",
    "#X_train.head()\n",
    "#X_validation.head(5)\n",
    "\n",
    "#np.unique(Y)\n",
    "\n",
    "#Y.value_counts()\n",
    "\n",
    "\n",
    "hrdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_optimizer = TPOTClassifier(generations=5, population_size=20, cv=5,\n",
    "                                    random_state=42, verbosity=2)\n",
    "\n",
    "\n",
    "pipeline_optimizer.fit(X_train, train_label)\n",
    "\n",
    "print(pipeline_optimizer.score(X_test, test_label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can tell TPOT to export the corresponding Python code for the optimized pipeline to a text file with the export function:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_optimizer.export('/Users/nanaakwasiabayieboateng/PythonMachineLearning/tpot_exported_pipeline.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hrdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import autosklearn.classification\n",
    "import sklearn.cross_validation\n",
    "import sklearn.datasets\n",
    "import sklearn.metrics\n",
    "\n",
    "digits = sklearn.datasets.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(X, y, random_state=1)\n",
    "\n",
    "automl = autosklearn.classification.AutoSklearnClassifier()\n",
    "automl.fit(X_train, y_train)\n",
    "y_hat = automl.predict(X_test)\n",
    "\n",
    "print(\"Accuracy score\", sklearn.metrics.accuracy_score(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "from sklearn.ensemble import RandomForestClassifier  \n",
    "from sklearn.cross_validation import cross_val_score  \n",
    "  \n",
    "mnist_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/mnist.csv.gz', sep='\\t', compression='gzip')  \n",
    "  \n",
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=10, n_jobs=-1),  \n",
    "                            X=mnist_data.drop('class', axis=1).values,  \n",
    "                            y=mnist_data.loc[:, 'class'].values,  \n",
    "                            cv=10)  \n",
    "  \n",
    "print(cv_scores) \n",
    "\n",
    "print(np.mean(cv_scores))  \n",
    "\n",
    "\n",
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),  \n",
    "                            X=mnist_data.drop('class', axis=1).values,  \n",
    "                            y=mnist_data.loc[:, 'class'].values,  \n",
    "                            cv=10)  \n",
    "  \n",
    "print(cv_scores) \n",
    "\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "from sklearn.ensemble import RandomForestClassifier  \n",
    "from sklearn.linear_model import LogisticRegression  \n",
    "from sklearn.cross_validation import cross_val_score  \n",
    "  \n",
    "hill_valley_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_without_noise.csv.gz', sep='\\t', compression='gzip')  \n",
    "  \n",
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),  \n",
    "                            X=hill_valley_data.drop('class', axis=1).values,  \n",
    "                            y=hill_valley_data.loc[:, 'class'].values,  \n",
    "                            cv=10)  \n",
    "  \n",
    "print(cv_scores)  \n",
    "\n",
    "print(np.mean(cv_scores)) \n",
    "\n",
    "cv_scores = cross_val_score(LogisticRegression(),  \n",
    "                            X=hill_valley_data.drop('class', axis=1).values,  \n",
    "                            y=hill_valley_data.loc[:, 'class'].values,  \n",
    "                            cv=10)  \n",
    "  \n",
    "print(cv_scores) \n",
    "\n",
    "print(np.mean(cv_scores)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "from sklearn.cross_validation import train_test_split  \n",
    "from tpot import TPOTClassifier  \n",
    "  \n",
    "hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\t', compression='gzip')  \n",
    "  \n",
    "X = hill_valley_noisy_data.drop('class', axis=1).values  \n",
    "y = hill_valley_noisy_data.loc[:, 'class'].values  \n",
    "  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,  \n",
    "                                                    train_size=0.75,  \n",
    "                                                    test_size=0.25)  \n",
    "  \n",
    "my_tpot = TPOTClassifier(generations=10)  \n",
    "my_tpot.fit(X_train, y_train)  \n",
    "  \n",
    "print(my_tpot.score(X_test, y_test))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AML  in TPOT package\n",
    "\n",
    "We will have a look at some very basic examples in the TPOT package. The most basic example is assigning and fitting a simple classifier or regressor in TPOT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tpot import TPOTClassifier, TPOTRegressor\n",
    " \n",
    "# create instance \n",
    "tpot = TPOTClassifier()\n",
    "# fit instance\n",
    "tpot.fit(X_train, y_train)\n",
    " \n",
    "# create instance\n",
    "tpot = TPOTRegressor()\n",
    "# fit instance\n",
    "tpot.fit(X_train, y_train)\n",
    " \n",
    "# evaluate performance on test data\n",
    "tpot.score(X_test, y_test)\n",
    " \n",
    "# export the script used to create the best model\n",
    "tpot.export('tpot_exported_pipeline.py')"
   ]
  }
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