Project 29 Bank Marketing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data preprocessing\n",
    "import pandas as pd\n",
    "\n",
    "#Linear Algebra\n",
    "import numpy as np\n",
    "\n",
    "#Data Visualization\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "#Algorithms\n",
    "#data preprocessing\n",
    "import pandas as pd\n",
    "\n",
    "#Linear Algebra\n",
    "import numpy as np\n",
    "\n",
    "#Data Visualization\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "#Algorithms\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import SVR\n",
    "from sklearn import linear_model\n",
    "from sklearn import model_selection \n",
    "from sklearn.linear_model import LogisticRegression,SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score,roc_curve,roc_auc_score\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV\n",
    "from sklearn import metrics  \n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank=pd.read_csv('bank-full.csv',delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\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",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attribute Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* age (numeric)\n",
    "* job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "* marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "* education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "* default: has credit in default? (categorical: 'no','yes','unknown')\n",
    "* housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "* loan: has personal loan? (categorical: 'no','yes','unknown')\n",
    "# related with the last contact of the current campaign:\n",
    "* contact: contact communication type (categorical: 'cellular','telephone')\n",
    "* month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "* day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
    "* duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "# other attributes:\n",
    "* campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "* pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "* previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "* poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "# social and economic context attributes\n",
    "* emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
    "* cons.price.idx: consumer price index - monthly indicator (numeric)\n",
    "* cons.conf.idx: consumer confidence index - monthly indicator (numeric)\n",
    "* euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
    "* nr.employed: number of employees - quarterly indicator (numeric)\n",
    "\n",
    "Output variable (desired target):\n",
    "* y - has the client subscribed a term deposit? (binary: 'yes','no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      "age          45211 non-null int64\n",
      "job          45211 non-null object\n",
      "marital      45211 non-null object\n",
      "education    45211 non-null object\n",
      "default      45211 non-null object\n",
      "balance      45211 non-null int64\n",
      "housing      45211 non-null object\n",
      "loan         45211 non-null object\n",
      "contact      45211 non-null object\n",
      "day          45211 non-null int64\n",
      "month        45211 non-null object\n",
      "duration     45211 non-null int64\n",
      "campaign     45211 non-null int64\n",
      "pdays        45211 non-null int64\n",
      "previous     45211 non-null int64\n",
      "poutcome     45211 non-null object\n",
      "y            45211 non-null object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "bank.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From here we can observe that previous,pday,campaign,duration,day,balance,age all these features are in integer format.\n",
    "* Other than these features all other features are in object format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From here we can observe that minimum age is 18 and maximum age is 95.\n",
    "* Minimum balance is -8019 and maximum balance is 102127\n",
    "* In Duration,balance can observe alot of difference present in this data ,so there is a outliers ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that there is no null data present in this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "may    13766\n",
       "jul     6895\n",
       "aug     6247\n",
       "jun     5341\n",
       "nov     3970\n",
       "apr     2932\n",
       "feb     2649\n",
       "jan     1403\n",
       "oct      738\n",
       "sep      579\n",
       "mar      477\n",
       "dec      214\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning month column\n",
    "bank['month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mn={'jan':0,'feb':1,'mar':2,'apr':3,'may':4,'jun':5,'jul':6,'aug':7,'sep':8,'oct':9,'nov':10,'dec':11}\n",
    "bank['month']=bank['month'].map(mn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     13766\n",
       "6      6895\n",
       "7      6247\n",
       "5      5341\n",
       "10     3970\n",
       "3      2932\n",
       "1      2649\n",
       "0      1403\n",
       "9       738\n",
       "8       579\n",
       "2       477\n",
       "11      214\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check data once again\n",
    "bank['month'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can observe one thing data had been chaged to numerical format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18df8540288>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='y',data=bank)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can observe that  client has not subscribed for credit deposit for no is more compared to subscription."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18df8ec7cc8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank['loan'].value_counts().plot.pie(autopct=\"%2.2f%%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In most cases in this dataset 84% of peoples don't have any loans and only 16% of people's have loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18df66c6108>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9,\n",
    "                      wspace=0.5, hspace=0.2)\n",
    "sns.barplot(x=\"age\", y=\"duration\", data=bank)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here you can observe that whose age is 89 duration is 2000 \n",
    "  * If duration is more then chances of credit deposit is very less in the sesnse there is more chances to say no.\n",
    "whose age is in between (18 to 50) dration is very less it's 250 chances of credit deposit is more chances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18df93f41c8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='balance',y='duration',hue='y',data =bank)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from here we can observe onething  i.e balance is in between 0-20000 and duration is in between 100-2000 then  the credit deposit is going to yes.m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check cor-relation\n",
    "corr_hmap=bank.corr()\n",
    "plt.figure(figsize=(8,7))\n",
    "sns.heatmap(corr_hmap,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Month  and day is complete highly negatively cor-relate with pdays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "list=['job','marital','education','default','housing','loan','contact','poutcome','y']\n",
    "for val in list:\n",
    "    bank[val]=le.fit_transform(bank[val].astype(str))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting and Removal Of Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age             AxesSubplot(0.125,0.657941;0.0824468x0.222059)\n",
       "job          AxesSubplot(0.223936,0.657941;0.0824468x0.222059)\n",
       "marital      AxesSubplot(0.322872,0.657941;0.0824468x0.222059)\n",
       "education    AxesSubplot(0.421809,0.657941;0.0824468x0.222059)\n",
       "default      AxesSubplot(0.520745,0.657941;0.0824468x0.222059)\n",
       "balance      AxesSubplot(0.619681,0.657941;0.0824468x0.222059)\n",
       "housing      AxesSubplot(0.718617,0.657941;0.0824468x0.222059)\n",
       "loan         AxesSubplot(0.817553,0.657941;0.0824468x0.222059)\n",
       "contact         AxesSubplot(0.125,0.391471;0.0824468x0.222059)\n",
       "day          AxesSubplot(0.223936,0.391471;0.0824468x0.222059)\n",
       "month        AxesSubplot(0.322872,0.391471;0.0824468x0.222059)\n",
       "duration     AxesSubplot(0.421809,0.391471;0.0824468x0.222059)\n",
       "campaign     AxesSubplot(0.520745,0.391471;0.0824468x0.222059)\n",
       "pdays        AxesSubplot(0.619681,0.391471;0.0824468x0.222059)\n",
       "previous     AxesSubplot(0.718617,0.391471;0.0824468x0.222059)\n",
       "poutcome     AxesSubplot(0.817553,0.391471;0.0824468x0.222059)\n",
       "y                  AxesSubplot(0.125,0.125;0.0824468x0.222059)\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.plot(kind='box',subplots=True,layout=(3,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe dataset contains outliers and it's better to drop those outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.60696496, 0.10381968, 0.27576178, ..., 0.25194037, 0.44489814,\n",
       "        0.36398261],\n",
       "       [0.28852927, 1.42400783, 1.3683719 , ..., 0.25194037, 0.44489814,\n",
       "        0.36398261],\n",
       "       [0.74738448, 0.71495069, 0.27576178, ..., 0.25194037, 0.44489814,\n",
       "        0.36398261],\n",
       "       ...,\n",
       "       [2.92540065, 0.20174582, 0.27576178, ..., 1.05047333, 0.56617504,\n",
       "        2.74738398],\n",
       "       [1.51279098, 1.02051619, 0.27576178, ..., 0.25194037, 0.44489814,\n",
       "        0.36398261],\n",
       "       [0.37068857, 0.71495069, 0.27576178, ..., 4.52357654, 1.57724822,\n",
       "        0.36398261]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing Outliers\n",
    "from scipy.stats import zscore\n",
    "z=np.abs(zscore(bank))\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    7,    34,    37, ..., 45204, 45208, 45210], dtype=int64),\n",
       " array([ 4,  5, 11, ..., 14, 11, 14], dtype=int64))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold=3\n",
    "np.where((z>3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.3806246098756345"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[7][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.045498739065863"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[34][5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_new=bank[(z<3).all(axis=1)]#removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45211, 17)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39456, 17)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5755 rows had been removed as an outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Distribution Of Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   58    4        1          2        0     2143        1     0        2   \n",
       "1   44    9        2          1        0       29        1     0        2   \n",
       "2   33    2        1          1        0        2        1     1        2   \n",
       "3   47    1        1          3        0     1506        1     0        2   \n",
       "4   33   11        2          3        0        1        0     0        2   \n",
       "\n",
       "   day  month  duration  campaign  pdays  previous  poutcome  \n",
       "0    5      4       261         1     -1         0         3  \n",
       "1    5      4       151         1     -1         0         3  \n",
       "2    5      4        76         1     -1         0         3  \n",
       "3    5      4        92         1     -1         0         3  \n",
       "4    5      4       198         1     -1         0         3  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=bank_new.iloc[:,:-1]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: y, dtype: int32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=bank_new.iloc[:,-1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18df93ee688>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x['duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "collist=x.columns\n",
    "ncol=16\n",
    "nrows=14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D0Nc4BBbdoTNlTaPdJYVCofAqbFNzFTmkUCgUilHFaLZEDul1GhU5pBjTmP1IkHr79u1Mnz6d9PR0nnmmb7Xfjo4O7rzzTtLT01myZAmFhYUAFBYWEhwcDDBTCHFcCPHSaPXZk4aijRs3UlZWhsFgoKSkhLVr1/LAAw/YhWKFELz44osAp6WUc6SUhz3WWR+nzh451FdGNSkymNL6NhqsBiSFQqHwRwbj7HLJOCSEuFEIkSuEyBdCPO5gv14I8YZ1/0EhRKp1e6oQos06YRjVSYNCoVAoRh+bIHVIoJbmdmUcUoxdzIMIwx7LmEwmHnroIbZt20ZOTg4bN24kJyenR5v169cTHR1Nfn4+jz76KI899ph935QpUwBypJSZUsoHGAXcYRjacLBoSN8z1OMUI4MtrSxU7yByKCIIQKWWKRQKv8atmkNCCC3wInATMBP4mhBiZq9ma4E6KWU68Hvg1932nbdOGEZt0qBQ+DBzhBCnrMZW5YlUeB22tLLgQC1tBlOPNDOFYizRFYbt28ahrKws0tPTSUtLIzAwkDVr1rBly5YebbZs2cI999wDwOrVq9m9e7ezilxehTLi+D61Lba0MkeRQxbjkBKlVigU/ozdOIR7IocWA/lSygtSyk5gE3Brrza3Aq9a/94MXCd8fTalUHiOZVZjqyrnqfA6bILUIQGWibpKLVOMVcxm/0grKy0tZcKECfbPKSkplJaWOm2j0+mIjIykpqYGgIKCArCklX0khPjCaPV7uLQbTORXNisD9hinzokgNUB4kI6QQC3vnRxTWuCpQohKIcRpT3dEoVD4BnIQ1VddMQ4lA93rcZZYtzlsI6U0Ag1ArHXfZCHEsf4mDUKI+4UQh4UQh6uqqlzokvtR3iWFQjEaDFXbA2DdunWkp6czffp0duzYMeB33nvvvWCJNrOl9maO3C+z0D1yCKBFGYcUYxR/SStzFAHU27/nrE1SUhJFRUUAOcAPgA1CiAhH5/GGuZ6N+tZOXtybz98PFPD2sRKP9kUxPOpaOgnQCgK0fZc0QghSooMpqm31QM+GTDVwo6c7oVAofAe7ccgF65ArxiFH39J7luCsTRkwUUo5n34mDVLKl6WUi6SUi+Lj413okkLh1+wUQhwRQtzvaKc3TcAVPTEPQ9sjJyeHTZs2kZ2dzfbt23nwwQcxmUyu6IWUdEvtPT7Sv9GmOWQzDqnIIcVYxTyIydRYJiUlheLiLh9gSUkJ48ePd9rGaDTS0NBATEwMer2e2FiLL1BKeQQ4D0xzdJ6RmOsNxbFnMku+u/EY9W0GJsaEcKyonqb2wQsW+4tT0d0OjeLiYpYtWwYwSwiRLYT4/nD6V9dqINRBSpmNSbGhVDZ12LWJxgDNQK2nO6FQKHyHwRTYcMU4VAJM6PY5BbjkrI0QQgdEArVSyg4pZQ0MPGlQKBQucVZKuQCLBthDQoirezfwZmOrv0ymnXE+5/iQtT22bNnCmjVr0Ov1TJ48mfT0dLKyslzSCxlNbNXKQgKUcUgxtpF+Uq3ssssuIy8vj4KCAjo7O9m0aROrVq3q0WbVqlW8+qpFPWDz5s0sX74cIQRVVVWYTCYAhBBpwFTgwij/hEGx52wFH+dVc+u88dyWmYwEcstVuXNHDEes3JlDQ6fT8bvf/Q4gG1iKZS7TW8vUZepaOwlxIEZtIzU2FIDDhXVDPYVCoVCMacz26F/3RA4dAqYKISYLIQKBNcDWXm22AvdY/14N7JFSSiFEvFXQesxMGhQKL8cAIKWsBN7GogmmGCPUVpUPWdvDmS6IC3ohyUKIk0KI3wsh9I765c5oM2OvtDJvrlgmhChUAu8KZ5gHUfp1LKPT6XjhhRe44YYbyMjI4I477mDWrFk8+eSTbN1qme6tXbuWmpoa0tPTee655+wRJPv372fu3LlgKViyGXhASulVUQ/dnRJlDW18dK6K2xeksCg1hsQIPSGBWi7WjKm0o1FjOGLlzhwaSUlJLFiwAAApZRNwhr5yFS5T29LpUIzaRkp0MFohOHTRqy7LYaEixBUKxWCwmYbcEjlk1RB6GNiB5QH+LyllthDiaSGEzbW0HogVQuRjSR+zlbu/GjgphDjBKE4a2jpN/OqDM/zy/RwahxAqPBZRpVV9n5aWFrDes0KIUOB6QAkWjiUc6J66qu0x2O1gCenHco1cBsQAj/VpjHujzYz2UvaWyfoY0BzyS4F3W2pHRkYGs2bN4vnnnwegtrYWLA6hPCHELiFEtEc76kEGE4Y91lm5ciXnzp3j/PnzPPHEEwA8/fTT9giioKAg3nzzTfLz88nKyiItLQ2A22+/nezsbLCUsl8gpXzX3X0bzPyme1tHx3ycV02AVsNPb84AujRpLjW0ua/DPsRwxMpdOVYIkQrMBw4OtY/1rZ0OxahtBGg1JEcHc6jAd4xD3hwhrlAovA85CA1F56b2nl/4AfBBr21Pdvu7Hfiqg+P+DfzblXO4k2d3nOUfBwoB2H22kje/fTmxYQ4d5grvZY4Q4hRgAoz+tnBzREVFBcAMq7FVB2yQUm73bK8UgyEmYRxnP3JN2yMlJaWHtkd/uiDOticlJQEgpewQQvwD+OFI/TYbJltamXWy3uT9xiG/xJbasWDBApqamli4cCErVqzglVdeAWiSUk4VQjyOxdnj0Kjo69hLv/p45NBYodNoprCmheIhigv/5aPznCptYPHkGKJCAu3bEyOCuFBVg9FkRudA1Nhf2HCwiK8vmdhj23DEyl04VoNljfCIlNJhrXmrtuL9ABMnTnTUhNqWTsZFBjvcZyM1NoQD+TW0G0wEBTg3JCkUCoUvYp2au2Qc8rm3YFuniTcOFbN6YQobv7WU0ro2Hnz9qMOXlMLr8UuPvjOsntocKeU8KeUsKeUvPd0nxeBIy5g3ZG2PVatWsWnTJjo6OigoKCAvL4/Fixf3qxdSVmYp3yssM/LbGIVIM5sgdajVONTY5tXRm5J+BN59OXS/e2pHeHg4GRkZlJaW2lJGaqzNXsVy3fglg5lMKUaW1g4jL+7N55VPC7n6N3vZkV3eZ143UHTR4Yt1mMySpZNje2xPDA/CaJZcHFsVrUaF4YiV93eswWAAmAK8LqV8y9n5B4qQMZrMNLYb7e8bZ6TGhmKSkhPF9QP9ZG9gMvAZMF0IUSKEWOvpDikUirFNl7Nr4LY+Zxz67EI1rZ0mbstM5vIpsfx81SwOFtTy1tHSgQ9WKBSKEUQ7DG2PWbNmcccddzBz5kxuvPFGXnzxRbRarVO9EIC77roLLFogp4A44H9G+jfaBakDdWiFoKbFqyvEXNmfwLu/hO4XFhZy7NgxlixZYotQtGmblQEJHu3cKOAsxa57Wtm+ffsAMq36VMeFEE86/ULFsOlt6NmeXU5tSyd3LprA7QtS+OhcFccHsdA3mMxkFdQyNSGM+PCekeQJEZbPeRVKlLo3wxErd+bQkFKydu1agHYp5XPD6V+91fkQou8/EWJibAgAhwrHRGpZgZQySUoZIKVMkVKuH8zBUkrlEFcoFD2wy1G7YBxyKa1sLHGwoJYAreB8VTNFta2suWwCG7KK+P2H51iVOZ4APw4ZHoPsFEJI4C9Sype773Al1Fih8EZWrlzJypUre2x7+umn7X/btD0c8cQTT9j1QAb6ToA9e/YghMgZzeg7m+aQViMI0WupbfZe45CU8pL130ohhE3gfb9nezW6NDc3c/vtt/OHP/yBiIgIl4/zpWewsxS7oHjL79J2iQ41SykzPdbRUcRmnOmdZuQJ2jpNHCuuZ1FqNPMmRLHmsgkcvFDDrjMVzJsQ5dJ3vH20lIY2A7dlju+zLyE8CIBzFc3cONutXR/T2NLMbM4Hk8nEfffdZ3doLFq0iFWrVrF27Vr+8z//k/T0dGJiYti0aRPQ06Gh0+nsDo1PPvmE1157DSBcCHHcerr/tkpYDApbefr+NIcs+3UkhOvJ8oOKZa9+WshT7+ZwS+Z4IoICPN0dhWLEKS4u5u6776a8vByNRsP999/P97//fZ566imAucN9zvgCg9Ec8jlLSc6lRqaPC7cbgTYdKub7102lpK6NLccvufVc27dvZ/r06aSnp9u9+9157rnnmDlzJnPnzuW6667j4sWL9n1arRZgptUD2bv6m2KAku2+6NFXguL905+A7ooVKwBm+7uArjdgq1am0QhCA3XUtnqtcUgjhAgH/xV4NxgM3H777dx111185StfASAxMREgAEAIkQRUOjrWl57BzlLsbNXKlOaQZzlT1ojJLFkw0fJo12gEV6THUd9q4EJVi9PjzFJS1dRBQXULz+/OIzkqmGmJ4X3aBeo0RIcEkF/ZPGBfKhvb2ZlTzqV6/xGwHqpYOVgcGufPnyc3N5ebbroJgKuuusq2UMmxSgdkDnXBVttiiRwK7adamY3JcaEcKazFaDIP5VRjhpf3W4pCe+M1OtC6qaOjgzvvvJP09HSWLFlCYWEhYIluDQ4Ohq5100uj2nEP4Wze2x0V1drl4Dlz5gyff/45L774Ijk5ObbdFcN9zvgCXfOZgdv6lHFISsnp0gZmj4/ssb28oZ2MpAj+tC8fk9k9oZYmk4mHHnqIbdu2kZOTw8aNG7tfiADMnz+fw4cPc/LkSVavXs2Pf/xj+z7rQ872YuwZo6sAVbJd0QtnD/9nnnmG6667DiwL+910VUtUeADbxFsjsEQOeW9amQ74xCrwngW8708C77bUjoyMDH7wgx/Yt1sXfDZRlnuALQ4O91m6p9jJvtXKwoQQJ4QQ24QQszzVR3/jZGk90SEBpER3iQ7PTIogKEDDsSLHkSB7z1bymx25/P7Dc/z14wvUtnRyy7zxTg198eF6zlf1bxySUvLg60fZl1vF3w8U0Gn0bSPDWKDOxcghgLT4MFo6TZy+5FD72mfotEbvVjR2eLgnPXFl3bR+/Xqio6PJz8/n0Ucf5bHHumohTJkyBbrWTQ+Mbu89wwBGj+40dzOAPO2ogS/jzMGj6MLsr5FDlxraqWs10Npp6rFdCMHDy9K5UNXCttNlbjlXVlYW6enppKWlERgYyJo1a2xCnnaWLVtGSIglz3np0qWUlJS45dzewEhGuaiS7QpH9Cege88999ia+bWArjdgixzSCmvkkPcahzqt4u5+KfB+4MABXnvtNfbs2UNmZiaZmZl88MEHPP744wARQog8YAXQ173ro/ROsbP5kjRC2J49J6WU84D/Bd5x9B2+LGLuCWpbOsmvbGZuSlQPw06AVkPGuAjOljf1iQQ5U9bI2lcPERyg5fYFydy9dBJ7fngNE2NCnJ4nPkzPhaoWzP04EI8W1XH4Yh1zkiNp7TTx/gcf9BsFYa38N6+bR/+bg/39iv6pa3HdOJRq1R36/ELNAC3HNgbr/VDe4F2RQ66sm7rP51avXs3u3bv9Wj9JGT0GT3cHj5UEIcRJIcTf/TmzQNrnMwO39Snj0OnSBgDGR/UtaVnX2smU+FBe2JPvlgdNaWkpEyZMsH9OSUnp94Zdv369PaQWoL29HSBDCPG5EEItZrvRq2S733n0FQPTW0C3W8l2vxDQ9WZsmkMajSBUr6Om2bu8lwoLttSOkydPcvz4cY4fP87KlSuJjY0FOCelnCqlvE5KOSYUXIeLoxS77oLUVj0mM4A1ND1ACBHX+3t8Kd3OG9h2ugyzhLkpkX32ZSRF0GYwcfhiV/RQY5uBfx0uZnZyJA9cM4WFk2KYkRRB0gClzuPDg2gzmChrbHe4f8PBIn638xyBOg1fnp9MiA4+ePmX/UZBWKnr5tH/22B++0jjC6nstfbIoYHTysKDAkhPCPN545BNVsPbIodcWTd1b6PT6YiMjKSmxvL/VVBQAJa0so+EEF8YrX57Cw6MHt0ZMKrVHxwXvR083/nOd8BSkCUTKAN+5+g4fxibrmplfhY5lH2pEY2AcRFBffZphODBa9M5W97Eh2ccSigMCkcGJmcD/n//938cPnyYH/3oR/ZtRUVFAGeArwN/EEJMcXSsP1ywvVEl2xX9MRwBXX+7lzyBPXJIIwjVa2lsN9o9mQqFN+Isxc6Whi6EoLy83L5dCLEYy/xpWKtMk1nyr0PFVDY5Nkj4OxsOFvHuiUvEhekdzuumJoSh1Qh2n9YqGuYAACAASURBVKmwb9ueXY7JLPnfr80nUOf6FDcuPBCA8050h6SUZF9q5OqpcQQFaIluLUYbmdRvFIRi5KlvNRCgFS7/X8eGBnKooNan30mdRkv2RFO7wcM96Ykr6yZnbZKSkmzrphzgB8AGIYTDCaAvzvX6m/e6GtXq646LfjQUkVKagb/iRKLE18cGuiKHXFFQ9Cnj0OnSBqbEhzl9SazKHE9qbAi/eC+H5g7jkM+z4WARKSkpFBcX27eVlJQwfnzfKhgffvghv/zlL9m6dSt6fVf5VFtbKeUFYB8w39G5/OGCVShcxdnDv6zMki7qLwK63kyX5pCwi4TWeW9qmULhNMXujX/+naZjH6ARgs2bNwPMska0/hFYI4cZhvyvw8X8+N8neemj8z69WB0qjW0GDhbUMjcl0qHzTR+gJS0ulF05FUgpqWnu4ERxPVdMiWVSbOigzhUfZpmfOdId2nCwiNL6NhraDNw42xKlGmxoQIbG0mAtpd5P9HiUNaVhsxBigqMGiqFT29LpUtSQjakJ4bR0mjh4wTcDIqWUtFilNZo7TAO0Hl1cWTd1b2M0GmloaCAmJga9Xm+LakVKeQQ4D0xzdB5fm+s5mvd2x9WoVl/GmYPHtjaw8mX8WKJE4qeaQ6dKG5iT3Df02Mabh0tYMXMcJXWt/OJdh+G/LnPZZZeRl5dHQUEBnZ2dbNq0yV65wcaxY8f49re/zdatW0lI6Mp0qauro6PDEu5pvYGvxGINdxtmP87RVfgm/Qnovvrqq7aPfieg620YzF2pOKF6y6TdiyuW+Q2nShv4OM83vKjuxlmK3er/+Abh81ei1cDDDz8MkG2NaF0qpfx0uOfdfMSiQ9huMLtUKcvfOH2pASlhbj/zuhlJERTWtHK+qoVP8qvtlcwGS5heR0SQzqkotS0y/YsZlrlcdIilRPiZsi5x494GrFtuuQXglJRyLvAhFk28PvhipMNoUdvSSah+YL0hG1MTwwgO0LI92z36o95Gh9Fsj3hsGYYTfCRwZd3UfT63efNmli9fjhCCqqoqTCaLsUsIkQZMBS6M8k8YdZzNe7szElGtYw1nDh5rIaiZQoiTwDLgUc/21HOYrf4nvzIOVTS2U9XUwRwHeendmRwXygPXTOGNw8X861Bxv237Q6fT8cILL3DDDTeQkZHBHXfcwaxZs3jyySfZutVSmf5HP/oRzc3NfPWrXyUzM9P+EDxz5gyLFi0CmAnsBZ6RUrrNOLQzu5xffXBmwMobCsVYoj8B3V27dgHMxs8EdL0Rk9mMRlgWSmFW41BVk3dpH/gbZ8oa2ZhVxH+uzyL7UoOnuzNmGEyO/mBp7TRytKiOb1+TRqBWQ54yDvUh+1Ij0xLDSHCQUmYjY5ylNP3GrCKOXKxj/oQoIoICBn0uIQRTEsI4X9nSZ58lpayBtLgwokIs6WdpkyZgbKwix1r5ylEUhDXSweap+yuw0NG5fS3SYTSpae6wv2dcIUCrYdmMeLadKqfD6F2RNe6ge1aEtxmHXFk3rV27lpqaGtLT03nuuefsQu/79+9n7ty5YFk3bQYe8Ac9PGfz3pdeeomXXnoJYESiWscazhw8r732GlhkSuZKKVdZdUn9kq75zMBtXX+iejknSywT3jnJkZyr6H+SNS4yiC9MjeOxt07ySX4108eF09Q2+NzclStXsnLlyh7bnn66q4Lghx9+6PC4K664glOnTiGEyJFSLhr0ifuhpcPIvnMWz9M/Py3k57fOHvCYD89UcKK4nivTBx+KrVCMFraHvyN2796NEOK0lPK6Ue6WohdGk7R7JiKDLYu0snqlqeJJDhbUEKjTYDZLNmUV84vb+neiKCx0r1bmbs5VNCMlzJ8QzY6oCkrrWt1+jrFMc4eRwuoWvrs8vd92USGBzEyKYP0nBWgEXDNt6MaVaQnhbD1xidc/v8hdSyfZt1c2dVDd3MkVU7oikmbPW4ipvozPTpzhPxYns2nTJjZs2NDj+3qlNKzCojOpcCM1LZ32lEBXSQgPoqalk+2ny7k1M3mEeuYZuhuEbIai8oZ2frczl3GRepbPSPRU14CB101BQUG8+eabfY67/fbbuf3220dk3eTN9DfvtfHwww/z3e9+N9ufxkUxeOyaQ/5UrexUST0aATPHDyxQq9NouG5GIvdekcr+vCp+syOXl/Zf4C8fnR+Fno4sB/KrAYsY7CfWv/ujrdPE3rOV1LR0siFr7FeuGC1U2p5C4RijWaK11sqMCNYhgJJ67yqp60+0G0ycr2rhsknRLJuRwH6VWuYyslu1MneTW26JOMlICmd8VBDlje32dBAFnLnUiAS7xk9/PH3rLBZNiua2zGRiB2ko6M7cCZG0GUzUtfZ0FmZbo4NmJnXNL7U6HRlf+T4bn/620yiIP/7xj9Dl0f8ecO+QOzdMNmYV8dpnhV4nUjxcapo77enLrpKeEEZcWCAv77/gc2XSe0QOdVr+PlPeSE1LJ9/feLxHW1+oVqdQKFzDLzWHPsmvZnZypMvCdIE6DVMTwjn+5PWcfOp65iRHsm7bWf5xoGCEezqy7M2tRK/T8IWpcVyobqGts/+w2RMl9faY5899VKDP3RwqrOXJLadZt005ARWK3hhNZvvLR6fREB6ko7TOP4xDeRVNHL1Y51WL/KNFlv5MSQhjcWoMF2taqXRSrlvRk5GMHDpT1kRIoJYJ0SGMjwzGYJIUVPdNafJXzlY0ER0SQEZS+IBtF6XGsPk7V7AoNWZY55yXEgVASbcortc/v8jp0gYmxoQQEdwzXW3G4mtI+87fyM/P54knngAsURA2CYF169ZBl07VMinl2WF1cBj899unOFPexGfnR1aKZDQNDq2dRtoMpkGllYHlfr52egLZlxrZfrp84APGEC1WEeqQQK3979pmi+af0YveSwqFYnQZzHzGJ4xD9a2dHC+u59pp8YN+MW04WMR7J8q4Y9EErp+ZyM/fzWHdtjNj0rsipWRfbhVT4sMYHxmMlAwocnm8uB6ABROjOVPW6JaKKZWN7V6X6+xOXv/8ImYJr35aSKdx7FaYUV4jxUhgMEs03UItYkL1FNb4/qK3urmDr/z5UzYfLWFnjvcsOE4UW1KuJ8aEsDA1GoDDF+s82aUxQ1cpe/d/d255E1MTw9FoBElRFk0dpQdlQUpJcW0rqbGhI6L35Izp48LRaQQl3YzZpfVtlDe2M39iVJ/28eF6GtuNVDd7t+B+U7vBnlJwqtR3rrEa67gPNnIIIHNCFPHhen6365xXGfOHS3OHZe0SERRgjyKqtVYLVRHvCoX/MhjNIZ8wDu3Pq8Ys4ZrpCQM3doJWI7gyPY41l03gLx9d4Kpf7+X3u87R0Dp2jERVTR2UNbQzNTGMhAhLaPW5iqZ+j8ktbyIyOICpCWF0GodfMaWx3cD1f9jP73bl+mz56oMFtWiEpcJM90ol/oDRZOZQYS2FysOtcILJJNF2e/kkROjJr2z2ufD93mzKKqKp3ci4iCAOXqj1GgP5qdJ6YkIDCQnUMXt8JHqdhsOFyjjkCl1pZe41UEgpOVveaBdTTggPQisEZ8r6f1/7C5ca2mnuMDIhJmRUzxug1ZASHdyjmMehwjoCtMIeVdQdm9aNtxf/uFDV9b72pap41c2WQgeDqVZmQyMEKzISya9s5p1jpe7umsewla+PCNbZ30E11rm4wWT2KUOYQqFwHb/THNqXW0lUSMCwF+oBWg1zU6LY8tCVXJYaw/O787j2t3vZmFWEeQw8UM9ZX/rTEsKJDdUTqNUMaBzKq2wiIVzP+KhgAE4P06v0/sky6lsNtBvMvHPcd164Niqb2ilraOfytFjAkpbnT7x5pIS3j5Xy32+f8nRXFF5Ku9GETtv1akkM19PQZuBSg2+nMr1/qpxFk6JZOSeJTpN5xNM3XOVkSQPJ1ud7oE7DrPERw37O+wu2177WzaJDVU0d1LUamG41Dmk1gvhw/YDva3/heJHlvZoSHTzq556ZFEFZQztFNa00tBk4WVLPnORIggL6GiDiw8eGcajUqvkWrtf1iIoa69giYgabVmZj1vgI5iRH8tyucwNKMIwVbAah8KAAWjtNmM2SGqsRzSyhrtU3nbYKhaJ/BuPsGvPGIbNZsv9cFV+YGu827172pUaWz0jg4WXppCeE8f/eOsWX/3SAD06V0drpHd7g3hhNZrIKapk1PoLo0EC0GkFafCi5/Uw2zWZJfmUzCeF6YsMCCQnU2oUXh8onedWMiwgiKiSAQ4W+p2F0rtwyCZyRFEFkcABny/1rMr/7TCVgiZ7ylcmUwr20dBjR67peLalxlgqI3mIsGQkqm9o5U9bIdRmJpMaGEKjTsDe30tPdoralk5K6NrtxCGB2ciTZlxrGhMPD05hHSJD6jPW9YTMOgSXCLtfP3ifOOFFSj04jGBfpvIT9SDFrvKWS37snL7H+4wt0GM09qpR1JyI4gOAALecrvTuS9pLVOJQaF0qxD1XFG05aGYAQgqVpsZTWt/HC3jx3ds1j2IxDEUGWMWnpNNqNaECPvxUKhf/gV5pD2ZcaqW7u7LEYcRfjo4K5LTOZP9yZSXljOw++fpSrn91Hca33vVzfO1lGdXMHP1gxzb5tWmI4eRXOPVql9W20G8wkRAShEYIZ48LJGWb01bGiOhZPjiE1NpRDhXU+l0qSX2mZvCeE65mWGEZ+P+Pri5wqrScoQIPJLId9rSh8k+ZexqHEiCBCArV86kL1xLHKQauY/xVTYtFpNUyJD2Nfruergtn0RZKjexqHWjpNFPiBDtRw6crRd691yFapbMa4rupX4yKCKK1vG5N6h+7meFE9SZFB6DSjP0WNDg1kakIYf/jwHC/uO8/s5Eh7ZHVvNMLihPPGyKHumoIldW2EBmoZHxVMfavBZ66x6hZrWpmLhWgcMTkulPkTonh5/wWv/H8cLM3dIofAIlBd3dJpN3Ar45DCVZraDezM9h79RMXwsM9nXGg75o1D+6ze2akJYSPy/UIIWjtNPLxsKq9/cwlmKXn94EWviiD66FwVWYW1XD01jusyEu3bpyWGUVrf1qO0ZXdsIewJ1tDojKQIzpQ18vrnF4ckVlzd3MGlhnbmJEcyMSaEqqYOimt9J4QZIL+qmYggHWF6HekJ4ZyrbPI5A5gzGtoMVDR2kDnBImqbo8RTFQ5o6TCh13WlYGiEYEp8GB/nV/us3sHnF2oI0+uYNd6y2E+PD6W0vs3jjgRb+tj4yG7GIWtkhEotGxg5QtXKzpZb0rljQgPt2xIjLFEy3pRaZpaSilGubGc0mTlV2jDqekPduW1+MvNSorgqPY5b543vt61WIzxiVBjMHK20vo2U6BCiQywGA19JLatp7iRQpyFwmM7hG2ePQ6sRPLU1e8zP51o6jAQFaOxpkM0dRmpbOkgItzxf+jMOqSIliu786M2T3P/aEaUx6iMMZj4z9o1D56pIjgq2W8lHCq1GcLGmla8uTKGx3cibh0tG9HyDYf0nBUQGB/DFmYk9tk9LtISs5zmZbNpSomyT0oykCJrajdS3Dc2rZPNSz06OZFKsZWJ3pMjzqWXufOHlVzaTnhCGEIJpiWHUtxqosuZz+zo2IcupCWGE6XU+JWypcB8tHcY+k/VZ4yOoaurg4AXfTC07WFDLotRou9ZSWrzFWeHpVLpTJQ1Mig0hOLDLWDc1MYxAnYZTJco4NBAjlVZ2tqypR0oZWCKHAHLLveO5ajSbeXn/BZb8aje/2TF6FdjPVTTTZjCREj0445A73/PRIYFs/s4VvHrf4gFTluLD9ZTWt3l1mnVpXRvJ0cFEh1iMkZ42WruLmuYOQgMHL0bdm/CgAFZkJPJxXjUfnBrbkRLNHSbC9Dp79G5Lh5Ha5k67PpaKHFK4iq3KbJvBe59tI0W7wcScn+1g++kyT3fFbTz9Xg4AwgXLz5g2DtW3dnKsqM5uBBkNJsWGkhih572Tl0btnP3R0GrgQH41mROi+oRg28bFmScy51IjE2NC7B6GjCSL17usfmhepdPWxcas5Ah7KolNWNJXOF/VwhTrws82vv6SWtY9pW5KfCj5PhCCrXA/Tb3SysDybNHrNLztQ1VhbFQ3d5Bf2cxSq0g9WO6R2NBAPvewMez0pQZmJ0f22Bag1ZCRFMFpFfk3ILZIN3dGDhlNlqqgtvetjciQAEIDtfaUM09zqLCOotpWpiWG8ad950fNGWAr8jDBA2LUQyE+TI+UUODF3vXSeovuWLQ1Uq3YQeRQXkUTf9qXz1tHvcfxORA1LZ1DFqPuzZK0WMZHBvGTd05xcQyn3LZ0GAntZhyqaemgpdNkzxDw1SrCCoU7uVTfRlOHkXXbRs8xMlr4fOTQx9YS9tMTRyalzBlzkiM5fLFu1MOtHfF5QQ0ms3RoIJsQE0JQgIZ3jl9y6FXLKWu0p0GAxbsfFKDhfFXfF6Pt+JrmDm56/mN+s+Nsnxfo8eJ60uJDiQgKQCMEc5IjOV7sO8ahupZOqpo6mGq93mz/uisNoLalk01ZRbR7qZU+v7IZvU5DdGgg6Qnhg14snKto4s/78tl6wjsMq4qRwRLW3tObG6DVMHt8JNtOl3u1h30oZBVYoiMXT46xb7MJnX52ocZjaQp1VjFqWxpZd2aPjyC7tNHtotSvfVboU2Whn9ySDbhW+tVV8qua6TSZmdErckgjBNPGhfdbRGI0OVxYS3JUMBu+tRSdRvB/n18clfMeL6onOiSgR8qdJ3A1EsnbK5Y1dxhpaDOQHB1MaKCWkEAtJQ5Eqf9+oICSujae3Z6L2Sypb+3kT/vy+YXV2+yN1DR3DlmMujcaIVizeCJmCV996bMxO3dtajcQHqSzp3YX1Vj+ryODAwjT66i1Viv77Y5c/rwvf8yn0SlGHqPJf68RNwcNe4zucz2f1xzal1tFVEgAKaOcmz5rfCRSwp6znq9G89n5GoICNEyI6etl02oE8ydEO8wXbe4wUljTwsxu3sugAC2Xp8X2a+x4fnceZ8oaqWs18Lud5+zbzWbJkaI6Fk2Ktm/LnBhFTlkj7QaTT+Qy2ybt060iovFheiKDAzjnBo9qW6eJv358gcffOsXP3/XOyVhuRTNT4sPQCEF6QhgVjR00DkLY8tfbzlJc18YTb5/yOQOBwoLZLGntNDnUgFgwKZrmDuOoLTJHi4MXaggO0DKnV4TO0rQYS0lsD6Vw2CKDevfLtq2pw+j2vp0pb+KRN4679Ts9SbnVAeTOUvYniy3/L3NTovrsm54YTm6553XszpQ1UtbQzvyJUcSF6blxdhL/PloyKo6LEyX1zJsQ5XYR8JEiLkyPEN5rHCq1RgklRwUjhCAlOpji2jYO5FezIauIwuoWpJTsPWsR0C9vbCe/qpk3DhVTUtfG+k8K7NXOvI3q5g63GYfA8n95zxWpGExm7vjLZ2MqispGXauB6JBAggIs72BbRFuoXkd0aAB1LZ20G0y8sDef4ro2zpR5hzFa4b10GB0/9/efq/JZ7cJOk9nTXXArHcau3+PTkUMms+Sjc1VMjAlxu1jkQCSEW4wCH3lBNZrPztdwWWqM06oeV6bHUtbQ3kOUesPBIn6/6xxSWlLAunPt9ARqWjrtk+LulNa3sSmrmK8tnsBV6XG8f6rMnrt+obqF+lYDiyZ1ec+XTo7FYJIeT61wF7YywzaPr013yJmm02DYeqKUhjYDafGhvHm42CvzwnPLG+2/Pd0qAO9q9FBrp5GP86tJjNDT1G7ko3Oev3cU7sfmlQxxoAMxOS6UqQlhvLA3n2of0uk6WFDLwknRBGh7PoMvn2JJM/OU7tDhwjqEgDkpfY1D8yZYDBOHL9a57XydRt+aTHXHnXOMk6X1hOt1pMWF9tk3fVw4da0GKps8e3+8dbQEjYB5VgPWHYtSaGo3jrhDrKXDyLmKJvt5xwIBWg0TokO8VoOvtN4yR7NVLJwQHUJhTQs/3nyS06UNPP1eDkW1rZQ3tvOFqXGARWB/Z04FAVrLdb/fC9/XBpOZquYOIoPdqzc6LiKIB69NJzkqmB/86wT/uf7gmFoA17d2EhUSaDea2dL/Q/U6YkICqW019EiBPFrkvneAwjdpNzh+t9/99yxu/t9PRrk3o4OvObC760a5Mp0Zs8ahzy/UUN3cwSwHIfMjjc0ocCC/GoMHrYvVzR3kVjTZFyGOuCLd8rI/32viklfRjE4jWDy557FfmptEoE7DhzkVfb7rjx/mAfDw8qlcYT2nLQrgUKEltWJBt8ihy6fEEqbXsf302Bb4s3G2vImokAB77jbA1MRwzlU0D9vT++6JMmJCA/nfr83HaJa8f8q7RNDqWzupaOxg2hCNQ5/m19BpNHPT7CSiQgLYocpj+iS2VNNYJykhK+ck0dxh5P+9dcqjz053Ud3cwdnyJpZ0SymzMSU+jLgwPZ95yDj+6flq5iRHOlw8TU8MJyY0kE/zq912Pm+q4Olu3ClIfbLEogOlcfCl8yda3p+2VEVPYDSZeef4JaaPi7AvMK+YEkdCuJ63jo5syuCxonrM0lIZcywxMynCrpU0Ugw1+vqiNa0oxWocmjU+gvzKZkrr24gP07Mvt9IuwrxgYjTJUcFsO1XOsaI6rkqPJy4skIMuXo+nSxt4+1jJqFQ3qmhsR0qIcrNxCCyGlPuunMyX5iRx5GIdN//vJ3x/0zGv1pWyUddqICYkAL1OQ6BWwzmrJmZooJaY0EDqWjp7ZAfY5nDFta1sOV5KWYN3RokpPIe3Sl2MJDZjiq9Udixv6Ar48Gnj0NbjlwgN1PbJ2x8tpiaE09Rh5JgHBZcPWCf2l6c5Nw7NTY4kKjiAzy7U2MU1TWbJiZJ6rkiP6yPmFxem5+qp8eSUNfLqp4UASCk5WlTHm0eKuWvpRJKjgokKCeT6mYm8cbiY1k4j6z8pYEJMMFPiu7yhQQFals1IYHt2uU88XHLLG5mWGN4j3H1qQhgNbQaqhuHprWxq59Pz1cxLiWRmUgTTE8NHVLejsd3APw4UONQdcIYtaspWYWdCdDCBOk0fo6Mzdp+tJDRQS1p8KF/MSGT3mQqfjjTwVwqrLddUbKje4f7EiCBunDWOXTkV/Of6gz1eWGMRmxF9eUZCn30W3aEYPjs/+rpDLdZ30xVT4hzu12gEl0+J5VM39q2lm6fN3VpGnqB7tK27Upwa2gzkXGpkwSTHkTGzx0cQrtfx6TCjzTYcLBqyMeHj/GqqmjpYMLGrj1qN4NbM8ezLrRzRqNZP8qvRaYS92ulYYVFqNMW1bV71POs0mmloM3CypIGEcD3xYZZn8oqZ4wBLmtmdl03ALOHX288SGxpIQrieK6ZYtNLM0vK+Xzw5hoMuaqf9z/s5HCqs44dvnhjR3wZwqd4y1u6OHLKh1QiuTI/jh9dP59pp8ezILmfZb/ex4rmP+J/3cjjrJcLx3TGazDS2G4gKCUQIQUxooH1uakkrC6S2pZO8ima0GkFSZBB51kIjr3xayMGCWn6zI7fHd/qCJIRieHT44VzdtmY1miXbvMxZPxRW/vFj+98+m1ZW0djO28dLyUiK6BPKP1pMiQ9DqxHsPtM3wma02H2mktjQQIfaBTZ0Wg1fzEikqLaVZ3ecxWyW7DlbSUObgXuvmOTwmGumxTNjXDg/25rNf/ztIMt/9xGbj5SwcFI0P7x+ur3dt65Oo77VwN3rs8ivbOaOhRP6TKK/9YXJNLQZ2OWBcZJSciC/mvzK4Uf2tBtMnC5tZF6vFA3b2A8nNPf9k2WYpeW7hBCsyhzPkYt1gzLeuIqUkodeP8rP383hrx9fcFknwaa3ZDPG6rQapiaEccqFcGspJXvOVnD1tHh0Gg03zBpHY7tx0OmGapLi/Zwpa0SnEUSFOp+wX5kex+qFKRy5WMdNz+/nlQMFNA1Cu8qb2Ha6nAkxwT2027qzfEYClU0dox4Jsi+3CqNZ2tNEHPGF9DjKG9s5XeqeRU5rN2PK87vz3PKdnmT5b/fZ/3ZXWtm+3EqMZsnyGYkO9+u0GhZPjuHT89Ue0x3alFVEdEgA03sVufjy/BSMZjmilVo/PV/NgonRdjHdsYJNjN4WQT3SHLxQw1/2n3f6TmxoNfD7D8+x4Be7ePtYKfMndmk4zUmJ5J2HrmTjt5YyPirYXjXvpjnjEEJw52UTAEt0YUp0MEsmx3KpoX1AD3r2pQY+v1BLVEgAhy/WcXKEI6lsemlRISMrXB4UoOX6WeN45LpprJw9DonFkHLjHz7m7r9n8c/PCt0yx3QHDW0GpIToEMv71ybqHhFkqV4WGxpIdXMH5yqamBQbQlJkMHnWyKK91pTRj/Oq+zXuq3mY/+HIue9Mh8hX6J5Kd8SN6feeoPezyW3GISHEjUKIXCFEvhDicQf79UKIN6z7DwohUrvt+3/W7blCiBtcOV9/SCl5ZpvFyHHt9L7e2tEiOFDL1I58fn739UyZks4zzzzTp01HRwd33nkn6enpLFmyhMLCQvu+devWAcwe6rgYTGb25lZyXUbCgGKZ8ydGcVlqNH/56ALznt7J3txK5k+IYpmT8dNqBHctmcRDy6ZQ3thOSnQwqxeksPFbS3uI/y2YGM13l6dz+GIdKdHB3HfV5D7fNTcliruXTuKz8zW8fnBQQrQR/V1zrvDH3fnc9beD/P1AAY//+9SwvNlHLtbRaTKzpFca3pzkSIIDtBzIH5qnV0rJpqxiZidHkBgRBMAtc8cDsOW4w0n4sMZlz9lKPs6r5oFrpqARgv9xsRJJVkEtCeF6xln7CLBwUjTHi+sHTA/KvtRIRWMHy2dYrrcvTI0jJFA7Eqllw75mRoPt27czffp00tOH9txIT09n+vTp7NixY8DvLCgoAJghhMizPqNHdCadVVhLSnSIUw00GwsmRvPwsqlEBgfw1Ls5LPnVbv7fWydHfEHRm4Hebf1RUN3Cx3lV3Dx3vNPIkptmJxGu1/HGoWK39NdVNh0qYnxkcoAp5wAAIABJREFUEEv7iSq9aU4SQQEaNh5yONkf9L3UPXLo+d15GMdY2mD3e+gnT/2ih+6PRljuSyDN0TzHVXZmVxAXpmf+BOcOnesyErlY08rRIUQlSympa+m0RwkPhu3btzMlfRp//94qkgp3oOvleJsSq6dz5+944JYrWbzYvfMZgMrGdk6VNnBlunODprdx4rN9/PCOZXzl2kW0Hfo3n+T1TNN0xzVjw2i23E8NbQYe2nCUizWtPPHOKYd6OM/tyqWhzUBiuB6NgPuu7Dk3y5wQxURrdNYf7szkG1em8oMVFsffotQYtj/yBV5buxiNECxJi6HtwhGWzJ/T7zvrK6vv4NLL36J+44/QtVbzz88s873+3llYrpkhva9zyxsJ1GlGrapdRHAAV02N574rJ/P4TTP4YkYiJ0vqeXJLNl987iOWrtvNwxuO8qd9+ew9W0lZQ9uwDEZDeTfZDGbJ0Zb/2/FRlvna5PgwhBBMig2lw2hZO0xLCCchXE9lUwfHi+u5UN1CSnQwVU0d9kIy//WvE2w/XW7/HVLKYT/Xhzv/YZjPmbGIK2OGm54z3bFdvu0ODEHN7f2nkedXNjPnqR3kV3qF4Pmg5zPdNYf+9kkBlU3eExU6WB58/WiPz66kyQ9oHBJCaIEXgZuAmcDXhBAzezVbC9RJKdOB3wO/th47E1gDzAJuBP5k/b4hYTJLfrszl7ePlXL1tHiPljo1m0yc3vx7Ylc/xfKf/B//+Of/cfzkqR5t1q9fT3R0NPn5+Tz66KM89thjAOTk5LBp0yaAbIY4LjuzK2hqN3LDrHEDthVCcFtmMs+vyWTl7CS+Mj+Z2xem9Bsqr9UIfnTDDD78wTW8tnYJCyZF95ksAvzX9dM5+dT1PHDNFKdVI35y80ymJ4bzk3dO85ePzg/4cjGZTAAT6f+a65e/7r/A7z88x22Z47lmWjxvHC7mv98euoFo6/FLBAdo+0xcA3UarpkWz7bT5UOypB8sqCW3oon/WNIVxTUxNoQr02P5x4HCHjoewx2XDqOJddvOkhobwn9dP43rZiSwN7fK7jHq77hP8qu5Kj2uxzVzZXocrZ2mAQV33z5Wik4jWGY1DgUFaLl2ejw7cypc/v8wmszUNHc41TVxxzUzGphNJh566CG2bdtGTk4OGzduJCenp4FuoOdGdnY227dv58EHH8RkMmHq5zutx1ZIKacCdVie1SPChapmTpY0MC0xzKX28eF67r96Ct+5ZgoZSRFsPlLCqhcO8KU/fswP3jjOU1uzef3gRY5crOuR4uNmBnq3OeWPu/MI0Gr4xpWpTtsEB2q5fWEKW05cIvvS6Iiani5t4JP8ar66aEK/joPI4AC+NGc8bx8ttWtFwdDvpdZe/0eLf7V7UNUMPUn3e+jdj7J49k9/p7O6y2gmEKxfvx7A2Hue4yrFta3szCnn5rlJDvWGbNyaOZ5wvY71n1xw+bsNJjPvHCtl1QsHmP+LXfzP+zlsPVHqcvSp7fcvuP9ZJj/wEhcP7aKk4FyPNuvXr2d+egoJ33yZhV+6y63zGYANWUVICbfMSxrsoR7BbDLxym9/yo9//yo5OTmY8z/hrT2f97jmh3vNgMVr/72Nx/jZlmwefeM4//3WKWpaOll71WRiQgJ5amt2DyNEzqVGXvv8IkvTYjjw+HKyf34jS/oxEk8fF87PbpnVYz49Y1wECVZH0JTYEBp2v8TSB39LTk4Of17/T/v7xRZF8twLf6a8Xcvj/9jJLXd9i8AjG3n3xCU+O3yi33cWcI4hvq+zLzUyNSHMrZUEXSUkUMfyGQn86Prp/NeKadyWmUxCeBCf5FXz7PZcvvHKIS5ft4fMp3ex+s+f8ugbx/ntjlw2ZRXxcV4VBdUtrqTVD/rddL7K8hy3aULOtGqy2nQyp1mjAQ0myYJJUSRZjUe/22lJJbM5JvflVvL3AwX8+2gJ+/Oq2JlTgZSSH755kqffy+mRZvO3/RdcNhj1N1exMZLrptHk8ws1bhGpd3XMGOZzpjfZlxrsGQN/3d/3XVTTLb3YkRH0g1NlNLUb+ceBQofff6GqmXv/kUWNk+Ik7xwr7TdK7aNzVU7voZrmDvuccajzmbZe0VKLf7mbUyUNHosQLKlrpXSQVSM7jCa+9vLnbOul++tKmrwrNSAXA/lSygvWL90E3Ap0vzpvBZ6y/r0ZeEFYzn4rsElK2QEUCCHyrd/3mQvnBSwGodK6Nj7Kq+K1zwo5V9HMoknR9igET3E+5zgxSRO5/MpM9pytoDp+Icu++1smXXcX8WF6QvQ6Pn3hFabe+A3Wf1LAlVffyMMPP4yUki1btrBmzRpOnjwppZSDHpczZY386oMzpMWHuhw9JYTg1sxkbs1MdntYaERQQL9hagFaDV9fMpHPL9SwbttZXtybz4qZ48icGMW8lEimJoSj12kwmM3odVqysrIAOga45vpgMksqm9p55dNC/vLRBb40J4nffnUebxwqZk5yJC/szQfgB9dPIz5MP+ANIqXEZJZ8er6Gt4+VcvvCFIIdVGH6+pKJbM8u57ld53jkumkO24BFh8MsJSYpkdIiFPnE26dIjNCzKnM87xzrihT6wYpp3P7nz/jJ26f56c0ziQ4NHPK4gEVn6NfbzpJf2cw/vnEZAVoNS6fEcra8iZ+8c5q/3r2IjKTwPmNiNJn56/4L1LcauG1+co9910yLJyokgBf25DMvJYrIkK5UIrNZYjCbOVvWxOsHL7JyThJxYV06NDfOTuKDU+W88mkhd18+qY/h0WyWGM2SmpYO3jpayqufFlLZ1MFLH53nq4sm8B9LJ5IWF2ZfZA1nbEaT8znHSU9PJy0tDYA1a9awZcsWZs7seldt2bKFp556CoDVq1f3eW7o9Xom///2zjzMiuJq3O+ZnRlgGBhElB0BF1SixLigEuWnkcQ1Jrh8RoyJMX5J1BgT/YwRzWoWk6hZTIziQtwF0RgEFTeUfV8FAWXf92VgZs7vj6o703PpvtvcO7fvTL3Pc5/bt2519anTp0+dqq6u7tmTo446KlJv3zKPOeYY3n77bTCDQgBPYPz031KVv7bW2G9NrbHhGlUOVNeybONufvbKfIoL8hosSp8IXduX0rV9KV8+vjOzVm1n9mfbmLhkI3sO1DRo/NuWFND7sNa0KSmkpCCPksJ8WhXmU1Jot4vy6VBWRGXrYsqKC2hVFPnf/FdWlE95q0KvjZcRv21rgKoy/dNtjJm1htGz1vC9Lx7FYW1KgrIDcOuQvrw2dy03PDmDX17an5O7V9CmJPl1MtT6DbXbtQqK1t3dO1hTy8zPtnPX6Hl0KCs+ZLaAH7ed15cJC9dz3chp/Pj8fgzudxizpqd2Le2JervH1j0HOGHEeEoK8xjavzPHHtGWLhWtqGxdTCu7OGpRfh4F+XmImKnOefbb64YE8zuSpHXpHt1Qf5dTxGzX1ulL637X1JrP7qpqdldVs2PvQTbvOcB7H0yiqvQwzvnHIgDKjjmLfUsnU1TZDTBB1iuvvAIQGQmvi3M0TrQYWbPv7jELKMrP4ztn94qVnbLiAq4/syd/enMpD0z4mOGn96CitIHdUlur7K+uYfmmPby5aAPPTl3F+p376d2xjNvP78eEhRuYtmIbg3/3Dpd87kiuPKUrfTq1oXVRQYOBqUgbN3b8u9S26cS0rYXc/ZVj2dj2Sma8N4EuPfvW5X3llVe45557eGRxAeM/rmXDuBFMX7mFN0aPaVQ8c6C6lknLNvPIu8s579hO9OrYmsnLs7cgd6J8snA2nbr04LAju1FUVMTVV13JEx9O4s6XB/Lzi/vTvqwoZZsBc24+2bSHn7w0lxmfbqNfpza8NnctB2uUmwb3pktFKbef3487Xp7Ht5+cQYeyIpZu3MXGXVW0Ky1iyDGdEJHAeCRRpk+fRs9evZm5rYgbRs2m3fFn89M/Pc6F197EC9NX85eJy5j36NNUnHkV3z6zF+9UlvL0A/fQdlANt//hUb4w5EKWbt5P+4rD6dW7d4M2a/ny5QdU9UCy7fW6HfuYumIr158Z38dlEhGhQ+tiOrQurnu0cN+BGtbv3M+GnftZv2M/G3dVsWzTbnbsPYj3pBfkCf0Ob8PFA47g0s91oWObBuv0Jd02gVneoLggj64VrfgIOO/YTrw2dy03n9uHuat3cEzn+kdFzziqkmkrtlFUkMf7Szdz9OFt6Nq+lBO7lPP4pJXsP1jD4H4dmbd6B79/YwlzV2/npZmrAbjthTm0LytizOy1PDP1M0ZN/Yw/fP1E8kS4e8x8WhXl861BPTn9qErmrd7B/DU7OLKiFftWL6J7z15spJylS7fy5Usu56WXR1N6WHfW79xPh7IiXh49hrvuvpuVm/dw0tmm37S3qrrR/SaobzfzJHYHWVXZd7CGd5ds4pevL+KwNsVcPOBITu5eQbvSQvYfrKW4II/iQrPo92db91Kr5obXnFXbWbxuJw++bfobw0/vwdn9OtK3UxtaFxdQWpRPvgi7bPuzbe8BqmuVgjxh654D5OUJbUsKaF9WRHFBPvNmTaNrj14UVRzO5r01nHfhZfzjqee46oabyRNzM7mxfqam1swI27W/mrU79vH6vHX8ZeIndXlWbtlL/3ve4PpBPalsU8ymnfvr6gfwxIcrObl7e45oV0J+nlBdq0yw6zEGDZDdNGomi9fvYuyctQw/vcch7dstz80GzM2C1sUFdf/X1irvLt3EdY9PY9jArvzqsuMbDBBX19Ry/7jFjF+4gcl3nptyPOP3KN2FD5s3s/WsLOOMozrQr1MbDmtbQkVpEa2LC+jQuojC/Dzy84TCfKGkIJ8bn55BeatCfnFp/7pyxEYvkdjExDdS9zty1hRjrzU1yqD7JwLw+g/OpH1ZkZnNbNeVM98H2LrnIGu372Ph2p2Ma+STGYkMDh0JeOfErwa+EJRHVatFZAfQwaZPjtr3SBLgG49NZfInWzjgGZE+vG0JV57Sjf5HtE3bApGpsnXTejoc1pmz+3bkxC7lvL6zL8sXzaFfpzbsrqpmb1U1+7ZvZmd+OT+3j+7sqCmk3+3Pc+q2lZz3xTO9xSWslzcWrOc7T82gTXEBT139hazcNUmFwvw8/nr1SUxYuIEH317GxCUb6xqaCF8f2IXfXn4ia9asAfCueulnc4dw0cMfsGCtWT/jylO6cu9F/W3HQ7jtvL7UqPK3dz7h2WmrKMgT8vPqOyGRDkitKrW1Zgq3d1JLdzvbxo8z+5g1VB55dzmPvLucwnxTdq3WDwgFTZApK8rnX8M/T2lRw0vx5O7t+f45R/HQ28t4edYa3r7t7JT1Mm7+Om4aNZNahW8N6ln3OGFBXh4PDBvAdY9PZeiD7yMCxQVmkKa2lrpBAID/d2ynQ9YvKSnM566hx3D7i3M58b7xFOXnkZdn7kp5H2vo1LaYOy44usG+Xz6+M89PW8V9ry3kvtcWUlxgOog1dlAoukk7q29HzuhdhAiMmvIpI+1i6Vd/oRu/vPT4lHXT1GzdtJ6uXbvW/e7SpQtTpkxpkGfNmjV1eQoKCigvL2fLli2sWbOGU089tcG+tt6+ZW7ZsoV27dqxZUvdzK6E/cy4+ev44fNzqIkMaNYG23CE0qJ8rvpCN9qmMPABxp5O69WhboF9VWX7voOs32EC7cjC75t2VVFdoxyoqaW6ppaDNcrBmlqqE5iFVlSQx3Wn9+DOoccAFBG/bWuAiHDb83NYvW0v15zanVuG9Il7zPLSQkZedwrffnI6wx+fBpjgNN/6oDyR+sEMAK0PCtSmJXOzqnN5CY8NH9hgsDaII9q14pFrBnLrc7O5adRMZt19XsrXUtCsvv0Ha3l51hpezuAi+41lz+KZ7M9rS2R+RX6bSg6sq1+YtVZpoJeoOCfmK99qapXvPj2T/Qdr+MvVJ9G5vFVceb5/Th8+2bSHB99ayoN2/abigjwUE/x6TV0Ezuhdya8vO56z+3YkL0+osC+M2LirimenfcaLM+rb2uKCvEOu6T2LP2C/tuGng3tz3ek9GLWiCxNnLm4g05o1a+jWrRsPndSJm5+ZxdN5xVz2xzcYenANg04/zZs1YT/z7ymf8X+jzYzrnpVl/OKS/nH2CA+RGDDCyccexaxFy3l93jr+M3cdPzjnqJRtRlU59ddvsWFnFaVF+fzlqpPYse8gZ/apZPPuKgZ0bcczU1fx9YFdWbF5Dy/NXE2eCFXVtRzetoQ/DRtQ95aqxrJmzRpOO74v3U7vwaRlm9lQU8aKGYuY2X4h7UoLGXRUJYuqtvHn64fQtX0p+QUFtK9ox9dOqOCvb65g8e6jGW9fd715PQx74D/8/msnNmizSKK9fvKjldz76kIK8oRhA7uGbiCxVVE+PSvL6FlZ1iC9plbZud8MBmzfc5DNu6v4ZNNufvX6Yn71+mKKCvK4/bx+fPusXpBC27SnqtrMHjzxiLqbbf2PLOft2wYD5i2JbUoK+cc1J7NlzwGOO6KcOat28J2zevHIe8u5/fx+bNhZxY1n9+a7o2bSpqSAey48jpGTVvDk5E9ZOnE3XzmhM8cdUc6/PljBsH9MRsQsL7B6214u++uHAJQU5tGhrJgbnppxqIyLP2D/pjyu+IfpFu6ev50D65bwr90D6vKsnb2Exc8up6CtieV31BQy8O6XOWdvw/iHJPzMo+8v5/5xi/npmHnU2s54QZ54OuH2O/L4HA3b3NXb9qX0mC+Y9aki8SrU37xIlD2LP2D/5ry6wYHd83dyYN0SxqgZE7v7K8em7Gf2HajhrN9NZNOuKu5+ZX5MOXZXVQeuJTji1eBxlikrtnLcz8YdWi97M+neVxfyy/8sqrtBVKvKwZp6BR0/YjwARfl5VNfWkidSF+s9N30Vz01fRZHtt+SLUFVdQ62aPkZJYX7K8UysV9mv2Lwn6TcXvjBjdfxMCeBdWDqTSLyBRRH5GnC+qn7L/r4GOEVVv+/Js8DmWW1/f4IZ0b0P+EhVn7bp/wJeV9WXoo5xA3CD/dkPaLhcfmwqiXMBpEi8ciuAtkBkIZ32mNF+r0M/DjNtNjLPuD+wESgBdgNtVLVjkF4gKd1kSg/pIhn5KoAjVLUVBNpcMjYTdt14iSVrXL3Y9Fy2mVRlaozNNKUeumEmPSTrNxZhAqHdQCQa7g7ssOVF+6JKYDlwNLDL+pmuGD9zfLRQjfTBQYTJvvxk6QO8EKdtS0UvYap3NInI1hg/E+a6xyJee14JdAL2qmoHqI9zVLXBc7U5ajOx6h+RLZ5fSlc8k21dRBMkT6IxYLpsJlt6STbWrQQOJ7E2C2szvj4GEtJN2OwlGYJkT6Vt2hJQVpjw2lIlUEvy8U+qfibsdpJNPxNW3aRDrpYWzyQjc3dV7ej7j5mqHvwBTgPe8Py+E7gzKs8bwGl2u8AKJtF5vfnS9QGmp7O8RMtthF6mZ0IvmdJDNs5TIrptTrpJVNaWoJdUZWqMbppSD5ggJ63+NKDuq+0+m4ECPx2F9Vw2lSzpvp7CWO9UZMuVaynNeolZZ9tu+16XTXVeslX/iGzJ+qVc1UWi8iRynaTTZrKllxTqOT3FNisl3xs2e2kq28pFPXjrhU9fyMeW0uZnwq6fbPqZsOomHXI1xteEVS9NIXMibyubBvQRkZ72TTdXAGOj8owFrrXblwNvq5FyLHCFmLeZ9cSMhk9N4Ji5QEp68aQ3V72kg0R02xJxegkmV3Szh/T7U7+6b7f7TLRlYMt8JYN1yzVyxWaampaol8bEOc0BF+clT0uxmWTrWUFqbVZz9zHJ0Fz1U1cvzICP8zPxaSl+JlM012spo8Rdc0jN84vfw4xM5gOPqeoCEbkPM0I1FvgX8JRdIGwrRvnYfM9jFn6qBv5XVZN/pVMIaYRenm/OekkHQbrNslhZx+klmBzTTdr9aXTdgUvtsX4CPCsivwBm2bId5JzNNBktUS/x2nObzfe6bA7EqX+5zdbi4rxYNCY2ziVSqOfhwB1234TbrObuY5KhufrgqHp1A37u+pOxaSl+JlM012sp42R7ClRjP8ANrtzMldsc5Au7brIlaxj1kg2ZmqPOw3BuwyBDcz7HYZQtzHUPc73CrLemli1sugiLPGGRI2xy5opeMi17runB2Ul45AurbrItV7aPn02Z4y5I7XA4HA6Hw+FwOBwOh8PhaL4ksuaQw+FwOBwOh8PhcDgcDoejmZJTg0Mi0lVEJorIIhFZICI32/QRIrJGRGbbz9AUyl4pIvPs/tNtWnsRmSAiS+13RRLl9fPIM1tEdorILemQ1edYXxKRJSKyTETuaGx5aZDnMRHZKCLzPWkp67IRcoRKL7EIsu00lBtTB3Zxv+fs/1NEpEc6jhtHprh1FZHBIrLDc538LI3HT8gWRaTGc/yEF7BrSp0ncKzhIrLJU49vpXqsdMnUiHIT9itieNDKMFdETvLsc63Nv1RErvWkn2zbgGV2X0lBxlD6HD/dZeAYoax7Y8i03sKss0y1STGOl3VdBNVZMhC3JSlX1nXjRzI+OQPHDp1OMt1GBRwzdHqIJpt2Yo+VVR2l4ldE5E4r7xIROT9DcoXWdsSnT57BY4WunxSLRNpmaWwfKtvPxyX5LF1n4CS73Qb4GDgWGAH8qJFlrwQqo9J+C9xht+8A7k+x7HxgPdA9HbL6lP0J0AsoAuYAx2b5PJ0FnATMT7cuc1kvceT1te1M6wC4Cfi73b4CeC4MdQUGA69l6PgJ2SKwO8w6T/BYw4GHM31Ok5GpEWUn7FeAocB/MW9EORWYYtPbA8vtd4XdrrD/TcW89lTsvheEpe6Z0F2unPfmqrew6ywTbVLYdRFUZ9Ict+WibgJky0qsF1adZLqNyhU9hMVOwqKjZP2K/W8OUAz0tPLnNze9xJFvJVF98mzZB1noJ6ViT1F5BtOIPlROzRxS1XWqOtNu7wIWAUdm8JAXA0/Y7SeAS1Is51zgE1X9NC1SNeQUYJmqLlfVA8CzGLmzhqq+h1kx30u6dJkoodNLLDJk24nowHteXgTOFUl+xkQyZOE6jiaTttiUOg+jjWdMpiT9ysXAk2qYDLQTkc7A+cAEVd2qqtuACcCX7H9tVfUjNS3rkyRvF2E8H0Cg7tJJaOveGDKst1DrrIn9dCh0EYK2yY9Q6MaPLMZ6odRJJtuogEOGUg/RZLlPkHUdpeBXLgaeVdUqVV0BLMPUI51kXS8hIZT9pFg0RTuVU4NDXuy0rs8BU2zS9+zUzMdSnJ6owHgRmSEiN9i0Tqq6DszJAA5LUdwrgGc8vxsrq5cjgVWe36vJfjDjR7p0mSi5opdD8LHtVElEB3V5VLUa2AF0aORxEyZOXU8TkTki8l8ROS6Nh03UFktEZLqITBaRRAOXptR5ojb+VetvXhSRrikcJxMypYugcxkkR6z01T7pyZCzPicNtOS6p0rO6CyNbVIQodNFBmLMVAmdbuLQFLFeLukkXW2UH7mkh2iaqk8QKh0l6FeaQuZQ6cUHvz55Jgh9PykWmepD5eTgkIi0Bl4CblHVncDfgN7AAGAd8IcUij1DVU8CLgD+V0TOSpOsRcBFwAs2KR2yNjiET5p7BV2O6sXHthtVnE9atA6ypqc4dZ0JdFfVE4GHgDFJlv2miMz3+SRzZ6Sbqg4ErgL+JCK9Ezm0T1qmdJ5IOa8CPVT1BOBN6u9+ZIqwXHdBciSbno5jtgRact1TJSd0luY2KfAwPmlZ00WGYsyUxfFJC52dNDHNQSfpaIuagx4yTWh0lIRfaQqZQ6OXADLSJ/ch1P2kWGSyD5Vzg0MiUohRxihVfRlAVTeoao2q1gL/JIXpd6q61n5vBEbbMjbYaZ7Y740piHwBMFNVN6RL1ihWA97ZAF2AtY0sMxOkQ5fJkCt6qcPPthtJIjqoyyMiBUA5mX38BHusmHVV1Z2quttuvw4UikhlouWr6hBV7e/zeYUEbdHjE5YD72BG5+PRlDqPeyxV3aKqVfbnP4GTUzhOWmVKM0HnMkiOWOldfNKTIed8ThppyXVPldDrLANtUhCh0UWmYsxGEBrdJEhTxHq5pJN0tVF+5JIeommqPkEodJSkX2kKmUOhlyAC+uSZILT9pFhkug+VU4ND9hm/fwGLVPUBT3pnT7ZLgaTeLCIiZSLSJrINnGfLGAtE3hRwLfBKCmJfieeRssbK6sM0oI+I9LSzlK7AyB020qHLZMgVvQDBtt1IEtGB97xcDrxt11zJGInUVUQOjzzTKyKnYHzVljSJENcWRaRCRIrtdiVwBrAwgbKbUudxjxXlby7CPJucSZr6ugs6l2OBb4jhVGCHnbr+BnCePb8VGF//hv1vl4icau3uGyTvo3LK56SZllz3VAm1zjLUJgURCl1kKsZsJKHQTRI0RayXSzpJSxsVUHYu6SGapuoTZF1HKfiVscAVYt6S1RPog3lhRjrJul6CiNEnzwSh7CfFokn6UBqCVckT/QCDMFO55gKz7Wco8BQwz6aPBTonWW4vzArlc4AFwF02vQPwFrDUfrdPstxSezLKPWmNkjXgOEMxq5V/EpE9y+fpGcwUyYOYEdfrG6vL5qCXOLL62nYmdADcB1xkt0swjzwuwzQ+vbJVV+BG4Eab53v2WpwDTAZOT+PxfW0RGAg8ardPt9fpHPt9fRh1nsCxfu3R40Tg6CY4vxm57pLxK5hpwH+xMswDBnrK+abV/TLgOk/6QEwA8gnwMCBhqXsmdJcr57056y3MOgvy0xk8XtZ1EVRnMhC35ZpuAuTKWqwXRp1kuo3KFT2EyU7CoKNU/Apwl5V3CUm+OTVX9BJDLt8+eVPaB1nuJ6VoT2nrQ4ktxOFwOBwOh8PhcDgcDofD0QLJqcfKHA6Hw+FwOBwOh8PhcDgc6cUNDjkcDofD4XA4HA6Hw+FwtGDc4JDD4XA4HA6Hw+FwOBwORwvGDQ45HA6Hw+FwOBwOh8PhcLRg3OCQw+FwOBwOh8PhcDid0HmVAAAQjUlEQVQcDkcLxg0OOQAQkQ9j/DdYRF5rSnlyCRE5QkRetNsDRGRoAvuEWqciMlxEHk5zmZeIyLGe3/eJyJB0HqOpEJERIvKjGP93FJEpIjJLRM5Mofw6/UfrLSyISA8RmZ9E/pEicnkmZQoLyeomhfID/XWuIyK7sy1DrmLt7qpG7D9cRI5Ip0zZIp6PdsRHRNqJyE2e36GOW6JJlw346KEu5nMEIyIXicgd2ZYjk+TaNREGcjn2byzWXk7PthzxcINDDgBUNfTGGkZEpEBV16pqpNM7AIg7ONRCuQSoG+RQ1Z+p6ptZlCeTnAssVtXPqer7jSyrgd4cDuevHQH0AFIeHAKGA81icMiRFtoBN8XN1QwQkYIYfzfQQ1TM5whAVceq6m+yLYcjc4hIfrL7NPPYPx6DgdDHby1mcEhExojIDBFZICI32LTrReRjEXlHRP7puVPfUUReEpFp9nNGdqXPPCKyWwy/E5H5IjJPRIZ5srQVkdEislBE/i4iOW079g7rYhF51NZ3lIgMEZFJIrJURE6xnw/t7I8PRaSf3Xe4iLwgIq8C4yOzBESkCLgPGCYis0VkWFAZ2UZE/kdEplo5HxGRfBG5zl4P7wJnePI2mPHhvbMvIj+2tjJHRH5j075tr5s59joqtSPlFwG/s8fs7S1XRM61OponIo+JSLFNXyki94rITPvf0U2kokMQkbtEZImIvAlEbKG3iIyzvuV9ETlaRAYAvwWG2rq2EpG/ich063/u9ZS5UkQq7fZAEXkn6piH6K2p6psgBSLyhIjMFZEX7bn+mT3/80XkHyIi0TsF5bG++H5rmx+LnXVl7fP31gbmisj3bfrJIvKu1f8bItK5aasfk3wx7coCERlv7WCAiEy2dRgtIhVQV++BdrtSRFba7eM81+lcEelj03fb78F23xfF+LNRHl0OtWkfiMiDkmN3N8VwSHskIq1F5C2PT7jYpvcQkUXROs9uLZJDRL5hz/McEXlKRLrbus61391svpH2nH4oIsul3j//BjjT2sutVifvW13NFM8dS4ny3baMgcCoiN9qeg00DvH30X7tURsRWSEihTZPWzG+uDCrFWgEklhM015MLDzX+qET7L4jxLS771h7+oEt9jdAb2sPv7Nprf38TVgIsIEg/xody/n6FqL0IJ6ZoSJSIiKP2/yzROSLnrJfFhMfLBWR3zaxKvz8yYVSP6P5TRHpZPONENOOj7fXwWUi8ltbp3Ge62Sl1LfPU0XkKJseVK53BnRva3PTxMwciduGZRvPNfWENIxxvmTTPwAu8+QP6jO8LyYujOSbJCIniMjZ1qZm233aZKGagcSo/0oxMdwHwNfEPw4ut/nybFmlIrJKRAol8dj/kNg4WzqLoYuE5ReRHsCNwK1W/jNFpJOYWHCO/Zxu9/mhGD8+X0RuiZIh0MfbfGVWlmlWtov96hQTVW0RH6C9/W4FzAeOBFYC7YFC4H3gYZvn38Agu90NWJRt+ZtAP7uBrwITgHygE/AZ0Bkz0rkf6GX/mwBcnm2ZG1nfHkA1cDxmkHQG8BggwMXAGKAtUGDzDwFestvDgdUem+oBzPf897DnOEFlDAZey1LdjwFeBQrt778C19rz3REoAiZ5roeR3vMN7LbfFwAfAqX2d0QfHTx5fwF8P6CckcDlQAmwCuhr058EbrHbKz373wQ8miWdnQzMA0rtOV0G/Ah4C+hj83wBeDvADiK6yQfeAU7w1K/Sbg8E3oneP1pvYflYu1fgDPv7MauT9p48TwEXRtcjRp53gD/Y7aHAm3b7u8BLnmsp4rc/BDratGHAY9nWi0c31cAA+/t54H+AucDZNu0+4E+eeg+025XASrv9EHC13S4CWtntyDU4GNgBdMH4sY+AQZ5rqqfN9wxZ8jcp6C5St6D2qABo69HVMozf9tV5tuuTRL2PA5Z4/EF7jJ++1v7+JjDGbo8EXrDn/FhgmcceXvOUWQqU2O0+wHS7HeS76+ww1z4E++ig9uhx4BK7fQPW7+Tqh8RimoeAe2z+c4DZdnuEtYdie01twfjXHtjYxmNfh/ibbNc9ARuos2sa+tfhNIzlYvkWrx7qfgO3AY/b7aMxfqrElr0cKLe/PwW6NqEu/PxJBSD297eob2tHAB/Yc34isBe4wP432nOdrATustvfwPqaGOUOpz6OeQ240m7fSJw2LNu25DnP0THOTzFtax9rG8979BAU719LfVvfl3o//Kqn7NaRfcPyCaj/j6wd/NiTLygOfgX4ot0eho3fSTz294uNs6KzOLaQjPwjgB95yn3Os08+xl9E/FiZreMC4HMk4ONtOb/Cxj6YWY8fA2XJ1DenZ38kyQ9EZA4wGegKXAO8q6pbVfUgJtCKMAR4WERmA2Mxs2ZCNaKbIQYBz6hqjapuAN4FPm//m6qqy1W1BtPRGJQtIdPIClWdp6q1mIvvLTVX0zzMRVgOvCDmDtEfMY1thAmqujWBY8QqI1uci3E+06yNnwvcinFem1T1AMZhxWMIJijaC+DRR39792AecDXx69wPcy4+tr+fAM7y/P+y/Z6BOS/Z4ExgtKruVdWdGL9Qgpke+oLV4yOYzqsfXxeRmcAsjD6ay2Niq1R1kt1+GuMXvijmLuI8TAfE7/zHyuN3vocAf1fVaqiztX5Af2CC1f9PMQFmWFihqrPt9gygN9BOVd+1adF27sdHwP+JyE+A7qq6zyfPVFVdbf3YbIzOjgaWq+oKm+eZRtQjWwS1RwL8SkTmAm9ibvR0svtE67xH04rcKM4BXlTVzVBn46dhblaBGUT1trtjVLVWVRdSX/9oCoF/2uvsBer9TpDvzmX8fDQEt0ePAtfZ7eswg0W5TryYZhDGjlDVt4EOIlJu9/2PqlZZ+9tIsE35+ZuwEGQDsfDGcrF8SxBenS7GDAL1tf+9pao7VHU/sBDonlRtGoefP+kCvGGvhdtp2O7+1/aF5mE6qeNsesR2Ijzj+T7NbscqN8Jp1Pez/h31X5htKjrGGYi5zpbaa+tpT96geP8F4CtiZmB9EzM4AuYm7ANiZuq1i8Q3IcMvxgPbRxCR1gTHwc9hBoUAruDQfkW82N+PbOosWhfnkrz80ZwD/A3Axjo7MDoerap7VHU3JiaOrF0az8cDnAfcYc/HO5i+SrdkhIr1jG2zQUQGY4Kh01R1r52etgQzg8KPPJvXLxBvzsSayqlxfuciVZ7tWs/vWsy18XNgoqpeaqcDvuPJvyfBY8QqI1sI8ISq3lmXIHIJcGlA/mrsI6giIpgZDJFy/OxgJOZO0xwRGY65MxRPnlhEzksN2fVZ0XXNA7ar6gC/zBFEpCfmbsvnVXWbiIzEOGvw6NaTlkv4+YW/Yu7SrhKREUTVS0RK4uTxO99+tibAAlU9jXDi9S81mDs4Qfjagar+W0SmAF/GBN/fsp26WMcpIP41lQsE1eFqzAzHk1X1oJhHRCI6i9ZFLj0aFeRPvXj/99Y1SFe3AhswswHyMDOAEz1WLpJwe6Sqk+w0/bOBfFXN2ALyTUi8mMavIxXRmZ8fiXeMbLfJfvjZQKx21hvLxfItQcTytdnUld81/hDwgKqOtf2iEZ7/qgBUtVZEDtoOJ9TbTgT12Y5VbiKE2aaidVjukxbBN963/c4JmNkdX8cMMKGqvxGR/2BmSU8WkSF2gDFMBPX9ItdNrDh4LPBrEWmPuSEdHbvEunaCYqJs6iyZNrMxsX2iPsXPx0f2/6qqLknyuHW0lJlD5cA2e4EeDZyKmXZ6tohUiFmI7que/OOB70V+iOdZ0WbOe5j1cvJFpCNmBHSq/e8UEekp5vnRYZgpqM2dcmCN3R6e4D67AO8ss1TKyDRvAZeLyGEA1nHPAgaLSAd7d+NrnvwrMY4dTOMWWZdhPPBNESn1lAOm/utsOVd7yonWTYTFQA+xz69jZ/WlXr2M8B5wqZh1Y9oAF2KmXq8Qka9B3RopJ/rs2xbTkO4Q8yz+BZ7/VlKv26/iT5DewkA3EYkMzlxJvV/YbO8o+S3aWZJAnmjGAzdaXx2xtSVAx8jxxTzLHoaZeUHsALZJ/dvrvHa+kno78K7v1QszA+hBTKB1QoLHWgz0sgEq1N+9yyWC2qNyYKPtvH2Rpr0bn0newsww7AB1Nv4h5o4rGF8ar931a3/W2buM12BmBECw7w6zr4mHn4+G4PYIzGMAz9A8Zg0lwntYHdhO/GY7wyaIXLOHIBtYiY9/9SHIt8TSg1enfTF36FPulKURP3/ijUevTbHcYZ7vj+x2IuVOpj7GuSIgTxiJjnHeBHpK/fqPV3ryxor3HwUeBKZFZqqJSG87C+R+YDpmxm/YCIrxALD+wzcOtrNepgJ/xjx6VxNVdqzYfyU+sXGWdeZnC0nJz6G+5C3MsgmRtTXbYnzKJWLWNCrD3LhP5sU2bwDftzfzEZHPJbEv0HIGh8ZhFk6dixnZnYy5gH8FTMGc4IWY4B3gB8BAMYtOLcQ8H9vcUcyzxXOBOZgR3h+r6nr7/0eYRfnmAyts3ubObzGj3pOoD6rjMRE4VuyC1CmWkVHsYwg/xSzAOBezrkdnzN2ejzDXw0zPLv/EDKROxTxPvMeWMw7TYZ1upy9GXhl7N+a6moBx/hGeBW4Xs0Ba3cLKdsr1dZhpqfMwI+B/T2edG4uqzsRMiZ2NWfsm4qivBq4X88jqAszgWfS+czCDbwswzwdP8vx9L/BnEXkfc8fMD1+9hYRFwLXWjtpjpsf+EzPFdQwwLXoHVd0eL48Pj2LWcphrdX2VmscfLwfut2mzCf9bIK7FLC4+F/Nmw/ts+u+B74p5RX2lJ/8wYL69vo7GdGbjYme93gSME7No5Abq27dcIag9GoVpn6djrr+w3WlNCVVdAPwSeNfa8wOYWOQ6ay/XADfHKWYuUC1mYctbsevJichkzKMu8Xz3SODvkoMLUsfw0UHtERhbqiA3H7tMhRHY2BYTz8UcIFDVLcAkMYuf/i5W3jAQwwaC/Gs0vr4ljh7+inn5wDx77OGqWkWWCfAnIzBx1vvA5hSLLhYzm/VmzMxEEiz3FuCHNo7sTO60R9Exzh8xa5T9x7atn3ryBsb7qjoD2EnDgehbrE3NAfYB/81cNVLGL8aLJlYc/BxmvcVDlqqIE/sHxcbZ1JmfLSQr/6uYAezZ9kbhzZhlFuZhHoU/zvqxkZiBtSmYtZpmJSHnzzE38eeKecTx58lWNLKAWItERFqr6m57N3o0ZjHTljDo0QB7Z2GmqjaXO7AOh8PRovG0bwL8BViqqn/MtlwOR1gQ88aci1X1mmzL4nCEHTGP2Q2MrGOU5L6lwD5VVRG5ArM4dfJvUWpC7Mzb11S1fxrKOgLzmNnRdiZn6Eln/XOdlqaLMD3XmQ1GiMgQzGMO4zF3sVsUHof1+yyL4nA4HI708W0RuRazRtgszEKRDocDEJGHMI/4Ds22LA5HC+BkzIt+BNiOWZi5RSAi38DM4vphrgwMOVo2LXrmkMPhcDgcDofD4XA4HA5HS6elrDnkcDgcDofD4XA4HA6Hw+HwwQ0OORwOh8PhcDgcDofD4XC0YNzgkMPhcDgcDofD4XA4HA5HC8YNDjkcDofD4XA4HA6Hw+FwtGDc4JDD4XA4HA6Hw+FwOBwORwvGDQ45HA6Hw+FwOBwOh8PhcLRg/j8f1BlgJTdjrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "for i in range(0,len(collist)):\n",
    "    plt.subplot(nrows,ncol,i+1)\n",
    "    sns.distplot(x[collist[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can dataset contains a skeness and it's better to remove this skewness by implmenting techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1,random_state=41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35510, 16)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3946, 16)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35510,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3946,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying Standard Scaling to get optimized result\n",
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying MachineLearning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc=DecisionTreeClassifier()\n",
    "rfc=RandomForestClassifier()\n",
    "lg=LogisticRegression()\n",
    "knc=KNeighborsClassifier()\n",
    "sgc=SGDClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** DecisionTreeClassifier ***********\n",
      "\n",
      "\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "                       max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort=False,\n",
      "                       random_state=None, splitter='best')\n",
      "\n",
      "\n",
      "accuracy score 0.8819057273188039\n",
      "Cross-val-score= 0.6693341859525501\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.93      0.93      3539\n",
      "           1       0.43      0.44      0.44       407\n",
      "\n",
      "    accuracy                           0.88      3946\n",
      "   macro avg       0.68      0.69      0.69      3946\n",
      "weighted avg       0.88      0.88      0.88      3946\n",
      "\n",
      "\n",
      "\n",
      "[[3299  240]\n",
      " [ 226  181]]\n",
      "\n",
      "\n",
      "roc_auc_score 0.6884508387757893\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** RandomForestClassifier ***********\n",
      "\n",
      "\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.9067410035478966\n",
      "Cross-val-score= 0.7786081805181306\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.98      0.95      3539\n",
      "           1       0.61      0.28      0.38       407\n",
      "\n",
      "    accuracy                           0.91      3946\n",
      "   macro avg       0.76      0.63      0.66      3946\n",
      "weighted avg       0.89      0.91      0.89      3946\n",
      "\n",
      "\n",
      "\n",
      "[[3466   73]\n",
      " [ 295  112]]\n",
      "\n",
      "\n",
      "roc_auc_score 0.6272784896689955\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** KNeighborsClassifier ***********\n",
      "\n",
      "\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "                     weights='uniform')\n",
      "\n",
      "\n",
      "accuracy score 0.8973644196654841\n",
      "Cross-val-score= 0.8875684828346984\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.94      3539\n",
      "           1       0.50      0.28      0.36       407\n",
      "\n",
      "    accuracy                           0.90      3946\n",
      "   macro avg       0.71      0.62      0.65      3946\n",
      "weighted avg       0.88      0.90      0.88      3946\n",
      "\n",
      "\n",
      "\n",
      "[[3427  112]\n",
      " [ 293  114]]\n",
      "\n",
      "\n",
      "roc_auc_score 0.6242254610437713\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** SGDClassifier ***********\n",
      "\n",
      "\n",
      "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
      "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
      "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
      "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
      "              power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n",
      "              validation_fraction=0.1, verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.9004054738976178\n",
      "Cross-val-score= 0.8665864551773487\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95      3539\n",
      "           1       0.68      0.07      0.12       407\n",
      "\n",
      "    accuracy                           0.90      3946\n",
      "   macro avg       0.79      0.53      0.53      3946\n",
      "weighted avg       0.88      0.90      0.86      3946\n",
      "\n",
      "\n",
      "\n",
      "[[3526   13]\n",
      " [ 380   27]]\n",
      "\n",
      "\n",
      "roc_auc_score 0.5313328561421243\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** LogisticRegression ***********\n",
      "\n",
      "\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.9011657374556513\n",
      "Cross-val-score= 0.8931205529208206\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.98      0.95      3539\n",
      "           1       0.55      0.22      0.32       407\n",
      "\n",
      "    accuracy                           0.90      3946\n",
      "   macro avg       0.73      0.60      0.63      3946\n",
      "weighted avg       0.88      0.90      0.88      3946\n",
      "\n",
      "\n",
      "\n",
      "[[3465   74]\n",
      " [ 316   91]]\n",
      "\n",
      "\n",
      "roc_auc_score 0.6013386810222074\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed=7\n",
    "#prepare models\n",
    "models=[]\n",
    "models.append(('DecisionTreeClassifier',dtc))\n",
    "models.append(('RandomForestClassifier',rfc))\n",
    "models.append(('KNeighborsClassifier',knc))\n",
    "models.append(('SGDClassifier',sgc))\n",
    "models.append(('LogisticRegression',lg))\n",
    "\n",
    "\n",
    "\n",
    "#evaluate each model in turn\n",
    "Model=[]\n",
    "cvs=[]\n",
    "score=[]\n",
    "rocscore=[]\n",
    "for name,model in models:\n",
    "    print('**************',name,'***********')\n",
    "    print('\\n')\n",
    "    Model.append(name)\n",
    "    model.fit(x_train,y_train)\n",
    "    print(model)\n",
    "    pred=model.predict(x_test)\n",
    "    print('\\n')\n",
    "    acc=accuracy_score(y_test,pred)\n",
    "    print('accuracy score',acc)\n",
    "    score.append(acc*100)\n",
    "    kfold=model_selection.KFold(n_splits=10,random_state=seed)\n",
    "    cv=model_selection.cross_val_score(model,x,y,cv=10,scoring='accuracy').mean()\n",
    "    print('Cross-val-score=',cv)\n",
    "    cvs.append(cv*100)\n",
    "    print('\\n')\n",
    "    print(classification_report(y_test,pred))\n",
    "    print('\\n')\n",
    "    cm=confusion_matrix(y_test,pred)\n",
    "    print(cm)\n",
    "    print('\\n')\n",
    "    false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test,pred)\n",
    "    roc_auc=roc_auc_score(y_test,pred)\n",
    "    print('roc_auc_score',roc_auc)\n",
    "    rocscore.append(roc_auc*100)\n",
    "    print('\\n')\n",
    "    plt.figure(figsize=(10,15))\n",
    "    plt.subplot(911)\n",
    "    plt.title(name)\n",
    "    print(sns.heatmap(cm,annot=True))\n",
    "    plt.subplot(912)\n",
    "    plt.title(name)\n",
    "    plt.plot(false_positive_rate,true_positive_rate,label='AUC'%roc_auc)\n",
    "    plt.plot([0,1],[0,1],'k--')\n",
    "    plt.xlabel('false_positive_rate')\n",
    "    plt.ylabel('true_positive_rate')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>cvs</th>\n",
       "      <th>score</th>\n",
       "      <th>rocscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>66.933419</td>\n",
       "      <td>88.190573</td>\n",
       "      <td>68.845084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>77.860818</td>\n",
       "      <td>90.674100</td>\n",
       "      <td>62.727849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>88.756848</td>\n",
       "      <td>89.736442</td>\n",
       "      <td>62.422546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>86.658646</td>\n",
       "      <td>90.040547</td>\n",
       "      <td>53.133286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>89.312055</td>\n",
       "      <td>90.116574</td>\n",
       "      <td>60.133868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model        cvs      score   rocscore\n",
       "0  DecisionTreeClassifier  66.933419  88.190573  68.845084\n",
       "1  RandomForestClassifier  77.860818  90.674100  62.727849\n",
       "2    KNeighborsClassifier  88.756848  89.736442  62.422546\n",
       "3           SGDClassifier  86.658646  90.040547  53.133286\n",
       "4      LogisticRegression  89.312055  90.116574  60.133868"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.DataFrame({'Model':Model,'cvs':cvs,'score':score,'rocscore':rocscore},columns=['Model','cvs','score','rocscore'])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>0.261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>month</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>job</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>education</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>housing</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>contact</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>marital</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>loan</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           importance\n",
       "feature              \n",
       "duration        0.261\n",
       "balance         0.113\n",
       "day             0.108\n",
       "age             0.104\n",
       "month           0.089\n",
       "poutcome        0.053\n",
       "job             0.052\n",
       "pdays           0.047\n",
       "campaign        0.038\n",
       "education       0.031\n",
       "housing         0.030\n",
       "contact         0.022\n",
       "previous        0.022\n",
       "marital         0.020\n",
       "loan            0.009"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.DataFrame({'feature':x.columns,'importance':np.round(rfc.feature_importances_,3)})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "importances.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18dfd7b8348>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances.plot.bar(color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From  here we can observe that defult and loan has very least priority.Because of that I will drop them from the dataset and train the classifier again. We could also remove more or less features, but this would need a more detailed investigation of the features effect on our model. But I think it’s just fine to remove only default and loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank.drop(['default','loan'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted insurance claim: [0 0 0 ... 0 0 0]\n",
      "actual insurance claim 18108    0\n",
      "5237     0\n",
      "10272    0\n",
      "18385    0\n",
      "34276    0\n",
      "        ..\n",
      "24142    0\n",
      "7485     0\n",
      "20670    0\n",
      "3229     0\n",
      "8266     0\n",
      "Name: y, Length: 3946, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "rfc.fit(x_train,y_train)\n",
    "pred=rfc.predict(x_test)\n",
    "print('predicted insurance claim:',pred)\n",
    "print('actual insurance claim',y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank=pd.DataFrame(pred)\n",
    "bank.to_csv('rfc_bank.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "#save the model as a pickle in a file\n",
    "joblib.dump(rfc,'bank.obj')\n",
    "#load the model from a file\n",
    "dct_from_joblib=joblib.load('bank.obj')\n",
    "#use the loaded model to make prediction\n",
    "dct_from_joblib.predict(x_test)"
   ]
  }
 ],
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