Term+Deposit+Opening+Decision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Term Deposit Opening Decision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.decomposition import FactorAnalysis\n",
    "from sklearn import preprocessing \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import resample\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import RFE\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import matthews_corrcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv file\n",
    "\n",
    "df=pd.read_csv(\"/Users/gulcanogundur/Desktop/Big Data Projelerim/bank-additional-full.csv\",sep=\";\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Describe Dataset \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null object\n",
      "marital           41188 non-null object\n",
      "education         41188 non-null object\n",
      "default           41188 non-null object\n",
      "housing           41188 non-null object\n",
      "loan              41188 non-null object\n",
      "contact           41188 non-null object\n",
      "month             41188 non-null object\n",
      "day_of_week       41188 non-null object\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null object\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null object\n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n",
      "None\n",
      "   age          job  marital            education  default housing loan  \\\n",
      "0   56    housemaid  married             basic.4y       no      no   no   \n",
      "1   57     services  married          high.school  unknown      no   no   \n",
      "2   37     services  married          high.school       no     yes   no   \n",
      "3   40       admin.  married             basic.6y       no      no   no   \n",
      "4   56     services  married          high.school       no      no  yes   \n",
      "5   45     services  married             basic.9y  unknown      no   no   \n",
      "6   59       admin.  married  professional.course       no      no   no   \n",
      "7   41  blue-collar  married              unknown  unknown      no   no   \n",
      "8   24   technician   single  professional.course       no     yes   no   \n",
      "9   25     services   single          high.school       no     yes   no   \n",
      "\n",
      "     contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
      "0  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "1  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "2  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "3  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "4  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "5  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "6  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "7  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "8  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "9  telephone   may         mon ...         1    999         0  nonexistent   \n",
      "\n",
      "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
      "0          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "1          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "2          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "3          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "4          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "5          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "6          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "7          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "8          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "9          1.1          93.994          -36.4      4.857       5191.0  no  \n",
      "\n",
      "[10 rows x 21 columns]\n",
      "               age      duration      campaign         pdays      previous  \\\n",
      "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
      "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
      "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
      "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
      "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
      "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
      "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
      "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
      "\n",
      "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
      "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
      "mean       0.081886       93.575664     -40.502600      3.621291   5167.035911  \n",
      "std        1.570960        0.578840       4.628198      1.734447     72.251528  \n",
      "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
      "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
      "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
      "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
      "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  \n"
     ]
    }
   ],
   "source": [
    "print(\"Describe Dataset\",\"\\n\")\n",
    "print(df.info())\n",
    "print(df.head(10))\n",
    "print(df.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous Variables \n",
      "\n",
      "Index(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
      "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuous Variables\",\"\\n\")\n",
    "continous_vars=df.describe().columns\n",
    "print(continous_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Variables \n",
      "\n",
      "Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
      "       'month', 'day_of_week', 'poutcome', 'y'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Categorical Variables\",\"\\n\")\n",
    "categorical_vars=df.describe(include=[object]).columns\n",
    "print(categorical_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing - Continous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous Variables Visualization \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7UAAAOVCAYAAAC29ccDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1057ac860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Continuous Variables Visualization\",\"\\n\")\n",
    "df.hist(column=continous_vars,figsize=(16,16))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing - Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Variables Visualization \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/IAAAOWCAYAAABMB6l0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDwAAAAOikQcAAIAB+T9H2wJZWDDauwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d031ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Categorical Variables Visualization\",\"\\n\")\n",
    "fig, axes = plt.subplots(4, 3, figsize=(16, 16))\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.7, hspace=0.3)\n",
    "for i, ax in enumerate(axes.ravel()):\n",
    "    if i > 10:\n",
    "        ax.set_visible(False)\n",
    "        continue\n",
    "    sns.countplot(y = categorical_vars[i], data=df, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert pdays column numeric to categorical    \n",
    "df[\"pdays_cat\"]=[0 if each==999  else 1 for each in df.pdays]\n",
    "df=df.drop([\"pdays\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvoAAAJ+CAYAAADCPqQbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d031e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heat map\n",
    "print(\"Correlation Matrix\",\"\\n\")    \n",
    "correlation=df.corr(method=\"pearson\")\n",
    "plt.figure(figsize=(25,10))\n",
    "sns.heatmap(correlation,vmax=1,square=True,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factor Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/decomposition/factor_analysis.py:228: ConvergenceWarning: FactorAnalysis did not converge. You might want to increase the number of iterations.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "fact_2c=FactorAnalysis(n_components=1)\n",
    "df[\"X_factor\"]=fact_2c.fit_transform(df[['euribor3m','nr.employed','cons.price.idx','emp.var.rate']])\n",
    "df=df.drop([\"euribor3m\",\"nr.employed\",\"cons.price.idx\",\"emp.var.rate\"],axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One - Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Describe Dataset_v2 \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 61 columns):\n",
      "age                              41188 non-null int64\n",
      "campaign                         41188 non-null int64\n",
      "previous                         41188 non-null int64\n",
      "cons.conf.idx                    41188 non-null float64\n",
      "pdays_cat                        41188 non-null int64\n",
      "X_factor                         41188 non-null float64\n",
      "job_admin.                       41188 non-null uint8\n",
      "job_blue-collar                  41188 non-null uint8\n",
      "job_entrepreneur                 41188 non-null uint8\n",
      "job_housemaid                    41188 non-null uint8\n",
      "job_management                   41188 non-null uint8\n",
      "job_retired                      41188 non-null uint8\n",
      "job_self-employed                41188 non-null uint8\n",
      "job_services                     41188 non-null uint8\n",
      "job_student                      41188 non-null uint8\n",
      "job_technician                   41188 non-null uint8\n",
      "job_unemployed                   41188 non-null uint8\n",
      "job_unknown                      41188 non-null uint8\n",
      "marital_divorced                 41188 non-null uint8\n",
      "marital_married                  41188 non-null uint8\n",
      "marital_single                   41188 non-null uint8\n",
      "marital_unknown                  41188 non-null uint8\n",
      "education_basic.4y               41188 non-null uint8\n",
      "education_basic.6y               41188 non-null uint8\n",
      "education_basic.9y               41188 non-null uint8\n",
      "education_high.school            41188 non-null uint8\n",
      "education_illiterate             41188 non-null uint8\n",
      "education_professional.course    41188 non-null uint8\n",
      "education_university.degree      41188 non-null uint8\n",
      "education_unknown                41188 non-null uint8\n",
      "default_no                       41188 non-null uint8\n",
      "default_unknown                  41188 non-null uint8\n",
      "default_yes                      41188 non-null uint8\n",
      "housing_no                       41188 non-null uint8\n",
      "housing_unknown                  41188 non-null uint8\n",
      "housing_yes                      41188 non-null uint8\n",
      "loan_no                          41188 non-null uint8\n",
      "loan_unknown                     41188 non-null uint8\n",
      "loan_yes                         41188 non-null uint8\n",
      "contact_cellular                 41188 non-null uint8\n",
      "contact_telephone                41188 non-null uint8\n",
      "month_apr                        41188 non-null uint8\n",
      "month_aug                        41188 non-null uint8\n",
      "month_dec                        41188 non-null uint8\n",
      "month_jul                        41188 non-null uint8\n",
      "month_jun                        41188 non-null uint8\n",
      "month_mar                        41188 non-null uint8\n",
      "month_may                        41188 non-null uint8\n",
      "month_nov                        41188 non-null uint8\n",
      "month_oct                        41188 non-null uint8\n",
      "month_sep                        41188 non-null uint8\n",
      "day_of_week_fri                  41188 non-null uint8\n",
      "day_of_week_mon                  41188 non-null uint8\n",
      "day_of_week_thu                  41188 non-null uint8\n",
      "day_of_week_tue                  41188 non-null uint8\n",
      "day_of_week_wed                  41188 non-null uint8\n",
      "poutcome_failure                 41188 non-null uint8\n",
      "poutcome_nonexistent             41188 non-null uint8\n",
      "poutcome_success                 41188 non-null uint8\n",
      "y_no                             41188 non-null uint8\n",
      "y_yes                            41188 non-null uint8\n",
      "dtypes: float64(2), int64(4), uint8(55)\n",
      "memory usage: 4.0 MB\n",
      "None \n",
      " \n",
      " \n",
      "\n"
     ]
    }
   ],
   "source": [
    "columns = df.select_dtypes(include=[object]).columns\n",
    "df = pd.concat([df, pd.get_dummies(df[columns])], axis=1)\n",
    "df = df.drop(['job', 'marital', 'education', 'default', 'housing', 'loan',\n",
    "       'contact', 'month', 'day_of_week','poutcome', 'y','duration'], axis =1)\n",
    "print(\"\\n\",\"Describe Dataset_v2\",\"\\n\")\n",
    "print(df.info(),\"\\n \\n \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "min_max_scaler=preprocessing.MinMaxScaler()\n",
    "data_scaled=pd.DataFrame(min_max_scaler.fit_transform(df),columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = data_scaled.y_yes\n",
    "data_scaled = data_scaled.drop(['y_yes','y_no'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test - Train Data Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_scaled, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def upSample(X_train, y_train):\n",
    "    df_all = pd.concat((X_train, pd.DataFrame({'value': y_train}, index=y_train.index)), axis=1)\n",
    "    \n",
    "    df_majority = df_all [df_all.value==0]\n",
    "    df_minority = df_all[df_all.value==1]\n",
    "     \n",
    "    # Upsample minority class\n",
    "    df_minority_upsampled = resample(df_minority, \n",
    "                                     replace=True,     # sample with replacement\n",
    "                                     n_samples=df_majority.shape[0],    # to match majority class\n",
    "                                     random_state=123) # reproducible results\n",
    "    # Combine majority class with upsampled minority class\n",
    "    df_upsampled = pd.concat([df_majority, df_minority_upsampled], axis=0)\n",
    "    y_upsampled = df_upsampled.value\n",
    "    X_upsampled = df_upsampled.drop('value', axis=1)\n",
    "\n",
    "    return X_upsampled, y_upsampled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "\n",
      "Classification Report \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.95      0.84      0.89      7303\n",
      "        1.0       0.33      0.63      0.44       935\n",
      "\n",
      "avg / total       0.88      0.82      0.84      8238\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train,y_train=upSample(X_train, y_train)\n",
    "lr = LogisticRegression(random_state = 42)\n",
    "lr.fit(X_train,y_train)\n",
    "prediction = lr.predict(X_test)\n",
    "print(\"Logistic Regression\",\"\\n\")\n",
    "print(\"Classification Report\",\"\\n\")\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAAEWCAYAAAATsp59AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XmcFMX9//HXe5dLVC6FFQEDCipq\nAt5HYkRBDqOCRqNoIhriGqP5xiMeqNEoMRITozFfNaKI4IV8PX7iLQHxBAS8ERVUlBUElFsEZPn8\n/uhaGHZnZ3tnj5md+Tx99GOmq6u7qnfdD1Vd3dUyM5xzLp8VZLoCzjmXaR4InXN5zwOhcy7veSB0\nzuU9D4TOubzngdA5l/c8EDrn8p4HwlokaYqk5ZKaZroudUXSQElvS1ol6WtJkyR1znS9ypN0oaSv\nJK2UdE+q34mkX0iaI2m1pA8kDUrYto+k58O5VrjpNvzO10laE5aP6uqcXN3xQFhLQjA4HDDg+Hou\nu1E9ldMVGAtcDLQEugC3A5tqsQxJqtH/l5L6AZcDvYHOwK7AtZXk7QDcD1wEtAAuAR6U1C5k+R4Y\nDwxNUeT5ZrZdWPaoSd1dZnggrD1nANOAe4EhiRskbSPpJkmfhxbKq5K2Cdt+Iul1SSskLZB0Zkif\nIuk3Ccc4U9KrCesm6TxJc4G5Ie1f4RirJM2SdHhC/kJJV0j6JLR8ZknqJOk2STeVq++Tki5Ico49\ngc/MbJJFVpvZo2b2RaoywrbDJM0I5z9D0mEJ5U2RdL2k14C1wK6SWkoaJWmRpC8l/UVSYczfxRBg\nlJnNNrPlwHDgzErydgRWmNmz4ZyeBr4FdgMws4/MbBQwO2bZriEyM19qYQHmAb8D9idqRRQlbLsN\nmAJ0AAqBw4CmwC7AamAw0BjYAegZ9pkC/CbhGGcCryasGzARaANsE9J+GY7RiKjV9hXQLGy7BHgP\n2AMQ0CPkPQhYCBSEfDsSBaOiJOe4K7AOuBk4Etiu3PbKymgDLAd+Feo2OKzvkHCuXwB7h+2Ngf8H\n3AlsC7QD3gDOCfl3AVYAu1Tyu3gHOCVhfcfw89ohSd5C4CWiVnwhMAgoAbYtl69r9OdSYf8pwFLg\na+A1oFem/1/0pfpLxiuQCwvwkxD8dgzrHwIXhu8FwHdAjyT7DQMer+SYcQLhUVXUa3lZucBHwMBK\n8s0Bjg7fzweeSXHMQ4i6iktDULy3LCBWVkYIgG+US5sKnJlwrtclbCsC1hMCfEgbDLwY8/fxCdA/\nYb1x+Hl1riT/UGANsJHoH4GfJclTWSA8GNie6B+2IUT/sO2W6f8nfane4l3j2jEEeMHMvg7rD7Kl\ne7wj0Izoj7O8TpWkx7UgcUXSxeGi/0pJK4iu4+0Yo6wxRK1Jwud9lRVoZtPM7Bdm1pbomuhPgSur\nKGNn4PNyaZ8TtZCTncsPiILXonDJYAVR67Ad8awhut5Xpuz76vIZJfUBbgR6AU2AI4C7JfWMU5CZ\nTbfoEsF6MxtD1Co8JmY9XZbwQFhD4VrfL4AjwijlV8CFQA9JPYi6TOsI15zKWVBJOkTXqZonrO+U\nJM/mUcxwPfCyUJfWZtYKWEnURa2qrPuBgaG+3Ym6pVUysxnAY8A+VZSxkCi4JdoF+DLZuYTjrCdq\nYbcKSwsz2ztOvYiu5/VIWO8BLDazb5Lk7Qm8bGYzzWxTOKfpQJ+YZZVnbPmZuwbCA2HNDQJKgb2I\n/qh6EgWTV4AzzGwTcA/wT0k7hwGFQ8PtHA8AfcLtG40k7ZDQEnkbOFFS8zBam2rUEqLu2UaiLmsj\nSVezdavobmC4pG5hZPZHknYAMLMSYAZRS/BRM/suWQFhYOfsshFVSXsSXVubVkUZzwC7SzotnOcp\n4ef1VLJyzGwR8AJwk6QWkgok7SbpiCp+BmXGAkMl7SWpNXAVURc+mRnA4WU/d0n7ErV03w3rktSM\nqLWIpGbhd4ekVpL6hbRGkk4naiE/H7OeLltkum/e0BfgOeCmJOm/IBqsaARsA9xC1AJaCbzMlgGO\nw4laIKuIWkJDQvqORMFgNVF3689UvEbYNWG9EBgVjrMIuBSYD/RJ2H4V8Fk45gygY8L+vwzHPDLF\nue4DPAksJup+zgf+BjSuqgyi66izwvnPAn6ScNwpJFwPDWktgTuIBi5WAm8Bp4Ztu4Tykw6WhDwX\nhXquAkYDTRO2zQZOT1g/n2iwazXwKXBxwrbO4eeSuMwP29qGc1xNNHgzjXCt1ZeGtSj8Ql2ek/RT\noi5yZ4tasc7lDe8aOyQ1Bv4A3O1B0OUjD4R5TlJ3om5de6Luu3N5x7vGzrm85y1C51zeq5eH9dPk\nTVXn6l6N7nksPHRw7L/T0qkPZe39ldkcCCk8dHCmq+BiKp36EACLv1yZ4Zq4uIo6tMx0FbKGd42d\nc+lTQfwlzuGim9QfkfRheFz0UEltJE2UNDd8tg55JelWSfMkvStpv4TjDAn550oaUnmJEQ+Ezrn0\nFRTGX+L5F/Ccme1J9GjkHKK5JSeZWTdgUlgHGAB0C0sx0Q34SGoDXEM0IcZBwDVlwbPS06jOOTvn\n3FZqsUUoqQXRI4qjAMxsg5mtAAYSTQxC+CybQXwgMNYi04BWktoD/YCJZrbMovkoJwL9U5XtgdA5\nlz4p9iKpWNLMhKW43NF2JXpWfrSktyTdLWlborkxF8Hm59DLZiHqwNazFpWEtMrSK5XVgyXOuSxX\njbcqmNlIYGSKLI2A/YDfm9l0Sf9iSzc4aenJikmRXilvETrn0le7gyUlQImZTQ/rjxAFxsWhy0v4\nXJKQv1PC/h2JpnyrLL1SHgidc2lTgWIvVTGzr4AFkspegNUb+ACYwJaJjocAT4TvE4AzwujxIcDK\n0HV+HugrqXUYJOlLFVOjedfYOZe+gloPIb8HHpDUhGhKtLOIGmzjJQ0lerfNySHvM0Szgc8jesXC\nWQBmtkzScKIp0iB6DcSyVIV6IHTOpU+1+7CImb0NHJBkU+8keQ04r5Lj3EM0IXIsHgidc+mr2Suo\ns4YHQudc+jwQOufyXi13jTPFA6FzLn21P1iSEblxFs65zPAWoXMu7/k1Qudc3vNA6JzLe941ds7l\nPW8ROufyXmHsCVezmgdC51za5C1C51ze80DonMt7PljinMt73iJ0zuW9+G+ny2oeCJ1z6fMWoXMu\n73kgdM7lPR8scc7lPQ+EzjnngdA5l+981Ng5l/e8a+yccx4InXP5zluEzrm854HQOec8EDrn8l2B\nP1ninMt73iJ0zuW7HLlGmBvtWudcZqgg/hLncNJ8Se9JelvSzJDWRtJESXPDZ+uQLkm3Spon6V1J\n+yUcZ0jIP1fSkKrK9UDonKsBVWOJ7Ugz62lmB4T1y4FJZtYNmBTWAQYA3cJSDNwBUeAErgEOBg4C\nrikLnpXxQOicS5sKCmIvNTAQGBO+jwEGJaSPtcg0oJWk9kA/YKKZLTOz5cBEoH+qAjwQOufSJ8Ve\nJBVLmpmwFCc5ogEvSJqVsL3IzBYBhM92Ib0DsCBh35KQVll6pXywxDlXA/G7vGY2EhhZRbYfm9lC\nSe2AiZI+rGbhliK9Ut4idM6lrxotwjjMbGH4XAI8TnSNb3Ho8hI+l4TsJUCnhN07AgtTpFfKA6Fz\nLn21GAglbStp+7LvQF/gfWACUDbyOwR4InyfAJwRRo8PAVaGrvPzQF9JrcMgSd+QVinvGjvnaqBW\n7yMsAh5XFDQbAQ+a2XOSZgDjJQ0FvgBODvmfAY4B5gFrgbMAzGyZpOHAjJDvOjNblqpgD4TOufTV\n4iN2ZvYp0CNJ+jdA7yTpBpxXybHuAe6JW7YHQudcDeTG1TUPhM659OXII3YeCNPQcrvm3DWsmL13\n64gZ/Ob6O+nYrg1XDz2J7p135pChf2LWh58C0OfAH/LX351Kk8aN2PD9Ri773wd5cdZstmvejJfu\nuGbzMTu224EHnn+Vi24ZW6G8y84YyK+P60Vp6SYuuHkML0x/t97ONReMuHE4r097ldatWjPmnnEA\nvDjlv4wecxeffzGfO28fzZ577AXAC/99jnEP37d5308+ncfdd95Ht6678/3333PLrX/nrXdmUaAC\nfjP0XHr99KgK5d3/4L08/cwECgoK+MPvL+agAw+tnxPNBA+E+euWC4fw/LR3+MWVt9C4USHNmzVl\nxepvOWnYP7njst9slffrlasZeMk/WPT1cvbetSPP3jKMXY4/jzVr17H/kGGb870x+noen/JGhbK6\nd+7AKX0O5YenXcLOO7bmhVuvZM9TLmTTppS3RbkE/fv9jBMGncxfR/x5c1qXLrvxl2tv5B8337BV\n3r59+tO3T/QQwiefzuOKP/2Rbl13B+C+B0bTqlVrHhz7KJs2bWLV6lUVypo//1MmTX6BMfeM4+tv\nlnLRH8/ngbGPUFiYGy85qsgDYUqS9iR6BKYD0c2MC4EJZjanrsqsD9s334bDe+7JWcPvAOD7jaWs\nXLOWlWvWJs3/9sfzN3+f/WkJzZo03tw6LNO14060a92SV96ueO/o8T89gIf/O5UN329k/qKlfFLy\nFQft1ZVp78+t3RPLYT177Meir7a+jazzD7pUud+kyS/Q56i+m9effnYC99/7fwAUFBTQqmWrCvu8\n+vrL9D6qL02aNGHn9h3o0KEjcz6czT57/6iGZ5GlcqRFWCdXOiVdBowj+ufiDaJhbAEPSbo81b7Z\nbtcO7Vi6YhX3XPVbZo65gZHDzqZ5s6ax9v35kQfx1sfztwqCAKf2PYzxk6Ym3adD29aULP5m83rJ\n0mV0aJvy+XFXSya/OJHeR/UDYPWa1QCMGv0fhhb/iqv/fDnLln1TYZ+lS5fSrm3R5vW2bdvx9ddL\n66fCmVDLs89kSl3VbihwoJmNMLP7wzKC6C7xoZXtlPgs4siRVT2JkxmNCgvZb/cu/OexiRwwZBjf\nfreey844vsr99urSkRt+dxrn/u3uCttO6XMo4154Pel+SvIvrnmvuM59MOd9mjZrxq5ddgOgtLSU\npUuXsM8+PRg18j723vuH3P6fWyvsZ8me5MqRVlNStfxkSabUVSDcBOycJL192JaUmY00swPM7IDi\n4mTPY2deyZJvKFm6jDc++ASAR1+czn67p+5mdWjbhkdHXMSZw2/n0y+XbLXtR113oVFhIW9+9Fkl\n5S2jY9EOm9c7tm3Dwq+X1/AsXFXKd4tbtmhJs2bN+OlPegHQ64g+fDy34qWMdm3bsWTp4s3rS5cu\nYccddqzz+mZKgRR7yWZ1FQgvACZJelbSyLA8RzSX2B/qqMx6sXjZShYs/obdd2kPwFEH7MMH80sq\nzd9yu+Y8edOlXHnHOF5/9+MK2089+jDGTUzeGgR48pVZnNLnUJo0bkTn9m3p2mkn3vhgXs1PxFVq\n06ZNTHlpMr2P3BIIJXHYoYfz1tuzAHjzzRlJrzP++NDDmTT5BTZs2MDCRV9S8uUCuu+5d73Vvb7l\nSIMQWR31syQVEHWFOxBdHywBZphZacxDWOGhg+ukbjXVo9sPGDmsmCaNG/HZl4v59fV30mu/7vzr\nojNp26oFK9as5Z2P5zPgwhFcceYJXH7G8cxd8NXm/ftfcANLl0cjjnMfuYVjL76Rjz7fcjH/uJ/s\nz/7du/Dnux4BYNiQQZx1bC82lpZy0S1jeW7aO/V7wjGUTn0IgMVfrsxwTSq6dvhVvPXOLFauXEGb\n1jtw1pln02L7Fvzr3zexYuVytttue7ru1o2bbvw3AG+9PYs777qN/9y29YMJX321iL/ccA1rvl1D\nq5atGHbp1RQV7cSrr73MRx/PYehZ5wAw9v57eObZJyksLOT3513EIQcfVu/nHEdRh5ZQw2Hfbf/4\nZOwA8u0/jsvacFhngbAWZG0gdBVlcyB0ydVGIGxx6VOxA8iqG4/N2kDo9xE659JWkLWhrXo8EDrn\n0pbsroaGyAOhcy5t3iJ0zuU9bxE65/Ketwidc3mvMEcioQdC51zavGvsnMt7OdIg9EDonEuftwid\nc3nPW4TOubyX7bPKxOWB0DmXNh81ds7lvRxpEHogdM6lz7vGzrm8lyNx0AOhcy593iJ0zuW9XBks\nye537DnnslpdvLNEUqGktyQ9Fda7SJouaa6khyU1CelNw/q8sL1zwjGGhfSPJPWrqkwPhM65tNXR\nW+z+AMxJWP8bcLOZdQOWs+WVwEOB5WbWFbg55EPSXsCpwN5Af+B2SYUpz6M6tXPOuUS13SKU1BH4\nGXB3WBdwFPBIyDIGGBS+DwzrhO29Q/6BwDgzW29mnwHziF4kVykPhM65tFWnRSipWNLMhCXZy8tv\nAS5ly/vPdwBWmNnGsF5C9GZMwucCgLB9Zci/OT3JPklVOlgiqU2qHc1sWartzrncV52xEjMbCYys\nbLukY4ElZjZLUq+y5GSHqmJbqn2SSjVqPKuKg+6a6sDOudxXy6PGPwaOl3QM0AxoQdRCbCWpUWj1\ndQTKXgJeAnQCSiQ1AloCyxLSyyTuk1SlXWMz62Jmu4bP8osHQedcrQ6WmNkwM+toZp2JBjsmm9np\nwIvASSHbEOCJ8H1CWCdsn2zRi9onAKeGUeUuQDfgjVRlV3kfYbj4eDrQxcyGS9oF2MnMUh7YOZf7\n6uk2wsuAcZL+ArwFjArpo4D7JM0jagmeCmBmsyWNBz4ANgLnmVlpqgLi3FB9O9GFy6OA4cBq4FHg\nwGqfjnMup9TVkyVmNgWYEr5/SpJRXzNbB5xcyf7XA9fHLS9OIDzYzPaT9FYoYHnZDY3OufyWK7ed\nxAmE34ebEQ1AUlu2DG075/JYrjxiFycQ3go8DhRJup7oouRVdVor51yDkCNxsOpAaGYPSJoF9A5J\ng8xsTqp9nHP5Id9mn2kOlHWPt6m76jjnGpJcuUZY5XlIuproeb42wI7AaEneNXbO1dWkC/UuTotw\nMLBvGKpG0gjgTeAvdVkx51z2y5trhMB8osdd1oX1psAndVUh51zD0SjLW3pxpZp04d9E1wTXA7Ml\nTQzrRwOv1k/1nHPZLNu7vHGlahHODJ+ziG6fKTOlzmrjnGtQcr5rbGZjKtvmnHOQHy1CACR1A24A\n9iK6VgiAz0DjnMuVFmGc24BGA3cQzeJwJDAWuK8uK+WcaxgKUOwlm8UJhNuY2SRAZva5mf2ZaCYa\n51yea1Sg2Es2i3P7zDpJBcBcSecDXwLt6rZazrmGIMvjW2xxWoQXED1i9z/A/sCv2DIrrHMuj+XN\nkyVmNiN8XQOcVbfVcc41JNkd3uJLdUP1k6R485OZHV8nNXLONRi50jVO1SL8R73VwjnXIGX7IEhc\nqW6ofqk+K+Kca3iy/baYuOLOR+iccxXkSIPQA6FzLn3ZPhocV1YHwtKpD2W6Cq6aijq0zHQVXD3K\n+Rahjxo756qSDy3CzI8ar9uY6Rq4uJpF/yttWL0+wxVxcTXZvmmNj1GY64HQR42dc1XJ+a5xGZ+G\nyzlXmRyJgz4Nl3MufbnyrLFPw+WcS1tBNZZsFqd+W03DJekEfBou5xwgxV+qPpaaSXpD0juSZku6\nNqR3kTRd0lxJD0tqEtKbhvV5YXvnhGMNC+kfSepXVdk+DZdzLm2FUuwlhvXAUWbWA+gJ9Jd0CPA3\n4GYz6wYsB4aG/EOB5WbWFbg55EPSXsCpwN5Af+B2SYWpCq4yEJrZDDNbY2YlZnaWmZ1oZtPinJVz\nLrfV5jVCi6wJq43DYkSX4h4J6WOAQeH7wLBO2N5bkkL6ODNbb2afAfOAg1KVHWfU+EWS3FhtZn6d\n0Lk8V50xEEnFQHFC0kgzG1kuTyHRK4S7ArcBnwArzKzspuISoEP43gFYAGBmGyWtBHYI6YmNtcR9\nkorziN0fE743A35ONILsnMtz1RkECUFvZBV5SoGekloRvU+9e7Js4TNZGLYU6ZWKM0P1rHJJr0ny\nm62dc3V2W4yZrZA0BTgEaCWpUWgVdgQWhmwlQCegRFIjoCWwLCG9TOI+SVUZ0CW1SVh2DCMwO1Xz\nvJxzOahQ8ZeqSGobWoJI2gboA8wBXgROCtmGAE+E7xPYMnB7EjDZzCyknxpGlbsA3YA3UpUdp2s8\niy3NzY3AZ2wZtXHO5THVbouwPTAmXCcsAMab2VOSPgDGSfoL8BYwKuQfBdwnaR5RS/BUADObLWk8\n8AFRzDovdLkrP48ogKbIIDUzs3Xl0pqaWV0/XW8+6UID4pMuNDhh0oUaRbKbn/ogdQBJcOGxe2Xt\n4yVxrnW+niRtam1XxDnX8EiKvWSzVPMR7kQ05LyNpH3Z8i9HC6IbrJ1zeS4fZp/pB5xJNOJyE1sC\n4SrgirqtlnOuIcj2Z4jjSjUf4RiiC5c/N7NH67FOzrkGIlcmZo0T0PcvG9IGkNQ6jN445/JcbU66\nkElxAuEAM1tRtmJmy4Fj6q5KzrmGIp/mIyyUtPnlBuFGx5q/7MA51+CpGks2i3ND9f3AJEmjiW6s\n/jXRLNXOuTyX7S29uOI8a3yjpHeJHncRMNzMnq/zmjnnsl4+3D6zmZk9BzwHIOnHkm4zs/PqtGbO\nuayXK6PGsQKhpJ7AYOAUomeNH6vLSjnnGoYciYMpnyzZnegh5sHAN8DDRM8mH1lPdXPOZbmcv6Ea\n+BB4BTjOzOYBSLqwXmrlnGsQsv0Z4rhSBfSfA18BL0q6S1Jvsn8U3DlXj3L+dZ5m9riZnQLsCUwB\nLgSKJN0hqW891c85l8UKChR7yWZx3mL3rZk9YGbHEk3A8DZweZ3XzDmX9XK+RZiMmS0zszv9DXbO\nOciD+Qidc64qWR7fYvNA6JxLW7Z3eePyQOicS1u2d3nj8kDonEtbnNd0NgQeCJ1zafMWoXMu7+VG\nGPRA6JyrgSy/Tzo2D4TOubR519g5l/d8sMQ5l/eUI1cJPRA659KWIz3jnLkx3DmXAQWKv1RFUidJ\nL0qaI2m2pD+E9DaSJkqaGz5bh3RJulXSPEnvStov4VhDQv65koZUeR7p/wicc/lO1fgvho3AxWbW\nHTgEOE/SXkSzXU0ys27AJLbMfjUA6BaWYuAOiAIncA1wMHAQcE1Z8KyMB0LnXNqk+EtVzGyRmb0Z\nvq8G5gAdgIHAmJBtDDAofB8IjLXINKCVpPZAP2BimC1rOTAR6J+qbL9G6JxLW129xU5SZ2BfYDpQ\nZGaLIAqWktqFbB2ABQm7lYS0ytIr5S1C51zaqtMilFQsaWbCUpz8mNoOeBS4wMxWpSo+SZqlSK+U\ntwidc2mrTnvQzEYCI1MeT2pMFAQfMLOy1wYvltQ+tAbbA0tCegnQKWH3jsDCkN6rXPqUVOV6i9A5\nl7YCKfZSFUWPqYwC5pjZPxM2TQDKRn6HAE8kpJ8RRo8PAVaGLvTzQF9JrcMgSd+QVilvEdbA+vXr\nOf2sM9jw/QZKN5bS7+i+/M/vzt+8ffgN1/PYE4/z1rSZACxctJDLrrqC1atXU7ppE3/8w4UccfhP\nKxz35dde4fq/jWDTplJOPuHnFA89u97OKR/0O64/zZs3p7CwkMLCQh6+bxwffvQhw28YzvoNGygs\nLOSqy67kh/v8kNFjR/P0c88AULpxI5/O/4yXJ75Ey5YttzpmyZclXHrFpaxctYrue3bnhuv+SuPG\njTNxevWqli8R/hj4FfCepLdD2hXACGC8pKHAF8DJYdszwDHAPGAtcBZErxSRNByYEfJdZ2bLUhUs\ns5Rd50wy1m3MdB1SMjPWfreWbZtvy/fff89pZ/6KKy8bRs8f9eC92e8z9oH7+e/k/24OhH+67hq6\n79md035xKvM+mUfx+ecy+dmJWx2ztLSUfsf/jNF33kVRUREnnXYK/xzxd7ru1jUTpxhfs+jf1A2r\n12e4IlXrd1x/xt33EK1bbbmjovi8c/jVab/k8B8fzsuvvsLosaMZPfKerfab8vIU7nvwPkb9Z1SF\nY158+R/pc2RvBvQbwHV/Hc4eu+/OKSedUufnUhNNtm8KNZxAZsZbC2MHkAP33Tlrb7/2rnENSGLb\n5tsCsHHjRjZu3IgQpaWl3PjPf3DJhRdvnR+xZs0aAFavWUO7tu0qHPPd99/jB5060aljJ5o0bsLP\n+h/DpCkv1v3J5DlJfPvttwCsWbOatm3bVsjzzPPPMqDfgArpZsYbM97g6N5HA3D8scczOU9+Z4UF\n8Zds5l3jGiotLeXEwSfzxRdfcNopg+nxox8x5oH76N3rSNqV+2M6/9zzGPrbs7n/oQf57rvvGD3y\n7grHW7xkMTvt1H7zelG7It597906P498IsE5550DEiefeDInn3gSl118Keec/1v+8a+bsE3GffeM\n3Wqf79Z9x2tTX+PKS6+ocLwVK1ew/fbb06hR9Oe0U7silixZXC/nkmm58qxxvcdpSWel2LZ5eH3k\nyJSDS1mjsLCQJ8Y/xksvTObd999jxqyZPPfC8/xy8OkV8j797NOccPwgXp44mZG33cGlV17Opk2b\ntsqT7EpFrkx1lC3GjhrL+AfGc8ettzPu/8Yx882ZPPzIeC696BL++/RELrnoEq4efs1W+7z08kvs\n26NnhWuDELUIK8iT31ltPmKXSZlosF5b2QYzG2lmB5jZAcXFSW8xylotWrTg4AMPYvqMN/hiwRf0\nPW4ARw04mu/WrePoY6Ob2h95/DEG9OsHwL49erJ+/QaWL1++1XF2Kiriq68WbV5fvGQx7dpV7EK7\n9JVdktihzQ707nUU789+nwlPTaDPUX0A6NenL+/Pfn+rfZ594bmk3WKA1q1as3r1ajZujK5pf7Vk\ncdLLHrmoNp8syaQ6CYThAehky3tAUV2UmQnLli1j1arofs9169bx+rSp7N19L16b/DKTn53I5Gcn\nsk2zZkx86jkA2rdvz9Tp0wD45NNPWL9hPW3atNnqmD/cex/mf/EFC0pK2PD9Bp5+7hmOOuLI+j2x\nHLb2u7WbrwWu/W4tr0+fStfdutK2bVtmzooGtabPmM4unXbZvM/qNauZ+eZMjqzk9yCJAw84kImT\nooGvCU9N4MgjetXtiWSJWn7WOGPq6hphEdHzfsvLpQt4vY7KrHdLvl7K5VddQemmTdimTfTv2y/l\nH8DlF1/CVdddw733j0USI667HkksXrKEq669mrtu+w+NGjXi6mFX8ptziyndtImfDzqBbl2zfMS4\nAfnmm2VccMkFQHR995h+A/icTMkaAAAHjklEQVTJYT+hefPmjPjH3ygtLaVpkyZcc+WWrvGkFydz\n2MGH0Xyb5lsd69z/+R3X/unPtGvbjgt/fyGXXnEp/77jf9lzjz05ceCJ9XpemaIsHwSJq05un5E0\nChhtZq8m2fagmZ0W4zBZf/uMS9CAbp9xkdq4fWb27MWxA8jeexdlbbOwTlqEZjY0xbY4QdA51wBk\n+7W/uPz2Gedc2nLljgYPhM65tOVGGPRA6JyrgRxpEHogdM6lz7vGzrm8lyNx0AOhcy593iJ0zuW9\n3AiDHgidczXgLULnXN7LkTjogdA5l7447yJpCDwQOufSlxtx0AOhcy59OdIg9EDonEtfts8zGJcH\nQudc2rxF6JzLez5Y4pzLezkSBz0QOufSlys3VOfIGweccy593iJ0zqUtRxqEHgidc+nLla6xB0Ln\nXNpyZdTYrxE659ImxV+qPpbukbRE0vsJaW0kTZQ0N3y2DumSdKukeZLelbRfwj5DQv65kobEOQ8P\nhM65tKkaSwz3Av3LpV0OTDKzbsCksA4wAOgWlmLgDogCJ3ANcDBwEHBNWfBMxQOhcy59tdgkNLOX\ngWXlkgcCY8L3McCghPSxFpkGtJLUHugHTDSzZWa2HJhIxeBagQdC51zaqtMilFQsaWbCUhyjiCIz\nWwQQPtuF9A7AgoR8JSGtsvSUfLDEOZe+aoyVmNlIYGQdlmwp0lPyFqFzLm0FUuwlTYtDl5fwuSSk\nlwCdEvJ1BBamSE99HunWzjnn6sEEoGzkdwjwREL6GWH0+BBgZeg6Pw/0ldQ6DJL0DWkpedfYOZe2\n2ryNUNJDQC9gR0klRKO/I4DxkoYCXwAnh+zPAMcA84C1wFkAZrZM0nBgRsh3nZmVH4CpWLZZld3n\nTDHWbcx0HVxczaJ/UzesXp/hiri4mmzfFGo42f6qr9fGDiAtdmyetXdfe4vQOZe2HHmwxAOhcy59\nHgidc3nP31ninHO5EQc9EDrn0pcjcdADoXOuBnIkEnogdM6lza8ROufyno8aO+fyXq5M1e/PGjvn\n8p63CJ1zacuRBqEHQudc+nIkDnogdM7VQI40CT0QOufSliNx0AOhcy59fh+hcy7v5UqL0G+fcc7l\nPW8ROufSlistwuyeqt85V9dqFsrWbYz/d9qsUdaGzWwOhDlLUnF4x6trAPz3lfv8GmFmFGe6Aq5a\n/PeV4zwQOufyngdC51ze80CYGX69qWHx31eO88ES51ze8xahcy7veSB0zuU9D4T1SFJ/SR9Jmifp\n8kzXx6Um6R5JSyS9n+m6uLrlgbCeSCoEbgMGAHsBgyXtldlauSrcC/TPdCVc3fNAWH8OAuaZ2adm\ntgEYBwzMcJ1cCmb2MrAs0/Vwdc8DYf3pACxIWC8Jac65DPNAWH+SPXDu9y45lwU8ENafEqBTwnpH\nYGGG6uKcS+CBsP7MALpJ6iKpCXAqMCHDdXLO4YGw3pjZRuB84HlgDjDezGZntlYuFUkPAVOBPSSV\nSBqa6Tq5uuGP2Dnn8p63CJ1zec8DoXMu73kgdM7lPQ+Ezrm854HQOZf3PBA2YJJKJb0t6X1J/yep\neQ2O1UvSU+H78almx5HUStLv0ijjz5L+GDe9XJ57JZ1UjbI6+6wxLi4PhA3bd2bW08z2ATYAv03c\nqEi1f8dmNsHMRqTI0gqodiB0Llt5IMwdrwBdQ0tojqTbgTeBTpL6Spoq6c3QctwONs+P+KGkV4ET\nyw4k6UxJ/xu+F0l6XNI7YTkMGAHsFlqjfw/5LpE0Q9K7kq5NONaVYQ7G/wJ7VHUSks4Ox3lH0qPl\nWrl9JL0i6WNJx4b8hZL+nlD2OTX9Qbr844EwB0hqRDTP4XshaQ9grJntC3wLXAX0MbP9gJnARZKa\nAXcBxwGHAztVcvhbgZfMrAewHzAbuBz4JLRGL5HUF+hGNNVYT2B/ST+VtD/Ro4T7EgXaA2OczmNm\ndmAobw6Q+DRHZ+AI4GfAf8I5DAVWmtmB4fhnS+oSoxznNmuU6Qq4GtlG0tvh+yvAKGBn4HMzmxbS\nDyGaCPY1SQBNiB4b2xP4zMzmAki6n+QvMj8KOAPAzEqBlZJal8vTNyxvhfXtiALj9sDjZrY2lBHn\n2ep9JP2FqPu9HdEjiWXGm9kmYK6kT8M59AV+lHD9sGUo++MYZTkHeCBs6L4zs56JCSHYfZuYBEw0\ns8Hl8vWk9qYBE3CDmd1ZrowL0ijjXmCQmb0j6UygV8K28seyUPbvzSwxYCKpczXLdXnMu8a5bxrw\nY0ldASQ1l7Q78CHQRdJuId/gSvafBJwb9i2U1AJYTdTaK/M88OuEa48dJLUDXgZOkLSNpO2JuuFV\n2R5YJKkxcHq5bSdLKgh13hX4KJR9bsiPpN0lbRujHOc28xZhjjOzpaFl9ZCkpiH5KjP7WFIx8LSk\nr4FXgX2SHOIPwMgw80opcK6ZTZX0Wrg95dlwnbA7MDW0SNcAvzSzNyU9DLwNfE7Ufa/Kn4DpIf97\nbB1wPwJeAoqA35rZOkl3E107fFNR4UuBQfF+Os5FfPYZ51ze866xcy7veSB0zuU9D4TOubzngdA5\nl/c8EDrn8p4HQudc3vNA6JzLe/8fMdjM2buR9UQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e27fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix\",\"\\n\")\n",
    "score = round(accuracy_score(y_test, prediction),3) \n",
    "cm1 = cm(y_test, prediction)\n",
    "sns.heatmap(cm1, annot=True, fmt=\".1f\", linewidths=.3, \n",
    "        square = True, cmap = 'PuBu')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.title('Accuracy Score: {0}'.format(score), size = 12)\n",
    "plt.show()\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Selection \n",
      "\n",
      "Index(['campaign', 'previous', 'cons.conf.idx', 'pdays_cat', 'X_factor',\n",
      "       'education_illiterate', 'month_mar', 'month_may', 'month_oct',\n",
      "       'poutcome_failure'],\n",
      "      dtype='object') \n",
      "\n"
     ]
    }
   ],
   "source": [
    "rfe=RFE(lr,10)\n",
    "rfe=rfe.fit(X_train,y_train)\n",
    "print(\"Feature Selection\",\"\\n\")\n",
    "print(X_train[X_train.columns[rfe.ranking_==1].values].columns,\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (mean): % 74.1203824523\n",
      "std: % 0.624419755001\n"
     ]
    }
   ],
   "source": [
    "accuracies = cross_val_score(estimator = lr, X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy (mean): %\",accuracies.mean()*100)\n",
    "print(\"std: %\",accuracies.std()*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (mean): % -0.258796175477\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(lr, X_train, y_train, scoring='neg_mean_absolute_error', cv=10,)\n",
    "print (\"MAE (mean): %\" , scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 0.18\n",
      "Mean Squared Error (MSE): 0.18\n",
      "Root Mean Squared Error (RMSE): 0.43\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(lr.predict(X_test), y_test)\n",
    "mse = mean_squared_error(lr.predict(X_test), y_test)\n",
    "rmse = np.sqrt(mse)\n",
    "print('Mean Absolute Error (MAE): %.2f' % mae)\n",
    "print('Mean Squared Error (MSE): %.2f' % mse)\n",
    "print('Root Mean Squared Error (RMSE): %.2f' % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cohen - Matthew Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohen Score 0.336758215327\n",
      "Matthew Score 0.361011121079\n"
     ]
    }
   ],
   "source": [
    "print(\"Cohen Score\",cohen_kappa_score(y_test, prediction))\n",
    "print(\"Matthew Score\",matthews_corrcoef(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Algorithm\n",
      "Classification Report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.93      0.82      0.87      7303\n",
      "        1.0       0.28      0.55      0.37       935\n",
      "\n",
      "avg / total       0.86      0.79      0.82      8238\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=100,weights=\"distance\")  \n",
    "classifier.fit(X_train, y_train)  \n",
    "y_pred = classifier.predict(X_test)  \n",
    "\n",
    "print(\"KNN Algorithm\")\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAAEWCAYAAAATsp59AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1215c1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix\",\"\\n\")\n",
    "score = round(accuracy_score(y_test, y_pred),3) \n",
    "cm1 = cm(y_test, y_pred)\n",
    "sns.heatmap(cm1, annot=True, fmt=\".1f\", linewidths=.3, \n",
    "        square = True, cmap = 'PuBu')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.title('Accuracy Score: {0}'.format(score), size = 12)\n",
    "plt.show()\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy (mean): %\",accuracies.mean()*100)\n",
    "print(\"std: %\",accuracies.std()*100)\n",
    "\n",
    "scores = cross_val_score(classifier, X_train, y_train, scoring='neg_mean_absolute_error', cv=10,)\n",
    "print (\"MAE (mean): %\" , scores.mean())\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 0.21\n",
      "Mean Squared Error (MSE): 0.21\n",
      "Root Mean Squared Error (RMSE): 0.46\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(classifier.predict(X_test), y_test)\n",
    "mse = mean_squared_error(classifier.predict(X_test), y_test)\n",
    "rmse = np.sqrt(mse)\n",
    "print('Mean Absolute Error (MAE): %.2f' % mae)\n",
    "print('Mean Squared Error (MSE): %.2f' % mse)\n",
    "print('Root Mean Squared Error (RMSE): %.2f' % rmse)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohen Score 0.262299680993\n",
      "Matthew Score 0.284067585545\n"
     ]
    }
   ],
   "source": [
    "print(\"Cohen Score\",cohen_kappa_score(y_test, y_pred))\n",
    "print(\"Matthew Score\",matthews_corrcoef(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Algorithm \n",
      "\n",
      "Classification Report \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.95      0.83      0.88      7303\n",
      "        1.0       0.32      0.63      0.43       935\n",
      "\n",
      "avg / total       0.88      0.81      0.83      8238\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10 ,n_jobs=-1,\n",
    "                            random_state=42, \n",
    "                            max_depth= 5\n",
    "                           # class_weight=\"balanced\",\n",
    "                            #max_features=10)\n",
    "                            )\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "rf_predict = rf.predict(X_test)\n",
    "print(\"Random Forest Algorithm\",\"\\n\")\n",
    "print(\"Classification Report\",\"\\n\")\n",
    "print(classification_report(y_test, rf_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAAEWCAYAAAATsp59AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121f55160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix\",\"\\n\")\n",
    "score = round(accuracy_score(y_test, rf_predict),3)\n",
    "cm1 = cm(y_test, rf_predict)\n",
    "sns.heatmap(cm1, annot=True, fmt=\".1f\", linewidths=.3, \n",
    "            square = True, cmap = 'PuBu')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.title('Accuracy Score: {0}'.format(score), size = 12)\n",
    "plt.show()\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (mean): % 74.301591895\n",
      "std: % 0.657612294845\n",
      "MAE (mean): % -0.25698408105\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "accuracies = cross_val_score(estimator = rf, X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy (mean): %\",accuracies.mean()*100)\n",
    "print(\"std: %\",accuracies.std()*100)\n",
    "\n",
    "scores = cross_val_score(rf, X_train, y_train, scoring='neg_mean_absolute_error', cv=10,)\n",
    "print (\"MAE (mean): %\" , scores.mean())\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 0.19\n",
      "Mean Squared Error (MSE): 0.19\n",
      "Root Mean Squared Error (RMSE): 0.44\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(rf.predict(X_test), y_test)\n",
    "mse = mean_squared_error(rf.predict(X_test), y_test)\n",
    "rmse = np.sqrt(mse)\n",
    "print('Mean Absolute Error (MAE): %.2f' % mae)\n",
    "print('Mean Squared Error (MSE): %.2f' % mse)\n",
    "print('Root Mean Squared Error (RMSE): %.2f' % rmse)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohen Score 0.32532218886\n",
      "Matthew Score 0.352462336608\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Cohen Score\",cohen_kappa_score(y_test, rf_predict))\n",
    "print(\"Matthew Score\",matthews_corrcoef(y_test, rf_predict))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAF7CAYAAABsGdmzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIwwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121d126a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranking = rf.feature_importances_\n",
    "features = np.argsort(ranking)[::-1][:10]\n",
    "columns = data_scaled.columns\n",
    "plt.title(\"Feature importances based on Random Forest Regressor\", y = 1.03, size = 18)\n",
    "plt.bar(range(len(features)), ranking[features], color=\"aqua\", align=\"center\")\n",
    "plt.xticks(range(len(features)), columns[features], rotation=80)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}