LR_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f10cd2b3",
   "metadata": {},
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffebc397",
   "metadata": {},
   "source": [
    "**Author:** Alva Rani James, PhD, **Year:** 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd0c2335",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sklearn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [4], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m \n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mrc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfont\u001b[39m\u001b[38;5;124m\"\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m preprocessing\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LogisticRegression\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4733db7",
   "metadata": {},
   "source": [
    "- **Tip:** Intall the packages, which are not avaliable by using the below command line trick in the juypter note by using \"!\" mark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d326e520",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install imblearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d0b153",
   "metadata": {},
   "source": [
    "# Logistic regression "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e8300c",
   "metadata": {},
   "source": [
    "Logistic Regression is a type of classification algorithm in Machine Learning that is utilized to estimate the probability of a categorical dependent variable. This approach is typically applied to binary variables, where the data is represented as either 1 (for success, yes, etc.) or 0 (for failure, no, etc.). Essentially, the logistic regression model predicts the likelihood of Y being 1 based on the values of X."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24971a30",
   "metadata": {},
   "source": [
    "## Logistic regression assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9332fb",
   "metadata": {},
   "source": [
    "To perform binary logistic regression:\n",
    "\n",
    "- It is essential that the dependent variable is binary and that the level 1 of the dependent variable signifies the intended outcome\n",
    "- It is imperative to include only the relevant variables and ensure that the independent variables are not highly correlated, that is, multicollinearity should be minimized\n",
    "- Additionally, the independent variables should exhibit a linear relationship with the log odds, and a substantial sample size is required for logistic regression to be effective \n",
    "\n",
    "Given these assumptions, we can now examine our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93e050b",
   "metadata": {},
   "source": [
    "# Download the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "419772d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-03-07 18:17:20--  https://raw.githubusercontent.com/madmashup/targeted-marketing-predictive-engine/master/banking.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4882918 (4.7M) [text/plain]\n",
      "Saving to: ‘banking.csv’\n",
      "\n",
      "100%[======================================>] 4,882,918   13.7MB/s   in 0.3s   \n",
      "\n",
      "2023-03-07 18:17:21 (13.7 MB/s) - ‘banking.csv’ saved [4882918/4882918]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! wget https://raw.githubusercontent.com/madmashup/targeted-marketing-predictive-engine/master/banking.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5627d35",
   "metadata": {},
   "source": [
    "# 2. Export the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780c8246",
   "metadata": {},
   "source": [
    "The data originates from the UCI Machine Learning repository and pertains to phone-based direct marketing campaigns carried out by a Portuguese banking institution. \n",
    "- The objective of the classification exercise is to anticipate whether the customer will agree to subscribe (1/0) to a term deposit, as denoted by the variable y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36719745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the data:  (41188, 21)\n",
      "columns: ['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y']\n"
     ]
    }
   ],
   "source": [
    "df =pd.read_csv(\"/home/ctg/Share/L_posts/data/banking.csv\",header=0)\n",
    "df =df.dropna()\n",
    "print(\"The shape of the data: \",df.shape)\n",
    "print(\"columns:\",list(df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f72fdf8",
   "metadata": {},
   "source": [
    "## Preprocessing or Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c5be00a",
   "metadata": {},
   "source": [
    "## 1.1 Input variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6bd05905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "job                object\n",
       "marital            object\n",
       "education          object\n",
       "default            object\n",
       "housing            object\n",
       "loan               object\n",
       "contact            object\n",
       "month              object\n",
       "day_of_week        object\n",
       "duration            int64\n",
       "campaign            int64\n",
       "pdays               int64\n",
       "previous            int64\n",
       "poutcome           object\n",
       "emp_var_rate      float64\n",
       "cons_price_idx    float64\n",
       "cons_conf_idx     float64\n",
       "euribor3m         float64\n",
       "nr_employed       float64\n",
       "y                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f7b463",
   "metadata": {},
   "source": [
    "## 1.2 Check the categorical columns and reduce the catogires for a better modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0ec98c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('job',\n",
       " 0        blue-collar\n",
       " 1         technician\n",
       " 2         management\n",
       " 3           services\n",
       " 4            retired\n",
       " 8             admin.\n",
       " 10         housemaid\n",
       " 19        unemployed\n",
       " 25      entrepreneur\n",
       " 68     self-employed\n",
       " 70           unknown\n",
       " 103          student\n",
       " Name: job, dtype: object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols= list(df.select_dtypes(include=['object']).columns)\n",
    "cat_df=[]\n",
    "for cat_col in cat_cols:\n",
    "    df_cate = cat_col,df[cat_col].drop_duplicates()\n",
    "    cat_df.append(df_cate)\n",
    "cat_df[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "634a1522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job',\n",
       " 'marital',\n",
       " 'education',\n",
       " 'default',\n",
       " 'housing',\n",
       " 'loan',\n",
       " 'contact',\n",
       " 'month',\n",
       " 'day_of_week',\n",
       " 'poutcome']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8622fef",
   "metadata": {},
   "source": [
    "The above datafarme gives you the list of values within each categorical columns. From the columns, the education column has got many categories, hence, we need to reduce those categorires. \n",
    "\n",
    "The following categories are their in education column:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c0ddeed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('education',\n",
       " 0                  basic.4y\n",
       " 1                   unknown\n",
       " 2         university.degree\n",
       " 3               high.school\n",
       " 7                  basic.9y\n",
       " 23      professional.course\n",
       " 28                 basic.6y\n",
       " 3059             illiterate\n",
       " Name: education, dtype: object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5463d904",
   "metadata": {},
   "source": [
    "We could group all the categories with basic* into one group and call them \"basic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72e3553f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['education']=np.where(df['education'] =='basic.9y', 'Basic', df['education'])\n",
    "df['education']=np.where(df['education'] =='basic.6y', 'Basic', df['education'])\n",
    "df['education']=np.where(df['education'] =='basic.4y', 'Basic', df['education'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c21478f",
   "metadata": {},
   "source": [
    "## 1.2.1 After grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80b7e17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     Basic\n",
       "1                   unknown\n",
       "2         university.degree\n",
       "3               high.school\n",
       "23      professional.course\n",
       "3059             illiterate\n",
       "Name: education, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4234fcc",
   "metadata": {},
   "source": [
    "# 2. Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9340e12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36548\n",
       "1     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfb95b42",
   "metadata": {},
   "outputs": [
    {
     "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=df,palette='pastel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0814a9b4",
   "metadata": {},
   "source": [
    "## 2.1 Get the percentage of subscriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae073011",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of no subscription is 88.73458288821988\n",
      "percentage of subscription 11.265417111780131\n"
     ]
    }
   ],
   "source": [
    "count_no_sub = len(df[df['y']==0])\n",
    "count_sub = len(df[df['y']==1])\n",
    "pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of no subscription is\", pct_of_no_sub*100)\n",
    "pct_of_sub = count_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of subscription\", pct_of_sub*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17891257",
   "metadata": {},
   "source": [
    "The distribution of instances across our classes is imbalanced, with a ratio of 89:11 between no-subscription and subscription cases. \n",
    "   - Prior to taking steps to rectify this issue, we must conduct further investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349e19ae",
   "metadata": {},
   "source": [
    "###  2.1.1 Mean on neumerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3765c056",
   "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>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.911185</td>\n",
       "      <td>220.844807</td>\n",
       "      <td>2.633085</td>\n",
       "      <td>984.113878</td>\n",
       "      <td>0.132374</td>\n",
       "      <td>0.248875</td>\n",
       "      <td>93.603757</td>\n",
       "      <td>-40.593097</td>\n",
       "      <td>3.811491</td>\n",
       "      <td>5176.166600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.913147</td>\n",
       "      <td>553.191164</td>\n",
       "      <td>2.051724</td>\n",
       "      <td>792.035560</td>\n",
       "      <td>0.492672</td>\n",
       "      <td>-1.233448</td>\n",
       "      <td>93.354386</td>\n",
       "      <td>-39.789784</td>\n",
       "      <td>2.123135</td>\n",
       "      <td>5095.115991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "y                                                                        \n",
       "0  39.911185  220.844807  2.633085  984.113878  0.132374      0.248875   \n",
       "1  40.913147  553.191164  2.051724  792.035560  0.492672     -1.233448   \n",
       "\n",
       "   cons_price_idx  cons_conf_idx  euribor3m  nr_employed  \n",
       "y                                                         \n",
       "0       93.603757     -40.593097   3.811491  5176.166600  \n",
       "1       93.354386     -39.789784   2.123135  5095.115991  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('y').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7732f2ce",
   "metadata": {},
   "source": [
    "### 2.1.1 Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be90ac2",
   "metadata": {},
   "source": [
    "- Customers who purchased the term deposit have a higher average age in comparison to those who did not\n",
    "- Additionally, the pdays (i.e., the number of days since the last time the customer was contacted) is lower for the customers who agreed to the term deposit offer, which is expected since customers are more likely to remember the previous call and increase the likelihood of a successful sale \n",
    "- Interestingly, the number of contacts or calls made during the current campaign is lower for customers who subscribed to the term deposit, which is somewhat unexpected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10f308a",
   "metadata": {},
   "source": [
    "### 2.1.2 Mean on categorical columns to get more sense of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "edd6c2ea",
   "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>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>38.187296</td>\n",
       "      <td>254.312128</td>\n",
       "      <td>2.623489</td>\n",
       "      <td>954.319229</td>\n",
       "      <td>0.189023</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>93.534054</td>\n",
       "      <td>-40.245433</td>\n",
       "      <td>3.550274</td>\n",
       "      <td>5164.125350</td>\n",
       "      <td>0.129726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>39.555760</td>\n",
       "      <td>264.542360</td>\n",
       "      <td>2.558461</td>\n",
       "      <td>985.160363</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.248995</td>\n",
       "      <td>93.656656</td>\n",
       "      <td>-41.375816</td>\n",
       "      <td>3.771996</td>\n",
       "      <td>5175.615150</td>\n",
       "      <td>0.068943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>41.723214</td>\n",
       "      <td>263.267857</td>\n",
       "      <td>2.535714</td>\n",
       "      <td>981.267170</td>\n",
       "      <td>0.138736</td>\n",
       "      <td>0.158723</td>\n",
       "      <td>93.605372</td>\n",
       "      <td>-41.283654</td>\n",
       "      <td>3.791120</td>\n",
       "      <td>5176.313530</td>\n",
       "      <td>0.085165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>45.500000</td>\n",
       "      <td>250.454717</td>\n",
       "      <td>2.639623</td>\n",
       "      <td>960.579245</td>\n",
       "      <td>0.137736</td>\n",
       "      <td>0.433396</td>\n",
       "      <td>93.676576</td>\n",
       "      <td>-39.495283</td>\n",
       "      <td>4.009645</td>\n",
       "      <td>5179.529623</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>42.362859</td>\n",
       "      <td>257.058140</td>\n",
       "      <td>2.476060</td>\n",
       "      <td>962.647059</td>\n",
       "      <td>0.185021</td>\n",
       "      <td>-0.012688</td>\n",
       "      <td>93.522755</td>\n",
       "      <td>-40.489466</td>\n",
       "      <td>3.611316</td>\n",
       "      <td>5166.650513</td>\n",
       "      <td>0.112175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>62.027326</td>\n",
       "      <td>273.712209</td>\n",
       "      <td>2.476744</td>\n",
       "      <td>897.936047</td>\n",
       "      <td>0.327326</td>\n",
       "      <td>-0.698314</td>\n",
       "      <td>93.430786</td>\n",
       "      <td>-38.573081</td>\n",
       "      <td>2.770066</td>\n",
       "      <td>5122.262151</td>\n",
       "      <td>0.252326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>39.949331</td>\n",
       "      <td>264.142153</td>\n",
       "      <td>2.660802</td>\n",
       "      <td>976.621393</td>\n",
       "      <td>0.143561</td>\n",
       "      <td>0.094159</td>\n",
       "      <td>93.559982</td>\n",
       "      <td>-40.488107</td>\n",
       "      <td>3.689376</td>\n",
       "      <td>5170.674384</td>\n",
       "      <td>0.104856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>37.926430</td>\n",
       "      <td>258.398085</td>\n",
       "      <td>2.587805</td>\n",
       "      <td>979.974049</td>\n",
       "      <td>0.154951</td>\n",
       "      <td>0.175359</td>\n",
       "      <td>93.634659</td>\n",
       "      <td>-41.290048</td>\n",
       "      <td>3.699187</td>\n",
       "      <td>5171.600126</td>\n",
       "      <td>0.081381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>25.894857</td>\n",
       "      <td>283.683429</td>\n",
       "      <td>2.104000</td>\n",
       "      <td>840.217143</td>\n",
       "      <td>0.524571</td>\n",
       "      <td>-1.408000</td>\n",
       "      <td>93.331613</td>\n",
       "      <td>-40.187543</td>\n",
       "      <td>1.884224</td>\n",
       "      <td>5085.939086</td>\n",
       "      <td>0.314286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>38.507638</td>\n",
       "      <td>250.232241</td>\n",
       "      <td>2.577339</td>\n",
       "      <td>964.408127</td>\n",
       "      <td>0.153789</td>\n",
       "      <td>0.274566</td>\n",
       "      <td>93.561471</td>\n",
       "      <td>-39.927569</td>\n",
       "      <td>3.820401</td>\n",
       "      <td>5175.648391</td>\n",
       "      <td>0.108260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>39.733728</td>\n",
       "      <td>249.451677</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>935.316568</td>\n",
       "      <td>0.199211</td>\n",
       "      <td>-0.111736</td>\n",
       "      <td>93.563781</td>\n",
       "      <td>-40.007594</td>\n",
       "      <td>3.466583</td>\n",
       "      <td>5157.156509</td>\n",
       "      <td>0.142012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>45.563636</td>\n",
       "      <td>239.675758</td>\n",
       "      <td>2.648485</td>\n",
       "      <td>938.727273</td>\n",
       "      <td>0.154545</td>\n",
       "      <td>0.357879</td>\n",
       "      <td>93.718942</td>\n",
       "      <td>-38.797879</td>\n",
       "      <td>3.949033</td>\n",
       "      <td>5172.931818</td>\n",
       "      <td>0.112121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    duration  campaign       pdays  previous  \\\n",
       "job                                                                    \n",
       "admin.         38.187296  254.312128  2.623489  954.319229  0.189023   \n",
       "blue-collar    39.555760  264.542360  2.558461  985.160363  0.122542   \n",
       "entrepreneur   41.723214  263.267857  2.535714  981.267170  0.138736   \n",
       "housemaid      45.500000  250.454717  2.639623  960.579245  0.137736   \n",
       "management     42.362859  257.058140  2.476060  962.647059  0.185021   \n",
       "retired        62.027326  273.712209  2.476744  897.936047  0.327326   \n",
       "self-employed  39.949331  264.142153  2.660802  976.621393  0.143561   \n",
       "services       37.926430  258.398085  2.587805  979.974049  0.154951   \n",
       "student        25.894857  283.683429  2.104000  840.217143  0.524571   \n",
       "technician     38.507638  250.232241  2.577339  964.408127  0.153789   \n",
       "unemployed     39.733728  249.451677  2.564103  935.316568  0.199211   \n",
       "unknown        45.563636  239.675758  2.648485  938.727273  0.154545   \n",
       "\n",
       "               emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "job                                                                     \n",
       "admin.             0.015563       93.534054     -40.245433   3.550274   \n",
       "blue-collar        0.248995       93.656656     -41.375816   3.771996   \n",
       "entrepreneur       0.158723       93.605372     -41.283654   3.791120   \n",
       "housemaid          0.433396       93.676576     -39.495283   4.009645   \n",
       "management        -0.012688       93.522755     -40.489466   3.611316   \n",
       "retired           -0.698314       93.430786     -38.573081   2.770066   \n",
       "self-employed      0.094159       93.559982     -40.488107   3.689376   \n",
       "services           0.175359       93.634659     -41.290048   3.699187   \n",
       "student           -1.408000       93.331613     -40.187543   1.884224   \n",
       "technician         0.274566       93.561471     -39.927569   3.820401   \n",
       "unemployed        -0.111736       93.563781     -40.007594   3.466583   \n",
       "unknown            0.357879       93.718942     -38.797879   3.949033   \n",
       "\n",
       "               nr_employed         y  \n",
       "job                                   \n",
       "admin.         5164.125350  0.129726  \n",
       "blue-collar    5175.615150  0.068943  \n",
       "entrepreneur   5176.313530  0.085165  \n",
       "housemaid      5179.529623  0.100000  \n",
       "management     5166.650513  0.112175  \n",
       "retired        5122.262151  0.252326  \n",
       "self-employed  5170.674384  0.104856  \n",
       "services       5171.600126  0.081381  \n",
       "student        5085.939086  0.314286  \n",
       "technician     5175.648391  0.108260  \n",
       "unemployed     5157.156509  0.142012  \n",
       "unknown        5172.931818  0.112121  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('job').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8653cd8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.101573</td>\n",
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       "      <th>single</th>\n",
       "      <td>33.158714</td>\n",
       "      <td>261.524378</td>\n",
       "      <td>2.53380</td>\n",
       "      <td>949.909578</td>\n",
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       "      <th>unknown</th>\n",
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       "      <td>312.725000</td>\n",
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       "      <td>937.100000</td>\n",
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      "text/plain": [
       "                age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "marital                                                                         \n",
       "divorced  44.899393  253.790330   2.61340  968.639853  0.168690      0.163985   \n",
       "married   42.307165  257.438623   2.57281  967.247673  0.155608      0.183625   \n",
       "single    33.158714  261.524378   2.53380  949.909578  0.211359     -0.167989   \n",
       "unknown   40.275000  312.725000   3.18750  937.100000  0.275000     -0.221250   \n",
       "\n",
       "          cons_price_idx  cons_conf_idx  euribor3m  nr_employed         y  \n",
       "marital                                                                    \n",
       "divorced       93.606563     -40.707069   3.715603  5170.878643  0.103209  \n",
       "married        93.597367     -40.270659   3.745832  5171.848772  0.101573  \n",
       "single         93.517300     -40.918698   3.317447  5155.199265  0.140041  \n",
       "unknown        93.471250     -40.820000   3.313038  5157.393750  0.150000  "
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     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "df.groupby('marital').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a190120c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>education</th>\n",
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       "      <th>Basic</th>\n",
       "      <td>42.163910</td>\n",
       "      <td>263.043874</td>\n",
       "      <td>2.559498</td>\n",
       "      <td>974.877967</td>\n",
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       "      <td>3.556157</td>\n",
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       "      <td>0.222222</td>\n",
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       "      <td>3.529663</td>\n",
       "      <td>5163.226298</td>\n",
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      "text/plain": [
       "                           age    duration  campaign       pdays  previous  \\\n",
       "education                                                                    \n",
       "Basic                42.163910  263.043874  2.559498  974.877967  0.141053   \n",
       "high.school          37.998213  260.886810  2.568576  964.358382  0.185917   \n",
       "illiterate           48.500000  276.777778  2.277778  943.833333  0.111111   \n",
       "professional.course  40.080107  252.533855  2.586115  960.765974  0.163075   \n",
       "university.degree    38.879191  253.223373  2.563527  951.807692  0.192390   \n",
       "unknown              43.481225  262.390526  2.596187  942.830734  0.226459   \n",
       "\n",
       "                     emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "education                                                                     \n",
       "Basic                    0.191329       93.639933     -40.927595   3.729654   \n",
       "high.school              0.032937       93.584857     -40.940641   3.556157   \n",
       "illiterate              -0.133333       93.317333     -39.950000   3.516556   \n",
       "professional.course      0.173012       93.569864     -40.124108   3.710457   \n",
       "university.degree       -0.028090       93.493466     -39.975805   3.529663   \n",
       "unknown                  0.059099       93.658615     -39.877816   3.571098   \n",
       "\n",
       "                     nr_employed         y  \n",
       "education                                   \n",
       "Basic                5172.014113  0.087029  \n",
       "high.school          5164.994735  0.108355  \n",
       "illiterate           5171.777778  0.222222  \n",
       "professional.course  5170.155979  0.113485  \n",
       "university.degree    5163.226298  0.137245  \n",
       "unknown              5159.549509  0.145003  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('education').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ed29c6",
   "metadata": {},
   "source": [
    "# 3. Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32413a15",
   "metadata": {},
   "source": [
    "## 3.1.1 on job title"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26f8aef",
   "metadata": {},
   "source": [
    "- generate functions for visualizations since we will be using teh same line of script multiple times for many columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "73889c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visuals(df, title,xla):\n",
    "    \"\"\"get the bar plots for the given columns\"\"\"\n",
    "    pd.crosstab(df.job,df.y).plot(kind='bar')\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xla)\n",
    "    plt.ylabel(\"Frequency of Purchase\")\n",
    "def visuals_2(df, catcol,title,xla,yla):\n",
    "    table=pd.crosstab(df[catcol],df.y)\n",
    "    table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xla)\n",
    "    plt.ylabel(yla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0290a228",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "%matplotlib inline\n",
    "visuals(df, \"Purchage Frequency for job title\",\"Job\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040679e3",
   "metadata": {},
   "source": [
    "- The frequency of purchase depends on job title. Hence, job title can be a good predictor of the outcome"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b29554",
   "metadata": {},
   "source": [
    "### 3.1.2 on marital status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3195972f",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "visuals_2(df, \"marital\",'Stacked Bar Chart of Marital Status vs Purchase','Marital Status','Proportion of Customers')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6e353de8",
   "metadata": {},
   "source": [
    "- marital status does not appear to be a strong predictor for the outcome variable, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7707ef",
   "metadata": {},
   "source": [
    "### 3.1.3 on eductaion status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c15b2e9",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "visuals_2(df, \"education\",'Stacked Bar Chart of Education vs Purchase','Education','Proportion of Customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd21ccc",
   "metadata": {},
   "source": [
    "- Education appears to be a good predictor of the outcome variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b77d5f9",
   "metadata": {},
   "source": [
    "### 3.1.3 Day of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76f6d498",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "visuals(df, \"Purchase Frequency for Day of Week\",\"Day of Week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924164c7",
   "metadata": {},
   "source": [
    "- Day of week may appear to be not a good predictor of the outcome \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2b128c",
   "metadata": {},
   "source": [
    "## 3.1.4 month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "520d642d",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "visuals(df, \"Purchase Frequency for month\",\"Month\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90b5f77",
   "metadata": {},
   "source": [
    "- Month might be a good predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440eaf8b",
   "metadata": {},
   "source": [
    "### 3.1.5 Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fd86e099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 27,
     "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": [
    "df.age.hist()\n",
    "plt.title('Histogram of Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b494f0c9",
   "metadata": {},
   "source": [
    "- most of the customers of the bank are in the age range of 30-40 years old"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d666ec5",
   "metadata": {},
   "source": [
    "## 3.1.6 Poutcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "055ac0c4",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "visuals(df, \"Purchase Frequency for month\",\"Poutcome\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf451ac4",
   "metadata": {},
   "source": [
    "- Poutcome seems to be a good predictor of the outcome variable.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b83e49",
   "metadata": {},
   "source": [
    "## Convert the categorical variables into dummy binaries\n",
    " - We are gonna use the following categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6269ab31",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9ae5e15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "for var in cat_vars:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(data[var], prefix=var)\n",
    "    data1=data.join(cat_list)\n",
    "    data=data1\n",
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "data_vars=data.columns.values.tolist()\n",
    "to_keep=[i for i in data_vars if i not in cat_vars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "36ddb315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx',\n",
       "       'cons_conf_idx', 'euribor3m', 'nr_employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54bfe3f0",
   "metadata": {},
   "source": [
    "#### Our final data columns will be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "881d3ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp_var_rate',\n",
       "       'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y',\n",
       "       'job_admin.', 'job_blue-collar', 'job_entrepreneur',\n",
       "       'job_housemaid', 'job_management', 'job_retired',\n",
       "       'job_self-employed', 'job_services', 'job_student',\n",
       "       'job_technician', 'job_unemployed', 'job_unknown',\n",
       "       'marital_divorced', 'marital_married', 'marital_single',\n",
       "       'marital_unknown', 'education_Basic', 'education_high.school',\n",
       "       'education_illiterate', 'education_professional.course',\n",
       "       'education_university.degree', 'education_unknown', 'default_no',\n",
       "       'default_unknown', 'default_yes', 'housing_no', 'housing_unknown',\n",
       "       'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes',\n",
       "       'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug',\n",
       "       'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may',\n",
       "       'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri',\n",
       "       'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue',\n",
       "       'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent',\n",
       "       'poutcome_success'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final=data[to_keep]\n",
    "data_final.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a700474",
   "metadata": {},
   "source": [
    "## Over-sampling to overcome the sample imbalance between subscriptions and non-subscriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0c1c2a",
   "metadata": {},
   "source": [
    "After constructing our training data, \n",
    "- I will apply the SMOTE algorithm (Synthetic Minority Oversampling Technique) to up-sample the no-subscription class\n",
    "- Essentially, SMOTE functions by generating artificial samples of the underrepresented class instead of duplicating existing ones\n",
    "- This is accomplished by selecting one of the k-nearest-neighbors at random and utilizing it to produce a comparable but randomly modified new instance\n",
    "- Our implementation of SMOTE will take place in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ec4293e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of oversampled data is  51134\n",
      "Number of no subscription in oversampled data 25567\n",
      "Number of subscription 25567\n",
      "Proportion of no subscription data in oversampled data is  0.5\n",
      "Proportion of subscription data in oversampled data is  0.5\n"
     ]
    }
   ],
   "source": [
    "X = data_final.loc[:, data_final.columns != 'y']\n",
    "y = data_final.loc[:, data_final.columns == 'y']\n",
    "os = SMOTE(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "columns = X_train.columns\n",
    "os_data_X,os_data_y=os.fit_resample(X_train, y_train)\n",
    "os_data_X = pd.DataFrame(data=os_data_X,columns=columns )\n",
    "os_data_y= pd.DataFrame(data=os_data_y,columns=['y'])\n",
    "# we can Check the numbers of our data\n",
    "print(\"length of oversampled data is \",len(os_data_X))\n",
    "print(\"Number of no subscription in oversampled data\",len(os_data_y[os_data_y['y']==0]))\n",
    "print(\"Number of subscription\",len(os_data_y[os_data_y['y']==1]))\n",
    "print(\"Proportion of no subscription data in oversampled data is \",len(os_data_y[os_data_y['y']==0])/len(os_data_X))\n",
    "print(\"Proportion of subscription data in oversampled data is \",len(os_data_y[os_data_y['y']==1])/len(os_data_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3493edc",
   "metadata": {},
   "source": [
    "#### The data after performing SMOTE  for balancing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9e0bfd47",
   "metadata": {},
   "outputs": [
    {
     "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(data = os_data_y,x='y',palette='hls')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25515cf1",
   "metadata": {},
   "source": [
    "- We now have an ideally balanced dataset! \n",
    "- I applied oversampling to the training data\n",
    "- This was done intentionally to prevent information from the test data being utilized in creating synthetic instances\n",
    "- By only oversampling the training data, we ensure that no information is transferred from the test data to the model training process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05fd5cda",
   "metadata": {},
   "source": [
    "## Construct the model with the best performing feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b103df4a",
   "metadata": {},
   "source": [
    "## How can we choose the best performing feature?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a6e62e",
   "metadata": {},
   "source": [
    "- One way to do so is by using Recursive feature elimination method (Readmore https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a882bcc6",
   "metadata": {},
   "source": [
    "- The Recursive Feature Elimination (RFE) algorithm operates by constructing a model multiple times and selecting the best or worst performing feature in each iteration\n",
    "- The chosen feature is then removed from the dataset, and the process is repeated with the remaining features\n",
    "- This recursive process continues until all features in the dataset have been evaluated\n",
    "- The primary objective of RFE is to choose features by iteratively considering smaller and smaller subsets of features\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "26e62acb",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/data/ajames/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False False False False False False False\n",
      " False False False False False False False False False False  True  True\n",
      "  True  True  True  True False  True  True  True False False False  True\n",
      "  True  True  True  True  True False False False False False False False\n",
      " False False False False False  True  True  True  True  True False False\n",
      " False]\n",
      "[36 37 33 38 31 30 28 32 20 35 15  8  9 10 14 39 13 12 19 11 17 16  1  1\n",
      "  1  1  1  1 41  1  1  1  3  2 42  1  1  1  1  1  1  5  4 21 23 24 25 26\n",
      " 34 18 22 40 27  1  1  1  1  1  7  6 29]\n"
     ]
    }
   ],
   "source": [
    "data_final_vars=data_final.columns.values.tolist()\n",
    "y      =['y']\n",
    "X      =[i for i in data_final_vars if i not in y]\n",
    "logreg = LogisticRegression()\n",
    "rfe    = RFE(estimator=LogisticRegression(), n_features_to_select=20)\n",
    "rfe    = rfe.fit(os_data_X, os_data_y.values.ravel())\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "568e8d1e",
   "metadata": {},
   "source": [
    "## Columns which the RFE metho supports? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "080589d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['marital_divorced', 'marital_married', 'marital_single',\n",
       "       'marital_unknown', 'education_Basic', 'education_high.school',\n",
       "       'education_professional.course', 'education_university.degree',\n",
       "       'education_unknown', 'housing_no', 'housing_unknown', 'housing_yes',\n",
       "       'loan_no', 'loan_unknown', 'loan_yes', 'day_of_week_fri',\n",
       "       'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue',\n",
       "       'day_of_week_wed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os_data_X[os_data_X.columns[rfe.support_].tolist()].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "99f048ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = os_data_X[os_data_X.columns[rfe.support_].tolist()]\n",
    "y = os_data_y['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3ef1b9eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.457815\n",
      "         Iterations 7\n",
      "                                                       Results: Logit\n",
      "============================================================================================================================\n",
      "Model:                                   Logit                               Pseudo R-squared:                    0.340     \n",
      "Dependent Variable:                      y                                   AIC:                                 46857.8129\n",
      "Date:                                    2023-03-07 19:42                    BIC:                                 47025.8148\n",
      "No. Observations:                        51134                               Log-Likelihood:                      -23410.   \n",
      "Df Model:                                18                                  LL-Null:                             -35443.   \n",
      "Df Residuals:                            51115                               LLR p-value:                         0.0000    \n",
      "Converged:                               1.0000                              Scale:                               1.0000    \n",
      "No. Iterations:                          7.0000                                                                             \n",
      "----------------------------------------------------------------------------------------------------------------------------\n",
      "                               Coef.         Std.Err.            z     P>|z|           [0.025                 0.975]        \n",
      "----------------------------------------------------------------------------------------------------------------------------\n",
      "marital_divorced               0.2603                 0.0589    4.4206 0.0000                  0.1449                 0.3757\n",
      "marital_married                0.7914                 0.0338   23.4427 0.0000                  0.7252                 0.8576\n",
      "marital_single                 0.9827                 0.0384   25.6005 0.0000                  0.9075                 1.0579\n",
      "marital_unknown                0.3845                 0.3688    1.0427 0.2971                 -0.3383                 1.1072\n",
      "education_Basic               -2.1012                    nan       nan    nan                     nan                    nan\n",
      "education_high.school         -1.8820                 0.0237  -79.5746 0.0000                 -1.9284                -1.8357\n",
      "education_professional.course -2.1144                 0.0398  -53.1173 0.0000                 -2.1924                -2.0364\n",
      "education_university.degree   -1.4377                 0.0071 -202.0387 0.0000                 -1.4517                -1.4238\n",
      "education_unknown             -2.0225                 0.0728  -27.7899 0.0000                 -2.1651                -1.8798\n",
      "housing_no                    -0.0918                 0.0606   -1.5153 0.1297                 -0.2105                 0.0269\n",
      "housing_unknown                0.9029 10346142767106344.0000    0.0000 1.0000 -20278067202438012.0000 20278067202438012.0000\n",
      "housing_yes                    0.1163                 0.0590    1.9724 0.0486                  0.0007                 0.2319\n",
      "loan_no                        2.6996                 0.0025 1095.2871 0.0000                  2.6948                 2.7045\n",
      "loan_unknown                   0.9029 10346142767106344.0000    0.0000 1.0000 -20278067202438012.0000 20278067202438012.0000\n",
      "loan_yes                       2.0478                 0.0494   41.4185 0.0000                  1.9509                 2.1447\n",
      "day_of_week_fri               -2.9526                 0.0200 -147.8848 0.0000                 -2.9917                -2.9134\n",
      "day_of_week_mon               -3.1085                 0.0207 -150.1916 0.0000                 -3.1491                -3.0679\n",
      "day_of_week_thu               -2.7495                 0.0066 -418.1946 0.0000                 -2.7624                -2.7366\n",
      "day_of_week_tue               -2.8561                 0.0154 -185.9759 0.0000                 -2.8862                -2.8260\n",
      "day_of_week_wed               -2.7589                 0.0133 -208.0982 0.0000                 -2.7849                -2.7329\n",
      "============================================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "795a1f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## remove columns with Pvalue greater than alpha (0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ff49377c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.547612\n",
      "         Iterations 7\n",
      "                         Results: Logit\n",
      "=================================================================\n",
      "Model:              Logit            Pseudo R-squared: 0.210     \n",
      "Dependent Variable: y                AIC:              56027.2286\n",
      "Date:               2023-03-07 19:46 BIC:              56133.3350\n",
      "No. Observations:   51134            Log-Likelihood:   -28002.   \n",
      "Df Model:           11               LL-Null:          -35443.   \n",
      "Df Residuals:       51122            LLR p-value:      0.0000    \n",
      "Converged:          1.0000           Scale:            1.0000    \n",
      "No. Iterations:     7.0000                                       \n",
      "-----------------------------------------------------------------\n",
      "                  Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
      "-----------------------------------------------------------------\n",
      "euribor3m         0.1724   0.0055  31.3940 0.0000  0.1616  0.1832\n",
      "job_blue-collar  -1.0763   0.0359 -29.9385 0.0000 -1.1467 -1.0058\n",
      "job_housemaid    -1.6958   0.1293 -13.1176 0.0000 -1.9492 -1.4424\n",
      "month_apr        -0.2657   0.0406  -6.5414 0.0000 -0.3453 -0.1861\n",
      "month_aug        -1.6702   0.0390 -42.7906 0.0000 -1.7467 -1.5937\n",
      "month_jul        -1.6076   0.0389 -41.3749 0.0000 -1.6838 -1.5315\n",
      "month_jun        -1.3558   0.0392 -34.6062 0.0000 -1.4326 -1.2790\n",
      "month_mar         0.7349   0.0856   8.5880 0.0000  0.5672  0.9026\n",
      "month_may        -1.5306   0.0294 -51.9744 0.0000 -1.5883 -1.4729\n",
      "month_nov        -1.7494   0.0457 -38.3212 0.0000 -1.8389 -1.6600\n",
      "month_oct         0.4949   0.0746   6.6378 0.0000  0.3488  0.6410\n",
      "poutcome_success  3.1883   0.0593  53.7274 0.0000  3.0720  3.3046\n",
      "=================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cols=['euribor3m', 'job_blue-collar', 'job_housemaid',  \n",
    "      'month_apr', 'month_aug' ,'month_jul', 'month_jun', 'month_mar', \n",
    "      'month_may', 'month_nov', 'month_oct', \"poutcome_success\"] \n",
    "X=os_data_X[cols]\n",
    "y=os_data_y['y']\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd5ab54",
   "metadata": {},
   "source": [
    "## Linear regression implementaion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1ab28765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af7d1e27",
   "metadata": {},
   "source": [
    "## Predicting the test set results and the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "62734c22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.83\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94d4e69",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c8be1567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8296069356626035\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = pd.crosstab(y_test, y_pred, rownames=['Actual'], colnames=['Predicted'])\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt='g')\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred))\n",
    "#plt.savefig('5samples_down_regulated.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15b19bb",
   "metadata": {},
   "source": [
    "- The result is telling us that we have 6810+5917 correct predictions\n",
    "- 1758+856 incorrect predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54cec37d",
   "metadata": {},
   "source": [
    "## Compute precision, recall, F-measure and support\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d529a8",
   "metadata": {},
   "source": [
    "- The **precision** is defined as tp / (tp + fp), where tp is the number of true positives and fp is the number of false positives. It represents the classifier's ability to avoid labeling negative samples as positive\n",
    "\n",
    "- The **recall** is calculated as tp / (tp + fn), where tp is the number of true positives and fn is the number of false negatives. It measures the classifier's ability to identify all positive samples\n",
    "\n",
    "- The **F-beta score** is a weighted harmonic mean of precision and recall, with the optimal value at 1 and the worst score at 0. The F-beta score assigns more weight to recall than precision, with the weight determined by the beta factor. A beta value of 1.0 indicates that recall and precision have equal importance\n",
    "\n",
    "- Finally, the support corresponds to the number of instances of each class in y_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7a188f74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.89      0.84      7666\n",
      "           1       0.87      0.77      0.82      7675\n",
      "\n",
      "    accuracy                           0.83     15341\n",
      "   macro avg       0.83      0.83      0.83     15341\n",
      "weighted avg       0.83      0.83      0.83     15341\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0569e5",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "Out of the complete set of test data, 83% of the term deposits that were promoted corresponded to the deposits that were preferred by the customers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bb38ae",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8dd46a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xEZZHQijDMIzKIi/fR3aejzyfkpvvY3tGjvuep+T6fOTm+cjND/ie7+OPDbvI8ylZuflk5uSTlecjMyef7Dy3n5mbT1ZuPlm5PrJy86m3cQ3XffpU2OSu6sqlMbA6IG2D37EiykVERuGiEtKiRYsKF84wDCMQVSUjJ5/5K7exIzOXrek5bEnPYVdWLnnegz83X9mRmcO2jFzyfEq+z0devpLvc1ueT8nL97ErK49d2Xn7JE+dmvHUiI+lRnwsifGx1IiPoUZCLLVrxJOUEEstfBz98cscOe1pNDZ8MyVVXbmAi9rmjxSTjqo+DTwN0KtXLxsyMwyjXOR6D/YdmbnsyMxlZ2Yuufk+76HvFMTMPzYBsDk9hy27s9mansPurDzSc/IIHF0SgVoJccTHCvGxMcTHxpCSFEf95ATiY2OIFSE2RoiLFWJEiIsRYmNiSK0RR50aCdRMiC08npGTT4t6Nb1yhITYGOK87wVlF3xvkJJIUnxsyRf76KPwyiNw2mnw8MNwwAFhacOqrlzW43oo/jT0PjdgGIZRDlSVn1Zt5+XvVrI7O48/NuwiOSmORWt2khQfQ1auL+SyujWvTaPUJDo2SSU1KZ5aibHUTIgjNgb6t2tAWnIidWvGExfGXsE+s2ULrFgBPXvCqFHQvj0ce2xYq6jqyuU7YKKIJKlqlpc2GBdrfkXEpDIMI2pQVa777y/8sWGX64VkuZ5IQe+iQ+MU0pIT2ZKew1EdGpLvU3q0qEvtGnHUrhlP7RrxpCbFkxi3p/cQGyPUiI+laZ0akb24sqIKL74I110HtWvDH39AYmLYFQtUsnIRkWTgQG83BmghIt2Brar6t4hMAPqo6iAvz2vAWGCKiIwH2gNjgHFmKWYYRjDemr+ab/7cRGZuPpm5PrbszubXtTsBOKV7U1I9ZVG7RjwdmqTQv90+e5ePDhYvhssug5kz4bDD4IknILaUIbN9oLJ7Lr2Ar/z2x3nbi8AIoAnQtuCgqu4QkcHAZGAesA24H3igkuQ1DKMKk5vv472f1rJ+ZxY7vfmRN35YBcBBjVJISoglJSmOozs25PpjO3BQ45QISxwhfvoJeveG1FR49lm44AKIqdhhuspe5/I1eybkgx0fESRtITCg4qQyDKOqk5fvIz07n905eaRn57Ery31++dtGpsxeAUBCXAy1a8RzYMNkLj+iLaf1aB5ZoasCq1dD8+bQrRuMHw8jR0KDyumpVfU5F8Mwqik7MnJ55fuVbM/IYXd2PunZnuLwPtOz89jtbaVNsH9w5eF0aVa7kiSPAtasgdGj4ZNP4LffoFkzuPHGShXBlIthGGFje0ZO4YK/vHwf63dksS0jl5x8H9m5+d6nj5x8H1N/WMXyzekApCUnkpwYS3JSHLUS4micmuS+J8aR7G3ueyzJic4iKzkxjuSkOOrXSqRBSmKEr7yKkJcHjz8Ot9wCubnuMy0tIqKYcjEMo0xk5eYzZ9kWPlm0nu0ZuWxJz2ZLeg5bduewIzO3TGU1SElkxvVHUDPBHkX7TGYm9O8P8+c766/Jk6Ft29LPqyDsFzUMo1g27MxixeZ03vhhFb+t38XSjbvJyS86RPWPNvXo2CSV+rUSaFanBslJccTHxBSa7aYmxXNAvRokxsWSEBdDYlwMCXExhYv/jH0kNxfi46FGDRg0CG64Ac48063cjCCmXAxjP0ZV2ZWdx8adWWzYmc2GnVms35nF9F83kJ6dx58bdxfJ36FxCv3bpdEgJZGhBzelae0kLAJGhFCF//4Xrr0W3n0XevSAiRMjLVUhplwMYz/j1e9XMn/FNt77eS1xsRJ0sjwlKY7aNeL5Z58WHNy8Nl2b1aZz01RTJFWFpUvhiivchH2PHhW6XqW8mHIxjGrOA9N/59ulW0jPziMjJ5+/t2YAcEC9GrSqX4sB7RrQMDWRRqlJNEpNomFKIrUS7dFQZZk0CW691Q2FPfww/OtfplwMwwg/W9Nz+Pr3jWzenc3m3Tl7PndlsyU9mw07swEY3KkRyYlx9G+Xxnn/aEnHJqkRltwoFxkZcMIJ8NBDzsS4imLKxTCqOAUxOHI8V+0uZoeL8ZGb7+O5Wcv54Jd1gFtI2CA5kbTkBBrXTqJLs1TqJydyRs/mtG2QHOErMcrFpk1w/fXOa/FJJznz4gpeXR8OTLkYRiWjqqzZnsm29Fyy8vLJ9oI1FX73+3x7wZq9JtWDcUC9Gnx89QBqJcTavEh1weeDF15w1l+7drlV9hAVigVMuRhG2MjL9/H5ko0s35xOjteryMn3MW/FVuJiYtiakcP2jBy2ewGiysLYEzsRH+vMdxPi/GJ2eCa9rdNqkWzzJNWHX3+FSy+FWbPc2pUnn4ROnSItVZmwu9EwysGGnVlM/OQ31u/IKpzj2JaRg7+v7tgYF8gpNkZQVfq3a0DdWvHUrZlASlI8jWsnkpbsgjklxsUE/UyIjSEmxnoi+x3z58OSJa7ncv75EV+zUh5MuRhGCeTm+1i6abdbA7Ijiw07s9iwK4v/zltNdp6PprWT6Nq8Nr1b1SMtOZH6yQkMbN+A5nVrEmtKwSgLH34IW7fC8OFuO/FEqFs30lKVG1Muxn5Lvk9ZvzPLzXfk5pORk8+2dDdstS0jh427snlu1vK9zqtbM57WabVo2zCZB8/qTkJcdIyBG1WU1avh6qvhf/+Df/wDzjvP9VSiWLGAKRejGpCX72NnVh7bM3KYu3wr6Tn5/LpmB/GxMWTk5pOR7eKaZ+bkk57j9jNy89meUbIfrNgYoWFKIi3r1+TGIR1olJoUWkxywwiFvDwXv/622yA/HyZMgGuuicohsGCYcjGqND6fkuvzkZev5Ob7+GLJRh7+4k9qJsSyKyuPHZm57M7OC3puQmwMzevWoGZiLDXj46hbK4FmdV1885oJsYXOEjs2SSExLpaaCbHUrZlAnZrx1K2VYJZXRsUyb55TJscd55xMtm4daYnCiikXI6Lk5Pm49r8/M3/FVnxKoYVVbr5TKMVZVbVpUIu+bepRu4YLV1unRjy1a8aTnBhPl2appCTFm/WUUfXYvh2++AJOP90Ngc2dC716VZveij/27zMqnO+WbmH20s1s3p3D1vRstuzO8Vy0Z7Mza0+v4+xeBxAfJ8TFFJjbFv0e73nR7d2qLh0a2+pyI4pQhTfegH//G7ZtgxUroEkTF3q4mmLKxahwbnlnIcs2p1O/VgL1aiVQv1YinZs6F+31kxOpVyuBozs2onHtpEiLahjh588/nf+vzz5zvZSPPnKKpZpjysXYJ/J9yraMHDbtyt6z7c5ms/e5aVc2q7ZmclK3pjx8ziGRFtcwKpedO13vRBUee8wtjKyCTiYrAlMuRplZuHoH/3lnIet3ZLElPYf8IPMiNeJjaZjqFgke2aEBZ/c6IAKSGkaE+Pln564lNRWefx769dsveiv+mHIxSiQv30d6dj67sp1V1u6sPN75aQ2/rN7BaYc0o1ndGqQluxjmDVISaeB9N5ftxn7Jhg1w3XXwyitu+Ou445zDyf0QewIYRfhr4y7W7cgiPTuPH1dt56kZy4LmS4iL4a5Tu1IjYf/o4htGifh88MwzMGYMpKc7z8VHHBFpqSKKKRcDcHMn7/+8ltFTf9rr2Pn9WtK5aW2Sk+JITowjOSmO5nVqmGIxjAJOPRXee88plCeegA4dIi1RxDHlsp/i8ymb07NZuSWDx7/6i69+31R4bOLpXenarA7JiXHUqRVPalJ8BCU1jCpKejokJbkJ+mHD3NqV4cOr5ZqV8mDKZT9kw84sjnv4G7am5xRJv/7Yg+jYJIWjOjSKkGSGESW89x5ceSVcey1cdRWcdVakJapymHLZT8jL97E1PYfV2zN59ptlbE3P4bQezTixW1Na169Fs7o1iI81B4yGUSJ//+2UybvvQpcubt2KERRTLtUQVSU9J5/5K7fxzo9r2J2dx2eLNxTJ06ZBLe49/WDiTKEYRmi89BJcdplbszJxolttH29DxsVhyqWaMX/lNs579nsyc/ML0zo0TqFPq3o0TE1kUMeGNK9bk96t6kVQSsOIIlTdPEqLFjBoEDzyCLRqFWmpqjymXKKcvHwfK7dmcP7zc9mescdDcIGFV/vGKXQ/oE5khTSMaGTbNmdanJwM99/vLMH2c/PislDpykVELgeuB5oAvwKjVfWbEvIfC9wOdAGygW+B61X1j4qXtmqyKyuXrek5XPDCDyzbnF6YfnDz2hx+YBpN69RgWN8W5i7eMMqDKrz6qpus37LFucUv6L0YIROychGRrsAlQFtgpKquE5FTgJWq+mOIZZwNPAxcDszyPj8WkU6q+neQ/K2Bd4FHgOFAMnAv8BFwYKiyVxeycvN596c13PjWwsI0ERgzpAN1ayZwao9mNilvGPvCsmVw8cXw5ZfQpw98+il07x5pqaKSkJSLiBwDvAd8DBwF1PAOtQVGAKeEWN81wBRVfcbbv1JEhgCXATcFyd8TiAduUtV8T5YJwJcikqaqm0OsN6pZtyOTAfd+RW7+Hh9et57QidZpbu4kxdahGEZ4yM2FX391CyEvvni/cTJZEYTac7kTuEZVHxeRXX7pXwPXhlKAiCTglMWkgEPTgUOLOW0ekAtcJCLPAjWB84EfqrNi+W7pFv7ems72jFy2Z+byxNdLAejZsi6n9WhG67RaHNo2LcJSGkY14bPPnB+wBx+Egw6ClSshMTHSUkU9oSqXzrihqEC2AqGaHaUBscCGgPQNwNHBTlDVFSIyGPgvMBmIAX4EjguWX0RGAaMAWrRoEaJYVYt7Pv6NJ2csLdyPjxXq1Uqgf7s0Jp3ZzYa9DCNcrF/v5lNefx3atYNbb4V69UyxhIlQlcs2oBmwIiC9B7C6jHUG+meXIGnugEhj4DngJeB1IAW4A5gmIkepqq9IwapPA08D9OrVK3h83CrI31syWLsjk027sgsVy2f/HkDTOjWoaXHcDSO85OfD00/DTTdBZiaMHeuswpIsWF04CVW5vAbcJyJn4RRBnIgMxA1xvRBiGZuBfKBxQHpD9u7NFPAvIF1VbyhIEJHzgFW4obRZIdZd5Zi/chs//r2NjxetZ/7KbUWOXXXUgbRrlBIhyQyjmrNjB9x2G/Ts6eZW2rePtETVklCVyy3AFGAlrqex2Pt8DbgrlAJUNUdE5gMFw1wFDAbeKua0mjiF5E/BftSODy1cvYPTn5hdJO3p4T1pnVaLBimJ1KmZECHJDKOasmsXPPWUW1Vfrx7MnesWQtqoQIURknJR1VxgmIjcihsKiwF+VNU/y1jfA8DLIjIXt17lUqAp8CQUWoL1UdVBXv4PgX+LyFicIksB7sb1XOaXse6Is3pbBg99/idf/rYRgGmX9KNny7rExtgNbhgVgiq8847zB7Z6NfToAUcdBa1bR1qyak9Ib/8icpuI1FTVZar6pqpOU9U/RaSGiNwWamWqOhUYjesJ/QQcDhyvqiu9LE1w5s0F+b8EzgVOxk3kf4qzHhuiqntWD0YBO7NyOXziV7w5fzVxMcKz/9eLPq3rmWIxjIpi5Uo46SQXCbJePZg92ykWo1IQ1dLnvUUkH2iiqhsD0usDG1W1yhmD9+rVS+fNmxdpMQq5+o0fefentQzp3Jgnh/eMtDiGUb1Rhd694bffYNw4uPpqiDNvV6EgIvNVdZ/dPYfa2sVZdB2CM0c2imHV1gzu+/R33vt5Lb1a1jXFYhgVyezZcPDBzh/Ys8+6HkuULkuIdkocFhORXSKyE6dYlonITr8tHTdMNa0yBI02dmfnMWfZFvrf+xXv/bwWgMfO7RFhqQyjmrJli1tRf9hhMMlbp929uymWCFJaz+UKXK/leeA/wA6/YznAClX9roJkixpUlZ9WbeenVdtZuHoHP6/ezrLN6RSMOA7q0JDJw3qQFF/lRg8NI7pRdXFWrrvOeTG+7jq3GRGnROWiqi8CiMhyYLZnNWYE8Mr3f3PrO4sAaJCSSLfmtTm5ezO6Nq/Nwc1qUz/ZVvwaRoUwZgzcey/06wdPPumGxIwqQaimyDMKvnur5hMCju/l0Xh/YuHq7QDMuvFImtetGVlhDKO6k5kJGRlQvz5ceCG0bQsXXQQxUbv0rVoSqlfkVOBR4CwCFIvHfjvek5mTz+8bdtMmrZYpFsOoaD79FC6/3M2nvPWWW11vK+yrJKGq+vuBbjjX+lm4tSfX4/yKnV0hkkUBK7ekc9ZT3/HL6u1c2N8WZRlGhbFuHZxzDgwZ4kyKr7gi0hIZpRCqKfJxwD9V9Rtvzct8VZ0qIutwAcTerDAJqyiZOfmc9vhstqTn8Mg/D+Gkbk0jLZJhVE+++MIthMzOhjvugBtuMM/FUUCoPZc6OL9i4CzG6nvfv6P4WCzVmoe++IMt6Tk8aorFMCqGXM9+qFs3OPZYWLjQucU3xRIVhKpclgJtvO9LgHPE+YE/jf1wEeWol+bx1Ixl9G5VlxNNsRhGeNm5062o79/fucdPS4Np01zMFSNqCFW5TAEKbPzuwQ2F5QD3ARPDL1bV5aOF65i+eANNaifxxHm22t4wwoYqvPkmdOwIjz7qXOJnZ0daKqOchGqK/KDf9y9FpAPQC/hTVRdWlHBVkedmLQfgq+uOsEWRhhEuNm2C88+Hjz92lmBvvw19+kRaKmMfKJcnN29dy98AInKOqr4RVqmqKJ/+6gJ7dWySaorFMMJJaqpTMA8+6CzBzMlk1FPqsJiIxIlIZxFpH5B+ioj8ArxYYdJVIRat2cFlr7gQMo+c0z2ywhhGdeCbb5xp8e7dbpL+++9h9GhTLNWE0hxXdgL+AH4BlojI/0SkoYh8iZuHmQ4cWOFSVgHu+GAxPoWXL+xjIYgNY1/YvBlGjoQBA5xL/OVuqNlW2FcvSvs17wGW44J1TcMtopwJfA0coKrXqeqqihSwKpCZk8+29BwOPzCN/u0aRFocw4hOVOGFF6BDB3j5ZbjxRli8GLp2jbRkRgVQWv+zDy5S5AIRmYVbjT9JVZ+teNGqDvdP/50/N+7mqA4NIy2KYUQ3L7/slMuTT0KXLpGWxqhASuu5NATWAKjqdiAD13PZr9iWkUvDlERuOr5jpEUxjOgiI8MtfFy1CkScP7CZM02x7AeUplwU8Pnt+3Ax7Pcb0rPz+HzJBnq2rBtpUQwjuvjoI+jcGcaPh/ffd2l169rcyn5CacNigotAWRDiOBn4xW8fAFVNrQjhqgIvz1nJjsxcLh7QpvTMhmHAmjXO6qtgQeTXX8PAgZGWyqhkSlMuF1SKFFWQ7Lx8Bt77Net3ZtG8bg16tLCei2GExPjx8MEHcNddLipkQrAoHUZ1J6RIlPsbL85ewTPfLGP9zizq10rg8WE9Ii2SYVRt5s1zSuTgg51yuf56aGO9/f0ZW60UwGeLNzD2vV8BmHxuD4Ye3CTCEhlGFWbHDrjlFpg8GYYOdXMr9eu7zdivMeXixy+rt3PxS/NIS05k2iX/oE2D5EiLZBhVE1XnqXj0aNiwwblsufPOSEtlVCFMufjx4mwXsubVi/qaYjGMknj1VRg+HHr0cL2VXr0iLZFRxTDl4vHV7xt5a8FqjuvSmIMam3sXw9iL7GznqqVDBzjzTMjJcZ6MY82Jq7E3ZnAOqCpXvLqApPgYxp9ii7sMYy9mzHCu8I85BrKynKPJkSNNsRjFErJyEZHLReRXEckQkTZe2hgROavixKt4snLzOfahmaTn5HP4gQ2on2whVA2jkE2bYMQIOOII13N56ilISoq0VEYUEJJyEZHRwC3A07iFlQWsAa4Iv1iVw4rN6fzfc3P5Y8NumtROMpNjw/Bn6VI46CB47TW4+WZYtAiOOy7SUhlRQqhzLpcCF6vqhyIy3i99AdA5/GJVPF//vpELpvyAKpz3jxbceXIXRKT0Ew2jurNzpwve1aYNXHghXHABdOoUaamMKCNU5dISWBQkPReoET5xKo/nv12BKkz/9wDaW3wWw4D0dLjjDnjmGVi4EJo1g/vui7RURpQS6pzLMiDYmNHxwOKyVOjN3SwXkSwRmS8i/UvJLyIyWkR+E5FsEVknIveUpc5ANuzMYtafm7jiyANNsRgGOHctnTvDvffCqafavIqxz4Tac5kEPCYiNXFzLv1EZDhwAzAy1MpE5GzgYeByYJb3+bGIdFLVv4s57X7gBOB6YCFQG9inZfPPf7scn8IphzTbl2IMI/rJy4Ozz4b//c8Nfc2cCf1LfN8zjJAISbmo6gsiEgfcDdQEXsZN5l+lqlPLUN81wBRVfcbbv1JEhgCXATcFZhaRg4ArgYNVdYnfoR/LUGcRcvJ8PDVjGY1SEzmwoS2UNPZTVF18lbg4aNIEJkyAa64xJ5NG2AjZFFlVn1HVlrgAYo1V9QBVfS7U80UkAegJTA84NB04tJjTTsYNyQ0RkWUiskJEXhSRcoeEfG6Wi9d9/qGtyluEYUQ3338PvXvDggVu/7HHYMwYUyxGWAnVFPlBEekBoKqbVXVjOepKA2KBDQHpG4DGxZzTBmdMcA4wAhgOdADeF5G9ZBeRUSIyT0Tmbdq0aa/C8n3KvZ/+RkpiHOf9o2U5LsEwopjt2+Hyy6FfP1i/HrZti7RERjUm1J5LX2CeiCwRkZtFpNU+1KkB+xIkrYAYIBEYrqozVfUbnILpA/Teq2DVp1W1l6r2atCgwV6Frd2eiSpcc0x7UpPi9+ESDCPKmDbNuW156im4+mpYsgQGDYq0VEY1JiTloqqHAm2BV4HzgKUi8o2IXCIioUbR2gzks3cvpSF792YKWAfkqeoffml/AnlAixDrLWTllgwAmtaJSutpwyg/v/0GBxwAP/wADz4IKWYlaVQsZZlzWa6q41W1E67X8D1wK7A2xPNzgPnA4IBDg4HZxZz2LRAnIm390trgDBFWhip7AS99twLAzI+N6k92tluzUhC7/qabYM4c58XYMCqB8jqujMcNVyXgeiOh8gAwQkQuEpGOIvIw0BR4EkBEJojIF375P8d5AXheRA4RkUOA53GKbV5ZhV67IxOA1mm1ynqqYUQPX37pIkKOHQtffeXS4uPNyaRRqZTFcWV7ERknIn/i1qgcBFwHNAq1DM9seTTOT9lPwOHA8apa0Atpght+K8jvw61x2QjMBD4FVgMne8fKxKZd2Zxma1uM6sqGDS7GyqBBkJ8Pn3wCDzwQaamM/ZSQ1rmIyDzgEOBn4AngNVVdX54KVfVx4PFijo0IkrYOOLM8dfmTk+djW0Yu9ZPN3NKopkyfDlOnwq23umGwGja3aESOUFfoT8dZbC0pNWcVZe7yreTk+ejVql6kRTGM8PHzz/Dnn3DGGXDeeXD44dC6daSlMoyQrcVujmbFAvDNn27dS1sLX2xUB3bvhuuug5494cYbnRsXEVMsRpWh2J6LiDwC3KSq6d73YlHVq8IuWRhRVZ6auQyAFvVqRlgaw9hH3n0XrrwSVq2Ciy+Ge+5xblwMowpR0h3ZFWcVVvA9alm8bicA/drUJyHOIjsbUczChXDKKdC1K7z+Ohx2WKQlMoygFKtcVPXIYN+jkd1ZeQBcfmTbUnIaRhUkNxdmzYIjj3RK5cMPYfBgZ15sGFWUUH2L3ea52w9MryEit4VfrPBS4Fsm1iJNGtHGd99Br15w9NFu4h7g+ONNsRhVnlDHiMYCwWbCa3rHqjRaoF1MtxjRwrZtcMklcOihsHUrvPUWHHhgpKUyjJAJdRawOOeShwBbwydOxaCe6GLaxYgGsrOhWzdYu9bFWBk3DpLNytGILkpULiKyC6dUFFgmIv4KJhZIwnPdEg3YqJhRpVm9Gpo3h8RE5xese3e3GUYUUlrP5Qpcr+V54D/ADr9jOcAKVf2ugmQLH8U59DeMqkBWlosEec898N//wkknwYgRkZbKMPaJEpWLqr4IICLLgdmqmlspUoUZm3IxqiyffeYCeP31F5x7LvTpE2mJDCMslLSIsp6qFsynLARSpJhxJb98VZKCCf3i5DeMiHDVVfDoo9CunVMyRx8daYkMI2yU1HPZJCJNvJDGmwk+uFQw0R8VvrxNtxgRJ9+LUBEbC//4B9Sr5+LXJyVFVi7DCDMlKZej2GMJFtWLKNUmXYyqwI8/wqWXwrBhrtdy7rmRlsgwKoySVujPCPY9GikcFousGMb+yq5dLnDXww9DWho0aRJpiQyjwgl1hX4nETnIb3+wiLwiIjeJSJUfEiuc0DftYlQ2n30GHTvCQw/BqFEulv2Z+xyeyDCqPKGu0H8Ot2ASEWkOvAvUA/4FjK8Y0SoC0y5GJZOQ4Hors2fDE09A3bqRlsgwKoVQlUtHXCx7cFEhv1fV44HhwD8rQrBwompzLkYlkZsLEyfCf/7j9gcOhAUL3OS9YexHhKpcYnGLJgEGAR9535cCjcItVLixYTGjUpg1Cw45xFl//f47+HwuPcbCPBj7H6He9YuAy0SkP065fOKlN8OZKVdtbELfqEi2boWLLoL+/d3k/bvvwptvmlIx9mtCvftvBC4GvgZeV9WFXvpJwNwKkKtCsEWURoWweTO88QZcfz0sXuzctxjGfk5IXpFVdaaINABSVXWb36GngIwKkSyM2DoXI+wsWeL8gN12G7RvD3//7RZEGoYBhN5zQVXzgUwR6SIinUUkSVVXeCv4qzS2zsUIG5mZcMstziX+Qw85T8ZgisUwAgh1nUuciNwHbAN+xvka2yYi94pIlQ+Jt8e3WGTlMKKcTz6BLl3grrvgnHPcmpXmzSMtlWFUSUINFnYvzuT4UmCWl9YfmIBTUNeFX7TwY8HCjHKzezcMHw7168OXX7p49oZhFEuoyuVcYKSqfuSXtlRENgHPUsWVi824GOUiP99N1J9zjosE+fnn0KGDC+ZlGEaJhDrnUhu3piWQpUCdsElTQRQsorRhMSNkChY+nncevP22S+vWzRSLYYRIqMrlZ+CqIOlXAz+FTZoKwnouRsjs3AlXXw29e8OqVfD663D66ZGWyjCijlCHxW4APhKRwcB3uOd1P6ApcFwFyRZ2rOdilMppp7k5lcsucxP3depEWiLDiErKss6lPc5RZQecVe9/gcdVdW0FyhcWzLWYUSLLlzvnkikpTqGIWLhhw9hHSh0WE5GWInIxcArwmqqerqqnqeot5VEsInK5iCwXkSwRme+5lAnlvHYisktEdpe1zoKBMbMWM4qQkwMTJkCnTnDnnS6tb19TLIYRBkpULiIyAPgVtxL/MeBHESm3F2QRORt4GLgb58J/NvCxiLQo5bwE4A1gZnnqtXUuxl7MnAndu8PNN8PQoS4ypGEYYaO0nsudwFdAc6A+8DxuzUt5uQaYoqrPqOoSVb0SWAdcVsp5E4FfcENxZWb1tkzAlIvh8dBDzhV+ZiZ88IFzMmmLIQ0jrJQ259IVGFAw/CUi1wIXi0jdAB9jpeL1PnoCkwIOTQcOLeG8ocAJQA+gXGY7+V7XJS3ZzEj3W3w+SE938yrHHw8bNzo3LjVrRloyw6iWlNZzqQMU+g5T1XSco8o65agrDRcXZkNA+gagcbATRKQJ8AwwXFV3lVaBiIwSkXkiMm/Tpk2F6QXDYrUSQjWOM6oVv/4KRxwB55/v9tu3h7vvNsViGBVIKOtcDhaRHgUbzlKsS0BaWQi03ZIgaQW8AjyhqnNCKlj1aVXtpaq9GjRo4FehLaLcL8nIgJtucnMrv/4KJ5xgpoOGUUmE8ir/KXs7FH7X77vieiSlsRnIZ+9eSkP27s0UcBQwUETGevsCxIhIHnC5qj4dQr3G/siPP7o1KytWwIgRcN99ztzYMIxKoTTl0jpcFalqjojMBwZTdGJ+MPBWMad1Ddg/GfgP0AdYE3rdZRDUiG5UXRf1gAPcNmWKm7w3DKNSKVG5qOrKMNf3APCyiMwFvsV5WW4KPAkgIhOAPqo6yKt/kf/JItIL8AWmh4oNi1Vj8vJg8mQXYvizz1wvZWa5LNcNwwgDlRrkW1WnAqOBW3A+yQ4HjvdTYk2AthVVvy2irKb88INb/Dh6tHMsuWNHpCUyjP2eSlUuAKr6uKq2UtVEVe2pqjP9jo1Q1VYlnDtFVZPLUWc5pTWqNOnpcMUVTrGsWwfTpsFHH1lUSMOoAlS6cokEtkK/mhIXB1995RTMb7/BmWfaj2wYVYT9QrkUYI+dasDSpW69yu7dbghs/nx45BFITY20ZIZh+FEm5SIiaSLSV0Siaqm7DYpVA7KzYfx46NzZBe/68UeXnpQUWbkMwwhKSMpFRFJEZBputf5soJmX/qSI3F5x4oWHPcNi1neJSr76ykWBvPVWOOkkNwTWPyRn2oZhRIhQey4TcQqlB5Dpl/4BcGq4hQo3hSv0IyyHUQ5UXYyVnBw3WT9tGjRtGmmpDMMohVCdbZ0EnKqqP4mI/yjTEqBN+MWqGKzjEiX4fPD88zBkiPNW/PLLULu2+QIzjCgi1J5LXWBLkPQUnEuXKo1ZIkcRCxe6Ia+LL4ZnnnFpTZqYYjGMKCNU5fIDrvdSQMHj+hLcHEyVpkBYm3OpwqSnw403Qo8e8Pvvzm3L7bdHWirDMMpJqMNiNwOfikhn75xrvO99gAEVJZyxH3H77TBpEowcCffeC/XrR1oiwzD2gZB6Lqo6GxfQKwFYCgwC1gL9VHVBxYkXJmxcrGqyapWz/ALXa5k5E557zhSLYVQDQo6epaoLgfMrUJYKQ7HJ/CpFXp5b+HjbbdCzJ8yY4RxNmnmxYVQbQlIuIlKisyZV3RoecSoO0y1VhO+/h0sugZ9/hqFD4bHHIi2RYRgVQKg9l82UvNA9lGBhEcNGxaoIH33kokE2bQpvvQWnnmpdSsOopoSqXI4M2I8HDgEuw7nPr9IoapZikUIV1q6FZs1g0CC44w64+mpISYm0ZIZhVCAhKRdVnREk+XMRWQZcBLwWVqnCjKoNi0WEP/+Eyy93k/ZLlkByMtxS5d9FDMMIA/vqFfknosQU2ToulUh2NowbB127wty5cNNNUKNGpKUyDKMSCdlaLBARScZFlVwVNmkqCJtyqUTWr3cx6//4A845Bx54wK2wNwxjvyJUa7FdFH1GC1ATSAeGVYBcYcUNi1nXpULJzYX4eGjUCA4/HB59FI45JtJSGYYRIULtuVwRsO8DNgHfq+q28IpUQZhuqRh8PucD7K67YPZs52jyueciLZVhGBGmVOUiInFALeAdVV1b8SKFH7WBsYrh55/h0kthzhw48kjXezEMwyCECX1VzQPuw5kfRydmLRZeVOH6693q+qVLnUv8L76A1q0jLZlhGFWEUK3F5gA9K1KQisasxcKICGzdChde6MyMzzvPGtgwjCKEOufyDDBJRFoA83ET+YVUdeeV2Xm+SIsQ/axcCaNHu1DDPXq4eZaYfbVkNwyjulLi00FEnheRVNwiyVbAA8AMYJ7f9kMFy7jP/LFhF3n5Nu9SLnJz4b77oFMnmD59jxdjUyyGYZRAaT2X84ExQFQPpu/IzOWQFnUiLUb0MXu2m7BfuBBOPNGZF7dsGWmpDMOIAkpTLgKgqisrQZYKYf7Kbfy6diejj24XaVGij88/h+3b4Z134OSTIy2NYRhRRChjG1E9nrRmeyYAfVtbAKpSUYVXXoGPP3b7N94IixebYjEMo8yEolzWi0h+SVuFS7kP5HiT+U1qJ0VYkirO7787r8XDh8Pzz7u0xETnbNIwDKOMhGItNgrYXsFyVBj/W7AagJoJVTrkTOTIyoIJE+Cee6BmTXjySbj44khLZRhGlBOKcnlfVTdWuCQVxKI1O4iLERqmWs8lKO+952KsDBsG99/vfIMZhmHsI6Upl6ieb9m0K5udWXlcMqBNpEWpWqxfDz/9BEOGwJlnOguwvn0jLZVhGNWI0uZcwr7sWkQuF5HlIpIlIvNFpH8JeY8QkXdFZJ2IZIjILyIyMtS6Nu3KBqBjk9QwSF4NyM+Hxx+Hgw5ycyuZmW5lvSkWwzDCTInKRVVjwjkkJiJnAw8Dd+PCJM8GPvZW/gfjUGAhcAbQBXgCeFpEzg2lvo8XrQOgTYNa+yZ4deDHH+HQQ+Ff/4LeveHbby2Al2EYFUa5g4WVk2uAKar6jLd/pYgMAS4DbgrMrKp3ByQ9ISJHAqcTQmjlXVl5AHRpWntfZI5+VqyAPn2gXj149VX45z/NF5hhGBVKpfnwEJEEnPPL6QGHpuN6KKGSCgSNISMio0RknojM27RpE+nZeaQlJxATsx8+SFXdynqAVq2cefFvv8G555piMQyjwqlMB1FpQCywISB9A9A4lAJE5ARgEPB0sOOq+rSq9lLVXg0aNGDGH5uoWzNhX2SOTlascO5auneHX35xacOHQ926kZTKMIz9iEh4Hwy0QJMgaXshIofhhsKuUtW5oVSUm+8jzxfVBm9lIzcXJk50Tia//homTXLfDcMwKpnKnHPZDOSzdy+lIXv3ZoogIocDHwG3qeoToVboUxjQLq2sckYn+fnQrx/Mnw+nnAKPPAIHHBBpqQzD2E+ptJ6LqubgYsEMDjg0GGc1FhQRGQB8DIxT1YfKUme+T4mt7q7hd+50n7GxMHIkvPsuvP22KRbDMCJKZT95HwBGiMhFItJRRB4GmgJPAojIBBH5oiCziByBUyxPAq+KSGNvaxBKZXk+H/Gx1XTyWhWmTIE2bdwqe4DLL4eTToqoWIZhGFDJpsiqOlVE6gO3AE2ARcDxfi79mwBt/U4ZAdQErvO2AlbigpeVSFauj9jqaCm2ZImLszJzplu70sY8EBiGUbWo9DEjVX1cVVupaqKq9lTVmX7HRqhqq4B9CbK1Cla2P1m5zhtytZvPnzQJunVzZsbPPAPffANdukRaKsMwjCJU9iLKSiMr10UCOKpDwwhLEiZU3fqURo3cIshJk6BBSKODhmEYlU61ne3elZ0LQKv6NSMsyT6ydi2cfbaz/gK3XuXFF02xGIZRpam2PZeCuZZocbXv8/nYvHkz27dvJz8/3/VUdu1yYYZHjoQ6ddxci2EYRjmJjY2lTp06pKWlEVPBlrTVVrmgkJoUPZe3evVqRIRWrVoRn5uLrFzpzItbt4YWLSApOpSkYRhVE1UlNzeXDRs2sHr1alq0KM5fcHiotsNiABJFPrTS09Np1qwZCQkJiM/nVtu3aQPt2pliMQxjnxEREhISaNasGenp6RVeX7VWLlFjhawKGRnEbPAcFaSkQNeuzotxFClIwzCqPhU9HFZYT6XUEgGUKOm5LFsGQ4fCpk2wbRv4nAk11d2zgGEY1Zpq/QSr0qolJwfuvhs6d3ZrVerWhY4dTakYhlEtqNZPsirdcfn7bxg3zvVafvsNUlOruMCR44gjjuCKK67YpzJatWrFpEmTwiRR9eLrr79GRNi8eXOl1HfkkUfy0ksvVUpd+xtnnHEGDzzwQKTFcKhqtdwat+2kvcZ/plWKTZtUH3lkz/5ffxV+Xbx4cQQE2nfOP/98HTp0aIXWsWXLFt25c2dIeceOHaudO3feK33jxo2anp5ebhkGDhyouNFWjY+P1zZt2uiYMWM0Kyur3GVWFbKzs3XdunXq8/kqvK4PPvhA27Ztq3l5eRVeV6Tw+Xw6duxYbdKkiSYlJenAgQN10aJFpZ736quvardu3bRGjRraqFEjHTZsmK5bt67w+LRp07Rnz55au3ZtrVmzpnbr1k2nTJlSpIxffvlF69atq9u3by+xrpKeN8A8DcMzuPr2XLQKDYv5fC4S5EEHwTXXuJ4KQNu2JZ9nAFCvXj1SUlL2qYwGDRpQs+a+Lai94IILWLduHX/99Rf33nsvkydP5vbbb9+nMkMhNze3QstPSEigcePGlTJH+fDDDzNixAhiY2PLXYbP53Nrwaoo9957L/fffz+PPvooP/zwAw0bNmTw4MHs2rWr2HO+/fZbhg8fzvnnn8+vv/7KO++8w+LFixk2bFhhnvr163PLLbcwZ84cfvnlFy644AIuvPBCPvroo8I8Xbt2pU2bNrzyyisVeo0hEQ4NVRW3Rm06aZ+7qkDPZdEi1cMPVwX3WcwbTHXtucyYMUP79OmjiYmJ2rBhQx09erRmZ2cXHt+9e7cOHz5ca9WqpQ0bNtS7775bhw4dqueff35hnoEDB+q//vWvwv233npLu3btqklJSVq3bl0dMGCArl+/Xl944YXC3kXB9sILL6iqasuWLfW+++4rLGPHjh166aWXauPGjTUxMVE7dOigb7zxRrHXESiDquppp52mPXr0KNz3+Xw6ceJEbdOmjSYlJWmXLl305ZdfLnLOnDlz9JBDDtHExETt3r27fvjhhwroV199paqqX331lQL64Ycfau/evTU+Pl7ff//9kMoeN26ctmjRQhMSErRRo0Y6fPjwIr9D3759tVatWpqamqp9+vTRhQsXFqlz06ZNRdq4S5cumpCQoM2bN9fx48cX6dm0bNlS77zzTh01apSmpKRos2bN9N577y22/VRd71FE9Oeffy6Sfv/992vXrl21Zs2a2rRpU73wwgt127ZthcdfeOEFrVWrln744YfauXNnjY2N1YULF2p2drbecMMN2qxZM61Zs6b26tVLP/nkk8Lz8vLydOTIkdqqVStNSkrSAw88UCdOnKj5+fklyrkv+Hw+bdy4sY4fP74wLSMjQ5OTk/XJJ58s9rz77rtPW7RoUSTt+eef11q1apVY3yGHHKJjxowpkjZu3Dg97LDDSjyvMnou0bPKsBzERHoOIycHBg92n88/D+efX6YJ+3Hv/8ritTsrUMC96dQ0lbEndg5LWWvWrOG4445j+PDhTJkyhaVLl3LRRRcRExPD/fffD8C1117LjBkzePvtt2natCl33nkn33zzDaeeemrQMtevX88555zDhAkTOP3009m9ezdz5swB4Oyzz2bRokV88MEHfP311wDUrl17rzJUleOOO45t27bxwgsv0L59e37//XeysrJCvraff/6Zb7/9llatWhWm3XLLLbz55ptMnjyZgw46iO+++46LL76YunXrMnToUHbv3s0JJ5zA4MGDefnll1m7di2jR48OWv6NN97I/fffz4EHHkhKSkqpZb/11ltMmjSJ119/na5du7Jx48bCdsnLy+Pkk0/mwgsv5NVXXyU3N5cFCxYU23uYP38+Z555JrfccgvDhg3jhx9+4JJLLiE1NZUrr7yyMN+DDz7IuHHjuP766/n444+56qqrOPzww+nXr1/QcmfNmkViYiKdOxe9v2JiYnjooYdo06YNK1eu5Morr+TKK6/k5ZdfLsyTlZXF+PHjeeqpp2jQoAFNmjThggsuYOnSpbz22ms0b96cjz76iBNPPJEffviBbt264fP5aNasGdOmTaNBgwbMnTuXUaNGUb9+fS688MJif9vOnTuzcuXKYo+3bNmSX3/9Neix5cuXs379eo455pjCtBo1ajBgwABmz57NJZdcEvS8ww47jJtvvpn333+fE044gS1btvDGG29w/PHHB82vqnz55Zf8/vvv3HXXXUWO9enTh/Hjx5OZmUmNGjWKvY6Kplorl4iplq++ggEDICEBpk2DDh0gbT+JiOnH448/TpMmTXj88ceJiYmhY8eO3HPPPVxyySXceeed+Hw+nn/+eV566SUGD3Yx5J577jmaN29ebJlr164lNzeXM844g5YtWwLQxc8rdHJyMnFxcTRuHBjwdA+ff/453333Hb/++isdO3YEoE0IYQuefvpppkyZQm5uLjk5OcTExDB58mTALYJ94IEHmD59Ov379wegdevWzJ07l8mTJzN06FBeffVV8vPzee6556hRowadO3fmP//5T5GhjwJuv/32wgdUKGWvXLmSJk2acMwxxxAfH0+LFi3o1asXADt37mT79u2ceOKJtPWGYjt06FDsdT7wwAMMHDiQcePGAdC+fXv+/PNPJk6cWES5HHPMMYWGFldeeSWPPPIIX3zxRbHKZeXKlTRs2HAvpeavYFu1asW9997LySefzIsvvli4JiM/P59HH32Unj17ArB06VJef/11VqxYUbjS/IorruDzzz/nqaee4vHHHyc+Pp477rijSNkLFizg9ddfL1G5fPTRRyUORcbHxxd7bP369QA0atSoSHqjRo1Ys2ZNsef169eP119/nWHDhpGZmUleXh6DBw/mxRdfLJJvx44dNGvWjOzsbGJjY5k8eTLHHXdckTxNmzYlNzeXtWvXFv7ekaB6K5fK7rmsXg2jR8Nbb8FzzzmfYIcfXu7iwtWDiBRLliyhX79+RRZtHX744eTk5PDXX3+h6txR9OnTp/B4rVq1iiiLQLp168bRRx9Nly5dOOaYYzj66KM544wzaFAGR54//vgjTZo0KVQsoXL22WczduxYdu7cycSJE6lbty6nn346AIsXLyYrK4shQ4YUue9yc3MLeze//fYbXbp0KfI22bdv36B1FSiGUMs+88wzefjhh2ndujXHHnssQ4YM4aSTTiIxMZF69eoxYsQIjj32WAYNGsSgQYM488wzOaCYaKVLlixh6NChRdIOP/xwxo0bx86dO0lNTQXg4IMPLpKnadOmbNy4sdj2y8zMJCmIt4kvv/ySCRMmsGTJEnbs2EF+fj45OTmsX7+epk2bAhAXF0f37t0Lz1mwYAGqSqdOnYqUlZ2dzVFHHVW4/+STT/Lss8+ycuVKMjMzyc3NLXwpKY7SjodC4LNHVUt8Hi1evJirrrqKW2+9lWOPPZZ169Zx/fXXc8kllxSxrEtJSeGnn35i9+7dfPHFF1xzzTW0atWKQYMGFeYpuL8yMzP3+Tr2hWqrXBStPMvevDyYPBluucV9v/tuOO+8Sqq86lLSH0pE8HkLRsvyEhAbG8v06dOZM2cO06dP57nnnuOmm25ixowZdOvWLWS5ykPt2rU58MADAXjllVfo3LkzU6ZMYcSIEYXX8v777+/ls6ngTbe0B4w/tWrVKvweStkHHHAAv//+O1988QWff/451157LePGjeP777+nVq1avPDCC4wePZpPPvmE9957j//85z+88847HHvssXvVXdrvFli3/7ECWYORlpbGtm3biqStXLmSoUOHcvHFF3PHHXdQv359FixYwD//+U9ycnIK8yUmJhbp8fh8PkSEH374YS85Ch6uU6dOZfTo0UyaNIlDDz2U1NRUJk+ezNtvv12sjLBvw2IFPeb169cXUd4bN27cqzfjz4QJE+jTpw/XX3894BR3rVq16N+/P3fddVdhWTExMYX3YPfu3VmyZAl33313EeWydetWgDK9cFUE1Va5QCUuG/m//4PXX4chQ5ySsciQAHTq1Ilp06bh8/kKey+zZs0iISGBtm3b4vP5iI+PZ+7cubRu3RqAjIwMFi1aVGJ3XkTo168f/fr147bbbqNz585MnTqVbt26kZCQUKolUY8ePVi3bh1Lliwpc++lgPj4eG6++WZuuukmzjrrLDp16kRiYiIrV64s8ubsT8eOHXnppZeKjIXPnTu31LpCKRsgKSmJoUOHMnToUMaMGUPjxo359ttvC4fXunXrRrdu3bjxxhs57rjjePHFF4Mql06dOjFr1qwiabNmzaJ58+b7ZLV3yCGHsGnTJjZv3kyaN0w8b948cnJyePDBBwuVxwcffBBSWarK+vXrOfLII4PmmTVrFn379i2yRmrp0qWllr0vw2KtW7emcePGfPbZZ/Tu3Rtw80XffPMN9913X7HnZWRk7DVcWLBf0suQz+cjOzu7SNqiRYto2rRpicqsMqi+ykVBKnLWZccO57U4OdnFrj/1VDjjjP1yIeTOnTv56aefiqTVqVOHyy+/nIceeojLL7+cq6++mmXLljFmzBiuuOKKQrPgkSNHcuONN5KWlkaTJk0YP3584VtpMObMmcPnn3/OscceS6NGjfjxxx9ZtWpV4fBIq1atWLlyJQsWLKBFixakpKSQmJhYpIxBgwbRt29fTj/9dB588EHat2/PX3/9RXp6OqecckrI133uuedy880389hjj3HDDTdw3XXXcd1116GqDBgwoNDYICYmhlGjRjFs2DBuueUWLr74Ym6++WbWrl3L3XffDZTce0tJSSm17ClTppCXl0ffvn1JTk5m6tSpxMfH065dO5YvX85TTz3FSSedRLNmzVi2bBm//PILl112WdD6rr32Wnr37s3tt9/Oueeeyw8//MD9999fKGt5OeSQQ2jYsCGzZs0qbOd27drh8/l46KGHOO2005gzZw4PPfRQqWW1b9+eYcOGMWLECO6//3569OjB1q1b+frrr2nTpg2nnXYa7du3Z8qUKXz88ccceOCBvPHGG8yYMYO6deuWWPa+DIuJCKNHj+auu+6iQ4cOtG/fnvHjx5OcnMy5555bmO///u//AAqHvE488UQuvvhinnjiicJhsdGjR9OjR4/C3updd91F3759adOmDdnZ2Xz00Ue8/PLLPProo0Vk+OabbxgyZEi5ryFshMPkrCpuDVp31AH3flmsuV258flUX39dtXFj1X//O2zFRrMpMgHmv4CefvrpqrrHFDkhIaHQFNl/4eGuXbv0vPPO05o1a2rDhg11woQJetRRR+mll15amMffDHjx4sU6ZMgQbdiwoSYkJGjbtm114sSJhXmzsrL09NNP1zp16pRoirxt2za96KKLNC0tTRMTE7Vjx446derUYq8zmCmyqupdd92l9evX1507d6rP59NHHnlEO3bsqAkJCZqWlqZHH320Tp8+vTD/d999p927d9eEhATt3r27vvnmmwronDlzVDW4WbCqllr222+/rf/4xz8KF9j16tVL33//fVVVXb9+vZ566qnatGlTTUhI0AMOOECvv/56zcnJKbbOAlPk+Pj4Yk2R/duzpDbyZ8yYMXrGGWcUSXv44Ye1adOmmpSUpEcddZROnTpVAV2+fLmq7jFFDiQnJ0fHjh2rrVu31vj4eG3UqJGeeOKJOm/ePFV1i0NHjhypderU0dq1a+vIkSN13Lhx2rJlyxJl3FcKFlEWmLkPGDCg0Oy7gIEDB+rAgQOLpD3yyCPaqVMnrVGjhjZu3Fj/+c9/6qpVqwqPjxkzRg888MBCE/x+/frpa6+9VqSMzMxMTU1N1e+++65EGSvDFDniSqCitnotO+iJj35TYgOXmT//VD3mGNdsvXqpejdxOIhW5RJusrKytFGjRjpp0qRIi1IpvPPOOyoieymT6sqGDRu0fv36unTp0kiLUi157LHHdPDgwaXms3Uu+0Bmbj5t0mqVnjFU3ngDRoxw5sWPPgqXXeaGxYx94scff2TJkiX06dOHXbt2MXHiRHbt2sXZZ58dadEqhBdffJE2bdpwwAEHsGjRIkaPHs2JJ55YOAdR3WnYsCHPP/88q1atCsn82ygb8fHxew2TRYpqq1x8PqVROEIc5+ZCfDz06AGnnw733QeeeaQRHh544AF+//33QnPTmTNnlrjWJZrZsGEDY8eOZd26dTRu3JihQ4cyceLESItVqZx00kmRFqHaMmrUqEiLUIi4XlD1I6lJO73jhfe5YUjxi8VKZONGuO462L0b/ve/8AoXhH2xXDIMwygLJT1vRGS+qvYKerAMVFvHlQrElicUpc8HzzzjVtW/8YaLt1KFneQZhmFURartsBiUw7fY8uVu8ePs2TBwIDzxhAvgVUmohr7IzjAMozxU1mhVte25QDl6LqmpLtTwlCnOP1glKpb4+PiIu2swDKP6k5mZWeJC0HBhyuX9991EfX4+1K8PixY578WV3INo2LAha9asISMjo9LeLAzD2H9QVTIyMlizZg0NGzas8Pqq9bCYz1fCQ3rVKrjqKnjnHTevsm4dNG8esRj2Bc4AC7z+GoZhhJv4+HgaNWpU+LypSKq1cmlSJ0gsg7w8eOQRuO02N3k/cSL8+9/O3DjCpKamVsqPbhiGUdFUa+XSKDVx78T8fGcNdsQR8Nhj4BfsyTAMwwgPlT4GJCKXi8hyEckSkfki0r+U/F1FZIaIZIrIGhG5TUI0qYorGOLatg1uvBF27YLERJg1y821mGIxDMOoECpVuYjI2cDDwN3AIcBs4GMRaVFM/lTgM2AD0Bu4CrgeuCaU+uJigNdec2tWJk2CL790B+rX3y+9FxuGYVQWlbpCX0S+B35R1Yv90v4E3lTVm4LkvwyYCDRS1Uwv7RbgMqC5liB8SoOWuuag1qR+OwN694annoJDDgn3JRmGYVQrom6FvogkAD2B6QGHpgOHFnNaP+CbAsXi8SnQFGhVUn1Nd26m1sIf4fHH4bvvTLEYhmFUIpU5oZ8GxOKGuPzZABxdzDmNgdVB8hccW+5/QERGAQWe27LjcljE5Ze7YF77N2nA5kgLUUWwttiDtcUerC32cFA4ComEtVjgUJYESSstf7B0VPVp4GkAEZkXjq5ddcDaYg/WFnuwttiDtcUeRGReOMqpzAn9zUA+rsfhT0P27s0UsL6Y/JRwjmEYhhFhKk25qGoOMB8YHHBoMM5qLBjfAf1FJCkg/1pgRbhlNAzDMMJDZa9zeQAYISIXiUhHEXkYNzn/JICITBCRL/zyvwZkAFNEpIuInAaMAR4oyVLM4+kKkD9asbbYg7XFHqwt9mBtsYewtEWlBwsTkcuBG4AmwCLg36o60zs2BThCVVv55e8KTAb6ANtwiuiOEJSLYRiGESGqbSRKwzAMI3JUa5f7hmEYRmQw5WIYhmGEnahVLpXpALOqU5a2EJEjRORdEVknIhki8ouIjKxMeSuSst4Xfue1E5FdIrK7omWsLMrxHxERGS0iv4lItneP3FNZ8lYk5WiLY0XkO++e2Oz9Z9pXlrwVhYgMEJH3vGegisiIEM4p17MzKpVLZTvArMqUtS1wrnYWAmcAXYAngKdF5NxKELdCKUdbFJyXALwBzKxwISuJcrbF/cDlwI1AR+B4qkGblON50Rp4F/jGy380UAP4qFIErliScYZUVwOlxlXfp2enqkbdBnwPPBOQ9icwoZj8lwE7gRp+abcAa/CMGqJ1K2tbFFPGNOCtSF9LpNoCeBB4ARgB7I70dUSiLXAuP3KBjpGWvQq0xRm4Bd+xfmlH4ryCpEX6esLYLruBEaXkKfezM+p6LpXtALMqU862CEYqzsw7ailvW4jIUOAE3BtZtaCcbXEysAwYIiLLRGSFiLwoIhUfbL0CKWdbzMMp2otEJFZEUoDzgR9UdX/zP1buZ2fUKRdKdoAZ6CqmgMbF5C84Fq2Upy2KICInAIOI/kVkZW4LEWkCPAMMV9VdFStepVKe+6IN0BI4B9eDGw50AN4XkWh8ThRQ5rZQ1RU4TyDjgGxgB9AV9xKyv1HuZ2c03zQV5gAzCilrW7hMIofhvCBcpapzK0KwCFCWtngFeEJV51SsSBGjLG0RAyTiFO1MVf0Gp2D64Mbao52Q20JEGgPPAS/hrv0IYBcwLcoVbXkp17MzGhvKHGDuoTxtAYCIHA58DNymqk9UjHiVSnna4ihgrIjkiUge7oFSy9sfVcw50UB52mIdkKeqf/il/QnkASUaRFRxytMW/wLSVfUGVf1RnQeR84CBlG24uTpQ7mdn1CkXNQeYhZSzLRCRATjFMk5VH6owASuRcrZFV6C733YbzoKmO/Df8EtZOZSzLb4F4kSkrV9aG1xYjpVhF7KSKGdb1MQpJH8K9qPumbmPlP/ZGWmLhXJaOZwN5AAX4UwmH8ZZPrT0jk8AvvDLXxungd/Amd+ehrOAuDbS1xKBtjgCSAfuw72RFGwNIn0tld0WQc4fQfWxFivrfRGDewjPwJnfHuJ9nwPERPp6KrktjgJ8wFigHdAD+AT4G6gV6evZx7ZIZs/LVAbuhao70KKYtij3szPiF7sPjXQ5TnNme3+KAX7HpgArAvJ3xdnsZ+GGAMYS5WbI5WkLb1+DbCsqW+5It0WQc6uNcilPW+Ccyf4XN7+wEXgVaBTp64hQW5wDLPCU0CbgfaBTpK8jDO1wRDH//ykltEW5np3muNIwDMMIO/vb+KFhGIZRCZhyMQzDMMKOKRfDMAwj7JhyMQzDMMKOKRfDMAwj7JhyMQzDMMKOKRcjqvCCnamIpEValvLieRy+rpQ8I6pT4DJj/8OUi1HpiMgUT0EEbt0jLRuAiHztJ1O2iPwhIjeLSGyYqugNPO5Xn4rIGQF5puLcr1QoAe2/W0R+DiU6YTHlBF6DsR9jysWIFJ/jVoT7b4siKlFRXsDJdBDwCDAeKLG3ESqquklVM0rJk6mqG8NRXwhcjLvWbjil9oKIHFtJdRvVFFMuRqTIVtX1AVueiFwjIr+ISLoXr/tZEalTXCEiUltEXhaRjV589GUiMjrg+NPe8V1eLPBeIciX4cm0QlUfA74ATvHKrOsF0trmxRX/XEQ6l0GmwmExEVnhJf/Xe/tf4aUXDouJSHvvWNeAax8lLr57vLffSUQ+9K5zo4i87rmPL43t3rUuVdW7ga3AMX719BaR6V5dO0Vkloj087+eYNfgHTtRXMz6LHEx7O/yAngZ1RxTLkZVwweMBjoD5+LiiTxaQv7x7Ank1AEYiQvBiogI8CHQzDt+CM5H0pdeoLCykAnEe9+nAH1x0Rv74BwAfiIiNUqTKQgFsVIKeg97xU5R5wZ/HjAs4NAwYKqq5nrXMxPX++uDi/ueDLwXagwSL+riWUA9XCTGAlKAl4H+Xtk/AR/5zXsFvQav9/Mq8Bju9xyJCyF8dyjyGFFOpB2p2bb/bbiHcx7OKWDB9nExeYfgnA3GePtH4BfLHHgPeKGYc4/yyq4RkP4TcEMJ8n0NPOZ9j/GTYSLOS65S1PFhbVy0wotKk8k7vgK4zm9fgTMC8ozAz4kmcDXO9X2BP8ADcIq4n7d/BwEen4G6Xtl9SpBFcYpzt/ebKC4GyoElnCM4B4bnlXINM4FbA9JO8eqqFk5jbSt+s56LESlmUjSWykUAInKUiHwmIqtFZBfwPyCB4kOqPgGc5U1ETxKRgX7HeuJic2zyJqt3e0NNXYC2wQrzY5SXNwunLF7Bhb3tiHuof1eQUVV3AAuBTiHIVF5ex8Ut7+/tnwssU9UCOXoCAwKuc5V3rLRrvR73GwzGKd6rVPWvgoMi0lBEnvIMG3bgvCY3pPQgYj2B/wTI9BpQi+gOL26EQFykBTD2WzL8H2AAItISN4z1DC7OxBZcLI3XcQpmL1T1Y++844BBwIci8l9VvQDX69jAngeyPztLkW8qe2Kor1XVfE9GKeEcDUGmcqGqG0Xkc9xQ2Ezv81W/LDG4tgtmdFBatNX13m/xl4icCSwQkQWq+pt3/EWgEfBv9rit/4JifpMAmcYRPPDaplLONaIcUy5GVaIX7oH1b7+H+QmlnaSqm3FzAi+LyMfA6yJyKS4eRyPAp6rLyijLjkDl57EY99Dsh3vIIyKpuDmWF0qTSVWzg5SZC4Ri5vwK8KiIPO3Vd7rfsQXAWcBKVc0NdnIoqOpfIvI/4F7gJC/5cFxv5kMAEWmEm1sp7RoWAB2KaUejmmPDYkZV4k/cPTlaRFqLyD9xk/vFIiJ3iMgpItJORDriIuUt8x7in+PC974rIsd5ZfYTkXEiEqw3Uyqq+ifwLvCUiPT3LLhewfWEXgtBpmCsAAaJSGMRqVtC9W/jjAqeA+Z6shQwGTf3M1VE+opIGxE5WpylXEoZL/N+4AQR6ePt/wGc51mj9cZFJcwJ4RruAM712qOLiHQQkTNE5N4yymNEIaZcjCqDqv6Cm7i+BtdDuIjS15ZkA3cBP+MUSQpwoleeAscDX+KG2n4HpuHWrqzdB1EvAObi5mLm4uZ1hqhqZmkyFcO1wJG4OZIfi8ukbm3M27j1KK8EHFsLHIabD/oE+BWncLK9LWRUdSFOMY/3kkbiLM/m4xTL8+wdP32va1DVT4GhXvpcbxuDCxdsVHMsEqVhGIYRdqznYhiGYYQdUy6GYRhG2DHlYhiGYYQdUy6GYRhG2DHlYhiGYYQdUy6GYRhG2DHlYhiGYYQdUy6GYRhG2Pl/YkyoE8A4Us4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "#plt.savefig('Log_ROC_5samples_down_regulated_with_features.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a77517a",
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   "source": [
    "- The ROC (Receiver Operating Characteristic) curve is a widely used tool for evaluating binary classifiers\n",
    "- It is plotted on a graph where the dotted line represents the ROC curve of a completely random classifier \n",
    "- A reliable classifier should aim to stay as distant as possible from this line, preferably towards the top-left corner of the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57120669",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57fef96f",
   "metadata": {},
   "source": [
    "Prior to applying the data to the LR model, several steps were taken,\n",
    "- Including data visualization to identify the most effective predictors and the least effective predictors\n",
    "- The imbalance in the y variable was resolved using the SMOTE method\n",
    "- Recursive feature elimination was also conducted to select the best features that would improve the accuracy of the model\n",
    "- Additionally, columns with insignificant P-values (>=0.5) were removed after the implementation of the LR method"
   ]
  }
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