Persoal_loan_cart_modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Context\n",
    "AllLife Bank has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).\n",
    "\n",
    "A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget.\n",
    "\n",
    "You as a Data scientist at AllLife bank has to build a model that will help marketing department to identify the potential customers who have higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.\n",
    "\n",
    "#### Objective\n",
    "\n",
    "1. To predict whether a liability customer will buy a personal loan or not.\n",
    "\n",
    "2. Which variables are most significant.\n",
    "\n",
    "3. Which segment of customers should be targeted more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Dictionary\n",
    "* ID: Customer ID\n",
    "* Age: Customer’s age in completed years\n",
    "* Experience: #years of professional experience\n",
    "* Income: Annual income of the customer (in thousand dollars)\n",
    "* ZIP Code: Home Address ZIP code.\n",
    "* Family: the Family size of the customer\n",
    "* CCAvg: Avg. spending on credit cards per month (in thousand dollars)\n",
    "* Education: Education Level. 1: Undergrad; 2: Graduate;3: Advanced/Professional\n",
    "* Mortgage: Value of house mortgage if any. (in thousand dollars)\n",
    "* Personal_Loan: Did this customer accept the personal loan offered in the last campaign?\n",
    "* Securities_Account: Does the customer have securities account with the bank?\n",
    "* CD_Account: Does the customer have a certificate of deposit (CD) account with the bank?\n",
    "* Online: Do customers use internet banking facilities?\n",
    "* CreditCard: Does the customer use a credit card issued by Universal Bank?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "from sklearn import metrics\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the datasest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Loan_Modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a copy of the dataset\n",
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIPCode</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal_Loan</th>\n",
       "      <th>Securities_Account</th>\n",
       "      <th>CD_Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
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       "      <td>49</td>\n",
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       "      <td>34</td>\n",
       "      <td>90089</td>\n",
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       "      <td>1.5</td>\n",
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       "      <th>2</th>\n",
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       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>94720</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>94112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>91330</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Age  Experience  Income  ZIPCode  Family  CCAvg  Education  Mortgage  \\\n",
       "0   1   25           1      49    91107       4    1.6          1         0   \n",
       "1   2   45          19      34    90089       3    1.5          1         0   \n",
       "2   3   39          15      11    94720       1    1.0          1         0   \n",
       "3   4   35           9     100    94112       1    2.7          2         0   \n",
       "4   5   35           8      45    91330       4    1.0          2         0   \n",
       "\n",
       "   Personal_Loan  Securities_Account  CD_Account  Online  CreditCard  \n",
       "0              0                   1           0       0           0  \n",
       "1              0                   1           0       0           0  \n",
       "2              0                   0           0       0           0  \n",
       "3              0                   0           0       0           0  \n",
       "4              0                   0           0       0           1  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID', 'Age', 'Experience', 'Income', 'ZIPCode', 'Family', 'CCAvg',\n",
      "       'Education', 'Mortgage', 'Personal_Loan', 'Securities_Account',\n",
      "       'CD_Account', 'Online', 'CreditCard'],\n",
      "      dtype='object')\n",
      "(5000, 14)\n"
     ]
    }
   ],
   "source": [
    "# Checking the columns name and shape of the data\n",
    "print(df.columns)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5000 rows and 14 columns.\n",
    "The variable \"Personal_Loan\" is our target variable.\n",
    "We are good with column names and no formatting is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  5000 non-null   int64  \n",
      " 1   Age                 5000 non-null   int64  \n",
      " 2   Experience          5000 non-null   int64  \n",
      " 3   Income              5000 non-null   int64  \n",
      " 4   ZIPCode             5000 non-null   int64  \n",
      " 5   Family              5000 non-null   int64  \n",
      " 6   CCAvg               5000 non-null   float64\n",
      " 7   Education           5000 non-null   int64  \n",
      " 8   Mortgage            5000 non-null   int64  \n",
      " 9   Personal_Loan       5000 non-null   int64  \n",
      " 10  Securities_Account  5000 non-null   int64  \n",
      " 11  CD_Account          5000 non-null   int64  \n",
      " 12  Online              5000 non-null   int64  \n",
      " 13  CreditCard          5000 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 547.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values in the dataset. There is no object datatype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>2500.500000</td>\n",
       "      <td>1443.520003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1250.75</td>\n",
       "      <td>2500.5</td>\n",
       "      <td>3750.25</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>45.338400</td>\n",
       "      <td>11.463166</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experience</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>20.104600</td>\n",
       "      <td>11.467954</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>73.774200</td>\n",
       "      <td>46.033729</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>98.00</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIPCode</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>93169.257000</td>\n",
       "      <td>1759.455086</td>\n",
       "      <td>90005.0</td>\n",
       "      <td>91911.00</td>\n",
       "      <td>93437.0</td>\n",
       "      <td>94608.00</td>\n",
       "      <td>96651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.396400</td>\n",
       "      <td>1.147663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCAvg</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.937938</td>\n",
       "      <td>1.747659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.881000</td>\n",
       "      <td>0.839869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortgage</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>56.498800</td>\n",
       "      <td>101.713802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.00</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal_Loan</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.294621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Securities_Account</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.104400</td>\n",
       "      <td>0.305809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD_Account</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>0.238250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.596800</td>\n",
       "      <td>0.490589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditCard</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.455637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean          std      min       25%  \\\n",
       "ID                  5000.0   2500.500000  1443.520003      1.0   1250.75   \n",
       "Age                 5000.0     45.338400    11.463166     23.0     35.00   \n",
       "Experience          5000.0     20.104600    11.467954     -3.0     10.00   \n",
       "Income              5000.0     73.774200    46.033729      8.0     39.00   \n",
       "ZIPCode             5000.0  93169.257000  1759.455086  90005.0  91911.00   \n",
       "Family              5000.0      2.396400     1.147663      1.0      1.00   \n",
       "CCAvg               5000.0      1.937938     1.747659      0.0      0.70   \n",
       "Education           5000.0      1.881000     0.839869      1.0      1.00   \n",
       "Mortgage            5000.0     56.498800   101.713802      0.0      0.00   \n",
       "Personal_Loan       5000.0      0.096000     0.294621      0.0      0.00   \n",
       "Securities_Account  5000.0      0.104400     0.305809      0.0      0.00   \n",
       "CD_Account          5000.0      0.060400     0.238250      0.0      0.00   \n",
       "Online              5000.0      0.596800     0.490589      0.0      0.00   \n",
       "CreditCard          5000.0      0.294000     0.455637      0.0      0.00   \n",
       "\n",
       "                        50%       75%      max  \n",
       "ID                   2500.5   3750.25   5000.0  \n",
       "Age                    45.0     55.00     67.0  \n",
       "Experience             20.0     30.00     43.0  \n",
       "Income                 64.0     98.00    224.0  \n",
       "ZIPCode             93437.0  94608.00  96651.0  \n",
       "Family                  2.0      3.00      4.0  \n",
       "CCAvg                   1.5      2.50     10.0  \n",
       "Education               2.0      3.00      3.0  \n",
       "Mortgage                0.0    101.00    635.0  \n",
       "Personal_Loan           0.0      0.00      1.0  \n",
       "Securities_Account      0.0      0.00      1.0  \n",
       "CD_Account              0.0      0.00      1.0  \n",
       "Online                  1.0      1.00      1.0  \n",
       "CreditCard              0.0      1.00      1.0  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Observations**\n",
    " 1. No useful information from ID & Zipcode.\n",
    " 2. Experience has negative value. We will need to look as experience cannot be negative.\n",
    " 3. Income, Age, CCAvg & Mortgage look good. We will explore them further during EDA.\n",
    " 4. We will need to check these variables --Family, Education, Personal_Loan, Securities_Account, CD_Account, Online, CreditCard, as the values do not look to be continuous values and so can be converted into categorical.\n",
    " 5. Avergae age is 45 years.\n",
    " 6. Approx. 94% of customers don't have CD_Account.\n",
    " 7. Approx. 90% of customers did not accept personal loan in the last campaign.\n",
    " 8. Approx. 71% of customers do not use credit cards.\n",
    " 9. Approx. 90% of customers do not have Securities account.\n",
    " 10. Approx. 60% of customers use internet banking facility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    5000\n",
       "Age                     45\n",
       "Experience              47\n",
       "Income                 162\n",
       "ZIPCode                467\n",
       "Family                   4\n",
       "CCAvg                  108\n",
       "Education                3\n",
       "Mortgage               347\n",
       "Personal_Loan            2\n",
       "Securities_Account       2\n",
       "CD_Account               2\n",
       "Online                   2\n",
       "CreditCard               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the unique value count of each variable\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Just for the EDA purposes, Columns with value count < 5 can be considered as categorical variables and those with count > 5 can be considered as continuous variables.\n",
    "2. Since, ID has 5000 unique values, implying all teh values are unique, we won't be using it further as it's not informative. We can drop it.\n",
    "3. ZIPCode is alos not informative, so we will drop that also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 12)\n"
     ]
    }
   ],
   "source": [
    "df.drop('ID', axis=1, inplace=True)\n",
    "df.drop('ZIPCode', axis=1, inplace=True)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the correlation between all the variables\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the Experience & Age have extreme high correlation of 0.99. This means we don't need to include Experience column for modelling and no need to treat the negative values. We can drop it for model building. We will use Age column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us convert the datatypes of variables as per the copunt of unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    45\n",
       "Experience             47\n",
       "Income                162\n",
       "Family                  4\n",
       "CCAvg                 108\n",
       "Education               3\n",
       "Mortgage              347\n",
       "Personal_Loan           2\n",
       "Securities_Account      2\n",
       "CD_Account              2\n",
       "Online                  2\n",
       "CreditCard              2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Family', 'Education', 'Personal_Loan', 'Securities_Account', 'CD_Account', 'Online', 'CreditCard']\n",
      "['Age', 'Experience', 'Income', 'CCAvg', 'Mortgage']\n"
     ]
    }
   ],
   "source": [
    "cat_col = [col for col in df.columns if df[col].nunique() <= 5]\n",
    "print(cat_col)\n",
    "num_col = [col for col in df.columns if df[col].nunique() > 5]\n",
    "print(num_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate analysis - Continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20,10))\n",
    "for i,col in enumerate(num_col):\n",
    "    ax=fig.add_subplot(2,3,i+1)\n",
    "    sns.distplot(df[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "1. Age & Experience look uniformly distributed.\n",
    "2. Income, CCAvg & Mortgage are rightly skewed.\n",
    "3. Mortgage has majority of 0 values indicating that majority of people do not have mortgages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate analysis - Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20,10))\n",
    "for i,col in enumerate(cat_col):\n",
    "    ax=fig.add_subplot(2,,i+1)\n",
    "    sns.countplot(df[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "1. Majority of customers have family size =1.\n",
    "2. Majority of customers are Undergraduates.\n",
    "3. Majority of customers did not buy personal loan.\n",
    "3. Majority of customers do not have securities account & CD_account in AllLife.\n",
    "4. Majority of customers use internet banking facilities.\n",
    "5. Majority of customers do not use Credit card of AllLife bank."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate analysis - relationship between numerical values with target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20,10))\n",
    "for i,col in enumerate(num_col):\n",
    "    ax=fig.add_subplot(2,3,i+1)\n",
    "    sns.barplot(x=df['Personal_Loan'], y=df[col])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "1. Age & Experience are not impacting Personal Loan\n",
    "2. Customers with higher income & high CCAvg & higher mortgage values are more likely to take personal loan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate analysis - relationship between categorical values with target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_col.remove('Personal_Loan')  # Removing the target variable\n",
    "fig=plt.figure(figsize=(20,10))\n",
    "for i,col in enumerate(cat_col):\n",
    "    ax=fig.add_subplot(2,3,i+1)\n",
    "    sns.barplot(y=df['Personal_Loan'], x=df[col], ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "1. Cusotmers with family size = 3 & 4 are more likely to take personal loan than the onews with family size = 1 & 2.\n",
    "2. Customers who are graduate & advanced/professionals are more likely to take personal loan than undergraduates.\n",
    "3. Customers with CD_Account & Securities_account are more likely to take personal loan.\n",
    "4. The variables Online & CreditCard do not affect the chances of customers taking personal loan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. There are no missing values as we have already checked.\n",
    "2. As we analysed earlier, Age & Experience are highly correlated. Hence, we can drop Experience.\n",
    "3. Also, Let's check for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 11)\n"
     ]
    }
   ],
   "source": [
    "# dropping Experience varibale\n",
    "df.drop('Experience', axis=1, inplace=True)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier detection using boxplot\n",
    "num_col.remove(\"Experience\")\n",
    "plt.figure(figsize=(20,30))\n",
    "\n",
    "for i, col in enumerate(num_col):\n",
    "                     plt.subplot(5,4,i+1)\n",
    "                     plt.boxplot(df[col],whis=1.5)\n",
    "                     plt.tight_layout()\n",
    "                     plt.title(col)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treating Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fucntion to treat outliers. Referred from case study.\n",
    "def treat_outliers(data,col):\n",
    "    '''\n",
    "    treats outliers in a varaible\n",
    "    col: str, name of the numerical varaible\n",
    "    df: data frame\n",
    "    col: name of the column\n",
    "    '''\n",
    "    Q1=data[col].quantile(0.25) # 25th quantile\n",
    "    Q3=data[col].quantile(0.75)  # 75th quantile\n",
    "    IQR=Q3-Q1\n",
    "    Lower_Whisker = Q1 - 1.5*IQR \n",
    "    Upper_Whisker = Q3 + 1.5*IQR\n",
    "    data[col] = np.clip(data[col], Lower_Whisker, Upper_Whisker) # all the values samller than Lower_Whisker will be assigned value of Lower_whisker \n",
    "                                                            # and all the values above upper_whishker will be assigned value of upper_Whisker \n",
    "    return data\n",
    "\n",
    "def treat_outliers_all(data, col_list):\n",
    "    '''\n",
    "    treat outlier in all numerical varaibles\n",
    "    col_list: list of numerical varaibles\n",
    "    df: data frame\n",
    "    '''\n",
    "    for c in col_list:\n",
    "        data = treat_outliers(data,c)\n",
    "        \n",
    "    return data\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us treat the outliers.\n",
    "df = treat_outliers_all(df, num_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets recheck the outliers after the treatment\n",
    "plt.figure(figsize=(20,30))\n",
    "\n",
    "for i, col in enumerate(num_col):\n",
    "                     plt.subplot(5,4,i+1)\n",
    "                     plt.boxplot(df[col],whis=1.5)\n",
    "                     plt.tight_layout()\n",
    "                     plt.title(col)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, now the outliers have been removed from the variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building - Logistic Regression\n",
    "\n",
    "### Model building approach\n",
    "1. Data preparation\n",
    "2. Partition the data into train and test set.\n",
    "3. Built a Logistic regression on the train data.\n",
    "4. Remove multicollinearity and insignificant variables, if any\n",
    "5. Choose optimal threshold, if required.\n",
    "6. Test the data on test set.\n",
    "\n",
    "### Model evaluation criterion\n",
    "- **We will be using RECALL as a metric to evaluate our model performance, because in this case, we are interested in corrrectly predicting the total no.of customers who actually bought personal loan. Hence, we need to focus on increasing the True Positive Rate to 1.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3500, 10)\n",
      "(3500,)\n",
      "(1500, 10)\n",
      "(1500,)\n"
     ]
    }
   ],
   "source": [
    "# Split train and test data\n",
    "X = df.drop('Personal_Loan', axis=1)\n",
    "y = df['Personal_Loan']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.30, random_state=1)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit Logistic regression (Statsmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.116647\n",
      "         Iterations 9\n",
      "                            Results: Logit\n",
      "======================================================================\n",
      "Model:                Logit             Pseudo R-squared:  0.627      \n",
      "Dependent Variable:   Personal_Loan     AIC:               838.5272   \n",
      "Date:                 2021-03-12 12:13  BIC:               906.2929   \n",
      "No. Observations:     3500              Log-Likelihood:    -408.26    \n",
      "Df Model:             10                LL-Null:           -1095.5    \n",
      "Df Residuals:         3489              LLR p-value:       3.3478e-289\n",
      "Converged:            1.0000            Scale:             1.0000     \n",
      "No. Iterations:       9.0000                                          \n",
      "----------------------------------------------------------------------\n",
      "                    Coef.   Std.Err.    z     P>|z|   [0.025   0.975] \n",
      "----------------------------------------------------------------------\n",
      "const              -15.0607   0.9036 -16.6678 0.0000 -16.8317 -13.2898\n",
      "Age                  0.0083   0.0081   1.0310 0.3025  -0.0075   0.0242\n",
      "Income               0.0560   0.0034  16.3210 0.0000   0.0492   0.0627\n",
      "Family               0.7718   0.0968   7.9756 0.0000   0.5822   0.9615\n",
      "CCAvg                0.3990   0.0662   6.0315 0.0000   0.2694   0.5287\n",
      "Education            1.8131   0.1459  12.4253 0.0000   1.5271   2.0991\n",
      "Mortgage             0.0011   0.0009   1.2050 0.2282  -0.0007   0.0029\n",
      "Securities_Account  -1.1677   0.3819  -3.0578 0.0022  -1.9162  -0.4193\n",
      "CD_Account           3.8456   0.4104   9.3699 0.0000   3.0412   4.6500\n",
      "Online              -0.6655   0.1965  -3.3869 0.0007  -1.0506  -0.2804\n",
      "CreditCard          -1.1624   0.2541  -4.5740 0.0000  -1.6605  -0.6643\n",
      "======================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train = sm.add_constant(X_train)\n",
    "X_test = sm.add_constant(X_test)\n",
    "\n",
    "logit = sm.Logit( y_train, X_train )\n",
    "\n",
    "lg = logit.fit()\n",
    "\n",
    "print(lg.summary2())\n",
    "\n",
    "# Let's Look at Model Performance\n",
    "y_pred_train = lg.predict(X_train) \n",
    "pred_train = list(map(round, y_pred_train))\n",
    "\n",
    "y_pred_test = lg.predict(X_test) \n",
    "pred_test = list(map(round, y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train data: 0.9577142857142857\n",
      "Accuracy on test data: 0.9506666666666667\n",
      "Recall on train data: 0.6737160120845922\n",
      "Recall on test data: 0.610738255033557\n",
      "f1 score on train data: 0.7508417508417509\n",
      "f1 score on test data: 0.7109375000000001\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on train data:',accuracy_score(y_train, pred_train) )\n",
    "print('Accuracy on test data:',accuracy_score(y_test, pred_test))\n",
    "\n",
    "print('Recall on train data:',recall_score(y_train, pred_train) )\n",
    "print('Recall on test data:',recall_score(y_test, pred_test))\n",
    "\n",
    "print('f1 score on train data:',f1_score(y_train, pred_train))\n",
    "print('f1 score on test data:',f1_score(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "const                 35.763416\n",
      "Age                    1.009979\n",
      "Income                 1.805320\n",
      "Family                 1.031279\n",
      "CCAvg                  1.699545\n",
      "Education              1.047229\n",
      "Mortgage               1.019407\n",
      "Securities_Account     1.144064\n",
      "CD_Account             1.354625\n",
      "Online                 1.040498\n",
      "CreditCard             1.109168\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "vif_series1 = pd.Series([variance_inflation_factor(X_train.values,i) for i in range(X_train.shape[1])],index=X_train.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. All the variables have p-value ~ 1. Hence, there is no multicollinearity between the predictor variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop insignificant variables and see if there are any changes in Recall values\n",
    "THe variables Age and Mortgage can be dropped as they have p-value > 0.05. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.116799\n",
      "         Iterations 9\n",
      "                            Results: Logit\n",
      "======================================================================\n",
      "Model:                Logit             Pseudo R-squared:  0.627      \n",
      "Dependent Variable:   Personal_Loan     AIC:               837.5924   \n",
      "Date:                 2021-03-12 12:14  BIC:               899.1976   \n",
      "No. Observations:     3500              Log-Likelihood:    -408.80    \n",
      "Df Model:             9                 LL-Null:           -1095.5    \n",
      "Df Residuals:         3490              LLR p-value:       4.4730e-290\n",
      "Converged:            1.0000            Scale:             1.0000     \n",
      "No. Iterations:       9.0000                                          \n",
      "----------------------------------------------------------------------\n",
      "                    Coef.   Std.Err.    z     P>|z|   [0.025   0.975] \n",
      "----------------------------------------------------------------------\n",
      "const              -14.6016   0.7735 -18.8779 0.0000 -16.1175 -13.0856\n",
      "Income               0.0557   0.0034  16.3396 0.0000   0.0490   0.0624\n",
      "Family               0.7647   0.0964   7.9281 0.0000   0.5756   0.9537\n",
      "CCAvg                0.3912   0.0657   5.9536 0.0000   0.2624   0.5200\n",
      "Education            1.8062   0.1454  12.4255 0.0000   1.5213   2.0911\n",
      "Mortgage             0.0011   0.0009   1.1605 0.2459  -0.0007   0.0029\n",
      "Securities_Account  -1.1696   0.3824  -3.0581 0.0022  -1.9191  -0.4200\n",
      "CD_Account           3.8682   0.4111   9.4102 0.0000   3.0625   4.6739\n",
      "Online              -0.6686   0.1963  -3.4061 0.0007  -1.0534  -0.2839\n",
      "CreditCard          -1.1685   0.2542  -4.5962 0.0000  -1.6668  -0.6702\n",
      "======================================================================\n",
      "\n",
      "Recall on train data: 0.6676737160120846\n",
      "Recall on test data: 0.610738255033557\n"
     ]
    }
   ],
   "source": [
    "#Age has highest p values among those with p-value greater than 0.05\n",
    "X_train1 = X_train.drop('Age', axis =1)\n",
    "X_test1 = X_test.drop('Age',axis =1)\n",
    "\n",
    "logit1 = sm.Logit(y_train, X_train1 )\n",
    "lg1 = logit1.fit()\n",
    "print(lg1.summary2())\n",
    "\n",
    "# Let's Look at Model Performance\n",
    "y_pred_train1 = lg1.predict(X_train1) \n",
    "pred_train1 = list(map(round, y_pred_train1))\n",
    "\n",
    "y_pred_test1 = lg1.predict(X_test1) \n",
    "pred_test1 = list(map(round, y_pred_test1))\n",
    "\n",
    "print('Recall on train data:',recall_score(y_train, pred_train1) )\n",
    "print('Recall on test data:',recall_score(y_test, pred_test1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No change in Recall score after dropping the variable - Age. Let's proceed furhter and drop MOrtgage as it has p-value > 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.116989\n",
      "         Iterations 9\n",
      "                            Results: Logit\n",
      "======================================================================\n",
      "Model:                Logit             Pseudo R-squared:  0.626      \n",
      "Dependent Variable:   Personal_Loan     AIC:               836.9252   \n",
      "Date:                 2021-03-12 12:15  BIC:               892.3699   \n",
      "No. Observations:     3500              Log-Likelihood:    -409.46    \n",
      "Df Model:             8                 LL-Null:           -1095.5    \n",
      "Df Residuals:         3491              LLR p-value:       6.4203e-291\n",
      "Converged:            1.0000            Scale:             1.0000     \n",
      "No. Iterations:       9.0000                                          \n",
      "----------------------------------------------------------------------\n",
      "                    Coef.   Std.Err.    z     P>|z|   [0.025   0.975] \n",
      "----------------------------------------------------------------------\n",
      "const              -14.5420   0.7694 -18.8994 0.0000 -16.0501 -13.0339\n",
      "Income               0.0559   0.0034  16.4035 0.0000   0.0492   0.0625\n",
      "Family               0.7666   0.0966   7.9373 0.0000   0.5773   0.9559\n",
      "CCAvg                0.3885   0.0657   5.9154 0.0000   0.2598   0.5173\n",
      "Education            1.8016   0.1451  12.4152 0.0000   1.5172   2.0860\n",
      "Securities_Account  -1.1635   0.3823  -3.0437 0.0023  -1.9127  -0.4143\n",
      "CD_Account           3.8716   0.4120   9.3982 0.0000   3.0642   4.6791\n",
      "Online              -0.6615   0.1960  -3.3754 0.0007  -1.0456  -0.2774\n",
      "CreditCard          -1.1708   0.2538  -4.6130 0.0000  -1.6683  -0.6734\n",
      "======================================================================\n",
      "\n",
      "recall on train data: 0.6676737160120846\n",
      "recall on test data: 0.610738255033557\n"
     ]
    }
   ],
   "source": [
    "# Let's drop Mortgage as it has p-value > 0.05\n",
    "\n",
    "X_train2 = X_train1.drop(['Mortgage'], axis =1)\n",
    "X_test2 = X_test1.drop(['Mortgage'],axis =1)\n",
    "\n",
    "logit2 = sm.Logit(y_train, X_train2 )\n",
    "lg2 = logit2.fit()\n",
    "print(lg2.summary2())\n",
    "\n",
    "# Let's Look at Model Performance\n",
    "y_pred_train2 = lg2.predict(X_train2) \n",
    "pred_train2 = list(map(round, y_pred_train2))\n",
    "\n",
    "y_pred_test2 = lg2.predict(X_test2) \n",
    "pred_test2 = list(map(round, y_pred_test2))\n",
    "\n",
    "print('recall on train data:',recall_score(y_train, pred_train2) )\n",
    "print('recall on test data:',recall_score(y_test, pred_test2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. After dropping both the variables, still there is no decrease in Recall score. Hence, we will go with this model - \"lg2\".\n",
    "2. Also, we can conclude that all the variables in \"lg2\" model are significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Significant variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['const', 'Income', 'Family', 'CCAvg', 'Education', 'Securities_Account',\n",
      "       'CD_Account', 'Online', 'CreditCard'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(X_train2.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AqC7YpiRJkurCiy/Cv/4F11wD++5b80uyLgwTCBWm0hWUbFOSJEk168MP4fzz81zDD34Au+8O3/oWLL100ZFVjS1MKkylKyjZpiRJkmpOSnDLLbDmmnDqqTBqVD4f0a6TB7ACoSqrZADa1iRJklRXnnkGDj0UHn8cNtgAfvtb2GKLoqNqM1YgVFUOQEuSpHbngw/glVfynMNTTzVU8gBWINQGrDJIkqS69sEHeT+HqVPzvMOmm8Krr8JiixUdWSFMINSq5m5ZcgBakiTVraY5hxNOyPs57LYbzJkDiyzSsMkD2MKkVjZ3y5JtSpIkqS49/zwMHAiDBsEKK8DDD+dkYhF/fbYCoVZny5IkSapbKeWVlJZcEt56C669FvbZp13u57CwTCC0QOa3d4MtS5IkqS598AGcdx6MHg2//z18/vMwbpyJQzOswWiBzG/vBluWJElSXZkzB266Ke/ncPrp0LEjfPRRvs/koVlWILTAbFGSJEntwssv5xmHYcNgww1h6FD48peLjqrmmUCoIk2tS7YoSZKkute0ktIKK8C//w3XXZfnHByQrogJhCpSnjzYoiRJkupS05zDPffkXaS7dIGRI/PQtCpmAqGK2bokSZLq0pw5cPPNcOKJMGFC3s9hxgxYbjmTh4VgAqFPaW6lJVuXJElSXXrrLfj2t/Ocw0Yb5URi882Ljqqu2eilT2lupSVblyRJUl35z3/y5xVXzJWG66+HJ580eWgFViD0qYpDU7XBdiVJklR3Zs6EX/wiD0aPHg3LLgv33190VO2KFQh9quJgtUGSJNWdOXPgt7/N+zmceSYMHJhXWFKrswIhwAFpSZJUx6ZPh223zXMO/frBLbfYqlRFJhANxgFpSZLUbsyYAUsvDZ07w3rrwaGHwt57u59DlfnVbTAOSEuSpLo3cyb85Cew6qowblw+d9VVsO++Jg9twApEA7JdSZIk1aU5c+Cmm/J+Dm++CbvvDosvXnRUDccEop1qrlUJbFeSJEl1atYs2GoreOwx2HhjuO022GyzoqNqSNZ42qnmWpXAdiVJklRnJk3Knzt2hO22gxtugCeeMHkokBWIdsxWJUmSVLdmzMj7Ofzyl/DAA7DFFnDqqUVHJUwg2pXytiVblSRJUl1q2s/hpJM+mXPo2bPoqFTGFqZ2pLxtyVYlSZJUd1KCHXbIqyl1757nHW6+GXr0KDoylbECUYfmNyBt25IkSaorEybA5z6Xl2DdfXfYay/Yc0+XZK1RflfqkAPSkiSpXZgxI881rL463HhjPrf//m4GV+OsQNQpKw2SJKluzZmTE4aTToKJE2HQINh666KjUoVMIGpcc+1KDkhLkqS6NmgQ3Hor9O8Pt98Om/pH0XpibajGNdeuZKuSJEmqO6+9BjNn5tv7758rEI8/bvJQh6xA1AHblSRJUt2aMQPOPTfv53DSSfCTn+QN4VS3TCAkSZLU+uaec9hzTzjggKKjUiswgZAkSVLrO/JIuPxyGDAA7rgDNtmk6IjUSkwgJEmS1Dpeew0WWwxWWQUOPhgGDoQ99nBJ1nbGBKIGla+85IpLkiSp5s2YAeecA+efnzeBu/pq6NMnf6jdMR2sQeUrL7nikiRJqllz5sD11+eN4M4+G3beGU4/veioVGVWIGqUKy9JkqSad8YZcOaZec7hzjudc2gQJhA1Zuiw1xn2ymQG9OpadCiSJEmf9uqr8O9/w5prwuDBsMYazjk0GL/TNaZp9sG2JUmSVFOmT4cf/xjWWguOOiqf+9zn8vKsJg8NxQpEQcoHpcuNmTiNAb26MmhAjwKikiRJmsucOfCb38DJJ8Nbb+Uh6XPOKToqFch0sSDlg9LlHJqWJEk1ZciQvAFcz57wxBN5c7hVVy06KhXICkSBHJSWJEk16ZVX4O2381D0fvvBCivALrtARNGRqQZYgShA06C0JElSTZk+Pbcqrb123gguJVhySdh1V5MH/ZcJRAEclJYkSTVl9my49tq8n8M55+SE4b77TBrULFuYCuKgtCRJqhn33APf/z5suincfTf07190RKphViDamO1LkiSpJrzyCtx7b7694445iXjsMZMHzZcJRBuzfUmSJBVq2jQ46aS8n8PgwfDxx3kfh29+05YlVcQEogC2L0mSpDY3ezZcc03eOfrcc2H33WHYMOjUqejIVGecgZAkSWoEI0bAgQfCwIG5XWnjjYuOSHXKCoQkSVJ79fLLcN11+Xb//vDII/DooyYP+kxMICRJktqbpjmHtdeGY46BKVPy+c03d85Bn5kJhCRJUnsx95zDHnvAc8/BsssWHZnaEWcgJEmS2ovx4+Gww6BfP+ccVDVVrUBExPYR8WJEjIuIE5u5f5mIuCcinomI5yJi/2rGI0mS1O7861+52gDQsyc89ZRzDqqqqiUQEdEBuAzYAegN7BERvee67HBgTEqpD7AVcH5ELFqtmCRJktqNadPghBOgd2/46U/h1Vfz+fXXd85BVVXNCkR/YFxK6eWU0n+AW4Cd5romAZ0jIoClgcnArCrGJEmSVN9mz4arroLVV4df/CLPOYwdm6sPUhuo5gxEd2B82fEEYMBc11wK3A28CXQGdkspzZn7iSLiYOBggB496nMDtqHDXueuUW8wZuI0enfrUnQ4kiSpXs2YASefDGuuCffem+cdpDZUzQpEc7WzNNfxdsAo4HNAX+DSiPjUb9cppStTSv1SSv1WXHHF1o+0DZQnDzv17V50OJIkqZ786195OdZZs2CZZeDJJ/OeDiYPKkA1E4gJwGplx6uSKw3l9gfuSNk44BVgrSrGVKje3bpw6+BNGTSgPqsokiSpjU2bBscfn+ccrroK/vnPfL5XL+ccVJhqtjA9BaweEb2AN4DdgUFzXfM68BXgkYhYGVgTeLmKMbWpprYlwNYlSZJUudmz4dpr4ZRT4J13YL/94OyzoVu3oiOTqpdApJRmRcQRwINAB+DalNJzEXFI6f4hwE+B6yPin+SWpxNSSpOqFVNbK29bsnVJkiRVLCW46KK8Idx998FGGxUdkfRfVd1ILqV0H3DfXOeGlN1+E/haNWMoWlPbkiRJUovGjYOzzoJf/Qq6dIG//hVWWslWJdUcd6JuReUtS2DbkiRJqsDUqTlxuPhiWHRR2Gcf2GYbWHnloiOTmlXVnagbTVPLUhPbliRJ0jylBFdemfdzOP982HvvvJ/DNtsUHZnUIisQrWTosNcZ9spkBvTqasuSJEmqzB13wFprwYUXOuegumEFopU0tS5ZcZAkSfM0bhzsuiu8+mqebbjtNnj4YZMH1RUTiFY0oFdX93iQJEmfNnUq/OhHeT+H+++HZ57J57t0cUhadccWpgrNPSA9NwemJUlSs66+Gk4+GSZNggMOyAPTq6xSdFTSQrMCUaG5B6Tn5sC0JElq1ogRsPba+fPVV5s8qO5ZgVgA7ukgSZLma+zY3K50/PEwcGAekF5sMVuV1G5YgZiPocNeZ7crHm+x+iBJksSUKfDDH8I668Bf/gKvvJLPL764yYPaFROI+WhqXbJFSZIkzdNvfpP3c7jggrwR3NixsOeeRUclVYUtTBWwdUmSJDUrpVxdePvtvMLShRfChhsWHZVUVVYgJEmSFtTYsbDTTnDLLfn4uOPgoYdMHtQQTCAkSZIqNWVKThbWWQf+9jeYMSOf79DBOQc1DBOIFgwd9jrDXplcdBiSJKkW3HxznnO48ELYd1946SU46KCio5LanDMQLWjaOM7haUmSGticObDIItCxY648XHQR9O1bdFRSYaxAzMeAXl0ZNKBH0WFIkqS29tJLsOOOcO65+XjnnXPbksmDGpwJhCRJUrn334djj83Vhocegs6d8/kI5xwkbGGSJEn6xB/+AAceCJMn588//SmsvHLRUUk1xQRCkiTp44+hUyfo3h369IHzz7dVSZoHW5jmwRWYJElqAC++CN/6Fhx6aD7eeGP4y19MHqQWmEDMgyswSZLUjr3/PhxzDKy7Ljz8MKy9dtERSXXDFqYWuAKTJEnt0J/+BLvvnpOIgw6CM890zkFaAFYgJElSY5g5M39eay0YMACefhquuMLkQVpAViAkSVL79uKLcNxx8NFHufqw2mpw331FRyXVLSsQkiSpfSqfc3jkEdhuu7yrtKTPxAqEJElqf554Ar7xDZgy5ZM5h5VWKjoqqV2wAiFJktqPyaUl2NddF7bdNs85DBli8iC1IhMISZJU/154IVccNt00bwq39NJwyy2w/vpFRya1OyYQkiSpfk2eDEcdBeutB48+mtuVUio6KqldcwZCkiTVpxdegM02c85BamNWICRJUn154438efXV84ZwzjlIbcoEQpIk1YcXXoCvfz3PNUyeDB06wGWXOecgtTETCEmSVNua5hzWXRceewxOPhmWWqroqKSG5QxEmaHDXueuUbksOmbiNHp361JwRJIkNbi33oJ11slzDgcfnOccVlyx6KikhmYFosxdo95gzMRpAPTu1oWd+nYvOCJJkhrUiy/mz6uskneTHjUKfv1rkwepBliBmEvvbl24dfCmRYchSVJjGjMGjjsO/vxneO45WGMNOOWUoqOSVKbhEwjbliRJqgHvvQdnnAGXX543gfv5z6Fnz6KjktSMhk8gmtqWenfrYtuSJElFmDkzzzm8+y4MHpwTCVuVpJrV0AnE0GGvM+yVyQzo1dW2JUmS2tqIEbDRRnlFpbPOgk02ySstSappDT1E3dS6ZNVBkqQ2NGYM7LAD9OsHf/97PnfggSYPUp1o6AQCYECvrgwa0KPoMCRJav/eew+OPDJv/Pb443DBBbnqIKmuNHQLkyRJaiOzZ8OAAfDKK3DIIXnOYYUVio5K0kIwgZAkSdWREjz0EGyxBXTokCsOX/iCrUpSnWv4FiZJklQFTXMO22wDt96az+24o8mD1A6YQEiSpNYzaRIccUSecxg2DC68EHbeueioJLUiW5gkSVLr+da34Kmn8pzD6ac75yC1QyYQkiRp4aUEDzwAX/5y3kH6/PNhmWXyxnCS2qWGbGEaOux1drviccZMnFZ0KJIk1a/nnoPtt4evfx2GDMnnBg40eZDauYZMIO4a9QZjJk6jd7cubiInSdKCmjQJDj8c+vSBJ5+Eiy6Co44qOipJbaRhW5h6d+vCrYM3LToMSZLqz0EHwT33fLKfw/LLFx2RpDbUkBUISZK0AFKCP/4Rxo/Px+eeC888A5deavIgNSATCEmSNG/PPgvbbZdXV7r44nxuzTWdc5AaWMMlEEOHvc6wVyYXHYYkSbXt3XfhsMPynMPw4Tl5OOecoqOSVAMabgbirlFvADg8LUlSS04/Ha68MicRp59uq5Kk/2q4CgTAgF5dGTSgR9FhSJJUO5rmHJ5+Oh+fdhqMHg2XXGLyIOl/NGQCIUmSypTPOVx4YT638srQu3excUmqSSYQkiQ1qrnnHH71K7jmmqKjklTjGm4GQpIklVxzTZ5zOPxw+MlPbFWSVBETCEmSGkVKeQO4RReF7bfPu0fvtBOsvXbRkUmqI7YwSZLUCP75T/ja13LC0LSfwxJLmDxIWmAmEJIktWfvvguHHgp9+8KIEXnO4e67i45KUh1rqATCTeQkSQ3nT3+Cq66CI46AcePgyCOhU6eio5JUxxpqBsJN5CRJ7V5KucIweTLsvz/ssQf07w9f+lLRkUlqJxqqAgFuIidJasdGj4Ztt4VvfxuuuCInExEmD5JaVcMkELYvSZLarXffhUMOgQ02yDtJX3IJPPJITh4kqZVVnEBExFLVDKTabF+SJLVbY8fCtdfm+YaxY/O8g3MOkqpkvglERAyMiDHA86XjPhFxedUja0VN1QfblyRJ7UJK8Ic/wM9+lo8HDoTXXoOLLoKuXYuNTVK7V0kF4kJgO+A9gJTSM8AW1QyqtVl9kCS1G6NHw1e+At/5Dtx6K/z73/l8t27FxiWpYVTUwpRSGj/XqdlViKWqrD5IkurapEkweHCec3jmGbj0Uhg5EhZbrOjIJDWYSpZxHR8RA4EUEYsCP6DUzlTrhg57nbtGvcGYidPo3a1L0eFIkrTwZsyAoUPhBz+A006D5ZYrOiJJDaqSBOIQ4GKgOzAB+D/gsGoG1VrKkwfblyRJdSUluOsueOo9YJ4AACAASURBVOABGDIEevaE8eNh2WWLjkxSg6skgVgzpbRn+YmI2Ax4rDohta7e3bpw6+BNiw5DkqTKPfMMHHMM/O1v0Lt33hSua1eTB0k1oZIZiEsqPFcThg57nd2ueJzdrnicMROnFR2OJEmVe/99OPjgPOcwejRcdllOJlxZSVINmWcFIiI2BQYCK0bEsWV3dQE6VDuwhVXetmTrkiSprnToAPfdB0cd5ZyDpJrVUgvTosDSpWs6l52fBuxcyZNHxPbk+YkOwNUppXObuWYr4CKgEzAppbRlRZG3wLYlSVJdaJpzuPZauOMO6NIlbwS3xBJFRyZJ8zTPBCKl9DDwcERcn1J6bUGfOCI6AJcB25KHr5+KiLtTSmPKrlkWuBzYPqX0ekSstMDvoEz5hnGSJNW08jmHddaBN96Az3/e5EFSzatkiPqDiDgPWAdYvOlkSmmb+TyuPzAupfQyQETcAuwEjCm7ZhBwR0rp9dJzvrMAsX+KG8ZJkmrejBlw7LFw9dV5tuHyy+Ggg6BjJf8kS1LxKhmivgl4AegFnAG8CjxVweO6A+Ub0E0onSu3BrBcRDwUESMiYp/mnigiDo6I4REx/N13323xRd0wTpJU0xZfPG8Ad8wxMG4cHHqoyYOkulJJArF8Suka4OOU0sMppQOATSp4XDRzLs113BHYCPgGsB1wakSs8akHpXRlSqlfSqnfiiuuWMFLS5JUI1LK8w2bbAJTpuRk4Ykn4PzzXZZVUl2qJIH4uPR5YkR8IyI2AFat4HETgNXKjlcF3mzmmgdSSjNTSpOAvwN9KnhuSZJq39NPw9Zbw/e+l1uX3iz9M2jFQVIdqySBOCsilgGOA34IXA0cXcHjngJWj4heEbEosDtw91zX3AV8OSI6RsSSwADg+YqjlySpFn38MRx4IGy0ETz7bJ5zGDUqbwonSXVuvn8CSSn9sXRzKrA1/Hcn6vk9blZEHAE8SF7G9dqU0nMRcUjp/iEppecj4gFgNDCHvNTrswv3ViRJKlhKEAGdOsF77+U5h1NPtVVJUrvS0kZyHYBdyYPPD6SUno2IbwInA0sAG8zvyVNK9wH3zXVuyFzH5wHnLXjokiTViKY5h1NPhXvugS9+EW6/HRappNAvSfWlpZ9s1wAHAssDv4qI64BfAr9IKc03eZAkqSE0zTnsvHPeSXrq1Hze5EFSO9VSC1M/YP2U0pyIWByYBHwppfRW24QmSVINSykvwXrllbD88vDrX+e5BwekJbVzLf155D8ppTkAKaWPgJdMHiRJDW/WrPw5ApZaKm8KN3YsHHKIyYOkhtDST7q1ImJ06XYAXywdB5BSSutXPTpJkmpFSnmu4fjj4Te/gS9/Oe/lIEkNpqUEYu02i0KSpFo2ciQcfTQ88gist56VBkkNbZ4/AVNKr7VlIJIk1aRjjoGLL85zDkOG5DmHDh2KjkqSCuMSEZIkze2jj2DOnHz7C1+A446DceNg8GCTB0kNzwRCkqQmKcHvfgdrrw033ZTPHXkknHceLLNMsbFJUo2oKIGIiCUiYs1qByNJUmFGjIAtt4Rdd4UuXaBnz6IjkqSaNN8EIiK+BYwCHigd942Iu6sdmCRJbea002DjjeGFF/K+DiNH5lWWJEmfUkkF4nSgPzAFIKU0CuhZvZAkSWoDH32UPwA22gh++MO8n8NBBznnIEktqCSBmJVSmlr1SCRJagtNcw5rrQW/+EU+t9NO+bZzDpI0X5UkEM9GxCCgQ0SsHhGXAP+oclySJLW+ESNgiy3ynMMyy+TbkqQFUkkCcSSwDvBvYCgwFTi6mkFJktTqzj8/zzm8+OIncw5bbVV0VJJUdyrZSnPNlNKPgR9XO5jPYuiw1xn2ymQG9OpadCiSpFrx4Yd5zmG55WDrrfOcw49/bKuSJH0GlVQgLoiIFyLipxGxTtUjWkh3jXoDgJ36di84EklS4VKC227L+zkce2w+t+GGzjlIUiuYbwKRUtoa2Ap4F7gyIv4ZEadUO7CFMaBXVwYN6FF0GJKkIg0fnpdg3W03WHZZ2GefoiOSpHaloo3kUkpvpZR+BRxC3hPitKpGJUnSwrjmmjznMHYsXHVVHpreeuuio5KkdqWSjeTWjojTI+JZ4FLyCkyrVj0ySZIq8eGHMGFCvr399nDiiTmBOPBA93OQpCqopAJxHfA+8LWU0pYppV+nlN6pclySJLUsJbj11ryfw1575ePu3eGcc6BLl6Kjk6R2a76rMKWUNmmLQCRJqtjw4XD00fDYY9C3L5x+OkQUHZUkNYR5JhARcVtKadeI+CeQyu8CUkpp/apHJ0nS3O68E777XVhpJbj6athvP1uVJKkNtVSBOKr0+ZttEYgkSfP0wQfw2mt5WdavfQ3OPBOOOspWJUkqwDxnIFJKE0s3D0spvVb+ARzWNuFJkhpaSnDzzXnOYccdYdYsWGopOPVUkwdJKkglQ9TbNnNuh9YORJKk//Hkk7DZZjBoECy/fG5X6jjf0T1JUpW1NANxKLnS8IWIGF12V2fgsWoHJklqYI88AltsASuvnPd22Hdf5xwkqUa09KecocD9wDnAiWXnp6eUJlc1KklS4/nwQxg9GgYMyJWHiy6CAw6Azp2LjkySVKalFqaUUnoVOByYXvZBRHStfmiSpIaQEtxyC6y5Zt4Ibvp0WGSRPCRt8iBJNaelBGJo6fMIYHjp84iy45oxdNjrDHvFoogk1Z2mOYc99oAVVoC77jJpkKQaN88WppTSN0ufe7VdOAvnrlFvALBT3+4FRyJJqtiLL+Z2pVVWgWuvhX32cc5BkurAfFdhiojNImKp0u29IuKCiOhR/dAWzIBeXRk0oObCkiSV++ADeOCBfHvNNeG3v4WXXoL99zd5kKQ6Ucl6eL8G+kREH+B44BrgRmDLagY2P0OHvf7fysOYidPo3c31wCWpZjXNOZxwAkycCK++Ct27w557Fh2ZJGkBVbIPxKyUUgJ2Ai5OKV1MXsq1UHeNeoMxE6cB0LtbF9uXJKlWDRsGAwfm/RxWXBH++tecPEiS6lIlFYjpEXESsDfw5YjoAHSqblgtaxqaHtCrK7cO3rTIUCRJLXn3XdhyS1huuTznsO++eYUlSVLdquSn+G7Av4EDUkpvAd2B86oa1Xw4NC1JNeyDD2BoaSG/FVeEO+/8ZM7B5EGS6t58f5KXkoabgGUi4pvARymlG6oe2Xw4NC1JNWbOHLjppjwcveee8Mwz+fwOO7g0qyS1I5WswrQr8CSwC7ArMCwidq52YJKkOvLEE3nOYa+9YOWV4ZFHoE+foqOSJFVBJTMQPwY2Tim9AxARKwJ/Bn5fzcAkSXXiww9hxx3zMqzXXZf3c7BVSZLarUp+wi/SlDyUvFfh4yRJ7dUHH8All8Ds2bDEEnD33XnOYb/9TB4kqZ2rpALxQEQ8CNxcOt4NuK96IUmSatacOXDzzXDiiTBhAqy1Fmy7LWyySdGRSZLaSCVD1D8CrgDWB/oAV6aUTqh2YJKkGtPcnMO22xYdlSSpjc2zAhERqwO/BL4I/BP4YUrpjbYKTJJUQ+bMge9/H95/H66/Hvbe21YlSWpQLf30vxb4I/A9YARwSZtEJEmqDTNnwtlnw7RpOVm4/fY85+BmcJLU0FqageicUrqqdPvFiBjZFgFJkgrWtJ/DiSfCm29Cz54waFCed5AkNbyWEojFI2IDIErHS5Qfp5RMKCSpvXn8cTj6aHjySejXD267DTbbrOioJEk1pKUEYiJwQdnxW2XHCdimWkFJkgpy+ukwfrxzDpKkeZpnApFS2rotA5EkFWDmTDjvPNh/f/j85+Haa2GZZWDppYuOTJJUo/zTkiQ1ojlz4MYbYY014Iwz4J578vnu3U0eJEktMoGQpEbz+OOw6aawzz45YXj0UTjiiKKjkiTViUp2opYktSe/+U3eRfqGG2DPPZ1zkCQtkPn+qxHZXhFxWum4R0T0r35okqRWMWMGnHZa3kka4Nxz834ODklLkhZCJRWIy4E55FWXzgSmA7cDG1cxLknSZzVnDvz2t3DSSXk/h44dYZNNYNlli45MklTHKkkgBqSUNoyIpwFSSu9HxKJVjkuS9Fk8/jgcdRQ89RRsvDH87ncwcGDRUUmS2oFKEoiPI6IDee8HImJFckVCklSr/v73XHW48ca8i7StSpKkVlLJvyi/Au4EVoqInwGPAmdXNSpJ0oKZMQNOPRV+//t8fPTR8OKLsNdeJg+SpFY1339VUko3AccD55B3p/52Sul31Q5MklSBOXPyqkprrAFnnQXDhuXziy0GSy1VbGySpHZpvi1MEdED+AC4p/xcSun1agYmSZqPYcPy/g3Dh0P//nD77Xl/B0mSqqiSGYh7yfMPASwO9AJeBNapYlySpPl57TWYONE5B0lSm5pvApFSWq/8OCI2BAZXLaL5mDzzP0x6ZTIDenUtKgRJKsaMGXkPh65d4dhjYZdd4JvfhCWXLDoySVIDWeA/V6WURlLgHhBTPvgYgJ36di8qBElqW+VzDj/7Wd4EDiDC5EGS1OYqmYE4tuxwEWBD4N2qRVSBAb26MmhAjyJDkKS2MWIEDB6cPw8YAHfckTeDkySpIJXMQHQuuz2LPBNxe3XCkST9j1mz4J138o7Se+zhnIMkqXAtJhClDeSWTin9qI3ikaTG1jTnMH06XHxxrjr861/QqVPRkUmSBLQwAxERHVNKs8ktS5KkapozB66/HlZfPc85vP9+PgcmD5KkmtJSBeJJcvIwKiLuBn4HzGy6M6V0R5Vjk6TG8OyzsO++MHJkrjjceadzDpKkmlXJDERX4D1gGz7ZDyIBJhCS9FmklFdS6tIlty7ddFOec4goOjJJkuappQRipdIKTM/ySeLQJFU1Kklqz6ZPz3MOo0fD3XdDjx7w/PMOSEuS6kJL/1p1AJYufXQuu930IUlaEHPmwHXX5f0czj4bll0W/v3vfJ/JgySpTrRUgZiYUjqzzSKRpPZs3DjYbbc857DJJvCHP+R5B0mS6kxLCYRNuJL0Wc2aBR07wsor589Dh8LuuzvnIEmqWy0lEF9psygkqb2ZPh3OOQfuvx+efBI6d4YnnjBxkCTVvXk23aaUJrdlIJLULsyeDddem/dzOOccWG89mFlaAdvkQZLUDlSyjKskqRITJ8I3vgFPPw2bbppXWOrfv+ioJElqVVVd9iMito+IFyNiXESc2MJ1G0fE7IjYuZrxSFJVfPRR/rzSSrDqqnnO4bHHTB4kSe1S1RKIiOgAXAbsAPQG9oiI3vO47ufAg9WKRZKqYto0OOkk+OIX4f33oUOHXHVwMzhJUjtWzQpEf2BcSunllNJ/gFuAnZq57kjgduCdKsYiSa1n9my45pq8n8O558JXv5pXW5IkqQFUcwaiOzC+7HgC8D+LnkdEd+A7wDbAxlWMRZJax/TpsMUWMGoUDBwI99wDG/vjS5LUOKpZgWiufp/mOr4IOCGlNLvFJ4o4OCKGR8Twjz/+uNUClKSKTZ2aP3funBOHm2+GRx81eZAkNZxqJhATgNXKjlcF3pzrmn7ALRHxKrAzcHlEfHvuJ0opXZlS6pdS6tepU6dqxStJnzZtGpx4Yh6OHjs2n7vsMjeDkyQ1rGq2MD0FrB4RvYA3gN2BQeUXpJR6Nd2OiOuBP6aU/lDFmCSpMrNnw3XXwY9/DO+8A/vum6sPkiQ1uKolECmlWRFxBHl1pQ7AtSml5yLikNL9Q6r12pL0mcyalduUnnoKNtsM7r0X+vUrOipJkmpCVTeSSyndB9w317lmE4eU0n7VjEWS5uvtt2HllaFjR/jud+G442DXXW1VkiSpTFU3kpOkujBtGpxwAvToAQ89lM+deCLstpvJgyRJc6lqBUKSatrs2XDttXDKKZ/MOayxRtFRSZJU00wgJDWmlGDbbeFvf3POQZKkBWALk6TG8uqrMGdObk3af3+49VZ45BGTB0mSKmQCIakxTJsGxx8Pa64JN9yQz+29t0PSkiQtIFuYJLVvc8857LcfbLdd0VFJklS3TCAktW+77Qa33w6bbw733QcbbVR0RJIk1TUTCEntz7hx0K0bLLUUHHJITiJ23tlWJUmSWoEzEJLaj6lT4Uc/gt694bzz8rmvfhV22cXkQZKkVmIFQlL9mz0brrkmzzlMmpRXVxo8uOioJElql6xASKp/hx+eE4a11oKnnsrJRLduRUclSVK7ZAVCUn0aOxaWXjonCocfnluVvvc9W5UkSaoyKxCS6suUKfDDH8I668Cpp+Zz663nkLQkSW3ECoSk+jBr1idzDu+9l+ccfvrToqOSJKnhWIGQVB/OOCMvydq7Nwwf7pyDJEkFqbsKxMz/zCo6BEltZexY+PjjnDQcfjj06eOcgyRJBavLCsROfbsXHYKkaiqfczj22HxulVWcc5AkqQbUXQKx1KIdGTSgR9FhSKqGWbNgyBBYfXW44ALYZx+4/vqio5IkSWXqLoGQ1I5dcQUcemiuPIwYAVdfnSsPkiSpZtTdDISkdmbsWHj7bdh8czjgAFh1VdhxR1uVJEmqUVYgJBVjyhQ47rhcbTj8cEgJllgCdtrJ5EGSpBpmAiGpbc2aBb/+dZ5zuPBC2HdfePBBkwZJkuqECYSktnXPPXDYYbnyMHIkXHWVcw6SJNURZyAkVd9LL8GYMfDtb+cWpQcfhG23teogSVIdsgIhqXrefz/v47DOOnDkkXlTuEUWga99zeRBkqQ6ZQIhqfXNmgWXX57nHC66CPbfH4YPh06dio5MkiR9RiYQklrfyJF5ZaX11su3r7wSVl656KgkSVIrMIGQ1DpefDEnCgD9+8MTT8Bf/wp9+xYblyRJalUmEJI+m/ffh2OOgXXXhRNPhKlT8/kBA5xzkCSpHTKBkLRwZs2Cyy6DL30JLr447yL9/POwzDJFRyZJkqrIZVwlLZwJE/IKSwMH5kHpPn2KjkiSJLUBKxCSKvfCC3Dmmfl2z54walSeczB5kCSpYZhASJq/yZPh6KPzqkrnnw+vv57Pr722cw6SJDUYEwhJ8/bxx3DppXk/h0suge9/H8aOhR49io5MkiQVxBkISfP2wQe5ZalPnzznsP76RUckSZIKZgVC0v964QU44oi8ytIyy8CIEfCXv5g8SJIkwARCUpPyOYcbb4Rnn83nV1vNOQdJkvRfJhBSo2vaz2HuOQd3kJYkSc1wBkIS/PrXsMEGcMEFtipJkqQWWYGQGtHzz8OgQTB1KnTsCA89BH/6k8mDJEmaLxMIqZFMngw/+EGec7j33rwRHMAKKzjnIEmSKmICITWClPJ8w5e+lOcdDjoIxo2DLbcsOjJJklRnnIGQGkEE3H8/bLghXHhhrkBIkiQtBCsQUnv1/POw007w8sv5+Lbb8pyDyYMkSfoMTCCk9ua99z6Zc3j4YRgzJp9femnnHCRJ0mdmAiG1J5dfnvdzuOwyOPjgvJ/DN79ZdFSSJKkdcQZCak+eew422ijPOay7btHRSJKkdsgKhFTPxoyB7beHRx/NxxdeCP/3fyYPkiSpakwgpHr03ntw5JF547cnnoA33sjnF13UOQdJklRVJhBSvbnqqryfw+WXw+DBeT+H3XYrOipJktQgnIGQ6kFK+XMETJ0KG28MF1xgq5IkSWpzViCkWjdmDOywAwwdmo+PPRYefNDkQZIkFcIEQqpVkybBEUfkOYdhw+Djj/P5RRZxzkGSJBXGFiapFt14Y94Mbvp0OOQQOP10WGGFoqOSJEkygZBqRkowZw506ABLLpnnHC68ENZZp+jIJEmS/ssWJqkWPPdc3s/h7LPz8Xe/m+ccTB4kSVKNMYGQijRpEhx+OPTpA08+CSuumM9HOOcgSZJqki1MUlFuvx0OPPCTOYczzoDlly86KkmSpBaZQEhtKSX4z39gscWgZ0/YZBP45S9tVZIkSXXDFiaprTz7LGy3HRx2WD7eaCO4/36TB0mSVFdMIKRqe/fdnDT06QPDh8MGGxQdkSRJ0kKzhUmqpgcegN13hxkzchJx+unOOUiSpLpmBUJqbSnlwWiA9daDrbeG0aPhkktMHiRJUt2zAiG1pmefhWOPzYPSf/sbdO8Od95ZdFSSJEmtxgqE1BrmnnP43vdyJUKSJKmdsQIhfVb/+Ad8/et5zuGII+AnP4GuXYuOSpIkqSqsQEgLIyV45518u08f2HFH+Oc/4eKLTR4kSVK7ZgIhLah//hO23RY22yzPOiy1FNxwA6y9dtGRSZIkVZ0JhFSpd96BQw6Bvn3h6afhqKNgEf8XkiRJjcUZCKkSY8bAppvCzJnOOUiSpIbmn0+leUkJXn01315rLTjgAOccJElSwzOBkJozenSec9hgA3jvvdyqdOGFzjlIkqSGZwIhlWuac9hggzzn8NOfQpcuRUclSZJUM5yBkJpMnJgrDDNnwpFHwmmn2aokSZI0l6pWICJi+4h4MSLGRcSJzdy/Z0SMLn38IyL6VDMe6VNSgmefzbe7dYOTTspzDhddZPIgSZLUjKolEBHRAbgM2AHoDewREb3nuuwVYMuU0vrAT4ErqxWP9CmjR8NXvpLblV56KZ874YQ8MC1JkqRmVbMC0R8Yl1J6OaX0H+AWYKfyC1JK/0gpvV86fAJYtYrxSNk778DgwTlxeOaZXG34wheKjkqSJKkuVHMGojswvux4AjCgheu/D9xfxXikPN+wzjowZQr84Ad5zmG55YqOSpIkqW5UM4GIZs6lZi+M2JqcQGw+j/sPBg4GWLrbF1srPjWKlODxx2HgQFhqKTjvvLwp3JprFh2ZJElS3almC9MEYLWy41WBN+e+KCLWB64GdkopvdfcE6WUrkwp9Usp9evUqVNVglU79cwzec5hs83g73/P5/bbz+RBkiRpIVUzgXgKWD0iekXEosDuwN3lF0RED+AOYO+U0ktVjEWN5p134OCD85zD6NFw2WW5AiFJkqTPpGotTCmlWRFxBPAg0AG4NqX0XEQcUrp/CHAasDxweUQAzEop9atWTGoQs2fDJpvA+PFw9NFw6qnOOUiSJLWSSKnZsYSa1fXza6fJrz1fdBiqNSnBgw/CtttChw5w333wxS/aqiRJkjQPETFiYf54X9WN5KQ2MWoUbLMN7LAD3HZbPvf1r5s8SJIkVYEJhOrX22/DQQfBhhvm3aMvvxx22aXoqCRJktq1ai7jKlXXjjvCyJFwzDF5zmHZZYuOSJIkqd0zgVD9SAnuuisvy9q5M1xySU4a1lij6MgkSZIahi1Mqg9PPw1bbw3f+Q5ceWU+17+/yYMkSVIbM4FQbXvrLTjwQNhoI3j22TzncNRRRUclSZLUsGxhUm075BC4917nHCRJkmqE+0CotqQEd9wBG28MPXrA2LH5nK1KkiRJrcp9IFT/muYcdt4ZLr00n1t9dZMHSZKkGmICoeKVzzk89xwMGQJnn110VJIkSWqGMxAq3llnwQ03wLHHwimnOOcgSZJUw5yBUNtLCW6/HXr1ylWHd96BqVNzu5IkSZLahDMQqg8jR8JWW8Euu3wy57DSSiYPkiRJdcIEQm3jrbfg+9+Hfv1gzJg853D11UVHJUmSpAXkDITaxg03wI03wnHH5TmHZZYpOiJJkiQtBGcgVB1Ncw5LLAHf+AZ89BFMmABf+lLRkUmSJAlnIFRLRoyALbfMcw5XXJHPLb64ycP/t3fn0VFV6d7Hvw8hEkDENIJLpS9gq4EARVACAZGhgyAgYVJEQKarLRcRlW4bHJZ0Cwo2vK2iIC9tBxQiqOCA4BUaX0YBmUeZFESCKIOChMiU7PePUykDBKhAkqokv89aWck5Z59znqpswn5q7322iIiISBGgBELyzr590Levt4r01q0wYQJ8+GGooxIRERGRPKQ5EJJ3Fi2CKVPgL3+BZ57RPAcRERGRIkgJhFw652D6dDh8GB56CLp0gYQEqFIl1JGJiIiISD7RECa5NKtXQ5MmXtLw9tteMmGm5EFERESkiFMCIbmzbx/06ePNc9i2zZvnsGCBlzyIiIiISJGnIUySO7t3w9Sp8OST8PTTmucgIiIiUswogZALcw7ef99bPfpvf/PmOOzZAxUrhjoyEREREQkBDWGS81u1Cu64A+67D2bOhBMnvP1KHkRERESKLSUQcq4ff/xtnsOOHfCvf8HKlVCqVKgjExEREZEQ0xAmOdfx494CcIMHe/Mcrroq1BGJiIiISJhQAiHePIf33oO5c+HNN71Hse7ZA+XKhToyEREREQkzGsJU3K1cCY0bQ9eu3toOhw97+5U8iIiIiEgOlEAUV4cOQa9eUL8+fP211/OwejVER4c6MhEREREJYxrCVFxdcQUsXAhDhsBTT2meg4iIiIgERQlEceEcvPsuTJwIs2Z5Q5S2bdOTlUREREQkVzSEqThYsQJuvx3uvx/274cffvD2K3kQERERkVxSAlGU/fIL9OwJDRrAzp3ePIdVq+D3vw91ZCIiIiJSSGkIU1HkHJhB2bLeMCXNcxARERGRPKIEoihxDqZNg1Gj4PPPvScqLV0KERGhjkxEREREiggNYSoqsuY5dOvmbe/f731X8iAiIiIieUgJRGF38uRv8xx27YLkZG9xuJiYUEcmIiIiIkWQhjAVVpmZUKKEt57D8ePw9NPeXAetIC0iIiIi+Ug9EIWNczB1Ktxyi7eCNHjrO7zwgpIHEREREcl3SiAKky+//G2eQ/nycOyYt98stHGJiIiISLGhBKIwcA769oWEhDPnOdSpE+rIRERERKSYUQIRzk6e9L6bQcWK3loO27dDnz7e/AcRERERkQKmVmg4ysyElBT4wx9g4UJv30svwYsvap6DiIiIiISUEohws3w5NGoEPXpApUpQpkyoIxIRERERCVACEU4GDICGDWH3bpg40ZvnEB8f6qhERERERAKUQIRaero3ZAkgNtZbz2H7dujdW/McRERERCTsqIUaKlnzHGJiYPJkb1//BQK1dwAAFylJREFU/lrPQURERETCmhKIUMg+z+Haa71F4URERERECgElEAXtqae8eQ7ffQeTJsGKFd62iIiIiEghUDLUARQLx4558xlKl/ZWkn7mGRgyBK68MtSRiYiIiIjkinog8lNmJkyZ4s1zGDnS23f33TB8uJIHERERESmUlEDkl2XLvKFJDzwA110HLVuGOiIRERERkcumBCI/jBzpTZLeswfeegu+/NIbuiQiIiIiUshpDkReOXYMjh+HChWgVStve/BgDVUSERERkSJFCcTlylrPYcgQSEyEt9+GunW9LxEREcl3p06dIjU1lePHj4c6FJGwFBUVReXKlYmMjMyT6ymBuBxLl8Ljj8PKlRAfDw8/HOqIREREip3U1FTKlStH1apVMbNQhyMSVpxzHDp0iNTUVKpVq5Yn19QciEs1YYI3r2HvXq/XYflyzXMQEREJgePHj1OhQgUlDyI5MDMqVKiQpz10SiByIy3NWwAOvMexDh0K27Z5T1oqobdSREQkVJQ8iJxfXv/7UKs3GJmZXi9DTAz06AHOwfXXw9/+pknSIiIiwpV50B5YtWoVAwcOPO/xb7/9lnfeeSfo8mdr1qwZMTEx1KlTh/j4eNatW3dZ8ealmTNnMjJrzazL9Ouvv9K0aVMyMjLy5Hr5YcSIEdx0003ExMQwZ86cHMusX7+ehg0bUrt2bdq1a8cvv/wSOLZhwwYaNmxIzZo1qV27dqB3oUWLFvz888/5Hr8SiItZuhQSEqBXL7jhBu8RrfqUQ0RERPJYvXr1GDNmzHmPn51AXKx8TlJSUli/fj39+/fnySefvORYs8uLhnpSUhJDhgzJg2ggOTmZTp06EREREVR55xyZmZl5cu9gfPXVV0ybNo3Nmzfz2Wef0b9//xzfwwcffJCRI0eyceNGOnbsyKhRowA4ffo0PXr0YPz48WzevJkFCxYEJkc/8MADjBs3Lt9fgxKIC5kxw5vX8P33MHmyN8+hUaNQRyUiIiKFwLp160hISMDn89GxY8fAJ8MrV67E5/PRsGFDnnzySWrVqgXAggULuPvuuwFYuHAhcXFxxMXFUbduXY4ePcqQIUNYvHgxcXFxvPzyy2eUT0tLo0+fPtSuXRufz8eMGTMuGFvDhg3Zu3cvAMeOHaNv377Ex8dTt25dPv74YwDS09Pp0qULPp+P++67jwYNGrBq1SrA63F57rnnaNCgAcuWLWPKlCnUr1+fuLg4Hn74YTIyMsjIyKB3797UqlWL2rVr8/LLLwMwZswYYmNj8fl8dO3aFYBJkyYxYMAAAHbv3k1iYiI+n4/ExES+8w8f7927NwMHDqRRo0bceOONTJ8+PcfXlpKSQvv27QPvS2JiIrfeeiu1a9cOvLZvv/2WGjVq0L9/f2699Vb27NnDqFGjiI+Px+fzMXTo0MD1OnTowG233UbNmjWZMGFCcL/8C/j444/p2rUrpUqVolq1atx0002sWLHinHLbtm2jSZMmANx5552B3+ncuXPx+XzUqVMHgAoVKgSSpaSkJKZOnXrZMV6MnsJ0trQ0+PZbqFULWrf2ehwGDICyZUMdmYiIiFzE3z/ZzFff/3LxgrkQe/1VDG1XM9fn9ezZk9dee42mTZvy3HPP8fe//51XXnmFPn36MGHCBBo1anTeT91Hjx7N2LFjuf3220lLSyMqKoqRI0cyevRoZs2aBXgJR5Zhw4ZRvnx5Nm7cCHDRYSyfffYZHTp0AOCFF17gj3/8I8nJyRw+fJj69evTokUL3njjDaKjo9mwYQObNm0iLi4ucP6xY8eoVasWzz//PFu2bOGll17iiy++IDIykv79+5OSkkLNmjXZu3cvmzZtAuDw4cMAjBw5kl27dlGqVKnAvuwGDBhAz5496dWrF8nJyQwcOJCPPvoIgH379rFkyRK2bt1KUlIS99xzzxnnnjx5kp07d1K1alXAe3zphx9+yFVXXcXBgwdJSEggKSkJ8BroEydOZNy4ccydO5cdO3awYsUKnHMkJSWxaNEimjRpQnJyMr/73e/49ddfiY+Pp3PnzlSoUOGM+z7xxBPMnz//nNfStWvXc37He/fuJSEhIbBduXLlQDKXXa1atZg5cybt27fn/fffZ8+ePQBs374dM6NVq1YcOHCArl278te//hWA6OhoTpw4waFDh86JMS8pgciSmen1Mjz1FJQpA1u3et8HDw51ZCIiIlLIHDlyhMOHD9O0aVMAevXqxb333svhw4c5evQojfwjGrp16xZICLK7/fbbGTRoEN27d6dTp05Urlz5gvebN28e06ZNC2xHR0fnWK579+4cO3aMjIwM1qxZA3ifaM+cOZPRo0cD3lOtvvvuO5YsWcJjjz0GeI1Zn88XuE5ERASdO3cG4PPPP2f16tXEx8cD3hyESpUq0a5dO3bu3Mmjjz5K27ZtadmyJQA+n4/u3bvToUOHQBKT3bJly/jggw8Ab0hOVuMYvN6AEiVKEBsby48//njOuQcPHuTqq68ObDvnePrpp1m0aBElSpRg7969gfOqVKkSaMjPnTuXuXPnUte/jldaWho7duygSZMmjBkzhg8//BCAPXv2sGPHjnMa51m9K8Fwzp2zL6dJzlnJ0/PPP09SUhJXXHEF4A1hWrJkCStXrqRMmTIkJiZy2223kZiYCEClSpX4/vvvlUDkuy++8NZzWLUK6teHV16BknprRERECptL6SkoSDk1HnMyZMgQ2rZty6effkpCQgLz5s276HWDedJOSkoKderUYciQITzyyCN88MEHOOeYMWMGMTExQccaFRUVGDbjnKNXr16MGDHinHLr169nzpw5jB07lvfee4/k5GRmz57NokWLmDlzJsOGDWPz5s0XjDn76ypVqtQF4ytduvQZjytNSUnhwIEDrF69msjISKpWrRo4Xjbb6BLnHE899RQPn7Wm14IFC5g3bx7Lli2jTJkyNGvWLMfHoeamB6Jy5cqB3gTw1jG5/vrrzzm3evXqzJ07F/B6HWbPnh04v2nTplxzzTUAtGnThjVr1gQSiOPHj1O6dOlzrpeXNAdi0SJo3Bj27fN6IJYtg4YNQx2ViIiIFGLly5cnOjqaxYsXAzB58mSaNm1KdHQ05cqVY/ny5QBn9Bpk980331C7dm0GDx5MvXr12Lp1K+XKlePo0aM5lm/ZsiWvv/56YPtCQ5giIyMZPnw4y5cvZ8uWLbRq1YrXXnst0CBfu3YtAI0bN+a9994DvIm/WcOjzpaYmMj06dPZv38/AD/99BO7d+/m4MGDZGZm0rlzZ4YNG8aaNWvIzMxkz549NG/enH/84x8cPnyYtLS0M67XqFGjwPuSkpJC48aNz/tazhYdHU1GRkagkX/kyBEqVapEZGQk8+fPZ/fu3Tme16pVK5KTkwOx7N27l/3793PkyBGio6MpU6YMW7duDfzezvbyyy+zbt26c75yGqKWlJTEtGnTOHHiBLt27WLHjh3Ur1//nHJZ72dmZibDhw+nX79+gVg3bNhAeno6p0+fZuHChcTGxgJeIvTDDz8EhnDll+L5MXtaGqxb5yUOjRvDuHHQs6fmOYiIiMglSU9PP2OY0aBBg3jrrbfo168f6enp3HjjjUycOBGAf//73zz00EOULVuWZs2aUb58+XOu98orrzB//nwiIiKIjY2ldevWlChRgpIlS1KnTh169+4dGG4D8Oyzz/LII49Qq1YtIiIiGDp0KJ06dTpvvKVLl+bPf/4zo0eP5vXXX+fxxx/H5/PhnKNq1arMmjWL/v3706tXL3w+H3Xr1sXn8+UYa2xsLMOHD6dly5ZkZmYSGRnJ2LFjKV26NH369Ak84WjEiBFkZGTQo0cPjhw5gnOOJ5544owhR+BNsu7bty+jRo2iYsWKgfctWC1btmTJkiW0aNGC7t27065dO+rVq0dcXBzVq1c/7zlbtmyhof9D5CuvvJIpU6Zw1113MX78eHw+HzExMWfMXbhUNWvWpEuXLsTGxlKyZEnGjh0b6M158MEH6devH/Xq1WPq1KmMHTsWgE6dOtGnTx/AS5IGDRpEfHw8ZkabNm1o27YtAKtXryYhIYGS+TySxoLtSgsXv6tSw/20e8ulnZx9nkN6OqSmah0HERGRQm7Lli3UqFEj1GEELS0tLbBuxMiRI9m3bx+vvvpqiKM6V0ZGBqdOnSIqKopvvvmGxMREtm/fHhiLH67Wrl3LP//5TyZPnhzqUArcY489RlJSUmA4U3Y5/Tsxs9XOuXq5vU/x6YFYssSb57B6NTRo4M1zUPIgIiIiBWz27NmMGDGC06dPU6VKFSZNmhTqkHKUnp5O8+bNOXXqFM453njjjbBPHgDq1q1L8+bNycjICHotiKKiVq1aOSYPea149EBs3Qo1angLwb30Etx/P5TQ9A8REZGioLD1QIiEQl72QBTdVvTRo/DJJ97P1avDu+/Ctm3QvbuSBxERERGRS1T0WtKZmTBxItxyC3TqBFkLc3TpoknSIiIiRVRhG1EhUpDy+t9H0UogFi+G+Hjo2xeqVPG2b7gh1FGJiIhIPoqKiuLQoUNKIkRy4Jzj0KFDREVF5dk1i84k6v374c47oWJFSEnx5jkEsaCKiIiIFG6VK1cmNTWVAwcOhDoUkbAUFRV10dXMcyNfEwgzuwt4FYgA3nTOjTzruPmPtwHSgd7OuTVB3+DoUZgxA3r3hkqVYPZsbxG4MmXy7DWIiIhIeIuMjKRatWqhDkOk2Mi3IUxmFgGMBVoDscD9ZhZ7VrHWwM3+rz8BbwR18ezzHPr0gQ0bvP2JiUoeRERERETyUX7OgagPfO2c2+mcOwlMA9qfVaY98LbzLAeuNrPrLnTRqBPpv81zqFoVli8Hny9fXoCIiIiIiJwpP4cw3QDsybadCjQIoswNwL7zXfTaA3shMhPeeQe6dtU8BxERERGRApSfCUROLfuzH48QTBnM7E94Q5wATlhq6ia6dYNu3S4zRCmmrgEOhjoIKdRUh+RyqQ5JXlA9kssVcykn5WcCkQr8Ptt2ZeD7SyiDc24CMAHAzFZdyop5IllUh+RyqQ7J5VIdkrygeiSXy8xWXcp5+TkHYiVws5lVM7MrgK7AzLPKzAR6micBOOKcO+/wJRERERERCa1864Fwzp02swHAHLzHuCY75zabWT//8fHAp3iPcP0a7zGuffIrHhERERERuXz5ug6Ec+5TvCQh+77x2X52wCO5vOyEPAhNijfVIblcqkNyuVSHJC+oHsnluqQ6ZFr2XUREREREgpWfcyBERERERKSICdsEwszuMrNtZva1mQ3J4biZ2Rj/8Q1mdmso4pTwFUQd6u6vOxvMbKmZ1QlFnBK+LlaHspWLN7MMM7unIOOT8BdMHTKzZma2zsw2m9nCgo5RwlsQ/5eVN7NPzGy9vw5pPqmcwcySzWy/mW06z/Fct6nDMoEwswhgLNAaiAXuN7PYs4q1Bm72f/0JeKNAg5SwFmQd2gU0dc75gGFoLKlkE2Qdyir3Et4DI0QCgqlDZnY1MA5Ics7VBO4t8EAlbAX5d+gR4CvnXB2gGfB//E+/FMkyCbjrAsdz3aYOywQCqA987Zzb6Zw7CUwD2p9Vpj3wtvMsB642s+sKOlAJWxetQ865pc65n/2by/HWIRHJEszfIYBHgRnA/oIMTgqFYOpQN+AD59x3AM451SPJLpg65IByZmbAlcBPwOmCDVPCmXNuEV69OJ9ct6nDNYG4AdiTbTvVvy+3ZaT4ym39+G/gf/M1IilsLlqHzOwGoCMwHpFzBfN36BYg2swWmNlqM+tZYNFJYRBMHXodqIG3EO9G4DHnXGbBhCdFRK7b1Pn6GNfLYDnsO/txUcGUkeIr6PphZs3xEojG+RqRFDbB1KFXgMHOuQzvwz+RMwRTh0oCtwGJQGlgmZktd85tz+/gpFAIpg61AtYBfwT+APzHzBY7537J7+CkyMh1mzpcE4hU4PfZtivjZda5LSPFV1D1w8x8wJtAa+fcoQKKTQqHYOpQPWCaP3m4BmhjZqedcx8VTIgS5oL9v+ygc+4YcMzMFgF1ACUQAsHVoT7ASP/aWl+b2S6gOrCiYEKUIiDXbepwHcK0ErjZzKr5JwJ1BWaeVWYm0NM/czwBOOKc21fQgUrYumgdMrP/Aj4AHtCnfZKDi9Yh51w151xV51xVYDrQX8mDZBPM/2UfA3eYWUkzKwM0ALYUcJwSvoKpQ9/h9WBhZtcCMcDOAo1SCrtct6nDsgfCOXfazAbgPdUkAkh2zm02s37+4+PxVrhuA3wNpONl4CJA0HXoOaACMM7/CfJp51y9UMUs4SXIOiRyXsHUIefcFjP7DNgAZAJvOudyfNSiFD9B/h0aBkwys414Q1EGO+cOhixoCTtmNhXvCV3XmFkqMBSIhEtvU2slahERERERCVq4DmESEREREZEwpARCRERERESCpgRCRERERESCpgRCRERERESCpgRCRERERESCpgRCRCTMmVmGma3L9lX1AmXT8uB+k8xsl/9ea8ys4SVc400zi/X//PRZx5Zeboz+62S9L5vM7BMzu/oi5ePMrE1e3FtEpDjTY1xFRMKcmaU5567M67IXuMYkYJZzbrqZtQRGO+d8l3G9y47pYtc1s7eA7c65Fy5QvjdQzzk3IK9jEREpTtQDISJSyJjZlWb2ub93YKOZtc+hzHVmtijbJ/R3+Pe3NLNl/nPfN7OLNewXATf5zx3kv9YmM3vcv6+smc02s/X+/ff59y8ws3pmNhIo7Y8jxX8szf/93ew9Av6ej85mFmFmo8xspZltMLOHg3hblgE3+K9T38yWmtla//cY/yq+zwP3+WO5zx97sv8+a3N6H0VE5FxhuRK1iIicobSZrfP/vAu4F+jonPvFzK4BlpvZTHdml3I3YI5z7gUziwDK+Ms+C7Rwzh0zs8HAILyG9fm0Azaa2W14q5M2wFvt9kszWwjcCHzvnGsLYGbls5/snBtiZgOcc3E5XHsacB/wqb+Bnwj8D/DfwBHnXLyZlQK+MLO5zrldOQXof32JwL/9u7YCTfyr+LYAXnTOdTaz58jWA2FmLwL/zznX1z/8aYWZzXPOHbvA+yEiUuwpgRARCX+/Zm+Am1kk8KKZNQEy8T55vxb4Ids5K4Fkf9mPnHPrzKwpEIvXIAe4Au+T+5yMMrNngQN4DfpE4MOsxrWZfQDcAXwGjDazl/CGPS3Oxev6X2CMP0m4C1jknPvVP2zKZ2b3+MuVB27GS56yy0qsqgKrgf9kK/+Wmd0MOCDyPPdvCSSZ2V/821HAfwFbcvEaRESKHSUQIiKFT3egInCbc+6UmX2L1/gNcM4t8icYbYHJZjYK+Bn4j3Pu/iDu8aRzbnrWhv+T/HM457b7eyfaACP8PQUX6tHIfu5xM1sAtMLriZiadTvgUefcnItc4lfnXJy/12MW8AgwBhgGzHfOdfRPOF9wnvMN6Oyc2xZMvCIi4tEcCBGRwqc8sN+fPDQHqpxdwMyq+Mv8C29oz63AcuB2M8ua01DGzG4J8p6LgA7+c8oCHYHFZnY9kO6cmwKM9t/nbKf8PSE5mYY3NOoOICthmAP8T9Y5ZnaL/545cs4dAQYCf/GfUx7Y6z/cO1vRo0C5bNtzgEfN3x1jZnXPdw8REfmNEggRkcInBahnZqvweiO25lCmGbDOzNYCnYFXnXMH8BrUU81sA15CUT2YGzrn1gCTgBXAl8Cbzrm1QG28uQPrgGeA4TmcPgHYkDWJ+ixzgSbAPOfcSf++N4GvgDVmtgn4v1ykx9wfy3qgK/APvN6QL4CIbMXmA7FZk6jxeioi/bFt8m+LiMhF6DGuIiIiIiISNPVAiIiIiIhI0JRAiIiIiIhI0JRAiIiIiIhI0JRAiIiIiIhI0JRAiIiIiIhI0JRAiIiIiIhI0JRAiIiIiIhI0JRAiIiIiIhI0P4/bQtSPYXeNhAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AUC ROC curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_test, y_pred_test2)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_test2)\n",
    "plt.figure(figsize=(13,8))\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')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try improving Recall using AUC-ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05327380293643781\n"
     ]
    }
   ],
   "source": [
    "# Optimal threshold as per AUC-ROC curve\n",
    "# The optimal cut off would be where tpr is high and fpr is low\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_test2)\n",
    "\n",
    "optimal_idx = np.argmax(tpr - fpr)\n",
    "optimal_threshold = thresholds[optimal_idx]\n",
    "print(optimal_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model prediction with optimal threshold\n",
    "pred_train_opt = (lg2.predict(X_train2)>optimal_threshold).astype(int)\n",
    "pred_test_opt = (lg2.predict(X_test2)>optimal_threshold).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train data: 0.8611428571428571\n",
      "Accuracy on test data: 0.8706666666666667\n",
      "Recall on train data: 0.9274924471299094\n",
      "Recall on test data: 0.912751677852349\n",
      "f1 score on train data: 0.5581818181818182\n",
      "f1 score on test data: 0.5836909871244634\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on train data:',accuracy_score(y_train, pred_train_opt) )\n",
    "print('Accuracy on test data:',accuracy_score(y_test, pred_test_opt))\n",
    "\n",
    "print('Recall on train data:',recall_score(y_train, pred_train_opt) )\n",
    "print('Recall on test data:',recall_score(y_test, pred_test_opt))\n",
    "\n",
    "print('f1 score on train data:',f1_score(y_train, pred_train_opt))\n",
    "print('f1 score on test data:',f1_score(y_test, pred_test_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, with the optimal threshold of 0.053 , we have achieved --\n",
    "1. Accuracy of 87%\n",
    "2. Recall score has improved a lot for test data from 61% to 91.3%, which is a good increase.\n",
    "3. Area Under Curve is 0.96 which is Excellent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=1)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the decision tree model\n",
    "dTree = DecisionTreeClassifier(criterion='gini', random_state=1)\n",
    "dTree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Function to calculate recall score\n",
    "# reference form MLS sessions\n",
    "def get_recall_score(model):\n",
    "    '''\n",
    "    model : classifier to predict values of X\n",
    "\n",
    "    '''\n",
    "    pred_train = model.predict(X_train)\n",
    "    pred_test = model.predict(X_test)\n",
    "    print(\"Recall on training set : \",metrics.recall_score(y_train,pred_train))\n",
    "    print(\"Recall on test set : \",metrics.recall_score(y_test,pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scoring the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set : 1.0\n",
      "Accuracy on test set : 0.9786666666666667\n",
      "Recall on training set :  1.0\n",
      "Recall on test set :  0.8926174496644296\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training set :', dTree.score(X_train, y_train))\n",
    "print('Accuracy on test set :', dTree.score(X_test, y_test))\n",
    "\n",
    "# Recall on train and test\n",
    "get_recall_score(dTree)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that ---\n",
    "1. Accuracy is 1 for training data beacuse it is capturing all the noise as well, implying it is overfitting the data.\n",
    "2. Recall is 100% for training data (because it's overfitting the data) and 89% on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will use RECALL as a metric of model evaluation instead of Accuracy because we need to increase our True Positive Rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Imp\n",
      "Education           0.403968\n",
      "Income              0.308514\n",
      "Family              0.160377\n",
      "CCAvg               0.047488\n",
      "Age                 0.046601\n",
      "CD_Account          0.025711\n",
      "Mortgage            0.006780\n",
      "Online              0.000561\n",
      "Securities_Account  0.000000\n",
      "CreditCard          0.000000\n"
     ]
    }
   ],
   "source": [
    "# importance of features in the tree building ( The importance of a feature is computed as the \n",
    "#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )\n",
    "\n",
    "print (pd.DataFrame(dTree.feature_importances_, columns = [\"Imp\"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = list(X_train.columns)\n",
    "importances = dTree.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='violet', align='center')\n",
    "plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the decision tree model, Education & Income are the most important variables for predicting the customers who are more likely to buy the personal loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing over fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using GridSearch for Hyperparameter tuning of our tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, max_leaf_nodes=10,\n",
       "                       min_impurity_decrease=0.001, random_state=1)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Choose the type of classifier. \n",
    "estimator = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "## add from article\n",
    "parameters = {'max_depth': np.arange(1,10), \n",
    "              'min_samples_leaf': [1, 2, 5, 7, 10,15,20],\n",
    "              'max_leaf_nodes' : [2, 3, 5, 10],\n",
    "              'min_impurity_decrease': [0.001,0.01,0.1]\n",
    "             }\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "acc_scorer = metrics.make_scorer(metrics.recall_score)\n",
    "\n",
    "# Run the grid search\n",
    "grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "estimator = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets plot the decision tree\n",
    "from sklearn import tree\n",
    "plt.figure(figsize=(15,10))\n",
    "tree.plot_tree(estimator,feature_names=list(X.columns),filled=True,fontsize=9,node_ids=True,class_names=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set :  0.9897142857142858\n",
      "Accuracy on test set :  0.9813333333333333\n",
      "Recall on training set :  0.9274924471299094\n",
      "Recall on test set :  0.8791946308724832\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on train and test\n",
    "print(\"Accuracy on training set : \",estimator.score(X_train, y_train))\n",
    "print(\"Accuracy on test set : \",estimator.score(X_test, y_test))\n",
    "# Recall on train and test\n",
    "get_recall_score(estimator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen, we have achieved 98% Accuracy with 87.9% Recall value, which is very good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing both the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train_Recall</th>\n",
       "      <th>Test_Recall</th>\n",
       "      <th>Test_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision treee with hyperparameter tuning</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Model  Train_Recall  Test_Recall  \\\n",
       "0                        Logistic Regression         0.921        0.912   \n",
       "1  Decision treee with hyperparameter tuning         0.927        0.879   \n",
       "\n",
       "   Test_Accuracy  \n",
       "0          0.872  \n",
       "1          0.981  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_frame = pd.DataFrame({'Model':['Logistic Regression','Decision treee with hyperparameter tuning'], \n",
    "                                 'Train_Recall':[0.921,0.927,], 'Test_Recall':[0.912,0.879], 'Test_Accuracy':[0.872,0.981]}) \n",
    "comparison_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above table, it seems like the Logistic Regression (Statsmodel) algorithm has given highest Recall. Hence, we can finalize Logistic Regression (Statsmodel) as our Final model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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