Skip to content

Instantly share code, notes, and snippets.

@shindishella

shindishella/Klasifikasi Loan Predict Datasets.ipynb

Created December 2, 2018 11:43
Show Gist options
  • Save shindishella/26bd89f5d533104c1bc46e05f279603b to your computer and use it in GitHub Desktop.
Save shindishella/26bd89f5d533104c1bc46e05f279603b to your computer and use it in GitHub Desktop.
Download ZIP
Klasifikasi Loan Predict Datasets | Kuis Data Mining A
Display the source blob
Display the rendered blob
Raw
Klasifikasi Loan Predict Datasets.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Klasifikasi Loan Prediction Datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rahayu Prihatini Saputri | 06211540000040\n",
"\n",
"Shindi Shella May Wara | 06211540000101"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Permasalahan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suatu Perumahan Keuangan ingin menguasai pasar pinjaman rumah. Mereka memiliki kehadiran di semua daerah perkotaan, semi perkotaan dan pedesaan. Pelanggan pertama kali mengajukan pinjaman rumah setelah perusahaan itu memvalidasi kelayakan pelanggan untuk pinjaman. Perusahaan ingin mengotomatisasi proses kelayakan kredit (real time) berdasarkan detail pelanggan yang diberikan saat mengisi formulir aplikasi online. Untuk mengotomatisasi proses ini, mereka telah memberikan masalah untuk mengidentifikasi segmen pelanggan, mereka memenuhi syarat untuk jumlah pinjaman sehingga mereka dapat secara khusus menargetkan pelanggan ini. Di sini mereka telah menyediakan satu set data parsial."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variabel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Loan_ID (Kode Loan ID)\n",
"\n",
"2. Gender (Jenis kelamin - Male/ Female)\n",
"\n",
"3. Married (Sudah Menikah? - Y/N)\n",
"\n",
"4. Dependents (Jumlah Tanggungan)\n",
"\n",
"5. Education (Pendidikan Terakhir - Graduate/ Under Graduate)\n",
"\n",
"6. Self_Employed (Punya Penghasilan? - Y/N)\n",
"\n",
"7. ApplicantIncome (Penghasilan Pemohon)\n",
"\n",
"8. CoapplicantIncome (Penghasilan orang terdekat)\n",
"\n",
"9. LoanAmount (Jumlah pinjaman dalam satuan thousands)\n",
"\n",
"10. Loan_Amount_Term (Jangka pinjaman dalam satuan bulan)\n",
"\n",
"11. Credit_History (Riwayat Peminjaman)\n",
"\n",
"12. Property_Area (Properti yang dimiliki - Urban/ Semi Urban/ Rural)\n",
"\n",
"13. Loan_Status (Status Peminjaman - Y/N) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Datasets"
]
},
{
"cell_type": "code",
"execution_count": 401,
"metadata": {},
"outputs": [],
"source": [
"# Importing required Packages\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 402,
"metadata": {},
"outputs": [],
"source": [
"# Read Test and Train\n",
"train=pd.read_csv(\"train.csv\")\n",
"test=pd.read_csv(\"test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 403,
"metadata": {},
"outputs": [],
"source": [
"# Copy of original data\n",
"train_original=train.copy()\n",
"test_original=test.copy()"
]
},
{
"cell_type": "code",
"execution_count": 404,
"metadata": {},
"outputs": [],
"source": [
"# drop data Loan_ID\n",
"train1=train.drop(\"Loan_ID\", axis=1)\n",
"test1=test.drop(\"Loan_ID\", axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mengatasi Missing Value"
]
},
{
"cell_type": "code",
"execution_count": 405,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Gender 13\n",
"Married 3\n",
"Dependents 15\n",
"Education 0\n",
"Self_Employed 32\n",
"ApplicantIncome 0\n",
"CoapplicantIncome 0\n",
"LoanAmount 22\n",
"Loan_Amount_Term 14\n",
"Credit_History 50\n",
"Property_Area 0\n",
"Loan_Status 0\n",
"dtype: int64"
]
},
"execution_count": 405,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(train1.isnull())"
]
},
{
"cell_type": "code",
"execution_count": 406,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Gender 11\n",
"Married 0\n",
"Dependents 10\n",
"Education 0\n",
"Self_Employed 23\n",
"ApplicantIncome 0\n",
"CoapplicantIncome 0\n",
"LoanAmount 5\n",
"Loan_Amount_Term 6\n",
"Credit_History 29\n",
"Property_Area 0\n",
"dtype: int64"
]
},
"execution_count": 406,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(test1.isnull())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Karena data train dan data test punya nilai missing, maka harus diatasi terlebih dahulu. Data yang berskala integer diatasi dengan diisi dari nilai rata-rata variabel tersebut. Sedangkan data yang berskala nominal akan diatasi dengan diisi dari nilai modus variabel tersebut."
]
},
{
"cell_type": "code",
"execution_count": 407,
"metadata": {},
"outputs": [],
"source": [
"# replacing the missing values with the mean by group\n",
"train1['LoanAmount']=train1.groupby('Loan_Status').LoanAmount.transform(lambda x: x.fillna(x.mean()))\n",
"test1['LoanAmount']=test1.LoanAmount.transform(lambda x: x.fillna(x.mean()))"
]
},
{
"cell_type": "code",
"execution_count": 408,
"metadata": {},
"outputs": [],
"source": [
"# replacing the missing values with the mode by group\n",
"train1['Gender']=train1.groupby('Loan_Status').Gender.transform(lambda x: x.fillna(x.mode()[0]))\n",
"train1['Married']=train1.groupby('Loan_Status').Married.transform(lambda x: x.fillna(x.mode()[0]))\n",
"train1['Dependents']=train1.groupby('Loan_Status').Dependents.transform(lambda x: x.fillna(x.mode()[0]))\n",
"train1['Self_Employed']=train1.groupby('Loan_Status').Self_Employed.transform(lambda x: x.fillna(x.mode()[0]))\n",
"train1['Credit_History']=train1.groupby('Loan_Status').Credit_History.transform(lambda x: x.fillna(x.mode()[0]))\n",
"train1['Loan_Amount_Term']=train1.groupby('Loan_Status').Loan_Amount_Term.transform(lambda x: x.fillna(x.mode()[0]))\n",
"test1['Gender']=test1.Gender.transform(lambda x: x.fillna(x.mode()[0]))\n",
"test1['Married']=test1.Married.transform(lambda x: x.fillna(x.mode()[0]))\n",
"test1['Dependents']=test1.Dependents.transform(lambda x: x.fillna(x.mode()[0]))\n",
"test1['Self_Employed']=test1.Self_Employed.transform(lambda x: x.fillna(x.mode()[0]))\n",
"test1['Credit_History']=test1.Credit_History.transform(lambda x: x.fillna(x.mode()[0]))\n",
"test1['Loan_Amount_Term']=test1.Loan_Amount_Term.transform(lambda x: x.fillna(x.mode()[0]))"
]
},
{
"cell_type": "code",
"execution_count": 409,
"metadata": {},
"outputs": [],
"source": [
"train1_copy=train1.copy()\n",
"test1_copy=test1.copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Coding Data"
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder"
]
},
{
"cell_type": "code",
"execution_count": 429,
"metadata": {},
"outputs": [],
"source": [
"# encode categorical features\n",
"train1['Gender']=LabelEncoder().fit_transform(train1['Gender'])\n",
"train1['Married']=LabelEncoder().fit_transform(train1['Married'])\n",
"train1['Dependents']=LabelEncoder().fit_transform(train1['Dependents'])\n",
"train1['Education']=LabelEncoder().fit_transform(train1['Education'])\n",
"train1['Self_Employed']=LabelEncoder().fit_transform(train1['Self_Employed'])\n",
"train1['Property_Area']=LabelEncoder().fit_transform(train1['Property_Area'])\n",
"train1['Loan_Amount_Term']=LabelEncoder().fit_transform(train1['Loan_Amount_Term'])\n",
"train1['Loan_Status']=LabelEncoder().fit_transform(train1['Loan_Status'])\n",
"train1['Gender']=train1['Gender'].astype('category')\n",
"train1['Married']=train1['Married'].astype('category')\n",
"train1['Dependents']=train1['Dependents'].astype('category')\n",
"train1['Education']=train1['Education'].astype('category')\n",
"train1['Self_Employed']=train1['Self_Employed'].astype('category')\n",
"train1['Property_Area']=train1['Property_Area'].astype('category')\n",
"train1['Credit_History']=train1['Credit_History'].astype('category')\n",
"train1['Loan_Status']=train1['Loan_Status'].astype('category')\n",
"train1['Loan_Amount_Term']=train1['Loan_Amount_Term'].astype('category')\n",
"\n",
"test1['Gender']=LabelEncoder().fit_transform(test1['Gender'])\n",
"test1['Married']=LabelEncoder().fit_transform(test1['Married'])\n",
"test1['Dependents']=LabelEncoder().fit_transform(test1['Dependents'])\n",
"test1['Education']=LabelEncoder().fit_transform(test1['Education'])\n",
"test1['Self_Employed']=LabelEncoder().fit_transform(test1['Self_Employed'])\n",
"test1['Property_Area']=LabelEncoder().fit_transform(test1['Property_Area'])\n",
"test1['Loan_Amount_Term']=LabelEncoder().fit_transform(test1['Loan_Amount_Term'])\n",
"test1['Gender']=test1['Gender'].astype('category')\n",
"test1['Married']=test1['Married'].astype('category')\n",
"test1['Dependents']=test1['Dependents'].astype('category')\n",
"test1['Education']=test1['Education'].astype('category')\n",
"test1['Self_Employed']=test1['Self_Employed'].astype('category')\n",
"test1['Property_Area']=test1['Property_Area'].astype('category')\n",
"test1['Credit_History']=test1['Credit_History'].astype('category')\n",
"test1['Loan_Amount_Term']=test1['Loan_Amount_Term'].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 430,
"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>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>144.294404</td>\n",
" <td>8</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>8</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>8</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>8</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>8</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Gender Married Dependents Education Self_Employed ApplicantIncome \\\n",
"0 1 0 0 0 0 5849 \n",
"1 1 1 1 0 0 4583 \n",
"2 1 1 0 0 1 3000 \n",
"3 1 1 0 1 0 2583 \n",
"4 1 0 0 0 0 6000 \n",
"\n",
" CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History \\\n",
"0 0.0 144.294404 8 1.0 \n",
"1 1508.0 128.000000 8 1.0 \n",
"2 0.0 66.000000 8 1.0 \n",
"3 2358.0 120.000000 8 1.0 \n",
"4 0.0 141.000000 8 1.0 \n",
"\n",
" Property_Area Loan_Status \n",
"0 2 1 \n",
"1 0 0 \n",
"2 2 1 \n",
"3 2 1 \n",
"4 2 1 "
]
},
"execution_count": 430,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train1.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Eksplorasi Data"
]
},
{
"cell_type": "code",
"execution_count": 431,
"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>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>614.000000</td>\n",
" <td>614.000000</td>\n",
" <td>614.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5403.459283</td>\n",
" <td>1621.245798</td>\n",
" <td>146.460374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6109.041673</td>\n",
" <td>2926.248369</td>\n",
" <td>84.040402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>150.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2877.500000</td>\n",
" <td>0.000000</td>\n",
" <td>100.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3812.500000</td>\n",
" <td>1188.500000</td>\n",
" <td>129.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5795.000000</td>\n",
" <td>2297.250000</td>\n",
" <td>164.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>81000.000000</td>\n",
" <td>41667.000000</td>\n",
" <td>700.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ApplicantIncome CoapplicantIncome LoanAmount\n",
"count 614.000000 614.000000 614.000000\n",
"mean 5403.459283 1621.245798 146.460374\n",
"std 6109.041673 2926.248369 84.040402\n",
"min 150.000000 0.000000 9.000000\n",
"25% 2877.500000 0.000000 100.250000\n",
"50% 3812.500000 1188.500000 129.000000\n",
"75% 5795.000000 2297.250000 164.750000\n",
"max 81000.000000 41667.000000 700.000000"
]
},
"execution_count": 431,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train1.describe()"
]
},
{
"cell_type": "code",
"execution_count": 432,
"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>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>367.000000</td>\n",
" <td>367.000000</td>\n",
" <td>367.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4805.599455</td>\n",
" <td>1569.577657</td>\n",
" <td>136.132597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>4910.685399</td>\n",
" <td>2334.232099</td>\n",
" <td>60.946040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2864.000000</td>\n",
" <td>0.000000</td>\n",
" <td>101.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3786.000000</td>\n",
" <td>1025.000000</td>\n",
" <td>126.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5060.000000</td>\n",
" <td>2430.500000</td>\n",
" <td>157.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>72529.000000</td>\n",
" <td>24000.000000</td>\n",
" <td>550.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ApplicantIncome CoapplicantIncome LoanAmount\n",
"count 367.000000 367.000000 367.000000\n",
"mean 4805.599455 1569.577657 136.132597\n",
"std 4910.685399 2334.232099 60.946040\n",
"min 0.000000 0.000000 28.000000\n",
"25% 2864.000000 0.000000 101.000000\n",
"50% 3786.000000 1025.000000 126.000000\n",
"75% 5060.000000 2430.500000 157.500000\n",
"max 72529.000000 24000.000000 550.000000"
]
},
"execution_count": 432,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test1.describe()"
]
},
{
"cell_type": "code",
"execution_count": 433,
"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>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ApplicantIncome</th>\n",
" <td>1.000000</td>\n",
" <td>-0.116605</td>\n",
" <td>0.565614</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CoapplicantIncome</th>\n",
" <td>-0.116605</td>\n",
" <td>1.000000</td>\n",
" <td>0.187669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LoanAmount</th>\n",
" <td>0.565614</td>\n",
" <td>0.187669</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ApplicantIncome CoapplicantIncome LoanAmount\n",
"ApplicantIncome 1.000000 -0.116605 0.565614\n",
"CoapplicantIncome -0.116605 1.000000 0.187669\n",
"LoanAmount 0.565614 0.187669 1.000000"
]
},
"execution_count": 433,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correlations = train1.corr() \n",
"correlations"
]
},
{
"cell_type": "code",
"execution_count": 434,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAJ6CAYAAADpfAJ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XecXHW5+PHPk4QQeksgkiABQRQLllAElCZcEGkWQMUKxnKRS7l6bRcQLFwLNprxWhC5Ahb8RQ0CIiAoJQEB6QIihBBIAgGCQMo+vz/mbDJZtpwNO3Nmdj7vvM4rc875zplnyu48+5zv93siM5EkSWpVI6oOQJIkqT8mK5IkqaWZrEiSpJZmsiJJklqayYokSWppJiuSJKmlmaxoyEXE/RHx5pW87xsj4q4GxDQpIjIiRg31sYeDiPhxRHyx6jhWVkRsFBF/ioinIuIbJdp/ICKubkZskl44k5VhKCLeHREzI2JhRDwcERdFxM5Vx9WbIoHYons9M6/KzK2qjKmndkl0ImLtiPhWRDxQvPf3FOtjq46tCaYA84C1M/O4oTxwRJwYET8dRPtdI6KreA+6l98MZUxSpzFZGWYi4ljgW8CXgY2AFwNnAAesxLGe9+Xc6l/YnSoiRgOXAa8A9gbWBnYE5gPbVRhas2wK3J6tM8vl7Mxcs27Zr7dG/jxJJWWmyzBZgHWAhcA7+2mzKrVkZnaxfAtYtdi3KzAL+C9gDnBOb9uKtm8FbgIWAH8BXl33GPcDby5ubwdcU7R7GDgNGF3s+xOQwNNF3Id0P17dsV4OXFHc/zZg/7p9PwZOB34HPAVcB7ykj+c9qXisKcXzfhg4rm7/CODTwL3UvuAvANYv9j1Q3HdhsbwB+Cfw+mL/YcX+rYv1I4BfD3TcYv8Oxeu3ALgZ2LVu3xXAycCfi+d3CTC2j+d3BPAIsGY/7/1Ar+UXi9sfAK7ucd8EtqhrewZwUfF6/BkYT+2z9DhwJ/DaHp+H/wRuAZ4AzgfGFPvGAr8tYnoMuAoY0Uf8OwIzimPMAHasi2cxsKiI58293HcDYBrwJHB98bpeXbf/28CDxf4bgDcW2/cujru4OPbNxfYPAncU78t9wEfqjrUrdZ/hHnGcCPwC+GnxWEfQz89I3Wv/ceDvxeOdDLykuM+T1D5T9e37/Nl0cWnXpfIAXIbwzaz9Yl0CjOqnzUnAtcCGwLjil9nJxb5di/v/D7WkZrU+tr0OeBTYHhgJvL/4QupOeu5nebLyempfyKOoJQx3AEfXxbPsS7AuhlnF7VWAe4DPAqOB3Ytf1lsV+39M7Qtuu+L45wLn9fG8JxWP9TNgDeBVwNy6OI8uXpeJxfP8HvCzHvcdVXe8n1AkO8BUasnIx+r2HVPiuBOoJTBvoZbU7Fmsjyv2X1Ec96XF634FcEofz+884Ox+3vcyr+VgkpV5xXs7Bvgj8A/gfcXn4YvA5XX3vZ9agrAxsH7xGfhose8rwFlFfKsAbwSil/jXp5YIvbd4r99VrG/QM/5+Xp8Livf+lcBDrJisHEYtoRkFHEctMe9OqE4EftrjePtSSxgC2AX4F/C6np/hXuI4kVric2Dxnq9GuZ+RadSqZa8AnqNWRduc2h8otwPvL9r2+7Pp4tKui6eBhpcNgHmZuaSfNu8BTsrMRzNzLvAFal8A3bqAEzLzucx8po9tHwa+l5nXZebSzDyb2i/QHXo+WGbekJnXZuaSzLyf2pf1LiWfzw7AmtS+oBdl5h+p/RX+rro2v8rM64vnfC7wmgGO+YXMfDoz/wb8qO5YHwE+l5mzMvM5al8q7+inTH9l3fN4I7Uv3e71XYr9Ax33MGB6Zk7PzK7MvBSYSS156fajzLy7eN0v6Of5bUDtr/K+lHktB+PC4r19FrgQeDYzf5KZS6lVTl7bo/13MnN2Zj4G/KbueSwGXgRsmpmLs9ZnqbdTOfsCf8/Mc4rP0s+oVXB6Pb1SLyJGAm8Hji/e+1uBs+vbZOZPM3N+cexvUEss++w7lZm/y8x7s+ZKalWvN9Y12TgiFtQtB9ftuyYzf12858+U/Bn5n8x8MjNvA24FLsnM+zLzCWoVru7Xu/TPptROTFaGl/nA2AHOg29M7RRGt38W27rNLb6A6GfbpsBx9b+MgU16HAeAiHhpRPw2IuZExJPU+tKU7fC5MfBgZnb1iHdC3fqcutv/ovaF3J8HexyrO+ZNgQvrns8dwFJq/X56cyXwxogYT+0v2POBnSJiErW/dm8qcdxNgXf2eB13pvblPdjnN7/H/Xoq81oOxiN1t5/pZb1nnH09j69Rq/hcEhH3RcSn+3i8np9bKB//OGpVi57v/TIRcVxE3BERTxTvwzr08zmNiH0i4tqIeKxo/5Ye7Wdn5rp1ywV1+x7scawyPyNlX+/SP5tSOzFZGV6uAZ6lVmLuy2xqv9C6vbjY1q23v2p7bnsQ+FKPX8arF3/t9nQmtb+At8zMtamdhogBnkd9rJtERP3n9MXUSvgra5Mex+p+7g8C+/R4TmMy8yF6eU0y8x5qX7pHAX/KzKeofSFPoXZ6oavEcR+k1geoft8amXnKSjyvPwD/FhFr9LF/MK/l08Dq3StFQtYQmflUZh6XmZtTq5IcGxF79NK05+cWyn8W5lI7ldnzvQdqw+Wp9ck6GFgvM9el1i+m+3O6wvsfEasCvwS+DmxUtJ9O+c91z8/TC/kZ6WkwP5tS2zBZGUaKkvDxwOkRcWBErB4RqxR/BX61aPYz4PMRMa4Y0no8tc5+g/F94KMRsX3UrBER+0bEWr20XYtaJ8CFEfEy4GM99j9C7dx7b66j9sX5qeJ57ErtC+28QcZb77+L1+UV1DpJnl9sPwv4UkRsClC8Pt0jqOZSOxXWM84rgSNZfsrnih7rAx33p8B+EfFvETEyIsYUw14nrsTzOofaF9UvI+JlETEiIjaIiM9GxFsY3Gt5M/CKiHhNRIyhduqqISLirRGxRUQEtc/J0mLpaTrw0mJY/qiIOATYmtqprH4Vp6Z+BZxYvPdbU+vL0W0tasnMXGBURBxPrX9It0eASXWJ3mhqp4nmAksiYh9gr/LP+nkG+hkZjMH8bEptw2RlmMnMU4Fjgc9T+2X6ILUv0F8XTb5IrV/ELcDfgBuLbYN5jJnUzo2fRq2T4z3UOmX25j+Bd1PrzPl9licH3U4Ezu7lvD6ZuQjYH9iHWofOM4D3Zeadg4m3hyuLeC8Dvp6ZlxTbv02tE+MlEfEUtU6x2xdx/Av4EvDnIs4d6o61FrVRTb2tD3TcB6kNKf8sy9+rT7ISP5dFf5g3U/sL/VKWj3oZC1w3mNcyM++m1hH7D9RGoDRy8rQti8dZSK0yeEZmXtFLTPOpjXI5jtopr08Bb83MeSUf50hqp0rmUOuM+6O6fRdT6/dxN7XTQ8+y4qmanxf/z4+IG4sq2lHU+hA9Tu3zPa1kHL0Z6GektEH+bEptI3rvyyZJktQarKxIkqSWZrIiSZJamsmKJElqaSYrkiSppZmsSJKklmayIkmSWprJiiRJamkmK5IkqaWZrEiSpJZmsiJJklqayYokSWppJiuSJKmlmaxIkqSWZrIiSZJamsmKJElqaSYrkiSppZmsSJKklmayIkmSWprJiiRJamkmK5IkqaWZrEiSpJZmsiJJklqayYokSWppJiuSJKmlmaxIkqSWZrIiSZJamsmKJElqaSYrkiSppZmsSJKklmayIkmSWprJiiRJamkmK5IkqaWZrEiSpJZmsiJJklqayYokSWppJiuSJKmlmaxIkqSWZrIiSZJamsmKJElqaSYrkiSppZmsSJKklmayIkmSWprJiiRJamkmK5IkqaWZrEiSpJZmsiJJkoZMRPwwIh6NiFv72B8R8Z2IuCcibomI1w10TJMVSZI0lH4M7N3P/n2ALYtlCnDmQAc0WZEkSUMmM/8EPNZPkwOAn2TNtcC6EfGi/o45aigD7EM24TEkSWol0awHWjzvvqZ+z44e95KPUKuIdJuamVMHcYgJwIN167OKbQ/3dYdmJCssnndfMx5GHWCVsZsDMGr0hIoj0XCxZNFDAMzdc5eKI9FwMe7SK6sOoaGKxGQwyUlPvSVy/SZcngaSJEnNNAvYpG59IjC7vzs0pbIiSZIapGtp1REM1jTgyIg4D9geeCIz+zwFBCYrkiRpCEXEz4BdgbERMQs4AVgFIDPPAqYDbwHuAf4FfHCgY5qsSJLUzrKr6ghWkJnvGmB/Av8+mGPaZ0WSJLU0KyuSJLWzrtaqrDSClRVJktTSrKxIktTGssX6rDSClRVJktTSrKxIktTO7LMiSZJULSsrkiS1M/usSJIkVctkRZIktTRPA0mS1M7a70KGg2ZlRZIktTQrK5IktTM72EqSJFXLyookSe3MSeEkSZKqZWVFkqQ25oUMJUmSKmZlRZKkdmafFUmSpGpZWZEkqZ3ZZ0WSJKlaVlYkSWpnXhtIkiSpWlZWJElqZ/ZZkSRJqpbJiiRJammeBpIkqZ05KZwkSVK1rKxIktTO7GArSZJULSsrkiS1M/usSJIkVcvKiiRJbSzT6fYlSZIqZWVFkqR25mggSZKkallZkSSpnTkaSJIkqVpWViRJamf2WZEkSaqWlRVJktpZl/OsSJIkVcpkRZIktTRPA0mS1M7sYCtJklQtKyuSJLUzJ4WTJEmqlpUVSZLamX1WJEmSqmVlRZKkdmafFUmSpGpZWZEkqZ1ZWZEkSaqWlRVJktpYphcylCRJqpSVFUmS2pl9ViRJkqplZUWSpHbmDLaSJEnVMlmRJEktzdNAkiS1MzvYSpIkVcvKiiRJ7cwOtpIkSdWysiJJUjuzz4okSVK1rKxIktTO7LMiSZJULSsrkiS1M/usSJIkVcvKiiRJ7czKiiRJUrWsrEiS1M4cDSRJklQtKyuSJLUz+6xIkiRVy2RFkiS1NE8DSZLUzuxgK0mSVC2TlSb7/JdP5U37HsqBh3201/2ZyZe/eSb7HPwhDnrfx7j9rnuaHKHa0TdPPYk7b7+aG2+4lNe+5pW9tjn5pP/iH/fOYMFjdzc5OrWbVSZvx3o/PIf1f3wuqx3y7l7brPqm3Vjvf89mve//mLU+899NjlAr6Opq7lIBk5UmO/Ate3LWqV/sc/9V18zggVmzmX7+DzjxU0dx8tdPa2J0akf77L07W26xGS/bemc+9rH/4vTTvtJru9/+9lLesNO+TY5ObWfECNb6xNE88dlP8dgR72fMbnsw8sWbrtBk5IQJrPau97Dg6H/n8Q9/gIVnfreiYNUpTFaabPJrXsU6a6/V5/7Lr76W/ffeg4hgm1e+nKeeWsjceY81MUK1m/32+zfOOfcXAFx3/Y2ss+46jB+/4fPaXXf9jcyZ82izw1ObGbXVy1k6+yG65jwMS5bw7BV/ZPSOO6/QZsw++/HstAvJhQsByAULqghV3bKruUsFTFZazCNz5zN+w7HL1jfacCyPzJ1XYURqdRM2Hs+sB2cvW39o1sNM2Hh8hRGpnY0YO5alc5cntV3z5jJy7NgV2oycOJGREzZh3W+dxrrfOYNVJm/X7DDVYQZMViLipRFxWUTcWqy/OiI+3/jQOlNmPm9bRFQQidpFb5+P3j5HUim9/b7p+XEaOZKREyay4Lj/4Mkvn8Rax36SWGPNpoSnXthnBYDvA58BFgNk5i3Aof3dISKmRMTMiJg5derUFx5lBxm/4VjmPLq8kvLIo/PYcOwGFUakVvSxj76fmTMuYeaMS5j98BwmbrLxsn0TJr6I2Q8/UmF0amddc+cyctzy04gjxo5j6fwVq7td8+by3DVXw9KldM2Zw9JZDzJywsRmh6oOUiZZWT0zr++xbUl/d8jMqZk5OTMnT5kyZeWj60C77rwD035/GZnJzbfewZprrsG4setXHZZazJlnnc3kbfdi8rZ7MW3axbz3Pe8AYPvtXseTTzxp3xSttCV33cnICRMZMX48jBrFmF13Z9E1f16hzXN/vprR27wWgFh7HUZO2ISlD8/u7XBqhg6orJSZFG5eRLyEohAYEe8AHm5oVMPYJ084hRl/vYUFC55kjwMP4+OHv5clS2q53yEH7cub3rAtV10zg30O/hCrjRnDyZ89puKI1eqmX3QZe++9O3fd8Wf+9cwzHHHEscv2zZxxCZO33QuAU77yOQ495CBWX3017r9vJj/80f9x0smnVhW2WlXXUhae9i3W+crXiREjePbi6Sz95/2s/v4PseTuO1l0zV9YPPN6Rr9+W9b737Ohq4unv38m+dSTVUeuYSwGOrcdEZsDU4EdgceBfwCHZeb9JR8jF8+774XEKC2zytjNARg1ekLFkWi4WLLoIQDm7rlLxZFouBh36ZUATets+Mz5X2hqJ7XVDjmh3+cWEXsD3wZGAv+bmaf02P9i4Gxg3aLNpzNzen/HHLCykpn3AW+OiDWAEZn51ED3kSRJnSciRgKnA3sCs4AZETEtM2+va/Z54ILMPDMitgamA5P6O+6AyUpErAu8rzjQqO6RB5l51OCfhiRJGlIV9SPpw3bAPUWhg4g4DzgAqE9WEli7uL0OMGCHpzJ9VqYD1wJ/A1rqFZEkSS1lAvBg3fosYPsebU4ELomITwBrAG8e6KBlkpUxmXnswM0kSVLTNbmyEhFTgPqhvlMzs3uekt76s/TsU/Mu4MeZ+Y2IeANwTkS8MrPv6XHLJCvnRMSHgd8Czy175EzngJckqcMUiUlfk6jNAjapW5/I80/zHA7sXRzrmogYA4wF+pxzoUyysgj4GvA5lmdHCWxe4r6SJKmRKrpeTx9mAFtGxGbAQ9Qmke156e4HgD2AH0fEy4ExwNz+DlomWTkW2CIzvUCNJEnqU2YuiYgjgYupDUv+YWbeFhEnATMzcxpwHPD9iDiGWvHjAznAPCplkpXbgH+9sPAlSVInKOZMmd5j2/F1t28HdhrMMcskK0uBmyLiclbss+LQZUmSqtZaQ5cbokyy8utikSRJaroyM9ieHRGjgZcWm+7KzMWNDUuSJJUywGVzhoMyM9juSm0O//upjZ/eJCLen5l/amxokiRJ5U4DfQPYKzPvAoiIlwI/A17fyMAkSVIJHdBnZUSJNqt0JyoAmXk3sErjQpIkSVquTGVlZkT8ADinWH8PcEPjQpIkSaV1QGWlTLLyMeDfgaOo9Vn5E3BGI4OSJEnqViZZGQV8OzNPBYiIkcCqDY1KkiSV01rT7TdEmT4rlwGr1a2vBvyhMeFIkiStqExlZUxmLuxeycyFEbF6A2OSJEklZdfwn2elTGXl6Yh4XfdKRLweeKZxIUmSJC1XprJyNPDziJhdrL8IOKRxIUmSpNIcDQSZOSMiXgZsRW000J1Oty9JkpqlTGUFYFtgUtH+tRFBZv6kYVFJkqRyOmA0UJlrA50DvAS4CVhabE7AZEWSJDVcmcrKZGDrzA64rKMkSWo5ZZKVW4HxwMMNjkWSJA1WBwxdLpOsjAVuj4jrgee6N2bm/g2LSpIkqVAmWTmx0UFIkqSV5NBlyMwrmxGIJElSb/pMViLiKWqjfqL4f9kuIDNz7QbHJkmSBtLJlZXMXKuZgUiSJPVmwGsDFfOsDLhNkiRVILO5SwXKXMjwFfUrETEKeH1jwpEkSVpRf31WPgN8FlgtIp7s3gwsAqY2ITZJkjSQDuiz0mdlJTO/UvRb+Vpmrl0sa2XmBpn5mSbGKEmSOliZocufiYgJwKb17TPzT40MTJIkleAMthARpwCHArez4oUMTVYkSVLDlZnB9iBgq8x8bsCWkiSpubKD+6zUuQ9YpdGBSJIk9aZMZeVfwE0RcRkrXsjwqIZFJUmSyrHPCgDTikWSJKnpyowGOrsZgUiSJPWmzGigLYGvAFsDY7q3Z+bmDYxLkiSVkJ08KVydHwFnAkuA3YCfAF4bSJIkNUWZZGW1zLwMiMz8Z2aeCOze2LAkSVIpXdncpQJlOtg+GxEjgL9HxJHAQ8CGjQ1LkiSppkyycjSwOnAUcDK1U0Hvb2RQkiSppA6YFK7MaKAZxc2FwAcbG44kSdKKBuyzEhGXRsS6devrRcTFjQ1LkiSV0gF9Vsp0sB2bmQu6VzLzceyzIkmSmqRMn5WuiHhxZj4AEBGbUrvqsiRJqloHzLNSJln5HHB1RFxZrL8JmNK4kCRJkpYr08H29xHxOmAHIIBjMnNewyOTJEkD64ALGfbZZyUiXlb8/zrgxcBsanOsvLjYJkmS1HD9VVaOAz4MfKOXfYmz2EqSVL1OnmclMz9c/L9b88KRJElaUZ/JSkS8rb87Zuavhj4cSZI0KB3QZ6W/00D79bMvAZMVSZLUcP2dBnJqfUmSVLkBhy5HxAbACcDO1CoqVwMnZeb8BscmSZIGkB0wKVyZ6fbPA+YCbwfeUdw+v5FBSZIkdSszg+36mXly3foXI+LARgUkSZIGoQM62JaprFweEYdGxIhiORj4XaMDkyRJgnKVlY8AxwI/LdZHAE9HxLFAZubajQpOkiQNoAMqK2WuDbRWMwKRJEnqTZnKSvcEcd2jga7KzF83NCpJklROB0y3P2CflYg4A/go8DfgVuCjEXF6owOTJEmCcpWVXYBXZmYCRMTZ1BIXSZJUtQ7os1JmNNBdwIvr1jcBbmlMOJIkSSsqU1nZALgjIq4v1rcFromIaQCZuX+jgpMkSf3LDqislElWjq+7HdQ62r4L+HhDIpIkSapTZujylRHxGuDdwMHAP4CzMvPKRgcnSZIG0MmVlYh4KXAotSrKfGrXA4rM3K1JsUmSJPVbWbkTuArYLzPvAYiIY5oSlSRJKqfDr7r8dmAOtWsDfT8i9qDWZ0WSJKlp+kxWMvPCzDwEeBlwBXAMsFFEnBkRezUpPkmS1OEGnGclM5/OzHMz863AROAm4NMNj0ySJA2sK5u7VKDMpHDLZOZjmfm9zNy9UQFJkiTVK3UhQ0mS1KI6YOjyoCorkiRJzWZlRZKkNlZcZ3hYs7IiSZJampUVSZLamX1WJEmSqmVlRZKkdmZlRZIkqVpNqaysMnbzZjyMOsiSRQ9VHYKGmXGXXll1CNJKyQ6orDQlWRk1ekIzHkYdoDtJWTzvvooj0XDR/cfUxycdXHEkGi7OuP+CqkMYduyzIklSO+uAyop9ViRJUkuzsiJJUjvrqjqAxrOyIkmSWprJiiRJammeBpIkqY11wtBlKyuSJKmlWVmRJKmdWVmRJEkqLyL2joi7IuKeiPh0H20OjojbI+K2iPi/gY5pZUWSpHbWQkOXI2IkcDqwJzALmBER0zLz9ro2WwKfAXbKzMcjYsOBjmtlRZIkDZXtgHsy877MXAScBxzQo82HgdMz83GAzHx0oINaWZEkqY212GigCcCDdeuzgO17tHkpQET8GRgJnJiZv+/voCYrkiSptIiYAkyp2zQ1M6d27+7lLj2zqVHAlsCuwETgqoh4ZWYu6OsxTVYkSWpnTe6zUiQmU/vYPQvYpG59IjC7lzbXZuZi4B8RcRe15GVGX49pnxVJkjRUZgBbRsRmETEaOBSY1qPNr4HdACJiLLXTQvf1d1ArK5IktbFW6rOSmUsi4kjgYmr9UX6YmbdFxEnAzMycVuzbKyJuB5YCn8zM+f0d12RFkiQNmcycDkzvse34utsJHFsspZisSJLUzlponpVGsc+KJElqaVZWJElqY2llRZIkqVomK5IkqaV5GkiSpHbmaSBJkqRqWVmRJKmN2cFWkiSpYlZWJElqZ1ZWJEmSqmVlRZKkNmafFUmSpIpZWZEkqY1ZWZEkSaqYlRVJktqYlRVJkqSKWVmRJKmdZVQdQcNZWZEkSS3NyookSW3MPiuSJEkVM1mRJEktzdNAkiS1seyyg60kSVKlrKxIktTG7GArSZJUMSsrkiS1sXRSOEmSpGpZWZEkqY3ZZ0WSJKliVlYkSWpjzrMiSZJUMSsrkiS1scyqI2g8KyuSJKmlWVmRJKmN2WdFkiSpYlZWJElqY1ZWJEmSKmayIkmSWpqngSRJamMOXZYkSaqYlRVJktqYHWwlSZIqZmVFkqQ2lmllRZIkqVJWViRJamPZVXUEjWdlRZIktTQrK5IktbEu+6xIkiRVy8qKJEltzNFAkiRJFbOyIklSG3MGW0mSpIpZWZEkqY151WVJkqSKmaxIkqSW5mkgSZLamB1sJUmSKmZlRZKkNuZ0+5IkSRWzsiJJUhtzun1JkqSKWVmRJKmNOSmcJElSxaysSJLUxhwNJEmSVDErK5IktTFHA6khvnnqSdx5+9XceMOlvPY1r+y1zckn/Rf/uHcGCx67u8nRqd18/sun8qZ9D+XAwz7a6/7M5MvfPJN9Dv4QB73vY9x+1z1NjlDtZutdtuGEy77FiVd8h70+dsDz9u9++L7896Wn8rmLvsZR5/43608YW0GU6iQmK022z967s+UWm/GyrXfmYx/7L04/7Su9tvvtby/lDTvt2+To1I4OfMuenHXqF/vcf9U1M3hg1mymn/8DTvzUUZz89dOaGJ3aTYwIDjnpcE77wJc5ec9jmLz/TozfYsIKbWbdfj+n7PdpvrTPJ/nrRddy0GcOqyhaQW00UDOXKpisNNl++/0b55z7CwCuu/5G1ll3HcaP3/B57a67/kbmzHm02eGpDU1+zatYZ+21+tx/+dXXsv/eexARbPPKl/PUUwuZO++xJkaodjLpNVsw959zmP/goyxdvJQbfvMXttlr2xXa3H3NbSx+dhEA//jr31l3/PpVhKoOMqhkJSLWaFQgnWLCxuOZ9eDsZesPzXqYCRuPrzAiDXePzJ3P+A2Xl+k32nAsj8ydV2FEamXrbrQ+j8+ev2z98Yfns85GfScjOx68O7ddcVMzQlMfujKaulShVLISETtGxO3AHcX6NhFxRkMjG6Yinv9GZyfM6KPK9Pb56u1zKAHQ22ejj99R2x34RjZ99eb8Yeq0BgelTld2NNA3gX8DpgFk5s0R8aa+GkfEFGAKwPe+970XGmPb+9hH38/hh78HgJkzb2LiJhsv2zdh4ouY/fAjVYWmDjB+w7HMeXR5JeWRR+ex4dgNKoxIrWzBnPmst/Hyz8d6L9qAJx59/HntttrpVex95EGcesiJLFm0pJkhqgdHA9XJzAd7bFraT9upmTk5MyeXzq1qAAAgAElEQVRPmTJlpYMbLs4862wmb7sXk7fdi2nTLua973kHANtv9zqefOJJ+6aooXbdeQem/f4yMpObb72DNddcg3Fj7WOg3v3z5nvZcNKL2GDiOEauMpLX77cjt1w6c4U2E18xiXd/+cOcecRXWTj/yYoiVScpW1l5MCJ2BDIiRgNHUZwS0uBMv+gy9t57d+6648/865lnOOKIY5ftmznjEiZvuxcAp3zlcxx6yEGsvvpq3H/fTH74o//jpJNPrSpstbBPnnAKM/56CwsWPMkeBx7Gxw9/L0uW1P7SPeSgfXnTG7blqmtmsM/BH2K1MWM4+bPHVByxWlnX0i7OP/6HHPmTzzFi5AiuueByHv77LN56zMH882/38rc/3MDbPnMYq64+hiPOqP3+evyheZz14a9WHLmGsyjTXyIixgLfBt4MBHAJ8B+ZOb/fO9bkqNETBm4llbBk0UMALJ53X8WRaLhYZezmAHx80sEVR6Lh4oz7L4Dad2VTXLfx25ra8XH72b9q+nmnUpWVzJwHvKfBsUiSJD1PqWQlIjYDPgFMqr9PZu7fmLAkSVIZnTCetGyflV8DPwB+A3Q1LhxJkqQVlU1Wns3M7zQ0EkmSNGhVTdTWTGWTlW9HxAnUOtY+170xM29sSFSSJEmFssnKq4D3Aruz/DRQFuuSJKkinTApXNlk5SBg88xc1MhgJEmSeiqbrNwMrAs41aokSS2kE0a9lE1WNgLujIgZrNhnxaHLkiSpocomKyc0NApJkrRSsnmT5Vam7Ay2V0bERsC2xabrM9NTQpIkqeFKXXU5Ig4GrgfeCRwMXBcR72hkYJIkaWBd2dylCmVPA30O2La7mhIR44A/AL9oVGCSJElQsrICjOhx2mf+IO4rSZIapIto6jKQiNg7Iu6KiHsi4tP9tHtHRGRETB7omGUrK7+PiIuBnxXrhwAXlbyvJEnqABExEjgd2BOYBcyIiGmZeXuPdmsBRwHXlTluqepIZn4S+B7wamAbYGpmfqp8+JIkqQNsB9yTmfcVE8meBxzQS7uTga8Cz5Y5aKnKSkRsBkzPzF8V66tFxKTMvL/M/SVJUmM0e+hyREwBptRtmpqZU4vbE4AH6/bNArbvcf/XAptk5m8j4j/LPGbZ00A/B3asW19abNu29+aSJGk4KhKTqX3s7i1zWjaGKCJGAN8EPjCYxyybrIyqvy5QZi6KiNGDeSBJkjT0Wmy6/VnAJnXrE4HZdetrAa8ErogIgPHAtIjYPzNn9nXQsiN65kbEsqn1I+IAYF7J+0qSpM4wA9gyIjYrihqHAtO6d2bmE5k5NjMnZeYk4Fqg30QFyldWPgqcGxGnUSvxPAi8byWehCRJGkKtNN1+Zi6JiCOBi4GRwA8z87aIOAmYmZnT+j9C78pOt38vsENErAlEZj61Mg8mSZKGt8ycDkzvse34PtruWuaYZUcDrQq8HZgEjCrOM5GZJ5W5vyRJaowW67PSEGVPA/0/4AngBuC5xoUjSZK0orLJysTM3LuhkUiSpEHrhMpK2dFAf4mIVzU0EkmSpF6UrazsDHwgIv5B7TRQAJmZr25YZJIkaUCtNBqoUcomK/s0NApJkqQ+9JusRMT6xU2HKkuS1IK6hn9hZcDKyg3U5vTva67/zYc8IkmSpDr9JiuZuVmzApEkSYPX1QF9VkqNBoqIy8pskyRJGmoD9VkZA6wBjI2I9Vh+OmhtYOMGxyZJkjRgn5WPAEdTS0xuYHmy8iRwegPjkiRJJWTVATTBQH1Wvg18OyI+kZnfbVJMkiRJy5S96vJ3I2JHigsZ1m3/SYPikiRJJXTCdPtlr7p8DvAS4CZgabE5AZMVSZLUUGVnsJ0MbJ2ZnXBqTJKkttEVDl3udiswvpGBSJIk9aZsZWUscHtEXE/tQoYAZOb+DYlKkiSV0gmnPMomKyc2MghJkqS+lB0NdGWjA5EkSYPXCaOByk63v0NEzIiIhRGxKCKWRsSTjQ5OkiSp7Gmg04BDgZ9TGxn0PmDLRgUlSZLK6Rr+g4FKJytk5j0RMTIzlwI/ioi/NDAuSZIkoHyy8q+IGA3cFBFfBR6mdoFDSZJUoS6Gf2ml7Dwr7y3aHgk8DWwCvL1RQUmSJHUrW1mZByzKzGeBL0TESGDVxoUlSZLK6IR5VspWVi4DVq9bXw34w9CHI0mStKKyycqYzFzYvVLcXr2f9pIkSUOi7GmgpyPidZl5I0BEvB54pnFhSZKkMhy6vNzRwM8jYnax/iLgkMaEJEmStFzZ6fZnRMTLgK2AAO7MzMUNjUySJA2oE6bb7zdZiYjdM/OPEfG2Hru2jAgy81cNjE2SJGnAysouwB+B/XrZl4DJiiRJFeqEocv9JiuZeULx/webE44kSdKKBjoNdGx/+zPz1KENR5IkDYajgWCtpkQhSZLUh4FOA32hWYFIkqTB64TRQKVmsI2IzSPiNxExNyIejYj/FxGbNzo4SZKkstPt/x9wAbXJ4DYGfg78rFFBSZKkcrqavFShbLISmXlOZi4plp/SGaOlJElSxcpOt395RHwaOI9aknII8LuIWB8gMx9rUHySJKkf6WigZbqvA/SRHts/RC15sf+KJElqiLLXBtqs0YFIkqTB64TRQKWSlYgYA3wc2JlaJeUq4KzMfLaBsUmSJJU+DfQT4Cngu8X6u4BzgHc2IihJkqRuZZOVrTJzm7r1yyPi5kYEJEmSyuuE00Blhy7/NSJ26F6JiO2BPzcmJEmSpOXKVla2B94XEQ8U6y8G7oiIvwGZma9uSHSSJKlfnTDpWdlkZe+GRiFJktSHskOX/wkQERsCY+q2P9DnnSRJUsN1dcCkcGUvZLh/RPwd+AdwJXA/cFED45IkSQLKd7A9GdgBuLuYIG4P7GArSVLlvJDhcoszcz4wIiJGZOblwGsaGJckSRJQvoPtgohYk9rMtedGxKPAksaFJUmSynCeleUOAJ4BjgZ+D9wL7NeooCRJkrqVHQ30dERsBGwLzAcuKk4LSZKkCnXCPCtlRwMdDFxP7VpABwPXRcQ7GhmYJEkSlO+z8jlg28x8FCAixgF/AH7RqMAkSdLAnGelrl13olKYP4j7SpIkrbSylZXfR8TFwM+K9UOA6Y0JSZIkldUJo4H6TVYiYgtgo8z8ZES8DdgZCOAa4NwmxCdJkjrcQKdyvgU8BZCZv8rMYzPzGGpVlW81OjhJkqSBTgNNysxbem7MzJkRMakhEUmSpNIculx3heVerDaUgUiSJPVmoMrKjIj4cGZ+v35jRBwO3FD2QZYsemhlYpP6tMrYzasOQcPMGfdfUHUI0krp6oDaykDJytHAhRHxHpYnJ5OB0cBBZR9k7p67rFx0Ug/jLr0SgI9POrjiSDRcdCcpi+fdV3EkGi78Y2ro9ZusZOYjwI4RsRvwymLz7zLzjw2PTJIkDajjhy53y8zLgcsbHIskSdLzlJ0UTpIktaDh32PFKfMlSVKLs7IiSVIb64Q+K1ZWJElSS7OyIklSG+uKqiNoPCsrkiSppVlZkSSpjXXCDLZWViRJUkuzsiJJUhsb/nUVKyuSJKnFmaxIkqSW5mkgSZLamJPCSZIkVczKiiRJbcyhy5IkSRWzsiJJUhsb/nUVKyuSJKnFWVmRJKmNORpIkiSpYlZWJElqY44GkiRJGoSI2Dsi7oqIeyLi073sPzYibo+IWyLisojYdKBjmqxIktTGsslLfyJiJHA6sA+wNfCuiNi6R7O/ApMz89XAL4CvDvQcTVYkSdJQ2Q64JzPvy8xFwHnAAfUNMvPyzPxXsXotMHGgg9pnRZKkNtZio4EmAA/Wrc8Ctu+n/eHARQMd1GRFkiSVFhFTgCl1m6Zm5tTu3b3cpdezRxFxGDAZ2GWgxzRZkSSpjWWTRwMVicnUPnbPAjapW58IzO7ZKCLeDHwO2CUznxvoMe2zIkmShsoMYMuI2CwiRgOHAtPqG0TEa4HvAftn5qNlDmqyIkmShkRmLgGOBC4G7gAuyMzbIuKkiNi/aPY1YE3g5xFxU0RM6+Nwy3gaSJKkNtZiHWzJzOnA9B7bjq+7/ebBHtPKiiRJamlWViRJamNOty9JklQxKyuSJLWx4V9XsbIiSZJanJUVSZLamH1WJEmSKmZlRZKkNtZq86w0gpUVSZLU0qysSJLUxpp9IcMqWFmRJEktzcqKJEltzD4rkiRJFbOyIklSG7PPiiRJUsVMViRJUkvzNJAkSW3MDraSJEkVs7IiSVIb60o72EqSJFXKyookSW1s+NdVrKxIkqQWZ2VFkqQ21tUBtRUrK5IkqaVZWZEkqY053b4kSVLFrKxIktTGnMFWkiSpYlZWJElqY44GkiRJqpiVFUmS2pijgSRJkipmsiJJklqap4EkSWpjDl2WJEmqmJUVSZLaWKYdbCVJkiplZUWSpDbmpHCSJEkVs7IiSVIbczSQJElSxaysSJLUxpxuX5IkqWJWViRJamOOBpIkSaqYlRVJktqYM9hKkiRVzMqKJEltzHlWJEmSKmZlRZKkNuY8K5IkSRUzWZEkSS3N00CSJLUxJ4WTJEmqmJWVJltl8nas+fFPECNG8MxFv+OZ8//veW1WfdNurP6+D0AmS+67l6e+cnLzA1Xb2HqXbXjn8R8kRo7gL+dfxiVn/r8V9u9++L7sdOgedC1ZylOPPclPP3Umjz00r6Jo1eo+/+VT+dOfr2f99dbl1z8963n7M5OvfOssrrpmBmPGrMqXPnccW2+1RQWRqpuTwmlojRjBWp84mic++ykeO+L9jNltD0a+eNMVmoycMIHV3vUeFhz97zz+4Q+w8MzvVhSs2kGMCA456XBO+8CXOXnPY5i8/06M32LCCm1m3X4/p+z3ab60zyf560XXctBnDqsoWrWDA9+yJ2ed+sU+9191zQwemDWb6ef/gBM/dRQnf/20JkanTmWy0kSjtno5S2c/RNech2HJEp694o+M3nHnFdqM2Wc/np12IblwIQC5YEEVoapNTHrNFsz95xzmP/goSxcv5Ybf/IVt9tp2hTZ3X3Mbi59dBMA//vp31h2/fhWhqk1Mfs2rWGfttfrcf/nV17L/3nsQEWzzypfz1FMLmTvvsSZGqJ66yKYuVSiVrETEZWW2qX8jxo5l6dxHl613zZvLyLFjV2gzcuJERk7YhHW/dRrrfucMVpm8XbPDVBtZd6P1eXz2/GXrjz88n3U26jsZ2fHg3bntipuaEZqGqUfmzmf8hst/b2204VgemetpRTVWv8lKRIyJiPWBsRGxXkSsXyyTgI37ud+UiJgZETOnTp06tBG3s4jnb+uZpI4cycgJE1lw3H/w5JdPYq1jP0mssWZTwlMb6vUz1ftfPtsd+EY2ffXm/GHqtAYHpeGst/4R0dvnUE2TTf5XhYE62H4EOJpaYnID0P2JfBI4va87ZeZUoDtLybk/P/cFhjk8dM2dy8hxGy5bHzF2HEvnr/gXSde8uSy+43ZYupSuOXNYOutBRk6YyJK772x2uGoDC+bMZ72NN1i2vt6LNuCJRx9/XrutdnoVex95EKceciJLFi1pZogaZsZvOJY5jy7/vfXIo/PYcOwG/dxDeuH6raxk5rczczPgPzNz88zcrFi2yUx7VQ3SkrvuZOSEiYwYPx5GjWLMrruz6Jo/r9DmuT9fzehtXgtArL0OIydswtKHZ1cRrtrAP2++lw0nvYgNJo5j5Cojef1+O3LLpTNXaDPxFZN495c/zJlHfJWF85+sKFINF7vuvAPTfn8ZmcnNt97Bmmuuwbix9oOqUldmU5cqlBq6nJnfjYgdgUn198nMnzQoruGpaykLT/sW63zl68SIETx78XSW/vN+Vn//h1hy950suuYvLJ55PaNfvy3r/e/Z0NXF098/k3zKLxj1rmtpF+cf/0OO/MnnGDFyBNdccDkP/30Wbz3mYP75t3v52x9u4G2fOYxVVx/DEWccC8DjD83jrA9/teLI1ao+ecIpzPjrLSxY8CR7HHgYHz/8vSxZUqvGHXLQvrzpDdty1TUz2OfgD7HamDGc/NljKo5YnSDKjM+OiHOAlwA3AUuLzZmZR5V4jJy75y4rH6FUZ9ylVwLw8UkHVxyJhosz7r8AgMXz7qs4Eg0Xq4zdHJZ3m2i4N07Yo6nljqseuqzpnZTKTgo3Gdg6O2HmGUmS1FLKJiu3AuOBhxsYiyRJGqROuDZQ2WRlLHB7RFwPPNe9MTP3b0hUkiRJhbLJyomNDEKSJK0cKyuFzLyy0YFIkiT1plSyEhFPsXyu1dHAKsDTmbl2owKTJEmC8pWVFa5qFREHAl60RpKkinXCQN2VuupyZv4a2H2IY5EkSXqesqeB3la3OoLavCvDP5WTJKnF2cF2uf3qbi8B7gcOGPJoJEmSeijbZ+WDjQ5EkiQNXnZAZaVUn5WImBgRF0bEoxHxSET8MiImNjo4SZKksh1sfwRMAzYGJgC/KbZJkqQKZWZTlyqUTVbGZeaPMnNJsfwYGNfAuCRJkoDyHWznRcRhwM+K9XcB8xsTkiRJKqsTRgOVrax8CDgYmEPtysvvKLZJkiQ1VNnRQA8AXmFZkqQW0wkz2JadFG4z4BPApPr7ZKYJjCRJaqiyfVZ+DfyA2iigrsaFI0mSBqMT+qyUTVaezczvNDQSSZKkXpRNVr4dEScAlwDPdW/MzBsbEpUkSSqlE2awLZusvAp4L7UrLXefBkq88rIkSWqwssnKQcDmmbmokcFIkiT1VDZZuRlYF3i0gbFIkqRB6nLo8jIbAXdGxAxW7LPi0GVJktRQZZOVExoahSRJWimd0MG21HT7mXll/QIsoTb9viRJ0jIRsXdE3BUR90TEp3vZv2pEnF/svy4iJg10zLKVFSLiNcC7qSUp/wB+WT50SZLUCK3UZyUiRgKnA3sCs4AZETEtM2+va3Y48HhmbhERhwL/AxzS33H7TVYi4qXAoSy/yvL5QGTmbiv9TCRJ0nC1HXBPZt4HEBHnAQcA9cnKAcCJxe1fAKdFRGQ/Fzka6DTQncAewH6ZuXNmfhdYunLxS5KkoZZN/hcRUyJiZt0ypS6cCcCDdeuzim301iYzlwBPABv09xwHOg30dmqVlcsj4vfAeUAM+MpJkqRhKTOnAlP72N1bjtCzYlKmzQr6TVYy80LgwohYAzgQOAbYKCLOBC7MzEv6u78kSWqsVuqzQq2Ssknd+kRgdh9tZkXEKGAd4LH+Dlp2NNDTmXluZr61eOCbgOf18JUkSR1tBrBlRGwWEaOpnZ2Z1qPNNOD9xe13AH/sr78KDG400Ehqk8ONAi4qFkmSVKFWmmclM5dExJHAxcBI4IeZeVtEnATMzMxpwA+AcyLiHmoVlUMHOm6pZCUiPkFtYrhHWPFChq8e9DORJEnDVmZOB6b32HZ83e1ngXcO5phlKyv/AWyVmfMHc3BJktRYLdZnpSFK9VmhNsToiUYGIkmS1JuylZX7gCsi4neseCHDUxsSlSRJKqWV+qw0Stlk5YFiGV0skiRJTVEqWcnMLzQ6EEmSpN6UHQ00DvgU8ApgTPf2zNy9QXFJkqQSMrsGbtTmynawPZfadYI2A74A3E9t4hdJkqSGKpusbJCZPwAWZ+aVmfkhYIcGxiVJkkroIpu6VKFsB9vFxf8PR8S+1Ob5n9iYkCRJkpYrm6x8MSLWAY4DvgusTe2ihpIkqUIDXFZnWCg7Gui3xc0ngN0aF44kSdKKSvVZiYiJEXFhRMyNiEci4pcR4WkgSZIq1gl9Vsp2sP0RtUs6vwiYAPym2CZJktRQZfusjMvM+uTkxxFxdCMCkiRJ5XVCn5WylZV5EXFYRIwslsMAr8AsSZIarmxl5UPAacA3gQT+AnywUUFJkqRyuqys1GTmA5m5f2aOy8wNM/NA4G0Njk2SJKn0aaDeHDtkUUiSpJWSTf5XhReSrMSQRSFJktSHsn1WejP8T5JJktTiOmE0UL/JSkQ8Re9JSQCrNSQiSZKkOv0mK5m5VrMCkSRJ6s0LOQ0kSZIqVtUU+M30QjrYSpIkNZyVFUmS2lgndLC1siJJklqalRVJktqY0+1LkiRVzMqKJEltzD4rkiRJFbOyIklSG3OeFUmSpIpZWZEkqY3ZZ0WSJKliVlYkSWpjzrMiSZJUMSsrkiS1sXQ0kCRJUrVMViRJUkvzNJAkSW3MDraSJEkVs7IiSVIbc1I4SZKkillZkSSpjTl0WZIkqWJWViRJamP2WZEkSaqYlRVJktqYlRVJkqSKWVmRJKmNDf+6CkQTyked8DpKklQvmvVAo0ZPaOr37JJFDzXtuXVrRrKikiJiSmZOrToODQ9+njTU/EypKvZZaS1Tqg5Aw4qfJw01P1OqhMmKJElqaSYrkiSppZmstBbPBWso+XnSUPMzpUrYwVaSJLU0KyuSJKmldVSyEhEHRURGxMtewDF+HBHvKG7/b0RsPXQRQkR8tsf6wqE8vgYvIsZHxHkRcW9E3B4R0yPipU143BMj4j+L2ydFxJuH+PhHR8Tqdev3R8TYoXwMNUYzfi8Mxe/LIYpjhc+pOlNHJSvAu4CrgUOH4mCZeURm3j4Ux6rz2YGbqFkiIoALgSsy8yWZuTW192ijZsaRmcdn5h+G+LBHA34JqC9D+vvyBfBzqs5JViJiTWAn4HCKH76I2DUi/hQRFxZ/MZ8VESOKfQsj4hsRcWNEXBYR43o55hURMbm4vXfR9uaIuKzYtl1E/CUi/lr8v1Wx/QMR8auI+H1E/D0ivlpsPwVYLSJuiohzezzWrsXj/SIi7oyIc4svUiJi2+L4N0fE9RGxVkSMiYgfRcTfisffre6xfx0Rv4mIf0TEkRFxbNHm2ohYv2j3kiK+GyLiqqr/uqrQbsDizDyre0Nm3gRcHRFfi4hbi9f4EKh9zorPy43F9gOK7ZOK9+3siLileB9XL/bdHxH/U7x310fEFj2D6FHR6+39nlS8TzcWy45F214/NxFxFLAxcHlEXN7jsSZFxB0R8f2IuC0iLomI1Yp9W0TEH4rHvrH4nEQfr8WuEXFlRFwQEXdHxCkR8Z4i5r9FxEuKduMi4pcRMaNYdhrqN7ETRMSmxWfvlv/f3rmHaFFFAfx3Wm1NUsmMHuq6BlKgoKRWltQW9l9RkiG12m5P/CMsy/wnCCuoCMpAKUoy6eEj6YFaka/KcDdXBV2fQbmbD0yz8rHWmrqnP+75+sbdb8ZZrfzaOT8Y5sy9M/eeuffMnTNn7nyfrcss/TYRWW3X+DIRudjSp4rILLOP7WYTubLajJeWnrZP43T5245tuylSbrvs1MkYqpqJBRgHvGVyDXAVUAE0A5cDJcBSYIzto0ClyU8DM0yeHdnnK2AYcBGwE+hv6T1t3R3oZPIo4EOTq4HtQA+gC/Aj0Nfymlrp3WTrCuAg0IfgZNYCI4Fzrazh0TqBJ4C3Le1KYIfVVQ18D3QzvQ8CE2y/acBjJi8HBph8DbDibPfhWbKbicC0Aul3mr2UEKIsO4BLre272z69rK0FKDebut7yZgGTTW4EnjL5XmCxyVMj+8wGxiT0d1egi6UNANYm2U2k3l6Rc2o0ncuB48AQS/8AGGfyamC0yV2s3ri2qAAOmFwK7AaesWMfBV41eU5EpzJg69nu92JfaDVOWNoioMrk+4FPTL6A/McUDwIvR+yrxvqmF/AL0Nny2oyXEXtK06dxuszGxs/oebTHTn3J5pKZyAohpDnP5Hm2DVCnqttV9QQwl+AAALQA801+L5JeiGuBlaraAKCqv1p6D2CBiGwiOAIDI8csV9WDqtoMbAH6pTiHOlXdpaotwHrCTeUKYI+qrrG6D6nqcdP3XUvbRnCIcvMsvlTVw6r6M2GAWGTpG4Fye6q6znRfD7xBGJycPCOBuap6QlX3Al8DwwmOyfMiUg8sA3qTf2W0U1VXmdzapuZG1iMS6o3r787ATBHZCCwAonOpCtnNqWjQEEECWEewi25Ab1X92OpuVtXfE9oCYI2q7lHVo8APwBJL3xjRYxQww2xtIdDd6nLaxwiC4wfh2s/ZVx/gC7ONJzl5HPpUVY+q6n5gH3lbjRsvIV2fxumSxOnYqZMRMvGvyyJyIXAzMEhElPAEqMBntP2jxbhvuZO+8ZaY/OcIjsFoESknRGJyHI3IJ0jXF4WOias76Y+mouW0RLZbrMxzgAOqOiSFTh2dzYSIRmvi2reSELEaqqrHRKSREIGAZFuLkwvVWyh/ErAXGEzov+ZI3j9ha+cRf85nYmsQ9B2hqn+k0MtJT85OpgOvqOpCEakgRFRytLGNuPFSRKYUOCauT+N0OY5NPxARIUQKY3VJOjknW2QlsjIGeEdV+6lquar2BRoI3v7VItJfwlyVsYQJZRDaJneTuieSXoha4EYR6Q8gNu+DEFnZbXJ1Sl2PiUjnlPsCbAMuE5HhVnc3EekErCTcOJHw5UoZ8F2aAlX1ENAgInfZ8SIig9uhU0diBVAqIg/lEqytfwPGikiJhPlMNwB1hD7fZ47KTZwcMSsTkVzUJDd5McfYyLo2QZ+4/u5BiLi0AOMJN5hTcZjwOjAVZhe7ROQOq7tUwryblRRui7QsAR7JbYiIO8mnRw35+SWV5O0rOg5VpSgnabw8U10agaEm306ICJ6Kdtmp0zHJirNyN+GLjigfEpyQWuBFYBPhgsztdwQYKCLrCE8Zz8YVbq9THgY+EpEN5F8fvQS8ICKrSHfzgPALkfXSaoJtQt1/Em5w063upYQn+deAEgv9zgeqLWyblkrgAStzM2FgyRyqqsBo4BYJny5vJjyZzgHqgQ0Eh2aKqv4EvA8ME5G1hDbcFiluK1Blr4h6Aq9H8kpFZDXhvf+kBH2S+rtKRL4lvO47kuL03gQ+b+fExfHARDuHGuASwjVTqC3SMpHQZvUisgWY0I5js0pXEdkVWR4ntON91jfjCbYEwV4XiMg3wP4UZSeNl2mJ02Um4cGujjAX7t+yU6eDkelfsEvN9/gAAAB3SURBVLWQ6GRVvbVAXpOqnv/fa+V0ROw14GJVHVQgrxEYZvMGHMdxnFZkJbLiOI7jOM7/lExHVhzHcRzHKX48suI4juM4TlHjzorjOI7jOEWNOyuO4ziO4xQ17qw4juM4jlPUuLPiOI7jOE5R486K4ziO4zhFzV+DgNA9wtHSfAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1308ce30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f,ax = plt.subplots(figsize=(10, 10))\n",
"sns.heatmap(correlations, annot=True, linewidths=.5, fmt= '.1f',ax=ax)\n",
"plt.title(\"Correlation between Columns of dataFrame\",y=1.08)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dari hasil korelasi tersebut terlihat bahwa variabel yang memiliki korelasi sedang adalah loan amount dan aplicantincome sebesar 0.6"
]
},
{
"cell_type": "code",
"execution_count": 435,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 422\n",
"0 192\n",
"Name: Loan_Status, dtype: int64"
]
},
"execution_count": 435,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train1['Loan_Status'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 436,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xf94d1b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f,ax=plt.subplots(1,2,figsize=(18,8))\n",
"train1['Loan_Status'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)\n",
"ax[0].set_title('Loan_Status')\n",
"ax[0].set_ylabel('')\n",
"sns.countplot('Loan_Status',data=train1,ax=ax[1])\n",
"ax[1].set_title('Loan_Status')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dari kriteria data terlihat bahwa jumlah loan_status yang diterima 422 orang dan loan status yang ditolak sebesar 192. Apabila dilihat dari pie chart terlihat bahwa proporsi loan_status yang diterima dibanding dengan yang ditolak adalah 69:31 "
]
},
{
"cell_type": "code",
"execution_count": 437,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10427f30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f,ax=plt.subplots(2,2,figsize=(10,10))\n",
"sns.violinplot(\"Gender\",\"ApplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[0,0])\n",
"ax[0,0].set_title('Gender and ApplicantIncome vs Survived')\n",
"ax[0,0].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Married\",\"ApplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[0,1])\n",
"ax[0,1].set_title('Married and ApplicantIncome vs Survived')\n",
"ax[0,1].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Self_Employed\",\"ApplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[1,0])\n",
"ax[1,0].set_title('Self_Employed and ApplicantIncome vs Survived')\n",
"ax[1,0].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Education\",\"ApplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[1,1])\n",
"ax[1,1].set_title('Education and ApplicantIncome vs Survived')\n",
"ax[1,1].set_yticks(range(0,110,10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 438,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xed7e1b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f,ax=plt.subplots(2,2,figsize=(10,10))\n",
"sns.violinplot(\"Gender\",\"CoapplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[0,0])\n",
"ax[0,0].set_title('Gender and CoapplicantIncome vs Survived')\n",
"ax[0,0].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Married\",\"CoapplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[0,1])\n",
"ax[0,1].set_title('Married and CoapplicantIncome vs Survived')\n",
"ax[0,1].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Self_Employed\",\"CoapplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[1,0])\n",
"ax[1,0].set_title('Self_Employed and CoapplicantIncome vs Survived')\n",
"ax[1,0].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Education\",\"CoapplicantIncome\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[1,1])\n",
"ax[1,1].set_title('Education and CoapplicantIncome vs Survived')\n",
"ax[1,1].set_yticks(range(0,110,10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 439,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAJdCAYAAAB3U9DaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xd4VFX6wPHvSZ8UagCBIF2RIqDYd61rRZFVdlf9KSK2tWBby9rWgq7rqquiu2IDF1RcFbuiIkpxBVxQBAWUkkASQkgjbZLJlPP7495JJmFmMiGZuTOZ9/M8PEy5c+fNzJ33vvecc89VWmuEEEIIIUR0SLA6ACGEEEII0USKMyGEEEKIKCLFmRBCCCFEFJHiTAghhBAiikhxJoQQQggRRaQ4E0IIIYSIIlKcRYhSappS6mur4/AnmmMTkaeU+j+l1OdhWO+JSqmCjl6vCC+l1IFKqRqlVOJ+vHaQUkorpZLCEVuL97pfKfVquN9HxAal1F1KqZfCsN6I7C/jujhTSl2glFqtlKpVSu0xb1+rlFJWxxYtIplc/bx3hrlT+CTS7x0qpdQrSqmHIvA+OUqphUqpUqVUpVJqg1JqWjjeS2v9mtb6tHCsW3QMpVSeUqpBKZXd4vF15u91UEe9l9Z6p9Y6U2vt7qh1RpqVBwZKqcFKKY9S6l9WvH8olFJLlVJXROB9RimlPldKVSil9iql1iqlzgrHe2mt/6q1DvvfFC5xW5wppf4EPA08BhwA9AH+CBwHpFgY2j7254i1k5gCOIDTlFJ9rQ7GYvOBfGAg0BOYChTvz4qsKLRFWOQCF3rvKKXGALb9XZm/7UK2lQ4xFagALlBKpVodjMU+BBZj7G97AzcAVfuzok6/X9Rax90/oCtQC5zfynKpwOPATowd4WzAZj53IlAA/AnYAxQBl/m8tifwAcaG9y0wE/ja5/kRGBtpOfAz8Huf514BngM+MeP8jZ/YLgM2AdXAduBqn+faFVuL9xkEaCApwOfzFLDL/PcUkGo+1x34CCjBSEwfATk+r11qvu9/zb/hcyC7xfq/BB4GvgNubfFcHnAbsN78jF7G+MEvMtf3BdDdZ/lJwE/AXvO9D/F5TgPDWnz+D7X2WQJXAU6gAagBPvTzGc0GHm/x2PvALebtO4BCM+afgVMCfA81wLgAz50IFPj5fH5j3r4feBt41fzO/wLUAT18lh8PlALJwDTv9hBC/P2Aheb3nAvc4LOczfwsK4CN5vdV4O9vkH9tzmF5wD3A/3weexy429yeB5mPTQS+N7/3fOB+n+UHmctejpHjlrfyWJL5uq7m763I3HYfAhLN5xLNOEox8tJ1BMgf5vJ/BraZ2/9G4Lc+z00DvjbXV2FuX2f6PD8YWGa+djHwLPBqqL8Rn+e6AvPMbXiH+bkmmM8NxchDZebf9BrQrcX3cCtGHqoE/gOktVj/NuAajH3IlBbPaeBaYIv5d8w033Ol+Z29CaT4LH8lsBVjv/EB0K/Fd5nks+xS4IrWPkuMHOsG6jHyzLN+PqNPgetbPPYDcB6ggCcx8mOl+VmM9rOObDPGbgG+h2m02A/hk5vZd794D7Abc9szl/ktsN68fb93ewgWv3k72P445P1lh/7GrU4yVvwDzgBcBEgYPss9ZX4pPYAsjKr/EfO5E811PIixQzsLsGMWBMAb5g8rAxiNkcS8O7wMjER5GZAEHIbxwx/lsxFWYrTiJdDix24uMxHjR6yAE8z3Pqy9sfl5n0EELs4eBFZhHAH1Ar4BZvps0OcD6eZn9xbwns9rl2IkrYMwduJLgb/5PH8g4AFGYhRG61u8d5753n2A/hiJ4TuMIiMVI6HeZy57EMaP+VTz87gdI8GlmM+3VpwF+ywblw3w+R1vftfKvN8dozDqBxxsPuebYIcGWM8XGIXsBcCBLZ47kdaLMycwGWN7spmfz5U+yz8GzDZvT6NpWw0WfwKwFqPYSwGGYOyQTzeX/RuwAuP3MwD4sWWc8m+/c1ge8BuMHckhGEWRt2XVtzg7ERhjfleHYhQIk1v8tudh5AJbK495i7P3gOfN53tj7LCuNp/7I7DZ/L57AF8RvDj7nc+29AeM32lfn+3QiVGQJGIUOLt8tsWVwD8wfu/HYxQ3+1OczcM44Mgy/9ZfgMvN54Zh5I1UjBy3HHiqxffwrfk39MA4YP6jz/O/xmj97w48A3zQ4r01xj6mCzDKXHaJ+VvqilGwXmouezLGfuIwM55ngOUtvstgxVmwz7Jx2QCf0VTgvz73R2Ic6KYCp2PkgW4Y+6NDvN9hi3UojCL0I4xc1KfF89NovThrtl/E2Iec6rP8W8Cfzdv301ScBYu/tf1xyPvLDv2NW51krPgHXAzsbvHYN+aXVYfxQ1cYiWKozzLHALnm7RPNZX1/DHuAo82N3wmM8HnurzTt8P4ArGjx/s/TVEy8Asxr49/0HnBje2Pzs95BBC7OtgFn+dw/HcgLsJ5xQIXP/aXAPT73rwU+9bl/D7DOvN0P48huvM/zecD/+dxfCDznc38GZjEI3Au86fNcgvkDO9G831px5vezbLlsgL9bYbRAHG/evxL40rw9zFzXb4DkVr7f7hjFzk/mZ7EOOMInxtaKs+Utnr/CJw6FkZy8MU6jaVsNFv9RwM4W670TmGve3g6c4fPcVS3jlH/794+m4uwe4BGMA87FGDuXxuLMz+ueAp40bw8ylx3i83ywx5IwDoYcmD0I5vMXAl+Zt7+keXFyGkGKMz/xrQPO9dkOt/o8l26u6wCMgzcXkOHz/Ou0sTjDyIcOYKTPY1cDSwOsZzLwfYvv4WKf+3/HPMgx779EUx46BiP39vZ5XgPH+dxfC9zhc/8JzGIQo7Xy7z7PZZrrG0RoxZnfz7LlsgH+7iyM/eFA8/7DwBzz9skYBe3RmC2OQdaTg9HCuQ3j4Hs5MNwnxtaKs3ktnn/IJ46WMd5PU3EWLP6A+2PauL/syH/xOuasDMj2HU+htT5Wa93NfC4B4ygpHVhrDlzci9E02st3PVprl899O8YPphdGIsv3eW6Hz+2BwFHe9Zrr/j+MpOPl+9p9KKXOVEqtUkqVm68/C6PZuL2xtUW/Fq/dYT6GUipdKfW8UmqHUqoK40fYrcU4gd1+4vOaitGFgNZ6F0b3xaUt3t93zFWdn/ve9TWLU2vtwfj7+4fwN0Lgz7JV2vg1v0HT2KCLaPq7tgI3YSSRPUqpN5RS/QKsp0Jr/Wet9SiMHeQ64L02nLzScnt6GzjGfL/jMZLgirbEj7Ed92uxHd9lxgfG594R25kIbD7GdzINowWoGaXUUUqpr5RSJUqpSoyWrewWi/nLNYHyz0CMFuQin+/8eYwWNGjjd66UmmqexOBd1+gW8TXmCK213byZab5Phda6NtT3CiAbo9W3ZR7rb8bX2/xdFpp57FX2/fz85jGllA2jZdD7e1+JcaBzUYvX728eq8HYX4WaxwJ9lq3SWlcDH2O03GP+7/27vsQouP4JFCulXlBKdQmwngKt9fVa66EY21ItfrbbIFpul68D55lj+c4DvtNa77MdBIuf4Pvjjtxftkm8FmcrMY6Wzg2yTCnGD2OU1rqb+a+r1jqUjbkE46hugM9jB/rczgeW+ay3mzbOhrrGZxkdaOXmhrgQY/xAH7Oo/ASjlaO9sbXFLowN23c9u8zbf8LotjtKa90FowAglBiVUscCw4E7lVK7lVK7MVppLtzPAcrN4jQLmgEYrWdgJNR0n+V9i+TWBPyefCwApiilBmL8HQsbX6z161rrX9HUHfVoq2+odSnGd+/tSqnFJ36zAO7V8mUt1rEXY5zf7zF2FgvMQqwt8edjtCT7bsdZWmvv2VdFdMx2JgIwd0S5GAdn7/hZ5HWMbrMBWuuuGGMIW/4G/X3vgbaFfIzcme3znXcxDxqgDd+5uT29CFwP9DTz2I9+4vOnCOiulMoI5b2CKMVoGWmZx7y54RGMz+JQM49dHGJ8YIx/6gL8yyeP9cc48NwfLfNYBsbwkUKMHADhz2MXKqWOweju/qrxxVrP0lofjtE1exDG+NLgb6h1PkZBN9p8qGUe8xd/yzy2EaNYOhMjj72+H/EH2x935P6yTeKyODN3TA9g/GimKKUylVIJSqlxGP3K3taVF4EnlVK9AZRS/ZVSp4ewfjdGorzfbEEaSfNWn4+Ag5RSlyilks1/RyilDgnxT0jB6CsvAVxKqTMxug9C+dtbiy2QVKVUms+/BIyN/R6lVC/zlP6/YBxZgtGMXAfsVUr1wGgiDtWlGF00IzG6Q8dh/IDTMX6EbfUmMFEpdYpSKhmjcHRgdGWD0Qp1kVIqUSl1BsYYvlAVY4wPCUhr/T3Gd/US8Jm5/aGUOlgpdbJZbNdjfF5+pytQSj2qlBqtlEpSSmVhjBnZqrUuw+hSSFNKTTT/vnswto/WvI6xozifIEktUPwYY22qlFJ3KKVs5uc3Wil1hPn8mxgFdnelVA5GV7PoeJcDJ7doRfLKAsq11vVKqSPZt9WmTbTWRRhF/RNKqS5m3hyqlPL+Zt4EblDG1C/dMQb8B5KBsbMtAVBKXUbTjrq1OHYAa4AHlFIpSqlfAee09roWOSwNo2vtTeBhpVSWWTDeQvM8VoORx/oTQtHh41JgDsaYP28eOw4Yp4wza9vqdeAypdQ4M2f8FVittc7TWpdgFGkXm7/D6RhjkkPVah7DaAAYiDH+9j/mPhJz33WUmXtqMXLZPnnMzAMPKKWGmdtNNjAdY+wwGAP0R5l/XxpGj0IoXsc46/N4jDFnbYqfIPvjduwv2y0uizMArfXfMX6Et2OM+ynGaJ6/g6ad9h0YA8dXmU3aX2C0BoXieowm490YfeVzfd67GqOYugDjaGg3RotJSKdZm6+/ASOpVGAk3A9CjCtobEHUYBQP3n8nY/T3r8E4O2cDxoB875xfT2EcnZRi/Pg+DSUw80f5e+AZrfVun3+5GF04bf5haK1/xjjifcaM5xzgHK11g7nIjeZj3ubs99qw+peBkWZzeLDXLcAYI+RbBKVijCMrxfguemN0C/qTDrxrxrgdI8lMMv++Sowxey/RdBQdypxOH2C0UBZrrX9oZdl94jcT1zkYO51c8+94CWMgMxgHQN6Wnc8xvj/RwbTW27TWawI8fS3woFKqGuPg6c0OeMupGAeIGzHyz9uAd6qbF4HPMHa03+G/Nc8b90aMMVUrMfLvGIyTXkJ1EUZLbjnGwV9r3WP9aZ7D6jAKmBkYv5ntGGc0vo5RVIGxDR+GMRD942B/jy+zkDsFY7yYbx5bi5EL9yePLcEYP7sQo+VwKE3ddGCMB70No6tzFE37sVA8jdE6XqGUmhXg/R0Yf3/LPNYF43uvwPi9l2G07LfUgDE27guMMx9/xDhInmau/xeMwukLjBMHQp3odQHGmMIvzV4FvwLFH8L+eH/2l+3mPVNDCCGEEEJEgbhtORNCCCGEiEZSnAkhhBBCRBEpzoQQQgghoogUZ0IIIYQQUSSmL2qbnZ2tBw0aZHUYQogIWrt2banWuuU8bjFH8pcQ8SfU/BXTxdmgQYNYsybQGeRCiM5IKdUprjQg+UuI+BNq/pJuTSGEEEKIKCLFmRBCCCFEFJHiTAghhBAiisT0mDMhOhOn00lBQQH19fVWhxIV0tLSyMnJITk52epQhBCtkPzVXHvzlxRnQkSJgoICsrKyGDRoEEopq8OxlNaasrIyCgoKGDx4sNXhCCFaIfmrSUfkL+nWFCJK1NfX07Nnz7hPbABKKXr27ClH4ULECMlfTToif0lxJkQUkcTWRD4LIWKL/GabtPezkOJMCCGEECKKSHEmhBBCCBFFpDgTIoplZmbu81hJSQm1tbVhe8+HH36YUaNGceihhzJu3DhWr14NwFNPPYXdbm/19aEuJ4To3Pzlr9raWkpKSsL2np0lf0lxJkSMKS8vp6CgICzrXrlyJR999BHfffcd69ev54svvmDAgAFA7CU3IUT0KSwspLy8PCzr7kz5S4ozIWJMYWEh06ZN49BDD+WUU05h586dAHz44YccddRRjB8/nt/85jcUFxcDcP/99zN9+nROPPFEhgwZwqxZswKuu6ioiOzsbFJTUwHIzs6mX79+zJo1i127dnHSSSdx0kknAXDNNdcwYcIERo0axX333Qfgdznfo+e3336badOmAfDWW28xevRoxo4dy/HHH9+xH5IQIioVFBRI/gqF1jpm/x1++OFaiM5i48aN+zyWkZGxz2MnnniifuSRR7TWWr/88sv63HPP1VprXV5erj0ej9Za6xdffFHfcsstWmut77vvPn3MMcfo+vp6XVJSonv06KEbGhr8xlBdXa3Hjh2rhw8frq+55hq9dOnSxucGDhyoS0pKGu+XlZVprbV2uVz6hBNO0D/88IPf5Xz/hrfeektfeumlWmutR48erQsKCrTWWldUVIT8mQBrdBTkn/b+k/wlOpO25i+PxyP5K8g/aTkTIsasW7eOs88+G4BLLrmEr7/+GjCOSE8//XTGjBnDY489xk8//dT4mokTJ5Kamkp2dja9e/duPCptKTMzk7Vr1/LCCy/Qq1cv/vCHP/DKK6/4XfbNN9/ksMMOY/z48fz0009s3LixTX/Hcccdx7Rp03jxxRdxu91teq0QIjZJ/gqNFGdCxDjvfDozZszg+uuvZ8OGDTz//PPNJkD0NvMDJCYm4nK5Aq4vMTGRE088kQceeIBnn32WhQsX7rNMbm4ujz/+OEuWLGH9+vVMnDgx4ISLvvP9+C4ze/ZsHnroIfLz8xk3bhxlZWWh/9FCiJhmNCJJ/gpEijMhYsz48eP55JNPAHjttdf41a9+BUBlZSX9+/cH4N///vd+rfvnn39my5YtjffXrVvHwIEDAcjKyqK6uhqAqqoqMjIy6Nq1K8XFxSxatKjxNb7LAfTp04dNmzbh8Xh49913Gx/ftm0bRx11FA8++CDZ2dnk5+fvV8xCiNjhzV9aa8lfQci1NYWIYna7nZycnMb7t9xyC3fffTd33303r776Kr169WLu3LmAMXD2d7/7Hf379+foo48mNze3ze9XU1PDjBkz2Lt3L0lJSQwbNowXXngBgKuuuoozzzyTvn378tVXXzF+/HhGjRrFkCFDOO644xrX0XK5v/3tb5x99tkMGDCA0aNHU1NTA8Btt93Gli1b0FpzyimnMHbs2PZ8VEKIKBMsf82fP5/evXtL/gpAeZsWY9GECRP0mjVrrA5DiA6xadMmDjnkkFaX+/nnnwE4+OCDwx2S5fx9JkqptVrrCRaF1GEkf4nOpK35a8iQISQnJ4c7LEu1J39Jt6YQQgghIsI7hiuWG4YiQbo1hYhDZWVlnHLKKfs8vmTJEnr27GlBREKIeKCUapwuYn/FQ/6S4kyIONSzZ0/WrVtndRhCiDjTES1n8ZC/pFtTCCGEEBEh3ZqhkeJMCCGEECKKSHEmhBAi4uSqEPFJWs5CI2POhOhErr/lNvaUlnfY+npn9+DZfzzW6nKffvopN954I263myuuuII///nPHRaD6HyWL1/OzJkzmTdvHn379rU6HBFFrMhh0Zi/pDgTohPZU1rOtj4ndNwKi5e1uojb7ea6665j8eLF5OTkcMQRRzBp0iRGjhzZcXGITuWzzz7D6XSydetWKc7ijO/lkPyJdA6L1vwl3ZpCiHb59ttvGTZsGEOGDCElJYULLriA999/3+qwRBRLSUkBoKGhweJIRKRFW3dmtOYvKc6EEO1SWFjIgAEDGu/n5ORQWFhoYUQi2qWlpQHgcDgsjkTEu2jNX1KcCSHaxd+RcGtdFyK+eYuz+vp6iyMR8S5a85cUZ0KIdsnJySE/P7/xfkFBAf369bMwIhHtbDYbYFwYW8QXbzEUDQUQRG/+kuJMCNEuRxxxBFu2bCE3N5eGhgbeeOMNJk2aZHVYIoqlp6cDUFtba3EkIt5Fa/6SszVjVGVlJU6nk+zsbKtDEVGkd3aPkM6wbNP6WpGUlMSzzz7L6aefjtvtZvr06YwaNarDYhCdj7c4q6urszgSEWmttZxFOodFa/6S4ixG3XTTTRQX7+aTTxZZHYqIIqHMSRYOZ511FmeddZYl7y1iT1KSseupqamxOBIRaa0VZ1bksGjMX1Kcxajc3FyrQxBCiHaRljMh/JMxZ0IIISwhLWfxJ9pOCIhWUpwJIYSwhEylIYR/UpzFOJfLZXUIQgixX6Q4i1/SchacFGcxTsZsCCFilVMu3ySEX1KcxTiZxFEIEauk5V8I/+RszRgnxZnwddefrqeytLjD1tc1uw9/feLZoMtMnz6djz76iN69e/Pjjz922HuLzs/t8VgdgrBIoAugSw4zSHEW4+RsJ+GrsrSYO4Zu7rD1Pbqt9WWmTZvG9ddfz9SpUzvsfUV8SEiQcUfxRikVsDADyWFe0q0ZoxITEwG5/Imw3vHHH0+PHq1fSUCIlhITpX0g3nhPBAhWoEVaNOYwKc5iVGZmJiAtZ0KI2JWcnGx1CMIiHunSDkqKsxiVkZEBSMtZvJGEJjqTlJQUq0MQEeZ2u4HoajmLRlKcxShvcSYtZ/FFEproTLwXQBfxRw40g5PiLEZ5jzil5Sy+SHEmOhObzWZ1CMIiksuCk9GYMco7ubIUZ8JX1+w+IZ2d1Jb1tebCCy9k6dKllJaWkpOTwwMPPMDll1/ecUGITsvbAyDiT6CWM8lhBinOYpxc/iS+tNYV0Np8PuGwYMGCiL+niG3e7Vi6NeNXoFwmOcwg3ZoxTi7fFF+kK0B0Bt68JS1n8UvGnAUnxVmMa5Br08UVKc5EZ+C9sokUZ/FLcllwUpzFOKfTaXUIogO1lrDiKaHF098ab7zFmXRrdi5t+c129paz9uYvKc5inHfOGBH70tLSKCsrC/qj7uwJzUtrTVlZGWlpaVaHIsLAO1ZWvt/OI5T8FS8HXB2Rv+SEgBjn8Uhx1lnk5ORQUFBASUlJwGUcDgcVFRVA5y/U0tLSyMnJsToMEQYOhwOA1NRUiyMRHSWU/KW1prjYuKj53r17KSsri1R4Edfe/CXFWYxTSho/O4vk5GQGDx4cdJklS5Ywc+ZMAJYuXRqBqIToeFKcdT6h5K+KigquvfZaACZPnsxNN90UidBikuzZY5z3IrIiPnh3akLEMu9YWbm2ZnzxjjUUrZPiLMYlJUnjZzyRee1EZ+A9y1yurRlffIsz2XcFJ8VZjJMNPL7I1CmiM/CeyJSYmGhxJCKSfOfllH1XcFKcxTg58owvUpyJzkCKs/jkW5zJeMPgpDiLcVKcxRcpzkRn4D3TWIqz+OI7LEOKs+CkOItxUpzFFynORGcQL/NdieZ8T2iSOe6Ck+IsxklxFl9cLpfVIQjRbtJyFp98W85sNpuFkUQ/Kc5inDQNxxe5XJfoDKTlLD75tvzLvis4Kc5ilDe3SctZfJHiTHQmMk9jfJFuzdBJcRajvGc7SXEWX6RbU3QG0nIWn3xbzqQ4C06KsxjlchktKDJXTHyR4kx0JtJyFl98izO5OkRwUpzFKJknKD5Jt6YQIlZJcRY6Kc5ilMdjdAvIkWd8kak0hBCxyvfgUhoWgpPiLEZJURaf5MLnQohYJfkrdFKcxShvbSYDa+OL3efyJ0LEKumej0++Lf/eue6Ef1KcxaikRONEAO/YMxEf7HYpzkTsKy4uBqCqqsriSEQk+U5CKyc3BSfFWYxKNM/SlCPQ+GK3260OQYh2814AW1r+44tvcSbjZ4OT4ixGeQdTSnEWX2pra6wOQQgh9ktdXVNxJgeawUlxFqO8Y87k6CN+NDQ04JTvWwgRo+x1TQVZbW2thZFEPynOYpyc/RI/ZHyOECKW+bacST4LToqzGCfFWfyQZCaEiGV1PmebV1ZWWhhJ9JPiLMZJt2b8qK6utjoEIYTYbw6fEwL27t1rYSTRT4qzGCctZ/GjZcuZnIouhIgVHo8Hp7OpMaG8vNzCaKKfFGcxTnbQ8aOmpibofSFigUyfEZ9a9vKUlZVaFElskOIsxkm3ZvxoWYzJGDQRi+QsvfjUctqncinOgpLiLMbJJTDih+8EjiBj0ERsku02PrW8mk3F3kppRQ1CirMY5/HI5ZvihcPhaJrgDtnJidgk2218atmQ4HS6mp29KZqT4izmqdYXEZ2C2+1GqcTG+9I9JGKRbLfCS6bTCEyKsxinlBRn8aJlF4AcdYpY5FucSbdW/EgyrwftSwr1wKQ4i3HJyclWhyAiJDExEa2bugakOBOxqOXYSREfUlJS9nlMrq8ZmBRnMc7f0YjonJKTk0F7AKO1QaZREbFI5maMT6mpqfv09MhsA4FJcRbj0tLSrA5BRIjNZmt2v+Wp6ULEAt+DCunWjB9KKVJTm++vWp7BKZpIcRbjUlNTrQ5BREhGRkaz+zLeUMQi34JMirP4kpGZaXUIMUOKsxiXnp5udQgiQrp27Wp1CEJ0KCnO4kvXLl2a3U9MTAywpJDiLEZ5c5oUZ/GjZXEm4w1FLPLdbmUS7fjSo0f3ZvclhwUmxVmMcjiMM55adnWJzqtHjx7mLaM7UxKbiEVSnMWvbt26NbsvY6YDk+IsRnnnh5HiLH40FWcGmUZFxCLfHbIUZ/GlZXHW8iQn0USKsxjlLc6ysrIsjkRESnp6erOznaQ4E7HId4csZ+vFl5bFmTQuBCbFWYyqqZGWs3jUrXvTmA0pzkQsyvQ5Y0/m6osvLYszGTMdWEjFmVJqSSiPicjxdgdkyqnJcSU7u2fjbSnOQic5LHr45iyZqy++dPE5WzNBKenWDCLoiGKlVBqQDmQrpbrTdJXtLkC/MMcmQiAtZ/Glu8+Rp5yG3jrJYdHHtziTGeLji+8wnPR0m8zVGERrp3tdDdyEkcTW0pTYqoB/hjEuESJpFo4vvtNpJCTIqIQQSA6LMlKcxS/f/ZW0mgUXtDjTWj8NPK2UmqG1fiZCMYk2kOIsvsgJIG0jOSz6+F7VRC6CHl98CzKbTKMRVEgTJWmtn1FKHQsM8n2N1npemOISIZKjj/jiW4zL7OqhkxwWnaQ4iy++42RTpDgLKqTiTCk1HxgKrAO85z5rQBKbxWTcUXzxnSNKpiEIneSw6GS3260OQURQs+IsOcXCSKJfqFOMTwBGajlX2KVPAAAgAElEQVRUF8JSvrOrS3HWJpLDolBdXZ3VIQiLJCXLFU6CCXVE8Y/AAeEMRAjROt+TAGQwdZtIDotC0nIWX3yvCJGQIL0+wYRaumYDG5VS3wIO74Na60lhiUoI4ZdvcpPirE0kh0UhaTmLL5KzQhdqcXZ/OIMQQoTGtyvT4XAEWVK0cL/VAYh9SctZfPE9AUSuqxpcqGdrLgt3IEKI1hkFmQaUnOnWBpLDopMcYMSXmpqaxtsej4yZDSbUszWrMfYIAClAMlCrte4S+FUiXGSnHL/sdjsK48coXQShkxwWnSSXxZfq6urG226XFGfBhNpy1mzmS6XUZODIsEQkWrVnzx6rQxAW8U1usmMLneSw6CQXPo8v5eXljbedLrmuajD7df0XrfV7wMkdHIsIUUVFhdUhCItUVlY23paWs/0nOSw6uGQHHVfKysoab7uckr+CCbVb8zyfuwkYcwbJfEEWKS0ttToEYZHy8qbCXIqz0EkOE8J6vvuuBofkr2BCPVvzHJ/bLiAPOLfDoxEhKS4utjoEYZESn+QmXUJtIjksCqnG69CLeLB79+7G23JwGVyoY84uC3cgInRFRUVWhyAsoLWmrKypOJMrBIROclh0SkySWeLjya6ipuLM6ZQu7WBCGnOmlMpRSr2rlNqjlCpWSi1USuWEOzjhX35+vtUhCAtUVFTg8klociWi0EkOi06+11oUnZvb7aZkT1Ovj1Na/oMK9YSAucAHQD+gP/Ch+ZiwQN6OnVaHICwg3dntIjksSvieZZySIhe/jhdlZWW43e7GgZ7SchZcqMVZL631XK21y/z3CtArjHGJAKqrq9lbUd76gqLT8R2vAdJy1kaSw6KE76DwtLQ0CyMRkdQ4HCfBaC11utySw4IItTgrVUpdrJRKNP9dDJS1+irR4XJzc60OQVik5VhD34ugi1ZJDosSvtuxFGfxw3twqROburLlpKbAQs3u04HfA7uBImCK+ZiIsLy8PKtDEBYpKipCJaeBeYabFGdtIjksShQWFjbettlsFkYiIsk7LMO3OJOuzcBCPVtzJzApzLGIEOTm5qISk9Fu2ajjTVFREe6UTBKdxpgdKc5CJzkseuzYsaPxdnp6uoWRiEgqLi5GpaSDz/Qp0nIWWKiT0A4GZgCDfF+jtZZkF2F5eXm407qRUFtidSgiwgoKd+FOySKx1hizo5TMERUqyWHRw3dohnRrxo/S0lLcyengbirIpDgLLNRJZt4DXsY4w8kTvnBEa/J27JTiLA55T0P39BrZ+JgUZ20iOSwKaK3Zum1b4305WzN+lJSU4kmyodxN1weWbs3AQi3O6rXWs8IaiWiVw+GgorwMT/+BVociIqzxNPTUput3y5lObSI5LArs2bOHmupqPElpJLjqSU1NtTokESGlZWV4knuT6JDiLBShFmdPK6XuAz4HHN4HtdbfhSUq4Zf3bBdPSqbFkYhI857h5vEpzuQKAW0iOSwK/PLLLwDoZBu46klMTLQ4IhEJHo+HmuoqdJ8Dmz3ucDgCvEKEWpyNAS4BTqapS0Cb90WEeOcH0ikZFkciIq2pOGsqzOWos00kh0WBzZs3g0pAJxotZnJSS3yoqanB4/Ggk5qfnes7IbFoLtTi7LfAEK21XKnUQhUVFYB51Cniirc40z6tpnV1dVaFE4skh0WBTZs3o9O7gzleUrrm40NVVRUAOql5N7bksMBCPWz5AegWzkBE6wJt4KLz2717Nyo1ExKauoFqa2osjCjmSA6zmNaazZs340zvCcrY9UjXfHyorKwE9t131UgOCyjUlrM+wGal1P9oPl5DTkOPIO9Rhu8kfg6HQwbVxoFdu3bhatGdXVm516JoYpLkMIsVFRVhr63Fk52NajByWUODNGTGg6birPnUKdXV1f4WF4RenN0X1ihESBr751VT60lNTY0UZ3GgoHAXntQezR7zdnOLkEgOs9iWLVsAcGdkk1i1CwC73W5lSCJCGouzZKM4S0nQNHhUY2+Q2FeoVwhY5ntfKXUccBGwzP8rRDi4XC6jW8tnfqvKykp69uxpYVQi3AJNoWKvq6e+vl4m8gyB5DDrbd++HZTCY+vW2K0pLSfxoby8HKDxhIDkBI1KSJADzCBCPlVGKTVOKfV3pVQe8BCwKWxRCb/cbjdKNf/KZOPu/HbtMloZfKfR6JpinHDovV6daJ3kMGvl5eVBWhYkNLUJSP6KD6WlpaikVEhs+u67pWj5/oMI2nKmlDoIuAC4ECgD/gMorfVJEYhNtKC1brwsmVYKpTVlZWXWBiXCrqCgAABPapfGx3qmeqhsSGD37t0MHCiTEgciOSx65BcU4Erp0uyx0hK50kk82LNnD54WY2a7pTgpLZXvP5DWWs42A6cA52itf6W1fgaQ02ssYpx2blZn5rizPXv2WBeQiIjG4iyta+NjvW3Gz9A7xYYISHJYlDB20M0n0N4tLb9xYWd+Pq4W3333FA8l8v0H1Fpxdj6wG/hKKfWiUuoUfC8pLyKq+ZxAxtfgvWqA6Lzy8/NRqemQ1HQdwq6pHlISobCw0MLIYoLksCjgcrmoqa72O0ejTKfQubndbnbt2tXs4BKgZ5qHktJSPB651K0/QYszrfW7Wus/ACOApcDNQB+l1HNKqdMiEJ/w4fF4mp0MAFBotqqIzitvx459uoMU0Cfd09iqJvyTHBYdvAWYvzkac3NzIx2OiKD8/HzcLhceW/dmj/dMc+N0uRtPFhDNhXRCgNa6Vmv9mtb6bCAHWAf8OayRiX0YxVnzr2znzh0WRSMiQWtNXt4O3Gn7zp96QJqTnTvyIh9UDJIcZi3vNEA6Yd9hztu2bYt0OCKCvNdT9aQ3n1Wgl81oMZOhGf615WzNRKVUPyATWARMC1dQwj+Xy7VPcVZWXiHdAp1YeXk59toaPLau+zx3QLqb3cXFxnYhWiU5zDqNVwJQ+/Yoe3feonP66aefUEkp++Qw77hZGZrhX0jznCmlZmBM4lhM84sGHxqmuIQfTqdzn+IMjFPUR48ebUFEIty8XT4tuwQA+qW7cbs9FBYWyhmbrZAcZq3ERHPi7BbX0hyQ4eLnzTKjSWe27ocfcKb32mff1SvNQ6JChmYEEGrL2Y3AwVrrUVrrMeY/SWoR1tDQ0KxboI955CHdAp1XsOKsb4bx/e/cuTOiMcUoyWEWSk42LjmndPMTZQd1cZG3YwcOh8Pfy0SMKysrY0deHu4uffd5LikBeqdrduyQoTn+hFqc5QOV4QxEtK6+vh6Pz6WbeqZ5yEhRbN261cKoRDjl5uaiUmx+z3Lrm27s6PLy8iIcVUySHGYhm83YfpXb2ezxwVku3G6PHGB2UmvXrgXA1aWf3+f7pzewfZvsv/wJ9dqa24GlSqmPaX7R4H+EJSrhl91ux9NsQK3mwIwGfvnlZ8tiEuG1ZetWnH5OBgCwJUFPmxRnIZIcZiGbzYZSCloUZwOzmlr/R44caUVoIoxWr16NSrHtczKA14GZbtbm7cZut5Oenh7h6KJbqC1nO4HFQAqQ5fNPRFBtrR0Skps9NjDTRe72XBkU3gm5XC7ycnPx2HoEXCYnvYFcOfIMheQwCymlsNnSUe6GZo9np7mxJStpOeuE3G43q7/9loasfn5PBAEYlOVCa82WLVsiHF30C/XC5w+EOxDRupraWnRi8+6tQVkuGvKd7Ny5kyFDhlgUmQiHwsJCnE4n7vQgxVmGm8/yC3A6nY3jesS+JIdZLz09ncoWLWcK6GtzUVCQb01QImw2bdpETXU1riGHB1xmaBejUWHjxo2MHTs2UqHFhJBazpRSvZRSjymlPlFKfen9F+7gRHO1tTXoxJRmjx2YKScFdFbeo8lAXQIAA7NcuNxuGVTbCslh1rOl28Czbwt/H5uLAjmppdNZtWoVKIWra/+Ay2SlaA7I0GzYsD6CkcWGULs1X8O4Rt1g4AEgD/hfmGISfmitsdfWopOaF2cHpLtJSpDirDPasmULJCTiCTDmDIxu7cZlRTCSwyyWnJQMet9L9fRI9VBWXt7i8nQi1q1cuQp3Zh/wc1UIXyO6Ovhh3bqmufAEEHpx1lNr/TLg1Fov01pPB44OY1yihZqaGjweDzoprdnjSQnQL8Mjl0DphH755RdjCo2EwD/TPuke0pKUTOTZOslhFvP4KcwAuqd6cLrcVFVVRTgiES4VFRVs27YVV5fArWZeI7s7qbXXSQ5rIdTizDtQoEgpNVEpNR7jEigiQrzXH/M3pUI/m5MdudsjHZIII601m3/+GVeQLk2ABAUDM51s3iQTebZCcpjFHA4HJCTu83hGklG0yZVOOo/GKTSCdGl6jeruRAHffvttmKOKLaEWZw8ppboCfwJuBV7CuICwiJCSkhIAdErGPs8dkO6muKTUuIKA6BQKCwups9vxZGS3uuyQLCdbt22V7z84yWEWq6jYi07a9+AyLcnozrTb7ZEOSYTJd999h0pKxRPkZCavrBTN4C5uVq1cGYHIYkeoFz7/SGtdqbX+UWt9ktb6cK31B+EOTjTZtWsXAJ6UzH2e62XzoLWmuLg40mGJMNm8eTMA7hbFWerOVbQcmTO0qwun0yXjDoOQHGat6upq6uvsePwcXCaZeyE5uOg81n73HQ2ZffxeblA57TjczafWGNfTweaff27sIRKhn62Zo5R6VylVopQqVkotVEpJl0AE7dixA5WY5LflLDvNGEi5Z8+eSIclwmTTpk2oxOR9LtuUYN83eQ3xOR1d+Cc5zFrbtxvDLjy2fU9uSTT30zJXY+ewZ88einfvxp217yWbAJTHjafFEeZh2Q1orfnmm28iEGFsCLVbcy7wAdAX6A98aD4mIuSXLVtw27r7ncyvW6oxZqO0tDTSYYkw+WH9elwZ2X6PPFvqmeqhexr8+OOPEYgsZkkOs5B32/TXTa/2aQsWsWz9emNaDHdWn5BfMyDTTS+bZsWKFeEKK+aEWpz10lrP1Vq7zH+vAL3CGJfw4XQ62bx5M64M/x951xQjue3duzeSYYkwqa2tZfu2bbgyQ0tuSsHwLg7W/7BOpiMITHKYhb799n/o9B5+T2jykm23c1i3bl3I4828lIIJ2fWsXbNGTgwxhVqclSqlLlZKJZr/LgbKwhmYaLJhwwacDQ0Bm4ltiZpEJcVZZ/H999/j8XgCft/+jOzupLSsnMLCwjBGFtMkh1mkrKyM9et/oKHrAL/PezsDPB7/U22I2KG15n9r1uDM7B1Sq7+vI3s34HK7pfXMFOqnNx34PbAbKAKmAJeFKyjR3Ndffw0Jibi69PP7vFKQkaKorq6OcGQiHFauXIlKTMad2Tvk14zsbgymXrNmTbjCinWSwyyyePFitNa4eg71+7x3JyTFWewrKCigePduXF3bPpxzSBcXvdM1ixd/HobIYk+oZ2vu1FpP0lr30lr31lpPBs4Lc2wCo0vziyVf4uw6ABIDXzsxPUlLc3An4HK5WLZ8udHK4GdOqED62Dz0zdCsWL48jNHFLslh1nC5XCx85x3cXfr6PRkAICXR6M50OByRDE2EwXIz/+xPcaYUHNenju+//56ioqKODi3mtK3dsblbOiwKEdDy5cupqtyLM3t40OUyklzSctYJfPPNN9RUV+Ps2baL2BtjNur4ft06KioqwhRdpyM5LMyWLFlCyZ49NPQeGXAZm1mc1dbWRiosEQZaaz5fvBhPVm90atZ+reOEvg4U8N5773VscDGoPcXZvqcNig6ltWbBG2+ArSvuVo5EMpI8VMqYs5j39tsLIS2r1e/bn2MPaMDj8fDBBzJ9V4gkh4WR0+lk7iuvoDN64up2YMDluqUaxZmcbR7b1q5dy468PBp6Bm9ICKZHmocJvRx89OEHcd/Y0J7iTE6tCbOVK1eydcsW6vuM8TuFhq+uKR7KyiS5xbI1a9awfv0P1Pca2ebBtAD9M9yM7dnAuwvfli6i0EgOC6P33nuP3UVF1PU7LGj+siVpMlNU40TbIvZorfn3vHmolHScPYe1a13nDKzDbq/j9ddf76DoYlPQPYBSqlopVeXnXzXgf3S66BAul4vnnpsNti4hbezZaR7KK/bS0NAQgehER3O5XPzzX/+CtCycvUfs93rOHljH3qpq5s+f34HRxS7JYdYoKytjzty5uLv2D6kVeEBGA5s2ySTKsWrx4sVsWL+eur5j2zRW1p+BWW6O6eNg4dtvx/XYs6DFmdY6S2vdxc+/LK11UqSCjEcLFy4kP38n9v5HQkLrrSh9042rBOzYsSPcoYkweOWVV8jdvp26nCPaldwO7ubi1wfUs2DB63I5JySHWeXZZ/9JXb2DugOPbrXVH+Dgrk62bdse911ZsWj37t3MeuYZPJm9cfba/wNLX1OG2knEyV8ffgi3290h64w17enWFGGya9cuXn55Dq6uA3B38z83UEuDs4xLn3ivyShix//+9z9ee+01nNnDcXUf1O71XTDcTkaSh7/cew+VlZXtD1CINlixYgVfffUljgMORad19btMQm3zIRjjzMv3LF26NAIRio5it9u58667qK1zUDfoVyEV4qHITvMwdXg1G378iVdffbVD1hlrpDiLMi6Xi4ceehinR1M/8JiQN/beNg890mDlSrk2WSzZsmUL99x7Lx5bd+oPPLpD1pmVrLlhVCV7dhdxz913yfgzETEVFRU89vgT6IyeNPQdG3A55W4+/GJwlpucTA8fffiBXCkgRtTV1XH33feQm5tL7ZATA06Vsr+O7dPAsX0czJ07l88/j7+5z6Q4izKvvPIKGzf+hP3AY9CpmSG/Tik4qlcd367+VlpLYsTWrVv506234dBJ1A4/Leg8dm11UDcXVx1iHHnec8/d2O32Dlu3EP5orXnkb3+juroa+6BfhzQcw0spOLW/nZ9/2WJMui2iWk1NDX+69Va+X/c9dYN+tV9nl7dGKZg+ooZDurt49NG/sWrVqg5/j2gmxVkUWb58Oa+++ioN2QcFnE07mF/3deB2u3njjTfCEJ3oSBs2bGDGDTdQVe+iZvjp6JT0Dn+Po/o0cPmIGtauWcPNN90o85+JsHrrrbf4dvVq6nKOaNN1Fb2O7+ugX6Zm9nPPSWtvFMvNzeWqq69m48ZN1A05EVcrc3C2R0oi3DSmigEZLu69526WLFkStveKNlKcRYlNmzYx86GH8GT2xjFw/7q3cjKNs1zefvutuD7LJdp9+umn3HzzLdTpZGoOPgtt8z8upyOc0M/BjWOqyN22hWv/eDU///xz2N5LxK/169cze/ZsXN0H4ux9yH6tIzEBLh5WTeGuXcyaNauDIxQd4YsvvuDqP/6RopIK7AefgavH4LC/py1Jc8fYvQzJdDBz5kxef/31uOj6luIsCuTm5nLrbbfjTEjDPuwUSNj/k8imDLGThIv7/nKvHH1GGafTydNPP83f/vY3HOnZRmHWhq7r/TU+28md4ypxVO7h+uuu5Z133omL5CYio6SkhHv/8hc8qVnUDfp1uwaFj+7h5OyBdXz88ccsWrSoA6MU7VFRUcFf/nIfDz30EPUp3ag+ZBLurAMi9v4ZyZrbx1VyVG8HL7zwAg8//HCnH6ohxZnFtm/fzo033Yzd6aFm+GnoZFu71pdt83D1iCp+2bKVxx9/PG5PQ442eXl5XP3HP/Luu+/S0Gc09oNOb/d33RZDu7qYeUQ5o7rVMWvWLO65527Kysoi9v6ic6qvr+fue+6hqrqW2qEnQ1JKu9d5/mA7I3u4eOzvf+err77qgCjF/tJas3jxYqZeeinLv/4aR//DqT34zLAMw2hNcgJcM6qG8wbbWbLkC6684nK2bNkS8TgiRYozC/3444/MuOFGquqdVA8/A53WpUPWe1gvJ+cPtrN48WJmzpyJ0+nskPWKtvN4PLz99ttceeWV5O4spG7YKTgOPHK/rgDQXlnJmpvHVHPRsFq+XfkN0y6dymeffSataGK/eDweHn74YX75+WdqB5+Ax9a9Q9abmAA3j6lkeFcnM2fOjKtxRtFk69atzLjhBh5++GEqPanUjpxEQ7+xluQurwQFkwfXcee4SuzlRVx77TW88cYbuFwuy2IKFynOLPLll19y0003U+tOoOagM9s07ih15yrwBG8RO3dwHX8YWsvSpUu5884/s1euuxlxeXl5XHf99Tz77LPUpfehetRkXN0HWhqTUnDGgfU8dEQFByRV8sgjj/DnO26XMYqiTbTWPPPMM6xYsYL6AUfh7h742pn7IzUR/nRoJcO7NDBz5kyef/556QWIkPLycp588kmuvPJKftq8hfpBx1E74uwOK747wojuLmZOKGdMVzuzZ8/muuuuZfv27VaH1aGkOIswl8vF888/z4MPPkiDrQc1B09sc4tZgr2cUC4LOHFgPZePqGHdd2u5/LJprFmzZj+jFm3R0NDAv//9by6/4go2b9lO3eDjqRt+Kjo58l0BgfTN8HD3+EouHl7Luu/+x6VTpzJv3jwZpyhCMn/+fLOLfhTOPiPD8h5pSXDHuEpO6lfPggULuOOO26UrPozq6uqYN28eF150Ee9/8CGOXiOoGn0+zl4Hd9jksh2pS4rmxjHVXDuqmqK8X7jqyiuZM2dOp8lhUpxFUHFxMTfedBMLFiygodcIag86A52cFtb3PKGfg/sP30uas5xbb72VWbNmySVSwmjNmjVcOu0y5s6dS32XAVSP+i2u7GFRmdwSFJw2oJ5Hj6xgXPca5syZw2XTLuWbb76Rrk4R0BtvvMGcOXNw9hyGY8CRYd22kxLgshG1XHZwDT98t5ZLp17CokWLZPvsQC6Xi/fff58LL7qIOXPmUGs7gJrRv8Ux8BhISrU6vKCUgqP7NPDIEeUcmW1n3rx5XDr1ElasWBHz24hcWy4CvIMqn3r6aeocTuqGnLBf85jtrwOz3Nx/eAX/2ZbOu++8w5IvFnPFlVdx1llnkZjYvovUCkNJSQn/+tdzfPXVl2Drgv2g03F37W91WCHpkebh+tE1/FRez/wtmrvuuosJEw7n2muvY8iQIVaHJ6LIG2+8wezZs3H2GEz94I67XE9rTurvYEQ3Jy//7OLRRx9lyReLmXHDjQwcaO0wgVjm8XhYsmQJL738MsW7d+PJ6kPdiIl4svpYHVqbZaVo/jiqhhP61TN/i4d7772XCRMOZ8aMG2J2G5HiLMxKSkp48qmn+Oa//8WT1Rv7Icd32MD/tkhNhKkH2Tm+r4PXtrh44okneP+9d7n8iis5+uijUVHYshMLXC4X77zzDi/PmUNDgxNHv/E09B3TrulQrDKqh4uHjijni8I03vthLVdcfjlnn3MO06dPp1u3jr00i4gtWmvmzJnD/PnzzcLshIgPDO+b4eGu8ZV8VZjKmz98x2XTpnHmWWcxbdo0evXqFdFYYpnWmtWrVzP7+RfIy92OzuhJ3fBTjVn+Y3w/cEh3FzMnVLCkMI13fljLZZddxqRJk7j00kvp3j16xsyFQsVy09+ECRN0tI6jcrvdvP/++7zw4os4HE7q+o3HecCoDklots2fkFhdzKjuDv48vu1dlFrDt3tSeHN7JiV1iuHDhjH10ks57rjjSGjDJVfi3Y8//sjjjz9BXl4urq451B94dFgL79Sdq0gu3YJyO+me6uaIXg1cfFB45vqpdirey7WxpNBGWloaF170f0yZMgWbLXLTfwSilFqrtZ5gdRztFc35y5fL5eLJJ5/k448/piH7IByDjm13Hsv4/nUSXPXMOq6cbqlt3wdVNSg+yLOxZJeNxMRkzp8yhd///vcxtwOOtJ9++onnZs/mxw0bIK0Ldf3G4+oxJGJFWerOVSQXb0ShGdHNxYGZrrDlsKoGxXt5Nr40c9j/XXwJU6ZMITXV2q7aUPOXFGdhsGHDBp586im2b9uGu0s/6gYe26E77fYWZ14uD3yzO5UPdmawx64YMngQF1x4ESeddBLJyR13ncfOpqqqiueff56PP/4YUjOpG3AUrm4Hhj3B2TZ/QlL17sb7I7o5ueuwqrC+567aBN7clsF3pSn07N6Nyy6/gjPOOIOkJOtaBqU4i5yamhoeeOBB/ve/b3H0HUtD/8M6ZDtvb3HmVVKXwMLt6awsTiUpOYkzzjiTP/zhD+TkdPy1HmNZUVERs2fPZtmyZaiUdOr6jsWZfRAkRHZYixU5rKg2gf+YOSy7Zw+mXTbd0hwmxZkFiouLef755/nyyy+NnXbOEbi6D+rwnXZHFWdebg+s2pPChzsy2VWr6Nm9G5PPO59zzjlHurNaWLZsGU/840mqqipp6D0KR//xHXrB8mCsSGxev+xN4j/bMtlSmciBA3KYfvkVnHDCCZZ0h0txFhk7d+7kzrvuorBwF/UDjzHO2usgHVWceRXVJvBpvo2vd6fh0vCrX/2a3/3ud4wZMyauh2zY7Xbmz5/Pm2+9hUcr6vuMpuGA0RHLWS1ZmcM2VyTx5vZMtlYmMiCnP1dceRXHH398xLePUPNX7A2MiUK1tbUsWLCA//znP7jc2jjC7HuoZT+AtkpMgOMOaOCYPuX8WJ7Mp/lOXn75ZebPm8epp53G5MmTGT48fBe3jQUVFRU89dRTLFu2DJ2RjX3kJDzpPa0OK2IO6ubinsP28l1pMm9tz+f+++/noOHDuPKqq5kwYUJc7wA7oxUrVvDXvz5CvVsbJ7d06Wt1SEH1zfBw2YhazhtiZ3F+Gl+sWsGKFSsYMmgQkyZP5tRTTyUjI8PqMCNq5cqVPP7EPygrLTHOrM05HJ0SX5+BrxHdXdxr5rC3cz3cd999HHzQcK686moOP/zwqMthUpy1g8vl4qOPPmLOnLlUVVXi7DEER86EiFwvMRwSFBza08mhPZ0U1CTyeUEaiz/9mI8//piRh4xg8m/P44QTTrC8zz7S1qxZw4MzH6KqugpH/8NpOGAMxOHYPKXg8F5OxmeX89/dqbybt4XbbruNcWPHcuVVVzFq1CirQ0ElDMcAACAASURBVBTt5HQ6mT17NgsXLsST2Qv78JNiKp91TdFMGVrHOYPqWFmcype7tvPUU0/x/Ozn+M2ppzFp0qROf6BZWVnJk08+ydKlS9Hp3bEfcjaezN5WhxUVmnJYRWMOu/XWW6Myh0m35n7QWrNixQpmz36eXbsKcWcdQP2AI/FkZEfk/Tu6WzOYWqfi692pLNmVzu5aRZesTM48ayLnnHNOpx/X4Xa7mTt3Lq++9ho6rSv2ISfiSe9hWTxWdgn44/TA0l1pfLAjg0oHHHvMMVx+xRUMHRreaWKkWzM8duzYwYMzH2Lb1i009BmJI+eIsI1J6uhuzUC0hu3VSXxZkMqqkjScbhg+bBgTzz6bU045haysrLC9txU2bdrEvX/5C6VlZTj6jjMPJKNnuqRozGFfFabxwc4MqswcduVVVzF48OCwvaeMOQuT9evX89zs2WzauBGd3p26/ofj7jogoqcgR7I489IaNlUksaQwjbWlqXg0TJhwOOeeO5ljjjnG0gHi4VBXV8eDDz7IypUracgejuPAoy3vpo62xOblcMPn+Wl8nJ9BnQtOOulkLr/8cvr3D888b1KcdSytNR988AHP/vOfuHQC9oHHhf0yY5EqznzVOhXfFKeyvMjGjuoEUpKTOf6EE5g4cSLjxo2Lum6ttlq0aBGPP/447uR0agefiCcz+qYXieYc9lm+jU/y06l3KU47/XSmT59O794d3+IoY846WH5+PrNnz+a///0vKiWd+kHH4cwebulFYCNJKRjZw8XIHjVUOGpZtiuNr35cy71r1tKzR3cmnTuZc845hx49rGtZ6ih79+7l9jvu4JdffjEGQvc+xOqQolpqIpwzqJ6T+zv4ZGcany/7kuXLlnHu5MlMnTqVrl1Dv26siKw9e/bw6N//zto1a3B37U/doF+jU8J7mbHUnatQLuMSO7M2ZDGkS/imU/CVkaw5NaeeU3PqyatOZOmuNL5Z9gVffPEF/fv15ayJZ3PGGWfQs2fsjSX99NNPefTvf8ed1Rf70JOifmb/aJOaCJMG1XFS/3o+zLPxxeef8uWSJZw/ZQoXX3yxJeMVpeWsFVVVVbzyyiu89977aJVA/QFjaOgzytJWFCtazvxxe2BdWTJLCm38WJ5MclIiJ550Mueffz4jRoywLK72sNvt3HjTTWzdup3aISd2+AWd2yNajzpb2utQvJObzrKiNNJtNi6+ZCrnnXdeh41VlJaz9tNa89lnn/H0rFnUOxqoyzkCZ68REekBiKbt2OGGNSUpLN1l4+e9SSQkJHDsscdw9tnncOSRR8bEvI+rVq3izjvvxJXVF/vw30T1BNjR9N0HU1qXwMLcdL7ZnUqP7t249voZnHzyyR3Suirdmu3kcrn48MMPeenlOdTW1tCQfRAN/Q9DJ1s7Cad3IlLcTjKSPPzqAEdEjjpbU1SbwOLCNL7enU69SzNy5CFceOFFMTWxrcfj4c4772L1t6uxDzsFd7foKcwgdhKbV0FNIv/ZlsEPZckMyOnP7Xf8mTFjxrR7vVKctU9ZWRmPPfYYq1atwpPVB/ugX0f0qiXRuh0X1SawrCiNr4ttVDmgf7++nD/ld5xxxhmkp4e3NXF/1dfXc8nUqeypcVEz4mxIjN7CDKL3uw9kW2US87ZkkluVyPhx47jp5pvbfTmoUPNXbOw1I2zTpk1cdfXVPP3001QlZFI78lwcg46zvDADSLCXo9xOFGB3JbCzJjp+jH0zPEw9yM5Tx5bxf8NrKc3byL333ssVl0/nyy+/xO12Wx1iqxYtWsTq1auoH3BU1BVmsSgn082fxlZx69gq6ssKuOGGGTz99NPY7dYfTMQjrTVLlixh6tRLWf3tGuoHHEXtwWdacjm5aNQ3w8MFw+w8dUwZ146qxlZbwKxZs5hy/vn885//pKioyOoQ9/HBBx9QsmcPdQOOivrCLBYN7erivsP3Mu3gGn75aR1XXnEFixYtish7y7fpo66ujpdeeomF77wDKenUDT0pLJPIdmbpSZrTB9Tzm/715sS2uTz44IOWTvoXipqaGp6bPRtPVh8ZY9bBDu3p5OEjynl7ezrvvfsuq1Z+w2OPP9Hpz/aNJpWVlTzxxD9YvnwZnsze2EeejrbJWEB/khLg6D4NHN2ngW2VSXxW4OCdt9/i3XfeYfJvf8sll1wSNeMo161bB7ZuUT8PXSxLUHByfweHZTfw3MYuPProo2zYsIEbb7wxrNNKScuZaePGjUyffjkLFy6kodcIqkf9FlePwVKY7SfvxLZ/PbKc60dX49mbz3333cftt99GQUGB1eHtY+XKldRUV1Pff4J852GQlgQXH2TnrsMqqS4vZsb117Flyxarw4oL3333HZdNn87yr1fg6H84tSPOksIsREO7urh2VA1PHFPBr/rUsnDh21x04QUsWLAAh8NhdXjk5u3AmRZD36W7gbS0NKZMmUJaWhp1rtjJtd1SNbePreScgXY++eQT7rj9NlwuV9jeL+6LM60177zzDjNmzKCoohr7wWfiGHgMJKZYHVqnkKDgyN4NPDShgouH1/LjujVcNu1S5s2bh8fjsTq8RqtXr0al2HBH82SNMZzYvA7u5uKe8RWo+kpuvOEGduzYYXVInZbH42Hu3Lnc8qc/UW53UTvibBr6jY2bM8w7Uo80D9NH1PLwEXsZZjOurTvj+usoKyuzNK5+fQ8gqaHG0hjaQrkaOPvss7n++uuZOHEi9hjLYYkJ8LuhdVx5SA3rfljPSy+9FLb3iutfqdvt5umnn2bWrFk4svpRfcgkaR4Ok8QEOG1APY8eWc7hPezMmTOHmTNnRsXRJxjdPu6UzKhuNYv1xObVL8PDPYdV4HbWs2DBAqvD6ZRqamq4++67+fe//42zx1CqD5kUsUmyOzPvOMobx1SxY/tWrvnj1Wzfvt2yeEaPHo2yl5NQt9eyGNpCJ6Xw0Ucf8cwzz/Dxxx+TnhSbJyT+uq+Dk/vX88Ybb7B27dqwvEfcFmdaax5//HHee+89Gg4YTd2w38jcMBHQLVVzzaga/jC0lq+++opbbr6Zuro6q8MiOTkZ5QlfE3VH6CyJDSA7zcNxfepYsuQL9u6NjR1LrNi7dy8zbriBlatWU3/g0dQP/rXlEyh3Nof3cnL3YXtpqC7l5ptutCyHTZ48GZvNRlr+amOm8GiXmEJ9fT0LFy6kvr4eWwznsIuG1QLGxPThELfF2ZtvvsmiRYtw9B2LY8CRUd1i0tkoBRMH1nPdqGp+2riR+fPnWx0SI0aMQNkrUA21VocSWCdKbAADM104nS7Lu4Y6E6Mwu5H/Z+88w6uotgb8rvRKDaGFDhZ6byKgKEU614afCoKNK3qvil4UG2CviA3rBVRAUbEgFkCxXUQREekgRUINoZf0/f3Yk3A4pBGSzDk5632ePJkzZc+amT1r1l5r7b23bPmbY40uIr1qY9/TbWUgPA9QNzaTWxof5OChwyxcuNAVGSpWrMgN119P8MHthG0vGQ+OkjthwRATJiXWuAxI42zPnj288cYbpFeoTVrN1m6Lc3qUEcUG0KFqGudXS+H9995j27Ztrspy4YUXAhC6Z42rcgQSvyaFU71aVerXr++2KGWCzMxMJkyYSGLido42upjM8r7ZG7ashOcBziqfQbWoLBYuWOCaDIMHD6Zfv36E71xB2I4//MODVgZYvS+EI2mGihUrlkj5AWmcffrpp6RnZNj5En2tVVkAZUmxgZ0yIyMzk99//91VORISErjooosI372SoGP7XZWlrGMMfLzZzirRq3cfnxxaxR/58MMPWbbsN47X7uDTubNlKTyfYeBgWjA1Smge2cIgItx+++1ceOGFhG//jfAtP0GW748r6c8cyxDeWFeOmjWqc+WVV5bIOUrMOBORt0Rkj4is9FhXSUTmi8gG539FZ72IyGQR2SgiK0SkRN1ZK1euJCuqMiY8piRPUyKUJcUGEBNq5feFjgGjR48mNiaW6E3fIunu58GVRTKy4I210Xy0OYpevXrxf//3f26LlCe+rMO8ycjI4L333iezXHXS484qzVOfPmUoPP/djnCOZxjOP/98V+UIDg7mvvvu49prryVs73qi13+BpLo3tV9ZZuPBEB5YWpF9qcHcc+84IiIiSuQ8Jek5mwr09lo3FlhojGkELHR+A/QBGjl/NwKvlKBchIT48di7ZUixAaw9YJ+FL0w2XKFCBR55eCKhGUeJ2jAfMtLcFqnMYAz8sTeU+5dW5IedEQwbNoyxY8cSGurTiepT8VEd5s2aNWtITt5LWpWz/S4a4K/8sieMtzfE0K5dW9q2dX82saCgIEaMGMGDDz5IVMZhYld/QkjSeg1zFhOZWfDRpkgeXlaeoNiqTJo0iaZNm5bY+UrMODPGfA/s81o9EJjmLE8DBnmsn24sPwMVRKTE/PLnnHMOQceSkRTfndMrEMjIgjlbYqhZozpdu3Z1WxwAmjdvzoQJEwhJ2U/MunlIqv+MIeSrbDoUzOPLy/PMinJQriYTJ07kuuuu8/lwpi/rMG+yp8TKCvO/aIC/YQwsSAznldWxNG3ShIkTH/apBv8FF1zAf996i2aNzyVyy49EblygeuwMyMiCH3aGM/aXSny8JYoeF13MG2++RfPmzUv0vKWdc1bVGLNTRP4FzAcaisi/gZrAgexwARAHnJNbASJyo4gsFZGlSUlJRRJi0KBBhIaEErnlR/ChgVADicwseGVVLH8fDuKGG2/yKeXWqVMnnnrySaJIIXbtXIIO73ZbJL/DGFizP4RJK2J5aGkFdmRW5LbbbmPqtOmuh4DOkKrA5U6ocwFQy1lfF7gjO9wJ7MLqtZMoDv2VG9nTCfnLeFf+yqE04bk/Y5m+Poa2bdvz6GOPl1hY60yoXr06kyY9xy233ELUsT3ErvqI0J1/uv69y4qqhPXjGc6pkE7tGN8dvigjy4atx/5SidfXxBBbvQGPPvoo48aNIyam5BtBpd4hQESaAjcA7YFDQD8gGriaE+GC/cCw3I43xrxmjGlrjGlbpUqVIskQFxfH3XffRfDhXURu+hYyfbeClEXSMmHK6hh+TQrjlltuoXv37m6LdApt2rRhyiuvEF+pHNHr5hG2YzkYH1BsznhVFcMzfVKxpWXC9zvCuW9pRR77vTwbUysxbNgwZsycxZAhQ3w9jFkYgjihv1oAoSLSCGgILPUId9YBToknFYf+yo2zzz6bWrXrELFntSaDlwDGwM+7w7jv10qsOhDJrbfeyuNPPEFsbKzbouVJUFAQl112GW+/PZ2OHdoTkfgrMWs+IfjQDtdksp3wggkC7m19iKvPOuaaLHlxIFX4dEskdy2pzJtrY6iYcBaPPvoor7/xJp07dy41OUrbXbEb6AT8DJQH9gDfAX2BC4AxHvt2KUlBLr74Yg4ePMiLL71E9Lp5HKvfDeNPc5T5KduPBvPyqnJsOxLEzTffzGWXXea2SHlSp04d3nzjDZ599lm++eYbQg7t4HjdLpiIcq7Ik1q7I0HH9hFyeBcd4tO4qpHvKLak40Es2hHOd7uiOJQK9erW4a6bL7c9YEtwcmAXOAz8YYw55oQtDwKDgcpA9miU04AJQKl9BUWEG64fyQMPPEDE1v+RUreL5p4VE9uPBjN9fQxr9odwVqOG3P2fsTRs2NBtsQpN1apVeezRR/nxxx+ZPPkF9qz7kvSKdUit1R4T7rvGZWlijM1/Xrg9gt+Swsk00Lp1K+6+4krat2/vSgpGaRtnn2LDlQOcvyDgX8ASoC3wsYjUAKLIw6snIjdiE26pXbv2GQlz6aWXUq1aNR597DGCV3/C8RqtSY9vDEEBOcJIiZJlYNGOcGZujCEiOpYnnriPDh06uC1WgcTExHD//ffTvn17Jj3/PCGrP+F4jVbO4J6BXU8ysmDZ3jAW7Yhg5b5QgkTo2KkTl156Ka1atfL5nLIi8iUwQETGAbdhG5nXY3XpFSIyHKjh/D5ldN3i1F/edO3alWHDhjFtmk2JS6nTGYKCi/UcgcTRdOHjLZEsSIwkKjqa22+/kX79+hEc7J/3tEuXLrRr14733nuPd955l9CVH5FatQlp1VsE7AwSh9KE/+0KZ9HOKHYcFWKjoxly6SUMGDCAWrVqFVxACVJixpmIzAS6A3Eikgg8CDwOfAJUAo4Df2JzOEIAweZt7AF+4dReUoANCwCvAbRt2/aMu6F06dKF6dOm8dRTT7Nkyc9E7F3PsYR2dgDHsvlxKXX+PhzM1PWxbDwYTKtWLRk37j7i4vxnnj8RoXfv3rRu3ZpnnnmWJUt+Jmz/Zo7X7hSQ8xXuPhbEoh0R/LA7kkOpUCWuMtddN4A+ffoQH+/DE8efJnnosLuBrsBEbFrGO8B52MZkAhCL1WFVgZF49dosbv3lzbBhwzDGMH36dIJTDtqIgHpHTouMLPhmewQfb43maDpccklfbrjhBipUqOC2aGdMeHg41157Lb179+b1119n/vz5hCdv5HiN1mTENQyIBmeWgT/3hfL9jnCWJYeTmQXnnnM21wwcZMeK8xFPf4kZZ8aYobmtF5ErsGHNFljltgZYD/QA2hhjEkWkH3kYZyVBXFwcjz/+GIsXL+bFl15ix4b5ZMXGk1K9FZnlaviUkZYVVQlzLBky04kOyfLJvKNsjqQLH2+OZP72SMrHxnLPPaPp2bOn33pU4uPjefzxx/jmm294fvILBK3+lLQq55Ca0KbMz8ua7SX7dkcEq/aFEhQURKdOnejfvz/t2rXzW29CfuSjw7pzQofdBaRivWQVsUZZFWf7IEp5SI3s4RQaNmzII48+SvCqORyv3or0ak0C4sN7JhgDy/aG8t6mWHYdFVq3bsU//3mLX4UwC0t8fDzjxo1jyJAhTH7hBdas/hGTtIbjCe19egDjMyHpeBDf7Qznx11R7EuB8uViGfKP3vTp08cnZylxo4tcDBAMJGETZoOBA8AR4Fvnwx2C9aydQkmFBUSEzp070759ez7//HOmv/0Oyeu/IismntSqTcmoWNsnlFt23lHw4d3Ujc3wyYTKtEyYnxjBZ39HczwD+vXrzw033EC5cu7kahUnIkKPHj1o3749U6dO5aOPPiL8wBYbEq9ylk/UkeIkO5fs+11RHHS8ZCNGDKRPnz4UZ0K7n1Eb6+lPwnrMMrDGWGPgC6AVVn+dEhcpybCmJ127duXss89m0qRJLF68mPB9f3E8oS2Z5Wq63tjMiqqEObwbwdCwnG/02Nt0KJiZG2NYdyCE2rUSeOy+W+jYsaPfNiQLy7nnnsvLL73EN998w8uvTCF53Rc2Hy2hnWu5tcWJZ+rFqn2hiAjt2rXjkr596dy5s093UBJTygPUOSNqb8Z2DkjF5qDdDTwE7MUabKFAjDEm35hR27ZtzdKlS0tEzrS0NL788kvenTGD3bt2QWR5UuIbk165oevx+ci18wg+vJsmFVMZ28p3RoHOMvDTrnA+2hJD8nHo0KE9N910s0+2SoqLjRs38vzzk/nzzxWY6Mol3vKMXDuPkMO76F3reIl1CDAG1h8M4attEfy2NxxB6NixI/0HDKB9+/aue8lE5DdjjGujforIYqA5Vo+NAT7GdgAYj+0g8Ae2c8DVxpg8LdiS1F/ZGGP4/vvveemll9mzZzeZ5aqTUrMNWTHuhp+jf59BUEYKk8/bR4Vw9wZJTToexOxNUfy8O5wK5ctx3YiR9O3b16eG9iktUlNTc/LR0jMySKnalLTqzYv9exezdBrBJoOpF3oPIVh85JZ60bdffy655BLXUy8Kq7/cqIEXAZ8Bkdju6AabwxGLVWo1sENpuNosDwsLY8CAAfTt25cffviBmTNnsW7dYiK3LyO1ciPSqp6ruRwOWQaWJoXx0eYYdhwVzmrUkHGj/knr1n42qXwRaNiwIZMnP893333Hiy+9xN51X5BesS6pCW39ruWZkWVHPf8qMYrNh4KJjYlm6NCBDBo0yHWF5mM8i+3QFInNHxNsh6YgYBXWs1YJ27HJVUSEbt260alTJ+bOncvUqdM4tGYumeVrklq9BZmx1dwW0RWOpgufbY3k68RIgkNCufrqKxg6dCjR0dFui+Yanvlor776KgsXLnQ8ru3IqFjXdY9rQRgDaw6E8NW2SJbvDUOCgujcuRP9+w+gbdu2rjcqTxc3jLO/gf7Avdics6bYkMBAYLsxppuI/AyE5XZwaYUFsgkODqZ79+5069aN1atX88EHH/Ddd98RtnslGRVqkRbf2Ofy0koLY2B5cigfbY5h6+Eg6tSuxYT/3MD5559f5sMBnogI3bt3p1OnTid6Qq2aQ2p8Y9JqtIDgXKuyz5A9Avanf1uPZ62Emtw+8nJ69erlkwNs+gBF1mGlrb+yCQsLY8iQIfTu3Zs5c+Yw6733Obx2Hpmx1Uit3twnwp2lgXeyf8+evRg5cqQ2PjyIj4/n/vvvZ+DAgUx6/nk2/fUtmeVrcrx2J59scKZnwc+7w/k6MYqth4OoUC6Wa4cNoX///n7V8cwbN8Ka5YCt2LBmQ2AOdgDaY9hQQCy2k8Alxph8Td3SCAvkxp49e/jss8/45NPPOHTwgA15VjmH9LhGpfIhdjusaZzeLh9tjmbToWBqVK/GdSNGcuGFF/pd66QkSEpK4vXXX+frr7+GsCiO12xDRuWGxfLxK86wZkaWDUN/ujWapONC43PP4dphw2nfvj1BPjycjA+ENYtFh7mlvwBSUlKYO3cu786Yyf59yZioSqRUbUpGpfqlMpRQaYc1jePdf39TDLuPCa1ateSf/7yFRo0alfi5/ZmMjAw++eQTXn/jDVJT00ip3oK0as3PqI4UV1gzewT/T/+OYX8K1K1Tm8suv8Lnx1b05bBmfay7PwEbDujuIcchbIcB3zPPPYiPj2fkyJFcc801LFq0iI/mzGHtmiVOyLMhafGNMZFlb0BbY2D1/hA+2hzDhoPBVKsaz903D6dnz54BmaORF1WqVOHee+9l8ODBPD95MmvX/EDWnjUcr93R9VyfbH7fG8qMjbHsPiacfVYjxoy83rXBFv0Qv9dhERERXHrppQwYMICFCxcyY+ZMtm3+HnYss7m1Vc52Pbe2uNhwMIRZG6PZcDCEOrVr8cQto7WuF5KQkBD+8Y9/0K1bN1544QUbNTqwlWN1upAVXdkVmbJzmz/eYhuVzZo24d5hw2nbtm2ZeqZufFFDsO7+xdiRtFsCY7G5Z8eMMY1E5AtymfoE3AsL5EZYWBg9e/akZ8+erF27ljlz5rBg4ULC9qwho3wCaVWblJmQ57oDIXy4KZq1B0KoEleZO+4YRp8+fXy6t4vbZPeEWrBgAa+8MoX9a+aSFncWaQltMaHuhAv3Hg/i3Q3R/LY3jDq1Enj0vn/SqVOnMqXUSoEi6zBf0l9gdVifPn3o1asXS5YsYcbMmfy54hcid/5BStzZpFdtjAlzPXWuSOw6FsTsv6L4NSmcShUrMGbM9fTu3VsbkkUgLi6O8ePH88MPP/DMs88iaz4ltXoL0mq0LNUe6qv3hTB9Yzl2HLG5zWOuv6HMGtpuhDWrAYnYFmY9bK+nscDFwO/YRNp9QCtjTL4xQjfDAnmxb98+PvvsM+bM+ZgDB/aXSLigNMOamw4F88GmaFbuC6VSxQpcfc219OvXj7Aw386j8jWOHTvGtGnTmD17NiY4jOM125Aed9ZpG+5FDWtmGfhyWwRzNsdASCjDh1/HpZde6pfGtQ+ENYtFh/mi/gJYs2YNM2fO4ocfvsdIEGmVG5JWrVmx5huVZFjzUJod2f/b7ZGEhoUz9KqruOyyy4iK8k8j09c4fPgwkydPZv78+WTFxHOsXtfTqhtFCWseTRdmbozi+50R1KxRnZtuHuW3uc0+G9Y0xuwSkQxgp/N3GJiO7cVZHTu8RgSQ6yzTvtby9KZSJTvR81VXXcWCBQuYOWsWf2eHC6o1sx9kP5hSZefRID7YZFud5WJjuPnmqxk0aJAmiBeRqKgoRo0aRa9evXj2uedY+edPhO7bxPE655V4ku3BNOHV1bGs3BdK586d+Ne//kXVqlVL9JxlGQ8dFonNPfsB2I7Vpw2wE5+vwA5SexK+rr/AenwnTBjP9u3bmTVrFvO++IKwvetJr1iXtBotyYqs6LaIuZKaCV/+Hcnn26JIywqib7++DB8+nMqV3Qm/lVViY2MZN24cHTt25OlnniF4zaccq9vF9ugsAX7fG8pb68pxOD2IoUOvYPjw4T6dU1ZclLrnDEBELgbewuZlRGF7OX2MnSkgHKv0Khpj8u3X7KstT0+ysrJYsmQJ099+mzWrV0N4NCnVmpMed3aRPWkl6Tk7kCp8tNm2UMLDw7n8iiu5/PLLA7qLeXGTlZXF559/zssvv0JKWhrHa7Rx5uosuBV4up6z1ftCmLKmPMeyQrn1ttvo16+fX7Y2PXHbc+bIMAW4DBvezMIOA9QG22uzDbALqGaMyXPOH3/QXwB79+5l9uzZfPzJJ6SmpJBeqT6pNVqdUV5tcXrOMrPg+53hzNkaw4EUOL9LF2648UafNX7LErt27eKBBx9k/bp1pFZrRlpCmwLDnIX1nGUZ+HRLJB9tjqJhg/r8Z+w9ZaIDh896zhyyp2x6BJiCHVk7COjtMX3Tey7JVqxkT3PTsWNHli1bxlv//S+rVi4mYs8qjtdo4zPjx2S3OuduiyLTBDN4yCCuvvpqKlb0zVayPxMUFET//v3p0KFDzlydoYe2c7ze+ZjQyGI7z487w3hzbSw1ExJ4dvyEMj0YcGkiIglAI6xxdgewFDuNUyK212Zd4FFsZwG/Jy4ujlGjRjF06FDee+89PvzwI0JXbSa9ckNSa7ZxLSfNGFixL5RZf8Ww/UgQjRufy8RR/6RZs2auyBOIVKtWjRcmT+aFF15g7ty5BKcc5Hj9bmfcmSQ1E15bHcOvSeFcfPHFjBkzJiC8ZZ6UunEmIpWwLv+9bFdssgAAIABJREFUWO9ZVey4QUeB/4lIKpBOKU/fVNKICG3atKF169b8/PPPTHn1Vbb+9S2Z5WqQUrsjWZHuTKprDCzeHcb7m2LZlwLnn38+N910EwkJCa7IE0hkz9X5ySef8NJLLxGy+mOO1ut+xjMMGANzt0Ywe1M0rVu3YuLEh9XzWby8hE3BeA2rv1phZwb4DlgCZDrbX/Q+0F/1F0CFChW46aabuPzyy5kxYwYfffQRYfu3kFKtGWlVm0Jw6X1OEo8EM2OjzYWtUb0a4+8aRdeuXf3eK+yPhIeHM2bMGBo0aMDkyZMJWv8lxxpeVOSGZlomPLuiPOsOhPLPf47isssuC8jn6sZgRoOxvZg8ja9DjiyHnN8VgbTcDjbGvGaMaWuMaeuPc/uJCJ06deKtN9/k9ttvJzbzINGrPyZsx3IwuabZlRjbjwbz+PLyTFkdS1ythjz//PNMnDhRDbNSREQYNGgQr776KjWrxhG1/itC96w5ozLnbI5k9qZoevTowRNPPKmGWTHiePUPYw0wTzKBeE7osGBsTu1J+Lv+AqhYsSK33HIL06dP5/zzOhG+fRmxqz8m+OD2Ej/30XTh7fVR3PdrBbamVWD06NFMm/423bp1C8gPuC8xePBgJk6cSHjaQaLXf4Wk5+pfyZf0LJi8shxrD4Rwz733cvnllwfsc3XDOGuINczKYxP/w4F/A9FAG2NMI+AB8pghoKwQHBzMwIEDmfHuu1zQrTvh25cRvfZzJLXkB5VNy4T3NkZx3y8VSEwvzx133MErU16lRYtT8peVUqJ+/fpMeeUVOrRvT8TWxYT//bN1gZ0mX22L4OMtUfTu3Ztx48b5ZW9MH+c87LhmsdjxzIKxObKxQBfssBpXAb8BPd0RsXSoWbMmEyZMYNKkSdSoHEvU+q+I2PwDZOTarj4jsgws2hHO3UsqsXB7FP0HDOSdd2f4bY/jskqXLl14/LHHCEs/TPT6ryAjtdDHGgNvrY1hRXIod945hosvvrgEJfV93DDOXgY2ANWw82cmc2J07V9FZAN2AuH03A4WkRtFZKmILE1KSiolkUuOChUq8OCDD/DAAw8QnXWU2DWfFdgCzYqqRFHTWTYcDOG+pZX4/O9ILu7Vm7ffeZcBAwboyP4+QExMDI8++gj/+Mc/CNu9mvCtP52WgfbLnjDe3RDN+eefz5gxY3x6lH9/xRhzD9AJO+l5Jaxhtgwb0swEvnD+KgO1vI8va/oLoGXLlvz3rbe4+uqrCd/3F7FrPyPoWHKxlb/7WBBPLC/PW2tjqHtWE1597TVuv/12ypcvewN9lwXatGnDY489RkjaIaL++gayChcRWrQjnJ92hTNs2DD69etXwlL6Pm5o7wzgTmNMBHAlVsH1xiq2ilir4yB55MOVhbBAblx44YW8/tpr1KlZnagNXxOyd0Oe+6bW7njaw3FkZMGsjVE8vKw8WVFVeOaZZxg7diwVKriT66bkTnBwMKNHj+bqq68mLGk94VsXF8pA2340mDfWlqNx43O5//77daDNkiUDuBPrGVsIdAYWYQ2yVsB87JROpyiosqq/wsPDuf7665k0aRKVokKIWTOXkOS/zqjMLGM9weN+rcjWlBjGjBnD5BdeLBM99so6bdu25T93303woZ02ClAAiUeCeWdDDG3btObaa68tBQl9n1I3zowxO4HRIrIHeBDrReuKDRFswuajpWCH2AgoEhISePnll2jVshWRm38gNGldsZSbdDyIh5dVYN7fkfTt24+p06bTpk2bYilbKX5EhOuvv54rr7ySsKS1hO5ene/+aZnwwsryRETFMH78BB0guOR5BPgSeNEYcxG2Q1NLbIjzEFAB600LOB3WvHlz3nrzTZo1bUrkpu8I3bWqSOUcTRee/zOWdzdE07pdB6ZOm14mhoEJJHr27Jmjw0L2b81zP2Ng2voYIqKiGXff/RrFcSh140xEqgAfYL1lYdhu5/OdzdudnLNU8sg5K4thAU+ioqJ44onHade+PRFbfiJk35YzKm/53lDuX1qR3RkxTJgwgTFjxuhI2X7CjTfeyPnnn0/EtiUEH96V536fbIlkx1HhvgcepCx5Y3yYOcClACJyNlaHVcCOd3Yv1pv2H3LJPSjr+gtsqsZTTz2ZU3dDd608reO3Hg7mgd8qsmJ/BLfddhuPPfY48fG+MSetcnqMHDmSBg0bEbX1J8hIAcAEBRPk8Wb8b1cY6w6EcNPNo3ToJg/cCGtWx7Y8Z2I7B8wxxsx2tp0rIhuBpLxkK6thAU/CwsKYOGEC55x7LlFbfiDo2P4ilTM/MYLnVpSjRp0GvPb6G3Tt2rWYJVVKkqCgIO655x6qVq1G1JYfITPdyTc8wbYjwcz723YAaNvW1XFZA4mtwCtY/bUca6ydgx0O6A7gJmfbKdnQgaC/wIY5H3roIbp27UrEtl8ISd5UqOPWHQjh0d8rYCIrM3nyCwwZMkS9ZX5MaGgo4+69BzJSCd++HAATGkV4sE3VyMiCj7bEcPZZjbjkkkvcFNXncCOsuQJojx1Be7cxZqjH5iexuWdNySPjPRBangARERE8PHEi5WKjidr8HWR599zPG2Nsftnb66Pp1LkzL7zwIjVr1ixBaZWSIioqinvuGQsphwjbuYLU2h1PejHe/yuKqOhoRo0a5ZqMgYajwwZiOy2Nc3RYVezQGc9hhwGKwuamnUSg6C+w+ZPjxo2jSdOmRG39CTl+MN/9V+8L4ek/yhNXrSavTHmVJk2alJKkSklSv359Ox9z0tpTRiP4365wko4Lw4Zfpx2YvHAjrCnAm8Bf2J6a2aRjB23sA7zh7NvY+/hAaXmCHZn7nrFjkWP7CNv5R6GOMQZmboxi3t+RDBw4kIkTJxIZWXyjziulT8uWLenWrRsRe1afNHbQpkPB/JEcxpVDr9Kea6WIo8OeAFKNMc96bPqMEzosFTDeOiyQ9Bc4HrQHHyQ6KsI2Mp2xHE3wyVkr248G89zK8tSoVYfnJ7+g4fkyxjXXXEOQCGEe+bPGwJeJUTSoX49OnTq5KJ1v4oapeh5wDbY7egMRWS4il2BDmQJ8hc1H24dtnQY0HTt25IILLiRi10ok9UiB+8/ZHMmX2yIZPHgw//73vzW5sowwYsQITGb6SZ1E5v0dSWx0NIMHD3ZRsoDkPGAIEOPor+XAEezYZgALsB2bpqA6jCpVqnDnHXcQdHQvoUnrAciKjsvZfjzDdmiJjC7Hk089TaVKlfIqSvFT4uPj6d69G+HJG7CvBmw6HELikSAGDdbQdW64Edb80Rgj2NblX8aYlsaYedjeTRudDgGfYycQPiUWF0hhgWxGjbqZoCAI27k83/2W7A7LGYD01ltv1QpfhqhTpw4tW7bMUW5H04Vle8O5uFcv7eBRyhhjfgTqYfVVS2NMS2wObV9s/tlr2CmeNuClwwJRfwF0796dps2aEbHzd8g6Odo7e1MUu44F8eBD49VjVobp3bs3JiONoLSjAPy4M5zw8DAuvPBClyXzTVwJ8orITGAxcLaIJIrISOBjoKYzCO3FwFyyTWwPAi0sALbV0b9fP8KSNyJpx3LdZ/vRYN5YF0vjxudy5513avy+DNKrVy9IsTkbvyaFk5EFffr0cVmqwCMP/fU40AI7GfrFzm/w0mGBqL/ADg8z4rrrIO04oR7jn+08Fsy32yPp178/rVq1clFCpaRp3bo1UdHRiGOc/54cQfv2HXR6uTxw5QtujBlqjKlujAk1xiQYY94E1gIrjTGNjDE9sAPS7nBDPl/k0ksvBWMITVp7yrYsA2+ujSUiKpbx4yfodCZllPbt2+csp2QK8VXiaNiwoYsSBSa56S9jTDJ2QvOfjTE9jDH7gARUh+XQqlUr6tWrT5hHaP6zrZGEhYczfPhw9wRTSoWQkBBaOlMEHs0IYl8KdO7c2WWpfBdfcq/8CjQSkXoiEoadPeBTl2XyGRISEmjdujXhyRtPGTH+ux3hbDwYzC2jb9WwQBmmcuXK1PSYlL5tu/YauvYtVIflg4jQu3cvgo7uRdLtmFcr94XR46KLNc8sQGjWrNlJv1u3bu2SJL6PzxhnxpgMYDS2Q8Aa4H1jTNGGly6j9OndG1KPEHxkd866tEyYsyWG5s2aBvxEsYHA2WedhXEG09ChBnwL1WEFkz3WYtDxfTnrevYs0/PDKx7Ur18/Z7lypYo6uHA++NQEfE7HgHluy+GrnHfeeQSHhJw0FcZPu8I5kAr3D79OvSgBQJ06dXKWGzRo4KIkSm6oDsuf6tWrU71GTXbu2J6zrnHjU0ZMUsootWvXzlludNbZ+s3KB5/xnCkFExUVRevWrQk7lJiz7psdUTRsUF/dwwFC9erVc5Y9FZ2i+AstWzTPWU6oWZOQEJ/yESgliGfajWdDUzkVNc78jPbt2sHxg4jJZNexYLYeDqJX7z7aAgkQPHNzdAgNxR8555xzcpZraQMjoAgJCSHByZvVWWvyR40zP6OF09sFk8m+VDvAbLdu3VyUSClN4uLiCt5JUXyYevXq5Syr9zfwyG5Uqi7LHzXO/Iz69esTGnZi6pOaNaprUmUAoaEAxd9J8Ohx3LdvXxclUdxg//79ANpDtwDUOPMzQkJCaOiRCN68RUsXpVFKm+zwtY5vpvgrFStWzFnWAUgDj8OH7UDaMTExLkvi22gmph9Sv3591qxZnbOsBBbTp0+nXLlybouhKEVC82MDmwYNGrBq1SpiY2PdFsWnUePMD6lVq1bOso51FXhono6iKP7KPffcw59//kn58uXdFsWnUePMD6lWrRoglCtXTscIUhRFUfyGhISEk/IOldzRnDM/JLsDwKFDh1yWRFEURVGU4kaNMz8kuwuyjhOjKIo/0rKl7cgUHBzssiSK4ptoWNMPiY+P5+GHHz4p90xRFMVfuO+++1i/fj0VKlRwWxRF8UnUOPNTunTp4rYIiqIoRSIuLk4HIVWUfNCwpqIoiqIoig+hxpmiKIqiKIoPocaZoiiKoiiKD6HGmaIoiqIoig+hxpmiKIqiKIoPocaZoiiKoiiKD6HGmaIoiqIoig+hxpmiKIqiKIoPIcYYt2UoMiKSBGx1Ww6XiAP2ui2E4gqB/uzrGGOquC3EmRLg+gu0HgcygfzsC6W//No4C2REZKkxpq3bciiljz57pSyg9Thw0WdfMBrWVBRFURRF8SHUOFMURVEURfEh1DjzX15zWwDFNfTZK2UBrceBiz77AtCcM0VRFEVRFB9CPWeKoiiKoig+hBpniqIoiqIoPoQaZ36IiPQWkXUislFExrotj1I6iMhbIrJHRFa6LYuiFBXVX4GL6rDCo8aZnyEiwcBLQB+gMTBURBq7K5VSSkwFersthKIUFdVfAc9UVIcVCjXO/I/2wEZjzCZjTBowCxjoskxKKWCM+R7Y57YcinIGqP4KYFSHFR41zvyPmsA2j9+JzjpFURRfR/WXohQCNc78D8llnY6HoiiKP6D6S1EKgRpn/kciUMvjdwKwwyVZFEVRTgfVX4pSCNQ48z9+BRqJSD0RCQOuBD51WSZFUZTCoPpLUQqBGmd+hjEmAxgNfAWsAd43xqxyVyqlNBCRmcBi4GwRSRSRkW7LpCing+qvwEZ1WOHR6ZsURVEURVF8CPWcKYqiKIqi+BBqnCmKoiiKovgQapwpiqIoiqL4EGqcKYqiKIqi+BBqnCmKoiiKovgQapwppY6IVBWRGSKySUR+E5HFIjK4GMrtLiJzi0NGRVEUb0TEiMjbHr9DRCSpOPSOiPzvNPd/SETGnOl5Fd9EjTOlVBERAT4GvjfG1DfGtMEORJnggiwhpX1ORVH8mqNAUxGJdH5fDGw/nQK89Y6IBAMYYzoXi4RKmUCNM6W0uRBIM8ZMyV5hjNlqjHlBRIJF5CkR+VVEVojITZDjEVskIh+IyFoRedcx8hCR3s66H4Eh2WWKSLSIvOWU9buIDHTWDxeR2SLyGfB1qV65oihlgS+Avs7yUGBm9gYRaS8i/3N0zv9E5Gxn/Ul6x9Fp34rIDOBPZ58jHuXc5aEHx3usHyci60RkAXB2yV+q4hbqOVBKmybAsjy2jQQOGmPaiUg48JOIZBtQrZxjdwA/AeeJyFLgdazBtxF4z6OsccA3xpgRIlIB+MVRaACdgObGmH3FeWGKogQEs4AHnFBmc+At4Hxn21qgqzEmQ0QuAh4F/uFsy9E7ItIdaA80NcZs9ixcRHoCjZztAnwqIl2xXrsrsbowBKtHfyuxq1RcRY0zxVVE5CWgC5AGbAWai8ilzubyWCWVBvxijEl0jlkO1AWOAJuNMRuc9e8ANzrH9gQGeORkRAC1neX5apgpilIUjDErRKQu1ms2z2tzeWCaiDQCDBDqsc1b7/zibZg59HT+fnd+x2D1YCwwxxhzDEBEdE7SMowaZ0pps4oTLUmMMbeISBywFPgbuNUY85XnAU4rM9VjVSYn6m5e848J8A9jzDqvsjpgW6CKoihF5VPgaaA7UNlj/UTgW2PMYMeAW+SxzVvv5KWHBHjMGPPqSStF/k3e+k4pY2jOmVLafANEiMgoj3VRzv+vgFEiEgogImeJSHQ+Za0F6olIA+f3UI9tXwG3euSmtSoW6RVFUWwoc4Ix5k+v9eU50UFgeBHL/goYISIxACJSU0Tige+BwSISKSKxQP8ilq/4Aeo5U0oVY4wRkUHAcyJyN5CEbUH+B5iNDVcuc4yqJGBQPmWliMiNwOcishf4EWjqbJ4ITAJWOGVtAfqVyEUpihJQOCkWz+ey6UlsWPMObEO0KGV/LSLnAoudtuUR4GpjzDIReQ9Yjk0B+aFIwit+gRijXlJFURRFURRfQcOaiqIoiqIoPoQaZ4qiKIqiKD6EGmeKoiiKoig+hBpniqIoiqIoPkSZNs6cSWobOsuRIvKZiBwUkdluywY5E9e+U8rnrOvclxLvqevG9Sm+i4jcKyJvlEC5w53puwKW0nyvvc77fx6zePgsIrLFGbFfURCRVc74mcVd7iIRub44yvJ540xEujhzlB0UkX0i8pOItCtCUZcCVYHKxpjL8jnfcBHJFJEjXn81inwRyik4c8slunTueiKSJSIvu3H+wlCcL3kB52kiIl+LyH4ROSAiv4nIJSVxLmPMo8aYEr+msoJjUBz30kMvuijPKQagMeZdY0xPt2QqDkRkqog87NK5H3LuaXs3zl8QpdyYHyl2nuTDIrJbRD53xnMrdowxTYwxi0qi7OLCp40zESkHzAVeACoBNYHxnDxafGGpA6w3xmQUYt/FxpgYr78dRTin4ptcC+wHrhQ7h2cg8xkwH9twiQduAw4VpSARCS5GuRRLfy89NNptgZTiwRl/8RpgHzDMZXFcRUS6YechHWqMiQXOBd4vYlllYvxWnzbOgLMAjDEzjTGZxpjjxpivjTErsncQkREissZp+X8lInW8CxGR8cADwBVO63NkUQVyWrN3icgKETkqIm+KSFUR+cKx+BeISEVn3+xWx40iskNEdorInfmUPcBxtx5wPCfnOuvvEpEPvfZ9QUQmOcvlHTl2ish2EXk4+0MpIsEi8rSI7BWRTUDfAq5vrIj85VzLahEZ7LFtuIj86JS3X0Q2i0gfj+31ROQ759j5QNzp3+Gc65kuIkkislVE7hORIGdbAxH5RkSSnWt6V+zE5tnHbhGRMc7zOSgi74lIhNcprgXuA9LxGmXbeV7/FJENznVMdM65WEQOicj7IhLmsf8NIrJRrFf3U3E8rLm1OMXDG5bfvRSRR7ATKb8oeXhLRORLERntte4PERkiludEZI9zD1aISNNcyogD6gGvG2PSnL+fjDE/esqYy/3JThWYKiKviMg8ETkK3CMiu8TDSBORwSKywlnOCXPnJ7+zfI6IzHfu6zoRudxjv8rOvT4kIr8ADQgwCnqvxSuMJ14pBnIiInFARLaJyHBnfV8R+d25t9tE5CGPYr93/h9w6mUn7zoiIp1F5Fen3v0qIp09ti1y3qefnHfra6cO5nZ9FUVkrqMD9jvLCYUtS0SuEas7kkVk3GneXk858rue68R+ew6LyCYRucljW3cRSRSRO533cKeIXOdV/PlADeBf2Iaip14Z7lzbc84z2uTIMtx5LntEZJjH/vnpTO9nf5JuKuBenvLMve5PDbHe3Uoe61o59TJURBqK/SYcdNa9l8etbod1ivwOYIzZZ4yZZow57CHj9R7n8K53RkRuEZENwAYRmSIiT3vJ+onYwYFz3o+C5Hd+52ljiMjFYr19B8Xqacnj+k4fY4zP/gHlgGRgGtAHqOi1fRCwEWtlh2A/uP/z2G6Ahs7yQ8A7hTjncODHfLZvAX7GehpqAnuAZUArIBw7KvSDzr51HRlmAtFAM+yo9xd5y4Q1RI8CF2Mny73bubYwoLqzrYKzb4hz3jbO74+BV51zxAO/ADc5227GTnNUC+t9/NaRKSSP67sMqzCCgCuc81b3uDfpwA1AMDAK2MGJwYwXA88696ErcDive46dky4xj23TgU+wE/3WBdYDI51tDZ17FA5UwSqPSV7P5xfnGioBa4CbPbafj/W8VsR6ZD/1OrfBzptXDmji7LsQqI+dmmU1MMzZ90JgL9DakecF4HuvZx/iUfYi4PpC3sucffO4R9cCP3n8bgwccOToBfwGVMAqi3Ozn6FXGQJswHqnBwFVC3oXOPmdmgocBM5z6ksE8Bdwscf+s4GxudT3/OSPBrYB12HremvnPjdx9p2FbVVHY2eE2O4tZ1n4c+ryRXlsy/e99j7W697Xxr6bQ7G6pjLQ0uO9bOY8z+bAbmBQPnU6p444cuzHeoNCnPL3Y1NJsuv0X1hdF+n8fjyP66uMnYM3CqsHZgMfe71LuZbl1KUjWB0UjtVJGfncy6nAw7msL+h6+mIbBgJ0A44BrT3uYwYwwbnHlzjbK3qU/6ZTj0Ox37khXvc1A/sOBAMPY+cefsm5pp7OM4xx9s9PZ+Y8+9yeYwH38pRnnst9+ga4weP3U8AUZ3kmMI4T+qFLHmWcDxzHRsbOA8K9ti/CQx/ipZscGec7zyzSefbbOKFPKzrl1/B+PwqQP08bA+t8OIRNmQoFbneeWZ56+7Tef7cVUIEC2psyFUh0LvxTnI8I8EV2BXR+BzkvQB2PB1YU4ywD+6HI/vvLY/sW4P88fn8IvOLx+1YcJeJRsc/x2P4k8Ka3TMD9wPte17Id6O5xrTc4y/2A1c5yVawBEelx7FDs5LvZFc/TOOlJAS+b1/1YDgz0uDcbPbZFOWVVwyr8DCDaY/uMvO45eRhnWEWUCjT2WHcTsCiPcgYBv3s9n6u97vcUj99veDyfTlgDKd5juwHO8/j9G/Afj9/P4BiDWOX6pMe2GKe8uhTOOMv1Xnrvm8d1x2IN5+y6/gjwlrN8IVY5dwSCCni+CcCLWOWchTV2G3nIWJBxNt1r+8MecnjL+BAn6nt+8l8B/OBV7qvAg079SOfkd+pRbznLwp9Tl49wsi7K1gH5vtfkb5zdA8wppAyTgOec5dzqdE4dwRoxv3gdvxgY7lGn7/PY9k/gy0LK0RLY7/E7z7KwUZJZHtuigTRO3zjL93py2f9j4F/OcnesMeB5r/YAHZ3lKOyHPdvwfRX4xOu+bvD43cy591U91iU79yVfnUnhjLO87uUpzzyX674e+MZZFqxR1NX5PR14DUgoxDPug02zOICt988CwR4yFmScXejxW7DGbLYcN2TL6P1+FCB/njYGtoH5s9c5Eykm48zXw5oYY9YYY4YbYxKwreQaWIUB9gY977h9D2Bj94L1aJ0JPxtjKnj8eYdNdnssH8/ld4zX/ts8lrc61+BNDWcbAMaYLOe47GuZBlztLF8NvO0s18Fa7Ts97sOrWA9adrne588TEblWRJZ7lNWUk8OTuzxkPOYsxjjn2W+MOVrYc+VBHNZb6HnsVpz7ICLxIjJLbPj2EPAOp4ZPd3ksH3PkQ0QisZ7Bdx35F2Nf4Ku8ji/s8/V+ZkewCrOw9S+ve1kgxrr7PweudFZdyYnr+gZrcL0E7BaR18Tmb+ZWTqIxZrRTx+tgDabphZQfTq5bYA3yIWJz+YYAy4wxp9SD/OR35OiQXQedevh/2EZAFWwLttB12s8Z5KWLXnfWn9Z77UUtrDF+CiLSQUS+dcJjB7EeusKmJ5z0PnjI5fk+5Ppu5iJHlIi86oToDmEbDRXk5LzGvMo66d44Oim5kNfgSb7XIyJ9RORnsaH3A1jvmOe9SjYn5zh7yjgY25id5/x+F+gjIlU89vfWOxhjctNF+erMQlKo55IHHwCdxKZ0dMUaStnzft6N/Sb/IjZlZ0RehRhjvjDG9Md6vwZiDbDT6UDk+cwN1sM+1Fl1FSf0y+nIn5+N4V3PDKfqwyLj88aZJ8aYtdhWTnb+zDZs+M5TeUUaY/7nmpC5U8tjuTY2fOXNDmxFAHKSRWthvWdgW2XNxeYO9eNERduGbTXFedyDcsaYJs72nbmcP1ecWPrrwGis674CsJLCxdF3AhVFJLow58qHvVjPSB2PdbU5cR8ew748zY0x5bCGamHj/IOx4cqXxeZG7cK+ZNcWQU449ZlFY8Mx27FGDtgWcjbVTqNsU4h9ZgJDnTyQSGxoyx5szGRjTBtsaPYs4K4CT2jMNqxBl/1+HcVDfhHJTf6T5DTGrMZ+GPpgFeKMIsi/DfjO672OMcaMwqYFZFDIOl2GKei9PunZcXLd20beeXozsNGJWsaY8sAUTrxfBdXJk94HD7m257JvQdwJnA10cN7zrs76wuqinHsjIlHY9/J0yfN6nMbHh8DTWG9WBayhVVhdNAxrAP3t6KHZ2Eb20HyPyp2CdGZ+daEgCtRDxpgDwNfA5dh3fqbJdmcZs8sYc4MxpgbWm/eyODmr+ZSXZYxZiPUO56qL8rgGb1lnApc637UO2Od1WvKTv43hXc+Ek9/JM8KnjTOxScF3ipMIKiK8TSdhAAAgAElEQVS1sJX3Z2eXKdgk5CbO9vIikucwGS5yv9MSbILNIcgtKfJ9oK+I9HASEe/EGl3/AzDGpGAt/BlYV/vfzvqd2Ir1jIiUE5EgsQns3TzKvU1EEsR2VBibj5zR2AqeBDbhlRMvR7443pGlwHgRCRORLngl2+eGiER4/mFDa+8Dj4hIrPNi3YH1kIENhx3BJqjWpBBGhwfDgLewIYKWzt95QEsRaXYa5WQzA7hORFo6yvpRYIkxZosxJgmrHK8Wm7w9gtNLXN+NzXPLj3lYhTwBeM/xtiIi7RwPSChWqaUAmd4Hi026Hi82aTdIbBLwCE68X38ATZzri8CGRwrDDGyvz67Yj85pyY/NgTtLbFJ3qPPXTkTONcZkAh8BDznvVGMCs6dbQe/1cmySeaiItMXmxWTzLnCRiFwuIiFiO1i0dLbFAvuMMSlih3fw9ConYd/PvOrlPOxzu8op9wps/tfcIlxfLNYzdEBssvaDp3HsB0A/sZ0ewrD1q6BvXbCXLgoj/+sJw+Z+JQEZYjvzFGpIEUdv9cA2srP1UAvgCYpQl513Ij+duRzoKiK1RaQ8NqxdWAp65tnMwDZy/4FHg0xELpMTHTn2Y78vuemigSJypaOTxKl73Tihi5ZjPfJRjnFXYKc+YzsXJGFTWb5yjLDTkp/8bYzPsfpxiNjOFbdxeoZvvvi0cYZNeOwALBHbG+xnrCfnTgBjzBxshZ4l1vW9EttiP1M6yanjnBVlbLVsvsMmFS4EnjbGnDJoozFmHdYL9AK2JdQf240+zWO3aVjD4m2vw6/FKovV2BfgA2wnArCesK+wH9pl2A9brjhej2eweRW7nXP9dBrXeRX2ee3DKtOCwmM1sQrY868BNm/vKLAJ+BH7srzlHDMemyB+EPty5Hk9nngoxElOay777zfgS4qmFBdicwU/xLaiGnAiTAc2z+EubEilCY6hXUiex7b69ovI5DzOn4q9/os4WaGUwz73/VgvVjK2he9NGjanZAE2/2UltkEw3Cl/PfbDtgDbcaCwA73OxObcfGOM2ZvXTnnJ74Q8e2Lv5Q5syOUJ7McQrGc3xlk/FfhvIeXyRz7z0kNznPUFvdf3Y+vjfuw743l//8aG4O7EvqvLscYB2HyjCSJyGJu79b7HccewuYE/iQ3zdPQ8oTEmGWtw3Imtc3cD/fKrA/kwCetN3YvV+18W9kBjzCrgFuw178Teg4LGVBzLyXrom/yux6mjt2Hvz36s7vu0kCJeAyw3duSBHF0ETOZEdOR0yVNnGmPmYx0CK7A5tIU2lgt65h58CjQCdhtj/vBY3w77/T7i7PMvY8zmXI7fj9WXG7C66B3gKWNMdoToOay+2o39DuYVovRmJqfqx0LLn5+N4dTry4DHsfWjEaf3vcyX7J4MSgkgInWBzUCoKdz4agWVVxvbQ6uaMaZIY1EpiqIoiuLb+LrnTHEQO2bNHdheSGqYKYqiKEoZJSCNM7ED1HmHLY+IyBS3ZcsNsYnmh7Dje51O7oWiKIqiKH6GhjUVRVEURVF8iID0nCmKoiiKovgqfj1BaFxcnKlbt67bYiiKUor89ttve40xVQre07dR/aUogUdh9ZdfG2d169Zl6dKlbouhKEopIiJlYkYA1V+KEngUVn9pWFNRFEVRFMWHUONMURRFURTFh1DjTFEURVEUxYfw65wzRSlLpKenk5iYSEpKitui+AQREREkJCQQGhrqtiiKohSA6q+TOVP9pcaZovgIiYmJxMbGUrduXUTEbXFcxRhDcnIyiYmJ1KtXz21xFEUpANVfJygO/aVhTUXxEVJSUqhcuXLAKzYAEaFy5craClcUP0H11wmKQ3+pcaYoPoQqthPovVAU/0Lf2ROc6b1Q40xRFEVRFMWHUONMUfyM9PR0MjMz3RZDUYpMVlYWu3fvdlsMRfFZ1DhTFB8mJibmlHWbNm0iMTGxxM75yCOP0KRJE5o3b07Lli1ZsmQJAJMmTeLYsWMFHl/Y/ZTA5YMPPuCKK65g586dbouilCC56a+SpqzoLzXOFMUPKalE+cWLFzN37lyWLVvGihUrWLBgAbVq1QL8T7kpvstPP/0EoN4zpVgpS/pLjTM/Ze3ataxdu9ZtMRQX2L59O8OHD6d58+b06NGDv//+G4DPPvuMDh060KpVKy666KKcD99DDz3EiBEj6N69O/Xr12fy5Ml5lr1z507i4uIIDw8HIC4ujho1ajB58mR27NjBBRdcwAUXXADAqFGjaNu2LU2aNOHBBx8EyHU/z9bzBx98wPDhwwGYPXs2TZs2pUWLFnTt2rV4b5Li0wQF2U9PRkaGy5Iopc3WrVvp0aOH6q+CMMb47V+bNm1MoNK9e3fTrVs3t8VQipHVq1efsi46OvqUdd27dzePPfaYMcaYN9980wwcONAYY8y+fftMVlaWMcaY119/3dxxxx3GGGMefPBB06lTJ5OSkmKSkpJMpUqVTFpaWq4yHD582LRo0cI0atTIjBo1yixatChnW506dUxSUlLO7+TkZGOMMRkZGaZbt27mjz/+yHU/z2uYPXu2GTZsmDHGmKZNm5rExERjjDH79+8v9D0Blhof0D9n+hfI+mvMmDGmW7duZsmSJW6LohQThdVf/fr1M1OnTjXGqP7K7089Z36KfcZKILJ8+XL69esHwDXXXMOPP/4I2EEge/XqRbNmzXjqqadYtWpVzjF9+/YlPDycuLg44uPj8wwnxcTE8Ntvv/Haa69RpUoVrrjiCqZOnZrrvu+//z6tW7emVatWrFq1itWrV5/WdZx33nkMHz6c119/XTs4BBjBwcGAes4CkcWLF3PVVVcBqr/yQ40zRfFzssfTufXWWxk9ejR//vknr7766kl5adlufrAfxvw+isHBwXTv3p3x48fz4osv8uGHH56yz+bNm3n66adZuHAhK1asoG/fvnnmwXmO9+O5z5QpU3j44YfZtm0bLVu2JDk5ufAXrfg1ISF2cho1yhXVX7mjxpmi+BmtWrVi3rx5ALz77rt06dIFgIMHD1KzZk0Apk2bVqSy161bx4YNG3J+L1++nDp16gAQGxvL4cOHATh06BDR0dGUL1+e3bt388UXX+Qc47kfQNWqVVmzZg1ZWVnMmTMnZ/1ff/1Fhw4dmDBhAnFxcWzbtq1IMiv+R7Zxlp6e7rIkSmnTuXNnZs2aBaj+yg+dW1NRfJhjx46RkJCQ8/uOO+5g3LhxjBs3jnfeeYcqVarw3//+F7CJs5dddhk1a9akY8eObN68+bTPd+TIEW699VYOHDhASEgIDRs25LXXXgPgxhtvpE+fPlSvXp1vv/2WVq1a0aRJE+rXr895552XU4b3fo8//jj9+vWjVq1aNG3alCNHjgBw1113sWHDBowx9OjRgxYtWpzJrVL8CDXOAoPc9NfkyZMZMWIETz31lOqvfBB/zl1q27atWbp0qdtiuEL37t0BWLRokatyKMXHmjVrOPfccwvcb926dQA0atQop9dbWSW3eyIivxlj2rokUrERyPrrySefZN68edx111307dvXbXGUYqCw+iuQOBP9VbY1u6KUYTRfR/FX1HOmKPmjYU1F8VOysrKKfGxycjI9evQ4Zf3ChQupXLnymYilKAUSGhoKqHGmFI1A0F9qnCmKn3ImxlnlypVZvnx5MUqjKIVHjTPlTAgE/aVhTUXxUzSsqfgr2WFNHedMUXJHjTNF8VPUOFP8Fc05U5T8UePMz/Hn3rbKmaHGmeKv6CC0ipI/mnPm52RmZuYoOiUwEBGMMbnmnI2+4y727N1XbOeKj6vEi88+VeB+X375Jf/617/IzMzk+uuvZ+zYscUmg1L2UONMyQs3dJgv6i/9qvs5ZX2cKyVvcvuw7dm7j7+qdiu+k+z+rlBy3HLLLcyfP5+EhATatWvHgAEDaNy4cfHJoZQpdG5NJS9KW4f5qv7SL7uf4znvlxJY+IrX4ZdffqFhw4bUr1+fsLAwrrzySj755BO3xVJ8mGzj7Ex6HCtKceCr+kuNMz9HjbPAxVe8Dtu3b6dWrVo5vxMSEti+fbuLEim+TrbH31caGErg4qv6S40zRfFTfOXDllunFG00KPmRXT+0Q5PiNr6qv9Q4UxQ/JTPTNzxnCQkJbNu2Led3YmIiNWrUcFEixdfJ/vhpWFNxG1/VX2qcKYqfkpmZ5ROeh3bt2rFhwwY2b95MWloas2bNYsCAAW6LpfgBvlB/lcDGV/WX9tZUFD8mKysrJ7kabLfxwvSwLCzxcZUK3CckJIQXX3yRXr16kZmZyYgRI2jSpEmxyaCUXdQ4U7wpbR3mq/pLjTNF8UOCxJBlhMzMzJOMs8KMSVYSXHLJJVxyySWunFvxX9Q4U7xxQ4f5ov7SsKai+CHBTr6qr3QKUJSioMaZouSOGmeK4ocEqXGmlAHUOFOU3FHjTFH8kCDsR017uyn+jNZfRckdNc4UxQ9Rz5lSFlDjTFFyR40zRfFDso0z/bgp/ozWX0XJHTXOFMUPyR6/Wj9uij+jnl9FyR0dSkNR/JQgOdU4u/fO0Rzcu7vYzlE+riqPPvNivvuMGDGCuXPnEh8fz8qVK4vt3ErZRxsXijeqwyxqnCmKnyKc+nE7uHc3/2mwttjO8cRfBe8zfPhwRo8ezbXXXlts51UCA/WcKd6oDrNoWFNR/BQR3xiKoGvXrlSqVPBMAoqSTfZchunp6S5Loii+qcPUOFMUP0UwPmGcKcrpsmDBAgBSUlJclkRRfBM1zhTFTxF8w3OmKKfL8ePHAQ1rKkpeqHGmKIqilCrZRllGRobLkiiKb6LGmaL4Keo5U/yVbONMPWeKkjvaW1NR/BgROel3+biqheqdVFjKx1UtcJ+hQ4eyaNEi9u7dS0JCAuPHj2fkyJHFJ4RS5sjuZazGmeKN6jCLGmeK4qfk5jMraDyfkmDmzJmlfk6lbKBhTcUb1WEWDWsqih/j7TlTFH9CB6FVlNxR48wP8cwz0pyjwEWfvOLvqHGmKLmjxpkf4jlwoxpnZYvTeZ4GKdOeM63biuJf6Dt7gjO9F2qc+SGpqak5y/oylB0iIiJITk4u9DM1BoKCyuYrbIwhOTmZiIgIt0VRFKUQnK7+KssUh/7SDgF+iKdxppQdEhISSExMJCkpKd/99uzezb7gLFIzg4jYf4D9+/eXkoSlS0REBAkJCW6LoZQgwcHBbougFBOF1V+BwpnqLzXO/BBP46ysek4CkdDQUOrVq1fgfnfc/m+6VTnAwu2RXHrFUG666aZSkE5Rih/VX2WHwuovpXDom+GHeBpnZTnnSMmbLAPpWRAeHu62KIpSZEJDQ90WQVF8EjXO/JC0tDS3RVBcJjXTGuWRkZEuS6IoRUeNM0XJHTXO/BA1zhQ1zhR/xTNhPCwszEVJFMV3UePMD/EcSkMJTFIc40x7Myr+hmfjUsPyipI7apz5ITrliZLtOYuKinJZEkU5PTxzZtU4U5TcUePMD1HjTElVz5nip6hxpigFo8bZ/7N33+FNVu0Dx78nSZuki9IyS9l7bxEURfTnxL0FX9crggy34p4gLkAUX19QQAXcAoKCIL4gqIigiOwCLaXQAXQ3Ozm/P54WCi2lQJMnac7nunqRnVubntzP/ZxznxCkkjPFWbrrjZpzpoSa8smZOrhQlMqp5CwEeb1evUNQdFZWOVOVByXUqDlninJyKjkLQSo5U9w+dVpTCU3lK2dqtaaiVE4lZyFIndZUXKX5ufpyU0KNSs4U5eRUchaCVOVM8UitcqaaeCqhpnxypvbWVJTKqeQsBKkmtIqndEGASs6UUONwOI5cVsmZolROJWchSCVnikSrnKkvNyXU2O12vUNQlKCnkrMQVP60gBLeVHKmhJryyZnP59MxEkUJXio5C0HqyFMpYzCoP2EltNhstiOX1fxZRamcGtlDUElJid4hKIqinJby45faJ1hRKqeSsxCkkjNFUUJVcXHxkctqioaiVE4lZyGoqKjoyGU1ZyO8SSn1DkFRTkn58av8KU5FUY5SyVkIKlSDm1JKJedKqCmfnKmzAIpSOZWchaCCgsIjl8sPdEr4UbtFKKEmP7/gyOXypzgVRTlKJWchqLj4aEJWUFBQxSOV2sootNOZakK1EmoKCo8eXBaWu6woylEqOQsxUkps5U4FqMEtPEWU/uWq5EwJNUVFRUeaKKuDS0WpnErOQozdbi+dZ6RVTtRpzfAUYdB+/2q3CCWUeL1eHHYb0mAC1MGlopyISs5CTPl96UANbuGqLDlTrQiUUHJkAZNB29nC4/GoFceKUgmVnIWY47+M1WrN8BRZumuTqpwpoeTIeCWObjt2/AGnoigqOQs5x293opKz8GQurZyprbyUUFJ2cCkNR5MzVf1XlIpUchZijj8FoCaEhyezUZ3WVELPkfFKiCO3qeRMUSpSyVmIOX6ja7VxcHiyGFXlTAk9Rw8ujyZnqvqvKBWp5CzEREZGHnNdlDsCVcJHWeVMfbEpoU4dYChKRSo5CzFWq/WY68dX0pTwYDGp5EwJPSaT1kIDeXTbMTU1Q1EqUt/sIcZqtR5TLTu+kqaEB6uqnCkhqGy8EuWSMzU1Q1EqUslZiDEajVijoo9cN5vNOkaj6MUgtHYaKjlTQklUVJR2QaqETFGqopKzEBQfH3/kssVi0TESRU9Wk6Ck3FZeihLsoqNLDyzLVcuMRuMJHq0o4UslZyGoXmLCkcvHz0FTwofVJFXlTAkpERERWCxWhDw6z0wlZ4pSkUrOQlBiYuKRy6pyFr6sRq+qnCkhJ65OHKJcv0Y1NUNRKlLJWQhKSDhaOVMDW/iyGn2UlBTrHYainJLEcuMXqEVNilIZlZyFoLp16x65rAa28GU1+SgpVsmZElrKV/5BVf8VpTIqOQtBWnKmtdNQyVl42b59+5HNzqOMkmKVnCkhJuG4yplKzhSlIpWchaDyqzXVZNrwsnr1anw+rUeUWhCghKLylX9QUzMUpTLVSs6EECuqc5sSGHFxcUcuq+QsvGRnZx+5bDFJbHZHuf0KlRNRY1jwOD45U9V/RanIVNWdQggLEAXUE0IcPZcGcUCSn2NTTiAmJkbvEBSd7MvIOHLZapRIKbHb7UebeyrHUGNY8Clf+QeVnClKZapMzoD7gAfRBrENHB3YCoFpfoxLqcKRRo5KWJFSkp6efuR6VOn+miUlJSo5OzE1hgWZ4w8uVfVfUSqqMjmTUr4NvC2EGCOlfCdAMSknoSbQhqeDBw9it9mOZBcWtb/mSakxLPio5ExRTu5klTMApJTvCCEGAC3KP0dK+bGf4lKqYDJV69em1DIpKSnHXLeWq5wpVVNjWPBQCwAU5eSq9S0vhPgEaA1sBMo2RZOAGth0UD45U5PBw8f27dtBCCj9nZdVzux2u55hhQQ1hgWPiIiIY66XrT5WFOWo6pZg+gCdpMoEFEU3W7ZsRUYlIEoOA9pqTVDJWTWpMSxICCGOua6SM0WpqLp9zjYDjfwZiFJ95b9f1HdNePB4PGzZugV3dP0jt6k5Z6dEjWFB4vgxy+12n+CRihK+qls5qwdsFUKsA5xlN0opr/JLVEqVyjrEK+Fjz549OB0OvEmNIGc7cDQ5czgceoYWKtQYFiSOH79UcqYoFVU3OXvBn0Eop8bpdJ78QUqtsmnTJgC8MQ2O3GYuXeSmTmtWywt6B6Bojv+8qoNNRamouqs1V/k7EKX6yq/OO37+hlI7/fPPP2CJQZqPtiGINGiVM5Wsn5waw4JHUVHRMddVcqYoFVV3tWYR2somgEggAiiRUsad+FmKvxQWFuodghJg/2zejDuq/jG3GQ1gMqjKWXWoMSx45OXlHXNdHVwoSkXVrZzFlr8uhLgGOMsvESknlZ+ff+SyqpzVfocOHSL38GG8TdtUuM9sFKryUA1qDAsehw4dOua6mnOmKBVVd7XmMaSUC4DBNRyLUk25ubl6h6AEUFnzWV90YoX7Io1qQcDpUGOYfnJyco65rpIzRamouqc1ryt31YDWM0j1cNDJwYMH9Q5BCaDU1FQAvNaECvdFGqVKzqpBjWHBY//+/cdcV8mZolRU3dWaV5a77AHSgKtrPBqlWrKysvQOQQmgjIwMhDkKTJEV7os0+NScnepRY1iQSNubfsx1r9d7gkcqSviq7pyzu/wdiFJ9Bw4c0DsEJYCysrLwRsRUel+kwacqZ9WgxrDgYLPZyD18CJ/RjMGrHVSo5ExRKqrWnDMhRLIQYr4QIkcIkS2E+FoIkezv4JTKpe/LOHJZ7RBQ+x0+nIvXZKn0PrPBh92udgg4GTWGBYeyU/QywnrkNjWGKUpF1V0QMAv4FkgCmgCLSm9TAqyoqIjioqOtNNS+dLVfUXExmMyV3mc2gl1t31QdagwLAnv27AHAFxl95DY1hilKRdVNzupLKWdJKT2lP7OB+id7klLz9u3bd8x1NbDVfi6XE2kwVnqf1SjV3prVo8awIJCSkoIwmU94sKEoiqa6ydkhIcQwIYSx9GcYcNifgSmVS08/djKtx+PRKRIlUHw+CVTezy7KJI/ZMUI5ITWGBYGdO1PwWOvqHYaiBL3qJmd3AzcBWUAmcEPpbUqA7d27F4ThSA8A1YC09jOZjCArr5BGmXyUlNhUBfXk1BimM4/Hw+49u/FGVezXpyjKsaq7WjMduMrPsSjVkJ6eDtY6YMsHpGqjEAYiIiIRvspXtMVESHxSq57FxsZW+hhFjWHBYN++fbhdLrxRiZgKMk7+BEUJY9VtQtsSGAO0KP8cKaUa7AIsbW86bnMcJnsBSDXfKBxERUVBUeWNOmMjtYpZXl6eSs6qoMYw/R2z00W55ExVfRWlouo2oV0AfIi2wkn9JenE4/GQlXkAX/3OIDJAaqs3ldotNiYaUW4/1fLiS5Ozw4cP06xZs0CGFWrUGKazlJQUhMGEz1LnmNtVnzNFqai6yZlDSjnVr5EoJ5WVlYXX68VnrYM2XdBLQUGB3mEpfhYdHY3BV/mWXXXNWp5x/GbSSgVqDNPZzp0peKPqgjh2qrNa1KQoFVU3OXtbCPE8sAw4MslJSvmnX6JSKlW2J500Hz19lZeXp1c4SoBERUVh8FX+BVbPoiVnmZmZgQwpFKkxTEdSSlJ2peCJqtj3V+1woSgVVTc56wrcDgzm6CkBWXpdCZCy5MxniTtym6qY1H4WiwVxguQs0gh1LRU3k1YqUGOYjrKzs7GVlOCrl1DhPrvdrkNEihLcqpucXQu0klKqvg062r9/P8IYgTQd3fokJ1ttgl7bGY0nbqUB0NjqJi0tNYARhSQ1hulo9+7dAHitFZMzNW9WUSqqbp+zv4F4fwainFxWVhY+cyyIow1Js3Ny1N50tZyU8pjf+fGSoz2kpaWpidVVU2OYjsqSM19UxeSssLCwwm2KEu6qWzlrCGwXQvzBsfM11DL0ANq//wCeyJhjbnM6XRQUFBAfr753aiun0wmi8u2bAJrHenFmuEhPT6dly5YBjCykqDFMR7t37wZrHBgjjrk9NkJy+LDaqEFRjlfd5Ox5v0ahnJSUksysTGRcqwr3ZWVlqeSsFisuLsZriDjh/W3qaD3Qtm7dqpKzE1NjmI52puzCbam4bVO82cvBnGwdIlKU4Fat05pSylXlfwAP2lYoSoAUFRXhdDjwmY9WzmIj1Eq9cHDo8GF85eYZHq+R1UdsJPz9998BjCq0qDFMP3a7XevPWMl8s3oWH1lZat6sohyvunPOEEL0EEK8LoRIA14BtvktKqWCsgFMljutWdZGITtbHXnWVlJKMjMzj0nKjycEdI53sv6P39X8wyqoMUwfqampSCkrnW/WwOqlsKiY4uJiHSJTlOBVZXImhGgnhHhOCLENeBfYBwgp5QVSyncDEqECHE3Ayn9JW00+oiOEOvKsxQ4fPozdZqvQVf14XRLc5OYVsGvXrgBFFhrUGKa/ss+kt5LkrJFVW8SSkaH22lSU8k5WOdsOXAhcKaU8V0r5DqCWhOmgLAE7voKSaPGqylkttnPnTqDyVW7l9ajnQghYtWpVIMIKJWoM01lKSgoiwnxM1b9M42jtV7F3795Ah6UoQe1kydn1QBbwPyHEDCHEhcCJ1/QrfpOVlYUwRoDRfMztiWYP2ZkHdIpK8bfNmzeDMOCNqlfl4+IiJR3j3fzvp5/Uqc1jqTFMZzt3puCxJFTaDqaB1YfJAGlpaYEPTFGCWJXJmZRyvpTyZqADsBJ4CGgohPiPEOLiAMSnlKqsxxmUVs5ycnSKSvG3DRv+xBddD4wnX1jdv6GT/QcOaAmdAqgxTG8ej4fdu3fjjUqs9H6TkDSJ9h3pg6Yoiqa6qzVLpJRzpZRDgGRgIzDOr5Epx8iopMcZQKLZR4nNribU1kL5+fns3LkDd1yTaj2+XwMnFpPgu+++83NkoUeNYfpITU3F43Hjja48OQNoGu0iZecOVfENEzk5OaxevVrvMILeqazWNAohkoAYYAlwp7+CUo4lpSQrMxNZyYq9+la1YrO2+uWXX5BS4olvWq3HW0zQv4GdFSt+JC8vz8/RhR41hgXejh07APBG1z/hY1rEesnLL1D7BIeJ119/nWeffVaNUSdRreRMCDEGyAaWA9+V/iz2Y1xKObm5uTidDnzmuAr31bdoE2oPHFDzzmqbH1esAEscvnKnhMzpa6mqvnBpUztut4cFCxb4P8AQosYwfWzfvl1bDGCOPeFjWsZ5jjxWqf3KTmGrZLxq1a2cPQC0l1J2llJ2Lf3p5s/AlKP2798PoM05O06D0spZ2WOU2iE7O5uNf/2FM6HVMfMMDbbcKp/XONpHz3ouvvn6K3Wq+1hqDNPB5i1bcVvrVbk3bPMYD0aDtsOFUvs1atQIQLWAOonqJmf7gAJ/BqKc2JHkrJJeV9ERkjizYN++fYEOS/GjRYsWIaXEXa/tKT/32pY2iopL+PLLL/0QWchSY1iA2Ww29qal4o2ueqVxpBGaxznEGkEAACAASURBVHjZohayhIUmTbQ5tOo7q2rV3VtzD7BSCPEdx24aPMkvUSnHyMjIAGGodM4ZQGOrm71qKXqtYbfbWbBwIe74ZlWeDjqRFrFe+tZ38sXnn3HVVVeRmHjiydhhRI1hAbZz506klHhjGpz0se3quFixfRsul4vIyMgARKfoJTo6GlC97U6mupWzdLS5GpFAbLkfJQD27dsHljgQlf+6mkR7SEtLVaudaokFCxZQXFSEq/Hpn3W7qbUNt8vJ+++/X4ORhTQ1hgVY2WlKXxWLAcq0j3fjdnvUvLMwkpaWqncIQa1alTMp5Yv+DkQ5sdS0NNyVLAYokxzt4af9Ng4dOkT9+icfCJXgVVBQwCdz5uCtk4yvGhWHE2kY5eOypjYWLV/OkCFD6N69ew1GGXrUGBZ4W7ZsAWsdZITlpI9tH+9BAH/99RfduqmpgOFgv9qyq0rVXa1ZXwjxhhDieyHET2U//g5O0Zo4Hti/v8q9FZNLt0DZs2dPoMJS/OQ///kPNpsNR9O+Z/xaV7WwUz9K8trEV7Hb7TUQXehSY1hgSSn5Z/Nm3CfY2cJQcuxKvZgISfM4Hxs2rA9EeEoQKC6xhf24VJXqntaci7ZHXUvgRSAN+MNPMSnlHDhwAK/Xi88af8LHNI3RkjPVZTu0/fLLLyxduhRnw674rHXP+PXMRri3fSEHMrOYPn16DUQY0tQYFkCZmZkUFhSccL6Z8Loq3NYx3snWLVvVF3YYsdlseocQtKqbnCVKKT8E3FLKVVLKu4Gz/RiXUio1VTsvX9WXdXSEJNGqKmehbP/+/Ux4dSIyOhFXk5419rod6nq4ONnO/PnzWbNmTY29bghSY1gAbdmyBaBaiwHKdKnrxuP1smnTJn+FpSgho7rJmbv030whxBVCiJ5oW6AoflaWcPksJ66cASRHudi9KyUQISk1LD8/nyfGjcPmdFPS6gIwGGv09W9uY6NlnJeJr04gMzOzRl87hKgxLIC2bNmCMEacUgW4fbybCAP88YcqaIaLqKgovUMIWtVNzl4RQtQBHgEeBT5A20BY8bOUlBSwxp904+tmMR7S9+3D7XZX+TgluBQWFvLwI4+wf38mJa0HIy0nXvhxuiIMMKpzIT6XjaefejJcTyWoMSyANm78G3d0/ROuMK9MpFFL0P5Y97sfI1OCRZTVgtVq1TuMoFXdjc8XSykLpJSbpZQXSCl7Sym/9Xdw4U5Kybbt23FHHdunypy+FnzeY25rGuPF6/WRpvqdhYycnBxGjR7NntQ0StoMxhvbyG/v1cDq4/5OhaSlpfHKyy/j9XpP/qRaRI1hgVNUVMTevWmn9XnukuBib/o+cnJy/BCZEkySkpL0DiGoVXe1ZrIQYr4Q4qAQIlsI8bUQQp0S8LPMzEzycnMrzNvQtvA5tqdZi1htf7qdO3cGKjzlDGzZsoXhw+8j40AWtrYX463j/z+nboluhrUt5tfffmPq1Klh1RdPjWGBs3HjRq357GkkZ90StMr/77+r6lltl9RE/flVpbo151nAt0BjoAmwqPQ2xY/+/PNPgGoNcg2tPqIjBNu2bfN3WMoZ8Pl8fPnll4wdO5Z8h5fi9lfgjWscsPe/KNnJ5c3sLFy4kA8//DBg7xsE1BgWIBs2bEAYI/BWo/ns8ZpEe0m0wtq1a/0QmRIMCgq0XdQaNDj9Po7hoLrbN9WXUpYfyGYLIR70R0DKUatXrwZL7EkXA4C2r3D7Ok42rFeTaYNVdnY2r7/xBhvWr8cT3wx7y4FgMgc8jptb27B5BHPmzMFqtTJ06NCAx6ADNYYFgJSSNb/8giu28WktbBECeibaWf3HOux2u5qTVAutXLkSgLi4mp9fW5tUt3J2SAgxTAhhLP0ZBhz2Z2DhLj8/n/Xr1+Oq00wbsaqhS4KLzKxs0tPT/Rydciq8Xi8LFy7kjjvu5M+//sbRfAD2NhfqkpiB9nG6s30J/Rs6mTFjBh9//LEucQSYGsMCYNeuXRw6eBBvfLPTfo0+9V04XW7WrVtXg5EpwaD8XFeTqbq1ofBU3eTsbuAmIAvIBG4A7vJXUAosWrQIr9eLu377aj+nd30XBgFLlizxY2TKqdi2bRsjRo5k8uTJlETWpajzNbgbdKh2wu0vBgH3dSrmnEZOZs6cyYcffljb56CpMSwAli9fDgYD7rqnn5y1r+Mh3gI/LF1ag5EpwSCj3JZNDodDx0iCX3VXa6ZLKa+SUtaXUjaQUl4DXOfn2MJWcXExX375Fd46TarcGeB4dc2SHokulny3WH3wdZaTk8P48eMZOXIku9IysLc6H1u7S5Dm4Nlr2yDg3o7FnN/YwSeffMKkSZNq7SpONYb5n9PpZOkPy3DHNQXTyffTPBGjAQY2tLF27VoOHjxYgxEqeitrTgyQl5enYyTBr/pNaCp6uMaiUI4xd+5cCgsLcDTpfcrPvayZnfzCIj777DM/RKacTFFREdOnT2fosGH8uOInnI27UdjlOjyJrXWvllXGIODuDiUMaW5n0aJFPP/cczidTr3DChQ1htWg5cuXU1iQj7thxzN+rfOTnIDk888/P/PAlKDx22+/Hbl84MABHSMJfmeSnAXfN00t8M8///D555/jqtcWX3TlmwZXpX28h7MaOPl03txjSsiKf9ntdubNm8ctt9zKvHnzsMU0pajLdbiS+4AxUu/wqiQE3NTaxtC2Jaz55RceeujBcDmqVWNYDXG5XMyZOxcZnYg39sxXHzew+ji3kYOFCxeonme1REFBAb/9dnQV7t40td1gVc4kOavVE1T0kJeXxwsvvoQvMgZns36n/Tq3trFhkm6ee/YZtYmwnzmdTr7++mtuufVWpk+fToEpnpLOV+NofX5QncKsjkuaOhjTpYiUHdsYOeK+cGhorMawGvLNN9+QlZmJvUnvGqsQX9PCDl4PU6ZMru3zIcPCt99+i8fjxmfSVuAeOpxHSUmJzlEFryqTMyFEkRCisJKfIkC1961BxcXFPProY+Tm5VHSatAZVVsSLT5GdiokNTWN1157rdbOI9KT2+1m4cKF3Hrbbbzzzjvk+qzYOlyBvd3F+I7b0SGU9G3g4qmeBdjzc7h/5IiQbwaqxjD/y8jIYNbs2Xjim9ZoM+V6Vh83ti7h119/Y/HixTX2ukrgFRQU8Olnn+Gp0xRZbpX6vn37dIwquFWZnEkpY6WUcZX8xEop1TrYGlJUVMQTT4xjd+oeSloPPq3TmcfrlujmptYlrFy5kokTJ6oErYZ4PB4WL17MrbfdxuTJkznkMmJrfym29pfhjW2od3g1onWch+d751HPZOPJceP44osvQrZyocYw/3K73bz40ku4vBJHs/41/voXJzvokuDmnalT2bRpU42/vhIY77//PjabDWdyn2Nu379/v04RBb8zOa2p1IDs7GxGjR7Nlm1bsbc8v0aPPK9o7uD6ljaWL1/OhAkT1KboZ8Dr9bJs2TKG3X47b775Jjl2sLW7mJL2V+CNq30FmHoWH8/2yqN3PQfvvfceEydODKeFAko1SCmZPHkyKTt3Ymt2DtIcU+PvYRAwolMRiZEuxj3xuNqeLgT9/PPPLFmyBGejrvii6gIQadAO9tSigBNTyZmONm3axH0jRrJvfya2tpfgSWhZ4+9xdUs7N7YqYcWKFTzy8EPk5+fX+HvUZlJKfv31V+66+24mTJhAZqELW9uLKOkwREukg3AFZk0xG2FUl2KubWnjhx9+4IGxYzh06JDeYSlB4uOPP+b777/HmdQDT0ILv71PXKTk8e75WHHw2KOPHNOOQQluu3fvZvyECfhi6uNK6nnk9giDpK4FtWitCio504HP52Pu3Lk8+OCDAdlf8coWDu7vXMS2rZu5b/i9pKSk+O29apPt27czZuxYnnrqKdJz8rG3HkRxx6u07ue1OCkrzyDg2pZ2xnYtInX3Tu67999s3bpV77AUnc2ZM4dZs2bhTmxzzJeuvyRafIzrnofZU8iDDz7AihUr/P6eypk5cOAAjz72OE5pxNZ6cIXtvJKsblL37NYpuuCnkrMAy8zM5KGHH2bGjBk445tT1PHKI6Vefzq7oYune+bjKjzI/SNH8OWXX+Lz+fz+vqHo0KFDTJgwgREjRrBlxy4czQdQ1OlaPAmtdE3KzOlrMdq0HYd+z4lkzs6ogL13n/ounuuVj8GZxwMPjNU6wSthR0rJ9OnT+eCDD3AntsbR8tyA/U00jPLxfO88WkU7ePnll5k+fToejycg762cmoyMDMY+8AD5RSWUtPk/ZGR0hce0inOze88ebDabDhEGP5WcBYjP52PBggXceeddbNq8FUeLc3C0GhTQHlit4ry80ieXrvF2pk2bxuOPP6ZOU5Xj9Xr55ptvGHb77Sz7cQXORt0o7HK9tt2SQf8/FYMtF+HV5g3mOY2kFwd2PnvTGC8v9MqjdbSD8ePHM2PGDJXghxGHw8FLL73MvHnzcNVvj6PleSAC+3cRGyF5okcBg5IczJs3j/tHjmDv3r0BjUGp2o4dO7h/1GgO5xdT3O4SfFEJlT6uc103Xq+PDRs2BDjC0KD/N04Y2L17N6NGj2bKlCnYzAkUdbpG2zNThypMbKTkga5F3Nm+mE1/beBftw9j4cKFYf8lu2/fPkaNHs3UqVMpiUyguPO1uJoGfwPZQIuNlDxe+uU4d+5cXnzxRbVQIAzk5OQweswY/ve/n3Am98HZfIBuVWSTQdvVYkyXIg7s3cXwe//NN998o1akB4HVq1czZsxYCp1eittfVmVboXbxHmIj4ccfVRW+MmopuR/ZbDZmz57NV199hTRGYm85EE9iG93nKwkBg5s46VzXzaydsUyePJkffljKo48+RqtWrXSNTQ/ff/89k6dMwSMF9lbn6376MtiZDHBX+xIaRXn5bNUqcg8fYvyEV4mLi9M7NMUPfvvtN8ZPmECJzYGtzUV4z2BT85rUt4GLtnVy+WB7LFOnTuWHpUt46OFH6NChg96hhR2fz8ecOXOYOXMmvpj62NpciIyoetqFyQADGtpZseYXDh8+TGJi6PaH9AdVOfMDKSXLli3jtqHD+OKLL3AktqWwy/V46rUNqi/9hlE+nuhewPCORaTv2sa/77mHN954g8OHD+sdWkC43W5ef/11Xn/9dRyWetq8siDdAzPYCAGXNytbaLKFMaPuD5vPTbhwuVy89957PPnkkxT5IinqdFXQJGZl4s2SR7oVcn/nIrL3pjBy5AgmT55MYWGh3qGFjaKiIp555hlmzpyJO7E1Je0vqzIxE24bTq82xg5OcuDxevnuu+8CFW7IUMlZDUtJSWH06DFMmDCBXLeRko5X4mxxDpTrihxMhIBzG7t4rV8uFzWxsfT77xh6223Mnj27Vk/UtNlsPPHEE1orgMbdsLW7GBkZuAn2tcXZDV081r2ArMwMHhg7hoMHD+odklIDdu3axfDh9/HFF1/gatCB4g5XIC119A6rUkJon8OJ/XL5vyZ2vv12IUNvu5Uvv/wSl8uld3i1WkpKCvcOH86vv63F0ayfNg/RUPUJOeHz4ivtad042keXBDcLF8xXizuOo5KzGlJQUMCkSZMYPnw4W1N2Y29xLiUdhuCLqa93aNUSGyEZ1s7GxH55dI0rZPbs2dx6y83MmTOH4uJivcOrUQ6Hg8efeII///oLe8uB2ubkAZ7YXJt0rOvhsW4FHM7O5IGxY1QFLYR5PB7mzp3LfffdR9qBbGxt/0+bX3aSL9xgEGXSxrCX++bTPDKfadOmcce/bmflypUhu8NFsJJS8u233zJy5P1k5RZS0v4y3A07n9ZZh/9LtnM4N4+ff/7ZD5GGLvWNdIZ8Ph+LFi3itqHD+HbRIpwNOlLY+To89duF5OmxhlE+xnQt5vneBbSIyOWDDz7g5ptuZObMmRQUFOgd3hmTUjJ+/Hg2//MP9pbna6ealTPWLt7DY93zOXwwmyfHPYHdbtc7JOUUpaamcv+oUcyYMQNHXFOKO12DN76p3mGdsmYxXh7vUcij3QsxFmfywgsvMGLEfaxfv14laTWgqKiIF154gUmTJuGMbkBxx6vwncHWdd0T3TSKlnw6b576/ZSjkrMzsGvXLkaNHs1bb71FoYimpNM1OJud7ddTmEd7XUnSikx+63XVuo6HR7oX8lLffDpE5/Pxxx9z0003Mnny5JBeuv7111+zevVqHE3PwpMYfosf/KlNHQ/3dyogZdcuXnnlZbV6LkSUVcv+fe+9pOzZi73VIBytL0BGWPzyfub0tQiPtsJ36j+xfhvDuiW6eaVvHvd0KOZQ+k4effRRHnzgAf755x+/vF84+Oeff7j7nntY9fPPOJN7Y2t7MTLCekavaRBwZbMSUnbtUv0Tywn+WnUQcrlczJ49m88++yzgqzDL97qyeQx+73XVItbLA12LySi2s2Sfhe8WLWThwoWcdVZfrr/+Bvr27YshCHqAVUdOTg7Tp8/AE99UK8ErNa5nPTdD25Qw55dfmT9/PjfccIPeISlV2LNnD69OnEjKzp2467bA2bz/GX/ZnozBlotAq5DsKozA5MfhwyDg/CQnAxo5+d9+C4t2bGLMmDH0O+ss7rr7brWys5rcbjcfffQRc+fORZpjKelwBb6YBjX2+uc0cvLTAStT355Cx44dado09Cq2NU0lZ6coJSWFV8aPZ29aGu56bXE0PStoJ/vXpOQYL/d2LOHm1jZ+2m/hp7//4Il1f5DcJImrr7mWSy+9lNjYWL3DrNK8efNwedw4mp0dkqecQ8X/JTv4JzeSGTOmM2DAAJKSat/G8KHO4/Hw2WefMWvWLHzGSOytL/DL3r7BIsIAFzd1cH6Sg2X7LCzZuI4RI9YxoH9/7rr7btq2VdMbTqRsf8w9u3dr33nNzgZjRI2+h0HA/Z0KeWGDkQcfGMsbb74Vlm2dyguNkkcQkFLyzTffcN+IEaQfyMHW9v9wtBwYFolZeXGRkmta2pnU/zAjOhVhKd7HtGnTuP6665g4cSLbtm0LynkDDoeDJUuW4k5ojTQHdxIZ6oSAO9sXY/C6eG/aNL3DUY6TkZHB6DFj+OCDD3DENaOo0zW1OjErz2zU9hp+6+xcrm9pY+P637j33nt55umn2b1b7fNYnsfj4aOPPmL48OGk7juAvc2F2ndeDSdmZepbfYzrkY+3JI+RI+5jwYIFYT01QlXOqsHpdDJp0iR++OEHPPHNsIdhUnY8kwEGNHIxoJGL9CIjK/Zb+N+PS1m6dCnt2rbh2uuuZ/DgwZjNwfH/acOGDTidDtwtWusdyunzurBYLAwZMoTFixdjD+Kl54kWHxc1sfHdr7+SmZlJ48aN9Q4p7EkpWbx4Me+88y5uCfZWg8J23qXVJLm6pZ3/a+pgabqFH9b9wppffmHQoEHcddddNG/eXO8QdZWSksKEV18ldc8e3AmtcDQ/G0z+mYNYXtMYLy/1yWX6tlimTJnC998t5t/3Dqdv376IMDvboSpnJ+FwOHj88cf54YcfcCb1xN7mwrBPzI7XLNbLXR1KmDogl3+1K6YkcyevvfYaN914AzNmzCA7O1vvENm+fTsIgbcG50kEmvC4GDJkCKNHj+aKK67A5gnuwWpwEycSqRpMBoGSkhJeeukl3nrrLeyWRK1aFqaJWXlRJsl1rey8dXYuVza38dualdx1552MHz+eAwcO6B1ewLndbj788EOtlUpGllYtaz0oIIlZmXiz5LHuhYzoVMThjF08/vjjjBhxH8uXL8ftdgcsDr2pylkVnE4n4558kr83bdK29UkM4apLAFhNkouSnVzYxMm2PBPLM1zMmzeXT+fN47zzz+df//qXbvMIcnJyEOaYkOjXdCLSFMnixYuRUkt4GpqC7/RxeYkWH63iPGzZvFnvUMLa3r17GffkU2RmHsDZpDeuxt3UnMvjxERIbmxt55KmDr5Lt7Lip+X876efuObaa7n99tupUyc4G/DWpLS0NF56+WVtblliGxzN+ulWiBBCOzNzVoPD/JxpZmnGDsaPH8+0d6Zy0cWXcMkll9CmTZtaXU0L3W+qAJg2bRob/9pYuhpTJWbVJQR0SvDQKaGIQ3YDK/Zb+GnNSlatWsn55w/izjvvpEWLFgGNyeFwIA3GgL5njTNG4rDl8vXXXwNgjQ/u5AwgyephS1qq3mGErQ0bNvDMs8/i8Ehs7S/DG9tI75CCWlyk5NY2Ni5p6mD+Hitff/0VS77/jqHDbuf6668PmmkaNW3JkiVMmjQJjzAF1f6pJoNWgR+U5GRzbgSrDjhZ8M1XfPXVVyQ3SWLQBYM577zzaNu2ba1L1FRydgIbNmzg22+/xdWwC556bfQOJ2TVs/q4uY2NK5rbWZpuYVlpknbRRRcxduwDAVvhGRUVdaQFiRI40RGS4sO1dxuwYLZixQrGj5+A1xJHSYf/Q5pj9A4pZCSYfdzTsYRLmjr4Yo+L6dOn893iRTz62OP07NlT7/BqjNfr5d1332X+/Pl445KwtzrvpBuW68EgtL513RLdFLlL+CMnknU5e5k7dw5z5syhXmIC/Qecw9lnn03Pnj2Jigq+/4ZTpZKzE/jkk0/AEoszuZfeodQKMRGSG1rbuaSZgyXpVpb8+CNbNm/mpZdfoU0b/ye/SUlJSGcJeFxgivT7+ymafKeB+vUS9Q4j7KxevZrx48fjjm6Arc1F6jN/mpJjvDzcrYjNuRHM3il56KGHuPzyyxk5cmTQtw46GY/HwyuvvMLKlStxNeyMs2nfkNjGLjZCMriJk8FNnBS6BBsPRbLxsJPlSxazaNEiTEYjnbt0pm/fs+jZsyft27fHZAq9VCf0Ig6AvLw8/v77b5yNuwffHKUQWrFXmdgIyU2tbfSs5+LdLXD/yJE88+yznHfeeX593y5dugBgKsrEUze8V2IFipSQXhJB4w6qz1kgbd68mRdefBFPVD1sbf/Pb60PwkmXBDfj++ayIDWKJUu+5491a3l14usBObD0l6lTp7Jy5UocyX1xN+4akPc0p68F6cUHTPgzjmYxHoa1O/3Kelyk5LwkJ+clOXH7iknJN/FPbiSb92zkg783ARBltdCtew969NB+2rRpExLJWvBHqIOMjAyklEG5sk94XAy5SluxJ6Xk5+++0Duk09K2joeX++Ty+sY6vPvOVM4991y/7jTQtWtX4urE4z60SyVnAZJaZORAiYFbzh+kdyhho6SkhJdefgWvKYoSlZjVKLMRbm5j46wGTt7eDGNGj+KFF1+iX79+eod2ylauXKlN22nUNWCJGZTtDgEg2J5fs5/NCEPZXGcPN2Oj0KW9x9Y8B1v/+Y21a9cCWrLWtVt3evToQffu3WnXrl1QJmvBF1EQiIgo/dBIn76BVCLUVuxVJS5ScnkzG+9vPcTmzZvp1q2b397LZDJx1ZVDmDNnDi5bLr6oBL+9l6JZnmElMiKCCy64QO9QwsbMmTPJycnG1v7y4G35E+LV/5ZxXp7vncukTfE8+eSTvPjiiwwcOFDvsKrN4/Hw7rRpyOhEnMm99Q7Hb+IiJWc1cHFWAxdQQr5TS9a25TnYsXktv//+OwAWs5mu3brRs2dPevbsSdu2bYMiWdM/giCUlJSE0WjEVJiJNz44Vq0cEYIr9qrildpxVG5urt/f66abbuKb+fPxpf9GSfvLVTsBP9qaa+KXLDO33XZjyM/NCRUlJSUsXvwd7sQ2eGMb6h3OCdWG6n9ds+Tpnnm8ujGeSW+9Sc+ePYmJCY0FF+vWrePQwYM42lwYEnPMakq8WXJ2QxdnNzyarO3Ij2B7voNt29bxxx9/AFplrVfvPvTt25c+ffrQpEkTXeJVyVkl4uLiOOecc1j92zqcST2C9wg0xOXYDczZFUPXLp0DcuQZFxfH2DFjmDhxIpEH/sLVRC328AebRzBzZxxJjRtxxx136B1O2Pj5559xOh24WgX3Zt61pfpvMcEd7Yp4cb2Rjz76iFGjRukdUrVs2rQJDEY8dZL1DkVX8WZJv4Yu+pUmawUuwba8CLbkOtiyYQ1r1qwBoFnTZM45dyDnnnsunTp1CljLDr8lZ0KImcAQIEdK2aX0tgTgc6AFkAbcJKXME9p/7dvA5YANuFNK+ae/YquOYcOGsWbNL1jSfsHR+gJVZalBHh8sz7AwPy0aQ4SVJ596GqMxMD3ILrnkEjZu3MjSpUvxmWPx1FMbHtckjw/e2RzLYaeRSeOerLV9oYJRRkYGCAO+6Hp6h1K1WlT9bxXnpXNdF3/9uUHvUKotLy8PERkFod73sYbViTxaWZOyhGy7gb8PR/LXoVS++CyDTz/9lKTGjbjk0su48sorSUjw79QYf9Y0ZwOXHnfbOGCFlLItsKL0OsBlQNvSn+HAf/wYV7W0a9eOe+/9NxF5aUQe2Kh3OLWClLAtz8Rz6+vy6a5ouvc+ixkffEBSUuBW8wkhePjhh+nVqxfW1NVEHNwZsPeu7XwSZu2IZktuBI8++hjdu3fXO6Swkp+fj4gwqwPJAJOA2RK47Y3OlNVq1VoKSR2S4tL5hjfccAMWiwV7kG5BJwQ0ivJxSVMH43oW8u65udzbsZg6jgxmzZrFrbfczLRp07DZ/NfD0W+VMynlz0KIFsfdfDUwSAjxAHAn0FIIkQl0BL4GlqFV1ZKEEB2klNv9FV913HzzzaSmprJs2TKQPu00mBr4TpnLC7/nmPlxv5XUQiMN6tfj5Sce4Nxzz9Wlq3NkZCQTJkzgmWefZf0faxCOQlzJvcJq/kVN8/pg5vZoVmdZuOOOO7jsssv0DinsJCcnI112hNuOjLDqHU5YyHUYSC2OpG+9+nqHUm1t2rRBepwYHPn4rHUD+t6hOt8wOkIysLGTgY2dZNoMLE6z8tVXX7L+j3VMnvI28fHxNf6egf42aggkAvcCvdBOYQ5Bq5hdwNGqWibwZGUvIIQYLoRYL4RYf/DgQb8GazAYGDduHFdccQXmzL+x7FkFClLjkgAAIABJREFUqst8teU6DHy128rDaxOZsS0GX53mPPTQQ3z08ScMHDhQ1+02LBYLE199lSFDhmDO2kTUzh8QruDuZO+LSkCWtkaoa/bSLCY4Vrm5ffDellhWZ1m48847ufPOO/UOKSyVrXY25afrHEl48EmYvi0WnyGSe++9V+9wqq1///4YDAYiDqYE/L3L5hu+8847fPfdd0SF4HzDxlE+7u1UwiPdCklL28uiRYv88j56LAjoCKyVUtpKv5xXAbcBbYBHSx+TCZxf2ZOllNOB6QB9+vTx+2/WYDDwyCOP0LhxYz788ENMjjxsrQYF/IgjVDi9sOFgJGuyLGzJjUAIQf8B/bnuuuvp1atXUO1/ZjKZePTRR+ncuTOTJk/GtHUBtqb98CS0CsoKqbPZ2RhsuZiKsujXwMVtbfVPJgtdgqmb49iZb2LUqFHceOONeocUtjp27Ei79u3ZmbYRd0Ir1ePMj2wewQfbYtiaZ+LRR8eSnBw6k+sTExMZPHgwK/63ElfDToHd1qsWzTcscQsk+G2+dKArZ9lAFnCVECIFiAIeAHxAPWBB6e3dgZqvE54mg8HAsGHDePPNN6lj8hGz9VsiM//WpQ9aWfVEAlEmX1BUT6SEHfkmPtwWzdhfEnl/aywHTUn86447mDtvHuPHT6B3795BlZiVd9lll/HBjBm0b9MS655VWFOWIxwFeocV9NKLjDy/IYG0EivPPvtsrUvMhBAzhRA5QojN5W5LEEIsF0KklP5bt/R2IYSYKoTYJYTYJIQI+FJgg8HAmNGjwVmCJX2tPnOKwkB6kZHn19flz8MWRowYwRVXXKF3SKfsnnvuwWQ0YN37q/qcnAIpYUuuiTc2xvGfrbF06tSRq6++2i/vFejK2bdok/8FWmKWA7gAB2ABJqOt4vwWiK7sBYQQw9EWDdCsWWB7kPXu3ZvZs2cxecoUVv/8MxF5e7G3OAdfVOD2DiyrnhiLsmkRe2ZbX5ypg3YDa7LM/JJtJccmsJjNDLroAi655BK6d+/u147/Na158+ZMK90A+IMPP8S0ZQHOBp1xNe6m9iWsxK9ZkczaEUtsfALvTHiV9u3b6x2SP8wG3gU+Lndb2aKmiUKIcaXXn+DYRU390BY1Bbx1fNeuXbn99tv55JNP8EXG4GpSezbp1pvDA9+lW/l+XzRxdeKZMvFFvzbO9qfGjRsz6v77mTJlCpGZf+NK6qF3SEHLJyG10MS6g5H8cdDKITvUja/D8OE3c9NNN/mtYa0/W2l8CgwC6gkhMoDngYnAQrSq2K/ADaX//gr0LL2/CJgAjK/sdQN9WvN4CQkJvPzSS6xcuZK3Jk3GsPVbXPU74GzSKyz6odk98EeOmTVZFrbnmxACevTowT2XXsbAgQOJiorSO8TTZjQaueGGG7jgggt4//33Wb58OZbDKdgbd8Ndv4Naeo62uGNOSjQrD1jo1q0rzz//AomJtXNj8yoWNc0praZFAHXRkrObgCbATrQDzAQhRGMpZWbAAi519913k52dXbqQyYurSe+gPE0fKnwSVmea+To1hnwnXHDBIMaOHUvduqE9teXqq6/mn3/+YcWKFVpbocTWeocUFKSEgw4D2/Ii2JwbwZZ8C8UuiclopE/fPgwffCHnn3++39sE+XO15q2V3S6EuAf4H3Au2twyI5APFAJutAraA4D9BM/XrXJW3qBBg+jduzczZ85kwYIFmPNSsSf1wl2/Xa1c9ZdWZGRFhoW1ORacXmiS1Jh7rr+ciy++mIYNg7cb+elITEzk6aef5sYbb+S9//yHjX/9jiV7C47G3XEntoUQqgjWpAMlBt7bWof0IgNDhw7lrrvuCoptTgKsMXAzcBZa1d8uhGiLNp4tl1KOLK2ojUBL1o5JzgIxfgkhePzxx4mM1CZfGxxFOFoNBEPY/a7OiE9q82cX7I1mX5GBTp06Mn7UaDp37qx3aDWi7HNy8OAhNv2zGrsw4klooXdYAeeTkFFiJCXfxI6CCHYUmMlzaPcl1o3nnAv60bdvX/r16xfQ3U70+GvNQTuluQtwAh2Aw6X3CbS2MSc8zNO7clZebGwsDzzwAJdffjlT3n6bLZt/xXxwO/bkvnjr6LPlQ00qa4GxYr+VPYVGzJERDL74Iq644go6d+4ctHPIakq7du2YPGkSGzZsYMYHH7Bj+y9YsjbhaNgVd722YVNJkxJWHjAzd1cM1uhYXnvtmZDc7LmGGCld0AQghPAA1wKNgKWlj/kIeBZtLDtGoMYvk8nEI488QnJyMu//97+YtuZha3k+vmj9q5y+qARkUTYCSZs4d1DMmy3P44O12ZEsTo/hQImgSVJjnn94OIMGDap1Y57ZbObVVyfw6GOPsW3b/7D7zq31jbltHsHuAhO7Ck2kFESwuygSu1v7U0ysG0+P/j3p3r073bp1o2XLlrr9zvVIzi4CvpZS3iOEuBhtTkd7IAboIqXMEEIMQdtJICS0bduWd995h1WrVvHef94nZ+cPeOok42zaNyRXdRa5BMsyLPy4P4oSt7Z9xZg7ruXiiy8Ou30ShRD06dOH3r17s3btWmbP/ogdO34tTdI6467XHoy1tyJR7BbM2h7DHwcj6d2rF089/XStPY1ZTTnABUKIp4E7ADPwb7RTnM2FEL8DCWgHoBV6/QSy8i+E4JZbbqFNmzaMnzABsX0RzqReuBp10bW672x2NqbDexAeB2O7FhFvDo4J6U4vrMk08/2+aA7aBa1atuC52//F+eefH7AdTPQQHR3NW2++ydNPP8Nff63G6SzW5qD5ISnREvMsBJIO8R6/J+ZSwmGHgZ0FJnYWRJBSGElGkQEJGISgRYvmXDywO507d6ZLly40btw4aBJwPb5V0oEBQoj5aL3NzGj9ziTwvRDCinZqs9L/Q8FyWvN4QggGDRpE//79mT9/Ph99/DGmLQtw1WuLK6kXMjL452LlOQVL0q38LzMKp0cy8Nxzue766+nRo0fQfGD1IoSgf//+nH322axbt46PP/mELZt/x5q1CUf9TrgadKh1cw635pn477Y6FLoNjBgxnJtuuimkFnn4yVdAJ+A5tFOWm9ASMYnWm/EhtDlnvwJXcNxuJ3pU/vv06cPsWbN46623+Pnnn4nMS8XWbAC+mNBpnOpPhS7BjxkWfjwQRbELOnZoz0P/uoP+/fuHzbgXFRXFa69N5M0332TZsmUY7Hk4Wp4LxppdDOVsdjYROTswSA9P9Sqs0dcuc8huYGteBFvzItheYCa39BRllNVCp85duKhrVzp37kzHjh2Jjq503WFQCHhyJqX8XQjhBPqjVcsWAq8Bo9Em1zqBPKDSkSOYTmtWxmw2c8stt3DppZfyySefMH/BAsy5qTgadcXVsEtQVlmK3YIFqVZ+OmDFh4ELL7yQoUOH0qJFC71DCzpCCPr160e/fv3YtGkTn3wyhz/+WIcl+x8c9drjbtQl5Luze3zw9Z4ovk+30qRJEhOfe762rsas0kkWNRmAvcCfaGcDDqKNWePR5qJlA9cQBFvRAdSpU4cXX3yRVatWMeXtqYhti3A16Bg2C5kqk2kzsDTdyppsC24vDBgwgFtuuYWuXbuGTVJWXmRkJE8++SStW7fmv//9LxHbvqWk5aCg36vV44Pt+RFsPBTB37kWsm3a7y4+LpYeZ/emW7dudO3alVatWoVUBTTgmYIQIg7oDOxHq5g1l1LmlG5+noHW76yEwPdgq1Hx8fGMGTOGa6+9lv/+97+sXr0a86Ed2JN6a6tiguCP3+uDnw5oG5Db3ILLLr+coUOHBnSvy1DWrVs33njjdVJSUpg7dy6rVq3CnLMVV2JbXI27Is2hdwo402bg/a1xpBYaufLKK7n//vu1vfjCUBWLmh4DvkRbBHAe4AHWolXQ9qCtPLcDTSt5rm6V/7Lqft++fZk5cybffPNN6UKmnrjrt6+VC5kqs6vAxHfpFv48aMYUYeLiSy/hpptuonnz5nqHpjshBDfffDMdOnTgxZdegu2LcSb1xNWoa1B9Psp6a/6abWbdQQs2N0REmOjVsxc39etHz549dZ0vVhP0KOO0Qms6K9AqZBuFENFoyVi+lLK/EGIJJ0jOgvW05okkJyfz8ssvs2nTJt55911Sdv6M7+B27E376XpaYWe+iVk7Y9lfbKBnzx6MHj2G1q3VUurT0bZtW1544QUyMjL49NNPWbp0KZGHduBOaI2rcTd81qDpp3xCUsKaLDMfp8QQaYnm5ZfHMXDgQL3DClaT0Kr829B2NVkALAHORkvM/kY73Tns+CcGQ+U/OjqaMWPGcNlll/HOO+/w99+/lVvIFDqd7k+FT8LfhyP4Pj2KHfkmYqKjGDrsOq677joSEhL0Di/odO/enVkzZ/LWW5P4+edVROTvw97iXN3HMp+E37Ii+T5DW0FrNkcy8PzzuOCCC+jVq1etOpDUIzkzAZFog1tT4CW0Ro4CiCndISCLE8w5C4bB7XR069aN/77/PsuWLeM/77+PYdsiXPXa4Urug4ywBCwOlxe+To1iabqVBg3q8/ITY3XbgLy2SU5O5rHHHuPOO+/k888/Z+G33xJxeBfuhJa4knoE7eIQhwdm7Yjht2wz3bt14+lnnqFBgwZ6hxXMJgGXllvUlI42lgmgR7lFTcP1DPJk2rRpw5QpU1izZg3T3nuPrJ3L8MYl4WjaN6CNtf3JJ+GPnEi+3RvNvmIDDerXY/ToW7j88stDuidjIGinwl9gxYoVTJ7yNsatC3E07q5V0XRYqb67QCsopBcZaNmiOY+PvJlBgwbV2t+jHslZBtppgF1oE2oPADtK7yubnZd7oieHWuWsPIPBwKWXXsrAgQP56KOP+OqrrzEXpGNLPisgpzr3Fhn5z9Y6HCgRDBkyhPvvv7/WfrD1VL9+fUaPHs2wYcP44osv+Pqbb3BuTsWd0BJnk15ISx29QzxiX7GRd7fUIdtm4O6772Lo0KEhNS9DJ6e9qCnYxi8hBAMHDqRfv34sXLiQ2R99jHHLQtyJbbTPaiD3XaxBPqm1w/h2r9YOo1nTZJ4a+y8GDx4cjr35TpsQgosuuohevXrx9ttvs2rVKiLz0rA1H4AvJnAHcP/bb+bjnTEkJCby/MOja2Vbk+MJqcO+WqXLzX8C2gFDgG/QOmx/LqW8TQixFuglpaxyqUifPn3k+vXr/R6vv6SmpvLa66+zfds2PHWScTQfUK3B0Lr9e4xF2XSu62Rcz6JqvdfqTDOzd8YQV6cuT4x7krPOOutMw1eqKT8/ny+++IKvvvoal9ulzUlrcnoreK3bv8dUlMWlTe1nvPH5msxIZu+MJSYunmefe56ePUNjqx8hxAYpZR+dY9iI1tusHtoCgZHAPrRWG060ZK2dlPKEZfFgHL+KioqYO3cuX331NV6fxNGgk1+2MIv+ax4Gj4Op5+TWaCsNKWH9wUi+So0hs0TQonkz/nXHnbW+HUag/PLLL7w1aRK5hw/jatAJZ3JvMEac0mvErP8Io/Qwe/AJazDHWHXAzIfbYzjrrL48++xzId/Oqbrjl14z/J4FRgE90HYF2Ip21Nms9LSmpfT2Wq1ly5ZMe/ddxo4dS7TjELHbFmLKTavR9/D4YOb2aGZsi6FLtx588OFMlZgFWHx8PMOHD+ezzz7l+uuuw5q/h9jNXxO5/0/wBv5j7vXBnJ1RTN8WS6cu3ZnxwYchk5gFg3KLmhyUW9SEVinLQBvLQnJRU2xsLCNGjGDOnE+46MILMGdtIm7zV0RkbwGfT+/wqrQj38RLf8bzzuZYIhOa8sILLzBz1mwGDx6sErMacs455/DJxx9zzTXXEHlwG7Fb5mMsyPDb++0qMDF7Rwx9+/Zh/PgJIZ+YnYqADx5CiATgM7R5ZcbSnx1og1lZnT+SKrZvEkKsF0KsP3iwQo/HkGM0Grnuuuv48MMPaNOyOdbdP2FO+xV83jN+7RK34I2/67DygIWhQ4fy5ptvhfx+cKEsISGBMWPG8MnHH3P+wHMwH9hI7Jb5WkIeoAp2UelnYlmGleuvv54333or3JvKno6TLWpqi7YlXYXxNVTGr0aNGvH0008zffp0enTphCX9d2K3LsCYvy9gn9XqOmg3MGVTLOP/rEO+sR6PPvooH86azaBBg1RfPj+Ijo7mwQcf5N133qFpgwSidi7DnLqmxg80S9yC97bGUb9BA5577nkiIk6tQhfq9PjkXot2ZFk++SosjaWsK11dtF5BFUgpp0sp+0gp+9SvX3uaKCYnJ/PetGnceuutRB7cTvTOHxBux2m/3iG7gZf/rEtKoZmnnnqKe++9Vx09BomkpCReeOEFpk6dSoukBlh3/4R1148I15mdpjyZg2WfiWIL48aNY8yYMWr+zekpv6ipG9qB5fGLmmKoZM5ZqI1f7dq1Y9Kkt5gwYQJJCdFEpSwnKmUZwl6gd2i4ffBtmpVx6+qyrTiWf//738yd9ylDhgxRn+sA6NKlCx98MEP7zjqcQuzWhRiKa+aAwydh+rYY8lxGnnv+hbCqmJXRIznriTbxvyOQjDaZ9sHS27JLH5NXentYiYiI4L777uPZZ58l0n6YmO2LEY5T76KcYzcwfmNdConmjTff5OKLL/ZDtMqZ6tatGx/MmM7IkSOxlmSVVtH2+OW99hQaefHPupSIGCZNmsyll17ql/cJE2WLmkxojWidwDlo42kTtMa0Qyt7YqhUzsoTQjBgwAA+mj2bUaNGEePOI2brfCIz1oNXn30xt+eZeOaPBL7aE8WAc8/jo48/YdiwYVgsgVv5rmhN1++77z6mvv029WLNRO/4joicbWdcXf18dxR/HYpk1KjRdOrUqYaiDS16JGfj0Zo3bkWbV+ZBa6fhAbJLTwk40I5MKwjFwe1UXXjhhbz99hSiTZKYnUsRzupN+gfIthmY8FddXMZoJk95W80lCnImk4mbb76ZWbNm0b5tK6y7V2JO+6VGTmuX2Z5n4tWN8Vjr1Ofdae/RtWvXGnvtcCSlzEJbZb4CWAf8C2iNNp5NRzu4fJtK5s2GWuWsvIiICG688UbmzpnDxRddhDlzE7Fb52Ms2B+wGNw++GxXFK/+VQdiGvLaa6/x4osvqdYvOuvWrRszP/yQs/r2xbL3N8x7fwV5enMUF6VZWJJu5eqrr+baa6+t4UhDR8CTMyllppRyvZSyB3Aj2inOfqV3dxRC7AIOc4LkLJQHt1PRuXNnJk96iyiTJGbnDwh3pVPwjlHoEry+KR5PRAyTp7xN27ZtAxCpUhP+v707j66qPtc4/n0zj8BFZRBQkEEZygwiNSYBbMGWKl5UKNYBrNiF1VrECUX0ltaKM2q7WqXY3lZrtbRWLVxbCy29Vq0WGYogUEDggoaZigGS9/6xN3ASkhAwyT4n5/mslUXOPnvv84aT9eY9v7Ft27Y8PmtW2K29ktxV86p8z8tzjm/BzBU70nhwSVNatm7LE0/+QKug1wEza0swZrYLwcKzOwlmba4n2E/zS0AhwezNRqd58+bccccdPProo7Q9pRk5q+aTue6v9T65ZePeVO555z94dUM2Xx45ktlznuHss88+9oXSIJo0acJ93/ve4RyWvfr1Kj9kekoqKdWsgvHqhix+tTaXoUOHcsMNNzT65TJqEsloSTObbWYfAQ8SjD3LJegi2EZQrDWtLrZkaDk7pEuXLjwwcyZpB/eRvXZBjZ9E9pfBI0ubsvtgBt+/fyadOnVquEClTqSlpTFx4kSmTZtG5qfbyVv5e2z/vyucU3raoKpXZ67Cyp1BYdaqTTseefQxTj45vvfISyBPAPkEk5ks/H4X8CfgdIJegByCJTYqaEz5q1evXjz91FOMGTOGjJJV5P/zN6Ts/aheXutvWzOY/k4zdqc047vf/S6TJ09uVKvBNxYpKSlMnDiRG2+8kbSdG8he/cejCjRPzyEz9ehuz3kbsnhudS7FxcXcfvvtST9GOorZmqcALxBMDDiNoHtgMUFR9lHYrVltFZIsLWeHdOvWjcmTv03q7v8jY/Ni4FDrScU/0T//IJc1u1OZeudddO3aNYJIpa4MGTKEBx98kCxKg27tWrSaVrbp36k8vLQpLVu34ZFHH9MWNXUkXPl/D1C5SaAMaMGRSU2pBItsV9DY8ldmZibXXXcdj8+aRYumueSufJX0rf+ssxmd5Q7Pr8nhyeX5dDmrG0/P/gmDBw+uk3tL/Rk1ahSTJ08mbddGsv616Ji/Dws2Z/KL1bkUFp7H1KlTNaGDaFrOWhOMO5tP0Fr2nLu/TJDcPhd2a+4mKNYEGDFiBEOHDiVzy1Js3y5KTxtUYfuM90rS+dPmLMaMGct5550XYaRSV3r27MkDM2eSXraPnNV/gPLaD7zeWWo8sKQZWXnNuH/mA1o+pW59HigiaC3LIyjCssPH5xKs3fhV4B0gaWbi9OjRg6ef+jHnnH02WRv+RtbaP3/mcZMHymHWsnxeXp/NyJEjefiRR7XsSwIZOXIk11xzDenb15CxZUm15y3bns6clXkMHDCAO++8S4VZKIribGn49Tyw0t3vDY+XA28TFGl9qWZvzcbULXA8Jk2aRE52Flkb36pwvLQMZq9qwhnt23P11VdHFJ3Uhx49ejDtrrtI2fsxmRveOvYFBAvMPrG8CXvLg+7t1q1b13OUycXdbwfOAf4FNCcozN4FFhLkrt+HXycR7LdZQWPOX/n5+cyYMYPx48eTvn0NOR+8dsLj0ErL4KElTXjn4wyuv/56Jk+enHTrXDUG48aNo7h4CJmb3iV1z9ajnt9RGqxldvrppzP9nnv0HseIojj7PPA1ggTX0cwWm9kFBNPRxxBMBFgCmJkdNYe2sXUL1Fbz5s0Zc9llpO38kJRPjmx78fqmLHZ8CjfedBMZGXW7xYpEr6CggEsvvZSMj98ndfdRvWRHeWFtDit3pnHzzVM0IaT+HAQmE7SM/REYDCwgKMj6AK8Bc4GjElRjz18pKSlcccUV3HrrraTv3ULuqvnHXaCVlsH97zVlxY4MbrvtNkaPHl1P0Up9MzOmTLmZFi1akrN+0VE9AHNW5nGAdO79r+9on+dKopitucjdDRgBrHH33u7+KsGMpzfdvQPBGkE7gAsbOr54NmrUKDIyMoN1ZICycuOVD3Pp17cvvXr1ijg6qS8TJkygZatWZH/4Zo1jN97fkcYrG4IuoPPPP78BI0w6M4B5wOPuPoxgI/TeBF2cu4FmBK1pSfvXZsSIEdxzzz2kfVJCzpqqZ+1Vpawcnlyez5pd6Uy7+26tx9cI5OTkcMuUm2HfLjK2Lj98fNn2dP5RksHV4yfQrt1RjcxJL572tlhMMGsT4ErgTYIFHStozN0Cx5Kfn09Bwblk7lwHOCt3pbO7FC659NKoQ5N6lJmZycRrr8U+2U7ajnVVnrO/DJ5e2YRTW7di0qRJDRtg8pkLjAYwszOB9gQFWTlwB0Fr2q1UMTQjmfJXQUEBU6ZMIXXXJjLXv1Gra372QS7/KMngxm99i6KiovoNUBpM//79GTRoEFlblh1edeCldTmccvJJXHzxxRFHF5+iWkrjWeAN4Ewz22hmE4DfAG3CrU/OB16mikkBjb1b4FiKi4vxA6VYedBV0KxJPv37H3ODe0lwhYWFtGzZioyw1bSy363PZusnxs1TbtEq6fVvPfADoBPBh8q5wFkE2zh9G5gYPlda+cJky18jRozg8ssvJ6NkFWnbat794n+3ZPD6pizGjh3LhReq06SxufLKK/GDpaSW7ubfB1N4f2ca/zn6Eg3HqUYkxZm7j3X31u6e7u5t3f1p4H1gmbt3dvehBPvWbY4ivnjWp0+fCpv5Dhx0jma3JIHU1FS+9KULSN2zhcqfWXaWGvM+zGHIkGL69u0bTYBJxN2XEAy5OABMdfexQEuCpTMeJtgXOIdgbFoFydRydshVV13FWV27krPhjWqXhdn6SQrPrMqnR/duTJgwoYEjlIbQtWtXOnU6Mg7WzDT8ogbx1K35NtDZzDqYWQbB5ICXIo4p7uTm5nLGGR0PP+7evXuE0UhDGj58OB3OOOOoVbNfWpfDQVIYP15/1BqCBW/A94FSd38o5qnfAY8TjKctBbzypKZkazmDYHHlqXfcgZUfJGPTuwB4asXWkp+uysPSs7jzrmn6sNmIFRcXHf6+W9eztDRKDeKmOHP3g8D1BOufrQCed/flNV+VnDp37sSh4SzaCSB5tGjRgp/Mnk1mZubhY3v2Gwu3ZDF8+Ajatm0bYXRJ5fPAxUBeONt8MbCXYG0zgD8QNG/+EE1qAqBdu3ZcdNGFZJSswj7dTXnukd0q3tuWztLt6Vw9fgKtWrWKMEqpb7Et+716a9/nmsRNcQbg7q+6exd37+juM6KOJ17F7o946qmnRhiJRG3B5kwOlKHlBhqQuy8COgCrw9nmvYFnCfbUnEuw+fkTwAdUmtSUjN2ah4wbN46UlJQK4yYdeGFtHm1Obc1FF10UXXDSIDp2PNLr06VLlwgjiX9xVZxJ7cQuLNqsWbMII5EoucOCLTn06d2bDh06RB1O0qhmQtN9QC/gEoIJTfeFp1cYIJiM3ZqHnHTSSRQVFpK5bfXhGXurdqazfk8KY786TguQJoGMjIzDwzLatDlqMQaJoeIsAcUm9crjjyR5rNmdxsefGF/44hejDiWpVDWhyd23AdcCf3P3oe6+HWiLJjVVMGzYMPxgKSmf7gKCRbTzcnMYNmxYxJFJQ7ngggsA1IV9DCrOElCwibVj2n40qb31UQbpaakUFBREHYoENKnpGPr160dmZhYp+3YAsGJnOucVFmn5lyRy00038eKLL5Kfnx91KHFNxVkCatKkCWB41duPSpJYuiOTnr16kZeXF3UogiY11UZmZiY9enSvkLkGDx4cWTzS8NLS0jRLsxZUnCWg7OzsqEOQiO0sTWHT3hQGDBgYdSgSQ5Oajq1Hjx4VHvfpo1l7IpWpOEtAGmdy8lKBAAAIcUlEQVQmq3cHa0FpT1VJNLEz9nKys8nNza3hbJHkpOJMJAGVfJpKelpqhT90Iokgdimg02K+F5EjVJyJJKj27TtoXzpJOLGz9Fq2bBlhJCLxS/tkJKiiomJatmwRdRgSofZa20wSUOwOFxoYLlI1FWcJavr0u6MOQSJSWrofCLbEEUlkwcxzEalM3ZoiCabcg/XtYneKEElEWohUpGoqzkQSlMbrSKKL3QhbRI5QcSaSoIKdIkQSV2pqatQhiMQlFWciCUrFmSQqrdUoUjMVZyIJSjtFSKKKnbEpIkdTcSaSoNT6IIlKuwKI1EzFmUiCGThgAEOGFEcdhsgJu+GGG+jYqRN5eXlRhyISl7TOmUiCuX/mzKhDEPlMCgsLKSwsjDoMkbilljMRERGROKLiTERERCSOqDgTERERiSMqzkRERETiiIozERERkTii4kxEREQkjqg4ExEREYkjKs5ERERE4oiKMxEREZE4Yu4edQwnzMw+BtZHHUdETgZKog5CIpHs7/3p7n5K1EF8Vkmev0C/x8ksmd/7WuWvhC7OkpmZ/d3d+0cdhzQ8vffSGOj3OHnpvT82dWuKiIiIxBEVZyIiIiJxRMVZ4vpR1AFIZPTeS2Og3+Pkpff+GDTmTERERCSOqOVMREREJI6oOBMRERGJIyrOEpCZDTezlWa22sxuizoeaRhmNtvMPjKzZVHHInKilL+Sl3JY7ak4SzBmlgo8AYwAugFjzaxbtFFJA5kDDI86CJETpfyV9OagHFYrKs4Sz0Bgtbuvdff9wHPAhRHHJA3A3f8MbI86DpHPQPkriSmH1Z6Ks8TTBvgw5vHG8JiISLxT/hKpBRVniceqOKb1UEQkESh/idSCirPEsxFoF/O4LbA5olhERI6H8pdILag4SzxvA53NrIOZZQBjgJcijklEpDaUv0RqQcVZgnH3g8D1wHxgBfC8uy+PNippCGb2LPAGcKaZbTSzCVHHJHI8lL+Sm3JY7Wn7JhEREZE4opYzERERkTii4kxEREQkjqg4ExEREYkjKs5ERERE4oiKMxEREZE4ouJMREQkhpmVmdnimK/bqjinyMxeruPXLTKzwTGPrzOzK+ryNSQxqDiTWjOzqWa23MyWhAnr7BrOnWNmo8PvC8LrFptZdhXntjezfZWSYZ0kJDPbWxf3qeH+h39OEWk09rl775iv+xrodYuAw8WZu//Q3X/aQK8tcSQt6gAkMZjZOcCXgb7uXmpmJwMZtbx8HPCAu/+khnPWuHvvzxqniEh9MbPhwCNACfBuzPHpwF53fyB8vAz4sruvCz9o3kywh+gSd/+amY0E7iTIodsIcmQ2cB1QZmaXA98Ehh66r5n1Bn4I5ABrgPHuvsPMFgBvAsVAM2CCu/+lfv8npL6p5UxqqzVQ4u6lAO5e4u6bzayfmS00s3fMbL6ZtY69yMyuAS4FppnZz4/3Rc1sr5l9P7z/H8xsoJktMLO1ZvaV8JyrzOy3ZjbPzFaa2d1V3MfMbKaZLTOzpWZ2WXj8Z2Z2Ycx5Pzezr5hZanj+22FL4cSY+zxuZv80s1eAFsf7M4lI3Muu1JJ/mZllAT8GRgIFQKtj3cTMugNTgSHu3gu4MXxqETDI3fsAzwG3uPs6guLr4bC1rnKB9VPgVnfvCSwFYvNcmrsPBL5V6bgkKBVnUlv/A7Qzs1Vm9qSZFZpZOjALGO3u/YDZwIzYi9z9KYK986a4+7ga7t+xUjIsCI/nAgvC++8BvgOcD4wC7o25fiDBp8/ewCVm1r/S/S8On+sFDANmhoXkU8DVAGbWlKBL4VVgArDL3QcAA4Cvm1mH8HXPBD4HfJ2YLggRaTQqd2v+EjgL+Je7f+DB1jr/XYv7DAFecPcSAHffHh5vC8w3s6XAFKB7TTcJc1Mzd18YHnoGOC/mlF+H/74DtK9FXBLn1K0pteLue82sH8EnxmLglwSFUg/gNTMDSAX+7wRforpuzf3AvPD7pUCpux8Ik1r7mPNec/dtAGb2a+Bc4O8xz58LPOvuZcBWM1sIDHD3l8zsCTNrQVDAvejuB83sC0DPmPFkTYHOBAnx0H02m9nrJ/jzikjiqW6/w4NUbOzICv+1aq6ZBTwU5p8iYPpnjKs0/LcM/V1vFPQmSq2FBckCYEFYHE0Clrv7OfX4sgf8yAaw5YRJyN3LzSz297dyAqz82Gp4jZ8RtLqNAcbHnP9Nd59f4SZmF1RxbxFp/N4HOphZR3dfA4yNeW4dwZhczKwv0CE8/kdgrpk97O7bzKx52HrWFNgUnnNlzH32AE0qv7C77zKzHWZWEHZ3fg1YWPk8aTzUrSm1YmZnmlnnmEO9gRXAKeFkAcwsPRxjEYXzzax5OBv0IuCvlZ7/M3BZOJbsFIIWsLfC5+YQjNXA3ZeHx+YD3wi7bjGzLmaWG95nTHif1gStiCLSuFQec3afu38KXAu8YmaLgPUx578INDezxcA3gFVwOJ/MABaa2XvAQ+H504FfmdlfCCYXHPI7YFSloR2HXEkwHGMJQf69F2m01HImtZUHzDKzZgRN+KsJEtWPgMfCMRFpBDOZlld7l+p1DBPbIbPd/bHjuH4RQQtYJ+AX7v73Ss/PBc4B3iNo+brF3bcAuPtWM1sB/Cbm/KcIuk3ftaDP9mOCom8uwTiSpQQJWJ9eRRoZd0+t5vg8grFnlY/vA75QzTXPEIwRiz32W+C3VZy7CugZc+gvMc8tBgZVcU1RzPclaMxZo2BHeoxEEpOZXQX0d/frT/D6HIJiq6+776rL2ERERI6XujUlqZnZMIKxJLNUmImISDxQy5k0GDP7HEHXY6xSd692pwEREZFko+JMREREJI6oW1NEREQkjqg4ExEREYkjKs5ERERE4oiKMxEREZE48v+qmMR+Len2dAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x16fa3eb0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f,ax=plt.subplots(2,2,figsize=(10,10))\n",
"sns.violinplot(\"Gender\",\"LoanAmount\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[0,0])\n",
"ax[0,0].set_title('Gender and LoanAmount vs Survived')\n",
"ax[0,0].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Married\",\"LoanAmount\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[0,1])\n",
"ax[0,1].set_title('Married and LoanAmount vs Survived')\n",
"ax[0,1].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Self_Employed\",\"LoanAmount\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[1,0])\n",
"ax[1,0].set_title('Self_Employed and LoanAmount vs Survived')\n",
"ax[1,0].set_yticks(range(0,110,10))\n",
"sns.violinplot(\"Education\",\"LoanAmount\", hue=\"Loan_Status\", data=train1,split=True,ax=ax[1,1])\n",
"ax[1,1].set_title('Education and LoanAmount vs Survived')\n",
"ax[1,1].set_yticks(range(0,110,10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 440,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x176ebc10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.PairGrid(train1, hue=\"Loan_Status\")\n",
"g.map_diag(plt.hist)\n",
"g.map_offdiag(plt.scatter)\n",
"g.add_legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Grafik tersebut menunjukkan hubungan antar variabel yang dianalisis pada masing-masing class. Berdasarkan grafik tersebut terlihat bahwa pada masing-masing class memiliki perbedaan pada tiap variabel yang diamati sehingga memungkinkan untuk dilakukan klasifikasi."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TRAIN-TEST SPLIT"
]
},
{
"cell_type": "code",
"execution_count": 441,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, auc, roc_curve\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 442,
"metadata": {},
"outputs": [],
"source": [
"X=train1.drop(\"Loan_Status\",axis=1)\n",
"y= train1['Loan_Status']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 123)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KLASIFIKASI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## K-Nearest Neighbour"
]
},
{
"cell_type": "code",
"execution_count": 443,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier"
]
},
{
"cell_type": "code",
"execution_count": 444,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7372708757637475"
]
},
"execution_count": 444,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn = KNeighborsClassifier(n_neighbors=5)\n",
"knn.fit(X_train, y_train)\n",
"knn.score(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 445,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
" 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1,\n",
" 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
" 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
" 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
]
},
"execution_count": 445,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yknn_predict=knn.predict(test1)\n",
"yknn_predict"
]
},
{
"cell_type": "code",
"execution_count": 446,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 447,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=yknn_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 449,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('knn.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 450,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 75\n",
"Y 292\n",
"dtype: int64"
]
},
"execution_count": 450,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output=pd.read_csv(\"knn.csv\")\n",
"output.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model K-Nearest Neighbour adalah 0,61111"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DECISION TREE"
]
},
{
"cell_type": "code",
"execution_count": 456,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import tree"
]
},
{
"cell_type": "code",
"execution_count": 457,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 457,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = tree.DecisionTreeClassifier(random_state=123)\n",
"clf.fit(X_train, y_train)\n",
"clf.score(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 459,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
" 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
" 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
" 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
" 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
" 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
" 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
" 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1,\n",
" 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
" 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
" 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 459,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_dt_predict=DecisionTree.predict(test1)\n",
"y_dt_predict"
]
},
{
"cell_type": "code",
"execution_count": 460,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 461,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=y_dt_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 462,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 463,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('decision_tree.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 464,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 185\n",
"Y 182\n",
"dtype: int64"
]
},
"execution_count": 464,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_dt=pd.read_csv(\"decision_tree.csv\")\n",
"output_dt.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Decision Tree adalah 0,61111"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BAGGING"
]
},
{
"cell_type": "code",
"execution_count": 484,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, \\\n",
" AdaBoostClassifier, GradientBoostingClassifier"
]
},
{
"cell_type": "code",
"execution_count": 485,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9837067209775967"
]
},
"execution_count": 485,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Bagging = BaggingClassifier(random_state=123)\n",
"Bagging.fit(X_train, y_train)\n",
"Bagging.score(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 486,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,\n",
" 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1,\n",
" 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
" 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
" 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
" 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 486,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_bagging_predict=Bagging.predict(test1)\n",
"y_bagging_predict"
]
},
{
"cell_type": "code",
"execution_count": 487,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 488,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=y_bagging_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 489,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 490,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('bagging.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 491,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 130\n",
"Y 237\n",
"dtype: int64"
]
},
"execution_count": 491,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_bagging=pd.read_csv(\"bagging.csv\")\n",
"output_bagging.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model bagging adalah 0,70833"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LOGISTIC REGRESSION"
]
},
{
"cell_type": "code",
"execution_count": 492,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import metrics"
]
},
{
"cell_type": "code",
"execution_count": 493,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=123, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 493,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logreg = LogisticRegression(random_state=123)\n",
"logreg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 494,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 494,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ylogistic_predict=logreg.predict(test1)\n",
"ylogistic_predict"
]
},
{
"cell_type": "code",
"execution_count": 495,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 496,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=ylogistic_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 497,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 498,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('reglogistik.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 499,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 59\n",
"Y 308\n",
"dtype: int64"
]
},
"execution_count": 499,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_lr=pd.read_csv(\"reglogistik.csv\")\n",
"output_lr.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Regresi Logistik adalah 0,77778"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LINEAR DISCRIMINANT ANALYSIS"
]
},
{
"cell_type": "code",
"execution_count": 500,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
]
},
{
"cell_type": "code",
"execution_count": 501,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n",
" solver='svd', store_covariance=False, tol=0.0001)"
]
},
"execution_count": 501,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lda = LinearDiscriminantAnalysis()\n",
"lda.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 502,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 502,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ylda_predict=lda.predict(test1)\n",
"ylda_predict"
]
},
{
"cell_type": "code",
"execution_count": 503,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 504,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=ylda_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 505,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 506,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('lda.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 507,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 59\n",
"Y 308\n",
"dtype: int64"
]
},
"execution_count": 507,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_lda=pd.read_csv(\"lda.csv\")\n",
"output_lda.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Linear Discriminant adalah 0,77778"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# QUADRATIC DISCRIMINANT ANALYSIS"
]
},
{
"cell_type": "code",
"execution_count": 508,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis"
]
},
{
"cell_type": "code",
"execution_count": 509,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
" store_covariance=False, store_covariances=None, tol=0.0001)"
]
},
"execution_count": 509,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qda = QuadraticDiscriminantAnalysis()\n",
"qda.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 510,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 510,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yqda_predict=qda.predict(test1)\n",
"yqda_predict"
]
},
{
"cell_type": "code",
"execution_count": 511,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 512,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=yqda_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 513,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 514,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('qda.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 515,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 73\n",
"Y 294\n",
"dtype: int64"
]
},
"execution_count": 515,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_qda=pd.read_csv(\"qda.csv\")\n",
"output_qda.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Kuadratik Discriminant adalah 0,7430555"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NEURAL NETWORK"
]
},
{
"cell_type": "code",
"execution_count": 516,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier"
]
},
{
"cell_type": "code",
"execution_count": 517,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
" beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
" hidden_layer_sizes=(100,), learning_rate='constant',\n",
" learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
" nesterovs_momentum=True, power_t=0.5, random_state=123,\n",
" shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
" verbose=False, warm_start=False)"
]
},
"execution_count": 517,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nn = MLPClassifier(random_state=123)\n",
"nn.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 518,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 518,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ynn_predict=nn.predict(test1)\n",
"ynn_predict"
]
},
{
"cell_type": "code",
"execution_count": 519,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 520,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=ynn_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 521,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 522,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('nn.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 523,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 1\n",
"Y 366\n",
"dtype: int64"
]
},
"execution_count": 523,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_nn=pd.read_csv(\"nn.csv\")\n",
"output_nn.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Naural Network adalah 0,715277"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GAUSSIAN PROCESS CLASSIFIER"
]
},
{
"cell_type": "code",
"execution_count": 532,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.gaussian_process import GaussianProcessClassifier"
]
},
{
"cell_type": "code",
"execution_count": 533,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GaussianProcessClassifier(copy_X_train=True, kernel=None,\n",
" max_iter_predict=100, multi_class='one_vs_rest', n_jobs=1,\n",
" n_restarts_optimizer=0, optimizer='fmin_l_bfgs_b',\n",
" random_state=123, warm_start=False)"
]
},
"execution_count": 533,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gpc = GaussianProcessClassifier(random_state=123)\n",
"gpc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 534,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
" 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
" 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
" 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
" 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
" 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
" 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
" 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
" 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
" 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
]
},
"execution_count": 534,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gpc_predict=gpc.predict(test1)\n",
"gpc_predict"
]
},
{
"cell_type": "code",
"execution_count": 535,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 536,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=gpc_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 537,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 538,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('gpc.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 539,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 295\n",
"Y 72\n",
"dtype: int64"
]
},
"execution_count": 539,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_gpc=pd.read_csv(\"gpc.csv\")\n",
"output_gpc.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Gaussian Process Classifier adalah 0,3402777"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stochastic Gradient Descent"
]
},
{
"cell_type": "code",
"execution_count": 540,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import SGDClassifier"
]
},
{
"cell_type": "code",
"execution_count": 541,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\asus\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
" \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
" eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
" learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
" n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
" shuffle=False, tol=None, verbose=0, warm_start=False)"
]
},
"execution_count": 541,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", shuffle=False)\n",
"sgd.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 542,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
" 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
" 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
" 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
" 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n",
" 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
" 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
" 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
" 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
" 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
" 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], dtype=int64)"
]
},
"execution_count": 542,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sgd_predict=sgd.predict(test1)\n",
"sgd_predict"
]
},
{
"cell_type": "code",
"execution_count": 543,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 544,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=sgd_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 545,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 546,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('sgd.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 547,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 276\n",
"Y 91\n",
"dtype: int64"
]
},
"execution_count": 547,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_sgd=pd.read_csv(\"sgd.csv\")\n",
"output_sgd.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Stochastic Gradient adalah 0,3819"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GradientBoostingClassifier"
]
},
{
"cell_type": "code",
"execution_count": 548,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import GradientBoostingClassifier"
]
},
{
"cell_type": "code",
"execution_count": 549,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
" learning_rate=0.1, loss='deviance', max_depth=3,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
" presort='auto', random_state=123, subsample=1.0, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 549,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gbc = GradientBoostingClassifier(random_state=123)\n",
"gbc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 550,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
" 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
" 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 550,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gbc_predict=gbc.predict(test1)\n",
"gbc_predict"
]
},
{
"cell_type": "code",
"execution_count": 551,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 552,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=gbc_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 553,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 554,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('gbc.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 555,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 97\n",
"Y 270\n",
"dtype: int64"
]
},
"execution_count": 555,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_gbc=pd.read_csv(\"gbc.csv\")\n",
"output_gbc.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Gradient Boosting adalah 0,736111"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ExtraTreesClassifier"
]
},
{
"cell_type": "code",
"execution_count": 556,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import ExtraTreesClassifier"
]
},
{
"cell_type": "code",
"execution_count": 557,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
" oob_score=False, random_state=123, verbose=0, warm_start=False)"
]
},
"execution_count": 557,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"etc = ExtraTreesClassifier(random_state=123)\n",
"etc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 558,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
" 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
" 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0,\n",
" 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
" 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1], dtype=int64)"
]
},
"execution_count": 558,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"etc_predict=etc.predict(test1)\n",
"etc_predict"
]
},
{
"cell_type": "code",
"execution_count": 559,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 560,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=etc_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 561,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 562,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('etc.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 563,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 124\n",
"Y 243\n",
"dtype: int64"
]
},
"execution_count": 563,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_etc=pd.read_csv(\"etc.csv\")\n",
"output_etc.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Extra Trees Classifier adalah 0,6875"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SGD - MultiClass"
]
},
{
"cell_type": "code",
"execution_count": 564,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.multiclass import OneVsRestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 565,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\asus\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
" \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"OneVsRestClassifier(estimator=SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
" eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
" learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
" n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
" shuffle=True, tol=None, verbose=0, warm_start=False),\n",
" n_jobs=1)"
]
},
"execution_count": 565,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"klasifikasi=OneVsRestClassifier(SGDClassifier())\n",
"klasifikasi.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 566,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 566,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"klasifikasi_predict=klasifikasi.predict(test1)\n",
"klasifikasi_predict"
]
},
{
"cell_type": "code",
"execution_count": 567,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv(\"submission.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 568,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=klasifikasi_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 569,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 570,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('sgd1.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 571,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 19\n",
"Y 348\n",
"dtype: int64"
]
},
"execution_count": 571,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_klasifikasi=pd.read_csv(\"sgd1.csv\")\n",
"output_klasifikasi.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model SGD Multiclass adalah 0,71527"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 572,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 573,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
" oob_score=False, random_state=123, verbose=0, warm_start=False)"
]
},
"execution_count": 573,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RF = RandomForestClassifier(random_state=123)\n",
"RF.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 574,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1,\n",
" 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
" 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
" 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
" 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
" 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
" 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
" 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
" 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
" 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1], dtype=int64)"
]
},
"execution_count": 574,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RF_predict=RF.predict(test1)\n",
"RF_predict"
]
},
{
"cell_type": "code",
"execution_count": 575,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv('submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 576,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=RF_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 577,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 578,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('RF.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 579,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 150\n",
"Y 217\n",
"dtype: int64"
]
},
"execution_count": 579,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_RF=pd.read_csv('RF.csv')\n",
"output_RF.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Random Forest adalah 0,6667"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Naive Bayes"
]
},
{
"cell_type": "code",
"execution_count": 580,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.naive_bayes import GaussianNB"
]
},
{
"cell_type": "code",
"execution_count": 581,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GaussianNB(priors=None)"
]
},
"execution_count": 581,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nb= GaussianNB()\n",
"nb.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 582,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 582,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nb_predict=nb.predict(test1)\n",
"nb_predict"
]
},
{
"cell_type": "code",
"execution_count": 583,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv('submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 584,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=nb_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 585,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 586,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('NB1.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 587,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 23\n",
"Y 344\n",
"dtype: int64"
]
},
"execution_count": 587,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_RF=pd.read_csv('NB.csv')\n",
"output_RF.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Naive Bayes adalah 0,6805"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from sklearn import svm\n",
"svclin = svm.SVC(kernel='linear')"
]
},
{
"cell_type": "code",
"execution_count": 589,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 589,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svclin.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 590,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8065173116089613"
]
},
"execution_count": 590,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svclin.score(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 591,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 591,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svclin_predict=svclin.predict(test1)\n",
"svclin_predict"
]
},
{
"cell_type": "code",
"execution_count": 592,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv('submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 593,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=svclin_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 594,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 595,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('SVM.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 596,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 60\n",
"Y 307\n",
"dtype: int64"
]
},
"execution_count": 596,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_RF=pd.read_csv('SVM.csv')\n",
"output_RF.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Suppport Vector Machine adalah 0,770888"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ridge Classification"
]
},
{
"cell_type": "code",
"execution_count": 597,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import RidgeClassifierCV"
]
},
{
"cell_type": "code",
"execution_count": 598,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), class_weight=None, cv=None,\n",
" fit_intercept=True, normalize=False, scoring=None)"
]
},
"execution_count": 598,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ridge= RidgeClassifierCV()\n",
"ridge.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 599,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 599,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ridge_predict=ridge.predict(test1)\n",
"ridge_predict"
]
},
{
"cell_type": "code",
"execution_count": 600,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv('submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 601,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=ridge_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 602,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 603,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('ridge.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 604,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 59\n",
"Y 308\n",
"dtype: int64"
]
},
"execution_count": 604,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_ridge=pd.read_csv('ridge.csv')\n",
"output_ridge.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Ridge Classifier adalah 0,77888"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machine CV"
]
},
{
"cell_type": "code",
"execution_count": 605,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.svm import SVC"
]
},
{
"cell_type": "code",
"execution_count": 606,
"metadata": {},
"outputs": [],
"source": [
"svmcv = SVC(gamma='auto')"
]
},
{
"cell_type": "code",
"execution_count": 607,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 607,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svmcv.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 614,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 614,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svmcv_predict=svclin.predict(test1)\n",
"svmcv_predict"
]
},
{
"cell_type": "code",
"execution_count": 615,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv('submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 616,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=svmcv_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 617,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 618,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('svmcv.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 619,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 60\n",
"Y 307\n",
"dtype: int64"
]
},
"execution_count": 619,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_RF=pd.read_csv('svmcv.csv')\n",
"output_RF.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Support Vector Machine CV adalah 0,770833"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adaboost Classifier"
]
},
{
"cell_type": "code",
"execution_count": 620,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import AdaBoostClassifier"
]
},
{
"cell_type": "code",
"execution_count": 621,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
" learning_rate=1.0, n_estimators=50, random_state=None)"
]
},
"execution_count": 621,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ada= AdaBoostClassifier()\n",
"ada.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 622,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
" 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
" 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
" 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
" 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
" 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
" 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
]
},
"execution_count": 622,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ada_predict=ada.predict(test1)\n",
"ada_predict"
]
},
{
"cell_type": "code",
"execution_count": 623,
"metadata": {},
"outputs": [],
"source": [
"# READ SUBMISSION FILE\n",
"submission=pd.read_csv('submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 624,
"metadata": {},
"outputs": [],
"source": [
"submission['Loan_Status']=ada_predict # Fill predictions in Loan_Status variable of submission file\n",
"submission['Loan_ID']=test_original['Loan_ID'] # Fill Loan_ID of submission file with the Loan_ID of original test file"
]
},
{
"cell_type": "code",
"execution_count": 625,
"metadata": {},
"outputs": [],
"source": [
"# Replacing 0 and 1 with N and Y in Loan_Status\n",
"submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
"submission['Loan_Status'].replace(1, 'Y',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 626,
"metadata": {},
"outputs": [],
"source": [
"# Converting submission file to .csv format\n",
"pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('ada.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 627,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Loan_Status\n",
"N 119\n",
"Y 248\n",
"dtype: int64"
]
},
"execution_count": 627,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_ridge=pd.read_csv('ada.csv')\n",
"output_ridge.groupby(['Loan_Status']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setelah dicek di website https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ didapatkan akurasi klasifikasi dengan model Adaboost Classifier adalah 0,6736"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Membandingkan Metode"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Berikut hasil nilai akurasi pada metode klasifikasi yang telah dijalankan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. K-Nearest Neighbour (0,6111)\n",
"\n",
"2. Decision Tree (0,6111)\n",
"\n",
"3. Bagging (0,7083)\n",
"\n",
"4. Regresi Logistik (0,7778)\n",
"\n",
"5. Linear Discriminant (0,7778)\n",
"\n",
"6. Quadratic Discriminant (0,7431)\n",
"\n",
"7. Neural Network (0,7153)\n",
"\n",
"8. Gaussian Process Classifier (0,3403)\n",
"\n",
"9. Stochastic Gradient Descent (0,3819)\n",
"\n",
"10. Gradient Boosting Classifier (0,7361)\n",
"\n",
"11. Extra Trees Classifier (0,6875)\n",
"\n",
"12. SGD - Multiclass (0,7153)\n",
"\n",
"13. Random Forest (0,6667)\n",
"\n",
"14. Naive Bayes (0,6805)\n",
"\n",
"15. Support Vector Machine (0,7708)\n",
"\n",
"16. Ridge Classification (0,7778)\n",
"\n",
"17. Support Vector Machine CV (0,7708)\n",
"\n",
"18. Adaboost Classifier (0,6736)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dari model klasifikasi diatas, yang cocok untuk mengklasifikasikan Loan Prediction Datasets adalah Regresi Logistik, Linear Discriminant, dan Ridge Classification."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment