Python Programming Help with the Project
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Project.zipFinal Project/.ipynb_checkpoints/Spr2019_Decision_Trees_Project1-checkpoint.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Decision Trees Project \n", "\n", "For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.\n", "\n", "Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public.\n", "\n", "We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values.\n", "\n", "Here are what the columns represent:\n", "* credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n", "* purpose: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n", "* int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n", "* installment: The monthly installments owed by the borrower if the loan is funded.\n", "* log.annual.inc: The natural log of the self-reported annual income of the borrower.\n", "* dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n", "* fico: The FICO credit score of the borrower.\n", "* days.with.cr.line: The number of days the borrower has had a credit line.\n", "* revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n", "* revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n", "* inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.\n", "* delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n", "* pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).\n", "\n", "* not.fully.paid : This is the class atribute you will build a decision tree to predict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import Libraries\n", "\n", "**Import the usual libraries for pandas and plotting. You can import sklearn later on.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the Data\n", "\n", "** Use pandas to read loan_data.csv as a dataframe called loans.**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "loans = pd.read_csv('loan_data.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Check out the info(), head(), and describe() methods on loans.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 9578 entries, 0 to 9577\n", "Data columns (total 14 columns):\n", "credit.policy 9578 non-null int64\n", "purpose 9578 non-null object\n", "int.rate 9578 non-null float64\n", "installment 9578 non-null float64\n", "log.annual.inc 9578 non-null float64\n", "dti 9578 non-null float64\n", "fico 9578 non-null int64\n", "days.with.cr.line 9578 non-null float64\n", "revol.bal 9578 non-null int64\n", "revol.util 9578 non-null float64\n", "inq.last.6mths 9578 non-null int64\n", "delinq.2yrs 9578 non-null int64\n", "pub.rec 9578 non-null int64\n", "not.fully.paid 9578 non-null int64\n", "dtypes: float64(6), int64(7), object(1)\n", "memory usage: 1.0+ MB\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>credit.policy</th>\n", " <th>int.rate</th>\n", " <th>installment</th>\n", " <th>log.annual.inc</th>\n", " <th>dti</th>\n", " <th>fico</th>\n", " <th>days.with.cr.line</th>\n", " <th>revol.bal</th>\n", " <th>revol.util</th>\n", " <th>inq.last.6mths</th>\n", " <th>delinq.2yrs</th>\n", " <th>pub.rec</th>\n", " <th>not.fully.paid</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9.578000e+03</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " <td>9578.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.804970</td>\n", " <td>0.122640</td>\n", " <td>319.089413</td>\n", " <td>10.932117</td>\n", " <td>12.606679</td>\n", " <td>710.846314</td>\n", " <td>4560.767197</td>\n", " <td>1.691396e+04</td>\n", " <td>46.799236</td>\n", " <td>1.577469</td>\n", " <td>0.163708</td>\n", " <td>0.062122</td>\n", " <td>0.160054</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.396245</td>\n", " <td>0.026847</td>\n", " <td>207.071301</td>\n", " <td>0.614813</td>\n", " <td>6.883970</td>\n", " <td>37.970537</td>\n", " <td>2496.930377</td>\n", " <td>3.375619e+04</td>\n", " <td>29.014417</td>\n", " <td>2.200245</td>\n", " <td>0.546215</td>\n", " <td>0.262126</td>\n", " <td>0.366676</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.000000</td>\n", " <td>0.060000</td>\n", " <td>15.670000</td>\n", " <td>7.547502</td>\n", " <td>0.000000</td>\n", " <td>612.000000</td>\n", " <td>178.958333</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.000000</td>\n", " <td>0.103900</td>\n", " <td>163.770000</td>\n", " <td>10.558414</td>\n", " <td>7.212500</td>\n", " <td>682.000000</td>\n", " <td>2820.000000</td>\n", " <td>3.187000e+03</td>\n", " <td>22.600000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>1.000000</td>\n", " <td>0.122100</td>\n", " <td>268.950000</td>\n", " <td>10.928884</td>\n", " <td>12.665000</td>\n", " <td>707.000000</td>\n", " <td>4139.958333</td>\n", " <td>8.596000e+03</td>\n", " <td>46.300000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.000000</td>\n", " <td>0.140700</td>\n", " <td>432.762500</td>\n", " <td>11.291293</td>\n", " <td>17.950000</td>\n", " <td>737.000000</td>\n", " <td>5730.000000</td>\n", " <td>1.824950e+04</td>\n", " <td>70.900000</td>\n", " <td>2.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>1.000000</td>\n", " <td>0.216400</td>\n", " <td>940.140000</td>\n", " <td>14.528354</td>\n", " <td>29.960000</td>\n", " <td>827.000000</td>\n", " <td>17639.958330</td>\n", " <td>1.207359e+06</td>\n", " <td>119.000000</td>\n", " <td>33.000000</td>\n", " <td>13.000000</td>\n", " <td>5.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " credit.policy int.rate installment log.annual.inc dti \\\n", "count 9578.000000 9578.000000 9578.000000 9578.000000 9578.000000 \n", "mean 0.804970 0.122640 319.089413 10.932117 12.606679 \n", "std 0.396245 0.026847 207.071301 0.614813 6.883970 \n", "min 0.000000 0.060000 15.670000 7.547502 0.000000 \n", "25% 1.000000 0.103900 163.770000 10.558414 7.212500 \n", "50% 1.000000 0.122100 268.950000 10.928884 12.665000 \n", "75% 1.000000 0.140700 432.762500 11.291293 17.950000 \n", "max 1.000000 0.216400 940.140000 14.528354 29.960000 \n", "\n", " fico days.with.cr.line revol.bal revol.util \\\n", "count 9578.000000 9578.000000 9.578000e+03 9578.000000 \n", "mean 710.846314 4560.767197 1.691396e+04 46.799236 \n", "std 37.970537 2496.930377 3.375619e+04 29.014417 \n", "min 612.000000 178.958333 0.000000e+00 0.000000 \n", "25% 682.000000 2820.000000 3.187000e+03 22.600000 \n", "50% 707.000000 4139.958333 8.596000e+03 46.300000 \n", "75% 737.000000 5730.000000 1.824950e+04 70.900000 \n", "max 827.000000 17639.958330 1.207359e+06 119.000000 \n", "\n", " inq.last.6mths delinq.2yrs pub.rec not.fully.paid \n", "count 9578.000000 9578.000000 9578.000000 9578.000000 \n", "mean 1.577469 0.163708 0.062122 0.160054 \n", "std 2.200245 0.546215 0.262126 0.366676 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 \n", "50% 1.000000 0.000000 0.000000 0.000000 \n", "75% 2.000000 0.000000 0.000000 0.000000 \n", "max 33.000000 13.000000 5.000000 1.000000 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>credit.policy</th>\n", " <th>purpose</th>\n", " <th>int.rate</th>\n", " <th>installment</th>\n", " <th>log.annual.inc</th>\n", " <th>dti</th>\n", " <th>fico</th>\n", " <th>days.with.cr.line</th>\n", " <th>revol.bal</th>\n", " <th>revol.util</th>\n", " <th>inq.last.6mths</th>\n", " <th>delinq.2yrs</th>\n", " <th>pub.rec</th>\n", " <th>not.fully.paid</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>debt_consolidation</td>\n", " <td>0.1189</td>\n", " <td>829.10</td>\n", " <td>11.350407</td>\n", " <td>19.48</td>\n", " <td>737</td>\n", " <td>5639.958333</td>\n", " <td>28854</td>\n", " <td>52.1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>credit_card</td>\n", " <td>0.1071</td>\n", " <td>228.22</td>\n", " <td>11.082143</td>\n", " <td>14.29</td>\n", " <td>707</td>\n", " <td>2760.000000</td>\n", " <td>33623</td>\n", " <td>76.7</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>debt_consolidation</td>\n", " <td>0.1357</td>\n", " <td>366.86</td>\n", " <td>10.373491</td>\n", " <td>11.63</td>\n", " <td>682</td>\n", " <td>4710.000000</td>\n", " <td>3511</td>\n", " <td>25.6</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>debt_consolidation</td>\n", " <td>0.1008</td>\n", " <td>162.34</td>\n", " <td>11.350407</td>\n", " <td>8.10</td>\n", " <td>712</td>\n", " <td>2699.958333</td>\n", " <td>33667</td>\n", " <td>73.2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>credit_card</td>\n", " <td>0.1426</td>\n", " <td>102.92</td>\n", " <td>11.299732</td>\n", " <td>14.97</td>\n", " <td>667</td>\n", " <td>4066.000000</td>\n", " <td>4740</td>\n", " <td>39.5</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " credit.policy purpose int.rate installment log.annual.inc \\\n", "0 1 debt_consolidation 0.1189 829.10 11.350407 \n", "1 1 credit_card 0.1071 228.22 11.082143 \n", "2 1 debt_consolidation 0.1357 366.86 10.373491 \n", "3 1 debt_consolidation 0.1008 162.34 11.350407 \n", "4 1 credit_card 0.1426 102.92 11.299732 \n", "\n", " dti fico days.with.cr.line revol.bal revol.util inq.last.6mths \\\n", "0 19.48 737 5639.958333 28854 52.1 0 \n", "1 14.29 707 2760.000000 33623 76.7 0 \n", "2 11.63 682 4710.000000 3511 25.6 1 \n", "3 8.10 712 2699.958333 33667 73.2 1 \n", "4 14.97 667 4066.000000 4740 39.5 0 \n", "\n", " delinq.2yrs pub.rec not.fully.paid \n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 1 0 0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploratory Data Analysis \n", "\n", "Let's do some data visualization! We'll use seaborn and pandas built-in plotting capabilities, but feel free to use whatever library you want. Don't worry about the colors matching, just worry about getting the main idea of the plot.\n", "\n", "** Create a histogram of two FICO distributions on top of each other, one for each credit.policy outcome.**\n", "\n", "*Note: This is pretty tricky, feel free to reference the solutions. You'll probably need one line of code for each histogram, I also recommend just using pandas built in .hist()*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,0,'FICO')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,6))\n", "loans[loans['credit.policy']==1]['fico'].hist(alpha=0.5,color='blue',\n", " bins=30,label='Credit.Policy=1')\n", "loans[loans['credit.policy']==0]['fico'].hist(alpha=0.5,color='red',\n", " bins=30,label='Credit.Policy=0')\n", "plt.legend()\n", "plt.xlabel('FICO')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Create a similar figure, except this time select by the not.fully.paid column.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,0,'FICO')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,6))\n", "loans[loans['not.fully.paid']==1]['fico'].hist(alpha=0.5,color='blue',\n", " bins=30,label='not.fully.paid=1')\n", "loans[loans['not.fully.paid']==0]['fico'].hist(alpha=0.5,color='red',\n", " bins=30,label='not.fully.paid=0')\n", "plt.legend()\n", "plt.xlabel('FICO')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Create a countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid. **" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1a21294550>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 792x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(11,7))\n", "sns.countplot(x='purpose',hue='not.fully.paid',data=loans,palette='Set1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Create more visualizations and interperet the results in the first part of the report.** \n", "You can create a scatterplot of pairs of attributes like we did in class example but since there are more attributes here, it will take longer to create them and will be harder to analyze, but if you clcik on images you can zoom them...\n", "Best solution is to create a subset of the dataset with some of the attributes and then create scatterplots. you can use what we used in class::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**this will take 1 minute to create graphs just wait until they show up. Agin this is not very practical, create a subset of attributes, and execute the following code on that new dataframe**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.pairplot(loans.dropna(), hue='credit.policy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### *****FIRST PART OF YOUR REPORT. ANALYSE AND DISCUSS THESE SUMAARY STATISTICS and THESE GRAPHS.****\n", "### What do they tell you about your data? About the relationship of the attributes, etc.." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting up the Data\n", "\n", "Let's get ready to set up our data for our Random Forest Classification Model!\n", "\n", "**Check loans.info() again.**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 9578 entries, 0 to 9577\n", "Data columns (total 14 columns):\n", "credit.policy 9578 non-null int64\n", "purpose 9578 non-null object\n", "int.rate 9578 non-null float64\n", "installment 9578 non-null float64\n", "log.annual.inc 9578 non-null float64\n", "dti 9578 non-null float64\n", "fico 9578 non-null int64\n", "days.with.cr.line 9578 non-null float64\n", "revol.bal 9578 non-null int64\n", "revol.util 9578 non-null float64\n", "inq.last.6mths 9578 non-null int64\n", "delinq.2yrs 9578 non-null int64\n", "pub.rec 9578 non-null int64\n", "not.fully.paid 9578 non-null int64\n", "dtypes: float64(6), int64(7), object(1)\n", "memory usage: 1.0+ MB\n" ] } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical Features\n", "\n", "Notice that the **purpose** column as categorical\n", "\n", "That means we need to transform them using dummy variables so sklearn will be able to understand them. Let's do this in one clean step using pd.get_dummies.\n", "\n", "Let's show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary.\n", "\n", "**Create a list of 1 element containing the string 'purpose'. Call this list cat_feats.**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "cat_feats = ['purpose']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now use pd.get_dummies(loans,columns=cat_feats,drop_first=True) to create a fixed larger dataframe that has new feature columns with dummy variables. Set this dataframe as final_data.**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**if you call head() now you will see that pandas created dummy variables called \n", "purpose_credit_card\tpurpose_debt_consolidation\tpurpose_educational, tc..**" ] }, { "cell_type": "code", "execution_count": 12, "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>credit.policy</th>\n", " <th>int.rate</th>\n", " <th>installment</th>\n", " <th>log.annual.inc</th>\n", " <th>dti</th>\n", " <th>fico</th>\n", " <th>days.with.cr.line</th>\n", " <th>revol.bal</th>\n", " <th>revol.util</th>\n", " <th>inq.last.6mths</th>\n", " <th>delinq.2yrs</th>\n", " <th>pub.rec</th>\n", " <th>not.fully.paid</th>\n", " <th>purpose_credit_card</th>\n", " <th>purpose_debt_consolidation</th>\n", " <th>purpose_educational</th>\n", " <th>purpose_home_improvement</th>\n", " <th>purpose_major_purchase</th>\n", " <th>purpose_small_business</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.1189</td>\n", " <td>829.10</td>\n", " <td>11.350407</td>\n", " <td>19.48</td>\n", " <td>737</td>\n", " <td>5639.958333</td>\n", " <td>28854</td>\n", " <td>52.1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>0.1071</td>\n", " <td>228.22</td>\n", " <td>11.082143</td>\n", " <td>14.29</td>\n", " <td>707</td>\n", " <td>2760.000000</td>\n", " <td>33623</td>\n", " <td>76.7</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0.1357</td>\n", " <td>366.86</td>\n", " <td>10.373491</td>\n", " <td>11.63</td>\n", " <td>682</td>\n", " <td>4710.000000</td>\n", " <td>3511</td>\n", " <td>25.6</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>0.1008</td>\n", " <td>162.34</td>\n", " <td>11.350407</td>\n", " <td>8.10</td>\n", " <td>712</td>\n", " <td>2699.958333</td>\n", " <td>33667</td>\n", " <td>73.2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>0.1426</td>\n", " <td>102.92</td>\n", " <td>11.299732</td>\n", " <td>14.97</td>\n", " <td>667</td>\n", " <td>4066.000000</td>\n", " <td>4740</td>\n", " <td>39.5</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " credit.policy int.rate installment log.annual.inc dti fico \\\n", "0 1 0.1189 829.10 11.350407 19.48 737 \n", "1 1 0.1071 228.22 11.082143 14.29 707 \n", "2 1 0.1357 366.86 10.373491 11.63 682 \n", "3 1 0.1008 162.34 11.350407 8.10 712 \n", "4 1 0.1426 102.92 11.299732 14.97 667 \n", "\n", " days.with.cr.line revol.bal revol.util inq.last.6mths delinq.2yrs \\\n", "0 5639.958333 28854 52.1 0 0 \n", "1 2760.000000 33623 76.7 0 0 \n", "2 4710.000000 3511 25.6 1 0 \n", "3 2699.958333 33667 73.2 1 0 \n", "4 4066.000000 4740 39.5 0 1 \n", "\n", " pub.rec not.fully.paid purpose_credit_card purpose_debt_consolidation \\\n", "0 0 0 0 1 \n", "1 0 0 1 0 \n", "2 0 0 0 1 \n", "3 0 0 0 1 \n", "4 0 0 1 0 \n", "\n", " purpose_educational purpose_home_improvement purpose_major_purchase \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " purpose_small_business \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Test Split\n", "\n", "Now its time to split our data into a training set and a testing set!\n", "\n", "** Use sklearn to split your data into a training set and a testing set as we've done in the past. Do not forget you class attribute is \"not.fully.paid\" **\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training a Decision Tree Model\n", "\n", "Let's start by training a single decision tree first!\n", "\n", "** Import DecisionTreeClassifier**" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data.**" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictions and Evaluation of Decision Tree\n", "**Create predictions from the test set and create a classification report and a confusion matrix.**" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.85 0.81 0.83 2431\n", " 1 0.16 0.20 0.18 443\n", "\n", "avg / total 0.74 0.72 0.73 2874\n", "\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1980 451]\n", " [ 355 88]]\n" ] } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tree Visualization \n", "\n", "Visualize the decision tree just like we did in class." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improve your results\n", "\n", "Using different values for the parameters come with DecisionTreeClassifier() method, try to improve your results.." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### *****SECOND PART OF YOUR REPORT: ANALYSE AND DISCUSS THESE HOW YOU IMPROVED YOUR RESULTS, WHAT HELPED AND WHY?****" ] }, { "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": 1 }
Final Project/.ipynb_checkpoints/Spr2019_Decision_Trees_Project2-checkpoint.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Decision Trees using Iris Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Iris Dataset\n", "\n", "You are working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\n", "\n", "We've been tasked by our company's Head of Data Science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\n", "\n", "<img src=\"images/petal_sepal.jpg\" />\n", "\n", "We've been given a data set from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\n", "\n", "### *Iris setosa*\n", "\n", "<img src=\"images/iris_setosa.jpg\" />\n", "\n", "### *Iris versicolor*\n", "<img src=\"images/iris_versicolor.jpg\" />\n", "\n", "### *Iris virginica*\n", "<img src=\"images/iris_virginica.jpg\" />\n", "\n", "The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future.\n", "\n", "**Note:** The data set we're working with is the famous UCI Machine Learning Repository [*Iris* data set](https://archive.ics.uci.edu/ml/datasets/Iris) — included with this notebook — which I have modified slightly for demonstration purposes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Section 1 - Reading Data and Creating ScatterPlot\n", "\n", "\n", "\n", "Import the required libraries" ] }, { "cell_type": "code", "execution_count": 11, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "df = pd.read_csv('iris.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now read your data into dataframe and use head() method to check your data" ] }, { "cell_type": "code", "execution_count": 12, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()\n", "#read the dataset file into a dataframe\n", "#display the head() of the dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Print out some summary statistics about the data set." ] }, { "cell_type": "code", "execution_count": 13, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>5.843333</td>\n", " <td>3.054000</td>\n", " <td>3.758667</td>\n", " <td>1.198667</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.828066</td>\n", " <td>0.433594</td>\n", " <td>1.764420</td>\n", " <td>0.763161</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>4.300000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0.100000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>5.100000</td>\n", " <td>2.800000</td>\n", " <td>1.600000</td>\n", " <td>0.300000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>5.800000</td>\n", " <td>3.000000</td>\n", " <td>4.350000</td>\n", " <td>1.300000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>6.400000</td>\n", " <td>3.300000</td>\n", " <td>5.100000</td>\n", " <td>1.800000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>7.900000</td>\n", " <td>4.400000</td>\n", " <td>6.900000</td>\n", " <td>2.500000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "count 150.000000 150.000000 150.000000 150.000000\n", "mean 5.843333 3.054000 3.758667 1.198667\n", "std 0.828066 0.433594 1.764420 0.763161\n", "min 4.300000 2.000000 1.000000 0.100000\n", "25% 5.100000 2.800000 1.600000 0.300000\n", "50% 5.800000 3.000000 4.350000 1.300000\n", "75% 6.400000 3.300000 5.100000 1.800000\n", "max 7.900000 4.400000 6.900000 2.500000" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\n", "\n", "We can even have the plotting package color each entry by its class to look for trends within the classes.\n", "**I am giving you the answer since the main assignment is after this**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 804.75x720 with 20 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "sns.pairplot(df.dropna(), hue='species') #this would only work if you called your dataframe df\n", ";" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FIRST PART OF YOUR REPORT. ANALYSE AND DISCUSS THESE SUMAARY STATISTICS and THESE GRAPHS.\n", "### What do they tell you about your data? About the relationship of the attributes, etc.." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now, complete the following sections similar to example we did in class.**\n", "##NOTE: The number of cells below does not mean the number of cells you need, you might complete the task with less or more cells" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EDA" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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AvwOWqepn+x32BvBP4fv3hI9J2KJAPp+PpX9+m0DBVEgbbtdnmAi+kpnsr6jgwIED9gRoGENQWVmJK8sF6UA+HG84njJTH0zCZTiuvq6W2QX+0P0UmiAZS/XH6rG8w9jWJ8ILSGqtALKDiDwuIneEH/4XUCwiB4EHgIeci2xwK1asoLOjHf+Yi205X6DkIsTt4bXXXrPlfIYxFAcOHiCYGwRA80Ptm8rKSidDso1JuAxH9fb2cvx4E1Nyg+RnCkePHnU6pISnqtTV1aHZI+hscYEry2USXEBVV6jq7eH7j6jqG+H7Par616o6Q1WvVNWEftd/+eVXwFuAlTfenhN6MugtuoD33nufjo4Oe85pGOcRCAQ4Un0ELQi/t+WHvhw6lBrDiibhMhx19OhRLFUmZAcZn+nnSGT83jinkydP4uvxQc7IzhPMDlJztMaeoAxHVVRUhCbLj5k9osny/QXGzsHn6+Gdd96x7ZyGcS7V1dWhgrsF4SeyQNKFgwcPOhqXXUzCZTgq0lU8MdtiYk6AqqrKlNo7KxbOWMUzApqjHDlyxPy8U8DLL7+MuNMIlMy09bxWdgmaO4aXX34ltFTfMGIoklhFhhKR0P2K/RUORmUfk3AZjjp48CBuF0zMDjIlJ0hnV7cZ5hpE3zLp3BGeKA86OzppaWkZcUyGc1paWvjgww/pLZ4BnnTbz+8bM4fa2qNs3rzZ9nMbxukOHDiAuOWM9zYr36KyspJgMOhcYDYxCZfhqH379jI1x8LjggvyguHn9jkcVWKrqqpCPNK30nC4NC+1JqSOVm+++SYBvx//2DkxOX+waDqSnsWSJS/H5PyGEVFZWRnq3To9MymAXl9vSjTETcJlOCYQCLC3vJwL8noBmJwTJM0N5eXlDkeW2A4ePBh6UxrpVJ3whFSTcCWvQCDAy6+8QjB/IppVGJuLuNz4SmezceMGamrMnD8jdg4cPICVd+bQdSqtVDQJl+GYgwcP0t3jY2ZBAACPCy7MC7B9+zaHI0tcqsr+A/ux8m2YT5MJLq/L1FlKYqtWreJEc3PMerciAmNmg8vFK6+8EtPrGKNXW1sbrS2tkNfvG+HHhw8fjndItjMJl+GYbdtCidXFhYG+5y4u8HPgwEGzofU51NbW0t3VDTZ1ZgTzg5TvNT2KyepPS5aAN49gwZSYXkfTswgUXsBbS5ea/RWNmDjnYiAPuLJdKbHFj0m4DMds2rSJiTlKYcapP7C5xX5UlS1bzrfF3egVGW7VIntWFmqRUnOkxnyIJqGKigr27N6Nr/RiW0tBnIt/3CX4enp4++23Y34tY/Tp26R6gHI3wazUKGFjEi7DEV1dXezcsZ1Li3xnPD8jL0hWmrBhwwaHIktse/bsQdKkb/7VSGmxoqrs3bvXnhMacbNkyZJQKYjSWXG5npVTiuaO5U9LlpgSEYbt+na9yD77e5qjZtK8YQxXWVkZ/kCQy0r8ZzzvdsGlRT7Wr11j3tQHsGPnDqxCa+QT5iOKAYGdO3fadEIjHk6cOBEqBVFyUUxKQZyLb+wc6uvq2LhxY9yuaYwOjY2NuLwucA/wTS+0nGzB7/cP8M3kEdOES0Rmicj2025tInJ/LK9pJIdVq1aRnS59E+ZPd3lpLydb29izZ48DkSWutrY2qiqr0FIbC5WmgRQK27dvt++cRsy9/vrrBAOBmE+W7y9YOB3JyGbxn/4U1+saqa+5uRnNOMd7W3gv9pMnT8YvoBiIacKlqhWqukBVFwBXAF3Aq7G8ppH4ent7WbtmNZcX9+AZ4DdwQYkfjyu0Ga9xyrZt21BVexMuIFgSZPee3fh8vsEPNhzX29vLK6++SrBgMuotGPwFdnK58JVezJayMqqqquJ7bSOlnTh5Aitj4FGNSCJmEq6huxk4pKpms7xRbvPmzXR2dXPV2FD9rRcqvLxQ4e37fpYH5hX1snzZh2ZY8TRbtmwJzd8qtve8OlYJ+APs2LHD3hMbMfHhhx/S2tJC77hLHLm+f8xsxOVh8eLFjlzfSE1tbW1o+jkak+FR82RfvR7PhOvvgP/u/6SIfFFEykSk7Pjx43EMx3DKBx98QE66MLcoNJxY3e6muv3MgftrxvXS1HzCzC0KU1XWb1yPVWLZ/1dbAuISNm3aZPOJDbupKn/4wx8hqxArb6IzQaRl0lsyg3ffey/pexyMxNHZ2Qlp5/hm2mnHJLG4JFwikg7cAZw18K+qv1bVhaq6sLS0NB7hGA7q6upi7ZrVXFk68HBixOUlfjLcwvvvvx+/4BJYTU0NDfUN6PgYbDTtAavUYt36dfaf27BVWVkZVVWV+MbNi0spiHPxj5tLwO/n1VfNDBHDHj3dPeA5xzfDz3d1dcUtnliIVw/XJ4GtqtoQp+sZCWrNmjX0+Hq5dlzveY/L9MAVpT2sWL6M3t7zHzsarFsXSoZ0XAwSLkDHK0drjnL06NGYnN+wx+9feglJzyJQfKGjcai3gEDhFJa8/Ard3d2OxmIkP1UNvc8PtEIR+p5P9s+CeCVcf88Aw4nG6PPuu+9Q4mXA1Yn9XTeul/aOTtavXx+HyBLbqtWrkEIZsEZNf7JdoAVoAdcKV+jxIHRCKJFbu3btCCM1YqWiooKtW7bgG3sJuM71yRQ//vGX0tHextKlS50OxUhyfn+o4PVgCVeyL+yJecIlIlnAxwGzCdco19TUxJayLVw3rhvXEEZD5hYFyM+Ad999N/bBJbCTJ0+yZ/ceguODQzpeWgTxh2/HBWkZwg87G6RAWLlq5QijNWLlhRdeRDzp+MdcPOxzpFevx9XVjKurmczyt0ivHn5jxsodh5U7jpde+u+kr480Ug0NDezbt8/pMJJWX8/VuTIS08M1NKraparFqtoa62sZie3999/HUuWjgwwnRrhdcO3YHjasX09r6+j99Vm9enWoHMTE2AwnRgQnBNmzew/Nzc0xvY4RvcOHD7Nq9Sp8Y+aMqNCpq7MZCfqRoB93+zFcnSP7v+6dMJ+mpuO89957IzpPsvvi//yffPGLXzy1PY0Rlb6eq3PN4XL1Oy5JmUrzRty89+47XJgfZHz20Es9fHR8L4FgkOXLl8cwssS2fMVyJNe+7XzORSeFtvlZtWpVbC9kRO13v/sd4vLgHzfX6VDOEMyfhOaU8tvfvUAgMPg0gVR1sqUFCPXiG9Hr6ekJ3TnXkKKAeOTUcUnKJFxGXFRWVnKosorrxkXXQpmSE2RSjvLeu+/EKLLE1tLSwrat2whODNq3nc+55IHkCcuWL4vxhYxoVFdX8+GHH+IbczGkZTodzplE8E1YwLH6ulG7olj1VM9zsteJckpk9aF6zt2LL2liVikaxlB88MEHuIS+YqdDJRIaVty9pzwlNi+N1qpVq7AsC50c2+FEAASCk4Ls3LHTtNQTyHPPPQcuD/7x85wOZUDBgiloTim/ee75UdnL1dHR0Xe/JdzTZUSnL1E932h52pk/62RkEi4j5lSVDz94n0uK/OSfq5LweVwdnvP14Ycf2h1awvvgww+QvNgPJ0bo5NCw4sqVZvJ8IqisrGTZ8uX4xs6BNO/gL3CCCL4Jl9FwrJ63337b6Wji7vQ5j2b+4/D0FdDNOPcxwfQgJ06ciE9AMTLkhEtEZorIsyLynogsi9xiGZyRGioqKqg/1sDVUfZuRYzxWlyYH2TFKBvqampqYsf2HQQnxWE4MSIvtFpxtA4PJZr/+s1vEHca/nGJ2bsVESyYjOaO4bnnnk/6lWTROn2HFNMzPDx9P8Nwm0K2y1nlbNSrNDQmdynPaHq4/gRsBb4LfPO0m2Gc14oVK3ALXFE6/KXjV43xsf/AQerq6myMLLGtXLkytDoxHsOJpwlOClJeXs6xY8fiel3jTBUVFaxetSpUdyvR5m71J0LPxCtoajrOm2++6XQ0cRX5O8lJ01E57cEOdXV1SLr0beEjLQOUs8mG443Hk3rYOpqEK6Cqz6jqJlXdErnFLDIjZaxeuYKLC/3kpA0/cVgYTtZWr15tV1gJ7/0P3kcKBPLie91IgpcsK0NF5FoR+QcR+Vzk5nRMdviv3/wGSctI+N6tCCtvAlbeeH77u98l/fL9aNTW1uJ2wcUFfmqPHnE6nKRUXV2N5ur5e/JzIRgMJvVuGIMmXCJSJCJFwJsi8mURGR95Lvy8YZzTkSNHqKmtG1HvFsCYLIvJucratWtsiiyxNTQ0UL6nPDScGG85QFFo/liiE5EXgKeBjwIfCd8WOhqUDfbt28eG9evxjZ03orpbcSWCb+JltJw8yRtvvOF0NHFTXV3NuCxlYk6Q+vqGUZVs2sGyLPZV7MPKP3+5IC0INQT3798fj7Bi4lxlxk63BVBO5Z6nDyMqcIHdQRmpI7Itz2UlI69EvaC4h6U7d9HR0UFOTs6Iz5fIIpPW4z2cGGFNsjiw8wC1tbVMnDjRkRiGaCEwR09fm58Cnnv+eSQtE//YOU6HEhUrbwLBvPG88OKL3HHHHWRknGcWdIqoPHiAyVl+JmUHsVQ5fPgws2bNcjqspFFVVUV3VzcUD3Jgfqg0xM6dO/nLv/zLuMRmt0F7uFR1uqpeAFwcvt93A5Lr3cCIuw0bNjAxRynxDr3Y6bksKPFjWRZlZWU2RJbYli1fFto70aG8UieF8pckKIK6GxgX7YtEJFNENonIDhHZIyKPDXDM50XkuIhsD9/+xZaIB3Ho0CHWr1sXmruVLL1bp+mdEOrleued1K+d197eTn1DI9Nyg0zLC/VGHzhwwOGoksumTZsA0DGDtJkErFKLDRs3kKztq2jmcK0b4nOGAYS2Ydi5cwfziuzpYr8wL4g3Tdi8ebMt50tUTU1NoeHECQ4MJ0ZkA4WhKveJSETeFJE3gBKgXETeFZE3IrchnMIH3KSq84EFwK0icvUAx/1RVReEb/9p4z/hnF566SXEk5Z0vVsRVt54NGcML/7+JYJBB3+H46C8vByAGfkBxngtstOl7zljaFauXBmaq5o1+LE6XmlsaOTgwYOxDywGBh1SFJFxwETAKyKXcWpoMY8h/YiM0WrXrl34/QHmFtmzsa3HBRfn+9hSltoJ17p1oXZMrPdOHIw1wWLfnn00NzdTXDxYf3/cPT2SF4eHICNVFNPCN8ebzc3NzSxbtozektngSdLhOBF84+bScHAZGzdu5Nprr3U6opjZuXMnboEL8gK4BC7K87Fj21anw0oadXV1lJeXY80b2giITlTYFtqX96KLLopxdPYbSg/XJwi9uU0CfgL8W/j2APCd2IVmJLtt27bhFphVYN8y3jlFAerqj6V0yYJ169YhOfFfndifTgjlHxs2bHA2kAGo6kpVXQncFrl/+nNDOYeIuEVkO9AIvK+qGwc47G4R2SkiS0Rkso3/hAG99dZbBIPBpO3diggWTkMysnn55ZedDiWmyso2c0F+kMxw18WcwgA1tXU0NjY6G1iSeP3110FApwyxrZMR6uV6a+lbSbk4YShzuH6rqjcCn1fVG0+73aGqr8QhRiNJbdu6hel5QbxDWZoxRBcXhpK37du323fSBOL3+ynbUkZwbByLnZ5LPkiWJGTCdZqPD/DcJ4fyQlUNquoCQo3JK0Wk/87QbwLTVPVS4APgtwOdR0S+KCJlIlJ2ehHMaKkqS//8NsG88ag3TlsLxIrLha9kJmVlZSlbDLSlpYV9+yqYV3Sq0Ou84lBvfmReknFunZ2dvP7G66FeqyjGyqwZFh3tHbz77ruxCy5GopnDNVVEHuh3+4KILIhZdEbS6unpYd++CmYX2DOcGDE5J0h2uqRswrVnzx56fb3oWMdHt0J7K5YG2bJ1C5Y18kUPdhKR/y0iu4BZ4R6oyK0K2BnNuVS1BVgB3Nrv+WZVjTSjnwWuOMfrf62qC1V1YWlpadT/loi9e/dyrL6OQEnyDZUMJFB8YWhbrxTdkmvdunWo6hkrsCdlW5R4YXXiLzZx3CuvvEJXZxfW7CjfW0qBIvjdC7/D77f38yXWokm4FgJfIjSfayLwReAG4FkR+Zb9oRnJrLy8nEAwyOxCe/8gXAIz83zs3LHN1vMmir5Ecvif2/YaCx3tHVRVVTkdSX8vAZ8C3gh/jdyuUNXPDvZiESkVkYLwfS9wC7Cv3zHjT3t4B7DXntAHtnbtWhAGezM4AAAgAElEQVQhUDg1lpeJG/UWoNnFrFm71ulQYmL58uWUeGFa7qmFASLwkdIeyso2n9qQ2ThLS0sLL/7+xdC0hcIoXywQvCRIY0Mjr732Wkzii5VoEq5i4HJVfVBVHySUgJUCHwM+H4PYjCS2c+dOBLgo3/5VSrMKAhytrU/JjWJ3796N5AskSDUALQ71tO3evdvhSM7iBtqArwDtp90YYkHm8cByEdkJbCY0h+stEXlcRO4IH3NvuGTEDuBeYvw+t3HjJqycMck7WX4A/ryJ7N61i66uLqdDsdWJEyfYvHkz14ztRvoN/V89thd/IMiKFSsciS0ZPPvss3R3dw95svxZxoZuv3nuN7S0tNgaWyxFk3BNAU7fldQPTFXVbkJLrA2jz47t25mca5E9gu18zmV2eB7Xrl27bD+3k1SVPXv3ECxKoKX02eDKdLFv377Bj42vLUBZ+OtxYD9wIHx/0C3HVHWnql6mqpeq6lxVfTz8/COq+kb4/sOqeomqzg/PW43ZD6Gnp4cDB/YTyB0/+MFJxMobTzAYTMTfnxF59913sSyLj44/e6PuC/KCTMhRlr71lgORJb5du3bx5ptvYs2whr8wSCC4IEhXVxe/+MUvbI0vlqJJuF4CNojIoyLyKLAW+G8RyQZM4RGjTyAQYM+e3czKP/vNyA7TcoNkuIUdO3bE5PxOaW5uprO9E0Y6X9oPXq+Xe+65B6/XG2oaDZdAMC/IoUOHRhiUvU4ryPwu8ClVLVHVYuB2IOkW81RWVqKqWNklTodiq2D435NKxUAty+L1115lZkGQidln99CIwI3juynfuzel/t126Onp4UdP/gjJFnTuCBvjeRCcFeS9994LDccngSEnXKr6A0LztlqAVuBLqvq4qnaq6mdiFaCRfCoqKujx9fatKLSbxwUz8v1s35Za87iqq6sB0PwRvhH5YdGiRdx7770sWrRoZAkXoHlK1eGqRK3u/BFV/XPkgaq+DVzvYDzDEvm/t7KindASpWDvmcl4MDaNoj5pXiTd2/fvSwXr1q2jrv4Yfzm5u++5Fyq8vFDh7Xv8sQm9ZHiExYsXOxFiwvrlL39J7dFaAgsDQ9tYcBA6R5EC4akfP8WJEydGfsIYi6aHC2Ab8CdCLchGEZlif0hGsotM/Laz/lZ/swv8VFZV0dbWFrNrxFtDQ0PozkjLCafB0qVL+dnPfsbSpUtDJT1HIht8PT46OjoGPzb+mkTkuyIyTUSmisi/Akk3uS9SOkHTs2N6HQn0npGMSyDGCRdgpWUzknIZiURV+f2LL1LihY+UnmrJVLe7qW539z3OTlOuH9/NBx+8f+rvepRbsWIFr732GtZMC8bYdFIXBK4M0Nbexg9++IOEW03d35ATLhH5GtAAvA+8BSwNfzWMM2zbupWJOUp+Rux6RC4uDKCqKTWs2Peh5D3/cYNKg+7ubpYsWUJ3d/eIEy71hv4fE7SY498TWrzzKvAaobfyv3c0omFoaWlBPGngsrFo3QDUk35GMq5x2Ksx6Mnk5Mnkmdh8Plu2bGFPeTmLpnThHuTT87apPaAWL774YnyCS2A1NTU8+dSTUAw6z+bPhfzQfK4tZVt4/vnn7T23zaLp4boPmBWeRHqpqs4LFwQ0jD5+v5+dO3cwpyC26yhm5AfIcAtbtgw6PzpptLe3Ix4Jrb9LJOHP5ETs4VLVE6p6X3gC/GXh+4k/ttCP3++PebIFgDv9zGTcHYflsC43vUlWL2kgqsqzv/4Pir1ww8TB399KMpUbJvTw1ltvUltbG4cIE1NXVxcPf+dheoI9BK8KRj+uNgQ6XbGmWjz//POsWbPG/gvYJJp/eg2huVuGcU579uyhx9fLJUWxG06E0Dyu2QW9bN400G4syam7uxtJc7q8/ADCeUB3d/f5j4sjEflp+Oubp29aHcXm1QklNBSSgP/3NrGsBFp5O0wffvghe/dVcNf0TtKG+Mn56ek9eFB+9atnYhtcggoGg3z/se9z5MgRAlcHIFYj5gJ6hSJFwmOPP5Zwi3wiomlSVQIrRGQpp5WBUNWf2B6VkbQ2bdqES2COTRtWn8+8Yj8v7q/l2LFjjBs3LubXizXpX9AnwSRYfC+Ev45oE+tEkZmZCcHk7wUakBXA6811OooR6erq4plf/j+m5Vn8xQClIM6lMEO5fWoXL69cxZYtW7jiigE3K0hZzzzzDBvWb8C6zMZ5W+fihsC1AVgG3/r2t/j1f/ya4uLiGF80OtH0cB0hNH8rHcg97WYYfTasX8dF+QGy4jA6Etm3bOPG1OjlcrvdkIhzPsMxeTxx+E8dIlWNjCW7gc0DbGCdVLKzs9GgHxJ80u9wuIJ+crJjuxgg1n7zm99wvKmZf5rZgSvKdseiqT2MzYKfPP10Um64PFxLlixh8eLFWDMsdEacVjh7Q0lX04kmvvXtbyVcwd1oykI8pqqPAU9H7ocfGwYQWml18FAl84vj01KfkGVRmkWib648ZDk5OVi9FiRa9YXwf2dOTo6zcQzs88B2EVkvIv9HRD4lIjGurWC/SEtc/In1AWEHd6CLkpLkrS+2e/du/vSnxdw00cdFBdEPjaa74fOz2qmpreW5556LQYSJZ8WKFfz85z9HJyi6IM5vaIUQuCrAgQMHeOSRRwgEYju9JRrRrFK8RkTKCe8nJiLzReSXMYvMSDrr168H4LLSoXe5v1Dh7VtS/cOynDNq2QxGBBYU9VC2eVNKtBwLCwtDyVbsV+pHRXpCTfqCggKHIzmbqn5OVWcCdwNHgf9HqNp8UhkzJjTeIr2JtzBhRCwL9XX2/fuSTVdXF0/88AcUZ8LfXTT8ZHhecYAbJvj4w3//Nzt3RrW3etLZunUrjz3+GFqsWFdZzkxNnADW5RabNm3iySefTJhyEdEMKf4U+AThGjequoPQPoqGAYQ23y3xwqQBqi+fS3W7m+6gi+6gi30taWfUshmKy0r9+Hr9bN26NdpwE87YsWNDdzqdjeMsneByuxJuPgSAiHxWRP4DWEJoA+pfAH/hbFTRmzRpEgCuntSpKwcgvnZQ7fv3JZuf/vSn1NXV87/mtI94msRnZnZR4lV+8Nj3U6p+4On27dvHQw8/hJVtEbwuaEtx0+HSCxTrEov333+fX/ziFwlRuDmqBZqqWtPvqeRfemLYoru7m7LNm7m8pOeszVxj6eLCAJkeSeilwEM1ZUqojrC0J9TkdKRdGD9+fELN4TrNT4EFwLPAvar6f1R1vcMxRW3cuHF4PB6k+6TTodjK1R2qvxX53U4mS5cu5Z133uHO6d227Jrh9cBXLmmjqbmJHyZBkc5oHT58mAe/8SA+t4/AXwT6ysk4SS9WrIsslixZkhA1uqIqCyEi1wIqIuki8g3Cw4uGsXnzZnr9fq4oje9KqzQXXFrkY83qVUn/BjZx4kTS0tIgwT5z3a1uZlw4w+kwBqSqJcA/A5nAEyKySUReGORlCcfj8TB12jTcXUlXQuy8XF3NiAjTp093OpSolJeX85Of/BtziwLcdUGPbee9MD/IZy7qYsOGjQmRANilrq6O+75+Hx3+jlCyNdLizXYR0PmKNc3iueeec3yrpWgSri8BXwEmEporsSD82DBYs2YN2WkS0+18zmXhmF5OtrSyd29y5/8ej4eZs2biOhGDyoDD1QNWh8WcOXOcjmRAIpIHTAGmAtMIbf2dlJn37Fmz8HQ1QwIMfdjF1dnEhIkTycoa6X5V8dPQ0MB3Hn6IgrQAX5kb/arEwXx8ko+/GO/j+eef58MPP7T35A5obGzkvvvvo6WjJZRsJdramnCNLp2k/OIXv+DNN990LJRoVik2qepnVHWsqo5R1c+qatLtWWbYLxAIsG7tGuYX9+BxIFeYXxzALbBq1ar4X9xml867FDkpkCALa+R46NNm3rx5DkdyTmuATwE7gb9V1Vmq+k8OxzQsc+bMQf09iC9F5veoktZ1nHlz5zodyZB1dHTwzW88SHdHKw9c2kpuuv3Jrwj888VdzCwI8qMnfpjU25OdPHmS++6/j8bmRgIfDYSaO4nIBdZVFjpOefrpp3n//fedCuP8ROTnIvKzc93iEaSR2Hbv3k1bewcL4zycGJGdplxc6GdNCiRcV155JWpp4qyzawBvlpfZs2c7HcmAwtuMfVlVX1LVo/2/LyI/dyKu4bjkkksAcLenxmbH4mtDe7v7/l2Jzufz8dC3v03NkSPcN6+NyTmx6yhNc8ED89spyQjw8EPfTtjK6OfT1tbG/V+/n7pjdaFkq8jpiAbhAutaCy1VnnjiCVavXu1ECIMqA7ac52aMcqtXr8bjgkvjVH9rIFeU+qmpreXIkSOOxWCHuXPnkpGZgdQlwMR5BfcxN1d+5MpEnTA/FNc5HcBQTZs2jZzcXFxt9U6HYgt3+N8xf/58hyMZnN/v55HvfY9du3bxvy/pYG6MtyYDyElTvr2gjfRgFw98/X5qavqvSUtcXV1dPPiNBzlcfThU3T1Zyqy5IXhtEKvQ4pFHHmHTpk1xvfygCZeq/vZ8t8hxydSSNOyjqqxdvYpLCv1kOviZHKn9tXbtWueCsEFGRgbXXH0N7nr3sAugaoGiaeFbqaIFwzzRCdBu5WMfM9Vf4sHlcnHF5ZeT3lGfEvO43G11FBQWMnXqVKdDOa9AIMCjjz7K+g0b+B+zO7l6XPwajiVei29f1kqwu5X77v1aUmxy7fP5eOjhh6ioqCBwVQDG2nt+2S7QArSAa4Ur9NhOaRD8aBAr1+Lh7zzMrl277D3/edg54yZpWpKGfaqrq6k71sDlURQ7jYWSTGVqnsXaFCgPcf3116Pdwx9W1AUKBUABWDdYw670LDWC2+PmmmuuGV4gRtQ+8pGPoD0dSLicQtJSi7S2Oq668spE24PzDH6/n0cffZQ1a9bwjzO7uGlS/N/HJmZbPLSgjZ72k9x371cTOukKBAI89thjbN+2HesjVmgJnc2kRRB/+HZckJYY/P6kQ+AvAgQyAnzzW9+M25BuAi2HSh49PT0cO3bM6TASQqS6/IIS5zfeXVDsY/fu3bS3tzsdyohcd911oWHFIw5+UFngPurmumuvS9QtfYYqcT/tBxBJbj0tyT007upoRP09CZ2s+3w+Hnnke6xevZrPzuziE1Oc261iSm6Qhy9rpbv1BF/76lcScmqEqvKTn/yENWvWYC2w0KlJ3gubGUq6urWbBx58IC6f6SbhGob77r+fv/mbv2H79u1Oh+K49evXMTnXojjT+T++y0r8WKpxH5e3W2ZmJjdcfwPuWrdzqxUbQsOJH//4xx0KwDb/7nQA0SgtLWXmzFmknTzsdCgj4jlRhSctjSuvvNLpUAbU09PDdx5+mLVr1/FPs7q41cFkK2JqbpDvXN5Cb8cJvvaVLyfcRPoXX3yRt956C2u2hV7k/Pu9LbIh8NEALR0tfOOb34h5Y93OhCupWpIjsbe8HCCpJjnGQldXF7t27WJ+UWJs/ndBXpCcdGHjxo1OhzJit912G9qrSK0zf1auKhd5+Xlce+21jlx/MCLypoi8ca5b5DhVfd7BMIfllltuRjqOIz2tTocyPGqRfvIw11x9dUL2jnZ0dPDggw+wZUsZ/3NOJx+f7HyyFTE5x+K7l7civjbu/dpXKQ9/1jht+fLlPPvss1hTLHRuiiRbEfkQuCZATU0N33vkezHd7NrOhCupWpLDdfp/RkNDaizfHq6tW7cSDFqOrk48nUtgbqGPTRs3JMS+WSOxYMECxo0fh6vKgU7oHpB64ZO3fjJU+T4xPQ3823luSeumm25CRPAc3+90KMPibq1FfZ0J2Tva0tLC/ffdS/nu3Xx5bgfXT0iMxuLpJmRbfPeKVrxWJ1+//z7H94k9dOgQT/zoCSgGXaip2bUyBoKXB9m6ZSu/+tWvYnaZodThGlFLUkQKRGSJiOwTkb0ikriD+kNQX39qyXYiT26Mh82bN5PhFi5yoLr8ucwr8nPiZAuVlZVOhzIiIsKn7/x0aOJ8nOtgSpWABZ/61Kfie+EoqOrK892cjm8kxowZw9VXX01G0wFIwu2qPA17ycsv4LrrEmsd1fHjx/nqV75MVeUhvj6/navHJkZDcSBjvBbfu6KFYk8P3/zmNxxbfd3V1cW/fvdf8bv9BK8JgtuRMOJCpyvWDIvFixfHrEbXUJrPI21J/jvwjqrOBuaT5PsvHj58GAD1ZFBZWeVsMA7bUraZWQW9pCXQTMBLwr1tW7Ykf4m42267DbfHjRyKY5NSwV3lZsFlC5Jiw2ERuSjcoCsXkcrIzem4RurTn/402tuFpzmx5vEMRnpa8bQc4Y5P3Z5QvaN1dXV89Stf5nh9Ld9a0MaCksRpJJ5LYYbyr5e3Mjmrl+9+918d2Qbo5z//OXW1dQSuTKD9EWNI5ytSJDz51JM0NTXZfv6h1OEadksyvM/Zx4D/Cp+rV1WTer3z/v37QYRA8Qyqj1Tj8yXO+H88NTU1caTmKJcUJVYrsSRTGZudGglXQUEBN914E+5qN8Trx1wP2qnc9Vd3xemCI/Yc8Ayh5QU3Ar8Dkm7z6v6uuuoqpk6bRsaxnbbW5LKyi1F3GupOI5g7Diu72LZzA6TV7cSTlsbdd99t63lHoqamhq999Su0n2jkoctaubgw8ZOtiNx05aHLWpmR5+cHjz/O22+/Hbdrb926laVLl2LNsqA0bpd1lgsCVwbo7OrkZz+zfyOdIfdNDLMleQGhQZHnRGSbiPyniGT3O+8XRaRMRMqOH0+U/UzObffu3WhWEcG8CVjBIPv27XM6JEdEVmjOScA3rzkFPnbu2E4wGHQ6lBG76667UL8i1fHp5XIfdFNUXMRHP/rRuFzPBl5V/RAQVa1W1e8DNzkc04i5XC7+8bOfha6TuE/Y12HXO/UarKxirKxieubcTu9U+2Z4SE8bac0HuH3RIoqL7U3khuvIkSPc+7Wv4mtv5juXtXJhfvK9J2R54FsL2phT6Oepp57kz3/+c8yvGQwG+cn//QmSK+glyT0fNmq5ELw4yIoVK2xvuEczGDSclqQHuBx4RlUvAzqBh04/QFV/raoLVXVhaWlip9E+n4+du3YRyBlHMC9UXtfpCY1O2bFjB940YWpu4r2BzS4I0NnVnfTzuCC0ofFFMy/CXTn8yvND1gY0wF1/dVcybeXTIyIu4ICIfFVE/goY43RQdrj55puZfsEFZB7dAlbi/Z31l15TRnpaGp/73OecDgUIDSPef9+9+DtP8p3LWpmSgO9VQ5XhDu29OLcowI9//FTMN1/+8MMPOVJ9hMDcQErP2zoXnalIlvDss8/augArmoRrOC3Jo8BRVY2s019CKAFLSjt27MDf20swfyJ4MtGcMaxfv8HpsByxY/s2LsrrxZWAK1ZmFYbG33bs2OFwJCMnItxz9z1oq0JjjK91KFRZPpEnyw/gfiALuBe4AvhH4J8Ge5GIZIrIJhHZISJ7ROSxAY7JEJE/ishBEdkoItNsjv283G43X/nyl6GnjbRju+N56ai52urxnKjk7//u7ygpcX5jvebmZr5+/330tJ/k4ctamRTDjajjJd0NX7+0nYsLAjzxxBOsW7cuZtf64+I/IvkSk0ryScENwVlBysvL2bvXvmnn0SRcUbckVfUYUCMis8JP3QwkRmGRYVi5ciXiTiOYPwEAf8Fk9u3bS2NjjD8JE0x7ezuHq48wK4FWJ56uJFMp8oaGf1PBTTfdRE5uDq5DMVyd4Ad3tZubbryJwsLC2F3HZqq6WVU7CPXP3auqd6nqUFpBPuAmVZ0PLABuFZGr+x3zBeCkqs4A/i/wYztjH4orr7ySv/jYx8io24b0JOgOClYQb/U6xowdy2c+8xmno6G7u5tvf+ubnDjeyDfntzI5BZKtiPRwT9e03ACPPvK9mNTpqqys5MD+AwSnB1OzBMQQ6VRFPGLrvLlo3sGH1ZIEvgb8XkR2Enpj+1G0QSaCQCDA8hUr8edPBldouCVQdAEQKgo3mkQy/hn5I0+4ugOC1+vlnnvuwev10h2w5y98Rm4v5XvitylpLGVkZHDnHXcidRIalI8BqRbUrwk12XkoRGShiOwCdgK7wj1WVwz2Og3pCD9MC9/6jx3cCfw2fH8JcLM4sDHg/ffdR0Z6GplVqxNyU+u02m3QdZIHH3iAzMxMR2NRVZ566ikOHjzI1+a1xWXO1gsVXqrb3VS3u/lhWQ4vVMR2OV+mBx6c30aex893//U7NDc323r+yE4dOinxftfiKg2sMRYbNtk3ijXkhGu4LUlV3R6eo3Wpqn5aVU+OJGCnbNiwgY72NgIlM/qeU28+mlPK2++862Bk8RdJuKbnjTzh6goIixYt4t5772XRokV02ZRwXZgf4FjDcVpaknpRbJ9Pf/rTCIJUxuDzXsF9yM3MWTOZM2eO/eePrd8AX1bVaao6DfgKofmmgxIRt4hsJzRY+/5pUx8iJgI1AKoaAFqBuM8GLy0t5d6vfQ1XWx2eBBtadLU3kF6/g09+8pMJsW/iK6+8wvLly/mbC7viVvqhut1Nd9BFd9DFvpY0qttjP+kpP125f14bbS0n+cHjj2PZWK9tz549uHJdzpWB8HNGIzxuK7QHoKVKQ30DJ0/ak7ZEs0pxWC3JVPHW0qVIehbB/ElnPN9bfBGVhw5SUVHhUGTxt3//fsZnh1bPjFSWR1m6dCk/+9nPWLp0KVkee1pV08MTZPfvT85q3f2NHTuWj370o7gPu8HuRvtx0DblnrvvsfnEcdGuqn1VClV1DTCksTdVDarqAmAScKWIzO13yEDZ7Vm/oPFYab1o0SKuve46Mo+W4eq0vz7QsAR8eCtXMmbMGO69916no6GmpoZfPfNL5hf7WTQ19cv1TM0N8o8zO9i6bRuvvvqqbeetq68jmOXgAgM/ZzTCHU24ckJ/7qcXPB+JaIYUh92STHaNjY1sWL8eX/EMcJ35IwuUXIi4PbzxxhvneHXqOXTwAJOz7dkSw+tRuru7WbJkCd3d3XhtSrgiK5ISbQPYkbjrrrvQHkWO2tvL5TroIic3hxtvvNHW88bJJhH5DxG5QUSuF5FfAitE5HIRGdICnXBtwBXArf2+dRSYDCAiHiAfODHA62O+0lpEeOjb36aoqBDvwWUQcDihUCWjchVufyePff/7ZGdnD/6aGPvFL36OSwP8y5xO4j/w64wbJvRyabGfZ3/9a9t6Ydo72tF0B4cT0zijEY6T9XPTQ186O+2ZyxFNwjXslmSye+utt7Asi8CY2Wd/05NBb+F03nvvfTo6Os7+forx+XzUH2tgYnZiL7HOSVMKMoXq6mqnQ7HN5ZdfzsRJE+2dPN8NUid86vZPkZGRYd9542cBMBN4FPg+cDFwLaFdMJ4+14tEpFRECsL3vcAtQP+iem9wap7qPcAydXCTzoKCAh5/7DFc/k4yKlc6Op8rrX4nnpPVfPnLX+aSSy5xLI6IXbt2sX79Bu6Y2klhxuiZeyQCn53ZRU9PN7///e9tOafH7UHUwYw1jTMa4Y4mXOGRWrvK5ETzzj3ilmQy8vv9vPb66wQLJqOZeQCkV68nvXp93zGBsXPw+XriWgXYKfX19agq47ISf+XP2Ew/tUePOh2GbUSEv/r0X0EzYNPUNKkUBOHOO++054Rxpqo3nud2vrI144Hl4cU8mwnN4XpLRB4XkTvCx/wXUCwiB4EH6FdD0Anz5s3ja1/9Kp6TR0irdaYGoLulhvSazdx4403cc09iDEMvWbKE7DTh45NTfyixvwnZFleN6eWtt96kq6trxOcrLS1FukdJF+EgIj8Huwr5RpNwDaslmexWrFhBy8mT+Meemkzs6mzG1XlqZYiVU4rmjOHlV16xdfJiIoqUwCjxJnYPF0BJZpCGY/aMvSeKW2+9lbS0NHv2V7TAfdjNwoULmTBhwsjP5wARGSsi/yUib4cfzxGRLwz2OlXdqaqXhRfzzFXVx8PPP6Kqb4Tv96jqX6vqDFW9UlUTopLuXXfdxa233kp67TbcJ+K7n6t0t+KtXMEFF1zIQw99GwcWbZ6lp6eHtWvWcO3YbjJHYZFOgBsn+ujq6mbz5s0jPteUyVOQdol9oeVk0Aout4tx48bZcrpoVikOtyWZ1JYseRm8+WdNlu/PN3YOdbW1tvzCJ7LIPIF8J8f4hyg/XTmZIqsUI/Ly8rjppptw17hDez6MxDHQLk3a3q2w54F3gUjGuJ9QCZuUJSI8+OCDXHzxHLyVq+I3iT7gI+vA++R4M3jyyR+FVpAlgN27d9Pr97OgJLH2dY2nmQUBvGlCWVnZiM81b948tFdt60VPZq4mF7NnzyY9Pd2e8w31wOG2JJNZqMpsOb4xcxhsFmawaDqSkc3ixYvjFJ0zuru7AfC6Ez/hyvQovl5/SuypeLo77rgjtL9izch6F1yVLgqLCrn22mttiswRJaq6mPBsi3D5htT6Dx9ARkYGP/rRExQXFpB18AOkd+RDSeelFpkHl+HubefJH/2I8ePHx/Z6UTh8+DAA05J4656R8rhgUnaAw4dH3uN5xRVXICKhun+jWTfQDFd+5ErbThnNkOLzjLKW5OLFixFPOoHSiwY/2OXGVzqbzZs3970BpKJAINStkohb+vTnDseYagnX3LlzmTJ1Cq6qEUye7wY5Jty+6PZk2jdxIJ0iUkx4ACRcLb7V2ZDio7i4mB//+CnS1I/34Adgxa7uVHr1BtyttXzjG9/g0ksvjdl1hiNSay8vCXrdY6kgPUiLDSsVi4uLmb9gfqgXfRT/SOVI6APkppvsG8CL5h17VLUkGxoaWLFiBb0lM8E9tO5E/5jZiMvDH//4xxhH55y0tNCSkUAS/CEGbF5hkihEQqsKaWbYqYUcDs3RuO2222yNzQEPEFpNeKGIrAV+R2h3i1Hhoosu4nvf/S7S3khG5ZqYrFz0NOwlraGcv/3bvw3VRUowqoqYKUcIoGrPHOLbF92Otis02GBYXsQAACAASURBVHK65KPgrnIz55I5TJs2zbbTRpNwjaqW5OLFi7EU/OP610I8jzQvvSUzeOfdd2lqSpDihDbLz88HoMOf+F1c7X4hNycblyuGexA65BOf+ARutzuUOEVLQ/smzl8wn4kTk3532guBTxJawPMucABIrQx7ENdffz1f+MIX8DQfxHPM3u2sXG31ZFSv58qrruJLX/qSree2S1FREarQ3pv470mx1NLroqjYnjpwN954IwWFBbj3j9JVCLWg7cpf3/PXtp42mk+iUdOSbGlp4bXXX8dffCGakRPVa/3j5xEMBlO2lytS2LGxO/H/EI93uxkz5rz7qyetgoICrr322lC3f7SN2ubQm8mi2xKvt2IYvqeqbUAhoVpavwaecTak+Pvc5z7Hxz52PRk1m3G32FMKRXwdZB1axsQJE3j0kUdwuxPzb37q1KkAVHckZnzxYCnUdKT1/SxGKi0tjX/4+38I9XClZt/BuSl49nqYMHECN9xwg62njibhGjUtyZdeegm/349/wvyoX6uZ+QSKLuTV117jxImzilInvenTpwNQE4f9wkaqpiuN6Rdc6HQYMXPrrbei3QrHonudHBYyMjO4/vrrYxNYfEWmNSwCfqWqr9NXH3r0EBG+852HmTp1Kt7KFYhvhEWYrSDeg8vIcMNTTz1Jbm6uPYHGwJw5c3C7XZSfcLJCprOq2tx0B5R58+bZds4777wz1Mu1a3TN5ZIaQVuUf/4f/2x7IyOahGtUtCQbGxt5+eVXQr1b3oJhnaN30mX09vby29/+1ubonJeVlcW0qVOoaEnsXLux28WJbhKiCnasXH311eTk5vRN7hySILhr3dxw/Q0Js6x/hGpF5D+AvwH+LCIZRPe+ljKysrJ44oc/JMMjeA8tgxHUBEw/sgnpaOQ7Dz9sW69JrGRlZXH55Zez8Xgm1ihKDE63sTEdj9tt6wbiXq+Xf/nCv4R6uGptO21iC4B7t5sLLryAW265xfbTR/PGNCpaks8++yz+YBD/pOHvy62Z+fhLZ/H6G2+k1NYyEQs/ciX7WtPpjt2iqBHb3hRq7S5cuNDhSGInLS2NW26+BXede+gbvNaD9iof//jHYxpbHP0NoR73W8N7IhYB33Q2JOdMmTKFhx96CGlvHHYlendLDWkNe7j77rttH1KJlU984laOd8GeE4ndEIyF3iCsrs/k6muusb0n8rbbbmP6BdPx7PSMvO5fEpAKQTuV+++7PyZzf6M5Y8q3JHfv3s27775L79hL0IyR/eL2TroCFTf//u8/w8Ht12LixhtvxB9UyhoTN99e35DBtKlTEr51PlK33HILGtSzauZogaIFZ//euWpc5Bfkc/nlqbEbl6p2qeorqnog/LheVd9zOi4n3XjjjXzyk58kvX4HrvYox5v93XirVjNt+vSEnSQ/kBtuuIHC/Dz+fCT+vbbdAcHr9XLPPffg9XrpDsR38v6a+nTae+Huu++2/dwej4cHvv4A2qlIeYovSmgH9z43N998MwsWLIjJJaJJmFK6JRkIBPj/nn4aycjGP/GykZ8wzUvPxMspK9vMsmXLRn6+BHLJJZcweeJEltVlOh3KgI60uznQ4uaTqTEp/Lzmzp1LcUnxWUVQdYGiC/olXAFw1bu46cabUq5UhnGm++67jzFjxuCtWgPW0Kv3ZFRvxBX08egjjyTVZubp6en87d//A7uaPexvie/80q6AsGjRIu69914WLVpEVxwTLr8Fb1Rnc/HsWTFrRM2fP59Fixbh2u+CkZf5+v/Zu+8wt8oz4f/f+0iaounNY3vcu41xw5higrGNwcZgahZYMJAApgVjYnoIoW6Sd5NsIGWzpJA3u9ns/haymwSzCXkJTmg2uOFex+M+1Z6uadLz++NoxuPxjD1F0hlJ9+e6dM2RdHTOrTPSo/s85yn9kwHXehfJyck8/HD4+gL2ZGqfmD6T/Pd//3f2FxbiG3YRuELT+LIlfxImNY9/+v6rVFXFzggalmVxw003safSxa4+Fm7D0/wkuwIkuwJMyGxmeAhGi151IJGkxIR+OWZQqFmWxdzL5mKVWme/rHgMjN8wd+7ciMSmnOP1enni8cfBV4nn6KZuvcZVeRh3xV6WLl3K6NHR19nkuuuuIyszg9/sTQnHcGRd8roNq1at4rXXXmPVqlV43ZHb+Z8PJVLug7vvuTes81o+8MADZGZm4l7n7nmv6Cgg+wTK4CsPfYXs7Oyw7SemLgn2VmFhIW/88pe0ZI/Enz0idBsWC9/IS6iurub73/9+6LbbDyxevJisjHTeKvT2qXBbOt7H8DQ/w9P8PDuzlqXjfX2K62idxScliVx3/Q2kp6f3aVvRYs6cOfZlxeIzF7hyREjPSA9pTybVf82aNYv58+eTeGzz2XstmgBJh9YwuKCA2267LTIBhpjX6+Wee5exp9LF2pLI9VhMdht8Ph9vvvkmPp+P5AglXFVNwu+KUph1/vnMmhW66Wc6k56ezhOPP4GpDP+lRZNpMJ7gLa/zphEhVQOuLS5mzZoV9pP0uE+4mpubefGllwlYCTSOCP2ccsabQ1PBdN57772YurSYlJTE0jvvYvtxN5sq+s/lqd/s8ZKUlMStt97qdCgRM3nyZNLS0+DoGVYKgKvExSWzL+m34ymp0Lv//vtxuSw8h9efcT132W6or+ShBx+MqkuJHV111VWMHTOaX+9N7dedekLhP/ck0xAQHl6+PCL7u+SSS1i0aBHWTiusY3OZaQYygUwIXBY4vWlEKAXA9ZkLb5KXJ598Mqy1hKAJFz//+c8p3LcX3/DZ4AlPg8vmwVMxqQP4x3/8DqWlpWHZhxOuu+46hhYU8G97UmnqB5M8fV7uZmO5hzvuvIusrCynw4kYl8vF7Itn4yo5w3g55XbvxNmzZ0c0NuWs/Px8brrxRjzlexBfF80aAgGSjm5i0qRJXHLJJZENMMRcLhdfXfkYlY3w1r6YGPakUztPuPnbsUT+7u9ujmjHoOXLlzNgwADcn7m73zO6H5PtAhXw+GOPtw3qHU5xnXBt3LiRf//Nb2jOG48/O4wfWrHwjZ5DfUMjr7zyCoE+jI/Tn7jdblZ89auU1Al/KHK2AX2jH365O41hQ4fwxS+GdjqGaHDBBRdgGg10MdauFAsul4vzzuv9cCcqOt188824PR48XUz7467Yh2ms5c477wz7GX4knHPOOVxzzRLePZzE/urYq81tDsAvdqWSPyCPu+66K6L7TklJ4flvPI/UC7I+yiewLAVrh8WiRYtCOkH1mcRtwlVdXc0LL74ISRk0Db8w7PszSRk0DLuQjRs38pvf/Cbs+4uU888/n8svv5zfH0jmUK1zH6c39yVTVg+PPf5E2wTb8eS8885DRJCSzn8wXaUuJk+ejNfrjXBkymk5OTksvPJKEsr3Qkvjac8nlG5n2LDhXHDBBQ5EFx733XcfGRkZ/HxnKv7YOL9ts6ooiaO1wldXPubI4MWTJ0/m7rvvxjpkIfujNEFvBPenbgqGFPDII49EbLdxmXAZY/jmN7/J8RMn8I2+LGS9Es+mJW8cLdkj+enPfsb27dsjss9IWL58Oampaby+Pc2Rwm1PpYs/HUpiyZIlYRs/pb/LzMxk1OhRWGWdfKWbwJwwWrsVx5YsWYIJtOA+vv+Ux6X+BFJbxrXXLompSd7T0tJY/sgKiqot/nw4etukdVRcb/G7omTmzp0b0lHle+q2225jxnkzcG1yQbR1wDfg+tSFq8XFiy+8GNGT0Nj5hvXAW2+9xUcffUTjkPMJpORGbsciNI68hIDHy3Pf+AY1NTWR23cYZWZm8tWVj7G/2uLtA5G9tNjoh9d3pJGXl8eDDz4Y0X33N9OnTUcq5OScEK2CDVzjNRlVMH78eIYNG46nYt8pj7sr9mJZVizNPNBm3rx5XHjhBbxZmEJFQ5TWxLRjDPxyZwqepPCOFdUdlmXx3NefIyM9A/ea6GrPJTsFiuGR5Y8wduzYiO477hKu7du386Mf/YiWrGG0DJwc+QDcifhGz6W0rCym2nPNnTuXefPm8tv9yRyM4MTW/7U3mWN1wlNPPxP3l8umTJmC8ZvTzjilQrBcFhMnTnQmMOU4EWHOnEvtkefbjeOSUHWIc6dMITOzd/PG9mciwooVjxIQN7/eHf1lw9pSD1uPu1m27D5ycyNYUdCF7OxsXnj+BaghetpzlYK1zWLevHksWbIk4ruPq4SroqKCp5/5Gn6Pl8ZRc8ChBqKB1AE0Dp3Fxx9/zK9+9StHYgiHFSseJT0tndd3pNISgTxyV/BS4vXXX6+Xy6AtoZKKUz/XclwYPXp0VHf3V3130UUXgTFIsB2XNNVB3XEudvDSVLgNHjyY25cu5dPShKieZ7HBD/++N5WxY0Zz7bXXOh1Om+nTp3Pvvffa7bn29fNaRJ/dbmvIkCE88cQTjnQQiZuEq7GxkaeffobKqmp8Yy4Ht7M/Pi3559CcO4Zf/OIXrF692tFYQiUzM5OvPvY4RdUWq8J8abHJDz/dkcbAgfncd999Yd1XtBgwYADpGelQ2e5BA1alxcQJWrsV7yZMmIAnIQH8TQBYNfYQNVOnTnUyrLC79dZbGZg/gF/vSSEQDbUwnVhVlMRxH6x49Kv9bhy9v//7v+fCCy/E9bmry17SjguAa60LT8DDKy+/4tjVkLhIuPx+Py+++CI7d+7EN2oOgZQcp0MCEZpGXkIgLZ+XXnqZzZs3Ox1RSMyZM4fLLruM/ylK5lhd+D5e/70/ieI64Yknn4r7S4mtRIRxY8dhVbU77j57/K0xY8Y4F5jqF9xuN+PHj0cCdoMbq64Ut8cT8XYskZaYmMh99z/AwRqLD44lOB1Oj51oFN456GXu3Ln9cpYIy7J49tlnyc3NtdtzNTkd0elkqz11zxOPP8HIkSMdiyPmE65AIMC3v/1tPvjgAxqHXxDaqXv6ynLjG3s5zW4vjz/xJLt27XI6opB45JFHSEhM5o1d4ZnT7EitxTsHklm4cKFeSuxg9OjRSHW79hTB9lxOFjKq/xgzejTit4dgt3yVDBs6LC6GUZk3bx4Txo/jv/en0BxlzWZ/tz+JFoRly5Y5HUqX0tPTefmll7EaLaxPrf7VnusoWLsslixZwhVXXOFoKDGdcPn9fr71rW/xxz/+kaaCGc40kj8bTzL14xfiC1isWPEoO3bscDqiPsvJyWHZffez/bibz0pDW5gbA/+6O4VkrzfueyV2ZtiwYXbD+Xr7vtTY7RQiORq16r/sz4EBE8DTWM3w4cOcDikiRIR7l91HuQ/+djR6arkqGoT3jyaxePHVFBQUOB3OGU2cOJGHv/IwckzsnoD9QR24P3Mzesxox3t2QgwnXI2NjbzwwgvBZGs6zQXTnQ6pSyYxlfrxV1Hnt3hkxQrWrz/zvGfRYMmSJYweNZLf7EsN6Rnl5xVuth53c/c998Zkz6q+GjJkiL3QOldxLSR7k8nIyHAsJtV/5Ofn2wsBP6axloEDBzobUATNnDmTSRMn8ocDKVEzGOqqA0kgFrfffrvToXTL9ddfz7x587C2WVDmcDB+cK1xkeRO4pWXX+kXnYZiMuGqrq5m5WOPsXr1ahqHzaJ5yHmO9UjsLpOURv2ExTRIEo89/jh//vOfnQ6pT1wuFw88+BBl9fD+kdB80AMG/nNfCkMKBvernjr9SesPqtRL29+BAwfGxJQtqu9ycuz2qxJogYC/7X48EBFuX7qUch98GuKa93Coaxb+eiyZyy9fEDWJsYjwxBNPUFBQgGetBxocjGWzwHF49mvPMnjwYOcCaSfmEq5Dhw5x3/33s2XLVhpGX0bLoCkh3X7CgU+w6iuw6itI2v42CQc+Cdm2TWIKdROvptmbx0svvcQbb7yBCUcjqAg5//zzmTplCm8f8IZkcut1pR4O1Vh86ct343ZHbxfvcBowYICdXAUvKVo+i0EDBzkblOo32mo6A3Y7rnirJb744osZUjCYdw/3/4mtVx9NoLHFcPPNNzsdSo94vV5efullXC0uXJ+6nGnPdRisvRZf/OIX+cIXvuBAAJ2LqYRr/fr1LFt2H0dLK6gfvwh/buh7Zll1FYi/GfE346opxqqrCO0O3In4xi+kOXcsb7zxBs8//zwNDQ6eJvSBiHDHnXdyvAE+Ke5buwlj4O2DyRQMHhSxiUajkdvtJjU9te3MUholrmoxuktEhorI+yKyQ0S2ichpE6qJyGUiUiUim4K355yINZRSUlLsheCAy/HWw9eyLK67/gb2VLoo6scTWwcMvHfEy5Rzz43KHsajR49mxYoVUELk23PVgXu9m3Hjx3H//fdHdt9nERMJlzGGt956i5UrV1JnPNRNXEIgPTqqYDtluWgadSlNQ2fx/vurefDBhygtLXU6ql6ZOXMmo0eN5E+Hk/vUY3FvlYvCKhd/d/Mt/W4cmv4mJzsHabB7KgZ8AbKyspwOqT9qAVYaYyYCFwIPicikTtb7wBgzLXh7MbIhhl5rOxYxgVPux5NFixbh8bhZ3Y8bz+844aa0HpZEcdOJq6++mvnz59vtucojtNPAyXZbL77wYr/rgRv1CVdLSwvf+973ePXVV2nKGELdpGswSWlOh9V3IjQPnkLDuAXsKzrAPffeG5U9GEWE666/gYM1Fvv6cEb5/pFEkpMSufLKK0MYXWzKzMi0x8IJzm+mDeZPZ4w5ZozZEFyuAXYA/bsbWAicvBRvOtyPH2lpacyZcxmflCT12yEiPjiaQIo3mTlz5jgdSq+JCI899hgDBw7E/WlkxueSrXa7raeefKrftNtqL6oTLp/Px9NPP8Pvfvc7mgZNoXHsAnD137OW3vBnDaNu4jVU+vw8vHw5H3/8sdMh9djll19OYoKHD4727my60Q9ry5KYN//yuLsE0htpaWlYLVZbAZeWFgMnIGEkIiOA6cDaTp6+SEQ+F5H/FZFzIhpYGFhWa5FvJ1zx2pli4cKF1DXDxrL+VQMC9jQ+n5Xb5V2010CmpKTwwvMvIA2CtS7M43OV2ONtXXPNNcydOzeMO+q9qE246urq+OrKlaz9dC2NI2bTPGxWv++J2FvGm0XdpKtp9KTzzDPP8N577zkdUo+kpKQw+5Iv8GlZYq+6Y28s99DYYhwftC5apKSkIM3SVsPV1m5HnUZEUoG3gBXGmOoOT28AhhtjpgI/AP6ni20sE5F1IrKurMzpvvBnFgi0fgFjs6zsrhkzZpCVkc4nJX0/QR+e5ifZFSDZFWBCZjPD0/rWQ2hDmV3eLViwoM+x9QcTJ07k3nvuRY4IUtS9z53JNJjMHmRnjeBe52bI0CH9YrytrkRlwtXY2MiTTz3Ftm3baRg9l5b8OJgnzuOlfsIiWlLzeemll/joo4+cjqhHLrvsMmqaYFdlzy9hrCtNIDMjnSlTQtvjNFZ5vV67hVJLu/vqNCLiwU62fm2M+W3H540x1caY2uDyO4BHRHI7We91Y8xMY8zMvLy8sMfdF36//4z344Xb7Wbu/MvZVJGIr6Vv21o63sfwND/D0/w8O7OWpeN9fdremuIEcrKzYqq8u/XWW5k2fRquTa6TYwSegZlmMNO6mXAZsNZbWI0WLzz/AklJ4Z3Hty+iMuF69dVX2fz55zSMmoM/Z5TT4USOKwHfuAX4vTk8//wLFBUVOR1Rt82aNQuPx82G8p5V4bcE4PPjicy+5AvaWL6bEhMTMS0Ggr+l/bkAcorY19J+Duwwxnyvi3UGBtdDRGZhl5ch7pYcWS0twexC7KK/ubnZwWicNW/ePJr9hg1l/acZSn0LbD6ewLz5l7e7/Bv9LMvia898jaSEJFyfhXaoCDkoyBHhnnvu6ffzgkbdf/TTTz/l7bffpmnwVPy5o50OJ/JcCfjGXk5TQPjmN78VNeN0eb1epk6dyubjPWuTsLfKja/ZcOGFF4YpstiTmJhoT+/TcvK+Os1sYCkwr92wD1eJyP0i0tqX/CZgq4h8DrwG3GKi5QvXhdYEywSbX8RzwjV58mRyc7JZW9J/2nGtL02gJUBMDn2Tn5/Pyq+uhHKQ3SG6pO0D1yYX50w+h1tuuSU02wyjsCdcIlIkIluCBdq6vm7v9Z/+FJLSaS6YEYrwopJJSKFh6Pns2LGdNWvWOB1Ot82adQFHa4XjDd3/sm097sYS0UmqeyAhwT5jl2Y55b46yRjzoTFGjDFT2g378I4x5ifGmJ8E1/mhMeYcY8xUY8yFxpjo67HSQWNjo70QrOFqux+HLMti7rz5bD6eQF1z/2jTtqYkgfy8XCZN6myEkui3YMECZs+ejWubC2r6uLHgpUQ3br72zNei4gpIpGq45gYLtJl92cixY8fYvWsXjQMmgtX/D244teSMQTxJ/OUvf3E6lG5rTZq2He/+GeW2EwmMnzCe1NTUcIUVc9rGngnWcMVj13/Vuaam1r75doIRzwkXwPz582kJwLp+0FuxpknYetzDvMsXxGzvURFh5cqVJCcl49rQx0uLh0GOCcvuXXZyDtl+LqouKR48eBCAQKqDDVP9TSQnJ3PTTTeRnJwM/ggMLtIZy6IlOSeq2nGNHj2atNQUtp/oXgLQ4IfCKhczZmjtVk+0JVj+DvdV3GubtcLSGi6we9ANHpjPJ8XOX3ZfW+rBb+xhdGJZbm4uDz7wIJTa7a96pRncn7sZM3YMN954Y2gDDKNIJFwGeFdE1ovIso5P9qRLdVuVYcC50eqkpYnFixezfPlyFi9ejLQ4lHABmEBU/ZhalsW06TPYWdW9wm1PpRu/genTp4c5stjS9pnQGi7VQcdLitE6bVioiAgLrlzItuPuHjV1CIePipMYMXxYVE7l01NXX3014yeMx7XF1TZ8TU/IDsH4DI+tfCyqyrdIJFyzjTEzgEXY02dc2v7JnnSpHj3abiTvqikOV6xnZdwJrFq1itdee41Vq1Zh3A61j/G34K4v7/e9MjqaNm0aZfVQ3o3CbccJN5ZlMXny5AhEFjtOnpjYf2Kpt5Pqm9aEywSL/pOXGOPXlVdeiQE+POZcLdfROos9lS4WLroqZi8ntmdZFo+ueBTjM8iuHr7fOnDtdbFw4cKoa+sW9pLYGHM0+LcU+G9gVm+3lZWVxYwZM0gs3wX+Pg6e0luuBHw+H2+++SY+n8+xke3dZTsx/mbmz5/vyP57a9q0aQDsPHH2NhM7KxMYP36cjiPVQ20Jl15SVB20JVgiIFbcX1IEGDJkCNOmTuGvx5IIONQHdfXRRFwuK66mLps0aRJz583FtccFPaholW2C23Jzzz33hC+4MAlrwiUiKSKS1roMXAFs7cs2v/zlL2Ma60g43OcOj1FLGutIOrKRGTNmRN3geKNGjSI1xcvOs7TjavTDvmoX06bp5cSe6phwaQ2XatW+0bxYrrgeFqK9a5ZcS0m9sO145E9OmvzwwbEkZs++hJycnIjv30n33H0P+HswTEQNWActbrjhBgYMGBDe4MIg3CVxPvBhcBybT4FVxpg/9mWDU6ZM4dprr8VTvBXX8QMhCTKqBPwk7fsLCW67t0e0VT+7XC6mTpvOjsozV9/vrXLjD5ysEVPd1/GSotZwqVZtCZYAmnC1mTNnDlkZ6bx7KPKDBH9SkkBNE1x//fUR37fThg4dyvz583Htc3VrcmvZLbjd7qgYc6szYU24jDGFwTFspgbHs3klFNv9yle+wthx40kuXI1VWxqKTUYHEyCx8K9YNSU88/TTDB061OmIemX69OmU1ENFsB1X67QY7W0Ptt+Kthq8/kBruFRXTk7lI2BZcTu1T0cJCQlce/0NbCz3cLQuct8XY+CPh5IZNXIEM2bE59iSN998M6bFnH2exSZwHXRx5RVXRm1NYFSWxImJifzj//k2A/Jy8e7+E1ZdudMhhZ8xJOz/EHdFIQ888EC/nQ29O1prrXYE23EtHe87bf6xHSc8jB83Vide7oXWhEv8csp9pU5NsEQTrnauv/56EjweVh2IXC3X5go3h2osbr7l1qi7WhEq48ePZ9I5k3AVnXlcLjkgmBYTVcNAdBSVCRdAdnY2r736fXIy0vHu+t/YTrpMgIT9H+Ap280dd9zBrbfe6nREfTJmzBh7PK4u2ks0+GFftZvpOv5Wr7Qfh8vlcsVtQa7OQiRqpgaLhKysLK5avJgPixPbat/DyRj4XZGXAXk5MT/21tlcvfhqTJWBE12v4zroYvSY0VE9bEbUJlwAgwYN4oc//AG5WRl4d/4vVk2J0yGFngmQuO+veMp2c+edd3L33Xc7HVGftY7HtaOL8bh2V9rtt+K1ir2v2mq0WsDl1totdbpAcqbTIfRLt912GyIufl8U/lqu7Sfc7K508fe3LT05O0ScmjNnDpbLQg53kejWgTluuGLBFZENLMSiOuECGDx4MD/+0Y8YlJ+Ld9cfsaqOOB1S6AT8JO55D3fFPpYtW8bdd98dM7UVM2bMoKweynynfwS3H/fgclmce+65DkQW/doKb79eTlSnai0/mgtmgDHavq+D/Px8rlq8mNVHkyjvpGwKFWPgzUIvuTnZLF68OGz7iRZpaWnMmDED17HOyys5an9uL7300k6fjxYx8W3Lz8/nRz/8IcOGFuDd/S6uEwedDqnv/C0k7X4X94kDPPLII9x+++1ORxRSraPHdzbNz45KDxMnTLCnTlI91nZJsZm4P3NWpzqZgAc04erC0qVLsVxu3ioMXy3XpnIPeypd3HnXl0hMdH5aof7gwgsuxFQbqDv9OSkRBhcMpqCgIPKBhVDMfNtycnL44Q9+wJjRo0na8/9wHS9yOqTe8zeTvPtPuKqP8uSTT0Z1I8GujBgxgoz0tNPG4/K1wP5ql7bf6oOEhOBgvC3g9uiQEOqkU6ZHM35NyDuRn5/P9dffwEfFiRyqDf1PZMDAf+7zUjB4kNZutXPeeXaZL2UdruIYsMotZp4304GoQitmEi6AjIwMXn31+0ycMIGkvX8JrjPWAwAAIABJREFUS9IVSMnBuDwYlwd/2kACKSHunupvJnn3u7hqinn2a1+L2S+kZVlMnTb9tHkV91a5CRiYOnWqQ5FFv7aEq7ndslKc/DyI8UNAE66uLF26lOTkZP6/vaGf5eLDYwkcrrW4d9l9OkZeOyNHjiTZmwwVHZ6oAtNsYqKJSUwlXACpqal873vfZeLEYNJVeSik228afhEBbw4Bbw4Nk66mafhFodt4oMWunasp5rnnnuOKK6K7geDZTJkyhbJ6Tpk0dlelG0tE50/sg5M/qkKCRxMudVLb5atAC8bfopezupCRkcHtS+9gY7mny97UvdHohzcLU5gwflxUD+0TDpZlMWHCBKzKU9MSOWH/PkycONGJsEIq5hIugJSUFL77ne8wZsxokve8h+XgZNfdZgIk7n0fV9URnnrqqaibI7E3WpOqvVUnC7Q9VR5GjRqp8yf2QftaraSkyI+crfqv1s+DtDSecl+d7qabbmJAXg7/sTclZHMs/ulgEscb4MGHvhIzHaBCadzYcUiVtM2SAUAlJCYlMmTIEMfiCpWYTLggWNP13e8yeNBAvHv+jPgqnQ6pa8aQcGAN7hMHePjhh1m0aJHTEUXE2LFj8XjcbQlXwEBhtYdzJkd/1bGT2idciQlag6FOaku4mn2n3FenS0xM5J5776Ow2mJNSd8vvdY0CX846GX27It1yrIujBo1CuM/teG8VAsjR4yMiQ4e0f8OziAzM5Pvfvc7pHmT8e55F1p6MCV5BLlLtuMp2c4tt9zCF7/4RafDiRiPx8PYMWMprLETrmP1Fr4WExNVx046JeHSS0aqndaev9JUD6A1yWexYMECRo8axZuFqbQEzr7+mfzP/iQaW+C+++4PTXAxaPjw4fZC9cnHXLUuRowY4Ug8oRbTCRfY43R965v/gKu5nqS974Pp47cmxKzqoyQeXMPFs2dz//3x90UcN348B2o8BAwcqLF7UI0fP97hqKJb+yRLG82r9loTLGm2Ey4deuXMXC4X9z/wAKX18JcjvT95KfNZ/L8jSSy66qqYSR7CoXV+YKkNXm5tgUB9IGrnDe4o5hMusNsKrfzqV3FVHcFzZKPT4bSRpnq8+1ZTUDCErz/7bExUmfbU2LFj8bUYyn0WB2vceNyuk2c5qlcsy2rr/aQ1XKq91NRUAKSp7pT7qmuzZs1i2tQp/K7IS0Mvp578bWESlsvNl7/85dAGF2PS0tJITUuF2uADwb/RPv5Wq7j5hV+8eDELFy4k4cgmrOqjTocDxpBY+FfctPDKyy/F7STNrWd7h+tcHK6zGDp0qHaVDgFPgt3mRBMu1V5CQgKWy4XVaP+S6SXFsxMR7l12H1WN8JfDPf8+FddbfFScyPXX30BeXl4YIowtBYMLkLpgDVewLdegQYOcCyiE4ibhEhFWrFjB4ILBePf/DYK9dJziLt6Gq+oIjyxfzqhRoxyNxUmtCdexeotjvgSGjxjpbEAxIiHRvpSoCZdqT0Twer1aw9VD5557LjNnnsfbB7009bCW63f7k/B4PNx6663hCS7GDB48GFe93bxE6u3ESxOuKOT1ennu619HmupJOLDGsTjEV0nS4c+46OKLueaaaxyLoz9IS0sjPS2Vo3UuyuqJia6//UFr70RNuFRHXm8K4m8KLmsNV3fdccedVDfCX492/ztV3iB8XJzI1dcsIScnxINkx6j8/HxMvQED1NknjxkZGU6HFRJxlXABTJo0idtvvx1P+Z6QD4raLSZA0v4P8Hq9PPH44zoWCzBo8GB2VdojzMfKmYzTWhMtTbhUR6mpKe2WtYaru6ZOncqkiRP54yFvt8flevdgEkYsbrnllvAGF0Py8/PtoSEa7RquAQMGxMzvZNwlXAB33HEHw4ePILnoI2hpiui+3SXbsWpKeHTFI3rGEzRw4CCKg1XI+fn5DkcTG1qnbNGES3WUlpoGgOVy6eejB0SEW269lZJ62Fh+9nG5Gvyw+lgyl112mZZrPTBgwAB7wQfiEwbmD3Q2oBCKy4QrISGBp556EtNUR8LhdRHbrzTWkHR4PbMuuIAFCxZEbL/9XW5ubtuyNioNDcE+I9RhIVRHKSn2ZcTkZG/M1BxEyiWXXEJOdhbvdaPx/CfFCdQ3G2688cYIRBY72n4D6sFqsGLqNyEuEy6Ac845hxtvuAFPyQ6smpLw79AYEos+JsHt4rGVK7Wgayc7O7vTZdV7rZ8vTbhUR609or1eHYOrp9xuN1dfs4QtFR4qGs5chv/tWBIjhg/TeWF7qPUEXOqFgC9wygl5tIvbhAvg3nvvJTc3l+SiDyHQywFWuslVsQ9X5SHuvfceBg6MnSrSUMjKympbTktLczCS2GGM3chEE67OichQEXlfRHaIyDYReaSTdUREXhORvSKyWURmOBFrqLU2lE/xxudQNH21cOFCDPBxcdffreJ6iz2VLhZdtVhPrnsoKyvLPmZVgEETrljh9Xp5/PHHoP4EnqObwrejZh/JB9cyYcJErV7uRPskSwun0Gpty6VO0wKsNMZMBC4EHhKRSR3WWQSMDd6WAf8c2RDDozXh8qZoD8XeKCgoYOLECawp7XoeyjXBZGz+/PmRCitmuFwuMjIzkBP2b0EstXWO64QL4KKLLmLBggUkHP0cqa8Iyz4SD3yCFWjiqaeexOVyhWUf0Ux7SoVea+KqCVfnjDHHjDEbgss1wA6g43DW1wK/MrY1QKaIRH032tYJq5O0wXyvzZ07jwPVFqW+zn9C15UnMvmcSScbgKseyc7Otmu4iK1mJnGfcAEsX76c9PR0kgs/gEBo51p0Hd+Pu6KQu+66K64HOD2TUaNGMW7sGO06HQaacJ2diIwApgNrOzxVALQfO+YwpydlUac14VK994UvfAGADWWnf78qGoSiaotLvnBppMOKGbk5uYixTxo14YoxGRkZPPH4Y0hdOZ6jIZxrsdlH8oGPGT1mDLfddlvothtjsrKy+NnPf8GDDz7odCgxRxOuMxORVOAtYIUxprrj05285LQRmERkmYisE5F1ZWVl4QgzpFoTrnicuzVUCgoKGFowmM0V9vdreJqf4Wl2O+Atwccuuugix+KLdu0HOo2VQU9BE642l156KVdccQUJRzdh1ZT2fYPGkLj/A1yBFp77+td1fkAVUa2N5jXh6pqIeLCTrV8bY37bySqHgaHt7g8BTpuI1RjzujFmpjFmZjR0YW8de6v1M6J65/wLLmRXZQItAVg63sfS8T4Ath73kJOd1TZtmeq59PR0wG7PFUuzIWjC1c6KFSvIzc0jef9fwd/cp225y3bhPnGQ+++/j5EjdX5A5QxtM9g5sRu5/RzYYYz5Xher/R64I9hb8UKgyhhzLGJBhon2XA2N6dOn0+g3FFaf/I4ZAzurEpk+4zztANQHrQlXkjcppo6jJlztpKam8tzXn4WGahIOfNLr7YiviqSDa5lx3nncdNNNIYxQqZ7RmtUuzQaWAvNEZFPwdpWI3C8i9wfXeQcoBPYCPwVi4pp3a8Klo8z3zZQpUwDYXXnyO1bms6hsMEydOtWpsGJCa0cqlxVbJ4xaGncwbdo0lt5+O//6r/+KP3Mo/uwe1k4F/CQXrsabnMSzX/uatpNQjpg0aRL79u2Lqer4UDLGfEjnbbTar2OAhyITUeRMmTKFxYsXc+211zodSlTLyspi8KCB7Ks6CDQCsDdY23XOOec4GFn0ax2cV878FY06mnB14ktf+hJr137Knv0fUZc6AJNw6gCBgZSuxwXxHNmA1Jbx9Msvx9SAbSq6PPTQQyxYsEAvZ6vTZGZm8uSTTzodRkyYOOkcNn1cDNQBUFjlJjHBo+23+ihWTxS1+qUTbrebb3zjOTyWIbHwb/aF+Xaahl9E0/DTe6BYNcUkHNvMVVddxaWXapdg5Ryv18u0adNiqv2DUv3NuHHjqPBBTZP9PSuqdTNq9Gi9lN9Hycn2tFOxVn5pwtWFoUOH8tCDD+KqOoK7bNfZX+BvIXn/BwwYMIDly5eHP0CllFKOGj16NACHal0YA4fqPIwZM9bhqKJffn4+ABdffLHDkYSWpuFncN1117F69Wo2bfmUusyhp11abM9zZD34qnjmmy/EbHWoUkqpk1oHsz5c52JQip+6JqMDXIfAiBEjeOedd2Lut1RruM5ARHjiiSdwCyQcWNP1evXHSSjeyuLFi5kxIybmt1VKKXUWOTk5pHiTOVZncbTObjA/fPhwh6OKDampqTHX6Sy23k0YFBQUcMcdS3Ef349V3ckQPMaQeGANKSmpPPDAA5EPUCmllCNEhCFDhnCs3kVxvf1zOnTo0LO8SsUrTbi64ZZbbiEnN5ekw5+d1oDeVXUEV/VR7rn7y22DtSmllIoPBUOGUtrgodTnwuNxEw2zDShnaMLVDYmJidx1551ITSlW9ZFTnks4tonc3DyWLFniUHRKKaWcMmjQICp8UOqzyM/Li7nLYCp09JPRTYsWLSIjM4uE4m1tj1m1ZVjVxdxyy806Z51SSsWh/Px8/AYKq93kDxrkdDiqH9OEq5sSEhK4dsk1uCoPIY32IHfusl14EhK46qqrHI5OKaWUE1ovIVY0WOTlDXA4GtWfacLVAwsXLgTAdbwQTICEE0XMufTStnmflFJKxZecnJMzj2RnZzsYiervIpJwiYhLRDaKyNuR2F+4DBkyhBEjR+KuPIRVW4ppbtAR5ZVSKo5lZma2LWvCpc4kUjVcjwA7IrSvsJp1/vm4a0twVR5GRHTcLaWUimPtE66MjAwHI1H9XdgTLhEZAiwGfhbufUXC5MmTMQE/7rJdDBk6TIeCUEqpOJaYmNi2rL8H6kwiUcP1feAJIBCBfYVd69xZVrOP8eN0ziyllIpn7SdY1va86kzCmnCJyNVAqTFm/RnWWSYi60RkXVlZWTjDCYlB7br96ojCSimlWsXa3H8qtMJdwzUbWCIiRcB/APNE5N/ar2CMed0YM9MYMzMaRuh1u93k5OYCdiN6pZRS8S0nx24sr2241Jm4w7lxY8zTwNMAInIZ8Jgx5vZw7jMSXnv1VY4ePaoN5pVSSvHjH/8z5eXl5AZPxpXqTFgTrlg1dOhQvZyolFIKsJuaDNJR5tVZRCzhMsasBlZHan9KKaWUUv2FjjSvlFJKKRVmmnAppZRSSoWZJlxKKaWUUmGmCZdSSimlVJhpwqWUUkopFWaacCmllFJKhZkmXEoppZRSYSbGGKdjaCMiZcABp+Poplyg3OkgYogez9CLlmM63BjT/+f16oYoKsOi5bMRTfSYhla0HM9ul1/9KuGKJiKyzhgz0+k4YoUez9DTY6q6op+N0NNjGlqxeDz1kqJSSimlVJhpwqWUUkopFWaacPXe604HEGP0eIaeHlPVFf1shJ4e09CKueOpbbiUUkoppcJMa7iUUkoppcJMEy6llFJKqTDThKsbROQuERnsdByxRkReFJHLe/G6y0Tk7XDE1J+IyGARebMXr/uZiEw6yzr3i8gdvY9ORQstv8JHy7Cuafl1Om3D1Q0ishp4zBizzulYoo2ICPbnLBDCbV6G/f+4upvru40xLaHav9Ni7f2o8NLyq2+0DAutWHovPRW3NVwikiIiq0TkcxHZKiI3i8h5IvJXEVkvIn8SkUEichMwE/i1iGwSkWQRmS8iG0Vki4j8QkQSg9v8lohsF5HNIvKd4GPXiMja4Pr/T0TynXzfvSUi3xaRB9vdf15EVorI4yLyWfA9vxB8boSI7BCRHwMbgKEi8svgcd4iIo8G1/tl8PgiIueLyMfB/8enIpImIkki8kbwNRtFZG4ncWWLyP8E979GRKa0i+91EXkX+FUEDlGfnOH4bg3ev0tE/ktE/gC8KyKWiPxYRLaJyNsi8k67Y7laRGYGl2tF5JXgcV3T+vkLbv+x4PKY4GfzcxHZICKjRSRVRN4L3t8iItdG/KCoLmn51XNahoWPll/dZIyJyxtwI/DTdvczgI+BvOD9m4FfBJdXAzODy0nAIWBc8P6vgBVANrCLk7WGmcG/We0euwf4rtPvvZfHazrw13b3twN3YHfdFezk/W3gUmAEEAAuDK57HvDndq9tPTa/BG4CEoBC4Pzg4+mAG1gJvBF8bAJwMHj8LwPeDj7+A+AbweV5wKbg8vPAeiDZ6WPXh+N7KbA1eP8u4DCQHbx/E/BO8LgPBE4AN3XyeTXANcHl/wM82+74PBZcXgtc3+7z7Q0e//TgY7nA3tbPsd6cv2n51atjpmVYZI+tll8dbm7i1xbgOyLybewv2QlgMvBnEQFwAcc6ed14YL8xZnfw/v8FHgJ+CDQAPxORVcFtAgwB/lNEBmF/KfeH5+2ElzFmo4gMELstSB728ZoCXAFsDK6WCozFLlQOGGPWBB8vBEaJyA+AVcC7HTY/HjhmjPksuK9qABG5BLswwhizU0QOAOM6vPYS7B8fjDF/EZEcEckIPvd7Y4yv7+8+/Lo4vgc7rPZnY8zx4PIlwH8Z+zJHsYi838Wmmzj5WVwPLGj/pIikAQXGmP8OxtEQfNwD/IOIXIr9w1MA5APFfXibKnS0/OohLcPCR8uv7onbhMsYs1tEzgOuAr4J/BnYZoy56CwvlS621yIis4D5wC3AV7DPVn4AfM8Y83uxr9s/H5p34Ig3sc9MBgL/gX0W+E1jzL+0X0lERgB1rfeNMSdEZCpwJXbh/nfAl9u/BPtMpqNOj3U31mndVl0nz/VnHY9vR+3fT3eODUCzCZ7mAX5O/853tZ3bsAvO84wxzSJShH32qPoBLb96Tcuw8NHy6yziuQ3XYKDeGPNvwHeAC4A8Ebko+LxHRM4Jrl4DpAWXdwIjRGRM8P5S4K8ikgpkGGPewa6inxZ8PgM4Ely+M5zvKQL+A7swvgn7y/Un4MvB946IFIjIgI4vEpFcwDLGvAV8HZjRYZWdwGAROT+4fpqIuIG/YX9xEJFxwDDsyx7ttV/nMqC89ewyCnU8vmfyIXBjsC1EPvYlih4LHqvDInIdgIgkiogX+3NbGiys5gLDe7N9FR5afvWalmHho+XXWcRtDRdwLvCPIhIAmoEHgBbgtWB1rhv4PrAN+zr9T0TEB1wEfAn4r+AX6jPgJ9htIH4nIknYWfejwf08H1z3CLAGGBmRdxcGxphtwSrcI8aYY8AxEZkIfBK8jFEL3I59JtJeAfCGiLQm+E932G6TiNwM/EBEkgEfcDnwY+zjvgX7f3OXMaYxuK9Wzwe3vRmoJ4p/FDoe3+BZdlfewq6N2Arsxm7HUNXLXS8F/kVEXsT+LnwR+DXwBxFZB2zC/kFR/YeWX72gZVj4aPl1djoshFJRSkRSjTG1IpIDfArMNsZoGyulVL8Xj+VXPNdwKRXt3haRTOzGzC/FemGllIopcVd+aQ2XUkoppVSYxW2jeaWUUkqpSNGESymllFIqzDThUkoppZQKM024VL8l9vxamU7HoZRSvaFlmGpPG80rpZRSSoWZ1nCpPhGRFBFZJfZM7VtF5GYRKRJ79vhPg7cxwXXzROQtEfkseJsdfDxVRN4Qe1b3zSJyY/DxouAIz4jI7cFtbRKRfxERV/D2y+B+t4jIo11HqpRSp9MyTEWKjsOl+mohcNQYsxggOMr1t4FqY8wsEbkDe8Trq4FXgX8yxnwoIsOwp9WYiD1VRpUx5tzgNrLa7yA4EvTN2APjNYvIj7GnwtiGPXHp5OB6WnWvlOopLcNURGjCpfpqC/AdEfk28LYx5oPgtBW/CT7/G+CfgsuXA5PaTWuRHpwK4nLsObgAe6LYDvuYD5wHfBZ8bTJQCvwBGCUiPwBWAe+G9q0ppeKAlmEqIjThUn1ijNktIucBVwHfFJHWAqN948DWZQu4yBjja78NsUugMzUmFOD/GmOePu0JkanAlcBDwN8BX+7VG1FKxSUtw1SkaBsu1SciMhioN8b8G/AdYEbwqZvb/f0kuPwu8JV2r53WxeOnVMcD7wE3iciA4PPZIjI82DbCMsa8hV2lPwOllOoBLcNUpGgNl+qrc4F/FJEA9kztDwBvAokishY7qb81uO5y4Ecishn7s/c34H7g5eDjWwE/8ALw29YdGGO2i8izwLsiYgX38xDgA94IPgZw2tmjUkqdhZZhKiJ0WAgVciJSBMw0xpQ7HYtSSvWUlmEqHPSSolJKKaVUmGkNl1JKKaVUmGkNl1JKKaVUmGnCpZRSSikVZppwKaWUUkqFmSZcSimllFJhpgmXUkoppVSYacKllFJKKRVmmnAppZRSSoWZJlxKKaWUUmGmCZdSSimlVJhpwqWUUkopFWaacCmllFJKhZkmXEoppZRSYaYJl1JKKaVUmGnCpZRSSikVZppwKaWUUkqFmSZcSimllFJhpgmXUkoppVSYuZ0OoL3c3FwzYsQIp8NQSkXQ+vXry40xeU7HEQpahikVX3pSfvWrhGvEiBGsW7fO6TCUUhEkIgecjiFUtAxTKr70pPzSS4pKKaWUUmGmCZdSSimlVJhpwqWUUkopFWaacCmllFJKhZkmXEoppZRSYaYJl1JKKaVUmGnCpZRSSikVZppwKaWUUkqFmSZcSsUgn8/Hli1bnA5DKaV6LBAIsH79ek6cOOF0KCGlCZdSMegnP/kJDz30EIcOHXI6FKWU6pFt27bx6KOP8uKLLzodSkhpwqVUDNq8eTMAdXV1DkeilFI9U1lZCcDevXsdjiS0NOFSKoY1Nzc7HYJSSvVIbW2t0yGEhSZcSsWwxsZGp0NQSqkeqampASBgAg5HElqacCkVwxoaGpwOQSmleqS6uhqAlpYWhyMJLU24lIpBIgJAfX29w5EopVTPtPZO9NX7YqpZhCZcSsWgQMCuitdG80qpaFNeXt62XFFR4WAkoaUJl1IxyBgDxG7jU6VU7CouKcaIXYaVlZU5HE3oaMKlVAyq99mXEquqqhyORCmlus8YQ3FxMeTZ948dO+ZsQCGkCZdSMag10dKESykVTaqqqvDV+zAD7BquI0eOOBxR6IQ14RKR8SKyqd2tWkRWhHOfSsW7xsZGGnx278Tjx487HI1SSnVf6+wYJtNgpVoxNVuGO5wbN8bsAqYBiIgLOAL8dzj3qVS8a9/ItKwidto/KKViX1FRkb2QBv5UP4X7Cx2NJ5QieUlxPrDPGHMggvtUKu609vAxKYbysvKzrK2UUv1HYWEh4hZIAZNuOHjwYMyMxxXJhOsW4DcR3J9Scam1V4/JNtTW1Opo80qpqLF7z25MhgEBMqGluYWDBw86HVZIRCThEpEEYAnwX508t0xE1onIuljq/qmUU0pLS+2F3A73lVKqH/P7/ezevZtAlj2OoMmyG87v2rXLybBCJlI1XIuADcaYko5PGGNeN8bMNMbMzMvLi1A4SsWukpISJEEw6XZhpQmXUioaHDhwgMaGRsgOPpAG4hF27NjhaFyhEqmE61b0cqJSEVFSUoLBIEXSdl8ppfq77du3A3ZzCAAEAlkBNm/Z7GBUoRP2hEtEvMAC4Lfh3pdSCo4VH7MTrjpNuLoiIkNF5H0R2SEi20TkkU7WuUxEqtoNa/OcE7EqFS+2bNmClWRB6snHTI5hf+H+mJimLOwJlzGm3hiTY4zRERiVioDS0lL7my1gJVt6SbFzLcBKY8xE4ELgIRGZ1Ml6HxhjpgVvL0Y2RKXiy4aNG/Bn++0G80Emz2CMYdu2bc4FFiI60rxSMaSxsZHamtq2b3YgOaAJVyeMMceMMRuCyzXADqDA2aiUil8lJSWUFJdg8gyySZBNwawrB7Bg06ZNjsYXCppwKRVD2nr6uuw/JslQUqqXFM9EREYA04G1nTx9kYh8LiL/KyLndPF67WmtVB99/vnnAJgBBqkUpDKYcLmBLFi/Yb1zwYWIJlxKxZC2UeaD32yTbE4ZeV6dSkRSgbeAFcaY6g5PbwCGG2OmAj8A/qezbWhPa6X6bsOGDUiiQMbpzwXyAuzauYv6+vrIBxZCmnApFUPa5k5s/WYnQV1tHc3NzY7F1F+JiAc72fq1Mea0Tj3GmGpjTG1w+R3AIyK5EQ5Tqbiwbv06ArmBU9pvtTIDDIFAoK0WLFppwqVUDDlx4oS90PrNTrT/VFVpn5X2RESAnwM7jDHf62KdgcH1EJFZ2EdVqwuVCrGjR49SWlKKGWA6XyEXxBI2bNgQ2cBCLKyTVyulIqstsQqeJZpE0/Z4bq5WzrQzG1gKbBGR1ta4zwDDAIwxPwFuAh4QkRbAB9xijOniF0Ep1VutiVSXCZfLHh5i3fp1EYwq9DThUiqGVFdX26PMS7DgSrD/aA3XqYwxH9LpxYtT1vkh8MPIRKRU/NqwYQNWsoU/zd/lOoEBAQq3F1JdXU16enoEowsdvaSoVAypra1FEtrlEcGEq6amxpmAlFLqDIwxrN+wnpbcljOeArWOxxXN7bg04VIqhtTW1mLc7arlPScfV0qp/ubo0aOcOH4CztbBNxvEJZpwKaX6h5qaGgKewMkHNOFSSvVjmzfb8ySa3LM0j3TZcyxqwqWU6heqa6vbkixAEy6lVL+2detWuxlEN5plBXIC7Nm7h8bGxvAHFgaacCkVQ2qqazAJ7c4UBawESxMupVS/tG3bNgJZnY+/1ZHJNgT8AXbv3h3+wMJAEy6lYkhNTU1bQ/k2idpLUSnV/zQ2NlJUVITJ6uZoK9n2H024lFKOamhooKmx6bSEK+AJUFlZ6UxQSinVhaKiIgKBQPcTriSwkiz27NkT3sDCRBMupWJE27Q+Sac+bpIM5RXlkQ9IKaXOYP/+/fZCd4fVEvCn+yncXxi2mMJJEy6lYkRZWRlgT1jdnkk2bc8ppVR/cejQITsLSe3+a0ya4cCBA0TjpA+acCkVI4qLi+2FlA5PeO0JrOvr6yMek1JKdeXIkSNYKdYpmYhsEqgEKsFabdn320sFX70vKgdz1oRLqRhx5MgRu6eP99THTap9Jnjo0KHIB6WUUl0oLinGn3wiwm+0AAAgAElEQVTqdD5SKUhz8FYmSOWpCZfx2uVZSUlJxOIMFU24lIoRBw4cwEq1wNXhiTT7z8GDByMek1JKdaW0tPS0JhBnlWz/KS+PvnapmnApFSN279nd+eSvaYAF+/bti3hMSinVGWMMVZVVkNjDFwY7BbV1EooimnApFQPq6uo4euRo592rLSADdu3aFfG4lFKqM42NjTQ3N/c84QoOe1NdXR3ymMJNEy6lYsDOnTsxxmCyO6+eD2QF2L5jO4FAoNPnlVIqktpmv/Cceb3TuAGJzunKNOFSKga0TgDbOhLzaXLtnj2FhdE5fo1SKrbU1dXZCz1NuAQsjxWVva414VIqBmzctBHJktOn9QkyuXbN18aNGyMYlVJKda6hoQEA4+rFeFruk6+PJppwKRXlGhoa2LplK/68ThrMt0oBSRXWr18fucCUUqoLTU1N9oK7Fy922W3Aoo0mXEpFuU2bNtHS0oLJP/OZoj/fz/oN6+2Gqkop5aC2hKsXWYhxGU24OiMimSLypojsFJEdInJRuPepVDxZs2YN4hbIO/N6ZqChsaHxZHsvpZRySFvC1XHcwG4wlonKE8dI1HC9CvzRGDMBmArsiMA+lYoLxhg++vgjAnmBsxdcA0BcwscffxyR2JRSqittNVS9qeGytIbrNCKSDlwK/BzAGNNkjKkM5z6ViidFRUWUFJdgBnej4akbAnkBPvzow6ic+FUpFTv6UsOF1e71USTcNVyjgDLgDRHZKCI/E5FTptYVkWUisk5E1pWVlYU5HKViy0cffQSAGdS9BMoMNhw7eowDBw6EMyyllDqjPiVcLmho1F6KHbmBGcA/G2OmA3XAU+1XMMa8boyZaYyZmZd3lkYoSqlTfPjhh/bYW8ndW781MdPLikopJ7UN69CbNlwuo8NCdOIwcNgYszZ4/03sBEwp1UeVlZXs2LGDwMBTR4+XTQKVQCVYqy37fisvSJbw4UcfRjZYpZRqpy1h6s2wEG7w+XwhjScSwppwGWOKgUMiMj740Hxgezj3qVS8WLt2rT2dT4f2W1IpSHPwViZIpZzyvH+gn21bt1FTUxPJcJVSqk19fb2dgfQmC9GEq0sPA78Wkc3ANOAfIrBPpWLemjVrkCSBzJ69zgw0GGP47LPPwhOYUkqdRV1dHZbHAjn7uqfxQIOvIeo6/4Q94TLGbAq20ZpijLnOGHMi3PtUKtYFAgE+W/cZ/gH+nhdY2SAJwrp168ISm1JKnU1dXV3P51Fs5bGHxIm2+RR1pHmlolBRURHVVdUwoBcvtiCQG2Ddek24lFLOqKmpwXh6WUPlObmNaKIJl1JRaMuWLQCYvN4VWCbXUHysmPLy8lCGpZRS3VJVXUXAEzj7ip0wCXa5V11dHcqQwk4TLqWi0Pbt27GSLEg5+7qdMTl2gbVjh078oJSKvMrKyrbEqccS7T9aw6WUCrtdu3fhz+hF+61WwYb2+/btC1lMSinVXVVVVW2JU48l2H8qK6Nr4hpNuJSKMoFAgEMHD2Ey+tBDxw1WmsX+/ftDF1gUEZGhIvK+iOwQkW0i8kgn64iIvCYie0Vks4joGIJKhUBLSwt1tXW9T7iCr4u2hKs3Q44ppRxUXl5Oc3MzpPZtO36vn0OHD4UmqOjTAqw0xmwQkTRgvYj82RjTfpzARcDY4O0C4J+Df5VSfVBVVWUvxFnCpTVcSkWZ1jlHjbdvY9AYr6G0tDQUIUUdY8wxY8yG4HINsAMo6LDatcCvjG0NkCkigyIcqlIxpzVRMomdlGHNkJyczE033URycjI0d7IBASvJ4sSJ6BplShMupaJMWyHT27PDVklQU11DINC7nkKxQkRGANOBtR2eKgDaVwEe5vSkDBFZJiLrRGRdazKslOpaWxmW1MmTzbB48WKWL1/O4sWLO0+4sJM1TbiUUmFVV1dnLyR0sUJ3zhChbfDAaJwiI1REJBV4C1hhjOnYx7yzLgmnnZIbY14PDu48My8vLxxhqn6uuLiYzz//3OkwosYZTxo9sGrVKl577TVWrVrV5eCogYQAx48fD1uM4aAJl+oXqquraWlpcTqMqNA26aurixW6eYbY2oKzsbEx1CFGBRHxYCdbvzbG/LaTVQ4DQ9vdHwIcjURsKro8+eSTPPzww3FfW9xdZ6zh8tjzJL755pv2yWAXCZdJMhyv1IRLqR5paGjg6quv5pVXXnE6lKjQlph29e3t5hlia/1Nc3NXGVnsEhEBfg7sMMZ8r4vVfg/cEeyteCFQZYw5FrEgVdRo7e0bj9+l3qiqqrLLn95O7QOQiD3bRhTRhEs5rnXwuvfee8/hSKJD21l0V2NwdfMMsfX10TYBbIjMBpYC80RkU/B2lYjcLyL3B9d5BygE9gI/BR50KFYVJeK1trinqqursRJ7OXF1qwSoq63D7/eHLK5w02EhlOOibQJSp9mVM3TSmkh1lzHmQ85S3Bs7E30oMhGpWODz+UhPT3c6jH6vurq66zao3RV8fW1tLRkZGX2OKRK0hks5LtqmZ3BaW8LVV8GEzbK0GFAqFOK5A0pP1NXVEXD3sb2b5+S2ooWWtMpx0TZ4ndNcrmBr+b7WcGnCpVRIRdOPv5N8Ph/G1cdxBN2mbVvRQkta5biKioq2Ze2peHZtCVKIEq62BE4p1Se1tbVOhxAVGpsau+5l3V3BYrCpqanP8USKJlzKcSUlJW3L5eXlDkYSHTThUqr/aP+DX10dXb3mnOL3+/vWYB6istOPJlzKcUeOHOl0WXUu1AlXyNqEKRWH2uYF7LCsuuZ2ueOy/NKESzmucN9eRqXblxJbx7NRXQtZwhUUTQWWUv1N+zao2h61e5KSkqCPozmIX05uK0powqUc5fP5OHToMFNymslIFPbs2eN0SP1eqGu4tNG8Ur3Xfj6/aJvbzympqalYzX0sd4JXclNSUvoeUIToOFzKUTt37iRgDKMzWjhQ08jWLZudDqnfC1kvxUCH7Smleqw1yUqwTNTN7eeU7OxspLGPNesNdu18VlZWaIKKAD21VY7auHEjlsC4DD/jM1s4dPiINpw/C48nOABNXwdYDnTYnlKqx1p7WQ9P83O8XY9r1bX8/HwCvgD0pVN6PWRlZ0VV+aUJl3LU2rVrGJnuJ8VjODfH/vZ99tlnDkfVv7W1WehrwtUCngSPXlJUqg/Ky8tJcgsDvX7Ky0qdDicqDBs2zF7ow5jXVo3F8OHDQxNQhGhJqxxTXl7Ojh07mZZjX4wfluonOxk++OBvDkfWv6WmptoLfR1+phlSUqOn/YNS/VFFRQWWGA7XujheWXlyrlPVpTFjxgAgJ3p5WTEAUiWMGT0mhFGFnyZcyjF/+ctfACiut/jXXcmIwPm5Daxds0an+zmD7OxsAKShb20gpEHIzsoORUhKxa3SkhIwAepbBL8/oO24uqGgoIDUtFTo7RXYKjAthkmTJoU0rnALe8IlIkUiskVENonIunDvT0UHYwz/+847jEgPUNFgcaDGbrg9e1ATzf8/e/cdHld5JX78e2ZGZVQsybKtLveOu2xcKC4UF3oJkIUsKUs2IWFDQnazv80Skk1fNskuCSEQWkgCAdOxKabZwbhjbNxwL3KRLblIVhvNzPn9MSMj2+qa4hmdz/PM47l37tx75nr0zrnvfYvXdyoZM2fr1atX4DZgF2cRcdQ4yMvNC01QxnRTZWUHSXBAgqNxuaz1NxhEhLFjxuIs71yHHTkcuNgcM2ZMKMMKu0jVcM1Q1bGqWhKh45lz3ObNm9mxcyfT80+fB6t/uo/idD8vvfhiTI0gHEkul4u8/Dyksgs1XH7QKo25NhDGnEs8Hg8VR4+T4FASHIHy6tChQ1GOKjZMnDgRPalntePSTEUTgo/eimae/Tsgh4TivsX06tUrQtGGht1SNFExf/583C5hWu7pDZFE4JKCWnbs3Mm6deuiFN25b9jQYTiPO5sdGqI9BRbHAT8MGTIk7LEaE68OHDiAqgZquIKTMdtsGe0zefJkAOTA6ReOOlYhE8gE/3R/YLkpD0i5MG3qtAhFGjqRSLgUeEtE1ojIHWe+KCJ3iMhqEVl95MiRCIRjou3gwYO8++67TM+vxd3MSHDT8jykJ8LTf/1r5IOLEaNGjcJf42/2tmKbBRYgR+TUfowxnbNv3z4AkpyKA+jpFvbs2RPdoGJEXl4eAwYOwLG/Y2mIHBTww4UXXhimyMInEgnXNFUdD8wB7hSRi5q+qKoPq2qJqpb07t07AuGYaPvLX/6CAz+zi+uafT3JCZcX1rJs+XI+/fTTCEcXGyZOnAgEC59OcBxyUFRchP3NGdN5O3fuBAKDngIUpnjYuWN7NEOKKbNmzgo0nK9p/3tkn9Azu2fMNZiHDiZcIjJVRD4vIl9ofLT1HlU9EPz3MPAiMKlzoZp4cPDgQRYuWMDFefVkJ7fcRuuy4jpSE+Cxxx6NYHSxo6ioiKLiog5fHQJQDxyBiy68qM1NjTEt27JlC3mpiiN43dM33cvuPXuor6+PbmAxYsaMGUAgiWoXDzjKHFwy65KYHD+w3RGLyFPA/cAFwMTgo9VG8CKSKiLpjc+By4ANnY7WxLzHH38cwc9V/Wtb3S7FBXOLa1m2bDmffPJJhKKLLZfMugSO0KGrQwgWbgqzZs0KS1zGdAd+v58Nn6xnUI/P2qEOzvDh8/nZvHlzFCOLHYWFhQwZOgRnaft6K8p+Qf3KzJkzwxxZeHQkRSwhcHvw66r6zeDjrjbekwN8ICLrgJXAAlV9o7PBmti2Y8cO3nzzDS4trG21dqvR5cV1ZCTBQw/93nosNmP27NkAyK4O3FZUcO52MmDggFODDxpjOm7Hjh2cqKxieNZn89MMzfQiAqtX2whI7XXJrEvQo2f3VmyOY5+D3Lxchg8fHv7AwqAjCdcGILcjO1fVnao6JvgYqao/6Vh4Jp489NDvSXHBVf2ab7t1pmQnXNe/mk8+2cAHH3wQ5uhiT15eHhMnTsS523lqXsQ2HQU9plxz9TVhjS2SROQ6EdkmIidEpFJEqkSkMtpxmfi2dOlSBBid3XBqXWqCMjjDy1Irr9rt1G3F0jYuHOuBw3DpJZci0sWJr6OkzYRLRF4VkVeAXsAmEXlTRF5pfIQ/RBMP1qxZw4oVK7mqXw1pCe2vrZqe7yEvVfnDQ7/H6+3KTKfx6dprr0VrFNrZE122C+4UN5dddll4A4usXwJXqWqGqvZQ1XRV7RHtoEz8UlXefXsRgzN9ZCadXp5N6uNhx86d7N69OzrBxZicnBxGjByBc3/rtxVlf6ApRGOCFovaU8N1P/A/wH3ANcBPg8uND2Napar8/vcP0ssNlxZ2rDGp0wE3Daxm775SFi5cGKYIY9eUKVPIyc3Bub0dbSBqwVnqZN7ceaSkpIQ/uMgpU1VrNGMiZtOmTezeu48Lcs+urZ+c48EhWHnVAdMvno4e01Znz5D9Qk5uDgMHDoxcYCHWZsKlqotVdTEwt/F503XhD9HEusWLF7N16zau619NYidmcpjQu4HBmT6eePwx6/1zBqfTyY033AjlQBtTuMmOwBXi9ddfH5HYwi14K/E6YLWI/E1EbmlcF1xvTFg8//zzuF3ClNyzZ5DPTFJKentY8Nqr1Na23jnIBEybFhjEtMVhbrzgOOLgwgsujNnbidCxNlyXNrNuTqgCMfHJ7/fz+GOPkpeqXJB3duHUHiJww4AayiuO8tprr4U4wtg3d+5ckpKTkO2tFEQ+cO5yMmXKFAoKCiIXXHhdGXz0INBX87Im666IYlwmjh04cIB3332XGS0M3Awwp7iOqpPVvPrqq5ENLkYVFRWRm5eLHGqhDCsH9emp0eljVXvacH1NRD4BhorI+iaPXcD68IdoYtny5cvZtXsPV/erOTVWTWeMyPIyJNPHM3/9i7XlOkNaWhpzZs/Buc8ZaFjaDCkVtE7jpnYLQFW/qKpfBP7Y+LzJOhvAzYTFE088gVOUOX1b7vwzONPHiCwvf/nzU1bL1U4TSybiqHA02wFIDgtOp5PRo0dHPrAQak8N118JXDG+wmdXj1cCE1T11jDGZuLA/PnP0dMdaNfQFSIwr7iWsiPlLF26NETRxY9rrrkG9Suyu/ms1rHTQV5+HhMmTIhwZBHxQDvXGdMlO3fu5K033+SSglqyklrv/HPDwBqOHT/Bs88+G6HoYtuYMWNQj0Iz/Ysd5Q6GDh1KcnJy5AMLofa04TqhqruBOwmMlNH4QEQSwhqdiWllZWWsXr2GGXm1uFr4pj31qZs9VU72VDn58eo0nvrU3eL+xvVuoKcbFiyw24pnGjBgAMNHDMe5p5kJrauAcrjqyqticnTmlojIFBH5DtBbRL7d5HEf0InWgsa0TFV58He/w93OoW2GZPoo6e3hr3/5MxUVFRGIMLaNHDkSADl6xkWjH+S4cN5550UhqtDqSOn7EYFxrbcC24LPd4nIRyISl5fNpmvef/99AKY207C00Z4qJ7U+B7U+B1uOJ7CnquXfSYfAlD51rFq1iqqqdoyS183MnTMXPaFw4vT1slcQkXgbCgIgEUgDXEB6k0clcEMU4zJxaPny5axctYpr+lWTnti+oW1uHlyLx1PPww8/HOboYl9+fj7uFDccO+OFqkD7rSFDhkQlrlDqSML1BoGeir1UNZtAg/lnga8DD4YjOBPbVq5cQUGakpPS3lE52za+twefz8/atWtDts94cfHFF+NwOM6al8y5P9D2Id4mqg72lv4hMFlVf9jk8StV3Rbt+Ez8aGho4IH/+1/yUpVLi9rfUzo3xc/lRXW88cbrbNmyJYwRxj4RYeCAgTgqT09L5ESgPIvl4SAadWhqH1V9s3FBVd8CLlLV5UBSyCMzMU1V2bRxI0MzutZ260wDevhwOWDjxo0h3W88yMzMZPSY0TgPNqklPAl6QrnoovibqLrJoMwPNB2M2QZlNqH24osvUrr/AP8w+GSLzSNack3/WtIT4YH/+1+boqwN/fr1w3HyjBNcFUjGCgsLoxNUCHXkq3NURP5NRPoGH/8KHBMRJ+2fWMR0ExUVFVTX1FKY5gvpfhMckJuq7N27N6T7jRdTJk8J3FYMnvbGbtZTpkyJYlRh0zgo8y6gFngk+DhJYCoyY7qsqqqKJx5/jFHZXsb26ngP6RQX3NC/mk82bGTJkiVhiDB+FBQU4K/zn55RnITsXtkkJcV+vU5HEq7PA4XAS8DLQHFwnRP4XOhDM7GssZFoz6TQ5+JZiV4qyo+EfL/x4FQvxATQTEWOCL1694qnsbdOaTIA8zhVvUlVXw0+Pg9c0Np7ReQxETksIs0mZiIyPTg348fBx73h+Azm3Pf0009TXV3DzYNqOr2Pi/M95Kcpjzz8B3y+0F6ExpPc3OB0zU1+Nhw1DvLz86MTUIi1O+FS1XJV/aaqjlPVsar6DVU9oqoeVd0eziBN7KmrC/TiSXaGvgrd7VRqazpf+MWzgQMHkuxORtMUHas4K5yMHTM2pkdnbofeIjKgcUFE+gNtNVh7ApjdxjZ/D5Z1Y1X1R12M0cSgyspKnp//HOfneOib3nyi1J6e1k5HoJZr777SU52JzNn69OkTeNLkVDvqHOTm5EYnoBBrYZzcs4nIEOAeoF/T96nqzNCHZWJd4w98OFosqILE0fAGoeR0Ohk2dBgf7/0YX60Pf62f4cOHRzuscLsbeF9EdgaX+wFfbe0NqrpERPqFNywT615++WVq6+q5emzLg5c29rQG2HK85XKppE8D+WnK03/9CzNnzoz3i6BO6dWrV+BJYw2XgtYo2dnZUYsplNqdcAHPAQ8Bf+S0/NOYszVOjlzrDX2hUusTUlJTQ77feDF48GDWbVgHxz9bjmeq+oaIDAaGBVdtUdVQTLo5RUTWAQeAe1S12Z4aInIHcAdAcXFxCA5rzgU+n4+XX3qR83p6KUrretMIh8DlhTU8vmU7mzdvZsSIESGIMr707Nkz8KTxdDeA+vWz9TGuI9UEXlX9vaquVNU1jY+wRWZiWlZWFgAnPK1/xWq9gtvt5oYbbsDtdrcrQTvR4CIrKz7+AMOhb9++qFeRMjm1HI9EZGbw3+uAecDA4GNeCCav/gjoq6pjCIxa/1JLG6rqw6paoqol8Tb0Rne2du1aDh8pZ3pB24OcttfUXA9JLuGNN94I2T7jSVJSUmAsrsaEK3jqG39PYl1HEq5XReTrIpInIj0bH2GLzMS0rKwsElxOyuta/4rVeIV58+Zx1113MW/ePGraSLhUoaLOQU5OTijDjSuN3aflkOBOcZOZmRnliMLm4uC/Vzbz6NLk1apaqaong88XAgki0qsr+zSx5b333iPZJYzv1RCyfbpdMC67nvffe9caz7cgMzPzs7YowXrqeEm4OnJL8R+D/363yToFBjSzrenmHA4Hubm5HK7Z1ep2KS5lwYIFACxYsIA+rtZbfVU1CDUNGje9VsIhLy8PAKkScgfkxm1bEVX9QfDfL4Z63yKSC5SpqorIJAIXpzY/Szehqny49ANG96wnMcSTRE3o7WH5hko2b94cF9PVhFrPrJ4cOHIgsNBdEy5V7R/OQEz8KSruy571pa1u43YptSdrmT9/fmA5s/WE62BNoMasqKgoNEHGoVMNTyFueve0RkR2AMuBvwNLVHVTO97zNDAd6CUipcAPgAQAVX2IwNRAXxMRL4Exvm5WG7Wy29i9ezcVR49x7fDQ1W41Oq+nFwHWrFljCVczMjMzEb+gKFIvp9bFg3bfUhSRFBH5vog8HFweLCJdqrY38a1fv34cqhF8IRyK60B14HIzXtslhUJCQgJp6WkAcdPYtA0jgD8A2cD9IrJTRF5s7Q2qeouq5qlqgqoWquqjqvpQMNlCVX+rqiNVdYyqTlbVDyPwOcw5Yt26dQAMz+r4QKdtSU9UitL9fGzTkzUrKyvrrFuKGRkZUYsnlDrShutxwANMDS6XAj8OeUQmbvTr1w+fH8pqQzeEQ+lJJ8lJiZ8NkGea1XhFGC9Xhm3wAQ3Bf/1AGXA4qhGZmLZx40YykqCPOzyTqAzJ8LBp00Zrx9WMzMzMwF+xAvXgTnGTkJAQ7bBCoiO/hANV9ZcECjZUtRaIz8YhJiT69w/chS49GbpGEPurnRT37YvDxuFqVVJiYBqMHj16RDmSiKgEfkNgip9/VNUpqtrqOFzGtGbzpo0MTPcQruaPA3v4qK2rtynKmnHqIjGYcGVmxc9FY0d+tTwi4iZY2SciAzlV4WfM2fr27YuIsC+ECVdpTSIDBsT+rPGRkpaWFu0QIuEWYAnwdeAZEfmhiMyKckwmRtXU1LCvdD/9e4Sv9ql/j8Ctyq1bt4btGLGqsYG8pgbacPXMjJ9mER1JuH4AvAEUichfgHeAfw1LVCYuJCcnU5ifF7KEq9IjHK9TBg60hKu9GgegjWeq+rKqfpfA6PILgduB16IalIlZu3btQlUpTgtfwpWX4ifBATt27AjbMWJVYw2XDlAcHkdctUPtSC/FRSLyETCZwK3Ef1HV8rBFZuLCoCFD+WTFAaC6y/vaXRVI3OJ95PRQcrvPntct3ojI88BYYDuBnopfAFZENSgTs/bs2QNAQRgTLqcD8lL11LHMZ04NAVEPUi9x1Q61zYRLRMafsepg8N9iESlW1Y/asQ8nsBrYr6rWs7EbGTZsGO+99x4nPEJGYtd61e84Efi6DhkyJBShxbXGsbeSkpKiHElE/Bz4SFWb/YUUkUtVdVGEYzIx6sCBA4hAr+TwNJhvlJPcwP7SfWE9RixqTLCkTvDX+eNmDC5oXw3X/7TymgLtmbz6X4DNQLdowWs+0zjOzNbjLib26dqYNluPu+jXt7i7tEvqksYhoxITE6McSfip6qo2NvkFYAmXaZfy8nIykwRXmPvlZCX72VRhY+me6VSNViWg8TPoKbQj4VLVGe3ZUUtXkSJSSGCes58A3+5whCamDRs2jOSkRDYd7VrC1eCHrScSmTe9JITRxb/ukHC1g/WmNu1WU1NDiiu8tVsQGPS5uqYWVY3b2SA6w+VykZqWysnKk0B8DW0Tyhz+Fy2s/w2BxvXNfoNF5A4RWS0iq48cORLCcMy5ICEhgXHjx7PuaDJdGaf702Mu6n3KpEmTQhdcHGsct8aGzwA+G0bRmDZFakIBS7FalpmZGajhwhKulpz1/QmORH9YVde09CZVfVhVS1S1pHfv3iEMx5wrpk27gMM1dKm34uojCSQnJTJ+/JlNCk1zvvKVrzB06NBT8yoaY9onNTWVGm/4L1RqvEKK2221W83IysyKu2l9ILQJV3OXBdOAq0RkN/AMMFNE/hzCY5oYcOGFF+JwOFhWdvZowX3TfbidftxOP8MyG+ibfna7Z58fVh5JZvKUqSQnJ0ci5Jg3adIkHnnkEWvvFrA72gGY2JGbm8uxOqWunZ0Ua72C2+3mhhtuwO12U+ttXwJVVuMkL89mzGhO06l84mnw5nYPC9EZqvrvwL8DiMh04B5VvTWcxzTnnqysLEpKSvjwk5XcOLAOR5Py6LahtewJDvfw/ZKTzb5//VEXlfVw6aWXRiJcEyNE5LrWXlfVF4L/trqdMU0NHToUCPSKHtmz7bkUa7zCvCvmcddddwGw+LW/tfkev8LOqkQumDSsa8HGKUu42rY7hPsycWbOnDn8cOVKNhx1MTq7YxPCLt6fRGZGDyZPnhym6EyMurKV1xR4IVKBmPgxevRoElxO1pYntCvhSnEpCxYsAGDBggX0cbXdBmz7CSdVHqWkxDoBNaexZj4hISGuhrZpzzhcIbmKVNX3gfc7EJuJIxdeeCE90tN4b7+nQwnX8XphbXkiN940N24mMDWhoapfjHYMJv6kpqZy/uTJLFu1lJsH1bY5PITbpdSerGX+/PmB5cy2E66/H0wiKTGBqVOnhiLkuJOeng5Asju+mpC0p4bLriJNlyUmJjJn7jzmP/s3TtTXkJHUvp5ASw4k4VO44gobL9e0THtagc4AACAASURBVETmASOBUyW0qv4oehGZWHbllVfxwQdLWVGWyLQ8T0j3XeURlh5K5pLLLyU1NTWk+44XjedF4qwvZ3vG4bKrSBMSV155JX/7299YcjCRK/u1Pe+5X2HxQTdjx46huLg4AhGaWCQiDwEpwAzgj8ANwMqoBmVi2vnnn0+/vsW8smcPU3I9p7U77ao39ibh8Sk333xz6HYaZxrngI23Hpwd6qUoIvNE5F9F5N7GR7gCM/GnuLiYUeeNZMlBd7vG5NpyzEVZDVxxRWuVrMYwVVW/ABxT1R8CU4CiKMdkYpjD4eAfb/8i+08Kyw6FbvDgSo/wZmkK06dPp1+/fiHbb7xpTLjiTbsTruBV5E3ANwmMuXUj0DdMcZk4NWfuPA5WCzsr2x6Ta+mhRNzJSVx00UURiMzEsNrgvzUikg80AP2jGI+JAzNmzGDggAG8sCsFb4gGnn9ldzIeH3z5y18OzQ7jVOPwP925hsuuIk2XXXzxxbicTlaUtX7V6PXD6vJkLrjwIht7y7TlNRHJBP4b+IhAj+lnohqRiXkOh4M7vvpVymqExQe6XstVUSe8XZrM5bNn07ev1VW0prHMj9So/5HSkYTLriJNl6WnpzOhpIQ15a1P9bPluItqjzJ9+vSIxWZi1i9V9biqPk+g1n0Y8OMox2TiwOTJkznvvJG8vDuVhi7Wcr2yyw3i5ItftGbRbYnXOWA7knDZVaQJialTp1JWA2W1LX/91pcnkOBy2jg1pj2WNT5R1XpVPdF0nTGdJSJ88Ytf4mgdfHCw80nA8Xph8cEk5s6bR26ujS7flngdAqgjA5/+UlXrgedF5DUC3a/rwhOWiWcTJkwAYNNRF7kpzXe53nQ8kZHnnYfb7Y5kaCaGiEguUAC4RWQcn83n2oNAr0VjuqykpIRBAwfw5r7tTM/3cGazor7pvlOzZfRN9zU7Pdk7pYHhbaxnYvs0JlzduQ2XXUWakCgqKiIzowdbTzSf79d5YU+VgzFjxkY4MhNjLgfuBwqBXwH/E3zcDfy/KMZl4oiIcM2111F60tFsZ5/bhtaeSrS+X3KS24bWnva6X2HJITcTSyZSWFgYqbBjmssV+G3odm24RCRXRCYQvIoUkfHBx3TsKtJ0gogwYuR57Kpqvop+z0knqjBixIgIR2Ziiao+qaozgNtVdUaTx9WNM2AYEwozZszA5XSy8nDHbyvurHRSUQuXXnZZGCKLT41zKV5zzTVRjiS02nNL8XLgdj67imxUiV1Fmk4aMmQIy5Z9iOfs2nf2VAW+loMHD45wVCZGLRWRR4F8VZ0jIiOAKar6aLQDM/EhPT2d0aNHs2HHGj7rP9Y+n1QkICI2F2wHpKen88Ybb8Rdk5I2a7jsKtKEQ//+/VGFA9VnV9GXnnSSnpZKdnZ2FCIzMehx4E0gP7i8FfhW9MIx8Wj0mDHsrXRQ18xFYmu2n3DRt7joVK2NaZ+UlJRu3YZrqYg8KiKvA4jICBGx0dtMpzSOQ3Og5uyv4IFqJ3379ou7PzYTNr1U9VnAD6CqXqCDP4vGtG7gwIEocLCZi8TW7K9NYNDgIeEJysSUjiRcdhVpQqagoAAR4VDN2YXXoboEimzuRNN+1SKSDSiAiEwGTkQ3JBNv8vMDP31HWhnO5kw+P1TUfvZe0711JOGyq0gTMklJSfTulc2hM2q46rxwvE6tN4/piG8DrwADRGQp8CcCU5AZEzK9evUC4Hh9+382KxsE1c/ea7q3jozDZVeRJqQKi4o4vKMMl3w2hPPh2kCNV0FBQbTCMrFnE/AiUANUAS8RqIE3JmR69OgBQFVD+5s6nAxua+23DHSshsuuIk1IFRQUUlZ3es7fOPq8JVymA/5EYDqfnwIPAIOBp6IakYk7TqeTtNQUqr0dSbgC5Vljsma6t47UcNlVpAmpgoICquoVfzI4gmVYWY0lXKbDhqrqmCbL74nIuqhFY+JWjx7pVHmOt3v7Ko/VcJnPdKSGy64iTUg1NiTNTvafmg6jrNZJRo800tLSohmaiS1rg00cABCR84Glrb1BRB4TkcMisqGF10VE/k9EtovIehEZH+KYTQzKzu7NsQ604Wps72VD3BjoWA2XXUWakGpsGD+hTwOTcxqAQA1XQYE1mDcdcj7wBRHZG1wuBjaLyCeAquroZt7zBPBbAheSzZlD4KJycHD/vw/+a7qxvPx8Vu/Y2O7tD9U6SE5KIjMzM4xRmVjRkYRrrYhMVtXl0L6rSGNa01jDdbjGCQQSrsN1CYwrLIpiVCYGze7oG1R1iYj0a2WTq4E/aWAyt+Uikikieap6sJMxmjjQv39/3npLqW4QUhPanuev9KSLvn2LbUxBA3Qs4erMVaQxLUpJSSErM4Oy2noAGoJj1lj7LdMRqronDLstAPY1WS4Nrjsr4RKRO4A7AIpt/Li4Nnz4cAC2HncxrnfDqfWNTSKa8vphR1UCc6efF7H4zLmtIwlXh68ijWlLYWERZfsrgMCAgooNEmjOCc1VSTRbpaGqDwMPA5SUlLRd7WFi1siRI0lKTGB9xekJ121Dz55fcetxF/VeZcKECZEM0ZzD2t36T1X3tPYIZ5AmfuXl51NeH8j7G0dwtoTLnANKgab3tguBA1GKxZwjkpKSmDhpEqvLk/G3kVqvOpxAYkICJSUlkQnOnPM60kvRmJDLy8ujojZQ/d446KklXOYc8AqBJhTSOMiztd8yALNmXcKxOth4tOUbRA1+WH7YzdRp00hJSYlgdOZc1pFbih0mIsnAEiApeKz5qvqDcB7TxJa8vDxU4Widg/I6BwkuJz179ox2WCbOicjTwHSgl4iUAj8AEgBU9SFgITAX2E5g7MEvRidSc6654IIL6JGexrv7PYzK9ja7zarDCVR5lCuuuCLC0ZlzWVgTLqAemKmqJ0UkAfhARF5v7OloTE5ODgDldQ4q6hz06d0bh8MqXk14qeotbbyuwJ0RCsfEkKSkJOZdcSV/e+ZpKupqyE4++97iolI3Bfl5djvRnCasv2wacDK4mBB8WKNSc0qfPn0AqKhzUFHvpHdObpQjMsaY1l1zzTWA8HZp0lmv7ap0su24k2uvu94uHs1pwv5tEBGniHwMHAYWqeqKcB/TxI7evXsDcLTewTGP81SNlzHGnKvy8vKYdsEFvH8gBc8ZI0K8uS8Jd3ISc+fOjU5w5pwV9oRLVX2qOpZAL59JInLaoCQicoeIrBaR1UeOHAl3OOYck5ycTFpqCsfqhWN10KtXr2iHZIwxbbruuuuo8igryhJPravyCCvKkrh89hybnsycJWL1nap6HHifM8bzUtWHVbVEVUsaaztM95Kdnc2+k058fptzzBgTG8aPH09hQT6LDyafWvfhoUQa/HD11VdHMTJzrgprwiUivUUkM/jcDVwCbAnnMU3sye7Vmz1Vgf4b1kPRGBMLRIQ5c+ex5ZiT8uAYgksPJTNk8CAGDhwY5ejMuSjcNVx5BCa5Xg+sItCG67UwH9PEmJ49e1Lnk1PPjTEmFsycORMIDANRXifsrHQwc9YlUY7KnKvCOiyEqq4HxoXzGCb2ZWZmNvvcGGPOZQUFBfQtLmJ9xU4SnYEO+NOmTYtyVOZcZX1WTdRlZGScem4JlzEmlkwomcjWE4lsOpZAds9Mm8DctMgSLhN1TROu9PT0KEZijDEdM3LkSOp9gd6KI0aOQqS5ec+NsYTLnAOaJllOpzOKkRhjTMeMGjWKxMQEABtZ3rQq3FP7GNMmG6/GGBOrcnNzWbjwdXw+H263O9rhmHOYJVwm6lJTU6MdgjHGdFpiYmLbG5luz24pmqizq0JjjDHxzhIuE3WWcBljjIl3lnCZqEtKSop2CMYYY0xYWcJlos7aPxhjjIl3lnCZqGtMuGwMLmOMMfHKeimaqEtKSuL2229n/Pjx0Q7FGGOMCQur4TLnhC996UuMHTs22mEYE/dqamp49NFH2bdvX7RDMaZbsYTLGGO6kdWrV/Pkk0/yxz/+MdqhGNOtWMJljDHdyJEjRwA4fvx4lCMxpnuxhMsYY7qR/fv3A3CyujrKkRjTvVjCZYwx3cju3bsB2LtnL36/P7rBGNONWMJljDHdyLbt2wGor6/jwIEDUY7GmO7DEi5jjOkmjh8/zonjx/FmDwRg586dUY7ImO7DEi5jjOkm9uzZA4A3qx8Ae/fujWI0xnQvlnAZY0w3UVZWBoA/JQtJdHPo0KEoR2RM92EJlzHGdBONQ0Foght1JdvQEMZEkCVcxhjTTdTW1gaeOBLwO1zU1dVFNyBjuhFLuIwxpptoaGgIPHE4UHF+tmyMCTtLuIwxpptoaGgAhzOwIA48Hku4jIkUS7iMMd2SiMwWkU9FZLuIfK+Z128XkSMi8nHw8ZVoxBlKtbW1iCsBAHUmUG2jzRsTMWFNuESkSETeE5HNIrJRRP4lnMczxpj2EBEn8DtgDjACuEVERjSz6d9UdWzwEfOzPVdUVKAuNxBoOF9RURHliIzpPsJdw+UFvqOqw4HJwJ0tFGoxZfv27bz//vvU19dHOxRjTOdMArar6k5V9QDPAFdHOaaw27Z9O96kHgD4kzOoqqq0pMuYCAlrwqWqB1X1o+DzKmAzUBDOY4abx+PhG9/8Jvfeey/PP/98tMMxxnROAbCvyXIpzZdN14vIehGZLyJFkQktPPbt28ehgwfxpecB4E/PBWDlypXRDMuYbiNibbhEpB8wDlgRqWOGw5tvvklNsN3D8y+8iMfjiXJExphOkGbW6RnLrwL9VHU08DbwZLM7ErlDRFaLyOojR46EOMzQeeGFF0Ac+LL7A+BP7QXuTJ6bP98msTYmAiKScIlIGvA88C1VrTzjtZgorAAqKyv5w8OPoOk51A2dzZHDZTz99NPRDssY03GlQNMaq0LgtJmcVbVCVRvbDTwCTGhuR6r6sKqWqGpJ7969wxJsV23evJkXX3yJht5D0MTUwEoR6vPGsH3bNl566aXoBmhMNxD2hEtEEggkW39R1RfOfD0WCqtGDz74IJWVldT1nYovsxBvz/488eST7Nu3r+03G2POJauAwSLSX0QSgZuBV5puICJ5TRavItAkIuaUlZXx//7jP/AnpuApmnjaa95eg/BlFvHAAw+wZs2aKEVoTPcQ7l6KAjwKbFbVX4XzWOG2e/duFi5ciCd3JP7UbADq+03Bj4NHHnkkytEZYzpCVb3AN4A3CSRSz6rqRhH5kYhcFdzsrmDv6nXAXcDt0Ym28w4dOsQ37/oXjh6vpHbQJeBKOn0DEeoGTseX1IN/+973LOkyJozCXcM1DbgNmNlkLJu5YT5mWCxatAjEQUPemM9WJqTg6TWEJX//OzU1NdELzhjTYaq6UFWHqOpAVf1JcN29qvpK8Pm/q+pIVR2jqjNUdUt0I+6YXbt28c9f+xpl5RXUDJ196kLxLK4kqofNweNK5Z7vfpfFixdHNlBjuolw91L8QFVFVUc3GctmYTiPGS7btm1DU7IgIfm09b4eefh9Pnbv3h2dwIwx5gyffvopX7/zGxw7WUf1sHn40/q0/oaEFKqHzaXBnc29997L66+/HplAjelGbKT5dlBVtu/YiS84fk1T/uQMAHbu3BnpsIwx5iz79u3jW3d/m2ovVA+bh6b0bN8bXcnUDp2Nt0c+P//5z/n73/8e3kCN6WYs4WqHV199lfIjh/FlFp/1miZngLsHf336aaqqqqIQnTHGBPh8Pu774Y+oqW+gZugcNPnsi8TEPctI3LOs+R04E6gbfCn+1F78+Mc/oby8PMwRG9N9WMLViurqah544AHuv/9+fBkFeHsNPHsjEWr7XsD+/Qf48lf+iY8++ijygRpjDLBs2TK2bf2U2uLJzSZbAI7qChzVrYwu73RRO3A6tXW1PPfcc2GK1JjuxxKuZhw7downn3ySGz93E889N5+GPsOpG3IpSPOny5+RT82wORw6dpJvfetb3P3tb7Nq1SpUzxxH0Rhjwmf58uWIKwlf9oAu7UeTM/D1KGDphy3UhBljOswV7QDOFT6fj7Vr1/Laa6+xeMkSfF4vvsxiPOfNDIzI3ERjdbyn75RT6/zpuVSPug5X2SY+Wr+BNau/Q0FBIVdeeQWXXXYZvXqdvg9jjAm1AwcO4Evu0eLFYUf43ZkcPLg1BFEZY6CbJ1x+v5/Nmzfz7rvv8vY773LsaAWSkIQnewgNOcNRd1az72uxOt7hwps3mpM5I3FV7GTfkS089NBD/OEPf2Dc+PFcMmsWF154IRkZGWH8VMaY7ur4iRP4Xcltb9gO6kqmweOhvr6epKSktt9gjGlVt0u4vF4v69at44MPPuD9xYupKC9HHE4aMgrxDhqDL6sYHF08LQ4n3t6D8fYejNSewFWxnY82beOjNWu4//77GTtuHBdfdBHTpk2jT582umsbY0w7+Hw+9pfuR9P7hmR//qQ0INDrcdCgQSHZpzHdWbdIuMrLy1m1ahXLli1jxcpV1NZUIw4XDT3y8Q24GG9W8dkjMLcgcc8yHDWBGq7kTa/hT80+7dbimdSdQUPhBBoKxuOoqcBZsYuPNu3gozVr+PWvf82AAQOZNm0q559/PiNGjMDl6hb/JcaYEFu2bBm1tTX4CvJDsj9/jzwQYdGiRZZwGRMCcfnrXl9fzyeffMKqVatYsWIlO3fuAEASU/BkFOIrKMaXUQDOhA7v21FdgfgaAHBWHWr/G0Xwp/bCn9qLhqISpO44rmN72V6+j51//jNPPfUUbncKJSUTmDRpEhMnTiQ/PzQFpzEmvh09epRf/fo34M7El9VyDVdHLhg1MZWG7IE8++yzXHDBBYwaNSossRvTXcRFwqWq7Nu3j+XLl7NixQo+/ngdDQ0ecDjwpeXgK5qIL6MQf0pPEIl2uCCCurNocGfRkD8GvPU4Txyg4UQpH6xae2rAwdy8PCaffz6TJ09m3LhxuN3uKAdujDnX7Nmzh+9979+pOHqM2mFzwdFyg/mOXjB6iieTUH2E79xzDz+87z6mTGm5Nt8Y07qYTbhUlW3btvHOO++weMkSDuzfH3jBnUlD9mB8GYX40nM7VYsVca4kfNn98WX3x6OK1J3AeWI/pSdKefnVBbz00kskJCRSUjKB6dOnc/HFF5OSkhLtqI0xUVRbW8uzzz7Ln/70FF6c1Ay9HH9a79AeJCGZmqFz0W1v8W//9m/Mnj2br3zlK9b21JhOiLmES1V57733eOrPf2bH9u0gDnw98vH2m4ovswhNSg9vAD4PbrebefPmsWDBAk76PKHdvwjqzsTrzsSbO5J6vw9H1SEaju9l2doNLFu2jP/51a+YO2cOt956qxV8xnQzZWVlvPrqq7zwwoucPFmFt2d/PH2noInhuQjTpFRqRlxJwv61vPnWIha9/TaXX3YZ1157LUOHDg3LMY2JRzGVcPl8Pu67777AbPYpWdT3m4q354CzJpQOJ/F6mHfVPO666y4Ann3ljfAe0OHEn1GAJ6MAT/FkHCcP03DkU15+5VXeePNNfvmLXzB27NjwxmCMiaqjR4/ywQcfsOjtt1m/bh2qijermIYRM/CnR+Ciy+GioWgi3j7DSDiwntfffIuFCxfSt18/Lrv0Ui666CL69g1N70hj4lVMJVwff/wxixcvxpM3hoaikqi0x1JXIgsWLABgwYIFqCuC7apE8Kfn4EnPoSF/LLp5AX94+GF+/+CDkYvBGBN2fr+frVu3smLFCj78cBlbtmwOzFzhzsCTPw5v78Hhr81vhial4+k/DU/RRFwV29lVsYNHHnmERx55hIKCQqZNm8rkyZMZNWqUjd1lzBliKuHKycnB4XSSULkfX03/s0aAjwhnIrVVR5k/f35gOT0Kg5j6vbgqdkJDDYUFBZE/vjEm5CoqKli1ahUrV65k5cpVVFaeAEDTetOQPw5vz76o+xzp+ONKxJszAm/OCKS+Guex3ew9vpf985/n2WefJSExkfHjxjFp0iQmTZpEcXExci7EbUwUxVTCVVhYyE9+/GN++rOfIRtewpdRQEPvoaEZrDQGSO1xXOXbSCrfhnpquPCii7j77rujHZYxphNUle3bt7NkyRI+WLo00CaVwPA1Den5eAeODQxfk3Bud5DRpFS8uSPx5o4EXwPOyoM0nChlxfpPWbFiBQB9cnKYNnUqF154IWPHjrXxBk23FHPf+mnTpvG3Z57hxRdf5IUXX6Ri+7uIK4GGjCK8WX3xZRSBKzHaYYaGKo6aoziP7yXh2G6kugIRYcqUKdx8883WdsuYGFRTU8PLL7/Mq6+9Rum+fYGmAmk5eAtL8GUW4k/JPjdqsTrDmYAvqxhfVjEeQOqqcJ4o5cDxfbz0yqu8+OKL9MjIZPbll3HjjTeSk5MT7YiNiZiYS7gA0tLSuO222/j85z/P2rVreffdd1my5O9Ubt8J4sCf1gdvRmFYCi9/avapgQP9Kdn4U7NDtm8gOCbXfpwnSkms3I/WVyMiDB8xgpkzbmHmzJk2EbYxMWr9+vX8v//4DypPnMCfnkND/wvwZvWFhAi1BQ13L+szaHI63uTheHOGU+/z4jxRird8O8/On8/zL7zAv9x1F9dcc01YYzDmXBGTCVcjp9NJSUkJJSUlfOc732HTpk0sW7aMD5ctZ+eO1VC6Gkl005Ceh69HAb6MAjQ4P1hnefpOOTV5dd2IK7r+Ifw+HCcP4zyxn4TKA0j1EVDFnZLKpMklTJkyhcmTJ9OzZ8+uH8sYE1U/+/nPqayuo3bkVfjTIj+kS8R7WTfldOHr2Q9fz3546k+StO0dfv3rX3PxxReTlZUVuTiMiZKYTriacjqdjBo1ilGjRnHHHXdQUVHB6tWrWb16NctXrODErp2BDVMyaehREBgYtUdeVNp+Sd0JnMdLcZ4oJaHqEOprwOFwMGzYMCZNmsvEiRMZPny4tXMwJs7k5+ezf/9+nEd3oQkpXb4A7Kio9rJu5PPgPLYHl6eK9B4ZNoOG6Tbi9hc9Ozubyy+/nMsvvxxVZefOnaxevZqVK1cGpv45tBFxuPD2yAu0/cosDtvAgagfR1UZrmN7SDixD2oDvY9y8/KYPH0eEydOZNy4caSlRbbwNcZE1r3/+Z/89re/5a233iLx0AZ86bl4s/oFyp/k8A/z4O+RR83hLTz3/Av43ZmBCaojoaEO54nSYBlYivoaGHneKP71X79LcnLkxlE0JppEVaMdwyklJSW6evXqsB+nvr6edevWsWzZMj5Y+iFlhw4CgcKoIXtgYDDVVhreJ296DWjjlqIqjupyXOXbSDy2G/XU4HS5mDB+PFOmTOH888+nsLAwpJ/LmFgkImtUtSTacYRCe8uwgwcPsnDhQt55991Aw3kITEvWIx9fj/xA7bsrPONYtav86iq/F0fVYZyVB05rKpGRmcmM6dOZPXs2I0aMCN/xjYmQjpRf3TLhaqqx9mvx4sUsevsd9pfuQ5wJeLIH0ZA/utnBBRP3LAMC7bnO3qEfZ8Uukso2ICeP4EpIYNrUqUyfPp0pU6bYHIjGnKE7JlxN7du3j+XLl7Ny5UrWfvwxnvp6ADStFw3p+fgy8vGn54as+UNYEi7146iuCHT4qTyA6+Rh1O8NNJUYPpxJEycyefJkhg4ditPpDN1xjYkyS7g6SVXZvHkzL730Eovefhuf348n5zwaCse3q7BzVJXh3vMhVFdQWFTEDddfz6WXXkp6euRHhDYmVnT3hKuphoYGNm7cyNq1a1m9eg2bNm3E5/MFmj+k5+DNKMKXVYwm9+j0MUKWcDXU4Tq+LzBsTdVBtKEOgH79+zOxpITx48czZswYayph4polXCFw+PBhHn30UV5//XU0NZvaIZehiaktbu86tImkvcvo1as3d379a8yYMQOHwxHBiI2JTZZwtay2tpZ169axatUqli1f/tntx9SeeDL74e01EE3u2GwXXUq4GupwHd2J6+gunFWHQJXMrCwmn38+kyZNYsKECdbj0HQrlnCF0LJly7j3Bz+g3plC9fArwZlw1jbO8m0k71jM1GnT+M/vf5/U1JYTM2PM6Szhar8DBw6wdOlS3n9/MRs2fIKq4u+Ri6fPcHxZ/aEdF3kdTrhUcVQdIqFsE67je8Dvp6i4mJkzZnDBBRcwePBgu7g03VZHyq+w9lIUkceAK4DDqnpeOI8VLlOmTOEnP/4x99xzDwkHPqahaOLpGzTU4d6zjPNGjebH//VfNpSDMSZs8vPzufHGG7nxxhs5fPgwixYt4uVXXuXQ9vcgaRX1eaPx9h4KjhC0k1LFeXwfSQc+Rk4eJjUtnXk33MDs2bMZNGhQ1/dvTDcT7uzgCeC3wJ/CfJywmjRpErNmzeLdxUtoyBtzWg/GhLKNqNfDd797jyVbxpiI6dOnD//wD//ALbfcwooVK3jyT39i08YPSSrbQF3h+YE5ZpuZZaM9s2M4qitI2rscR+VBcnJyufWr32H27NkkJYWn56Qx3UFYMwRVXSIi/cJ5jEj53Oc+xzvvvIOrYjvenGB3ZvWTVL6ViZMm0a9fv6jGZ4zpnhwOx6kZKVasWMHvfvcge7YtwpdZTH2/aWjS6U0cmu1d3cjnJbF0NQllG0lPT+crd9/NlVdeaReTxoRA1G+8i8gdIrJaRFYfOXIk2uG0aNiwYQwcNIikI1sg2O7NeWwvWl/NVVddFeXojDHdnYgwefJkHn/8Mb7+9a/jri0jdcMLOMu3t+v9jpNHSN34IgmHNnDVlVfyzNNPc+2111qyZUyIRD3hUtWHVbVEVUt69+4d7XBaJCLccP31UH0UR+UBABLLNtC7Tx+mTp0a5eiMMSbA5XJx88038+QTTzB86CCSd7xP4s4l4Pc1/wZVXIc24t70Kr1SE/nNb37DPffcY8PZGBNiUU+4Ysmll15KZlYWiQfX4zh5GEflIW763OfsCtCYGCQis0XkUxHZLiLfa+b1JBH5W/D1FbHWPKKgoIDfPvAAt912GwlHu4749wAACx1JREFUtuLe8jp460/fSJXEPctI2rOMqVMm8+STTzB+/PjoBGxMnLOEqwMSExO5/rrrcJ7YT+Ke5bjdKVxxRRinxzDGhIWIOIHfAXOAEcAtInLmXDNfBo6p6iDg18AvIhtl17lcLv7pn/6JH/zgByTUHCFl65vgawi8qEri7g9JKNvEzTffzE9/+lOr1TImjMKacInI08AyYKiIlIrIl8N5vEiYM2cOAM6Th5k5c4ZN1WNMbJoEbFfVnarqAZ4Brj5jm6uBJ4PP5wOzRJrp9hcDZs2axY9+9CPk5JFTU5O5jmwl4fBmbrnlFr72ta/ZWFrGhFm4eyneEs79R0OfPn1wuVx4vV7OP//8aIdjjOmcAmBfk+VS4Mw/6FPbqKpXRE4A2UB5RCIMsQsvvJDbbr2Vp556CkftCZwnyxgzdixf/epXidE80piYYpc0nXDnnXdy3nmjGDduXLRDMcZ0TnMZxpnTbrRnm5jpaQ1w6623MqGkhIG9UhgyZCjf+fa3rWbLmAix1t6dcP3113P99ddHOwxjTOeVAkVNlguBAy1sUyoiLiADOHrmjlT1YeBhCEztE5ZoQ8TtdvPrX/0q2mEY0y3ZpY0xpjtaBQwWkf4ikgjcDLxyxjavAP8YfH4D8K6eS5PPGmNiitVwGWO6nWCbrG8AbwJO4DFV3SgiPwJWq+orwKPAUyKynUDN1s3Ri9gYE+ss4TLGdEuquhBYeMa6e5s8rwNujHRcxpj4ZLcUjTHGGGPCzBIuY4wxxpgws4TLGGOMMSbMLOEyxhhjjAkzS7iMMcYYY8LMEi5jjDHGmDCTc2kcPxE5AuyJdhzt1IsYnVPtHGXnM/Ri5Zz2VdXe0Q4iFGKoDIuV70YssXMaWrFyPttdfp1TCVcsEZHVqloS7TjihZ3P0LNzalpi343Qs3MaWvF4Pu2WojHGGGNMmFnCZYwxxhgTZpZwdd7D0Q4gztj5DD07p6Yl9t0IPTunoRV359PacBljjDHGhJnVcBljjDHGhJklXO0gIreLSH6044g3IvIjEbmkE++bLiKvhSOmc4mI5IvI/E68748iMqKNbf5ZRL7Q+ehMrLDyK3ysDGuZlV9ns1uK7SAi7wP3qOrqaMcSa0RECHzP/CHc53QC/x9XtHN7l6p6Q3X8aIu3z2PCy8qvrrEyLLTi6bN0VLet4RKRVBFZICLrRGSDiNwkIhNEZLGIrBGRN0UkT0RuAEqAv4jIxyLiFpFZIrJWRD4RkcdEJCm4z5+LyCYRWS8i9wfXXSkiK4Lbvy0iOdH83J0lIr8Qka83Wb5PRL4jIt8VkVXBz/zD4Gv9RGSziDwIfAQUicgTwfP8iYjcHdzuieD5RUQmisiHwf+PlSKSLiLJIvJ48D1rRWRGM3H1FJGXgsdfLiKjm8T3sIi8BfwpAqeoS1o5vxuCy7eLyHMi8irwlog4RORBEdkoIq+JyMIm5/J9ESkJPj8pIj8Jntfljd+/4P7vCT4fFPxurhORj0RkoIikicg7weVPROTqiJ8U0yIrvzrOyrDwsfKrnVS1Wz6A64FHmixnAB8CvYPLNwGPBZ+/D5QEnycD+4AhweU/Ad8CegKf8lmtYWbw36wm674C/E+0P3snz9c4YHGT5U3AFwj0JBECyftrwEVAP8APTA5uOwFY1OS9jefmCeAGIBHYCUwMru8BuIDvAI8H1w0D9gbP/3TgteD6B4AfBJ/PBD4OPr8PWAO4o33uunB+LwI2BJdvB0qBnsHlG4CFwfOeCxwDbmjm+6rAlcHnvwS+3+T83BN8vgK4tsn3OyV4/nsE1/UCtjd+j+0R/YeVX506Z1aGRfbcWvl1xsNF9/UJcL+I/ILAH9kx4DxgkYgAOIGDzbxvKLBLVbcGl58E7gR+C9QBfxSRBcF9AhQCfxORPAJ/lLvC83HCS1XXikgfCbQF6U3gfI0GLgPWBjdLAwYTKFT2qOry4PqdwAAReQBYALx1xu6HAgdVdVXwWJUAInIBgcIIVd0iInuAIWe89wICPz6o6rsiki0iGcHXXlHV2q5/+vBr4fzuPWOzRap6NPj8AuA5DdzmOCQi77Wwaw+ffRfXAJc2fVFE0oECVX0xGEddcH0C8FMRuYjAD08BkAMc6sLHNKFj5VcHWRkWPlZ+tU+3TbhUdauITADmAj8DFgEbVXVKG2+VFvbnFZFJwCzgZuAbBK5WHgB+paqvSOC+/X2h+QRRMZ/AlUku8AyBq8Cfqeofmm4kIv2A6sZlVT0mImOAywkU7p8DvtT0LQSuZM7U7LluxzaN+6pu5rVz2Znn90xNP097zg1AgwYv8wAfZ//Nt7SffyBQcE5Q1QYR2U3g6tGcA6z86jQrw8LHyq82dOc2XPlAjar+GbgfOB/oLSJTgq8niMjI4OZVQHrw+Ragn4gMCi7fBiwWkTQgQ1UXEqiiHxt8PQPYH3z+j+H8TBHwDIHC+AYCf1xvAl8KfnZEpEBE+pz5JhHpBThU9XngP4HxZ2yyBcgXkYnB7dNFxAUsIfCHg4gMAYoJ3PZoquk204HyxqvLGHTm+W3NB8D1wbYQOQRuUXRY8FyVisg1ACKSJCIpBL63h4OF1Qygb2f2b8LDyq9OszIsfKz8akO3reECRgH/LSJ+oAH4GuAF/i9YnesCfgNsJHCf/iERqQWmAF8Engv+Qa0CHiLQBuJlEUkmkHXfHTzOfcFt9wPLgf4R+XRhoKobg1W4+1X1IHBQRIYDy4K3MU4CtxK4EmmqAHhcRBoT/H8/Y78eEbkJeEBE3EAtcAnwIIHz/gmB/5vbVbU+eKxG9wX3vR6oIYZ/FM48v8Gr7JY8T6A2YgOwlUA7hhOdPPRtwB9E5EcE/hZuBP4CvCoiq4GPCfygmHOHlV+dYGVY+Fj51TYbFsKYGCUiaap6UkSygZXANFW1NlbGmHNedyy/unMNlzGx7jURySTQmPm/4r2wMsbElW5XflkNlzHGGGNMmHXbRvPGGGOMMZFiCZcxxhhjTJhZwmWMMcYYE2aWcJlzlgTm18qMdhzGGNMZVoaZpqzRvDHGGGNMmFkNl+kSEUkVkQUSmKl9g4jcJCK7JTB7/MrgY1Bw294i8ryIrAo+pgXXp4nI4xKY1X29iFwfXL87OMIzInJrcF8fi8gfRMQZfDwRPO4nInJ3y5EaY8zZrAwzkWLjcJmumg0cUNV5AMFRrn8BVKrqJBH5AoERr68A/hf4tap+ICLFBKbVGE5gqowTqjoquI+spgcIjgR9E4GB8RpE5EECU2FsJDBx6XnB7azq3hjTUVaGmYiwhMt01SfA/SLyC+A1Vf17cNqKp4OvPw38Ovj8EmBEk2ktegSngriEwBxcQGCi2DOOMQuYAKwKvtcNHAZeBQaIyAPAAuCt0H40Y0w3YGWYiQhLuEyXqOpWEZkAzAV+JiKNBUbTxoGNzx3AFFWtbboPCZRArTUmFOBJVf33s14QGQNcDtwJfA74Uqc+iDGmW7IyzESKteEyXSIi+UCNqv4ZuB8YH3zppib/Lgs+fwv4RpP3jm1h/WnV8cA7wA0i0if4ek8R6RtsG+FQ1ecJVOmPxxhjOsDKMBMpVsNlumoU8N8i4icwU/vXgPlAkoisIJDU3xLc9i7gdyKynsB3bwnwz8CPg+s3AD7gh8ALjQdQ1U0i8n3gLRFxBI9zJ1ALPB5cB3DW1aMxxrTByjATETYshAk5EdkNlKhqebRjMcaYjrIyzISD3VI0xhhjjAkzq+EyxhhjjPn/7dgxDQAAAICg/q1N4QcpnDOHCwBgJrgAAGaCCwBgJrgAAGaCCwBgJrgAAGYBhPMjQXhK/3YAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 10))\n", "\n", "for column_index, column in enumerate(df):\n", " if column == 'species':\n", " continue\n", " plt.subplot(2, 2, column_index + 1)\n", " sb.violinplot(x='species', y=column, data=df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Test Split\n", "\n", "Let's split up the data into a training set and a test set!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision Trees\n", "\n", "We'll start just by training a single decision tree." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prediction and Evaluation \n", "\n", "Let's evaluate our decision tree." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tree Visualization" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improve your results\n", "\n", "***Using different values for the parameters come with DecisionTreeClassifier() method, try to improve your results..***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### *****SECOND PART OF YOUR REPORT: ANALYSE AND DISCUSS THESE HOW YOU IMPROVED YOUR RESULTS, WHAT HELPED AND WHY?****" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }
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Final Project/images/iris_virginica.jpg
Final Project/images/petal_sepal.jpg
Final Project/iris.csv
sepal_length,sepal_width,petal_length,petal_width,species 5.1,3.5,1.4,0.2,setosa 4.9,3.0,1.4,0.2,setosa 4.7,3.2,1.3,0.2,setosa 4.6,3.1,1.5,0.2,setosa 5.0,3.6,1.4,0.2,setosa 5.4,3.9,1.7,0.4,setosa 4.6,3.4,1.4,0.3,setosa 5.0,3.4,1.5,0.2,setosa 4.4,2.9,1.4,0.2,setosa 4.9,3.1,1.5,0.1,setosa 5.4,3.7,1.5,0.2,setosa 4.8,3.4,1.6,0.2,setosa 4.8,3.0,1.4,0.1,setosa 4.3,3.0,1.1,0.1,setosa 5.8,4.0,1.2,0.2,setosa 5.7,4.4,1.5,0.4,setosa 5.4,3.9,1.3,0.4,setosa 5.1,3.5,1.4,0.3,setosa 5.7,3.8,1.7,0.3,setosa 5.1,3.8,1.5,0.3,setosa 5.4,3.4,1.7,0.2,setosa 5.1,3.7,1.5,0.4,setosa 4.6,3.6,1.0,0.2,setosa 5.1,3.3,1.7,0.5,setosa 4.8,3.4,1.9,0.2,setosa 5.0,3.0,1.6,0.2,setosa 5.0,3.4,1.6,0.4,setosa 5.2,3.5,1.5,0.2,setosa 5.2,3.4,1.4,0.2,setosa 4.7,3.2,1.6,0.2,setosa 4.8,3.1,1.6,0.2,setosa 5.4,3.4,1.5,0.4,setosa 5.2,4.1,1.5,0.1,setosa 5.5,4.2,1.4,0.2,setosa 4.9,3.1,1.5,0.1,setosa 5.0,3.2,1.2,0.2,setosa 5.5,3.5,1.3,0.2,setosa 4.9,3.1,1.5,0.1,setosa 4.4,3.0,1.3,0.2,setosa 5.1,3.4,1.5,0.2,setosa 5.0,3.5,1.3,0.3,setosa 4.5,2.3,1.3,0.3,setosa 4.4,3.2,1.3,0.2,setosa 5.0,3.5,1.6,0.6,setosa 5.1,3.8,1.9,0.4,setosa 4.8,3.0,1.4,0.3,setosa 5.1,3.8,1.6,0.2,setosa 4.6,3.2,1.4,0.2,setosa 5.3,3.7,1.5,0.2,setosa 5.0,3.3,1.4,0.2,setosa 7.0,3.2,4.7,1.4,versicolor 6.4,3.2,4.5,1.5,versicolor 6.9,3.1,4.9,1.5,versicolor 5.5,2.3,4.0,1.3,versicolor 6.5,2.8,4.6,1.5,versicolor 5.7,2.8,4.5,1.3,versicolor 6.3,3.3,4.7,1.6,versicolor 4.9,2.4,3.3,1.0,versicolor 6.6,2.9,4.6,1.3,versicolor 5.2,2.7,3.9,1.4,versicolor 5.0,2.0,3.5,1.0,versicolor 5.9,3.0,4.2,1.5,versicolor 6.0,2.2,4.0,1.0,versicolor 6.1,2.9,4.7,1.4,versicolor 5.6,2.9,3.6,1.3,versicolor 6.7,3.1,4.4,1.4,versicolor 5.6,3.0,4.5,1.5,versicolor 5.8,2.7,4.1,1.0,versicolor 6.2,2.2,4.5,1.5,versicolor 5.6,2.5,3.9,1.1,versicolor 5.9,3.2,4.8,1.8,versicolor 6.1,2.8,4.0,1.3,versicolor 6.3,2.5,4.9,1.5,versicolor 6.1,2.8,4.7,1.2,versicolor 6.4,2.9,4.3,1.3,versicolor 6.6,3.0,4.4,1.4,versicolor 6.8,2.8,4.8,1.4,versicolor 6.7,3.0,5.0,1.7,versicolor 6.0,2.9,4.5,1.5,versicolor 5.7,2.6,3.5,1.0,versicolor 5.5,2.4,3.8,1.1,versicolor 5.5,2.4,3.7,1.0,versicolor 5.8,2.7,3.9,1.2,versicolor 6.0,2.7,5.1,1.6,versicolor 5.4,3.0,4.5,1.5,versicolor 6.0,3.4,4.5,1.6,versicolor 6.7,3.1,4.7,1.5,versicolor 6.3,2.3,4.4,1.3,versicolor 5.6,3.0,4.1,1.3,versicolor 5.5,2.5,4.0,1.3,versicolor 5.5,2.6,4.4,1.2,versicolor 6.1,3.0,4.6,1.4,versicolor 5.8,2.6,4.0,1.2,versicolor 5.0,2.3,3.3,1.0,versicolor 5.6,2.7,4.2,1.3,versicolor 5.7,3.0,4.2,1.2,versicolor 5.7,2.9,4.2,1.3,versicolor 6.2,2.9,4.3,1.3,versicolor 5.1,2.5,3.0,1.1,versicolor 5.7,2.8,4.1,1.3,versicolor 6.3,3.3,6.0,2.5,virginica 5.8,2.7,5.1,1.9,virginica 7.1,3.0,5.9,2.1,virginica 6.3,2.9,5.6,1.8,virginica 6.5,3.0,5.8,2.2,virginica 7.6,3.0,6.6,2.1,virginica 4.9,2.5,4.5,1.7,virginica 7.3,2.9,6.3,1.8,virginica 6.7,2.5,5.8,1.8,virginica 7.2,3.6,6.1,2.5,virginica 6.5,3.2,5.1,2.0,virginica 6.4,2.7,5.3,1.9,virginica 6.8,3.0,5.5,2.1,virginica 5.7,2.5,5.0,2.0,virginica 5.8,2.8,5.1,2.4,virginica 6.4,3.2,5.3,2.3,virginica 6.5,3.0,5.5,1.8,virginica 7.7,3.8,6.7,2.2,virginica 7.7,2.6,6.9,2.3,virginica 6.0,2.2,5.0,1.5,virginica 6.9,3.2,5.7,2.3,virginica 5.6,2.8,4.9,2.0,virginica 7.7,2.8,6.7,2.0,virginica 6.3,2.7,4.9,1.8,virginica 6.7,3.3,5.7,2.1,virginica 7.2,3.2,6.0,1.8,virginica 6.2,2.8,4.8,1.8,virginica 6.1,3.0,4.9,1.8,virginica 6.4,2.8,5.6,2.1,virginica 7.2,3.0,5.8,1.6,virginica 7.4,2.8,6.1,1.9,virginica 7.9,3.8,6.4,2.0,virginica 6.4,2.8,5.6,2.2,virginica 6.3,2.8,5.1,1.5,virginica 6.1,2.6,5.6,1.4,virginica 7.7,3.0,6.1,2.3,virginica 6.3,3.4,5.6,2.4,virginica 6.4,3.1,5.5,1.8,virginica 6.0,3.0,4.8,1.8,virginica 6.9,3.1,5.4,2.1,virginica 6.7,3.1,5.6,2.4,virginica 6.9,3.1,5.1,2.3,virginica 5.8,2.7,5.1,1.9,virginica 6.8,3.2,5.9,2.3,virginica 6.7,3.3,5.7,2.5,virginica 6.7,3.0,5.2,2.3,virginica 6.3,2.5,5.0,1.9,virginica 6.5,3.0,5.2,2.0,virginica 6.2,3.4,5.4,2.3,virginica 5.9,3.0,5.1,1.8,virginica
Final Project/loan_data.csv
credit.policy,purpose,int.rate,installment,log.annual.inc,dti,fico,days.with.cr.line,revol.bal,revol.util,inq.last.6mths,delinq.2yrs,pub.rec,not.fully.paid 1,debt_consolidation,0.1189,829.1,11.35040654,19.48,737,5639.958333,28854,52.1,0,0,0,0 1,credit_card,0.1071,228.22,11.08214255,14.29,707,2760,33623,76.7,0,0,0,0 1,debt_consolidation,0.1357,366.86,10.37349118,11.63,682,4710,3511,25.6,1,0,0,0 1,debt_consolidation,0.1008,162.34,11.35040654,8.1,712,2699.958333,33667,73.2,1,0,0,0 1,credit_card,0.1426,102.92,11.29973224,14.97,667,4066,4740,39.5,0,1,0,0 1,credit_card,0.0788,125.13,11.90496755,16.98,727,6120.041667,50807,51,0,0,0,0 1,debt_consolidation,0.1496,194.02,10.71441777,4,667,3180.041667,3839,76.8,0,0,1,1 1,all_other,0.1114,131.22,11.00209984,11.08,722,5116,24220,68.6,0,0,0,1 1,home_improvement,0.1134,87.19,11.40756495,17.25,682,3989,69909,51.1,1,0,0,0 1,debt_consolidation,0.1221,84.12,10.20359214,10,707,2730.041667,5630,23,1,0,0,0 1,debt_consolidation,0.1347,360.43,10.4341158,22.09,677,6713.041667,13846,71,2,0,1,0 1,debt_consolidation,0.1324,253.58,11.83500896,9.16,662,4298,5122,18.2,2,1,0,0 1,debt_consolidation,0.0859,316.11,10.93310697,15.49,767,6519.958333,6068,16.7,0,0,0,0 1,small_business,0.0714,92.82,11.51292546,6.5,747,4384,3021,4.8,0,1,0,0 1,debt_consolidation,0.0863,209.54,9.487972109,9.73,727,1559.958333,6282,44.6,0,0,0,0 1,major_purchase,0.1103,327.53,10.73891524,13.04,702,8159.958333,5394,53.4,1,0,0,0 1,all_other,0.1317,77.69,10.52277288,2.26,672,3895.958333,2211,88.4,0,0,0,0 1,credit_card,0.0894,476.58,11.60823564,7.07,797,6510.958333,7586,52.7,1,0,0,0 1,debt_consolidation,0.1039,584.12,10.49127422,3.8,712,2760,8311,59.8,0,0,0,0 1,major_purchase,0.1513,173.65,11.00209984,2.74,667,1126.958333,591,84.4,3,0,0,0 1,all_other,0.08,188.02,11.22524339,16.08,772,4888.958333,29797,23.2,1,0,0,0 1,all_other,0.0863,474.42,10.81977828,2.59,797,11951,5656,27.6,0,0,0,0 1,credit_card,0.1355,339.6,11.51292546,7.94,662,1939.958333,21162,57.7,0,0,0,0 1,credit_card,0.0788,484.85,11.73606902,7.05,782,5640.041667,16931,34.6,1,0,0,0 1,debt_consolidation,0.1229,320.19,11.26446411,8.8,672,3760.958333,4822,58.1,0,0,1,0 1,all_other,0.0901,159.03,12.4292162,10,712,1553.958333,14354,36.6,0,2,0,0 1,all_other,0.0743,155.38,11.08214255,0.28,802,4649.958333,1576,5.7,1,0,0,0 1,debt_consolidation,0.1375,255.43,9.998797732,14.29,662,1318.958333,4175,51.5,0,1,0,0 1,all_other,0.0743,155.38,12.20607265,0.28,772,4516.958333,3164,13.7,0,0,0,0 1,all_other,0.0743,155.38,12.20607265,3.72,812,6778.958333,85607,0.7,0,0,0,0 1,debt_consolidation,0.0807,156.84,11.51292546,2.3,742,3148.958333,9698,19.4,0,0,0,0 1,credit_card,0.1028,275.38,9.798127037,6.4,692,7469.958333,8847,26.9,1,1,0,0 1,home_improvement,0.0743,155.38,11.91839057,0,777,7128.958333,6053,19.5,0,0,0,0 1,home_improvement,0.0807,78.42,11.60823564,11.33,762,6059.958333,7274,13.1,0,0,0,0 1,credit_card,0.087,158.3,11.22524339,15.55,757,4770,66033,23,0,0,0,0 1,all_other,0.0743,155.38,11.46163217,3.83,772,2535,3660,6.8,0,0,0,0 1,credit_card,0.0743,155.38,11.35040654,0.31,787,3540,216,0.6,0,0,0,0 1,credit_card,0.1122,164.23,11.28978191,1.21,672,3990,27185,16.1,3,0,1,0 1,debt_consolidation,0.0996,161.25,10.77895629,8.03,707,4320,28329,48.6,1,0,0,0 1,home_improvement,0.0933,159.77,12.10071213,11.93,717,4498.958333,60568,39.2,1,0,0,0 1,home_improvement,0.0807,156.84,12.10071213,5.55,742,4019,40934,26.3,0,0,0,0 1,debt_consolidation,0.0743,155.38,11.69524702,2.29,777,3540,8379,16.9,0,0,0,0 1,small_business,0.087,94.98,10.1266311,14.54,752,2460,3660,7.8,1,0,0,0 1,home_improvement,0.1028,97.2,12.20607265,0,677,3779.958333,0,0,0,1,0,0 1,credit_card,0.1122,164.23,10.30895266,18.64,702,5190,15840,47.1,0,0,0,0 1,credit_card,0.0838,167.02,11.21182037,14.37,797,5610.958333,6844,14.4,0,0,0,0 1,educational,0.0775,168.6,8.987196821,3,762,6659.958333,1321,16.5,0,0,0,0 1,all_other,0.0838,160.72,10.37349118,14.78,772,4379,4737,23.9,0,0,0,0 1,credit_card,0.0901,159.03,11.19821472,9.96,722,3089.958333,23489,37.6,0,0,0,0 1,debt_consolidation,0.0743,178.69,11.73606902,0.27,792,7488.958333,2817,10.2,0,0,0,0 1,credit_card,0.0743,155.38,10.71441777,1.12,782,5807,1783,0,1,0,0,0 1,small_business,0.1028,323.98,10.217422,10.7,712,1379,3534,54.4,0,0,0,0 1,small_business,0.1059,813.65,10.77895629,4.05,762,2729.958333,2422,23.3,0,0,0,0 1,educational,0.0996,112.87,9.510444964,16.44,712,1649.958333,2864,41.1,1,0,0,0 1,debt_consolidation,0.1596,94.88,10.86283777,12.57,627,1409,3075,92.3,1,0,0,0 1,small_business,0.1059,398.69,11.00209984,17.12,747,14008.95833,17214,8.1,2,1,0,0 1,credit_card,0.1154,39.6,9.903487553,2.04,662,1139,1153,75.8,2,0,0,0 1,credit_card,0.1343,678.08,11.88448902,10.15,682,4209.958333,41674,74.1,0,0,0,1 1,credit_card,0.1249,401.37,11.22524339,15,682,4479,43039,93.4,3,0,0,0 1,all_other,0.0964,205.45,11.00209984,7.72,712,2293.958333,3362,69.3,0,0,0,0 1,debt_consolidation,0.1186,397.75,11.08214255,7.14,682,8730,11868,71.9,0,2,0,0 1,all_other,0.0743,155.38,10.59663473,2.55,772,8189.958333,2562,14,2,0,0,0 1,all_other,0.1028,113.39,9.903487553,1.5,682,1410,1882,32.4,0,0,0,0 1,debt_consolidation,0.1186,99.44,10.49551521,7.47,667,2550,1064,70.9,2,0,1,0 1,credit_card,0.0807,109.79,11.49272276,8.52,752,4979.958333,6432,6,0,1,0,0 1,all_other,0.1059,32.55,10.43382164,14.47,687,1110,4485,36.9,1,0,0,1 1,all_other,0.0996,112.87,11.26446411,11.06,702,4979.958333,16310,20.5,0,1,0,0 1,debt_consolidation,0.0964,160.51,11.77528973,8.55,712,5940,20969,40.2,0,0,0,0 1,debt_consolidation,0.1375,340.57,10.46310334,17.38,707,2699.958333,22213,65.9,0,0,0,0 1,home_improvement,0.0775,156.11,12.32385568,6.48,752,5339.958333,2160,2.3,1,0,0,0 1,credit_card,0.0743,155.38,11.15625052,4.56,812,9809,813,1,0,0,0,0 1,home_improvement,0.0807,235.25,11.91839057,2.72,757,5202,23678,24.4,4,0,0,0 1,debt_consolidation,0.0996,257.99,10.77895629,15.13,722,2640,11289,47.8,3,0,0,0 1,small_business,0.1501,225.37,12.26904744,6.45,677,6240,56411,75.3,0,0,0,1 1,home_improvement,0.0964,80.26,11.22524339,20,772,4920.958333,269,3.8,3,0,0,1 1,home_improvement,0.128,84,10.37349118,20,682,5492,3408,35.1,1,0,0,1 1,debt_consolidation,0.1122,507.46,10.59663473,13.5,712,5368.958333,6513,34.3,3,0,1,1 1,educational,0.0743,124.31,10.59663473,3.45,772,1739.958333,330,11,0,0,0,0 1,credit_card,0.087,63.32,11.15625052,6.07,712,2550,5967,19.8,1,0,0,0 1,all_other,0.1091,130.79,9.798127037,18,707,2192,5533,79.6,1,0,0,0 1,home_improvement,0.0775,93.67,11.73606902,2.14,762,8370,21050,1,0,0,0,0 1,all_other,0.0775,93.67,10.81977828,5.35,762,8370,21050,0.7,0,0,0,0 1,all_other,0.087,88.65,11.69524702,15,742,3026,8155,14.9,1,0,0,0 1,all_other,0.0964,128.41,11.51292546,15,722,3026,8155,14.9,1,0,0,0 1,home_improvement,0.087,94.98,11.03488966,12.23,737,2729.958333,3678,30.7,0,0,0,0 1,debt_consolidation,0.1217,179.8,10.46310334,7.1,662,2944,2040,36.2,2,0,0,0 1,credit_card,0.1533,444.05,11.00209984,19.52,667,2700.958333,33074,68.8,2,0,0,0 1,all_other,0.0775,124.89,11.03165864,3.46,747,2519,1918,17.1,0,0,0,0 1,credit_card,0.1533,644.3,11.40756495,5.24,662,1770,22379,62.2,0,0,0,0 1,small_business,0.0964,642.02,11.47210347,2.86,737,2190,10296,42.5,0,0,0,1 1,small_business,0.1154,104.76,9.61580548,12.24,672,1289.958333,2867,55.1,3,0,0,1 1,debt_consolidation,0.1122,157.66,10.30895266,12.36,682,1770,12310,86.1,0,0,0,0 1,debt_consolidation,0.087,383.87,11.91839057,4.8,767,13349.95833,61921,39.1,0,0,0,0 1,credit_card,0.1122,217.6,11.38509209,18,702,3725,16738,26.1,2,0,0,0 1,all_other,0.0807,297.99,11.05089001,3.49,792,5939,40384,9.2,1,0,0,0 1,debt_consolidation,0.0775,156.11,10.71441777,5,762,2131,1359,5.6,2,0,0,0 1,debt_consolidation,0.1154,329.95,12.32385568,20,727,10292,107501,44.8,2,0,0,0 1,all_other,0.0807,313.67,12.06104687,0.72,787,8010,3054,5.6,3,0,0,0 1,all_other,0.0807,297.99,11.40756495,2.44,792,8717.958333,40384,9.6,0,0,0,0 1,home_improvement,0.0933,239.65,11.44035477,9.66,792,4529,128000,0,3,0,0,0 1,debt_consolidation,0.0712,61.87,11.6784399,0.43,772,5936,4709,18.5,0,0,0,0 1,all_other,0.1186,94.47,10.71441777,2.8,667,5460,2850,75,0,1,0,0 1,small_business,0.1501,73.68,11.47210347,16.89,702,4080,51,0,0,0,0,1 1,home_improvement,0.0807,313.67,11.91839057,5.84,792,5364.958333,37152,3.7,0,0,0,0 1,debt_consolidation,0.0933,543.2,11.22524339,9.55,762,5699.958333,3839,16.6,1,0,0,0 1,home_improvement,0.0807,313.67,11.35040654,3.11,797,5363.958333,37152,3.7,0,0,0,0 1,credit_card,0.1217,266.37,11.15625052,10.2,672,3598.958333,26283,39.5,0,1,0,1 1,debt_consolidation,0.1028,210.6,10.71441777,5.87,692,1741.958333,5607,42.2,0,0,0,0 1,all_other,0.1249,234.15,10.04324949,5.01,662,1110,1061,81.9,3,0,0,0 1,debt_consolidation,0.1028,323.99,11.69524702,14.4,722,4679.958333,56740,77.5,1,0,0,0 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1,debt_consolidation,0.1071,192.35,10.51325312,16.24,697,4919.958333,3382,36.1,0,0,0,0 1,educational,0.1166,52.89,11.00209984,18.4,677,5370,926,4.5,3,1,0,0 1,debt_consolidation,0.1261,251.3,10.6454249,5.71,667,1500,8944,57,1,0,0,0 1,all_other,0.0832,125.94,10.37361617,15.07,762,2460,1152,16.5,0,0,0,0 1,all_other,0.0737,31.05,10.4341158,0.35,807,7139.958333,102,0.3,1,0,0,0 1,all_other,0.1166,247.9,11.01862914,17.59,687,2850,6550,47.8,2,0,0,0 1,home_improvement,0.08,235.03,10.77895629,4.28,812,7950,52,0.3,0,0,0,0 1,home_improvement,0.1229,250.15,11.7829526,3.18,672,3540,56481,61.7,0,0,1,0 1,debt_consolidation,0.1103,245.65,11.65268741,7.55,687,4949.958333,34753,95.2,0,0,0,0 1,all_other,0.0976,64.31,11.28983191,17.01,712,3479.958333,29889,37.4,1,0,0,0 1,educational,0.0832,236.14,10.69194491,3.74,747,3389.958333,11585,27.1,1,0,0,0 1,educational,0.0832,191.27,11.51292546,7.87,757,10470,8681,23.2,0,0,0,0 1,all_other,0.1197,66.4,10.77895629,10.4,667,3839.958333,16746,69.2,1,0,0,0 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1,debt_consolidation,0.0976,241.16,10.02552818,6.74,717,1679.958333,7313,29.6,0,0,0,1 1,debt_consolidation,0.0863,237.21,10.46321762,6,747,5850,5541,26.5,2,0,0,0 1,debt_consolidation,0.1039,48.68,10.46310334,13.17,697,4229.958333,19,0.2,1,0,0,1 1,debt_consolidation,0.1324,253.58,10.30895266,13.44,667,1289.958333,9142,81.6,1,0,0,0 1,educational,0.1103,163.77,10.81977828,16.56,687,4499.958333,5584,80.9,2,0,1,0 1,credit_card,0.1008,232.6,10.54534144,18.09,722,2369.958333,12391,66.6,1,0,0,0 1,debt_consolidation,0.0945,96.03,10.71441777,5.17,722,2460,5275,48,0,0,0,0 1,educational,0.1008,96.92,10.49127422,6.9,697,2339.958333,8349,43.5,0,0,0,1 1,debt_consolidation,0.1324,253.58,10.05173513,16.76,667,2820,4013,66.9,0,0,0,1 1,home_improvement,0.0832,157.43,10.33501019,10.13,742,4307,20337,39.2,3,0,0,0 1,debt_consolidation,0.1166,135.52,10.59653473,3.99,677,2460,7102,18.7,0,0,0,0 1,all_other,0.1039,64.91,9.998979534,8.13,692,1559.958333,1599,9.6,2,1,0,0 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1,all_other,0.097,44.18,10.59568428,20.84,757,7829.958333,14813,36.8,0,0,0,0 1,debt_consolidation,0.1191,201.52,10.79949394,14.33,682,4533.958333,16979,41,0,1,0,1 1,all_other,0.097,76.31,10.30895266,15.04,747,1679.958333,2420,5.8,0,0,0,0 1,major_purchase,0.1475,259.08,10.30895266,20.4,662,3240,1244,44.4,2,0,0,0 1,all_other,0.1033,180.76,10.30908599,13.88,752,2670,5063,40.8,6,0,0,0 1,educational,0.1633,105.97,10.91508846,26.05,672,5430,19487,89.2,2,0,0,0 1,debt_consolidation,0.1096,185.69,11.08214255,12.04,697,8219.958333,7350,37.3,0,0,0,0 1,educational,0.1191,216.45,10.83467754,20.69,707,5669.958333,16881,50.7,0,0,0,0 1,credit_card,0.1222,193.26,10.91508846,14.84,677,3240,7440,89.6,0,0,0,0 1,educational,0.1286,50.44,10.48514436,13.45,662,4619.958333,10363,71,2,0,1,0 1,all_other,0.1096,113.71,11.22524339,9.92,687,6240,22796,51.9,1,0,0,1 1,all_other,0.1096,110.43,10.30895266,0.6,722,1440,767,4.2,3,0,0,0 1,debt_consolidation,0.1096,185.69,10.70817611,18.52,712,2250,25925,66.1,0,0,0,0 1,all_other,0.1033,109.43,10.30895266,5.52,712,1649.958333,598,2.9,1,0,0,0 1,major_purchase,0.1222,145.78,10.24313332,17.63,677,2550,7474,35.8,2,1,0,0 1,debt_consolidation,0.0832,174.74,10.71163613,13.64,777,4559.958333,5578,9,2,0,0,0 1,all_other,0.1222,79.97,10.63585545,22.36,677,5580,12785,77.2,2,0,0,0 1,debt_consolidation,0.0832,47.23,10.54534144,2.53,732,2789.958333,3421,22.1,1,0,0,0 1,all_other,0.1222,165.77,11.42954386,5.06,722,6389.958333,5424,98.6,0,0,0,0 1,debt_consolidation,0.1696,135.41,10.46310334,29.42,672,5339.958333,536,9.2,2,0,0,0 1,educational,0.1191,116.1,12.07254125,2.47,677,1770,23806,60.6,1,0,0,0 1,debt_consolidation,0.1096,110.43,10.71441777,13.6,722,4470,23280,92,3,0,0,0 1,debt_consolidation,0.08,159.82,10.87804719,6.23,752,4649.958333,10399,31.3,1,0,0,0 1,debt_consolidation,0.1146,263.66,11.28978191,21.72,697,7577,73150,72.9,0,0,0,1 1,credit_card,0.1159,194.82,11.51292546,20.29,707,6119.958333,57152,78.7,0,0,0,0 1,debt_consolidation,0.1001,69.39,10.46310334,1.2,722,3330,2400,35.8,0,0,0,0 1,credit_card,0.1538,243.97,11.28978191,9.24,667,6540,33102,99.4,2,0,1,1 1,major_purchase,0.0768,96.69,10.6214249,9.39,787,5044.958333,15715,21.5,1,0,0,0 1,credit_card,0.1128,218.6,11.1844214,16.86,697,2208,21659,49.9,1,0,0,1 1,debt_consolidation,0.1033,180.76,10.6454249,0,772,7529.958333,0,0,6,0,0,0 1,debt_consolidation,0.0832,141.68,10.59663473,13.02,752,6300,12183,45.1,0,0,0,0 1,all_other,0.0832,174.74,11.31447453,4.36,752,3420,26040,49.1,3,0,0,0 1,all_other,0.1128,139.71,10.91508846,4.65,682,3209.958333,2801,63.7,0,0,0,1 1,debt_consolidation,0.1128,167.65,11.05879518,14.68,687,4770,17550,38.6,2,0,0,1 1,debt_consolidation,0.1159,158.5,10.81977828,20.45,707,5820,11781,58.9,2,0,0,0 1,credit_card,0.0832,135.39,10.93310697,17.04,752,7499.958333,68895,41.9,2,0,0,0 1,all_other,0.0737,31.05,11.25631561,0.74,782,12407,2440,8.6,0,0,0,0 1,credit_card,0.1254,226.78,11.37779279,20.95,702,6630,31518,93.2,0,0,0,0 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1,major_purchase,0.1537,375.47,9.94251594,5.02,687,1205,1889,43.9,2,0,0,0 1,debt_consolidation,0.1253,167.34,11.24504602,23.29,712,4530.041667,40752,89.5,0,0,0,0 1,debt_consolidation,0.1316,675.41,11.65268741,9.52,717,8253.041667,22424,34.4,0,0,0,0 1,debt_consolidation,0.1442,275.07,10.47925786,24.63,677,5880,8928,62.2,2,0,0,0 1,all_other,0.0737,60.54,10.54534144,14.49,777,4950.041667,1595,5.1,0,0,0,0 1,credit_card,0.0932,191.69,10.96819829,3.41,737,6870.041667,7711,58.4,3,0,0,0 1,credit_card,0.0768,155.96,11.6351431,1.07,787,3150.041667,4333,17.1,0,0,0,0 1,educational,0.1379,170.38,11.35040654,0.42,737,1140,890,6.4,1,0,0,0 1,major_purchase,0.08,282.03,10.85591733,8.4,792,3870.041667,2475,12.3,1,0,0,0 1,small_business,0.1284,672.36,11.15625052,16.87,747,5001,4304,4.1,4,0,0,0 1,educational,0.1284,50.43,11.40756495,17.32,692,7320,27248,79.4,1,0,0,0 1,all_other,0.1253,80.32,9.998615898,23.73,707,1620.041667,3228,38.4,3,0,0,0 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1,small_business,0.1695,356.27,11.00209984,11.18,662,3336.958333,6070,88,1,0,0,0 1,credit_card,0.1379,340.76,10.4487146,14.71,692,2520,8316,52.6,0,0,0,0 1,all_other,0.1218,399.6,11.04852212,9.03,712,7321,16353,31.9,0,0,0,0 1,all_other,0.1347,67.85,9.884916167,16.32,692,2189.958333,6191,72,1,0,0,0 1,small_business,0.0963,513.51,11.69524702,2.5,802,12330,8823,7.4,1,0,0,0 1,debt_consolidation,0.1284,242.05,10.81985828,13.17,702,10259.95833,19434,43.2,2,0,0,0 1,home_improvement,0.1253,301.2,11.4294569,20.3,722,4446.958333,42813,0,2,0,0,1 1,all_other,0.1496,207.87,11.20504069,18.43,662,5517,14417,82.9,0,0,0,0 1,home_improvement,0.1347,542.77,12.25486281,14.93,707,6359.958333,46392,81.1,1,0,0,0 1,all_other,0.1505,190.81,10.66904841,12.7,667,3719.958333,1953,57.4,2,1,0,0 1,credit_card,0.0932,191.69,11.1124479,19.45,747,7409.958333,42518,68.9,2,0,0,0 1,all_other,0.0932,191.69,10.95080655,11.56,777,6810,14650,24.5,5,0,0,0 1,small_business,0.1253,167.34,11.56171563,23.9,707,7500,70432,89.3,0,0,0,0 1,small_business,0.1221,666.3,11.69524702,8.22,782,6450,3606,4.5,0,0,0,0 1,all_other,0.1505,260.19,12.36563722,15.01,672,7500,82153,88.1,0,0,0,0 1,all_other,0.1442,51.58,11.23537852,11.4,677,3510,457,18,0,0,0,0 1,debt_consolidation,0.1505,294.88,9.998797732,13.75,672,1740,11131,57.7,1,0,0,1 1,major_purchase,0.1253,187.42,10.46310334,14.43,702,8130.958333,15341,40.2,1,0,0,1 1,all_other,0.1126,312.21,11.1844214,3.73,747,2070,700,14.6,7,0,0,0 1,all_other,0.1316,303.94,10.59663473,9.66,727,4019.958333,1813,27.1,1,0,1,0 1,debt_consolidation,0.1347,244.25,11.39075783,12.83,687,3510,28767,74.9,1,0,0,0 1,major_purchase,0.1505,156.12,11.31447453,11.56,667,3630,0,0,1,2,1,0 1,debt_consolidation,0.1411,329.46,10.71441777,22.48,682,2309.958333,13575,73.8,0,0,0,1 1,debt_consolidation,0.1347,284.95,11.03488966,13.53,692,3990,9631,74.7,3,0,0,0 1,small_business,0.1347,814.15,11.51292546,7.25,737,2160,5,0.2,3,0,0,0 1,credit_card,0.1379,511.14,10.96819829,11.07,707,3120,15166,94.8,2,0,0,0 1,home_improvement,0.1442,55.02,10.15424627,12.98,692,1260,0,0,0,0,1,0 1,all_other,0.1221,333.15,9.952277717,8.46,717,1829.958333,6711,53.3,1,0,0,0 1,small_business,0.1316,413.69,10.08580911,11.35,707,2130,4850,53.9,1,0,0,1 1,all_other,0.1158,528.22,12.61153775,7.6,697,4214,41491,60.9,0,0,0,0 1,debt_consolidation,0.1379,238.53,10.20359214,21.91,697,3180,7494,94.9,0,0,0,0 1,debt_consolidation,0.1411,676.02,11.08214255,18.85,702,8250,11667,33.1,1,0,0,1 1,all_other,0.1347,169.62,11.28983191,3.67,697,5100,3048,33.9,0,1,0,0 1,credit_card,0.1316,506.56,11.20504069,18.87,727,9450,49542,54.6,2,0,0,0 1,debt_consolidation,0.0932,319.47,10.47344963,19.88,762,2760,12255,24.2,0,0,0,0 1,small_business,0.0932,63.9,11.06663836,12.04,742,2760,0,0,0,0,0,0 1,small_business,0.1442,171.92,11.58084576,1.96,697,2669.958333,4233,46,0,0,0,1 1,debt_consolidation,0.1411,164.3,10.50232405,14.67,672,1394,8038,51.5,0,0,0,0 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1,debt_consolidation,0.1316,405.25,11.33857208,9.4,702,3299.958333,15127,45.6,0,0,1,0 1,debt_consolidation,0.1316,324.2,10.69194491,5.21,702,13770,7491,71.3,0,1,0,0 1,credit_card,0.1537,684.73,10.84534856,11.72,687,5939.958333,43620,92.9,0,0,0,0 1,credit_card,0.1474,442.08,11.22524339,14.96,692,3270,24769,90.1,2,1,0,0 1,debt_consolidation,0.08,222.49,11.08220409,13.25,757,6510,4409,12.4,3,0,0,0 1,debt_consolidation,0.1411,313.2,11.51292546,24.34,692,13410,46268,92.9,1,1,0,1 1,credit_card,0.1189,414.55,11.00209984,14.68,737,4230,12663,34.4,1,0,0,0 1,debt_consolidation,0.1537,139.39,10.23995979,23.83,672,1139.958333,4071,78.3,0,0,0,0 1,debt_consolidation,0.1682,533.43,11.98292909,22.57,682,3600.041667,15214,68.7,0,2,0,0 1,home_improvement,0.1095,654.28,11.11167148,18.89,777,8909.958333,12683,18.1,0,0,0,0 1,home_improvement,0.1568,385.02,11.15625052,14.97,662,12539.95833,9649,50.5,0,0,0,1 1,debt_consolidation,0.1635,706.59,11.51288546,20.93,677,4863,22739,59.5,0,0,0,0 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1,debt_consolidation,0.1537,209.08,11.38531934,13.24,662,5489.958333,24214,68,0,0,0,0 1,all_other,0.0932,159.74,10.75363848,5.05,792,5430,1767,14.7,0,0,0,0 1,debt_consolidation,0.0788,375.37,11.60823564,16.27,762,4793,12311,24.9,2,0,0,0 1,small_business,0.1347,407.08,11.28978191,23.83,692,6390,8762,31.9,3,0,0,0 1,small_business,0.1316,290.43,10.73639668,7.98,702,3881,553,2.8,1,0,0,0 1,credit_card,0.1379,68.16,10.88025231,18.07,692,3329.958333,16306,95.9,0,0,0,0 1,all_other,0.1442,55.02,8.998136761,19.14,682,3779.958333,2816,93.9,2,0,0,0 1,all_other,0.0963,144.43,10.54534144,0.82,747,6870,752,27.9,1,0,0,1 1,debt_consolidation,0.1442,524.34,11.00209984,20.64,712,4529.958333,51790,96.1,1,0,0,1 1,debt_consolidation,0.1537,519.21,10.59653473,16.89,662,3809.958333,15586,83.3,0,0,0,0 1,all_other,0.16,140.63,9.775654181,1.7,677,1350,301,8.9,0,0,0,0 1,debt_consolidation,0.1189,596.96,11.1844214,22.3,747,3689.958333,23518,32.9,2,0,0,0 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1,debt_consolidation,0.0963,385.14,11.15625052,8.86,757,4859.958333,14745,34.6,2,0,0,0 1,small_business,0.1979,287.2,10.87804719,15.26,692,3509.958333,11835,98.6,1,0,0,1 1,debt_consolidation,0.0963,144.43,11.05089001,13.33,732,2130,4004,17.8,3,0,0,0 1,educational,0.1189,165.82,10.30895266,18.2,732,1800,1984,24.8,0,0,0,1 1,debt_consolidation,0.1411,316.62,10.34174248,19.9,667,4949.958333,978,27.2,0,3,0,0 1,debt_consolidation,0.1379,204.46,11.15625052,7.99,672,8159.958333,7635,88.8,1,0,1,0 1,all_other,0.0737,99.35,10.50506754,18.15,797,7620,19617,24,0,0,0,0 1,credit_card,0.08,219.36,11.08521475,10.25,757,3990,15223,25.8,1,0,0,0 1,all_other,0.0963,320.95,10.68510338,1.35,737,7559.958333,1876,12.3,2,0,0,1 1,credit_card,0.1284,504.27,11.46163217,9.75,697,7919.958333,33981,77.1,0,0,1,1 1,major_purchase,0.0932,281.14,10.07744086,2.37,782,4229.958333,1483,5.3,2,0,0,0 1,all_other,0.0963,256.76,10.77895629,9.2,762,2309.958333,5060,19.5,0,0,0,0 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1,credit_card,0.1148,659.37,11.26446411,5.72,757,3616,8863,27.4,1,0,0,0 1,educational,0.1253,167.34,10.59663473,10.56,692,4410,15041,66.6,0,0,0,0 1,all_other,0.0859,63.23,9.903687533,8.7,747,4289.958333,4622,19.3,0,0,0,1 1,debt_consolidation,0.1253,669.33,12.05989014,6.45,722,3420,23973,44.9,1,0,0,0 1,educational,0.1287,48.77,9.740968623,7.41,692,2009.958333,0,0,0,1,0,0 1,debt_consolidation,0.1114,393.65,11.2395407,15.06,742,4860,18862,47.5,1,0,0,0 1,debt_consolidation,0.0859,252.89,10.1266311,20.3,797,6690,3301,27.3,0,0,0,0 1,credit_card,0.1253,401.6,11.85651517,5.59,702,5009.958333,4381,16.5,2,0,0,0 1,educational,0.1253,401.6,12.06681058,6.33,717,9120,38384,90.1,1,0,0,0 1,small_business,0.1531,313.34,11.32900263,3.45,742,5520,6218,8.1,0,0,1,0 1,credit_card,0.1183,198.81,11.00209984,15.5,702,3809.958333,55333,39,2,0,0,0 1,educational,0.0894,57.19,10.53209621,21.54,742,3179.958333,2940,32.3,5,0,0,0 1,debt_consolidation,0.1218,666,11.57332418,13.84,732,9373.958333,22233,76.7,1,0,0,0 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1,major_purchase,0.1357,118.89,11.38509209,16.05,672,7080.041667,10977,70.8,0,0,2,0 1,credit_card,0.1148,197.82,11.22524339,11.9,707,4110.041667,18103,59.5,3,0,0,0 1,all_other,0.1287,84.09,10.44488118,13.86,687,6780.041667,7565,27,1,2,0,0 1,all_other,0.1183,139.17,9.287301413,0,747,2970.041667,0,0,2,0,0,0 1,debt_consolidation,0.1496,623.61,10.84349481,10.71,717,5400,18632,98.1,0,0,0,0 1,debt_consolidation,0.0894,317.72,11.48246626,7.62,737,4830.041667,20574,38.2,2,0,0,0 1,debt_consolidation,0.0774,187.32,11.40756495,15.89,762,8730,37397,29,0,0,0,0 1,debt_consolidation,0.1183,99.41,10.6454249,14.46,697,3090.041667,6297,76.8,1,0,0,0 1,debt_consolidation,0.1496,242.52,10.6873891,12.66,662,5250.041667,3294,70.1,1,0,0,1 1,home_improvement,0.1183,497.01,11.00209984,11.7,732,3721.041667,30697,55.9,0,0,0,0 1,debt_consolidation,0.1287,269.07,10.81977828,5.3,692,8070,7710,35.1,0,2,0,0 1,home_improvement,0.0774,312.19,10.83958091,7.98,777,3510.041667,696,15.5,0,0,0,0 1,all_other,0.1114,656.07,11.69524702,9.55,772,6120,26701,43.1,0,0,0,0 1,debt_consolidation,0.1183,430.75,11.33857208,12.87,717,2220,14331,37.8,1,0,0,0 1,debt_consolidation,0.1183,298.21,10.4341158,17.12,712,3030.041667,13533,42.3,1,0,0,0 1,debt_consolidation,0.1253,689.41,11.51372515,14.45,722,4291,13171,51.8,3,0,0,0 1,debt_consolidation,0.1322,338.01,10.49127422,8.3,687,2280.041667,8989,36.2,0,0,0,0 1,credit_card,0.1357,285.34,10.98305293,21.34,677,6270.041667,55072,87.8,0,0,0,0 1,all_other,0.0894,285.95,10.75790288,21.19,737,5820,10325,30.1,0,0,1,0 1,all_other,0.1461,172.38,10.49127422,16.57,742,5400,0,34.5,0,0,0,0 1,all_other,0.1114,65.61,10.08580911,21.45,712,4350,560,70,3,1,0,0 1,all_other,0.1183,99.41,10.77895629,23.93,712,5580.041667,0,0,2,0,0,0 1,credit_card,0.0894,476.58,11.51292546,2.14,782,11010.04167,8057,17.4,2,0,0,0 1,all_other,0.1496,173.23,10.30895266,24.4,662,2880,1451,46.8,0,1,0,1 1,all_other,0.1357,326.1,11.00209984,13.2,677,4050,9164,47,1,0,0,0 1,credit_card,0.1148,412.11,11.08214255,1.63,732,3600,19898,36.4,2,0,0,0 1,debt_consolidation,0.1348,474.94,10.71441777,9.17,692,3540.041667,10163,61.6,1,0,0,0 1,educational,0.1531,217.6,9.862665558,15.13,662,8490.041667,4815,94.4,0,1,0,0 1,debt_consolidation,0.0894,381.26,11.51092346,15.64,757,12990.04167,4779,17.1,0,0,0,0 1,debt_consolidation,0.0894,476.58,11.56171563,12.53,777,10800.04167,8118,5.9,0,0,0,0 1,all_other,0.1913,293.77,11.65268741,11.79,667,4410,17267,29.1,3,0,0,0 1,debt_consolidation,0.0894,142.98,10.1064284,13.86,762,2970.041667,4659,30.1,0,0,0,0 1,home_improvement,0.0894,397.15,12.10598708,4.26,772,5103,31141,0.4,2,0,0,1 1,all_other,0.1357,54.35,9.422220731,21.46,712,1470,0,0,3,0,0,1 1,debt_consolidation,0.1322,212.95,10.30895266,5.28,772,11040.04167,14581,98,1,0,0,0 1,debt_consolidation,0.1114,590.47,11.15625052,10.7,752,4320,22093,23.7,0,0,0,0 1,debt_consolidation,0.1322,507.01,11.2009507,1.66,692,3602,17109,81.5,2,0,0,0 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1,debt_consolidation,0.1531,522.23,10.59663473,12.33,682,1440,5365,78.9,0,0,0,0 1,all_other,0.1496,225.2,11.00209984,1.08,697,1200.041667,0,0,3,3,0,1 1,debt_consolidation,0.1322,338.01,11.46163217,8.29,687,4290,14822,76.4,0,0,0,1 1,debt_consolidation,0.0894,222.41,10.77895629,14.4,722,3750.041667,1821,31.4,2,0,0,0 1,debt_consolidation,0.1183,497.01,11.00209984,7.78,727,6150,12609,67.1,0,0,0,0 1,all_other,0.1148,158.25,11.50690739,23.76,742,6240,57542,24.2,0,0,0,0 1,all_other,0.1114,459.25,12.03765399,4.76,662,3300,7044,61.8,1,0,0,0 1,major_purchase,0.1461,310.28,10.30895266,15.44,677,3690,3971,58.4,0,0,0,1 1,debt_consolidation,0.1287,311.11,9.998615898,15.44,687,4573.041667,4806,27.2,0,0,0,0 1,debt_consolidation,0.1531,348.16,11.36210258,16.33,672,10845,17768,65.8,1,0,1,0 1,credit_card,0.1322,287.31,11.22524339,7.73,687,7950,4167,19.8,1,1,0,0 1,all_other,0.1426,343.06,10.67359577,0.78,692,4800.041667,966,48.3,3,0,0,0 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1,all_other,0.0894,266.89,10.62132735,13.9,737,5730.041667,9851,43.2,0,0,0,0 1,credit_card,0.1322,676.02,11.57119437,9.32,707,5010.041667,16821,80.5,1,0,0,0 1,debt_consolidation,0.1426,514.59,11.77528973,24.95,702,8310,74469,81.3,0,0,0,0 1,debt_consolidation,0.1183,331.34,11.12726298,16.94,717,2130,14078,82.8,3,0,0,0 1,all_other,0.1218,352.98,11.45105006,8.49,737,5040,9044,24.1,1,1,0,0 1,all_other,0.1183,82.84,10.08580911,7.2,692,3570,4484,65,0,0,0,0 1,all_other,0.1136,246.84,10.49127422,18.87,697,4590.041667,14225,53.7,1,0,0,0 1,debt_consolidation,0.1148,428.59,10.71441777,6,732,3300,11574,14.9,1,1,0,1 1,credit_card,0.1253,669.33,11.36986308,20.13,717,6210,51324,63.6,0,0,0,0 1,credit_card,0.1392,682.74,11.58988651,4.62,742,4440,4588,91.8,0,0,0,0 1,credit_card,0.1062,150.59,11.15625052,19.94,707,12150,34299,68.3,0,0,0,0 1,debt_consolidation,0.1183,281.64,11.33857208,13.63,727,5550.041667,44643,90,1,0,0,0 1,debt_consolidation,0.1565,258.9,11.1844214,9.32,662,5040,5001,84.8,0,1,1,0 1,debt_consolidation,0.1025,323.85,11.44035477,2.71,717,6138.041667,9612,41.1,3,0,0,0 1,major_purchase,0.1114,196.83,11.08214255,0.52,782,3420.041667,906,11.6,0,0,0,0 1,debt_consolidation,0.1183,265.08,10.89673933,21.98,712,2190,8959,65.9,2,0,0,0 1,all_other,0.1183,115.97,11.40756495,4.33,717,3510.041667,11999,94.5,0,0,0,1 1,small_business,0.167,170.41,11.03488966,23.86,677,4590.041667,3024,24.4,3,0,0,0 1,debt_consolidation,0.0774,202.93,9.798127037,18,787,4110.041667,2816,13.2,0,0,0,0 1,all_other,0.1114,147.62,9.392661929,17,772,14640,5326,40,1,0,0,0 1,debt_consolidation,0.1025,777.24,11.06663836,7.59,782,6300.041667,14882,28.2,0,0,0,0 1,credit_card,0.0859,316.11,10.89673933,9.98,752,6930,21131,32.2,0,0,0,0 1,debt_consolidation,0.0894,460.69,10.83958091,10.96,777,6399.041667,1461,3.1,0,0,0,0 1,credit_card,0.0859,189.67,11.68687877,8.21,742,6120,27116,41.6,1,0,0,0 1,major_purchase,0.0774,140.49,10.59663473,16.02,747,3815.041667,1580,29.3,1,0,0,0 1,credit_card,0.16,316.42,11.3736634,10.86,687,5550.041667,14679,86.3,3,0,0,0 1,debt_consolidation,0.1739,358.48,10.87804719,22.35,672,14580,19099,99,1,0,1,0 1,debt_consolidation,0.1461,689.51,11.03488966,9.41,682,4050,20976,39.1,0,1,0,1 1,major_purchase,0.1843,872.88,13.30468493,7.09,662,6120,58222,39,1,0,0,1 1,home_improvement,0.1357,509.53,11.95761129,8.88,687,3359.041667,92755,49.2,1,0,0,0 1,all_other,0.0859,126.45,11.71993963,7.67,742,5850,32282,66.6,2,0,0,0 1,all_other,0.1183,165.67,9.903487553,17.64,722,5490,784,41.3,0,0,0,0 1,all_other,0.167,170.41,11.2009507,19.98,692,3723.041667,15934,99,3,0,0,1 1,debt_consolidation,0.1218,807.53,11.12726298,18.67,737,5550.041667,63437,65.2,0,0,0,0 1,all_other,0.0774,156.1,11.11988288,0.6,752,3090.041667,1248,9.3,0,0,0,0 1,all_other,0.1426,274.45,10.87804719,22.46,672,4110.041667,3527,84,3,0,0,0 1,debt_consolidation,0.1148,553.87,11.41861479,10.77,747,7290,17502,65.8,0,0,0,0 1,all_other,0.1183,49.71,9.392661929,1.5,732,1680.041667,246,24.6,0,0,0,0 1,educational,0.1253,334.67,12.16002871,12.2,712,8340,444,2.5,0,0,0,0 1,home_improvement,0.1114,492.06,11.46163217,24.87,747,9788,50662,14.8,0,1,0,0 1,small_business,0.1565,349.87,10.71441777,5.41,752,4410,5695,59.9,0,0,0,1 1,credit_card,0.1114,426.45,11.15434871,10.43,742,5226.041667,12724,50.9,1,0,0,0 1,debt_consolidation,0.1148,692.34,11.22524339,15.63,747,7680,23860,55.5,1,0,0,0 1,major_purchase,0.1218,99.9,9.980448594,1.67,727,3090.041667,37,1.1,0,0,0,0 1,all_other,0.1148,263.75,10.73639668,15.1,737,2400.041667,13415,43.6,0,0,0,0 1,debt_consolidation,0.1287,373.33,11.04292184,19.99,692,3450.041667,9505,59.8,0,0,0,0 1,debt_consolidation,0.1287,504.5,10.54534144,21.98,702,1920.041667,15432,64,2,0,0,0 1,all_other,0.1253,702.8,10.46310334,6.65,727,1650.041667,7191,27,0,0,0,0 1,major_purchase,0.1183,297.38,10.08580911,20.65,732,2069.041667,2678,33.5,1,0,0,0 1,debt_consolidation,0.1496,840.15,11.02755639,20.16,702,3690,21111,75.7,3,0,0,0 1,all_other,0.1148,758.28,11.60823564,11.04,772,4920.041667,60184,46.7,0,0,0,0 1,all_other,0.0859,94.84,11.64780595,0.16,752,1956.041667,531,35.4,0,0,0,0 1,home_improvement,0.1114,131.22,10.96819829,5.86,717,4230.041667,823,5.1,0,0,0,0 1,credit_card,0.1253,267.74,11.28426673,7.81,692,3780.041667,10750,47.1,0,0,0,0 1,all_other,0.1392,170.69,10.42228135,15.68,687,1500,3450,32.2,1,0,0,0 1,debt_consolidation,0.1357,390.64,10.49127422,12.1,682,3540.041667,13782,22.7,0,0,0,0 1,home_improvement,0.1253,66.94,9.92329018,24.35,717,3390.041667,0,0,1,1,0,0 1,credit_card,0.1496,692.93,11.28978191,20.76,677,3240,22737,87.4,2,0,0,0 1,major_purchase,0.1114,492.06,11.04292184,9.35,767,5370.041667,19639,39.1,2,0,0,1 1,all_other,0.16,98.45,10.6454249,22.31,697,3006.041667,14850,99,0,0,0,0 1,debt_consolidation,0.0859,269.48,10.89673933,10.56,782,4110.041667,3168,15.1,0,0,0,0 1,home_improvement,0.1565,874.66,11.1124479,13.65,687,6510,11325,43.9,1,0,0,0 1,debt_consolidation,0.1183,215.38,10.37972173,22.25,722,4020,1604,72.9,0,0,0,0 1,home_improvement,0.1392,573.5,11.09522582,17.49,687,4620.041667,19158,64.7,1,1,0,0 1,debt_consolidation,0.1148,296.72,10.1266311,16.7,722,3390.041667,1557,51.9,0,0,0,0 1,credit_card,0.1218,719.28,12.36307639,12.88,732,7530.041667,58974,71.7,0,0,0,0 1,debt_consolidation,0.0859,379.33,11.3736634,18.8,772,4627.041667,17757,23.5,0,0,0,0 1,all_other,0.0894,190.63,10.26813067,15.08,722,2220,3322,21.3,0,0,0,0 1,debt_consolidation,0.1461,206.86,10.71441777,16.67,662,2310.041667,7367,79.8,2,0,0,0 1,debt_consolidation,0.1392,409.65,11.96400108,18.75,702,7110.041667,139636,95.6,0,0,0,0 1,debt_consolidation,0.1426,514.59,11.00429742,8.94,682,2400.041667,14917,64.3,0,0,0,0 1,debt_consolidation,0.1461,137.91,9.574983486,4.92,682,1350.041667,1737,48.2,1,0,0,0 1,debt_consolidation,0.1426,686.12,11.1124479,11.23,687,2220,20833,84.7,2,0,0,0 1,debt_consolidation,0.0859,221.28,10.6454249,13.86,757,5730.041667,0,0,0,0,0,0 1,small_business,0.1635,529.94,11.8493977,11.25,702,5512,21229,64.7,2,0,0,0 1,all_other,0.1461,96.54,9.501516334,3.14,672,15990,709,22.9,0,0,0,0 1,home_improvement,0.1287,168.17,11.58988651,16.04,682,5430,1405,23.4,1,0,0,0 1,debt_consolidation,0.1426,111.5,9.92329018,22.41,667,3450.041667,7986,81.5,1,0,1,1 1,debt_consolidation,0.1809,904.88,11.57119437,16.37,672,5106,22792,76.7,2,0,0,0 1,debt_consolidation,0.1287,403.6,10.59663473,19.32,697,2640.041667,10602,60.6,2,0,0,1 1,debt_consolidation,0.0774,199.8,9.655026193,12.69,757,5850,7633,28.8,0,1,0,0 1,debt_consolidation,0.1114,485.5,10.30895266,15.52,737,4020,8458,28.6,1,0,0,0 1,major_purchase,0.0894,254.18,10.51867319,21.41,742,14923,3276,11.3,1,0,0,0 1,debt_consolidation,0.1114,295.24,10.08580911,12.1,737,2370.041667,9107,63.2,1,0,0,0 1,debt_consolidation,0.1531,870.39,11.69524702,8.39,692,3480.041667,14927,39.1,0,0,0,0 1,all_other,0.1183,662.68,12.3883942,2.73,737,4800,1260,7.2,0,0,1,1 1,debt_consolidation,0.1183,53.02,11.05879518,12.26,712,3900.041667,3221,74.9,2,0,0,0 1,debt_consolidation,0.0894,254.18,11.00209984,3.62,747,3060.041667,7610,34,0,0,0,0 1,home_improvement,0.1148,593.44,11.42954386,7.84,752,14761.04167,2522,34.5,4,1,0,0 1,home_improvement,0.1183,828.35,12.3883942,14.77,757,3750.041667,51669,26.7,3,0,0,0 1,debt_consolidation,0.1426,343.06,11.08214255,3.42,677,3530.041667,10141,47.4,0,1,1,0 1,all_other,0.1287,336.34,11.35040654,15.8,697,5010.041667,7241,44.7,2,0,0,0 1,debt_consolidation,0.1719,107.24,9.862665558,5.13,667,1161,1441,32,0,1,0,0 1,debt_consolidation,0.0859,158.06,10.30895266,9.04,757,8160.041667,363,2,0,0,0,1 1,major_purchase,0.0774,312.19,10.28629797,21.4,772,4410,2656,5.2,0,0,0,0 1,all_other,0.1392,136.55,10.36121362,17.77,677,1590.041667,8768,61.7,1,0,0,0 1,debt_consolidation,0.1774,720.42,11.14908203,23.17,662,7170.041667,24390,64,0,0,0,0 1,debt_consolidation,0.1183,331.34,10.858999,10.78,762,4717,12356,52.6,1,0,0,0 1,all_other,0.1218,99.9,11.19821472,21.11,687,5310.041667,15421,67.6,0,0,0,0 1,all_other,0.1114,164.02,11.28978191,11.07,722,4800,918,35.3,1,0,0,0 1,small_business,0.1531,417.79,11.00209984,14.3,732,2371.041667,19068,89.9,0,0,0,0 1,debt_consolidation,0.0859,158.06,10.81977828,8.02,737,3780.041667,13736,30.7,1,0,0,0 1,small_business,0.1719,185.89,11.14186178,0.57,687,6840.041667,3594,83.6,1,0,1,0 1,all_other,0.0705,247.2,11.15625052,7.92,782,8760,5236,8.7,2,0,0,0 1,debt_consolidation,0.1253,334.67,11.36093911,10.81,702,3750.041667,23152,71.5,0,0,0,0 1,all_other,0.074,86.97,9.655026193,15.46,762,10443.04167,641,40.1,0,0,0,0 1,home_improvement,0.1531,870.39,12.32385568,1.35,702,6510,0,0,1,2,0,0 1,debt_consolidation,0.1183,662.68,11.56171563,13.87,757,5471,11179,3.5,0,0,0,0 1,debt_consolidation,0.1183,331.34,10.83958091,13.84,757,3060.041667,12375,76.6,0,0,0,0 1,all_other,0.1426,274.45,11.21721122,12.21,702,4380,35638,97.7,1,0,0,1 1,educational,0.0774,218.54,11.56171563,7.43,747,6930,31183,35.1,1,0,0,0 1,all_other,0.0705,231.75,11.21721122,4.26,772,6091.041667,25768,2.2,2,0,0,0 1,debt_consolidation,0.16,351.58,11.36674295,21.01,662,8100,37398,89.3,3,1,0,0 1,debt_consolidation,0.1253,368.13,10.6454249,18.89,702,3060.041667,6811,25.7,1,0,0,0 1,debt_consolidation,0.1148,494.53,10.69194491,12.19,742,2490,5737,29.4,0,0,0,0 1,educational,0.1183,397.61,10.42228135,22.57,712,5850,5744,55.8,0,0,0,0 1,debt_consolidation,0.1496,372.44,10.54534144,22.45,672,3367.041667,15146,77.3,1,1,0,0 1,all_other,0.1183,662.68,11.22524339,7.42,752,3480.041667,11300,47.1,1,0,0,0 1,credit_card,0.1253,284.47,10.6454249,21.97,707,3390.041667,8249,85.9,2,0,0,0 1,all_other,0.1496,519.7,10.51867319,14.14,677,4067,7186,55.7,1,0,0,1 1,debt_consolidation,0.1253,669.33,10.91508846,2.71,767,3870.041667,0,0,1,0,0,0 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1,debt_consolidation,0.1025,485.78,11.73606902,14.96,737,8490,8443,43.7,0,0,0,1 1,all_other,0.1273,201.4,10.95080655,7.73,687,4680.041667,0,0,0,1,2,0 1,all_other,0.1607,879.85,11.81303006,20.88,682,10620,13018,68.4,0,0,1,0 1,all_other,0.1459,121.49,10.49127422,18.9,662,8370,6968,60.1,0,0,1,0 1,debt_consolidation,0.1099,114.58,10.98529272,0.63,697,4890.041667,11279,19.5,1,1,0,0 1,all_other,0.1311,168.73,10.26813067,20.04,702,2070.041667,438,17.5,0,0,0,0 1,debt_consolidation,0.1645,884.44,12.10071213,13.96,687,9600.041667,16960,90.7,1,0,1,1 1,educational,0.1136,52.66,10.1266311,0.96,702,3210.041667,66,0.5,2,0,1,0 1,debt_consolidation,0.183,907.6,12.36734079,7.28,672,6570,28897,46.9,1,1,0,1 1,debt_consolidation,0.1273,134.27,10.59663473,9.36,697,4921.041667,4498,73.7,3,0,0,0 1,all_other,0.1422,428.56,10.46310334,12.75,712,3450.041667,6,1.5,0,0,0,0 1,debt_consolidation,0.0751,155.55,10.35469055,11.58,737,2071.041667,2493,9.9,2,0,0,0 1,debt_consolidation,0.0788,375.37,10.71441777,7.92,757,8640.041667,8647,26.2,1,0,0,0 1,credit_card,0.1099,818.38,11.3736634,22.83,737,7350,42752,39.2,3,0,0,0 1,all_other,0.0788,65.69,9.952277717,21.52,752,1688.041667,564,7.6,0,0,0,0 1,credit_card,0.0751,264.43,11.08214255,20.05,757,2910,4384,19.2,3,0,0,0 1,educational,0.1422,411.41,11.22524339,17.56,682,5581,17233,91.2,0,0,0,0 1,credit_card,0.1062,488.4,11.3736634,14.95,732,5430,33403,40.4,2,0,0,0 1,debt_consolidation,0.1311,242.97,10.2791805,9.68,697,4290.041667,6659,80.2,0,0,1,0 1,debt_consolidation,0.1459,449.77,11.8493977,11.07,677,5700.041667,4954,82.2,1,0,1,0 1,debt_consolidation,0.1496,103.94,10.50506754,23.84,677,1410,2207,45,1,0,0,0 1,debt_consolidation,0.1533,626.91,11.17043516,15.75,692,4080,24322,95,1,0,0,1 1,all_other,0.1025,323.85,11.15625052,9.09,747,4170.041667,2205,24.5,3,0,0,1 1,home_improvement,0.157,280.09,10.87426647,7.36,662,4770,10973,87.8,0,1,0,1 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1,debt_consolidation,0.1459,689.3,11.58058411,16.63,687,4170.041667,9431,87.3,2,0,0,0 1,credit_card,0.1062,130.24,10.22194128,5.76,707,3630,4423,40.2,1,0,0,1 1,debt_consolidation,0.1607,299.15,10.36722157,23.07,672,1489,9640,87.6,0,0,0,1 1,debt_consolidation,0.1496,692.93,12.46843691,12.58,687,9660.041667,38910,93.3,2,0,0,0 1,credit_card,0.0639,45.91,11.2515607,2.38,772,3510.041667,3947,7.6,0,0,0,0 1,debt_consolidation,0.0988,322.11,11.54482134,14.44,727,5730.041667,18111,69.1,0,0,0,0 1,credit_card,0.1533,348.29,11.33413367,19.23,677,5910,18533,90.4,0,0,0,0 1,credit_card,0.0988,128.85,10.858999,2.47,717,2383.041667,3187,37.1,0,0,0,0 1,debt_consolidation,0.1311,212.6,12.04355372,24.52,687,3479.041667,11011,88.1,2,0,0,0 1,debt_consolidation,0.1533,452.77,10.7480872,8.87,677,2820.041667,13222,91.8,1,0,1,0 1,debt_consolidation,0.0788,437.93,10.75790288,4.98,747,2940,4482,19.4,0,0,0,0 1,debt_consolidation,0.1273,839.16,11.3736634,10.62,712,4230.041667,19002,53.1,0,0,0,0 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1,all_other,0.1114,393.65,11.22524339,7.82,737,3179.958333,10320,8.9,1,0,0,1 1,home_improvement,0.1379,851.89,11.23848862,4.45,717,5249.958333,165,2.5,2,0,0,1 1,major_purchase,0.1287,840.83,11.45952469,0.13,757,2190,14,0.2,2,0,0,1 0,debt_consolidation,0.1608,35.2,10.30895266,23.84,642,2490,10125,90.4,1,0,0,0 0,debt_consolidation,0.1608,246.38,12.1521443,22.47,647,7212,150971,92.3,4,0,0,1 0,debt_consolidation,0.16,787.53,11.26446411,19.34,682,3780.041667,15897,66,5,0,1,0 0,educational,0.164,126.4,10.30895266,29.6,677,1643,3043,58.5,1,0,0,1 0,debt_consolidation,0.1482,242.05,10.71441777,19.12,652,2847,26531,52.6,33,0,0,1 0,debt_consolidation,0.1482,172.89,10.6454249,18.51,652,4020,9792,64,8,0,0,1 0,debt_consolidation,0.138,149.96,10.73639668,16.7,662,4339,314,12.6,4,1,0,0 0,all_other,0.1482,311.2,11.39414193,19.34,657,4139.958333,38329,77.3,0,0,0,1 0,debt_consolidation,0.1703,570.69,11.35510138,25.32,672,4149.958333,38781,54.5,3,0,0,0 0,credit_card,0.1324,404.03,12.20607265,21.31,697,5879.958333,152416,80.1,0,0,0,0 0,debt_consolidation,0.1229,236.81,10.1266311,15.55,672,990,10410,63.9,1,0,0,0 0,home_improvement,0.1229,250.15,10.73639668,3.03,672,983,5556,27.1,2,0,0,0 0,debt_consolidation,0.1387,231.98,11.51292546,14.64,657,3498,39411,97.8,1,0,0,0 0,all_other,0.1387,163.75,10.77895629,9.18,652,7556,4945,64.2,2,2,1,0 0,debt_consolidation,0.1671,385.27,10.91871823,26.52,667,2672.958333,21300,65.5,4,0,0,0 0,all_other,0.0775,156.11,11.15625052,8.81,772,6389.958333,0,7.3,4,0,0,0 0,all_other,0.0838,204.84,10.78931908,4,742,3661,0,2.9,3,0,0,0 0,all_other,0.0807,136.45,11.69524702,4,742,4080,0,22.1,6,0,0,0 0,all_other,0.0933,80.69,11.60823564,10,712,2819.958333,0,17.2,6,0,0,0 0,all_other,0.0933,124.62,11.49272276,10,707,4079.958333,0,56.8,1,0,1,0 0,all_other,0.1154,164.98,10.91508846,10,697,3190,2447,65.5,4,0,0,1 0,all_other,0.1028,164.42,11.46163217,10,702,2310,0,20.1,3,1,0,0 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0,all_other,0.0838,81.94,8.779557456,6.46,722,1230.041667,0,0.1,2,0,0,1 0,all_other,0.0743,93.23,11.28978191,0.39,797,4441,0,0.5,0,0,0,0 0,all_other,0.0743,77.69,11.10682015,10.36,787,5250.041667,0,1.3,5,0,0,0 0,all_other,0.0775,156.11,12.61153775,5.38,757,5641,0,23.5,4,0,0,0 0,all_other,0.1407,34.21,10.1266311,16.27,642,567.0416667,0,85,0,0,0,0 0,all_other,0.1091,45.78,10.59663473,8.61,672,8490.041667,0,0.5,0,0,1,0 0,all_other,0.1122,164.23,10.23995979,3.51,672,1139,0,56.2,4,1,0,0 0,all_other,0.0901,38.17,10.49127422,3.27,707,4680,0,2.2,5,1,0,0 0,debt_consolidation,0.0964,141.25,10.30895266,3.72,697,1050,3493,63.5,0,0,0,0 0,debt_consolidation,0.1059,195.28,9.903487553,12.9,697,4169.958333,13660,66,4,0,0,0 0,debt_consolidation,0.0712,61.87,11.91839057,5.6,802,8489.958333,150786,2.2,0,0,0,0 0,educational,0.147,345.18,10.81977828,7.22,652,2820,10025,85,0,2,0,0 0,debt_consolidation,0.0964,61,11.51292546,10,707,6360.041667,0,41.5,6,0,0,0 0,all_other,0.1312,180.57,8.29404964,15,662,178.9583333,0,47.8,1,0,0,0 0,credit_card,0.1312,202.51,9.392661929,4.4,657,898.9583333,5251,49.3,9,0,0,0 0,credit_card,0.128,67.2,9.210340372,3.48,647,1169.958333,571,61.9,0,1,0,0 0,debt_consolidation,0.1312,226.98,11.00209984,13.16,672,5880,5513,88.9,9,0,1,1 0,credit_card,0.1501,693.45,11.28978191,3.11,672,3570,7883,59.3,6,1,0,1 0,credit_card,0.1059,325.46,10.91508846,17.96,727,3180,6559,24.8,5,0,0,0 0,credit_card,0.1691,142.43,11.12726298,19.62,657,2009.958333,1943,25.6,3,1,0,0 0,all_other,0.1438,89.35,10.6454249,16.91,642,5549,4139,52.4,18,4,1,1 0,debt_consolidation,0.1091,114.44,9.61580548,11.52,682,990,3570,72.4,0,0,0,0 0,debt_consolidation,0.1186,265.17,11.05089001,10.42,687,6388.958333,7406,58.8,14,0,1,1 0,home_improvement,0.1312,135.01,10.37349118,7.91,652,5609.958333,9129,73.3,3,0,1,0 0,credit_card,0.147,345.18,11.08214255,6.37,647,1896,2211,73.7,6,0,0,0 0,educational,0.1028,38.88,8.29404964,8.1,682,368,900,37.5,1,0,0,0 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0,all_other,0.1531,167.12,10.95619516,24.39,667,4110.041667,35122,91.5,4,0,0,0 0,all_other,0.1287,336.34,11.56347598,5.59,732,7320,4547,68.9,6,0,0,0 0,debt_consolidation,0.1357,339.69,10.37349118,22.95,687,3150.041667,10624,62.9,4,0,0,0 0,all_other,0.1774,432.26,10.27642947,11.36,682,5671.041667,13328,93.9,7,0,1,0 0,all_other,0.1774,86.46,10.81977828,12.02,677,5850,388,24.2,9,0,0,0 0,major_purchase,0.1704,231.89,11.15625052,20.81,672,1440,7483,94.8,6,0,0,0 0,all_other,0.1635,211.98,9.798127037,10.6,667,2791.041667,4511,63.5,4,0,0,0 0,debt_consolidation,0.1357,823.74,12.20607265,14.3,722,5371.041667,155946,90.1,0,0,0,1 0,all_other,0.1287,67.27,10.13459927,13.24,682,3330,4925,40.7,4,3,0,0 0,debt_consolidation,0.1461,172.38,10.30895266,4.4,697,1112,2972,59.4,5,0,0,0 0,debt_consolidation,0.1843,727.4,11.65268741,5.25,662,9511,14548,87.6,4,0,1,0 0,small_business,0.1426,514.59,11.28978191,17.86,782,11220,235868,17.5,0,0,0,0 0,home_improvement,0.1843,436.44,10.37349118,0,667,6905,0,0,6,0,0,1 0,major_purchase,0.1392,136.55,10.34174248,13.24,682,5851,577,14.1,5,2,1,0 0,credit_card,0.1183,480.45,11.40756495,17.81,712,8160.041667,136960,55.6,1,0,0,0 0,small_business,0.1531,870.39,11.91839057,22.7,762,6960,248480,42.4,0,0,0,0 0,small_business,0.1635,169.58,10.56617553,5.35,692,3390.041667,837,34.9,4,0,0,0 0,all_other,0.1183,331.34,11.08214255,23.54,722,5460,28045,51.8,5,0,0,1 0,educational,0.1426,267.59,9.878169745,15.45,707,1710.041667,3910,38.2,6,1,0,1 0,debt_consolidation,0.1496,554.32,11.15625052,17.06,692,4230.041667,19697,88.7,5,0,0,0 0,debt_consolidation,0.1218,666,11.61728548,11.78,737,5972.041667,28749,53.9,4,0,0,0 0,home_improvement,0.1531,87.04,11.91839057,22.82,667,4110.041667,59509,85.8,5,0,0,0 0,debt_consolidation,0.1392,474.51,11.17605315,24.05,717,3210.041667,2061,34.3,7,0,0,0 0,all_other,0.167,461.52,11.61186541,11.7,667,8100,4040,91.8,5,2,1,0 0,debt_consolidation,0.1461,744.67,12.3883942,13.86,697,4470.041667,219250,91.8,1,0,0,1 0,educational,0.1287,121.08,10.08580911,3.4,707,1380.041667,2650,26.2,4,0,0,0 0,debt_consolidation,0.1322,338.01,11.28978191,16.84,697,3570.041667,13948,39,4,1,0,0 0,debt_consolidation,0.1322,253.51,10.70324447,22.52,702,4530.041667,7202,34.5,4,0,0,1 0,debt_consolidation,0.1218,466.2,10.73630972,5.32,727,2280.041667,11781,37.8,5,0,0,0 0,all_other,0.1287,67.27,10.08580911,20.45,687,2011.041667,7946,78.7,4,0,0,0 0,all_other,0.1287,840.83,11.28978191,12.97,732,1890,6999,29.2,5,0,0,0 0,debt_consolidation,0.1357,679.37,10.30895266,9.44,712,5390.041667,3366,14,4,0,0,0 0,all_other,0.1218,199.8,10.40426284,4.51,727,3570.041667,3106,25.9,5,0,0,0 0,credit_card,0.1253,384.87,11.00209984,11.56,722,3614,25088,75.6,5,0,0,1 0,all_other,0.1136,213.93,10.7342204,2.43,702,2070.041667,5319,46.7,4,0,0,0 0,all_other,0.1607,281.56,11.22524339,20.56,677,4080,21166,72.7,7,0,0,0 0,debt_consolidation,0.0988,233.53,11.73606902,7.97,727,4350,7248,40.5,4,0,0,0 0,debt_consolidation,0.1273,671.33,11.22524339,18.74,722,2340.041667,32837,64.8,4,0,0,0 0,debt_consolidation,0.1062,390.72,11.58988651,13.68,737,7470.041667,17042,46.7,5,1,0,0 0,all_other,0.1719,536.2,11.98292909,19.94,662,4440,226936,94.7,0,0,0,0 0,debt_consolidation,0.1062,398.86,11.15625052,24.27,717,9330.041667,74403,68.8,1,0,0,0 0,debt_consolidation,0.1843,691.03,11.12726298,20.12,662,1640.041667,1890,19.1,5,4,0,0 0,credit_card,0.1533,362.22,11.12726298,24.78,682,3710,46338,55.5,5,0,0,0 0,all_other,0.1422,102.86,10.93310697,24.32,717,4230.041667,6725,98.9,5,0,0,0 0,credit_card,0.1947,922.42,11.56171563,19.81,662,3380.041667,20622,76.4,5,0,0,0 0,all_other,0.1311,134.98,10.29552964,2.35,682,4530.041667,1465,34.9,4,0,0,0 0,credit_card,0.0788,437.93,11.82041016,15.87,757,6720.041667,232755,23,0,0,0,0 0,all_other,0.157,840.26,12.18075484,11.42,702,7290,79524,92.7,5,0,0,0 0,all_other,0.1062,146.52,10.62132735,7.02,707,8013,2282,69.2,4,0,0,1 0,debt_consolidation,0.1099,98.21,11.08214255,19.57,687,7350,237551,36.9,0,0,0,0 0,credit_card,0.1099,425.56,11.57119437,16.36,722,7500.041667,22354,65.6,4,0,0,0 0,major_purchase,0.2017,149.01,10.59663473,12.39,677,3140.041667,200,13.3,10,3,0,0 0,debt_consolidation,0.1273,335.67,10.40426284,19.05,702,6480.041667,8853,37.5,4,0,1,0 0,debt_consolidation,0.1533,870.71,11.85651517,23.82,707,4800,259128,94.4,0,0,0,0 0,debt_consolidation,0.1136,625.32,10.98529272,8.95,732,3750.041667,18995,62.9,4,0,0,0 0,small_business,0.1496,831.52,12.76568843,11.38,717,8520.041667,1207359,56,7,0,0,1 0,small_business,0.1348,542.79,12.61153775,3.12,737,7410.041667,211931,10,7,2,0,1 0,debt_consolidation,0.1607,114.38,10.64998591,16.64,672,5910,11854,71.9,7,0,1,0 0,credit_card,0.1422,244.28,11.56171563,0.7,687,7920.041667,505,9,6,1,0,0 0,small_business,0.1533,870.71,11.8493977,23.81,712,7775.041667,60048,80.1,5,0,0,0 0,debt_consolidation,0.1311,192.35,11.00209984,5.72,692,10236,2077,1.1,4,3,0,1 0,debt_consolidation,0.1025,323.85,11.17043516,13.98,732,3750,6639,43.6,4,0,0,0 0,all_other,0.1348,468.16,10.97287698,15.16,702,3180.041667,14466,50.2,5,0,0,0 0,debt_consolidation,0.1311,236.22,10.85707407,13.11,687,3270,8076,36.1,4,0,0,0 0,debt_consolidation,0.1348,271.4,11.19821472,12.44,682,4230.041667,11706,34.9,4,0,0,0 0,all_other,0.0788,78.21,10.81977828,20.38,732,7410.041667,16150,32.6,4,0,0,0 0,debt_consolidation,0.157,262.59,10.66895539,21.35,662,4260.041667,9659,84.7,4,0,0,0 0,educational,0.1607,147.82,9.862665558,16.19,667,1260.041667,4445,53.5,4,0,0,1 0,home_improvement,0.1607,87.99,10.77895629,14.2,667,4080,1530,36.4,7,0,0,1 0,home_improvement,0.2164,729.7,11.87756858,8.63,667,8280.041667,55442,66.9,9,0,1,1 0,all_other,0.1459,137.86,10.08580911,1.15,732,1230.041667,972,11.3,5,0,0,0 0,home_improvement,0.1348,508.87,11.73606902,16.85,707,7440.041667,206877,92.5,1,0,0,1 0,debt_consolidation,0.1311,337.45,10.69194491,23.62,702,3780.041667,6255,56.9,5,2,0,0 0,debt_consolidation,0.1385,545.67,11.77528973,10.8,697,4110,197716,74.9,4,0,0,0 0,small_business,0.1533,870.71,11.84222921,16.16,707,4230.041667,56909,49.8,5,0,0,0 0,home_improvement,0.1311,674.9,12.29225034,9.94,717,5730.041667,39576,27.7,5,0,0,1 0,debt_consolidation,0.1385,136.42,11.00209984,18.18,677,3423.041667,15301,85,4,0,0,0 0,credit_card,0.1025,466.35,12.20607265,13.97,722,6120.041667,338935,78.3,2,0,0,0 0,debt_consolidation,0.1533,696.57,11.80559508,17.21,682,2790.041667,38578,86.9,4,0,0,0 0,credit_card,0.1273,688.11,11.31447453,21.13,732,5881,35227,54.3,5,0,0,0 0,all_other,0.1867,547.36,11.40756495,15.76,667,10050.04167,13255,88.4,7,0,0,0 0,all_other,0.0788,115.74,10.99909533,10.17,722,4410,11586,61.6,4,0,0,0 0,debt_consolidation,0.1348,508.87,10.93310697,17.76,717,3870.041667,8760,28.2,6,0,0,0 0,debt_consolidation,0.1099,556.5,11.22524339,17.84,727,6840.041667,18753,29,4,0,0,1 0,all_other,0.1385,511.56,12.32385568,12.33,687,6420.041667,385489,51.2,4,0,0,0 0,all_other,0.1459,396.35,10.30895266,21.04,697,3390,26117,78.4,6,0,0,1 0,all_other,0.2164,551.08,11.00209984,24.06,662,1800,16441,49.8,9,0,0,1 0,all_other,0.1311,101.24,10.96819829,8.23,687,2790.041667,1514,13.8,5,0,0,0 0,all_other,0.1979,37.06,10.6454249,22.17,667,5916,28854,59.8,6,0,1,0 0,home_improvement,0.1426,823.34,12.4292162,3.62,722,3239.958333,33575,83.9,5,0,0,1 0,all_other,0.1671,113.63,10.6454249,28.06,672,3210.041667,25759,63.8,5,0,0,1 0,all_other,0.1568,161.01,11.22524339,8,677,7230,6909,29.2,4,0,1,1 0,debt_consolidation,0.1565,69.98,10.11047245,7.02,662,8190.041667,2999,39.5,6,0,0,1 0,all_other,0.1461,344.76,12.18075484,10.39,672,10474,215372,82.1,2,0,0,1 0,all_other,0.1253,257.7,11.14186178,0.21,722,4380,184,1.1,5,0,0,1 0,debt_consolidation,0.1071,97.81,10.59663473,13.09,687,3450.041667,10036,82.9,8,0,0,1 0,home_improvement,0.16,351.58,10.81977828,19.18,692,1800,0,3.2,5,0,0,1 0,debt_consolidation,0.1392,853.43,11.26446411,16.28,732,4740,37879,57,6,0,0,1
Final Project/Spr2019_Decision_Trees_Project1.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Decision Trees Project \n", "\n", "For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.\n", "\n", "Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public.\n", "\n", "We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values.\n", "\n", "Here are what the columns represent:\n", "* credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n", "* purpose: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n", "* int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n", "* installment: The monthly installments owed by the borrower if the loan is funded.\n", "* log.annual.inc: The natural log of the self-reported annual income of the borrower.\n", "* dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n", "* fico: The FICO credit score of the borrower.\n", "* days.with.cr.line: The number of days the borrower has had a credit line.\n", "* revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n", "* revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n", "* inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.\n", "* delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n", "* pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).\n", "\n", "* not.fully.paid : This is the class atribute you will build a decision tree to predict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import Libraries\n", "\n", "**Import the usual libraries for pandas and plotting. You can import sklearn later on.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the Data\n", "\n", "** Use pandas to read loan_data.csv as a dataframe called loans.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "loans = pd.read_csv('loan_data.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Check out the info(), head(), and describe() methods on loans.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "loans.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploratory Data Analysis \n", "\n", "Let's do some data visualization! We'll use seaborn and pandas built-in plotting capabilities, but feel free to use whatever library you want. Don't worry about the colors matching, just worry about getting the main idea of the plot.\n", "\n", "** Create a histogram of two FICO distributions on top of each other, one for each credit.policy outcome.**\n", "\n", "*Note: This is pretty tricky, feel free to reference the solutions. You'll probably need one line of code for each histogram, I also recommend just using pandas built in .hist()*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(10,6))\n", "loans[loans['credit.policy']==1]['fico'].hist(alpha=0.5,color='blue',\n", " bins=30,label='Credit.Policy=1')\n", "loans[loans['credit.policy']==0]['fico'].hist(alpha=0.5,color='red',\n", " bins=30,label='Credit.Policy=0')\n", "plt.legend()\n", "plt.xlabel('FICO')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Create a similar figure, except this time select by the not.fully.paid column.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(10,6))\n", "loans[loans['not.fully.paid']==1]['fico'].hist(alpha=0.5,color='blue',\n", " bins=30,label='not.fully.paid=1')\n", "loans[loans['not.fully.paid']==0]['fico'].hist(alpha=0.5,color='red',\n", " bins=30,label='not.fully.paid=0')\n", "plt.legend()\n", "plt.xlabel('FICO')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Create a countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid. **" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(11,7))\n", "sns.countplot(x='purpose',hue='not.fully.paid',data=loans,palette='Set1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Create more visualizations and interperet the results in the first part of the report.** \n", "You can create a scatterplot of pairs of attributes like we did in class example but since there are more attributes here, it will take longer to create them and will be harder to analyze, but if you clcik on images you can zoom them...\n", "Best solution is to create a subset of the dataset with some of the attributes and then create scatterplots. you can use what we used in class::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**this will take 1 minute to create graphs just wait until they show up. Agin this is not very practical, create a subset of attributes, and execute the following code on that new dataframe**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.pairplot(loans.dropna(), hue='credit.policy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### *****FIRST PART OF YOUR REPORT. ANALYSE AND DISCUSS THESE SUMAARY STATISTICS and THESE GRAPHS.****\n", "### What do they tell you about your data? About the relationship of the attributes, etc.." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting up the Data\n", "\n", "Let's get ready to set up our data for our Random Forest Classification Model!\n", "\n", "**Check loans.info() again.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical Features\n", "\n", "Notice that the **purpose** column as categorical\n", "\n", "That means we need to transform them using dummy variables so sklearn will be able to understand them. Let's do this in one clean step using pd.get_dummies.\n", "\n", "Let's show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary.\n", "\n", "**Create a list of 1 element containing the string 'purpose'. Call this list cat_feats.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cat_feats = ['purpose']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now use pd.get_dummies(loans,columns=cat_feats,drop_first=True) to create a fixed larger dataframe that has new feature columns with dummy variables. Set this dataframe as final_data.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**if you call head() now you will see that pandas created dummy variables called \n", "purpose_credit_card\tpurpose_debt_consolidation\tpurpose_educational, tc..**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "final_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Test Split\n", "\n", "Now its time to split our data into a training set and a testing set!\n", "\n", "** Use sklearn to split your data into a training set and a testing set as we've done in the past. Do not forget you class attribute is \"not.fully.paid\" **\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training a Decision Tree Model\n", "\n", "Let's start by training a single decision tree first!\n", "\n", "** Import DecisionTreeClassifier**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictions and Evaluation of Decision Tree\n", "**Create predictions from the test set and create a classification report and a confusion matrix.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tree Visualization \n", "\n", "Visualize the decision tree just like we did in class." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improve your results\n", "\n", "Using different values for the parameters come with DecisionTreeClassifier() method, try to improve your results.." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### *****SECOND PART OF YOUR REPORT: ANALYSE AND DISCUSS THESE HOW YOU IMPROVED YOUR RESULTS, WHAT HELPED AND WHY?****" ] }, { "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": 1 }
Final Project/Spr2019_Decision_Trees_Project2.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Decision Trees using Iris Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Iris Dataset\n", "\n", "You are working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\n", "\n", "We've been tasked by our company's Head of Data Science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\n", "\n", "<img src=\"images/petal_sepal.jpg\" />\n", "\n", "We've been given a data set from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\n", "\n", "### *Iris setosa*\n", "\n", "<img src=\"images/iris_setosa.jpg\" />\n", "\n", "### *Iris versicolor*\n", "<img src=\"images/iris_versicolor.jpg\" />\n", "\n", "### *Iris virginica*\n", "<img src=\"images/iris_virginica.jpg\" />\n", "\n", "The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future.\n", "\n", "**Note:** The data set we're working with is the famous UCI Machine Learning Repository [*Iris* data set](https://archive.ics.uci.edu/ml/datasets/Iris) — included with this notebook — which I have modified slightly for demonstration purposes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Section 1 - Reading Data and Creating ScatterPlot\n", "\n", "\n", "\n", "Import the required libraries" ] }, { "cell_type": "code", "execution_count": 11, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "df = pd.read_csv('iris.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now read your data into dataframe and use head() method to check your data" ] }, { "cell_type": "code", "execution_count": 12, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()\n", "#read the dataset file into a dataframe\n", "#display the head() of the dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Print out some summary statistics about the data set." ] }, { "cell_type": "code", "execution_count": 13, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>5.843333</td>\n", " <td>3.054000</td>\n", " <td>3.758667</td>\n", " <td>1.198667</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.828066</td>\n", " <td>0.433594</td>\n", " <td>1.764420</td>\n", " <td>0.763161</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>4.300000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0.100000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>5.100000</td>\n", " <td>2.800000</td>\n", " <td>1.600000</td>\n", " <td>0.300000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>5.800000</td>\n", " <td>3.000000</td>\n", " <td>4.350000</td>\n", " <td>1.300000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>6.400000</td>\n", " <td>3.300000</td>\n", " <td>5.100000</td>\n", " <td>1.800000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>7.900000</td>\n", " <td>4.400000</td>\n", " <td>6.900000</td>\n", " <td>2.500000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "count 150.000000 150.000000 150.000000 150.000000\n", "mean 5.843333 3.054000 3.758667 1.198667\n", "std 0.828066 0.433594 1.764420 0.763161\n", "min 4.300000 2.000000 1.000000 0.100000\n", "25% 5.100000 2.800000 1.600000 0.300000\n", "50% 5.800000 3.000000 4.350000 1.300000\n", "75% 6.400000 3.300000 5.100000 1.800000\n", "max 7.900000 4.400000 6.900000 2.500000" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\n", "\n", "We can even have the plotting package color each entry by its class to look for trends within the classes.\n", "**I am giving you the answer since the main assignment is after this**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 804.75x720 with 20 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "sns.pairplot(df.dropna(), hue='species') #this would only work if you called your dataframe df\n", ";" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FIRST PART OF YOUR REPORT. ANALYSE AND DISCUSS THESE SUMAARY STATISTICS and THESE GRAPHS.\n", "### What do they tell you about your data? About the relationship of the attributes, etc.." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now, complete the following sections similar to example we did in class.**\n", "##NOTE: The number of cells below does not mean the number of cells you need, you might complete the task with less or more cells" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EDA" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 10))\n", "\n", "for column_index, column in enumerate(df):\n", " if column == 'species':\n", " continue\n", " plt.subplot(2, 2, column_index + 1)\n", " sb.violinplot(x='species', y=column, data=df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Test Split\n", "\n", "Let's split up the data into a training set and a test set!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision Trees\n", "\n", "We'll start just by training a single decision tree." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prediction and Evaluation \n", "\n", "Let's evaluate our decision tree." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tree Visualization" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improve your results\n", "\n", "***Using different values for the parameters come with DecisionTreeClassifier() method, try to improve your results..***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### *****SECOND PART OF YOUR REPORT: ANALYSE AND DISCUSS THESE HOW YOU IMPROVED YOUR RESULTS, WHAT HELPED AND WHY?****" ] }, { "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": 1 }
