├── README.md ├── IRIS Pattern Recognition ├── iris.py └── data.csv ├── China GDP Estimation └── china_gdp.csv ├── Loan Prediction ├── main.py └── loan.csv ├── Bigmart Sales Prediction ├── main.py └── test.csv ├── CO2 Emissions └── Simple Multivariate Linear Regression │ └── C02Emissions.ipynb ├── Drug Prediction └── DecisionTrees.ipynb └── Customer Churn ├── KNN.ipynb └── teleCust1000t.csv /README.md: -------------------------------------------------------------------------------- 1 | # Data Analytics Projects 2 | 3 | ### Machine Learning concepts using Python with real world datasets. 4 | 5 | 1. [Customer Churn](https://github.com/arjunmann73/Machine-Learning/tree/master/Customer%20Churn): K Nearest Neighbours 6 | 2. [CO2 Emissions](https://github.com/arjunmann73/Machine-Learning/tree/master/CO2%20Emissions): Linear Regression 7 | 3. [China-GDP Estimation](https://github.com/arjunmann73/Machine-Learning/tree/master/China%20GDP%20Estimation): Non Linear Regression 8 | 4. [Drug Prediction](https://github.com/arjunmann73/Machine-Learning/tree/master/Drug%20Prediction): Decision Tree 9 | 5. [IBM Classification Project](https://github.com/arjunmann73/Machine-Learning/tree/master/IBM%20Classification%20Project): KNN, SVM, Decision Tree 10 | 6. [IRIS Pattern Recognition](https://github.com/arjunmann73/Data-Analytics-Projects/tree/master/IRIS%20Pattern%20Recognition): Logistic Regression 11 | 7. [Loan Prediction](https://github.com/arjunmann73/Data-Analytics-Projects/tree/master/Loan%20Prediction): Logistic Regression 12 | 8. [Bigmart Sales Prediction](https://github.com/arjunmann73/Data-Analytics-Projects/tree/master/Bigmart%20Sales%20Prediction): Multivariate Linear Regression 13 | -------------------------------------------------------------------------------- /IRIS Pattern Recognition/iris.py: -------------------------------------------------------------------------------- 1 | ''' 2 | IRIS DATASET 3 | ''' 4 | 5 | # required libraries 6 | import pandas as pd 7 | from sklearn.linear_model import LogisticRegression 8 | from sklearn.model_selection import train_test_split 9 | from sklearn.preprocessing import LabelEncoder 10 | from sklearn.metrics import accuracy_score 11 | 12 | 13 | # read the dataset 14 | data = pd.read_csv('data.csv') 15 | print(data.head()) 16 | 17 | print('\n\nColumn Names\n\n') 18 | print(data.columns) 19 | 20 | #label encode the target variable 21 | encode = LabelEncoder() 22 | data.Species = encode.fit_transform(data.Species) 23 | 24 | print(data.head()) 25 | 26 | # train-test-split 27 | train , test = train_test_split(data,test_size=0.2,random_state=0) 28 | 29 | print('shape of training data : ',train.shape) 30 | print('shape of testing data',test.shape) 31 | 32 | # seperate the target and independent variable 33 | train_x = train.drop(columns=['Species'],axis=1) 34 | train_y = train['Species'] 35 | 36 | test_x = test.drop(columns=['Species'],axis=1) 37 | test_y = test['Species'] 38 | 39 | # create the object of the model 40 | model = LogisticRegression() 41 | 42 | model.fit(train_x,train_y) 43 | 44 | predict = model.predict(test_x) 45 | 46 | print('Predicted Values on Test Data',encode.inverse_transform(predict)) 47 | 48 | print('\n\nAccuracy Score on test data : \n\n') 49 | print(accuracy_score(test_y,predict)) 50 | 51 | -------------------------------------------------------------------------------- /China GDP Estimation/china_gdp.csv: -------------------------------------------------------------------------------- 1 | Year,Value 2 | 1960,59184116488.9977 3 | 1961,49557050182.9631 4 | 1962,46685178504.3274 5 | 1963,50097303271.0232 6 | 1964,59062254890.1871 7 | 1965,69709153115.3147 8 | 1966,75879434776.1831 9 | 1967,72057028559.6741 10 | 1968,69993497892.3132 11 | 1969,78718820477.9257 12 | 1970,91506211306.3745 13 | 1971,98562023844.1813 14 | 1972,112159813640.376 15 | 1973,136769878359.668 16 | 1974,142254742077.706 17 | 1975,161162492226.686 18 | 1976,151627687364.405 19 | 1977,172349014326.931 20 | 1978,148382111520.192 21 | 1979,176856525405.729 22 | 1980,189649992463.987 23 | 1981,194369049090.197 24 | 1982,203549627211.606 25 | 1983,228950200773.115 26 | 1984,258082147252.256 27 | 1985,307479585852.339 28 | 1986,298805792971.544 29 | 1987,271349773463.863 30 | 1988,310722213686.031 31 | 1989,345957485871.286 32 | 1990,358973230048.399 33 | 1991,381454703832.753 34 | 1992,424934065934.066 35 | 1993,442874596387.119 36 | 1994,562261129868.774 37 | 1995,732032045217.766 38 | 1996,860844098049.121 39 | 1997,958159424835.34 40 | 1998,1025276902078.73 41 | 1999,1089447108705.89 42 | 2000,1205260678391.96 43 | 2001,1332234719889.82 44 | 2002,1461906487857.92 45 | 2003,1649928718134.59 46 | 2004,1941745602165.09 47 | 2005,2268598904116.28 48 | 2006,2729784031906.09 49 | 2007,3523094314820.9 50 | 2008,4558431073438.2 51 | 2009,5059419738267.41 52 | 2010,6039658508485.59 53 | 2011,7492432097810.11 54 | 2012,8461623162714.07 55 | 2013,9490602600148.49 56 | 2014,10354831729340.4 57 | -------------------------------------------------------------------------------- /Loan Prediction/main.py: -------------------------------------------------------------------------------- 1 | ''' 2 | LOAN DATASET 3 | ''' 4 | 5 | # required libraries 6 | import pandas as pd 7 | from sklearn.linear_model import LogisticRegression 8 | from sklearn.model_selection import train_test_split 9 | from sklearn.preprocessing import LabelEncoder 10 | from sklearn.metrics import accuracy_score 11 | 12 | 13 | # read the dataset 14 | data = pd.read_csv('loan.csv') 15 | print(data.head()) 16 | 17 | print('\n\nColumn Names\n\n') 18 | print(data.columns) 19 | 20 | #label encode the target variable 21 | encode = LabelEncoder() 22 | data.Loan_Status = encode.fit_transform(data.Loan_Status) 23 | 24 | # drop the null values 25 | data.dropna(how='any',inplace=True) 26 | 27 | 28 | # train-test-split 29 | train , test = train_test_split(data,test_size=0.2,random_state=0) 30 | 31 | 32 | 33 | # seperate the target and independent variable 34 | train_x = train.drop(columns=['Loan_ID','Loan_Status'],axis=1) 35 | train_y = train['Loan_Status'] 36 | 37 | test_x = test.drop(columns=['Loan_ID','Loan_Status'],axis=1) 38 | test_y = test['Loan_Status'] 39 | 40 | # encode the data 41 | train_x = pd.get_dummies(train_x) 42 | test_x = pd.get_dummies(test_x) 43 | 44 | print('shape of training data : ',train_x.shape) 45 | print('shape of testing data : ',test_x.shape) 46 | 47 | # create the object of the model 48 | model = LogisticRegression() 49 | 50 | model.fit(train_x,train_y) 51 | 52 | predict = model.predict(test_x) 53 | 54 | print('Predicted Values on Test Data',predict) 55 | 56 | print('\n\nAccuracy Score on test data : \n\n') 57 | print(accuracy_score(test_y,predict)) 58 | -------------------------------------------------------------------------------- /Bigmart Sales Prediction/main.py: -------------------------------------------------------------------------------- 1 | ''' 2 | The following code is for the Linear Regression 3 | Created by- ANALYTICS VIDHYA 4 | ''' 5 | 6 | # importing required libraries 7 | import pandas as pd 8 | from sklearn.linear_model import LinearRegression 9 | from sklearn.metrics import mean_squared_error 10 | 11 | # read the train and test dataset 12 | train_data = pd.read_csv('train.csv') 13 | test_data = pd.read_csv('test.csv') 14 | 15 | print(train_data.head()) 16 | 17 | # shape of the dataset 18 | print('\nShape of training data :',train_data.shape) 19 | print('\nShape of testing data :',test_data.shape) 20 | 21 | # Now, we need to predict the missing target variable in the test data 22 | # target variable - Item_Outlet_Sales 23 | 24 | # seperate the independent and target variable on training data 25 | train_x = train_data.drop(columns=['Item_Outlet_Sales'],axis=1) 26 | train_y = train_data['Item_Outlet_Sales'] 27 | 28 | # seperate the independent and target variable on training data 29 | test_x = test_data.drop(columns=['Item_Outlet_Sales'],axis=1) 30 | test_y = test_data['Item_Outlet_Sales'] 31 | 32 | ''' 33 | Create the object of the Linear Regression model 34 | You can also add other parameters and test your code here 35 | Some parameters are : fit_intercept and normalize 36 | Documentation of sklearn LinearRegression: 37 | 38 | https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html 39 | 40 | ''' 41 | model = LinearRegression() 42 | 43 | # fit the model with the training data 44 | model.fit(train_x,train_y) 45 | 46 | # coefficeints of the trained model 47 | print('\nCoefficient of model :', model.coef_) 48 | 49 | # intercept of the model 50 | print('\nIntercept of model',model.intercept_) 51 | 52 | # predict the target on the test dataset 53 | predict_train = model.predict(train_x) 54 | print('\nItem_Outlet_Sales on training data',predict_train) 55 | 56 | # Root Mean Squared Error on training dataset 57 | rmse_train = mean_squared_error(train_y,predict_train)**(0.5) 58 | print('\nRMSE on train dataset : ', rmse_train) 59 | 60 | # predict the target on the testing dataset 61 | predict_test = model.predict(test_x) 62 | print('\nItem_Outlet_Sales on test data',predict_test) 63 | 64 | # Root Mean Squared Error on testing dataset 65 | rmse_test = mean_squared_error(test_y,predict_test)**(0.5) 66 | print('\nRMSE on test dataset : ', rmse_test) 67 | -------------------------------------------------------------------------------- /IRIS Pattern Recognition/data.csv: -------------------------------------------------------------------------------- 1 | "SepalLengthCm","SepalWidthCm","PetalLengthCm","PetalWidthCm","Species" 2 | 5.1,3.5,1.4,0.2,"Iris-setosa" 3 | 4.9,3,1.4,0.2,"Iris-setosa" 4 | 4.7,3.2,1.3,0.2,"Iris-setosa" 5 | 4.6,3.1,1.5,0.2,"Iris-setosa" 6 | 5,3.6,1.4,0.2,"Iris-setosa" 7 | 5.4,3.9,1.7,0.4,"Iris-setosa" 8 | 4.6,3.4,1.4,0.3,"Iris-setosa" 9 | 5,3.4,1.5,0.2,"Iris-setosa" 10 | 4.4,2.9,1.4,0.2,"Iris-setosa" 11 | 4.9,3.1,1.5,0.1,"Iris-setosa" 12 | 5.4,3.7,1.5,0.2,"Iris-setosa" 13 | 4.8,3.4,1.6,0.2,"Iris-setosa" 14 | 4.8,3,1.4,0.1,"Iris-setosa" 15 | 4.3,3,1.1,0.1,"Iris-setosa" 16 | 5.8,4,1.2,0.2,"Iris-setosa" 17 | 5.7,4.4,1.5,0.4,"Iris-setosa" 18 | 5.4,3.9,1.3,0.4,"Iris-setosa" 19 | 5.1,3.5,1.4,0.3,"Iris-setosa" 20 | 5.7,3.8,1.7,0.3,"Iris-setosa" 21 | 5.1,3.8,1.5,0.3,"Iris-setosa" 22 | 5.4,3.4,1.7,0.2,"Iris-setosa" 23 | 5.1,3.7,1.5,0.4,"Iris-setosa" 24 | 4.6,3.6,1,0.2,"Iris-setosa" 25 | 5.1,3.3,1.7,0.5,"Iris-setosa" 26 | 4.8,3.4,1.9,0.2,"Iris-setosa" 27 | 5,3,1.6,0.2,"Iris-setosa" 28 | 5,3.4,1.6,0.4,"Iris-setosa" 29 | 5.2,3.5,1.5,0.2,"Iris-setosa" 30 | 5.2,3.4,1.4,0.2,"Iris-setosa" 31 | 4.7,3.2,1.6,0.2,"Iris-setosa" 32 | 4.8,3.1,1.6,0.2,"Iris-setosa" 33 | 5.4,3.4,1.5,0.4,"Iris-setosa" 34 | 5.2,4.1,1.5,0.1,"Iris-setosa" 35 | 5.5,4.2,1.4,0.2,"Iris-setosa" 36 | 4.9,3.1,1.5,0.1,"Iris-setosa" 37 | 5,3.2,1.2,0.2,"Iris-setosa" 38 | 5.5,3.5,1.3,0.2,"Iris-setosa" 39 | 4.9,3.1,1.5,0.1,"Iris-setosa" 40 | 4.4,3,1.3,0.2,"Iris-setosa" 41 | 5.1,3.4,1.5,0.2,"Iris-setosa" 42 | 5,3.5,1.3,0.3,"Iris-setosa" 43 | 4.5,2.3,1.3,0.3,"Iris-setosa" 44 | 4.4,3.2,1.3,0.2,"Iris-setosa" 45 | 5,3.5,1.6,0.6,"Iris-setosa" 46 | 5.1,3.8,1.9,0.4,"Iris-setosa" 47 | 4.8,3,1.4,0.3,"Iris-setosa" 48 | 5.1,3.8,1.6,0.2,"Iris-setosa" 49 | 4.6,3.2,1.4,0.2,"Iris-setosa" 50 | 5.3,3.7,1.5,0.2,"Iris-setosa" 51 | 5,3.3,1.4,0.2,"Iris-setosa" 52 | 7,3.2,4.7,1.4,"Iris-versicolor" 53 | 6.4,3.2,4.5,1.5,"Iris-versicolor" 54 | 6.9,3.1,4.9,1.5,"Iris-versicolor" 55 | 5.5,2.3,4,1.3,"Iris-versicolor" 56 | 6.5,2.8,4.6,1.5,"Iris-versicolor" 57 | 5.7,2.8,4.5,1.3,"Iris-versicolor" 58 | 6.3,3.3,4.7,1.6,"Iris-versicolor" 59 | 4.9,2.4,3.3,1,"Iris-versicolor" 60 | 6.6,2.9,4.6,1.3,"Iris-versicolor" 61 | 5.2,2.7,3.9,1.4,"Iris-versicolor" 62 | 5,2,3.5,1,"Iris-versicolor" 63 | 5.9,3,4.2,1.5,"Iris-versicolor" 64 | 6,2.2,4,1,"Iris-versicolor" 65 | 6.1,2.9,4.7,1.4,"Iris-versicolor" 66 | 5.6,2.9,3.6,1.3,"Iris-versicolor" 67 | 6.7,3.1,4.4,1.4,"Iris-versicolor" 68 | 5.6,3,4.5,1.5,"Iris-versicolor" 69 | 5.8,2.7,4.1,1,"Iris-versicolor" 70 | 6.2,2.2,4.5,1.5,"Iris-versicolor" 71 | 5.6,2.5,3.9,1.1,"Iris-versicolor" 72 | 5.9,3.2,4.8,1.8,"Iris-versicolor" 73 | 6.1,2.8,4,1.3,"Iris-versicolor" 74 | 6.3,2.5,4.9,1.5,"Iris-versicolor" 75 | 6.1,2.8,4.7,1.2,"Iris-versicolor" 76 | 6.4,2.9,4.3,1.3,"Iris-versicolor" 77 | 6.6,3,4.4,1.4,"Iris-versicolor" 78 | 6.8,2.8,4.8,1.4,"Iris-versicolor" 79 | 6.7,3,5,1.7,"Iris-versicolor" 80 | 6,2.9,4.5,1.5,"Iris-versicolor" 81 | 5.7,2.6,3.5,1,"Iris-versicolor" 82 | 5.5,2.4,3.8,1.1,"Iris-versicolor" 83 | 5.5,2.4,3.7,1,"Iris-versicolor" 84 | 5.8,2.7,3.9,1.2,"Iris-versicolor" 85 | 6,2.7,5.1,1.6,"Iris-versicolor" 86 | 5.4,3,4.5,1.5,"Iris-versicolor" 87 | 6,3.4,4.5,1.6,"Iris-versicolor" 88 | 6.7,3.1,4.7,1.5,"Iris-versicolor" 89 | 6.3,2.3,4.4,1.3,"Iris-versicolor" 90 | 5.6,3,4.1,1.3,"Iris-versicolor" 91 | 5.5,2.5,4,1.3,"Iris-versicolor" 92 | 5.5,2.6,4.4,1.2,"Iris-versicolor" 93 | 6.1,3,4.6,1.4,"Iris-versicolor" 94 | 5.8,2.6,4,1.2,"Iris-versicolor" 95 | 5,2.3,3.3,1,"Iris-versicolor" 96 | 5.6,2.7,4.2,1.3,"Iris-versicolor" 97 | 5.7,3,4.2,1.2,"Iris-versicolor" 98 | 5.7,2.9,4.2,1.3,"Iris-versicolor" 99 | 6.2,2.9,4.3,1.3,"Iris-versicolor" 100 | 5.1,2.5,3,1.1,"Iris-versicolor" 101 | 5.7,2.8,4.1,1.3,"Iris-versicolor" 102 | 6.3,3.3,6,2.5,"Iris-virginica" 103 | 5.8,2.7,5.1,1.9,"Iris-virginica" 104 | 7.1,3,5.9,2.1,"Iris-virginica" 105 | 6.3,2.9,5.6,1.8,"Iris-virginica" 106 | 6.5,3,5.8,2.2,"Iris-virginica" 107 | 7.6,3,6.6,2.1,"Iris-virginica" 108 | 4.9,2.5,4.5,1.7,"Iris-virginica" 109 | 7.3,2.9,6.3,1.8,"Iris-virginica" 110 | 6.7,2.5,5.8,1.8,"Iris-virginica" 111 | 7.2,3.6,6.1,2.5,"Iris-virginica" 112 | 6.5,3.2,5.1,2,"Iris-virginica" 113 | 6.4,2.7,5.3,1.9,"Iris-virginica" 114 | 6.8,3,5.5,2.1,"Iris-virginica" 115 | 5.7,2.5,5,2,"Iris-virginica" 116 | 5.8,2.8,5.1,2.4,"Iris-virginica" 117 | 6.4,3.2,5.3,2.3,"Iris-virginica" 118 | 6.5,3,5.5,1.8,"Iris-virginica" 119 | 7.7,3.8,6.7,2.2,"Iris-virginica" 120 | 7.7,2.6,6.9,2.3,"Iris-virginica" 121 | 6,2.2,5,1.5,"Iris-virginica" 122 | 6.9,3.2,5.7,2.3,"Iris-virginica" 123 | 5.6,2.8,4.9,2,"Iris-virginica" 124 | 7.7,2.8,6.7,2,"Iris-virginica" 125 | 6.3,2.7,4.9,1.8,"Iris-virginica" 126 | 6.7,3.3,5.7,2.1,"Iris-virginica" 127 | 7.2,3.2,6,1.8,"Iris-virginica" 128 | 6.2,2.8,4.8,1.8,"Iris-virginica" 129 | 6.1,3,4.9,1.8,"Iris-virginica" 130 | 6.4,2.8,5.6,2.1,"Iris-virginica" 131 | 7.2,3,5.8,1.6,"Iris-virginica" 132 | 7.4,2.8,6.1,1.9,"Iris-virginica" 133 | 7.9,3.8,6.4,2,"Iris-virginica" 134 | 6.4,2.8,5.6,2.2,"Iris-virginica" 135 | 6.3,2.8,5.1,1.5,"Iris-virginica" 136 | 6.1,2.6,5.6,1.4,"Iris-virginica" 137 | 7.7,3,6.1,2.3,"Iris-virginica" 138 | 6.3,3.4,5.6,2.4,"Iris-virginica" 139 | 6.4,3.1,5.5,1.8,"Iris-virginica" 140 | 6,3,4.8,1.8,"Iris-virginica" 141 | 6.9,3.1,5.4,2.1,"Iris-virginica" 142 | 6.7,3.1,5.6,2.4,"Iris-virginica" 143 | 6.9,3.1,5.1,2.3,"Iris-virginica" 144 | 5.8,2.7,5.1,1.9,"Iris-virginica" 145 | 6.8,3.2,5.9,2.3,"Iris-virginica" 146 | 6.7,3.3,5.7,2.5,"Iris-virginica" 147 | 6.7,3,5.2,2.3,"Iris-virginica" 148 | 6.3,2.5,5,1.9,"Iris-virginica" 149 | 6.5,3,5.2,2,"Iris-virginica" 150 | 6.2,3.4,5.4,2.3,"Iris-virginica" 151 | 5.9,3,5.1,1.8,"Iris-virginica" 152 | -------------------------------------------------------------------------------- /CO2 Emissions/Simple Multivariate Linear Regression/C02Emissions.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Multivariate Linear Regression using Scikit-Learn" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Importing required packages" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import matplotlib.pyplot as plt\n", 24 | "import pandas as pd\n", 25 | "import pylab as pl\n", 26 | "import numpy as np\n", 27 | "%matplotlib inline" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "## Downloading the dataset" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 4, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | "--2019-09-18 12:57:47-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv\n", 47 | "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n", 48 | "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n", 49 | "HTTP request sent, awaiting response... 200 OK\n", 50 | "Length: 72629 (71K) [text/csv]\n", 51 | "Saving to: ‘FuelConsumption.csv’\n", 52 | "\n", 53 | "FuelConsumption.csv 100%[===================>] 70.93K 81.1KB/s in 0.9s \n", 54 | "\n", 55 | "2019-09-18 12:57:50 (81.1 KB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n", 56 | "\n" 57 | ] 58 | } 59 | ], 60 | "source": [ 61 | "!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": {}, 67 | "source": [ 68 | "## Reading the data into a Panda's DataFrame" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 6, 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "data": { 78 | "text/html": [ 79 | "
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MODELYEARMAKEMODELVEHICLECLASSENGINESIZECYLINDERSTRANSMISSIONFUELTYPEFUELCONSUMPTION_CITYFUELCONSUMPTION_HWYFUELCONSUMPTION_COMBFUELCONSUMPTION_COMB_MPGCO2EMISSIONS
02014ACURAILXCOMPACT2.04AS5Z9.96.78.533196
12014ACURAILXCOMPACT2.44M6Z11.27.79.629221
22014ACURAILX HYBRIDCOMPACT1.54AV7Z6.05.85.948136
32014ACURAMDX 4WDSUV - SMALL3.56AS6Z12.79.111.125255
42014ACURARDX AWDSUV - SMALL3.56AS6Z12.18.710.627244
\n", 195 | "
" 196 | ], 197 | "text/plain": [ 198 | " MODELYEAR MAKE MODEL VEHICLECLASS ENGINESIZE CYLINDERS \\\n", 199 | "0 2014 ACURA ILX COMPACT 2.0 4 \n", 200 | "1 2014 ACURA ILX COMPACT 2.4 4 \n", 201 | "2 2014 ACURA ILX HYBRID COMPACT 1.5 4 \n", 202 | "3 2014 ACURA MDX 4WD SUV - SMALL 3.5 6 \n", 203 | "4 2014 ACURA RDX AWD SUV - SMALL 3.5 6 \n", 204 | "\n", 205 | " TRANSMISSION FUELTYPE FUELCONSUMPTION_CITY FUELCONSUMPTION_HWY \\\n", 206 | "0 AS5 Z 9.9 6.7 \n", 207 | "1 M6 Z 11.2 7.7 \n", 208 | "2 AV7 Z 6.0 5.8 \n", 209 | "3 AS6 Z 12.7 9.1 \n", 210 | "4 AS6 Z 12.1 8.7 \n", 211 | "\n", 212 | " FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG CO2EMISSIONS \n", 213 | "0 8.5 33 196 \n", 214 | "1 9.6 29 221 \n", 215 | "2 5.9 48 136 \n", 216 | "3 11.1 25 255 \n", 217 | "4 10.6 27 244 " 218 | ] 219 | }, 220 | "execution_count": 6, 221 | "metadata": {}, 222 | "output_type": "execute_result" 223 | } 224 | ], 225 | "source": [ 226 | "df = pd.read_csv(\"FuelConsumption.csv\")\n", 227 | "\n", 228 | "# take a look at the dataset\n", 229 | "df.head()" 230 | ] 231 | }, 232 | { 233 | "cell_type": "markdown", 234 | "metadata": {}, 235 | "source": [ 236 | "## Extracting variables for multivariate linear regression" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": 8, 242 | "metadata": {}, 243 | "outputs": [ 244 | { 245 | "data": { 246 | "text/html": [ 247 | "
\n", 248 | "\n", 261 | "\n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | "
ENGINESIZECYLINDERSFUELCONSUMPTION_CITYFUELCONSUMPTION_HWYFUELCONSUMPTION_COMBCO2EMISSIONS
02.049.96.78.5196
12.4411.27.79.6221
21.546.05.85.9136
33.5612.79.111.1255
43.5612.18.710.6244
53.5611.97.710.0230
63.5611.88.110.1232
73.7612.89.011.1255
83.7613.49.511.6267
\n", 357 | "
" 358 | ], 359 | "text/plain": [ 360 | " ENGINESIZE CYLINDERS FUELCONSUMPTION_CITY FUELCONSUMPTION_HWY \\\n", 361 | "0 2.0 4 9.9 6.7 \n", 362 | "1 2.4 4 11.2 7.7 \n", 363 | "2 1.5 4 6.0 5.8 \n", 364 | "3 3.5 6 12.7 9.1 \n", 365 | "4 3.5 6 12.1 8.7 \n", 366 | "5 3.5 6 11.9 7.7 \n", 367 | "6 3.5 6 11.8 8.1 \n", 368 | "7 3.7 6 12.8 9.0 \n", 369 | "8 3.7 6 13.4 9.5 \n", 370 | "\n", 371 | " FUELCONSUMPTION_COMB CO2EMISSIONS \n", 372 | "0 8.5 196 \n", 373 | "1 9.6 221 \n", 374 | "2 5.9 136 \n", 375 | "3 11.1 255 \n", 376 | "4 10.6 244 \n", 377 | "5 10.0 230 \n", 378 | "6 10.1 232 \n", 379 | "7 11.1 255 \n", 380 | "8 11.6 267 " 381 | ] 382 | }, 383 | "execution_count": 8, 384 | "metadata": {}, 385 | "output_type": "execute_result" 386 | } 387 | ], 388 | "source": [ 389 | "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n", 390 | "cdf.head(9)" 391 | ] 392 | }, 393 | { 394 | "cell_type": "markdown", 395 | "metadata": {}, 396 | "source": [ 397 | "## Creating the Train/Test splits" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | "execution_count": 14, 403 | "metadata": {}, 404 | "outputs": [], 405 | "source": [ 406 | "msk = np.random.rand(len(df)) < 0.8\n", 407 | "train = cdf[msk]\n", 408 | "test = cdf[~msk]" 409 | ] 410 | }, 411 | { 412 | "cell_type": "markdown", 413 | "metadata": {}, 414 | "source": [ 415 | "## Training the Machine Learning model" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": 15, 421 | "metadata": {}, 422 | "outputs": [ 423 | { 424 | "name": "stdout", 425 | "output_type": "stream", 426 | "text": [ 427 | "Coefficients: [[13.07415933 5.46618242 6.98436196 1.89725897]]\n" 428 | ] 429 | } 430 | ], 431 | "source": [ 432 | "from sklearn import linear_model\n", 433 | "regr = linear_model.LinearRegression()\n", 434 | "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n", 435 | "y = np.asanyarray(train[['CO2EMISSIONS']])\n", 436 | "regr.fit (x, y)\n", 437 | "# The coefficients\n", 438 | "print ('Coefficients: ', regr.coef_)" 439 | ] 440 | }, 441 | { 442 | "cell_type": "markdown", 443 | "metadata": {}, 444 | "source": [ 445 | "## Prediction " 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": 17, 451 | "metadata": {}, 452 | "outputs": [ 453 | { 454 | "name": "stdout", 455 | "output_type": "stream", 456 | "text": [ 457 | "Residual sum of squares: 676.42\n", 458 | "Variance score: 0.83\n" 459 | ] 460 | } 461 | ], 462 | "source": [ 463 | "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n", 464 | "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n", 465 | "y = np.asanyarray(test[['CO2EMISSIONS']])\n", 466 | "print(\"Residual sum of squares: %.2f\"\n", 467 | " % np.mean((y_hat - y) ** 2))\n", 468 | "\n", 469 | "# Explained variance score: 1 is perfect prediction\n", 470 | "print('Variance score: %.2f' % regr.score(x, y))" 471 | ] 472 | } 473 | ], 474 | "metadata": { 475 | "kernelspec": { 476 | "display_name": "Python 3", 477 | "language": "python", 478 | "name": "python3" 479 | }, 480 | "language_info": { 481 | "codemirror_mode": { 482 | "name": "ipython", 483 | "version": 3 484 | }, 485 | "file_extension": ".py", 486 | "mimetype": "text/x-python", 487 | "name": "python", 488 | "nbconvert_exporter": "python", 489 | "pygments_lexer": "ipython3", 490 | "version": "3.7.2" 491 | } 492 | }, 493 | "nbformat": 4, 494 | "nbformat_minor": 2 495 | } 496 | -------------------------------------------------------------------------------- /Drug Prediction/DecisionTrees.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "DecisionTrees", 7 | "provenance": [], 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | } 14 | }, 15 | "cells": [ 16 | { 17 | "cell_type": "markdown", 18 | "metadata": { 19 | "id": "view-in-github", 20 | "colab_type": "text" 21 | }, 22 | "source": [ 23 | "\"Open" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": { 29 | "id": "qrBqRtPtBKSd", 30 | "colab_type": "text" 31 | }, 32 | "source": [ 33 | "

Decision Trees

" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": { 39 | "id": "d_AGyuEBBPSe", 40 | "colab_type": "text" 41 | }, 42 | "source": [ 43 | "## Importing the required libraries" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "metadata": { 49 | "id": "qCm-mz5kBKqw", 50 | "colab_type": "code", 51 | "colab": {} 52 | }, 53 | "source": [ 54 | "import numpy as np \n", 55 | "import pandas as pd\n", 56 | "from sklearn.tree import DecisionTreeClassifier" 57 | ], 58 | "execution_count": 0, 59 | "outputs": [] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": { 64 | "id": "f20EcchGBY6i", 65 | "colab_type": "text" 66 | }, 67 | "source": [ 68 | "## About the dataset\n", 69 | "\n", 70 | "We have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y." 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "metadata": { 76 | "id": "nlmvk3LOBSfO", 77 | "colab_type": "code", 78 | "colab": { 79 | "base_uri": "https://localhost:8080/", 80 | "height": 210 81 | }, 82 | "outputId": "654a0194-b3ac-449d-a04a-696a12a639bd" 83 | }, 84 | "source": [ 85 | "!wget -O drug200.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv" 86 | ], 87 | "execution_count": 2, 88 | "outputs": [ 89 | { 90 | "output_type": "stream", 91 | "text": [ 92 | "--2019-09-30 11:54:49-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv\n", 93 | "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n", 94 | "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n", 95 | "HTTP request sent, awaiting response... 200 OK\n", 96 | "Length: 6027 (5.9K) [text/csv]\n", 97 | "Saving to: ‘drug200.csv’\n", 98 | "\n", 99 | "\rdrug200.csv 0%[ ] 0 --.-KB/s \rdrug200.csv 100%[===================>] 5.89K --.-KB/s in 0s \n", 100 | "\n", 101 | "2019-09-30 11:54:49 (1.25 GB/s) - ‘drug200.csv’ saved [6027/6027]\n", 102 | "\n" 103 | ], 104 | "name": "stdout" 105 | } 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "metadata": { 111 | "id": "LhOP7zrrBekn", 112 | "colab_type": "code", 113 | "colab": { 114 | "base_uri": "https://localhost:8080/", 115 | "height": 195 116 | }, 117 | "outputId": "5463b3ee-baf3-4b96-f61a-5e213f20dd05" 118 | }, 119 | "source": [ 120 | "my_data = pd.read_csv(\"drug200.csv\", delimiter=\",\")\n", 121 | "my_data[0:5]" 122 | ], 123 | "execution_count": 3, 124 | "outputs": [ 125 | { 126 | "output_type": "execute_result", 127 | "data": { 128 | "text/html": [ 129 | "
\n", 130 | "\n", 143 | "\n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | "
AgeSexBPCholesterolNa_to_KDrug
023FHIGHHIGH25.355drugY
147MLOWHIGH13.093drugC
247MLOWHIGH10.114drugC
328FNORMALHIGH7.798drugX
461FLOWHIGH18.043drugY
\n", 203 | "
" 204 | ], 205 | "text/plain": [ 206 | " Age Sex BP Cholesterol Na_to_K Drug\n", 207 | "0 23 F HIGH HIGH 25.355 drugY\n", 208 | "1 47 M LOW HIGH 13.093 drugC\n", 209 | "2 47 M LOW HIGH 10.114 drugC\n", 210 | "3 28 F NORMAL HIGH 7.798 drugX\n", 211 | "4 61 F LOW HIGH 18.043 drugY" 212 | ] 213 | }, 214 | "metadata": { 215 | "tags": [] 216 | }, 217 | "execution_count": 3 218 | } 219 | ] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": { 224 | "id": "8wtilElgBuRH", 225 | "colab_type": "text" 226 | }, 227 | "source": [ 228 | "## Pre processing\n", 229 | "\n", 230 | "1. Converting the data into an array.\n", 231 | "2. Changing categorical variables to integer values.\n", 232 | "3. Converting the output variable to an array." 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "metadata": { 238 | "id": "zhr3oBiYBhN7", 239 | "colab_type": "code", 240 | "colab": { 241 | "base_uri": "https://localhost:8080/", 242 | "height": 105 243 | }, 244 | "outputId": "bd643051-e96b-454d-980d-265ab631d951" 245 | }, 246 | "source": [ 247 | "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\n", 248 | "X[0:5]" 249 | ], 250 | "execution_count": 4, 251 | "outputs": [ 252 | { 253 | "output_type": "execute_result", 254 | "data": { 255 | "text/plain": [ 256 | "array([[23, 'F', 'HIGH', 'HIGH', 25.355],\n", 257 | " [47, 'M', 'LOW', 'HIGH', 13.093],\n", 258 | " [47, 'M', 'LOW', 'HIGH', 10.113999999999999],\n", 259 | " [28, 'F', 'NORMAL', 'HIGH', 7.797999999999999],\n", 260 | " [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)" 261 | ] 262 | }, 263 | "metadata": { 264 | "tags": [] 265 | }, 266 | "execution_count": 4 267 | } 268 | ] 269 | }, 270 | { 271 | "cell_type": "code", 272 | "metadata": { 273 | "id": "qW08URHeCIAp", 274 | "colab_type": "code", 275 | "colab": { 276 | "base_uri": "https://localhost:8080/", 277 | "height": 105 278 | }, 279 | "outputId": "33cc580e-3566-4fe5-a20f-51e06e993706" 280 | }, 281 | "source": [ 282 | "from sklearn import preprocessing\n", 283 | "le_sex = preprocessing.LabelEncoder()\n", 284 | "le_sex.fit(['F','M'])\n", 285 | "X[:,1] = le_sex.transform(X[:,1]) \n", 286 | "\n", 287 | "\n", 288 | "le_BP = preprocessing.LabelEncoder()\n", 289 | "le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])\n", 290 | "X[:,2] = le_BP.transform(X[:,2])\n", 291 | "\n", 292 | "\n", 293 | "le_Chol = preprocessing.LabelEncoder()\n", 294 | "le_Chol.fit([ 'NORMAL', 'HIGH'])\n", 295 | "X[:,3] = le_Chol.transform(X[:,3]) \n", 296 | "\n", 297 | "X[0:5]\n" 298 | ], 299 | "execution_count": 5, 300 | "outputs": [ 301 | { 302 | "output_type": "execute_result", 303 | "data": { 304 | "text/plain": [ 305 | "array([[23, 0, 0, 0, 25.355],\n", 306 | " [47, 1, 1, 0, 13.093],\n", 307 | " [47, 1, 1, 0, 10.113999999999999],\n", 308 | " [28, 0, 2, 0, 7.797999999999999],\n", 309 | " [61, 0, 1, 0, 18.043]], dtype=object)" 310 | ] 311 | }, 312 | "metadata": { 313 | "tags": [] 314 | }, 315 | "execution_count": 5 316 | } 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "metadata": { 322 | "id": "uKJKr9ApCK3X", 323 | "colab_type": "code", 324 | "colab": { 325 | "base_uri": "https://localhost:8080/", 326 | "height": 122 327 | }, 328 | "outputId": "4fdd0c7f-220f-49a5-8a63-fd097019db54" 329 | }, 330 | "source": [ 331 | "y = my_data[\"Drug\"]\n", 332 | "y[0:5]" 333 | ], 334 | "execution_count": 6, 335 | "outputs": [ 336 | { 337 | "output_type": "execute_result", 338 | "data": { 339 | "text/plain": [ 340 | "0 drugY\n", 341 | "1 drugC\n", 342 | "2 drugC\n", 343 | "3 drugX\n", 344 | "4 drugY\n", 345 | "Name: Drug, dtype: object" 346 | ] 347 | }, 348 | "metadata": { 349 | "tags": [] 350 | }, 351 | "execution_count": 6 352 | } 353 | ] 354 | }, 355 | { 356 | "cell_type": "markdown", 357 | "metadata": { 358 | "id": "_yUW7Y4bCg5H", 359 | "colab_type": "text" 360 | }, 361 | "source": [ 362 | "## Setting up the decision tree" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "metadata": { 368 | "id": "YC48rc2JCbra", 369 | "colab_type": "code", 370 | "colab": {} 371 | }, 372 | "source": [ 373 | "from sklearn.model_selection import train_test_split" 374 | ], 375 | "execution_count": 0, 376 | "outputs": [] 377 | }, 378 | { 379 | "cell_type": "code", 380 | "metadata": { 381 | "id": "n8LSXpn9Ck6l", 382 | "colab_type": "code", 383 | "colab": {} 384 | }, 385 | "source": [ 386 | "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)" 387 | ], 388 | "execution_count": 0, 389 | "outputs": [] 390 | }, 391 | { 392 | "cell_type": "markdown", 393 | "metadata": { 394 | "id": "4fRF9zFBCqi1", 395 | "colab_type": "text" 396 | }, 397 | "source": [ 398 | "## Training the Machine Learning Model" 399 | ] 400 | }, 401 | { 402 | "cell_type": "code", 403 | "metadata": { 404 | "id": "xl6bc3pACmia", 405 | "colab_type": "code", 406 | "colab": { 407 | "base_uri": "https://localhost:8080/", 408 | "height": 122 409 | }, 410 | "outputId": "ef444191-0764-48a5-9265-2b59b828e517" 411 | }, 412 | "source": [ 413 | "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n", 414 | "drugTree # it shows the default parameters" 415 | ], 416 | "execution_count": 9, 417 | "outputs": [ 418 | { 419 | "output_type": "execute_result", 420 | "data": { 421 | "text/plain": [ 422 | "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n", 423 | " max_features=None, max_leaf_nodes=None,\n", 424 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 425 | " min_samples_leaf=1, min_samples_split=2,\n", 426 | " min_weight_fraction_leaf=0.0, presort=False,\n", 427 | " random_state=None, splitter='best')" 428 | ] 429 | }, 430 | "metadata": { 431 | "tags": [] 432 | }, 433 | "execution_count": 9 434 | } 435 | ] 436 | }, 437 | { 438 | "cell_type": "code", 439 | "metadata": { 440 | "id": "LxHbu7fOCvfu", 441 | "colab_type": "code", 442 | "colab": { 443 | "base_uri": "https://localhost:8080/", 444 | "height": 122 445 | }, 446 | "outputId": "c94eb74e-0c19-47d8-c7ad-49bf13f9cda6" 447 | }, 448 | "source": [ 449 | "drugTree.fit(X_trainset,y_trainset)" 450 | ], 451 | "execution_count": 10, 452 | "outputs": [ 453 | { 454 | "output_type": "execute_result", 455 | "data": { 456 | "text/plain": [ 457 | "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n", 458 | " max_features=None, max_leaf_nodes=None,\n", 459 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 460 | " min_samples_leaf=1, min_samples_split=2,\n", 461 | " min_weight_fraction_leaf=0.0, presort=False,\n", 462 | " random_state=None, splitter='best')" 463 | ] 464 | }, 465 | "metadata": { 466 | "tags": [] 467 | }, 468 | "execution_count": 10 469 | } 470 | ] 471 | }, 472 | { 473 | "cell_type": "markdown", 474 | "metadata": { 475 | "id": "V7YKivRNC9Zk", 476 | "colab_type": "text" 477 | }, 478 | "source": [ 479 | "## Prediction" 480 | ] 481 | }, 482 | { 483 | "cell_type": "code", 484 | "metadata": { 485 | "id": "alef99wcC5BA", 486 | "colab_type": "code", 487 | "colab": {} 488 | }, 489 | "source": [ 490 | "predTree = drugTree.predict(X_testset)" 491 | ], 492 | "execution_count": 0, 493 | "outputs": [] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "metadata": { 498 | "id": "C7fQwYy-C7T-", 499 | "colab_type": "code", 500 | "colab": { 501 | "base_uri": "https://localhost:8080/", 502 | "height": 140 503 | }, 504 | "outputId": "3cdfd7ed-1775-4bf0-a131-50f74a384975" 505 | }, 506 | "source": [ 507 | "print (predTree [0:5])\n", 508 | "print (y_testset [0:5])\n" 509 | ], 510 | "execution_count": 12, 511 | "outputs": [ 512 | { 513 | "output_type": "stream", 514 | "text": [ 515 | "['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n", 516 | "40 drugY\n", 517 | "51 drugX\n", 518 | "139 drugX\n", 519 | "197 drugX\n", 520 | "170 drugX\n", 521 | "Name: Drug, dtype: object\n" 522 | ], 523 | "name": "stdout" 524 | } 525 | ] 526 | }, 527 | { 528 | "cell_type": "markdown", 529 | "metadata": { 530 | "id": "6j1NoMiGDGXp", 531 | "colab_type": "text" 532 | }, 533 | "source": [ 534 | "## Evaluation of the decision tree" 535 | ] 536 | }, 537 | { 538 | "cell_type": "code", 539 | "metadata": { 540 | "id": "MEZ0ApgqDA14", 541 | "colab_type": "code", 542 | "colab": { 543 | "base_uri": "https://localhost:8080/", 544 | "height": 34 545 | }, 546 | "outputId": "fbb7187b-8d60-48ad-bcef-f99023235196" 547 | }, 548 | "source": [ 549 | "from sklearn import metrics\n", 550 | "import matplotlib.pyplot as plt\n", 551 | "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))" 552 | ], 553 | "execution_count": 13, 554 | "outputs": [ 555 | { 556 | "output_type": "stream", 557 | "text": [ 558 | "DecisionTrees's Accuracy: 0.9833333333333333\n" 559 | ], 560 | "name": "stdout" 561 | } 562 | ] 563 | } 564 | ] 565 | } -------------------------------------------------------------------------------- /Customer Churn/KNN.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Classification using K-Nearest Neighbours using Sckikit-Learn" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Importing the required packages" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import itertools\n", 24 | "import numpy as np\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "from matplotlib.ticker import NullFormatter\n", 27 | "import pandas as pd\n", 28 | "import numpy as np\n", 29 | "import matplotlib.ticker as ticker\n", 30 | "from sklearn import preprocessing\n", 31 | "%matplotlib inline" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "## Downloading and reading the dataset into a Panda's Dataframe" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 3, 44 | "metadata": {}, 45 | "outputs": [ 46 | { 47 | "name": "stdout", 48 | "output_type": "stream", 49 | "text": [ 50 | "--2019-09-19 09:58:16-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/teleCust1000t.csv\n", 51 | "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n", 52 | "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n", 53 | "HTTP request sent, awaiting response... 200 OK\n", 54 | "Length: 37048 (36K) [text/csv]\n", 55 | "Saving to: ‘teleCust1000t.csv’\n", 56 | "\n", 57 | "teleCust1000t.csv 100%[===================>] 36.18K 138KB/s in 0.3s \n", 58 | "\n", 59 | "2019-09-19 09:58:18 (138 KB/s) - ‘teleCust1000t.csv’ saved [37048/37048]\n", 60 | "\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "!wget -O teleCust1000t.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/teleCust1000t.csv" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 7, 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "data": { 75 | "text/html": [ 76 | "
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regiontenureagemaritaladdressincomeedemployretiregenderresidecustcat
0213441964.0450.0021
13113317136.0550.0064
236852124116.01290.0123
32333301233.0200.0111
4223301930.0120.0043
\n", 186 | "
" 187 | ], 188 | "text/plain": [ 189 | " region tenure age marital address income ed employ retire gender \\\n", 190 | "0 2 13 44 1 9 64.0 4 5 0.0 0 \n", 191 | "1 3 11 33 1 7 136.0 5 5 0.0 0 \n", 192 | "2 3 68 52 1 24 116.0 1 29 0.0 1 \n", 193 | "3 2 33 33 0 12 33.0 2 0 0.0 1 \n", 194 | "4 2 23 30 1 9 30.0 1 2 0.0 0 \n", 195 | "\n", 196 | " reside custcat \n", 197 | "0 2 1 \n", 198 | "1 6 4 \n", 199 | "2 2 3 \n", 200 | "3 1 1 \n", 201 | "4 4 3 " 202 | ] 203 | }, 204 | "execution_count": 7, 205 | "metadata": {}, 206 | "output_type": "execute_result" 207 | } 208 | ], 209 | "source": [ 210 | "df = pd.read_csv('teleCust1000t.csv')\n", 211 | "df.head()" 212 | ] 213 | }, 214 | { 215 | "cell_type": "markdown", 216 | "metadata": {}, 217 | "source": [ 218 | "### To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array:" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 8, 224 | "metadata": {}, 225 | "outputs": [ 226 | { 227 | "data": { 228 | "text/plain": [ 229 | "array([[ 2., 13., 44., 1., 9., 64., 4., 5., 0., 0., 2.],\n", 230 | " [ 3., 11., 33., 1., 7., 136., 5., 5., 0., 0., 6.],\n", 231 | " [ 3., 68., 52., 1., 24., 116., 1., 29., 0., 1., 2.],\n", 232 | " [ 2., 33., 33., 0., 12., 33., 2., 0., 0., 1., 1.],\n", 233 | " [ 2., 23., 30., 1., 9., 30., 1., 2., 0., 0., 4.]])" 234 | ] 235 | }, 236 | "execution_count": 8, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | } 240 | ], 241 | "source": [ 242 | "X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values #.astype(float)\n", 243 | "X[0:5]\n" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 9, 249 | "metadata": {}, 250 | "outputs": [ 251 | { 252 | "data": { 253 | "text/plain": [ 254 | "array([1, 4, 3, 1, 3])" 255 | ] 256 | }, 257 | "execution_count": 9, 258 | "metadata": {}, 259 | "output_type": "execute_result" 260 | } 261 | ], 262 | "source": [ 263 | "y = df['custcat'].values\n", 264 | "y[0:5]" 265 | ] 266 | }, 267 | { 268 | "cell_type": "markdown", 269 | "metadata": {}, 270 | "source": [ 271 | "## Normalizing the data\n", 272 | "\n", 273 | "\n", 274 | "Data Standardization give data zero mean and unit variance, it is good practice, especially for algorithms such as KNN which is based on distance of cases:" 275 | ] 276 | }, 277 | { 278 | "cell_type": "code", 279 | "execution_count": 12, 280 | "metadata": {}, 281 | "outputs": [ 282 | { 283 | "data": { 284 | "text/plain": [ 285 | "array([[-0.02696767, -1.055125 , 0.18450456, 1.0100505 , -0.25303431,\n", 286 | " -0.12650641, 1.0877526 , -0.5941226 , -0.22207644, -1.03459817,\n", 287 | " -0.23065004],\n", 288 | " [ 1.19883553, -1.14880563, -0.69181243, 1.0100505 , -0.4514148 ,\n", 289 | " 0.54644972, 1.9062271 , -0.5941226 , -0.22207644, -1.03459817,\n", 290 | " 2.55666158],\n", 291 | " [ 1.19883553, 1.52109247, 0.82182601, 1.0100505 , 1.23481934,\n", 292 | " 0.35951747, -1.36767088, 1.78752803, -0.22207644, 0.96655883,\n", 293 | " -0.23065004],\n", 294 | " [-0.02696767, -0.11831864, -0.69181243, -0.9900495 , 0.04453642,\n", 295 | " -0.41625141, -0.54919639, -1.09029981, -0.22207644, 0.96655883,\n", 296 | " -0.92747794],\n", 297 | " [-0.02696767, -0.58672182, -0.93080797, 1.0100505 , -0.25303431,\n", 298 | " -0.44429125, -1.36767088, -0.89182893, -0.22207644, -1.03459817,\n", 299 | " 1.16300577]])" 300 | ] 301 | }, 302 | "execution_count": 12, 303 | "metadata": {}, 304 | "output_type": "execute_result" 305 | } 306 | ], 307 | "source": [ 308 | "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n", 309 | "X[0:5]" 310 | ] 311 | }, 312 | { 313 | "cell_type": "markdown", 314 | "metadata": {}, 315 | "source": [ 316 | "## Train and Test Data split" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": 13, 322 | "metadata": {}, 323 | "outputs": [ 324 | { 325 | "name": "stdout", 326 | "output_type": "stream", 327 | "text": [ 328 | "Train set: (800, 11) (800,)\n", 329 | "Test set: (200, 11) (200,)\n" 330 | ] 331 | } 332 | ], 333 | "source": [ 334 | "from sklearn.model_selection import train_test_split\n", 335 | "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n", 336 | "print ('Train set:', X_train.shape, y_train.shape)\n", 337 | "print ('Test set:', X_test.shape, y_test.shape)" 338 | ] 339 | }, 340 | { 341 | "cell_type": "markdown", 342 | "metadata": {}, 343 | "source": [ 344 | "## Importing KNN library" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": 14, 350 | "metadata": {}, 351 | "outputs": [], 352 | "source": [ 353 | "from sklearn.neighbors import KNeighborsClassifier" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": 18, 359 | "metadata": {}, 360 | "outputs": [], 361 | "source": [ 362 | "from sklearn import metrics" 363 | ] 364 | }, 365 | { 366 | "cell_type": "markdown", 367 | "metadata": {}, 368 | "source": [ 369 | "## Training the Machine Learning models for different K's" 370 | ] 371 | }, 372 | { 373 | "cell_type": "code", 374 | "execution_count": 19, 375 | "metadata": {}, 376 | "outputs": [ 377 | { 378 | "data": { 379 | "text/plain": [ 380 | "array([0.3 , 0.29 , 0.315, 0.32 , 0.315, 0.31 , 0.335, 0.325, 0.34 ])" 381 | ] 382 | }, 383 | "execution_count": 19, 384 | "metadata": {}, 385 | "output_type": "execute_result" 386 | } 387 | ], 388 | "source": [ 389 | "Ks = 10\n", 390 | "mean_acc = np.zeros((Ks-1))\n", 391 | "std_acc = np.zeros((Ks-1))\n", 392 | "ConfustionMx = [];\n", 393 | "for n in range(1,Ks):\n", 394 | " \n", 395 | " #Train Model and Predict \n", 396 | " neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n", 397 | " yhat=neigh.predict(X_test)\n", 398 | " mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n", 399 | "\n", 400 | " \n", 401 | " std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n", 402 | "\n", 403 | "mean_acc" 404 | ] 405 | }, 406 | { 407 | "cell_type": "code", 408 | "execution_count": 20, 409 | "metadata": {}, 410 | "outputs": [ 411 | { 412 | "data": { 413 | "image/png": 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\n", 414 | "text/plain": [ 415 | "
" 416 | ] 417 | }, 418 | "metadata": { 419 | "needs_background": "light" 420 | }, 421 | "output_type": "display_data" 422 | } 423 | ], 424 | "source": [ 425 | "plt.plot(range(1,Ks),mean_acc,'g')\n", 426 | "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n", 427 | "plt.legend(('Accuracy ', '+/- 3xstd'))\n", 428 | "plt.ylabel('Accuracy ')\n", 429 | "plt.xlabel('Number of Nabors (K)')\n", 430 | "plt.tight_layout()\n", 431 | "plt.show()" 432 | ] 433 | }, 434 | { 435 | "cell_type": "code", 436 | "execution_count": 21, 437 | "metadata": {}, 438 | "outputs": [ 439 | { 440 | "name": "stdout", 441 | "output_type": "stream", 442 | "text": [ 443 | "The best accuracy was with 0.34 with k= 9\n" 444 | ] 445 | } 446 | ], 447 | "source": [ 448 | "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) " 449 | ] 450 | } 451 | ], 452 | "metadata": { 453 | "kernelspec": { 454 | "display_name": "Python 3", 455 | "language": "python", 456 | "name": "python3" 457 | }, 458 | "language_info": { 459 | "codemirror_mode": { 460 | "name": "ipython", 461 | "version": 3 462 | }, 463 | "file_extension": ".py", 464 | "mimetype": "text/x-python", 465 | "name": "python", 466 | "nbconvert_exporter": "python", 467 | "pygments_lexer": "ipython3", 468 | "version": "3.7.2" 469 | } 470 | }, 471 | "nbformat": 4, 472 | "nbformat_minor": 2 473 | } 474 | -------------------------------------------------------------------------------- /Bigmart Sales Prediction/test.csv: -------------------------------------------------------------------------------- 1 | Item_Weight,Item_Visibility,Item_MRP,Outlet_Establishment_Year,Item_Outlet_Sales,Item_Fat_Content_LF,Item_Fat_Content_Low Fat,Item_Fat_Content_Regular,Item_Fat_Content_low fat,Item_Fat_Content_reg,Item_Type_Baking Goods,Item_Type_Breads,Item_Type_Breakfast,Item_Type_Canned,Item_Type_Dairy,Item_Type_Frozen Foods,Item_Type_Fruits and Vegetables,Item_Type_Hard Drinks,Item_Type_Health and Hygiene,Item_Type_Household,Item_Type_Meat,Item_Type_Others,Item_Type_Seafood,Item_Type_Snack Foods,Item_Type_Soft Drinks,Item_Type_Starchy Foods,Outlet_Size_High,Outlet_Size_Medium,Outlet_Size_Small,Outlet_Location_Type_Tier 1,Outlet_Location_Type_Tier 2,Outlet_Location_Type_Tier 3,Outlet_Type_Grocery Store,Outlet_Type_Supermarket Type1,Outlet_Type_Supermarket Type2,Outlet_Type_Supermarket Type3 2 | 18.1,0.022526293,95.0094,2007,1713.7692,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 3 | 6.615,0.093574769,199.4426,2002,3361.6242,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 4 | 11.85,0.050186726,164.1526,2002,3124.5994,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 5 | 13.5,0.159968994,147.6102,1999,1603.9122,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 6 | 14.0,0.02976887,145.4786,1999,1300.3074,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 7 | 12.15,0.131383762,246.046,1999,1231.73,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 8 | 11.15,0.032229348,163.2526,1987,1480.0734,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 9 | 8.645,0.144006886,94.741,2009,1061.951,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 10 | 16.75,0.0,255.3988,1997,4625.9784,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 11 | 13.15,0.061381589,179.9976,1998,181.0976,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 12 | 4.555,0.034410585,111.3544,1999,1342.2528,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 13 | 12.911574527641704,0.009921107,183.6924,1985,555.2772,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 14 | 12.911574527641704,0.167725251,128.3678,1985,127.1678,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 15 | 8.985,0.183688161,100.27,1999,998.7,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 16 | 9.695,0.114170506,158.9604,2002,1743.0644,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 17 | 12.911574527641704,0.073055148,33.7216,1985,1280.9992,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1 18 | 15.15,0.066311153,145.776,2002,3954.852,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 19 | 10.195,0.045165796,118.4808,2009,1640.5312,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 20 | 10.0,0.03773288,128.2994,1987,1413.4934,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0 21 | 8.42,0.0,229.0352,2004,5496.8448,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 22 | 16.75,0.029799965,39.1822,2002,314.2576,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 23 | 6.675,0.0,92.9462,2009,1018.0082,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 24 | 11.8,0.057538034,149.8366,2002,1662.5026,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 25 | 12.911574527641704,0.07480619599999999,112.9176,1985,3435.528,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 26 | 11.65,0.034049703,112.286,2007,452.744,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0 27 | 20.6,0.023585598,94.7778,2007,1314.2892,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 28 | 5.925,0.09646733,42.8086,1997,669.1289999999998,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 29 | 10.195,0.0,139.5838,2009,4495.4816,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 30 | 11.6,0.077284566,172.4106,1999,4277.765,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 31 | 19.75,0.041330512,115.8466,1987,2474.7786,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 32 | 16.7,0.052708138,114.7176,2007,2404.8696,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 33 | 19.75,0.018019662,181.566,2004,2876.256,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 34 | 5.78,0.014584584,146.3102,2002,2187.153,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 35 | 11.1,0.294948897,159.0604,1998,792.302,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 36 | 9.5,0.074460855,253.6724,1999,5033.448,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 37 | 10.195,0.146960375,142.2838,2002,1685.8056,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 38 | 9.5,0.0,110.4228,2007,1547.3192,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 39 | 16.7,0.070312473,189.6214,2007,2826.321,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 40 | 15.7,0.027729547,169.37900000000005,2009,1528.011,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 41 | 12.6,0.074221559,255.9356,2009,4832.3764,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 42 | 12.911574527641704,0.037711338,41.548,1985,1358.2320000000002,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 43 | 11.65,0.01941154,40.3164,2002,502.0132,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 44 | 12.911574527641704,0.067441726,57.4272,1985,111.8544,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 45 | 16.85,0.026627857,93.712,2009,745.696,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 46 | 8.1,0.134870732,41.948,2009,199.74,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 47 | 17.2,0.156298858,162.1578,1997,1283.6624,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 48 | 12.911574527641704,0.048980156,148.705,1985,2996.1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 49 | 9.0,0.065515067,178.137,2009,1058.622,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 50 | 9.195,0.015856295,81.6592,2004,1403.5064,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 51 | 17.25,0.079113947,99.2068,1998,97.2068,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0 52 | 20.7,0.021518435,157.5288,2009,1571.2879999999998,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 53 | 7.365,0.042552205,225.172,2004,3395.58,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 54 | 7.31,0.026830586,108.057,1999,2087.283,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 55 | 7.285,0.050065211,157.4288,2002,2042.6744,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 56 | 20.85,0.021362955,103.2306,1999,2404.2038,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 57 | 6.13,0.0,54.1298,1997,1348.245,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 58 | 12.1,0.016921927,178.366,2007,4314.384,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 59 | 20.1,0.075049323,110.4228,2007,1105.228,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 60 | 12.15,0.042378864,125.4046,1999,1867.569,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 61 | 14.1,0.066979957,196.7084,1999,3174.5344,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 62 | 12.911574527641704,0.126585095,122.4098,1985,120.5098,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 63 | 18.7,0.044024163,125.902,1998,126.502,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 64 | 13.85,0.05640612900000001,231.43,1999,3029.39,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 65 | 11.3,0.05643481700000001,247.8118,1997,2964.1416,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 66 | 5.21,0.011072173,257.8962,2007,4402.9354,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 67 | 14.65,0.171355643,47.6692,2004,739.038,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 68 | 20.35,0.054277132,117.3466,2009,824.9262,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 69 | 16.5,0.012627329,38.7506,1987,759.012,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 70 | 7.06,0.043968652,57.6904,1999,585.904,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0 71 | 16.1,0.065183228,148.076,1997,1464.76,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 72 | 20.5,0.036346224,72.9696,2004,596.5568,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 73 | 11.6,0.144604071,240.8222,2009,3585.333,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 74 | 20.85,0.039624006,117.2808,1999,1523.3504,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 75 | 15.85,0.05754691400000001,55.8956,2009,491.3604,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 76 | 14.3,0.065339802,89.1856,2002,1845.5976,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 77 | 11.65,0.019402371,38.7164,1999,579.246,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 78 | 16.1,0.100388706,76.0328,1999,1853.5872,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 79 | 6.385,0.123710526,37.1874,1997,247.0118,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0 80 | 8.18,0.08233301900000001,58.1588,1987,343.5528,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 81 | 12.911574527641704,0.037569401,120.7098,1985,361.5294,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 82 | 20.1,0.074931201,108.9228,2009,1105.228,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 83 | 11.15,0.0,65.0142,1999,1120.5414,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0 84 | 14.5,0.064048405,153.4682,2004,1677.1502,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 85 | 16.35,0.104463896,227.1062,1998,225.7062,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 86 | 14.5,0.01950565,163.921,1999,2446.815,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0 87 | 7.47,0.153011599,215.2218,2007,3419.5488,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 88 | 13.15,0.155105614,157.2604,1998,316.9208,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 89 | 12.911574527641704,0.058446424,172.1422,1985,5518.1504,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 90 | 19.6,0.033970195,55.1614,2002,1381.535,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 91 | 19.5,0.01430324,57.2614,2002,1547.3192,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 92 | 12.911574527641704,0.048841794,64.7168,1985,3068.0064,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1 93 | 9.3,0.042377219,123.7388,2002,1609.9044,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 94 | 13.15,0.020757926,84.3566,2002,761.0094,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 95 | 20.2,0.064351908,259.4646,2004,2061.3168,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0 96 | 8.1,0.134532392,39.448,1999,878.8560000000001,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 97 | 12.911574527641704,0.040636926,224.6088,1985,4474.176,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 98 | 10.5,0.011351778,236.5248,2007,4029.4216,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 99 | 12.911574527641704,0.12978357699999998,78.2328,1985,1004.0264,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 100 | 11.1,0.135692831,220.0482,2009,2628.5784,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 101 | 7.895,0.094567181,104.5332,1997,1845.5976,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 102 | 9.8,0.015089965,249.6408,1997,1001.3632,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 103 | 16.2,0.0,190.8162,2004,3271.0754,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 104 | 6.78,0.066997921,186.524,2007,2609.936,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 105 | 9.0,0.038925251,34.919000000000004,1997,476.047,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0 106 | 17.75,0.014577412,160.7262,2004,2864.2716,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 107 | 13.65,0.031573246,99.47,2009,299.61,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 108 | 12.911574527641704,0.110762642,108.5912,1985,1637.868,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,1 109 | 20.35,0.014909465,232.3958,2007,6076.0908,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 110 | 6.11,0.0,131.2968,1998,260.9936,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 111 | 11.8,0.170251293,116.4834,2004,1958.1178,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 112 | 10.1,0.027041859,77.567,1987,306.268,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 113 | 17.6,0.010031539,161.5552,1987,1787.0072,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0 114 | 9.8,0.073289899,120.8098,2007,1325.6078,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 115 | 12.15,0.131714183,245.846,2009,4434.228,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 116 | 8.75,0.07462720099999999,187.8556,1997,1314.2892,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 117 | 7.825,0.079613553,65.0826,2004,1162.4868,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 118 | 12.911574527641704,0.0514111,33.0558,1985,984.7182,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 119 | 17.35,0.041538713,93.1804,1999,1378.206,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 120 | 12.911574527641704,0.174232377,146.6102,1985,1895.5326,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1 121 | 13.15,0.037952777,89.5856,1997,1494.0552,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0 122 | 18.25,0.184041545,110.157,2009,1867.569,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 123 | 11.0,0.008944844,122.3756,2004,2059.9852,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 124 | 12.911574527641704,0.075192072,56.0614,1985,2597.2858,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 125 | 10.0,0.037977917,128.6994,2007,642.497,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 126 | 12.911574527641704,0.01268995,56.4588,1985,973.3996,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1 127 | 6.385,0.14032811,109.1596,1998,107.8596,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 128 | 12.911574527641704,0.21799414,266.5884,1985,1324.942,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 129 | 10.0,0.067312613,234.359,1999,3072.667,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 130 | 12.911574527641704,0.00994477,177.83700000000005,1985,6528.169,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 131 | 21.25,0.024705597000000003,145.1102,2002,1603.9122,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 132 | 7.72,0.08854386800000001,117.5466,2002,824.9262,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 133 | 7.895,0.061124626,58.5588,1987,171.7764,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 134 | 12.911574527641704,0.035997636,78.6618,1985,80.5618,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0 135 | 6.675,0.021710275,34.9874,2002,247.0118,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 136 | 12.911574527641704,0.026015519,255.8356,1985,2543.356,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1 137 | 8.5,0.16383895099999998,51.3324,1998,311.5944,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 138 | 8.26,0.034544435,116.0834,2009,1267.0174,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 139 | 19.75,0.034027909,212.0902,2009,3185.853,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 140 | 5.48,0.015131684,83.025,1999,1414.825,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 141 | 6.385,0.0,108.9596,1997,1186.4556,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 142 | 11.15,0.105736638,104.4648,2009,2181.1608,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 143 | 17.75,0.157075658,240.5538,1999,3845.6608,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 144 | 17.75,0.075959623,112.4544,1997,2460.7968,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 145 | 13.15,0.038029746,88.6856,2002,1757.7120000000002,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 146 | 12.911574527641704,0.0,37.0506,1985,986.7156,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 147 | 12.911574527641704,0.086360962,151.2682,1985,2439.4912,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 148 | 5.785,0.040351229,181.366,2004,1977.426,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0 149 | 19.85,0.044460448,88.7856,1987,1142.5128,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 150 | 8.6,0.032621545,143.2154,1997,2127.231,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 151 | 12.911574527641704,0.041621987,253.6014,1985,6630.0364,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 152 | 11.8,0.0,46.5402,1998,229.701,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 153 | 20.1,0.054584207,193.382,2004,4247.804,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 154 | 15.0,0.07131206400000001,125.7362,1987,3020.0688,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 155 | 12.3,0.069852615,107.4938,2007,2358.2636,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 156 | 12.911574527641704,0.082152451,179.9002,1985,4119.3046,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 157 | 13.0,0.099887103,45.90600000000001,2007,838.908,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 158 | 11.8,0.008609602,114.8492,2007,1390.1904,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 159 | 10.195,0.159936948,143.2154,1997,2552.6772,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 160 | 15.1,0.100423086,144.7786,2007,2600.6148,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 161 | 9.6,0.022274264,101.699,2004,2992.771,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 162 | 15.6,0.035506142000000004,112.4518,1997,3301.7022,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0 163 | 5.325,0.139464425,53.2298,2007,1240.3854,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 164 | 11.6,0.144832027,239.8222,2007,4063.3774,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 165 | 12.911574527641704,0.074517508,227.372,1985,452.744,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 166 | 17.6,0.041851756,162.7526,2007,3124.5994,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 167 | 9.695,0.129008866,226.9404,2007,2700.4848,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 168 | 9.0,0.038917891,35.91900000000001,2004,1171.808,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0 169 | 15.1,0.099839365,143.3786,2004,1733.7432,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 170 | 6.15,0.046342889000000005,97.3384,1999,886.8456,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 171 | 16.85,0.026561057,93.712,1999,466.06,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 172 | 12.911574527641704,0.135226962,56.293,1985,113.186,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 173 | 10.0,0.07675510599999999,141.618,1998,139.81799999999998,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 174 | 9.21,0.008782561,119.8414,2002,1340.2554,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 175 | 14.6,0.151180862,47.5692,2009,443.4228,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0 176 | 17.35,0.056235142,102.0016,2007,1619.2256,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 177 | 11.15,0.085931544,169.679,1997,1018.674,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0 178 | 17.6,0.039443715,95.841,2007,1641.197,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 179 | 12.911574527641704,0.048830264000000005,113.1176,1985,2404.8696,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1 180 | 18.85,0.039723999,41.048,2002,918.804,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 181 | 11.65,0.019372253,40.6164,1997,308.9312,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 182 | 9.5,0.041851461,31.49,1987,466.06,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0 183 | 8.155,0.119937231,189.153,2009,2087.283,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 184 | 6.675,0.041913535,90.7462,2004,1665.8316,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0 185 | 13.85,0.030920531,141.0154,2009,1701.7848,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0 186 | 8.645,0.14371350800000002,96.641,2002,868.8689999999998,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 187 | 5.82,0.080804019,171.479,2002,4074.696,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 188 | 19.35,0.033082215,172.5738,2004,4865.6664,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 189 | 11.0,0.008960445,119.1756,1999,1696.4584,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 190 | 15.0,0.0,235.6248,1999,1896.1984,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 191 | 12.911574527641704,0.138008431,54.8298,1985,1348.245,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 192 | 14.35,0.091054989,231.8984,1999,4633.968,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 193 | 19.5,0.030819397,86.054,2009,2336.958,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 194 | 12.911574527641704,0.13948429199999998,94.312,1985,2516.724,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 195 | 7.435,0.205605116,207.7638,1998,621.1914,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0 196 | 14.7,0.035016091,144.3128,1998,431.4384,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 197 | 18.85,0.137410256,161.7578,1999,1604.578,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 198 | 15.1,0.059417055,237.9248,1999,2133.2232,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 199 | 13.1,0.037574137,174.2054,2004,3677.2134,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 200 | 15.7,0.0,112.4544,1987,1230.3984,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 201 | 8.38,0.046960237,111.857,1999,2966.139,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 202 | 15.85,0.228469522,93.3094,1998,285.6282,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 203 | 12.3,0.069446588,106.3938,2004,857.5504,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0 204 | 9.195,0.108330902,183.6634,2009,2726.451,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 205 | 6.035,0.065729039,188.324,1987,1491.392,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 206 | 19.5,0.07729230200000001,235.3958,1999,6309.7866,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0 207 | 10.5,0.026520107,144.8128,2007,2157.192,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 208 | 12.911574527641704,0.024766802,151.0392,1985,3281.0624,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 209 | 12.911574527641704,0.066459891,184.2292,1985,4560.73,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 210 | 12.1,0.011539622,163.5526,1997,2302.3364,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 211 | 16.2,0.016747538,98.6726,2007,1272.3438,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 212 | 12.911574527641704,0.033436336000000004,107.3912,1985,3057.3536,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 213 | 21.1,0.020718655,130.4994,1997,2184.4898,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 214 | 4.635,0.141686037,126.9994,2007,1798.9916,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 215 | 11.8,0.076839735,34.9558,1999,373.5138,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 216 | 18.6,0.080956792,95.2436,2007,1796.3284,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 217 | 12.6,0.031663212,173.2764,2009,1030.6584,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 218 | 14.3,0.034404732,98.1726,2004,489.36300000000006,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 219 | 8.39,0.024304264,114.0176,2009,1488.7288,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 220 | 16.75,0.032771033,192.3162,2007,2308.9944,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 221 | 12.35,0.185704641,77.1328,1987,2239.7512,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 222 | 11.15,0.032387589,164.3526,2009,2960.1468,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 223 | 6.61,0.0,186.4898,2002,2993.4368,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 224 | 19.5,0.01581384,183.2608,2007,2940.1728,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 225 | 14.5,0.097750937,159.7262,2007,5728.5432,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 226 | 12.911574527641704,0.076851759,111.857,1985,109.857,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 227 | 14.1,0.087920675,228.5668,1987,1151.8339999999996,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 228 | 10.65,0.08530861099999999,230.2668,2002,5759.17,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 229 | 12.911574527641704,0.111777297,124.6046,1985,2863.6058,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 230 | 12.911574527641704,0.102422487,131.4968,1985,1565.9616,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 231 | 13.85,0.030795085,143.3154,1997,1985.4156,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0 232 | 15.1,0.076385385,88.48299999999998,2009,988.713,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 233 | 14.0,0.060427062,153.5656,1997,3089.312,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 234 | 12.911574527641704,0.088394115,194.7452,1985,5872.356,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 235 | 19.1,0.177236497,172.3422,2004,2586.633,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 236 | 13.1,0.075515154,167.2158,1987,3008.0844,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 237 | 6.8,0.06276237400000001,50.4034,1998,48.6034,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 238 | 12.15,0.062541552,34.3532,2009,251.6724,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0 239 | 17.0,0.136008489,171.7106,2002,1711.106,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 240 | 13.1,0.0,189.253,1997,1518.024,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0 241 | 20.85,0.037396901,193.8478,2004,2712.4692,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 242 | 12.911574527641704,0.054366282,199.6084,1985,7142.7024,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 243 | 12.911574527641704,0.035574413,131.4284,1985,5404.9644,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 244 | 14.5,0.05891884400000001,169.7448,1999,3238.4512,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 245 | 11.1,0.11089655,191.5846,2002,2101.9306,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 246 | 16.75,0.021220113,55.1298,2004,1240.3854,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 247 | 6.71,0.03558013,217.5166,2004,4572.0486,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 248 | 7.905,0.055098434,109.2254,2002,976.7286,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 249 | 10.0,0.0,246.9144,2009,3185.1872,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 250 | 16.0,0.173462841,157.6972,2009,2960.1468,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0 251 | 20.25,0.025953257,179.5976,1997,2535.3664,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 252 | 12.65,0.156286566,238.9538,1999,4086.0146,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 253 | 18.1,0.022395357,96.5094,2004,1332.9316,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 254 | 10.895,0.044991875999999986,107.228,1998,426.112,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 255 | 6.325,0.125688044,100.9042,2009,2083.2882,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 256 | 15.35,0.073283193,91.812,1997,1771.0279999999998,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0 257 | 15.35,0.073697713,91.912,2007,559.2719999999998,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 258 | 19.2,0.099991245,112.7886,1987,1779.0176,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 259 | 16.7,0.054618573,65.5168,2004,1022.6688,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0 260 | 12.5,0.062343432,199.7426,1997,593.2278,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 261 | 9.195,0.108501676,183.6634,2007,2362.9242,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 262 | 12.911574527641704,0.041821227,107.628,1985,213.056,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0 263 | 17.0,0.087387669,125.073,1998,369.519,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 264 | 12.911574527641704,0.099780431,225.2088,1985,447.4176,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 265 | 12.911574527641704,0.078576075,78.467,1985,229.701,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0 266 | 12.911574527641704,0.037712875,64.4826,1985,2518.7214,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 267 | 13.65,0.081096613,261.2936,2007,3131.9232,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 268 | 18.2,0.097865088,221.8456,1998,884.1824,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 269 | 17.6,0.041700756,164.8526,2002,2302.3364,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 270 | 17.5,0.0075551759999999985,145.9102,2002,2187.153,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 271 | 16.35,0.029445361,257.6646,1997,2061.3168,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 272 | 14.65,0.0,56.4614,1998,55.2614,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0 273 | 7.975,0.014628382,82.325,2004,1331.6,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 274 | 12.911574527641704,0.012865901,59.3536,1985,122.5072,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 275 | 6.55,0.024521191,100.6332,2004,2665.8632,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 276 | 19.5,0.015749341,182.2608,1999,3307.6944,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 277 | 11.8,0.057746382,152.4366,2007,3929.5516,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 278 | 8.395,0.039572298,100.9042,2002,1686.4714,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 279 | 10.395,0.037033223,227.9352,2009,4809.7392,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 280 | 12.911574527641704,0.076868664,62.1194,1985,123.8388,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 281 | 12.65,0.063246037,159.2578,2007,2406.867,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 282 | 21.1,0.020760673,128.7994,2002,1798.9916,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 283 | 6.965,0.0,159.4604,1987,3169.208,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 284 | 11.5,0.037782943,106.6254,2007,325.5762,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 285 | 17.5,0.015623753999999997,182.6266,2009,3135.2522,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 286 | 7.81,0.067592098,246.0486,2002,7086.1094,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 287 | 19.35,0.040154087000000005,164.6868,2007,1146.5076,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 288 | 10.195,0.107564024,149.0076,1999,1182.4608,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 289 | 20.0,0.028180789,46.8744,2002,905.488,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 290 | 9.8,0.056375879,84.6908,2004,1006.6896,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 291 | 5.78,0.07426435599999999,264.7568,1999,4745.8224,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 292 | 12.85,0.033220169,196.6768,1997,2759.0752,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 293 | 10.1,0.054939848,199.5084,2007,3372.9428,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 294 | 7.39,0.092927148,249.5066,1999,4267.1122,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 295 | 13.6,0.049547993,108.6912,1987,2402.2064,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 296 | 12.911574527641704,0.042270751,162.52100000000004,1985,4567.388,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 297 | 20.7,0.048718334,38.7506,1987,227.7036,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 298 | 18.85,0.141642219,168.1132,1997,2874.9244,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 299 | 8.63,0.031143591,183.9582,1999,5386.9878,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 300 | 5.44,0.017047751,174.637,1987,2999.429,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 301 | 18.85,0.141524348,168.4132,1987,2874.9244,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 302 | 13.6,0.119418124,231.03,2002,5359.69,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 303 | 10.1,0.167155198,241.7512,1997,5331.7264,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0 304 | 12.911574527641704,0.0,164.5526,1985,2302.3364,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 305 | 8.06,0.021372636,231.7326,2004,5313.7498,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 306 | 14.85,0.019590769,261.591,2009,2366.919,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 307 | 7.5,0.0,237.7906,2004,1188.453,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 308 | 11.8,0.107662745,224.1772,2007,2890.9036,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 309 | 13.1,0.037793818,173.8054,2007,2801.6864,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 310 | 12.911574527641704,0.044122209,173.8054,1985,175.1054,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0 311 | 15.0,0.0,47.2744,2004,679.1160000000001,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0 312 | 20.35,0.054046705999999986,119.5466,2004,353.5398,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0 313 | 12.911574527641704,0.0,248.8092,1985,6474.2392,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1 314 | 10.0,0.073493831,118.344,1999,1797.66,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 315 | 17.1,0.128390273,111.9886,2004,1445.4518,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 316 | 8.75,0.07461309,187.4556,2004,3755.112,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 317 | 12.911574527641704,0.050535312,130.031,1985,129.83100000000002,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 318 | 12.911574527641704,0.036150153,219.5482,1985,5695.2532,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 319 | 15.25,0.065999008,177.96599999999995,1999,2336.958,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0 320 | 9.5,0.07434526799999999,251.9724,1997,3523.4136,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0 321 | 7.84,0.153494979,48.935,1997,798.96,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 322 | 10.0,0.089135671,146.9102,2004,3207.8244,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 323 | 7.52,0.044272226,181.395,2007,2563.33,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 324 | 10.395,0.0,51.4008,2009,556.6088,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 325 | 17.35,0.167351411,176.0712,2004,2109.2544,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0 326 | 9.8,0.0268952,128.40200000000002,2002,2403.538,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0 327 | 13.15,0.056543464,140.5812,2002,427.4436,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 328 | 8.27,0.127927931,184.8924,1997,4442.2176,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0 329 | 8.945,0.088002959,261.391,2009,1840.937,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 330 | 10.3,0.011189235,86.654,2007,2423.512,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0 331 | 12.5,0.07153422599999999,124.902,2009,1771.0279999999998,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0 332 | 8.895,0.072511335,177.437,1999,3528.74,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0 333 | 12.911574527641704,0.021863506,247.0092,1985,5478.2024,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 334 | 16.6,0.0,117.3124,1987,2014.7108,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0 335 | 5.03,0.014472516,122.0756,1998,121.1756,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0 336 | 15.0,0.049357077,62.6168,2007,958.752,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0 337 | 9.395,0.15930433300000002,226.172,2004,3848.324,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0 338 | 12.911574527641704,0.150122794,154.5314,1985,1396.1826,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 339 | 12.15,0.015523707,212.0928,2009,2735.1064,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 340 | 12.911574527641704,0.024032484,124.973,1985,2463.46,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1 341 | 10.195,0.126383094,112.1886,1997,667.1316,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0 342 | 14.7,0.072592873,48.5034,2009,874.8612,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0 343 | -------------------------------------------------------------------------------- /Loan Prediction/loan.csv: -------------------------------------------------------------------------------- 1 | Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status 2 | LP001002,Male,No,0,Graduate,No,5849,0,,360,1,Urban,Y 3 | LP001003,Male,Yes,1,Graduate,No,4583,1508,128,360,1,Rural,N 4 | LP001005,Male,Yes,0,Graduate,Yes,3000,0,66,360,1,Urban,Y 5 | LP001006,Male,Yes,0,Not Graduate,No,2583,2358,120,360,1,Urban,Y 6 | LP001008,Male,No,0,Graduate,No,6000,0,141,360,1,Urban,Y 7 | LP001011,Male,Yes,2,Graduate,Yes,5417,4196,267,360,1,Urban,Y 8 | LP001013,Male,Yes,0,Not Graduate,No,2333,1516,95,360,1,Urban,Y 9 | LP001014,Male,Yes,3+,Graduate,No,3036,2504,158,360,0,Semiurban,N 10 | LP001018,Male,Yes,2,Graduate,No,4006,1526,168,360,1,Urban,Y 11 | LP001020,Male,Yes,1,Graduate,No,12841,10968,349,360,1,Semiurban,N 12 | LP001024,Male,Yes,2,Graduate,No,3200,700,70,360,1,Urban,Y 13 | LP001027,Male,Yes,2,Graduate,,2500,1840,109,360,1,Urban,Y 14 | LP001028,Male,Yes,2,Graduate,No,3073,8106,200,360,1,Urban,Y 15 | LP001029,Male,No,0,Graduate,No,1853,2840,114,360,1,Rural,N 16 | LP001030,Male,Yes,2,Graduate,No,1299,1086,17,120,1,Urban,Y 17 | LP001032,Male,No,0,Graduate,No,4950,0,125,360,1,Urban,Y 18 | LP001034,Male,No,1,Not Graduate,No,3596,0,100,240,,Urban,Y 19 | LP001036,Female,No,0,Graduate,No,3510,0,76,360,0,Urban,N 20 | LP001038,Male,Yes,0,Not Graduate,No,4887,0,133,360,1,Rural,N 21 | LP001041,Male,Yes,0,Graduate,,2600,3500,115,,1,Urban,Y 22 | LP001043,Male,Yes,0,Not Graduate,No,7660,0,104,360,0,Urban,N 23 | LP001046,Male,Yes,1,Graduate,No,5955,5625,315,360,1,Urban,Y 24 | LP001047,Male,Yes,0,Not Graduate,No,2600,1911,116,360,0,Semiurban,N 25 | LP001050,,Yes,2,Not Graduate,No,3365,1917,112,360,0,Rural,N 26 | LP001052,Male,Yes,1,Graduate,,3717,2925,151,360,,Semiurban,N 27 | LP001066,Male,Yes,0,Graduate,Yes,9560,0,191,360,1,Semiurban,Y 28 | LP001068,Male,Yes,0,Graduate,No,2799,2253,122,360,1,Semiurban,Y 29 | LP001073,Male,Yes,2,Not Graduate,No,4226,1040,110,360,1,Urban,Y 30 | LP001086,Male,No,0,Not Graduate,No,1442,0,35,360,1,Urban,N 31 | LP001087,Female,No,2,Graduate,,3750,2083,120,360,1,Semiurban,Y 32 | LP001091,Male,Yes,1,Graduate,,4166,3369,201,360,,Urban,N 33 | LP001095,Male,No,0,Graduate,No,3167,0,74,360,1,Urban,N 34 | LP001097,Male,No,1,Graduate,Yes,4692,0,106,360,1,Rural,N 35 | LP001098,Male,Yes,0,Graduate,No,3500,1667,114,360,1,Semiurban,Y 36 | LP001100,Male,No,3+,Graduate,No,12500,3000,320,360,1,Rural,N 37 | LP001106,Male,Yes,0,Graduate,No,2275,2067,,360,1,Urban,Y 38 | LP001109,Male,Yes,0,Graduate,No,1828,1330,100,,0,Urban,N 39 | LP001112,Female,Yes,0,Graduate,No,3667,1459,144,360,1,Semiurban,Y 40 | LP001114,Male,No,0,Graduate,No,4166,7210,184,360,1,Urban,Y 41 | LP001116,Male,No,0,Not Graduate,No,3748,1668,110,360,1,Semiurban,Y 42 | LP001119,Male,No,0,Graduate,No,3600,0,80,360,1,Urban,N 43 | LP001120,Male,No,0,Graduate,No,1800,1213,47,360,1,Urban,Y 44 | LP001123,Male,Yes,0,Graduate,No,2400,0,75,360,,Urban,Y 45 | LP001131,Male,Yes,0,Graduate,No,3941,2336,134,360,1,Semiurban,Y 46 | LP001136,Male,Yes,0,Not Graduate,Yes,4695,0,96,,1,Urban,Y 47 | LP001137,Female,No,0,Graduate,No,3410,0,88,,1,Urban,Y 48 | LP001138,Male,Yes,1,Graduate,No,5649,0,44,360,1,Urban,Y 49 | LP001144,Male,Yes,0,Graduate,No,5821,0,144,360,1,Urban,Y 50 | LP001146,Female,Yes,0,Graduate,No,2645,3440,120,360,0,Urban,N 51 | LP001151,Female,No,0,Graduate,No,4000,2275,144,360,1,Semiurban,Y 52 | LP001155,Female,Yes,0,Not Graduate,No,1928,1644,100,360,1,Semiurban,Y 53 | LP001157,Female,No,0,Graduate,No,3086,0,120,360,1,Semiurban,Y 54 | LP001164,Female,No,0,Graduate,No,4230,0,112,360,1,Semiurban,N 55 | LP001179,Male,Yes,2,Graduate,No,4616,0,134,360,1,Urban,N 56 | LP001186,Female,Yes,1,Graduate,Yes,11500,0,286,360,0,Urban,N 57 | LP001194,Male,Yes,2,Graduate,No,2708,1167,97,360,1,Semiurban,Y 58 | LP001195,Male,Yes,0,Graduate,No,2132,1591,96,360,1,Semiurban,Y 59 | LP001197,Male,Yes,0,Graduate,No,3366,2200,135,360,1,Rural,N 60 | LP001198,Male,Yes,1,Graduate,No,8080,2250,180,360,1,Urban,Y 61 | LP001199,Male,Yes,2,Not Graduate,No,3357,2859,144,360,1,Urban,Y 62 | LP001205,Male,Yes,0,Graduate,No,2500,3796,120,360,1,Urban,Y 63 | LP001206,Male,Yes,3+,Graduate,No,3029,0,99,360,1,Urban,Y 64 | LP001207,Male,Yes,0,Not Graduate,Yes,2609,3449,165,180,0,Rural,N 65 | LP001213,Male,Yes,1,Graduate,No,4945,0,,360,0,Rural,N 66 | LP001222,Female,No,0,Graduate,No,4166,0,116,360,0,Semiurban,N 67 | LP001225,Male,Yes,0,Graduate,No,5726,4595,258,360,1,Semiurban,N 68 | LP001228,Male,No,0,Not Graduate,No,3200,2254,126,180,0,Urban,N 69 | LP001233,Male,Yes,1,Graduate,No,10750,0,312,360,1,Urban,Y 70 | LP001238,Male,Yes,3+,Not Graduate,Yes,7100,0,125,60,1,Urban,Y 71 | LP001241,Female,No,0,Graduate,No,4300,0,136,360,0,Semiurban,N 72 | LP001243,Male,Yes,0,Graduate,No,3208,3066,172,360,1,Urban,Y 73 | LP001245,Male,Yes,2,Not Graduate,Yes,1875,1875,97,360,1,Semiurban,Y 74 | LP001248,Male,No,0,Graduate,No,3500,0,81,300,1,Semiurban,Y 75 | LP001250,Male,Yes,3+,Not Graduate,No,4755,0,95,,0,Semiurban,N 76 | LP001253,Male,Yes,3+,Graduate,Yes,5266,1774,187,360,1,Semiurban,Y 77 | LP001255,Male,No,0,Graduate,No,3750,0,113,480,1,Urban,N 78 | LP001256,Male,No,0,Graduate,No,3750,4750,176,360,1,Urban,N 79 | LP001259,Male,Yes,1,Graduate,Yes,1000,3022,110,360,1,Urban,N 80 | LP001263,Male,Yes,3+,Graduate,No,3167,4000,180,300,0,Semiurban,N 81 | LP001264,Male,Yes,3+,Not Graduate,Yes,3333,2166,130,360,,Semiurban,Y 82 | LP001265,Female,No,0,Graduate,No,3846,0,111,360,1,Semiurban,Y 83 | LP001266,Male,Yes,1,Graduate,Yes,2395,0,,360,1,Semiurban,Y 84 | LP001267,Female,Yes,2,Graduate,No,1378,1881,167,360,1,Urban,N 85 | LP001273,Male,Yes,0,Graduate,No,6000,2250,265,360,,Semiurban,N 86 | LP001275,Male,Yes,1,Graduate,No,3988,0,50,240,1,Urban,Y 87 | LP001279,Male,No,0,Graduate,No,2366,2531,136,360,1,Semiurban,Y 88 | LP001280,Male,Yes,2,Not Graduate,No,3333,2000,99,360,,Semiurban,Y 89 | LP001282,Male,Yes,0,Graduate,No,2500,2118,104,360,1,Semiurban,Y 90 | LP001289,Male,No,0,Graduate,No,8566,0,210,360,1,Urban,Y 91 | LP001310,Male,Yes,0,Graduate,No,5695,4167,175,360,1,Semiurban,Y 92 | LP001316,Male,Yes,0,Graduate,No,2958,2900,131,360,1,Semiurban,Y 93 | LP001318,Male,Yes,2,Graduate,No,6250,5654,188,180,1,Semiurban,Y 94 | LP001319,Male,Yes,2,Not Graduate,No,3273,1820,81,360,1,Urban,Y 95 | LP001322,Male,No,0,Graduate,No,4133,0,122,360,1,Semiurban,Y 96 | LP001325,Male,No,0,Not Graduate,No,3620,0,25,120,1,Semiurban,Y 97 | LP001326,Male,No,0,Graduate,,6782,0,,360,,Urban,N 98 | LP001327,Female,Yes,0,Graduate,No,2484,2302,137,360,1,Semiurban,Y 99 | LP001333,Male,Yes,0,Graduate,No,1977,997,50,360,1,Semiurban,Y 100 | LP001334,Male,Yes,0,Not Graduate,No,4188,0,115,180,1,Semiurban,Y 101 | LP001343,Male,Yes,0,Graduate,No,1759,3541,131,360,1,Semiurban,Y 102 | LP001345,Male,Yes,2,Not Graduate,No,4288,3263,133,180,1,Urban,Y 103 | LP001349,Male,No,0,Graduate,No,4843,3806,151,360,1,Semiurban,Y 104 | LP001350,Male,Yes,,Graduate,No,13650,0,,360,1,Urban,Y 105 | LP001356,Male,Yes,0,Graduate,No,4652,3583,,360,1,Semiurban,Y 106 | LP001357,Male,,,Graduate,No,3816,754,160,360,1,Urban,Y 107 | LP001367,Male,Yes,1,Graduate,No,3052,1030,100,360,1,Urban,Y 108 | LP001369,Male,Yes,2,Graduate,No,11417,1126,225,360,1,Urban,Y 109 | LP001370,Male,No,0,Not Graduate,,7333,0,120,360,1,Rural,N 110 | LP001379,Male,Yes,2,Graduate,No,3800,3600,216,360,0,Urban,N 111 | LP001384,Male,Yes,3+,Not Graduate,No,2071,754,94,480,1,Semiurban,Y 112 | LP001385,Male,No,0,Graduate,No,5316,0,136,360,1,Urban,Y 113 | LP001387,Female,Yes,0,Graduate,,2929,2333,139,360,1,Semiurban,Y 114 | LP001391,Male,Yes,0,Not Graduate,No,3572,4114,152,,0,Rural,N 115 | LP001392,Female,No,1,Graduate,Yes,7451,0,,360,1,Semiurban,Y 116 | LP001398,Male,No,0,Graduate,,5050,0,118,360,1,Semiurban,Y 117 | LP001401,Male,Yes,1,Graduate,No,14583,0,185,180,1,Rural,Y 118 | LP001404,Female,Yes,0,Graduate,No,3167,2283,154,360,1,Semiurban,Y 119 | LP001405,Male,Yes,1,Graduate,No,2214,1398,85,360,,Urban,Y 120 | LP001421,Male,Yes,0,Graduate,No,5568,2142,175,360,1,Rural,N 121 | LP001422,Female,No,0,Graduate,No,10408,0,259,360,1,Urban,Y 122 | LP001426,Male,Yes,,Graduate,No,5667,2667,180,360,1,Rural,Y 123 | LP001430,Female,No,0,Graduate,No,4166,0,44,360,1,Semiurban,Y 124 | LP001431,Female,No,0,Graduate,No,2137,8980,137,360,0,Semiurban,Y 125 | LP001432,Male,Yes,2,Graduate,No,2957,0,81,360,1,Semiurban,Y 126 | LP001439,Male,Yes,0,Not Graduate,No,4300,2014,194,360,1,Rural,Y 127 | LP001443,Female,No,0,Graduate,No,3692,0,93,360,,Rural,Y 128 | LP001448,,Yes,3+,Graduate,No,23803,0,370,360,1,Rural,Y 129 | LP001449,Male,No,0,Graduate,No,3865,1640,,360,1,Rural,Y 130 | LP001451,Male,Yes,1,Graduate,Yes,10513,3850,160,180,0,Urban,N 131 | LP001465,Male,Yes,0,Graduate,No,6080,2569,182,360,,Rural,N 132 | LP001469,Male,No,0,Graduate,Yes,20166,0,650,480,,Urban,Y 133 | LP001473,Male,No,0,Graduate,No,2014,1929,74,360,1,Urban,Y 134 | LP001478,Male,No,0,Graduate,No,2718,0,70,360,1,Semiurban,Y 135 | LP001482,Male,Yes,0,Graduate,Yes,3459,0,25,120,1,Semiurban,Y 136 | LP001487,Male,No,0,Graduate,No,4895,0,102,360,1,Semiurban,Y 137 | LP001488,Male,Yes,3+,Graduate,No,4000,7750,290,360,1,Semiurban,N 138 | LP001489,Female,Yes,0,Graduate,No,4583,0,84,360,1,Rural,N 139 | LP001491,Male,Yes,2,Graduate,Yes,3316,3500,88,360,1,Urban,Y 140 | LP001492,Male,No,0,Graduate,No,14999,0,242,360,0,Semiurban,N 141 | LP001493,Male,Yes,2,Not Graduate,No,4200,1430,129,360,1,Rural,N 142 | LP001497,Male,Yes,2,Graduate,No,5042,2083,185,360,1,Rural,N 143 | LP001498,Male,No,0,Graduate,No,5417,0,168,360,1,Urban,Y 144 | LP001504,Male,No,0,Graduate,Yes,6950,0,175,180,1,Semiurban,Y 145 | LP001507,Male,Yes,0,Graduate,No,2698,2034,122,360,1,Semiurban,Y 146 | LP001508,Male,Yes,2,Graduate,No,11757,0,187,180,1,Urban,Y 147 | LP001514,Female,Yes,0,Graduate,No,2330,4486,100,360,1,Semiurban,Y 148 | LP001516,Female,Yes,2,Graduate,No,14866,0,70,360,1,Urban,Y 149 | LP001518,Male,Yes,1,Graduate,No,1538,1425,30,360,1,Urban,Y 150 | LP001519,Female,No,0,Graduate,No,10000,1666,225,360,1,Rural,N 151 | LP001520,Male,Yes,0,Graduate,No,4860,830,125,360,1,Semiurban,Y 152 | LP001528,Male,No,0,Graduate,No,6277,0,118,360,0,Rural,N 153 | LP001529,Male,Yes,0,Graduate,Yes,2577,3750,152,360,1,Rural,Y 154 | LP001531,Male,No,0,Graduate,No,9166,0,244,360,1,Urban,N 155 | LP001532,Male,Yes,2,Not Graduate,No,2281,0,113,360,1,Rural,N 156 | LP001535,Male,No,0,Graduate,No,3254,0,50,360,1,Urban,Y 157 | LP001536,Male,Yes,3+,Graduate,No,39999,0,600,180,0,Semiurban,Y 158 | LP001541,Male,Yes,1,Graduate,No,6000,0,160,360,,Rural,Y 159 | LP001543,Male,Yes,1,Graduate,No,9538,0,187,360,1,Urban,Y 160 | LP001546,Male,No,0,Graduate,,2980,2083,120,360,1,Rural,Y 161 | LP001552,Male,Yes,0,Graduate,No,4583,5625,255,360,1,Semiurban,Y 162 | LP001560,Male,Yes,0,Not Graduate,No,1863,1041,98,360,1,Semiurban,Y 163 | LP001562,Male,Yes,0,Graduate,No,7933,0,275,360,1,Urban,N 164 | LP001565,Male,Yes,1,Graduate,No,3089,1280,121,360,0,Semiurban,N 165 | LP001570,Male,Yes,2,Graduate,No,4167,1447,158,360,1,Rural,Y 166 | LP001572,Male,Yes,0,Graduate,No,9323,0,75,180,1,Urban,Y 167 | LP001574,Male,Yes,0,Graduate,No,3707,3166,182,,1,Rural,Y 168 | LP001577,Female,Yes,0,Graduate,No,4583,0,112,360,1,Rural,N 169 | LP001578,Male,Yes,0,Graduate,No,2439,3333,129,360,1,Rural,Y 170 | LP001579,Male,No,0,Graduate,No,2237,0,63,480,0,Semiurban,N 171 | LP001580,Male,Yes,2,Graduate,No,8000,0,200,360,1,Semiurban,Y 172 | LP001581,Male,Yes,0,Not Graduate,,1820,1769,95,360,1,Rural,Y 173 | LP001585,,Yes,3+,Graduate,No,51763,0,700,300,1,Urban,Y 174 | LP001586,Male,Yes,3+,Not Graduate,No,3522,0,81,180,1,Rural,N 175 | LP001594,Male,Yes,0,Graduate,No,5708,5625,187,360,1,Semiurban,Y 176 | LP001603,Male,Yes,0,Not Graduate,Yes,4344,736,87,360,1,Semiurban,N 177 | LP001606,Male,Yes,0,Graduate,No,3497,1964,116,360,1,Rural,Y 178 | LP001608,Male,Yes,2,Graduate,No,2045,1619,101,360,1,Rural,Y 179 | LP001610,Male,Yes,3+,Graduate,No,5516,11300,495,360,0,Semiurban,N 180 | LP001616,Male,Yes,1,Graduate,No,3750,0,116,360,1,Semiurban,Y 181 | LP001630,Male,No,0,Not Graduate,No,2333,1451,102,480,0,Urban,N 182 | LP001633,Male,Yes,1,Graduate,No,6400,7250,180,360,0,Urban,N 183 | LP001634,Male,No,0,Graduate,No,1916,5063,67,360,,Rural,N 184 | LP001636,Male,Yes,0,Graduate,No,4600,0,73,180,1,Semiurban,Y 185 | LP001637,Male,Yes,1,Graduate,No,33846,0,260,360,1,Semiurban,N 186 | LP001639,Female,Yes,0,Graduate,No,3625,0,108,360,1,Semiurban,Y 187 | LP001640,Male,Yes,0,Graduate,Yes,39147,4750,120,360,1,Semiurban,Y 188 | LP001641,Male,Yes,1,Graduate,Yes,2178,0,66,300,0,Rural,N 189 | LP001643,Male,Yes,0,Graduate,No,2383,2138,58,360,,Rural,Y 190 | LP001644,,Yes,0,Graduate,Yes,674,5296,168,360,1,Rural,Y 191 | LP001647,Male,Yes,0,Graduate,No,9328,0,188,180,1,Rural,Y 192 | LP001653,Male,No,0,Not Graduate,No,4885,0,48,360,1,Rural,Y 193 | LP001656,Male,No,0,Graduate,No,12000,0,164,360,1,Semiurban,N 194 | LP001657,Male,Yes,0,Not Graduate,No,6033,0,160,360,1,Urban,N 195 | LP001658,Male,No,0,Graduate,No,3858,0,76,360,1,Semiurban,Y 196 | LP001664,Male,No,0,Graduate,No,4191,0,120,360,1,Rural,Y 197 | LP001665,Male,Yes,1,Graduate,No,3125,2583,170,360,1,Semiurban,N 198 | LP001666,Male,No,0,Graduate,No,8333,3750,187,360,1,Rural,Y 199 | LP001669,Female,No,0,Not Graduate,No,1907,2365,120,,1,Urban,Y 200 | LP001671,Female,Yes,0,Graduate,No,3416,2816,113,360,,Semiurban,Y 201 | LP001673,Male,No,0,Graduate,Yes,11000,0,83,360,1,Urban,N 202 | LP001674,Male,Yes,1,Not Graduate,No,2600,2500,90,360,1,Semiurban,Y 203 | LP001677,Male,No,2,Graduate,No,4923,0,166,360,0,Semiurban,Y 204 | LP001682,Male,Yes,3+,Not Graduate,No,3992,0,,180,1,Urban,N 205 | LP001688,Male,Yes,1,Not Graduate,No,3500,1083,135,360,1,Urban,Y 206 | LP001691,Male,Yes,2,Not Graduate,No,3917,0,124,360,1,Semiurban,Y 207 | LP001692,Female,No,0,Not Graduate,No,4408,0,120,360,1,Semiurban,Y 208 | LP001693,Female,No,0,Graduate,No,3244,0,80,360,1,Urban,Y 209 | LP001698,Male,No,0,Not Graduate,No,3975,2531,55,360,1,Rural,Y 210 | LP001699,Male,No,0,Graduate,No,2479,0,59,360,1,Urban,Y 211 | LP001702,Male,No,0,Graduate,No,3418,0,127,360,1,Semiurban,N 212 | LP001708,Female,No,0,Graduate,No,10000,0,214,360,1,Semiurban,N 213 | LP001711,Male,Yes,3+,Graduate,No,3430,1250,128,360,0,Semiurban,N 214 | LP001713,Male,Yes,1,Graduate,Yes,7787,0,240,360,1,Urban,Y 215 | LP001715,Male,Yes,3+,Not Graduate,Yes,5703,0,130,360,1,Rural,Y 216 | LP001716,Male,Yes,0,Graduate,No,3173,3021,137,360,1,Urban,Y 217 | LP001720,Male,Yes,3+,Not Graduate,No,3850,983,100,360,1,Semiurban,Y 218 | LP001722,Male,Yes,0,Graduate,No,150,1800,135,360,1,Rural,N 219 | LP001726,Male,Yes,0,Graduate,No,3727,1775,131,360,1,Semiurban,Y 220 | LP001732,Male,Yes,2,Graduate,,5000,0,72,360,0,Semiurban,N 221 | LP001734,Female,Yes,2,Graduate,No,4283,2383,127,360,,Semiurban,Y 222 | LP001736,Male,Yes,0,Graduate,No,2221,0,60,360,0,Urban,N 223 | LP001743,Male,Yes,2,Graduate,No,4009,1717,116,360,1,Semiurban,Y 224 | LP001744,Male,No,0,Graduate,No,2971,2791,144,360,1,Semiurban,Y 225 | LP001749,Male,Yes,0,Graduate,No,7578,1010,175,,1,Semiurban,Y 226 | LP001750,Male,Yes,0,Graduate,No,6250,0,128,360,1,Semiurban,Y 227 | LP001751,Male,Yes,0,Graduate,No,3250,0,170,360,1,Rural,N 228 | LP001754,Male,Yes,,Not Graduate,Yes,4735,0,138,360,1,Urban,N 229 | LP001758,Male,Yes,2,Graduate,No,6250,1695,210,360,1,Semiurban,Y 230 | LP001760,Male,,,Graduate,No,4758,0,158,480,1,Semiurban,Y 231 | LP001761,Male,No,0,Graduate,Yes,6400,0,200,360,1,Rural,Y 232 | LP001765,Male,Yes,1,Graduate,No,2491,2054,104,360,1,Semiurban,Y 233 | LP001768,Male,Yes,0,Graduate,,3716,0,42,180,1,Rural,Y 234 | LP001770,Male,No,0,Not Graduate,No,3189,2598,120,,1,Rural,Y 235 | LP001776,Female,No,0,Graduate,No,8333,0,280,360,1,Semiurban,Y 236 | LP001778,Male,Yes,1,Graduate,No,3155,1779,140,360,1,Semiurban,Y 237 | LP001784,Male,Yes,1,Graduate,No,5500,1260,170,360,1,Rural,Y 238 | LP001786,Male,Yes,0,Graduate,,5746,0,255,360,,Urban,N 239 | LP001788,Female,No,0,Graduate,Yes,3463,0,122,360,,Urban,Y 240 | LP001790,Female,No,1,Graduate,No,3812,0,112,360,1,Rural,Y 241 | LP001792,Male,Yes,1,Graduate,No,3315,0,96,360,1,Semiurban,Y 242 | LP001798,Male,Yes,2,Graduate,No,5819,5000,120,360,1,Rural,Y 243 | LP001800,Male,Yes,1,Not Graduate,No,2510,1983,140,180,1,Urban,N 244 | LP001806,Male,No,0,Graduate,No,2965,5701,155,60,1,Urban,Y 245 | LP001807,Male,Yes,2,Graduate,Yes,6250,1300,108,360,1,Rural,Y 246 | LP001811,Male,Yes,0,Not Graduate,No,3406,4417,123,360,1,Semiurban,Y 247 | LP001813,Male,No,0,Graduate,Yes,6050,4333,120,180,1,Urban,N 248 | LP001814,Male,Yes,2,Graduate,No,9703,0,112,360,1,Urban,Y 249 | LP001819,Male,Yes,1,Not Graduate,No,6608,0,137,180,1,Urban,Y 250 | LP001824,Male,Yes,1,Graduate,No,2882,1843,123,480,1,Semiurban,Y 251 | LP001825,Male,Yes,0,Graduate,No,1809,1868,90,360,1,Urban,Y 252 | LP001835,Male,Yes,0,Not Graduate,No,1668,3890,201,360,0,Semiurban,N 253 | LP001836,Female,No,2,Graduate,No,3427,0,138,360,1,Urban,N 254 | LP001841,Male,No,0,Not Graduate,Yes,2583,2167,104,360,1,Rural,Y 255 | LP001843,Male,Yes,1,Not Graduate,No,2661,7101,279,180,1,Semiurban,Y 256 | LP001844,Male,No,0,Graduate,Yes,16250,0,192,360,0,Urban,N 257 | LP001846,Female,No,3+,Graduate,No,3083,0,255,360,1,Rural,Y 258 | LP001849,Male,No,0,Not Graduate,No,6045,0,115,360,0,Rural,N 259 | LP001854,Male,Yes,3+,Graduate,No,5250,0,94,360,1,Urban,N 260 | LP001859,Male,Yes,0,Graduate,No,14683,2100,304,360,1,Rural,N 261 | LP001864,Male,Yes,3+,Not Graduate,No,4931,0,128,360,,Semiurban,N 262 | LP001865,Male,Yes,1,Graduate,No,6083,4250,330,360,,Urban,Y 263 | LP001868,Male,No,0,Graduate,No,2060,2209,134,360,1,Semiurban,Y 264 | LP001870,Female,No,1,Graduate,No,3481,0,155,36,1,Semiurban,N 265 | LP001871,Female,No,0,Graduate,No,7200,0,120,360,1,Rural,Y 266 | LP001872,Male,No,0,Graduate,Yes,5166,0,128,360,1,Semiurban,Y 267 | LP001875,Male,No,0,Graduate,No,4095,3447,151,360,1,Rural,Y 268 | LP001877,Male,Yes,2,Graduate,No,4708,1387,150,360,1,Semiurban,Y 269 | LP001882,Male,Yes,3+,Graduate,No,4333,1811,160,360,0,Urban,Y 270 | LP001883,Female,No,0,Graduate,,3418,0,135,360,1,Rural,N 271 | LP001884,Female,No,1,Graduate,No,2876,1560,90,360,1,Urban,Y 272 | LP001888,Female,No,0,Graduate,No,3237,0,30,360,1,Urban,Y 273 | LP001891,Male,Yes,0,Graduate,No,11146,0,136,360,1,Urban,Y 274 | LP001892,Male,No,0,Graduate,No,2833,1857,126,360,1,Rural,Y 275 | LP001894,Male,Yes,0,Graduate,No,2620,2223,150,360,1,Semiurban,Y 276 | LP001896,Male,Yes,2,Graduate,No,3900,0,90,360,1,Semiurban,Y 277 | LP001900,Male,Yes,1,Graduate,No,2750,1842,115,360,1,Semiurban,Y 278 | LP001903,Male,Yes,0,Graduate,No,3993,3274,207,360,1,Semiurban,Y 279 | LP001904,Male,Yes,0,Graduate,No,3103,1300,80,360,1,Urban,Y 280 | LP001907,Male,Yes,0,Graduate,No,14583,0,436,360,1,Semiurban,Y 281 | LP001908,Female,Yes,0,Not Graduate,No,4100,0,124,360,,Rural,Y 282 | LP001910,Male,No,1,Not Graduate,Yes,4053,2426,158,360,0,Urban,N 283 | LP001914,Male,Yes,0,Graduate,No,3927,800,112,360,1,Semiurban,Y 284 | LP001915,Male,Yes,2,Graduate,No,2301,985.7999878,78,180,1,Urban,Y 285 | LP001917,Female,No,0,Graduate,No,1811,1666,54,360,1,Urban,Y 286 | LP001922,Male,Yes,0,Graduate,No,20667,0,,360,1,Rural,N 287 | LP001924,Male,No,0,Graduate,No,3158,3053,89,360,1,Rural,Y 288 | LP001925,Female,No,0,Graduate,Yes,2600,1717,99,300,1,Semiurban,N 289 | LP001926,Male,Yes,0,Graduate,No,3704,2000,120,360,1,Rural,Y 290 | LP001931,Female,No,0,Graduate,No,4124,0,115,360,1,Semiurban,Y 291 | LP001935,Male,No,0,Graduate,No,9508,0,187,360,1,Rural,Y 292 | LP001936,Male,Yes,0,Graduate,No,3075,2416,139,360,1,Rural,Y 293 | LP001938,Male,Yes,2,Graduate,No,4400,0,127,360,0,Semiurban,N 294 | LP001940,Male,Yes,2,Graduate,No,3153,1560,134,360,1,Urban,Y 295 | LP001945,Female,No,,Graduate,No,5417,0,143,480,0,Urban,N 296 | LP001947,Male,Yes,0,Graduate,No,2383,3334,172,360,1,Semiurban,Y 297 | LP001949,Male,Yes,3+,Graduate,,4416,1250,110,360,1,Urban,Y 298 | LP001953,Male,Yes,1,Graduate,No,6875,0,200,360,1,Semiurban,Y 299 | LP001954,Female,Yes,1,Graduate,No,4666,0,135,360,1,Urban,Y 300 | LP001955,Female,No,0,Graduate,No,5000,2541,151,480,1,Rural,N 301 | LP001963,Male,Yes,1,Graduate,No,2014,2925,113,360,1,Urban,N 302 | LP001964,Male,Yes,0,Not Graduate,No,1800,2934,93,360,0,Urban,N 303 | LP001972,Male,Yes,,Not Graduate,No,2875,1750,105,360,1,Semiurban,Y 304 | LP001974,Female,No,0,Graduate,No,5000,0,132,360,1,Rural,Y 305 | LP001977,Male,Yes,1,Graduate,No,1625,1803,96,360,1,Urban,Y 306 | LP001978,Male,No,0,Graduate,No,4000,2500,140,360,1,Rural,Y 307 | LP001990,Male,No,0,Not Graduate,No,2000,0,,360,1,Urban,N 308 | LP001993,Female,No,0,Graduate,No,3762,1666,135,360,1,Rural,Y 309 | LP001994,Female,No,0,Graduate,No,2400,1863,104,360,0,Urban,N 310 | LP001996,Male,No,0,Graduate,No,20233,0,480,360,1,Rural,N 311 | LP001998,Male,Yes,2,Not Graduate,No,7667,0,185,360,,Rural,Y 312 | LP002002,Female,No,0,Graduate,No,2917,0,84,360,1,Semiurban,Y 313 | LP002004,Male,No,0,Not Graduate,No,2927,2405,111,360,1,Semiurban,Y 314 | LP002006,Female,No,0,Graduate,No,2507,0,56,360,1,Rural,Y 315 | LP002008,Male,Yes,2,Graduate,Yes,5746,0,144,84,,Rural,Y 316 | LP002024,,Yes,0,Graduate,No,2473,1843,159,360,1,Rural,N 317 | LP002031,Male,Yes,1,Not Graduate,No,3399,1640,111,180,1,Urban,Y 318 | LP002035,Male,Yes,2,Graduate,No,3717,0,120,360,1,Semiurban,Y 319 | LP002036,Male,Yes,0,Graduate,No,2058,2134,88,360,,Urban,Y 320 | LP002043,Female,No,1,Graduate,No,3541,0,112,360,,Semiurban,Y 321 | LP002050,Male,Yes,1,Graduate,Yes,10000,0,155,360,1,Rural,N 322 | LP002051,Male,Yes,0,Graduate,No,2400,2167,115,360,1,Semiurban,Y 323 | LP002053,Male,Yes,3+,Graduate,No,4342,189,124,360,1,Semiurban,Y 324 | LP002054,Male,Yes,2,Not Graduate,No,3601,1590,,360,1,Rural,Y 325 | LP002055,Female,No,0,Graduate,No,3166,2985,132,360,,Rural,Y 326 | LP002065,Male,Yes,3+,Graduate,No,15000,0,300,360,1,Rural,Y 327 | LP002067,Male,Yes,1,Graduate,Yes,8666,4983,376,360,0,Rural,N 328 | LP002068,Male,No,0,Graduate,No,4917,0,130,360,0,Rural,Y 329 | LP002082,Male,Yes,0,Graduate,Yes,5818,2160,184,360,1,Semiurban,Y 330 | LP002086,Female,Yes,0,Graduate,No,4333,2451,110,360,1,Urban,N 331 | LP002087,Female,No,0,Graduate,No,2500,0,67,360,1,Urban,Y 332 | LP002097,Male,No,1,Graduate,No,4384,1793,117,360,1,Urban,Y 333 | LP002098,Male,No,0,Graduate,No,2935,0,98,360,1,Semiurban,Y 334 | LP002100,Male,No,,Graduate,No,2833,0,71,360,1,Urban,Y 335 | LP002101,Male,Yes,0,Graduate,,63337,0,490,180,1,Urban,Y 336 | LP002103,,Yes,1,Graduate,Yes,9833,1833,182,180,1,Urban,Y 337 | LP002106,Male,Yes,,Graduate,Yes,5503,4490,70,,1,Semiurban,Y 338 | LP002110,Male,Yes,1,Graduate,,5250,688,160,360,1,Rural,Y 339 | LP002112,Male,Yes,2,Graduate,Yes,2500,4600,176,360,1,Rural,Y 340 | LP002113,Female,No,3+,Not Graduate,No,1830,0,,360,0,Urban,N 341 | LP002114,Female,No,0,Graduate,No,4160,0,71,360,1,Semiurban,Y 342 | LP002115,Male,Yes,3+,Not Graduate,No,2647,1587,173,360,1,Rural,N 343 | LP002116,Female,No,0,Graduate,No,2378,0,46,360,1,Rural,N 344 | LP002119,Male,Yes,1,Not Graduate,No,4554,1229,158,360,1,Urban,Y 345 | LP002126,Male,Yes,3+,Not Graduate,No,3173,0,74,360,1,Semiurban,Y 346 | LP002128,Male,Yes,2,Graduate,,2583,2330,125,360,1,Rural,Y 347 | LP002129,Male,Yes,0,Graduate,No,2499,2458,160,360,1,Semiurban,Y 348 | LP002130,Male,Yes,,Not Graduate,No,3523,3230,152,360,0,Rural,N 349 | LP002131,Male,Yes,2,Not Graduate,No,3083,2168,126,360,1,Urban,Y 350 | LP002137,Male,Yes,0,Graduate,No,6333,4583,259,360,,Semiurban,Y 351 | LP002138,Male,Yes,0,Graduate,No,2625,6250,187,360,1,Rural,Y 352 | LP002139,Male,Yes,0,Graduate,No,9083,0,228,360,1,Semiurban,Y 353 | LP002140,Male,No,0,Graduate,No,8750,4167,308,360,1,Rural,N 354 | LP002141,Male,Yes,3+,Graduate,No,2666,2083,95,360,1,Rural,Y 355 | LP002142,Female,Yes,0,Graduate,Yes,5500,0,105,360,0,Rural,N 356 | LP002143,Female,Yes,0,Graduate,No,2423,505,130,360,1,Semiurban,Y 357 | LP002144,Female,No,,Graduate,No,3813,0,116,180,1,Urban,Y 358 | LP002149,Male,Yes,2,Graduate,No,8333,3167,165,360,1,Rural,Y 359 | LP002151,Male,Yes,1,Graduate,No,3875,0,67,360,1,Urban,N 360 | LP002158,Male,Yes,0,Not Graduate,No,3000,1666,100,480,0,Urban,N 361 | LP002160,Male,Yes,3+,Graduate,No,5167,3167,200,360,1,Semiurban,Y 362 | LP002161,Female,No,1,Graduate,No,4723,0,81,360,1,Semiurban,N 363 | LP002170,Male,Yes,2,Graduate,No,5000,3667,236,360,1,Semiurban,Y 364 | LP002175,Male,Yes,0,Graduate,No,4750,2333,130,360,1,Urban,Y 365 | LP002178,Male,Yes,0,Graduate,No,3013,3033,95,300,,Urban,Y 366 | LP002180,Male,No,0,Graduate,Yes,6822,0,141,360,1,Rural,Y 367 | LP002181,Male,No,0,Not Graduate,No,6216,0,133,360,1,Rural,N 368 | LP002187,Male,No,0,Graduate,No,2500,0,96,480,1,Semiurban,N 369 | LP002188,Male,No,0,Graduate,No,5124,0,124,,0,Rural,N 370 | LP002190,Male,Yes,1,Graduate,No,6325,0,175,360,1,Semiurban,Y 371 | LP002191,Male,Yes,0,Graduate,No,19730,5266,570,360,1,Rural,N 372 | LP002194,Female,No,0,Graduate,Yes,15759,0,55,360,1,Semiurban,Y 373 | LP002197,Male,Yes,2,Graduate,No,5185,0,155,360,1,Semiurban,Y 374 | LP002201,Male,Yes,2,Graduate,Yes,9323,7873,380,300,1,Rural,Y 375 | LP002205,Male,No,1,Graduate,No,3062,1987,111,180,0,Urban,N 376 | LP002209,Female,No,0,Graduate,,2764,1459,110,360,1,Urban,Y 377 | LP002211,Male,Yes,0,Graduate,No,4817,923,120,180,1,Urban,Y 378 | LP002219,Male,Yes,3+,Graduate,No,8750,4996,130,360,1,Rural,Y 379 | LP002223,Male,Yes,0,Graduate,No,4310,0,130,360,,Semiurban,Y 380 | LP002224,Male,No,0,Graduate,No,3069,0,71,480,1,Urban,N 381 | LP002225,Male,Yes,2,Graduate,No,5391,0,130,360,1,Urban,Y 382 | LP002226,Male,Yes,0,Graduate,,3333,2500,128,360,1,Semiurban,Y 383 | LP002229,Male,No,0,Graduate,No,5941,4232,296,360,1,Semiurban,Y 384 | LP002231,Female,No,0,Graduate,No,6000,0,156,360,1,Urban,Y 385 | LP002234,Male,No,0,Graduate,Yes,7167,0,128,360,1,Urban,Y 386 | LP002236,Male,Yes,2,Graduate,No,4566,0,100,360,1,Urban,N 387 | LP002237,Male,No,1,Graduate,,3667,0,113,180,1,Urban,Y 388 | LP002239,Male,No,0,Not Graduate,No,2346,1600,132,360,1,Semiurban,Y 389 | LP002243,Male,Yes,0,Not Graduate,No,3010,3136,,360,0,Urban,N 390 | LP002244,Male,Yes,0,Graduate,No,2333,2417,136,360,1,Urban,Y 391 | LP002250,Male,Yes,0,Graduate,No,5488,0,125,360,1,Rural,Y 392 | LP002255,Male,No,3+,Graduate,No,9167,0,185,360,1,Rural,Y 393 | LP002262,Male,Yes,3+,Graduate,No,9504,0,275,360,1,Rural,Y 394 | LP002263,Male,Yes,0,Graduate,No,2583,2115,120,360,,Urban,Y 395 | LP002265,Male,Yes,2,Not Graduate,No,1993,1625,113,180,1,Semiurban,Y 396 | LP002266,Male,Yes,2,Graduate,No,3100,1400,113,360,1,Urban,Y 397 | LP002272,Male,Yes,2,Graduate,No,3276,484,135,360,,Semiurban,Y 398 | LP002277,Female,No,0,Graduate,No,3180,0,71,360,0,Urban,N 399 | LP002281,Male,Yes,0,Graduate,No,3033,1459,95,360,1,Urban,Y 400 | LP002284,Male,No,0,Not Graduate,No,3902,1666,109,360,1,Rural,Y 401 | LP002287,Female,No,0,Graduate,No,1500,1800,103,360,0,Semiurban,N 402 | LP002288,Male,Yes,2,Not Graduate,No,2889,0,45,180,0,Urban,N 403 | LP002296,Male,No,0,Not Graduate,No,2755,0,65,300,1,Rural,N 404 | LP002297,Male,No,0,Graduate,No,2500,20000,103,360,1,Semiurban,Y 405 | LP002300,Female,No,0,Not Graduate,No,1963,0,53,360,1,Semiurban,Y 406 | LP002301,Female,No,0,Graduate,Yes,7441,0,194,360,1,Rural,N 407 | LP002305,Female,No,0,Graduate,No,4547,0,115,360,1,Semiurban,Y 408 | LP002308,Male,Yes,0,Not Graduate,No,2167,2400,115,360,1,Urban,Y 409 | LP002314,Female,No,0,Not Graduate,No,2213,0,66,360,1,Rural,Y 410 | LP002315,Male,Yes,1,Graduate,No,8300,0,152,300,0,Semiurban,N 411 | LP002317,Male,Yes,3+,Graduate,No,81000,0,360,360,0,Rural,N 412 | LP002318,Female,No,1,Not Graduate,Yes,3867,0,62,360,1,Semiurban,N 413 | LP002319,Male,Yes,0,Graduate,,6256,0,160,360,,Urban,Y 414 | LP002328,Male,Yes,0,Not Graduate,No,6096,0,218,360,0,Rural,N 415 | LP002332,Male,Yes,0,Not Graduate,No,2253,2033,110,360,1,Rural,Y 416 | LP002335,Female,Yes,0,Not Graduate,No,2149,3237,178,360,0,Semiurban,N 417 | LP002337,Female,No,0,Graduate,No,2995,0,60,360,1,Urban,Y 418 | LP002341,Female,No,1,Graduate,No,2600,0,160,360,1,Urban,N 419 | LP002342,Male,Yes,2,Graduate,Yes,1600,20000,239,360,1,Urban,N 420 | LP002345,Male,Yes,0,Graduate,No,1025,2773,112,360,1,Rural,Y 421 | LP002347,Male,Yes,0,Graduate,No,3246,1417,138,360,1,Semiurban,Y 422 | LP002348,Male,Yes,0,Graduate,No,5829,0,138,360,1,Rural,Y 423 | LP002357,Female,No,0,Not Graduate,No,2720,0,80,,0,Urban,N 424 | LP002361,Male,Yes,0,Graduate,No,1820,1719,100,360,1,Urban,Y 425 | LP002362,Male,Yes,1,Graduate,No,7250,1667,110,,0,Urban,N 426 | LP002364,Male,Yes,0,Graduate,No,14880,0,96,360,1,Semiurban,Y 427 | LP002366,Male,Yes,0,Graduate,No,2666,4300,121,360,1,Rural,Y 428 | LP002367,Female,No,1,Not Graduate,No,4606,0,81,360,1,Rural,N 429 | LP002368,Male,Yes,2,Graduate,No,5935,0,133,360,1,Semiurban,Y 430 | LP002369,Male,Yes,0,Graduate,No,2920,16.12000084,87,360,1,Rural,Y 431 | LP002370,Male,No,0,Not Graduate,No,2717,0,60,180,1,Urban,Y 432 | LP002377,Female,No,1,Graduate,Yes,8624,0,150,360,1,Semiurban,Y 433 | LP002379,Male,No,0,Graduate,No,6500,0,105,360,0,Rural,N 434 | LP002386,Male,No,0,Graduate,,12876,0,405,360,1,Semiurban,Y 435 | LP002387,Male,Yes,0,Graduate,No,2425,2340,143,360,1,Semiurban,Y 436 | LP002390,Male,No,0,Graduate,No,3750,0,100,360,1,Urban,Y 437 | LP002393,Female,,,Graduate,No,10047,0,,240,1,Semiurban,Y 438 | LP002398,Male,No,0,Graduate,No,1926,1851,50,360,1,Semiurban,Y 439 | LP002401,Male,Yes,0,Graduate,No,2213,1125,,360,1,Urban,Y 440 | LP002403,Male,No,0,Graduate,Yes,10416,0,187,360,0,Urban,N 441 | LP002407,Female,Yes,0,Not Graduate,Yes,7142,0,138,360,1,Rural,Y 442 | LP002408,Male,No,0,Graduate,No,3660,5064,187,360,1,Semiurban,Y 443 | LP002409,Male,Yes,0,Graduate,No,7901,1833,180,360,1,Rural,Y 444 | LP002418,Male,No,3+,Not Graduate,No,4707,1993,148,360,1,Semiurban,Y 445 | LP002422,Male,No,1,Graduate,No,37719,0,152,360,1,Semiurban,Y 446 | LP002424,Male,Yes,0,Graduate,No,7333,8333,175,300,,Rural,Y 447 | LP002429,Male,Yes,1,Graduate,Yes,3466,1210,130,360,1,Rural,Y 448 | LP002434,Male,Yes,2,Not Graduate,No,4652,0,110,360,1,Rural,Y 449 | LP002435,Male,Yes,0,Graduate,,3539,1376,55,360,1,Rural,N 450 | LP002443,Male,Yes,2,Graduate,No,3340,1710,150,360,0,Rural,N 451 | LP002444,Male,No,1,Not Graduate,Yes,2769,1542,190,360,,Semiurban,N 452 | LP002446,Male,Yes,2,Not Graduate,No,2309,1255,125,360,0,Rural,N 453 | LP002447,Male,Yes,2,Not Graduate,No,1958,1456,60,300,,Urban,Y 454 | LP002448,Male,Yes,0,Graduate,No,3948,1733,149,360,0,Rural,N 455 | LP002449,Male,Yes,0,Graduate,No,2483,2466,90,180,0,Rural,Y 456 | LP002453,Male,No,0,Graduate,Yes,7085,0,84,360,1,Semiurban,Y 457 | LP002455,Male,Yes,2,Graduate,No,3859,0,96,360,1,Semiurban,Y 458 | LP002459,Male,Yes,0,Graduate,No,4301,0,118,360,1,Urban,Y 459 | LP002467,Male,Yes,0,Graduate,No,3708,2569,173,360,1,Urban,N 460 | LP002472,Male,No,2,Graduate,No,4354,0,136,360,1,Rural,Y 461 | LP002473,Male,Yes,0,Graduate,No,8334,0,160,360,1,Semiurban,N 462 | LP002478,,Yes,0,Graduate,Yes,2083,4083,160,360,,Semiurban,Y 463 | LP002484,Male,Yes,3+,Graduate,No,7740,0,128,180,1,Urban,Y 464 | LP002487,Male,Yes,0,Graduate,No,3015,2188,153,360,1,Rural,Y 465 | LP002489,Female,No,1,Not Graduate,,5191,0,132,360,1,Semiurban,Y 466 | LP002493,Male,No,0,Graduate,No,4166,0,98,360,0,Semiurban,N 467 | LP002494,Male,No,0,Graduate,No,6000,0,140,360,1,Rural,Y 468 | LP002500,Male,Yes,3+,Not Graduate,No,2947,1664,70,180,0,Urban,N 469 | LP002501,,Yes,0,Graduate,No,16692,0,110,360,1,Semiurban,Y 470 | LP002502,Female,Yes,2,Not Graduate,,210,2917,98,360,1,Semiurban,Y 471 | LP002505,Male,Yes,0,Graduate,No,4333,2451,110,360,1,Urban,N 472 | LP002515,Male,Yes,1,Graduate,Yes,3450,2079,162,360,1,Semiurban,Y 473 | LP002517,Male,Yes,1,Not Graduate,No,2653,1500,113,180,0,Rural,N 474 | LP002519,Male,Yes,3+,Graduate,No,4691,0,100,360,1,Semiurban,Y 475 | LP002522,Female,No,0,Graduate,Yes,2500,0,93,360,,Urban,Y 476 | LP002524,Male,No,2,Graduate,No,5532,4648,162,360,1,Rural,Y 477 | LP002527,Male,Yes,2,Graduate,Yes,16525,1014,150,360,1,Rural,Y 478 | LP002529,Male,Yes,2,Graduate,No,6700,1750,230,300,1,Semiurban,Y 479 | LP002530,,Yes,2,Graduate,No,2873,1872,132,360,0,Semiurban,N 480 | LP002531,Male,Yes,1,Graduate,Yes,16667,2250,86,360,1,Semiurban,Y 481 | LP002533,Male,Yes,2,Graduate,No,2947,1603,,360,1,Urban,N 482 | LP002534,Female,No,0,Not Graduate,No,4350,0,154,360,1,Rural,Y 483 | LP002536,Male,Yes,3+,Not Graduate,No,3095,0,113,360,1,Rural,Y 484 | LP002537,Male,Yes,0,Graduate,No,2083,3150,128,360,1,Semiurban,Y 485 | LP002541,Male,Yes,0,Graduate,No,10833,0,234,360,1,Semiurban,Y 486 | LP002543,Male,Yes,2,Graduate,No,8333,0,246,360,1,Semiurban,Y 487 | LP002544,Male,Yes,1,Not Graduate,No,1958,2436,131,360,1,Rural,Y 488 | LP002545,Male,No,2,Graduate,No,3547,0,80,360,0,Rural,N 489 | LP002547,Male,Yes,1,Graduate,No,18333,0,500,360,1,Urban,N 490 | LP002555,Male,Yes,2,Graduate,Yes,4583,2083,160,360,1,Semiurban,Y 491 | LP002556,Male,No,0,Graduate,No,2435,0,75,360,1,Urban,N 492 | LP002560,Male,No,0,Not Graduate,No,2699,2785,96,360,,Semiurban,Y 493 | LP002562,Male,Yes,1,Not Graduate,No,5333,1131,186,360,,Urban,Y 494 | LP002571,Male,No,0,Not Graduate,No,3691,0,110,360,1,Rural,Y 495 | LP002582,Female,No,0,Not Graduate,Yes,17263,0,225,360,1,Semiurban,Y 496 | LP002585,Male,Yes,0,Graduate,No,3597,2157,119,360,0,Rural,N 497 | LP002586,Female,Yes,1,Graduate,No,3326,913,105,84,1,Semiurban,Y 498 | LP002587,Male,Yes,0,Not Graduate,No,2600,1700,107,360,1,Rural,Y 499 | LP002588,Male,Yes,0,Graduate,No,4625,2857,111,12,,Urban,Y 500 | LP002600,Male,Yes,1,Graduate,Yes,2895,0,95,360,1,Semiurban,Y 501 | LP002602,Male,No,0,Graduate,No,6283,4416,209,360,0,Rural,N 502 | LP002603,Female,No,0,Graduate,No,645,3683,113,480,1,Rural,Y 503 | LP002606,Female,No,0,Graduate,No,3159,0,100,360,1,Semiurban,Y 504 | LP002615,Male,Yes,2,Graduate,No,4865,5624,208,360,1,Semiurban,Y 505 | LP002618,Male,Yes,1,Not Graduate,No,4050,5302,138,360,,Rural,N 506 | LP002619,Male,Yes,0,Not Graduate,No,3814,1483,124,300,1,Semiurban,Y 507 | LP002622,Male,Yes,2,Graduate,No,3510,4416,243,360,1,Rural,Y 508 | LP002624,Male,Yes,0,Graduate,No,20833,6667,480,360,,Urban,Y 509 | LP002625,,No,0,Graduate,No,3583,0,96,360,1,Urban,N 510 | LP002626,Male,Yes,0,Graduate,Yes,2479,3013,188,360,1,Urban,Y 511 | LP002634,Female,No,1,Graduate,No,13262,0,40,360,1,Urban,Y 512 | LP002637,Male,No,0,Not Graduate,No,3598,1287,100,360,1,Rural,N 513 | LP002640,Male,Yes,1,Graduate,No,6065,2004,250,360,1,Semiurban,Y 514 | LP002643,Male,Yes,2,Graduate,No,3283,2035,148,360,1,Urban,Y 515 | LP002648,Male,Yes,0,Graduate,No,2130,6666,70,180,1,Semiurban,N 516 | LP002652,Male,No,0,Graduate,No,5815,3666,311,360,1,Rural,N 517 | LP002659,Male,Yes,3+,Graduate,No,3466,3428,150,360,1,Rural,Y 518 | LP002670,Female,Yes,2,Graduate,No,2031,1632,113,480,1,Semiurban,Y 519 | LP002682,Male,Yes,,Not Graduate,No,3074,1800,123,360,0,Semiurban,N 520 | LP002683,Male,No,0,Graduate,No,4683,1915,185,360,1,Semiurban,N 521 | LP002684,Female,No,0,Not Graduate,No,3400,0,95,360,1,Rural,N 522 | LP002689,Male,Yes,2,Not Graduate,No,2192,1742,45,360,1,Semiurban,Y 523 | LP002690,Male,No,0,Graduate,No,2500,0,55,360,1,Semiurban,Y 524 | LP002692,Male,Yes,3+,Graduate,Yes,5677,1424,100,360,1,Rural,Y 525 | LP002693,Male,Yes,2,Graduate,Yes,7948,7166,480,360,1,Rural,Y 526 | LP002697,Male,No,0,Graduate,No,4680,2087,,360,1,Semiurban,N 527 | LP002699,Male,Yes,2,Graduate,Yes,17500,0,400,360,1,Rural,Y 528 | LP002705,Male,Yes,0,Graduate,No,3775,0,110,360,1,Semiurban,Y 529 | LP002706,Male,Yes,1,Not Graduate,No,5285,1430,161,360,0,Semiurban,Y 530 | LP002714,Male,No,1,Not Graduate,No,2679,1302,94,360,1,Semiurban,Y 531 | LP002716,Male,No,0,Not Graduate,No,6783,0,130,360,1,Semiurban,Y 532 | LP002717,Male,Yes,0,Graduate,No,1025,5500,216,360,,Rural,Y 533 | LP002720,Male,Yes,3+,Graduate,No,4281,0,100,360,1,Urban,Y 534 | LP002723,Male,No,2,Graduate,No,3588,0,110,360,0,Rural,N 535 | LP002729,Male,No,1,Graduate,No,11250,0,196,360,,Semiurban,N 536 | LP002731,Female,No,0,Not Graduate,Yes,18165,0,125,360,1,Urban,Y 537 | LP002732,Male,No,0,Not Graduate,,2550,2042,126,360,1,Rural,Y 538 | LP002734,Male,Yes,0,Graduate,No,6133,3906,324,360,1,Urban,Y 539 | LP002738,Male,No,2,Graduate,No,3617,0,107,360,1,Semiurban,Y 540 | LP002739,Male,Yes,0,Not Graduate,No,2917,536,66,360,1,Rural,N 541 | LP002740,Male,Yes,3+,Graduate,No,6417,0,157,180,1,Rural,Y 542 | LP002741,Female,Yes,1,Graduate,No,4608,2845,140,180,1,Semiurban,Y 543 | LP002743,Female,No,0,Graduate,No,2138,0,99,360,0,Semiurban,N 544 | LP002753,Female,No,1,Graduate,,3652,0,95,360,1,Semiurban,Y 545 | LP002755,Male,Yes,1,Not Graduate,No,2239,2524,128,360,1,Urban,Y 546 | LP002757,Female,Yes,0,Not Graduate,No,3017,663,102,360,,Semiurban,Y 547 | LP002767,Male,Yes,0,Graduate,No,2768,1950,155,360,1,Rural,Y 548 | LP002768,Male,No,0,Not Graduate,No,3358,0,80,36,1,Semiurban,N 549 | LP002772,Male,No,0,Graduate,No,2526,1783,145,360,1,Rural,Y 550 | LP002776,Female,No,0,Graduate,No,5000,0,103,360,0,Semiurban,N 551 | LP002777,Male,Yes,0,Graduate,No,2785,2016,110,360,1,Rural,Y 552 | LP002778,Male,Yes,2,Graduate,Yes,6633,0,,360,0,Rural,N 553 | LP002784,Male,Yes,1,Not Graduate,No,2492,2375,,360,1,Rural,Y 554 | LP002785,Male,Yes,1,Graduate,No,3333,3250,158,360,1,Urban,Y 555 | LP002788,Male,Yes,0,Not Graduate,No,2454,2333,181,360,0,Urban,N 556 | LP002789,Male,Yes,0,Graduate,No,3593,4266,132,180,0,Rural,N 557 | LP002792,Male,Yes,1,Graduate,No,5468,1032,26,360,1,Semiurban,Y 558 | LP002794,Female,No,0,Graduate,No,2667,1625,84,360,,Urban,Y 559 | LP002795,Male,Yes,3+,Graduate,Yes,10139,0,260,360,1,Semiurban,Y 560 | LP002798,Male,Yes,0,Graduate,No,3887,2669,162,360,1,Semiurban,Y 561 | LP002804,Female,Yes,0,Graduate,No,4180,2306,182,360,1,Semiurban,Y 562 | LP002807,Male,Yes,2,Not Graduate,No,3675,242,108,360,1,Semiurban,Y 563 | LP002813,Female,Yes,1,Graduate,Yes,19484,0,600,360,1,Semiurban,Y 564 | LP002820,Male,Yes,0,Graduate,No,5923,2054,211,360,1,Rural,Y 565 | LP002821,Male,No,0,Not Graduate,Yes,5800,0,132,360,1,Semiurban,Y 566 | LP002832,Male,Yes,2,Graduate,No,8799,0,258,360,0,Urban,N 567 | LP002833,Male,Yes,0,Not Graduate,No,4467,0,120,360,,Rural,Y 568 | LP002836,Male,No,0,Graduate,No,3333,0,70,360,1,Urban,Y 569 | LP002837,Male,Yes,3+,Graduate,No,3400,2500,123,360,0,Rural,N 570 | LP002840,Female,No,0,Graduate,No,2378,0,9,360,1,Urban,N 571 | LP002841,Male,Yes,0,Graduate,No,3166,2064,104,360,0,Urban,N 572 | LP002842,Male,Yes,1,Graduate,No,3417,1750,186,360,1,Urban,Y 573 | LP002847,Male,Yes,,Graduate,No,5116,1451,165,360,0,Urban,N 574 | LP002855,Male,Yes,2,Graduate,No,16666,0,275,360,1,Urban,Y 575 | LP002862,Male,Yes,2,Not Graduate,No,6125,1625,187,480,1,Semiurban,N 576 | LP002863,Male,Yes,3+,Graduate,No,6406,0,150,360,1,Semiurban,N 577 | LP002868,Male,Yes,2,Graduate,No,3159,461,108,84,1,Urban,Y 578 | LP002872,,Yes,0,Graduate,No,3087,2210,136,360,0,Semiurban,N 579 | LP002874,Male,No,0,Graduate,No,3229,2739,110,360,1,Urban,Y 580 | LP002877,Male,Yes,1,Graduate,No,1782,2232,107,360,1,Rural,Y 581 | LP002888,Male,No,0,Graduate,,3182,2917,161,360,1,Urban,Y 582 | LP002892,Male,Yes,2,Graduate,No,6540,0,205,360,1,Semiurban,Y 583 | LP002893,Male,No,0,Graduate,No,1836,33837,90,360,1,Urban,N 584 | LP002894,Female,Yes,0,Graduate,No,3166,0,36,360,1,Semiurban,Y 585 | LP002898,Male,Yes,1,Graduate,No,1880,0,61,360,,Rural,N 586 | LP002911,Male,Yes,1,Graduate,No,2787,1917,146,360,0,Rural,N 587 | LP002912,Male,Yes,1,Graduate,No,4283,3000,172,84,1,Rural,N 588 | LP002916,Male,Yes,0,Graduate,No,2297,1522,104,360,1,Urban,Y 589 | LP002917,Female,No,0,Not Graduate,No,2165,0,70,360,1,Semiurban,Y 590 | LP002925,,No,0,Graduate,No,4750,0,94,360,1,Semiurban,Y 591 | LP002926,Male,Yes,2,Graduate,Yes,2726,0,106,360,0,Semiurban,N 592 | LP002928,Male,Yes,0,Graduate,No,3000,3416,56,180,1,Semiurban,Y 593 | LP002931,Male,Yes,2,Graduate,Yes,6000,0,205,240,1,Semiurban,N 594 | LP002933,,No,3+,Graduate,Yes,9357,0,292,360,1,Semiurban,Y 595 | LP002936,Male,Yes,0,Graduate,No,3859,3300,142,180,1,Rural,Y 596 | LP002938,Male,Yes,0,Graduate,Yes,16120,0,260,360,1,Urban,Y 597 | LP002940,Male,No,0,Not Graduate,No,3833,0,110,360,1,Rural,Y 598 | LP002941,Male,Yes,2,Not Graduate,Yes,6383,1000,187,360,1,Rural,N 599 | LP002943,Male,No,,Graduate,No,2987,0,88,360,0,Semiurban,N 600 | LP002945,Male,Yes,0,Graduate,Yes,9963,0,180,360,1,Rural,Y 601 | LP002948,Male,Yes,2,Graduate,No,5780,0,192,360,1,Urban,Y 602 | LP002949,Female,No,3+,Graduate,,416,41667,350,180,,Urban,N 603 | LP002950,Male,Yes,0,Not Graduate,,2894,2792,155,360,1,Rural,Y 604 | LP002953,Male,Yes,3+,Graduate,No,5703,0,128,360,1,Urban,Y 605 | LP002958,Male,No,0,Graduate,No,3676,4301,172,360,1,Rural,Y 606 | LP002959,Female,Yes,1,Graduate,No,12000,0,496,360,1,Semiurban,Y 607 | LP002960,Male,Yes,0,Not Graduate,No,2400,3800,,180,1,Urban,N 608 | LP002961,Male,Yes,1,Graduate,No,3400,2500,173,360,1,Semiurban,Y 609 | LP002964,Male,Yes,2,Not Graduate,No,3987,1411,157,360,1,Rural,Y 610 | LP002974,Male,Yes,0,Graduate,No,3232,1950,108,360,1,Rural,Y 611 | LP002978,Female,No,0,Graduate,No,2900,0,71,360,1,Rural,Y 612 | LP002979,Male,Yes,3+,Graduate,No,4106,0,40,180,1,Rural,Y 613 | LP002983,Male,Yes,1,Graduate,No,8072,240,253,360,1,Urban,Y 614 | LP002984,Male,Yes,2,Graduate,No,7583,0,187,360,1,Urban,Y 615 | LP002990,Female,No,0,Graduate,Yes,4583,0,133,360,0,Semiurban,N -------------------------------------------------------------------------------- /Customer Churn/teleCust1000t.csv: -------------------------------------------------------------------------------- 1 | region,tenure,age,marital,address,income,ed,employ,retire,gender,reside,custcat 2 | 2,13,44,1,9,64.000,4,5,0.000,0,2,1 3 | 3,11,33,1,7,136.000,5,5,0.000,0,6,4 4 | 3,68,52,1,24,116.000,1,29,0.000,1,2,3 5 | 2,33,33,0,12,33.000,2,0,0.000,1,1,1 6 | 2,23,30,1,9,30.000,1,2,0.000,0,4,3 7 | 2,41,39,0,17,78.000,2,16,0.000,1,1,3 8 | 3,45,22,1,2,19.000,2,4,0.000,1,5,2 9 | 2,38,35,0,5,76.000,2,10,0.000,0,3,4 10 | 3,45,59,1,7,166.000,4,31,0.000,0,5,3 11 | 1,68,41,1,21,72.000,1,22,0.000,0,3,2 12 | 2,5,33,0,10,125.000,4,5,0.000,1,1,1 13 | 3,7,35,0,14,80.000,2,15,0.000,1,1,3 14 | 1,41,38,1,8,37.000,2,9,0.000,1,3,1 15 | 2,57,54,1,30,115.000,4,23,0.000,1,3,4 16 | 2,9,46,0,3,25.000,1,8,0.000,1,2,1 17 | 1,29,38,1,12,75.000,5,1,0.000,0,4,2 18 | 3,60,57,0,38,162.000,2,30,0.000,0,1,3 19 | 3,34,48,0,3,49.000,2,6,0.000,1,3,3 20 | 2,1,24,0,3,20.000,1,3,0.000,0,1,1 21 | 1,26,29,1,3,77.000,4,2,0.000,0,4,4 22 | 3,6,30,0,7,16.000,3,1,0.000,1,1,2 23 | 1,68,52,1,17,120.000,1,24,0.000,0,2,1 24 | 3,53,33,0,10,101.000,5,4,0.000,1,2,4 25 | 3,55,48,1,19,67.000,1,25,0.000,0,3,1 26 | 3,14,43,1,18,36.000,1,5,0.000,0,5,3 27 | 2,1,21,0,0,33.000,2,0,0.000,1,3,3 28 | 2,42,40,0,7,37.000,2,8,0.000,1,1,4 29 | 3,25,33,1,11,31.000,1,5,0.000,0,4,3 30 | 1,9,21,1,1,17.000,2,2,0.000,1,3,1 31 | 2,13,33,1,9,19.000,4,0,0.000,1,2,2 32 | 1,56,37,1,6,36.000,1,13,0.000,1,2,2 33 | 1,71,53,1,27,155.000,5,12,0.000,0,2,4 34 | 1,35,50,1,26,140.000,2,21,0.000,1,4,3 35 | 1,11,27,1,8,55.000,5,0,0.000,0,3,2 36 | 2,60,46,1,13,163.000,3,24,0.000,0,2,4 37 | 3,20,35,1,11,52.000,4,0,0.000,0,2,2 38 | 2,54,60,0,38,211.000,4,25,0.000,0,1,4 39 | 1,44,57,1,1,186.000,2,17,0.000,0,2,3 40 | 1,11,41,1,0,39.000,1,1,0.000,1,2,3 41 | 2,72,57,0,34,22.000,2,35,1.000,1,1,3 42 | 3,10,41,0,7,30.000,1,7,0.000,0,1,3 43 | 2,15,28,0,0,29.000,2,4,0.000,1,1,3 44 | 2,27,28,1,4,23.000,2,8,0.000,0,5,1 45 | 1,9,36,1,14,62.000,4,10,0.000,0,6,4 46 | 1,64,43,1,20,76.000,4,20,0.000,1,4,3 47 | 1,65,41,1,3,74.000,4,16,0.000,0,2,2 48 | 1,49,51,1,27,63.000,4,19,0.000,0,5,2 49 | 3,47,41,1,0,36.000,4,8,0.000,0,2,4 50 | 3,9,34,1,9,33.000,2,8,0.000,1,4,1 51 | 1,5,36,0,14,29.000,2,9,0.000,1,1,3 52 | 2,30,34,1,4,27.000,2,1,0.000,0,5,1 53 | 1,56,52,1,28,49.000,2,12,0.000,0,4,2 54 | 3,10,22,0,0,24.000,4,0,0.000,0,1,4 55 | 1,7,26,1,3,26.000,2,2,0.000,0,5,1 56 | 1,52,27,0,6,47.000,3,5,0.000,0,2,1 57 | 2,36,45,1,1,94.000,2,18,0.000,1,5,1 58 | 3,27,34,0,8,21.000,3,4,0.000,1,1,2 59 | 1,58,62,1,36,27.000,1,0,0.000,0,2,1 60 | 2,41,52,0,27,30.000,2,2,0.000,1,1,3 61 | 2,47,40,1,16,127.000,4,12,0.000,1,4,2 62 | 2,55,39,1,15,137.000,2,20,0.000,1,2,3 63 | 2,56,50,1,1,80.000,2,24,0.000,1,4,4 64 | 3,35,55,0,24,30.000,2,2,0.000,0,1,1 65 | 2,69,51,1,11,438.000,4,23,0.000,1,2,4 66 | 2,44,39,0,16,79.000,2,16,0.000,0,1,4 67 | 3,36,47,1,16,63.000,1,23,0.000,0,2,1 68 | 3,28,51,1,22,40.000,3,10,0.000,1,6,3 69 | 1,72,67,1,44,51.000,1,34,1.000,0,2,3 70 | 1,16,27,0,5,37.000,3,5,0.000,0,4,1 71 | 3,3,43,0,19,61.000,4,11,0.000,1,1,1 72 | 1,40,57,1,15,22.000,2,9,0.000,0,2,1 73 | 3,16,39,0,6,44.000,4,3,0.000,1,1,2 74 | 1,20,42,0,4,17.000,3,5,0.000,0,1,1 75 | 2,48,43,0,13,110.000,5,15,0.000,1,3,2 76 | 2,64,48,1,13,91.000,2,24,0.000,0,2,3 77 | 1,8,46,0,3,46.000,2,8,0.000,0,1,1 78 | 1,40,38,1,10,85.000,3,12,0.000,0,2,3 79 | 2,38,71,0,27,10.000,2,12,1.000,0,1,1 80 | 1,67,68,0,28,244.000,1,47,0.000,1,1,3 81 | 1,26,42,0,0,80.000,4,15,0.000,1,1,4 82 | 3,41,34,1,15,83.000,5,1,0.000,1,2,4 83 | 2,2,31,0,2,21.000,1,6,0.000,0,1,1 84 | 3,20,48,1,29,24.000,2,3,0.000,0,2,4 85 | 2,31,53,0,2,351.000,3,31,0.000,1,1,3 86 | 2,45,52,1,4,169.000,4,29,0.000,1,4,2 87 | 2,33,27,1,7,46.000,2,7,0.000,0,4,1 88 | 1,10,26,0,6,34.000,3,3,0.000,0,1,2 89 | 2,62,54,1,2,50.000,4,3,0.000,0,2,4 90 | 2,4,35,0,16,161.000,5,6,0.000,1,1,4 91 | 1,42,47,1,17,212.000,4,17,0.000,0,2,3 92 | 3,27,51,1,3,80.000,5,11,0.000,0,3,2 93 | 2,38,42,0,1,30.000,4,7,0.000,1,1,1 94 | 3,52,61,1,3,53.000,5,1,0.000,1,2,2 95 | 3,35,33,0,9,73.000,5,4,0.000,1,1,4 96 | 3,3,20,1,1,17.000,2,0,0.000,1,4,1 97 | 2,17,33,0,9,23.000,5,3,0.000,0,1,4 98 | 1,55,53,1,21,34.000,1,8,0.000,0,2,3 99 | 3,43,36,1,5,107.000,1,19,0.000,1,3,2 100 | 2,47,25,1,5,21.000,1,1,0.000,1,2,3 101 | 3,65,58,0,30,83.000,2,16,0.000,1,1,2 102 | 1,13,20,0,1,17.000,3,0,0.000,0,5,4 103 | 3,64,25,1,4,76.000,3,2,0.000,1,6,3 104 | 2,12,24,0,2,19.000,1,0,0.000,0,1,3 105 | 1,29,32,1,0,49.000,4,3,0.000,1,3,2 106 | 2,23,47,0,21,173.000,3,22,0.000,1,1,2 107 | 2,14,35,0,4,47.000,3,10,0.000,1,1,2 108 | 1,35,61,0,23,41.000,2,11,0.000,0,1,1 109 | 3,26,39,0,8,105.000,3,13,0.000,1,1,4 110 | 1,13,54,0,2,31.000,4,2,0.000,0,1,1 111 | 2,69,40,1,13,41.000,2,17,0.000,0,3,2 112 | 3,61,50,1,30,102.000,4,9,0.000,1,3,2 113 | 2,45,22,1,2,36.000,4,0,0.000,1,5,2 114 | 2,3,37,0,13,24.000,1,3,0.000,1,1,1 115 | 2,53,22,0,1,25.000,4,0,0.000,0,2,2 116 | 1,59,42,0,1,68.000,2,21,0.000,1,1,3 117 | 1,13,34,0,11,20.000,3,0,0.000,1,1,1 118 | 3,31,31,0,4,48.000,1,15,0.000,0,1,3 119 | 1,40,29,1,6,40.000,2,8,0.000,1,5,3 120 | 1,48,55,0,15,79.000,1,25,0.000,0,1,3 121 | 3,3,31,0,1,28.000,1,0,0.000,0,1,1 122 | 2,51,48,0,12,64.000,2,22,0.000,1,1,1 123 | 3,33,41,0,15,37.000,2,11,0.000,0,1,1 124 | 3,14,47,1,7,48.000,1,13,0.000,0,2,1 125 | 1,17,42,1,6,131.000,5,6,0.000,0,3,2 126 | 3,19,29,1,9,18.000,2,2,0.000,0,3,1 127 | 2,58,36,0,10,38.000,2,14,0.000,1,1,2 128 | 3,22,23,0,3,37.000,2,4,0.000,1,3,4 129 | 2,29,31,1,1,60.000,4,6,0.000,0,5,2 130 | 3,14,22,1,2,13.000,3,0,0.000,1,4,2 131 | 2,70,64,1,18,98.000,1,31,0.000,1,2,4 132 | 1,62,52,1,2,195.000,1,23,0.000,0,2,3 133 | 2,7,35,1,0,47.000,1,14,0.000,0,5,1 134 | 1,56,47,1,19,65.000,2,22,0.000,0,3,4 135 | 1,50,50,1,22,150.000,5,5,0.000,1,4,2 136 | 2,57,39,0,6,106.000,5,8,0.000,0,1,3 137 | 3,2,40,0,19,51.000,4,14,0.000,1,1,1 138 | 2,40,38,0,17,96.000,4,0,0.000,1,1,1 139 | 2,6,43,0,4,33.000,5,7,0.000,0,2,3 140 | 3,23,25,1,6,38.000,2,1,0.000,1,3,3 141 | 3,11,32,0,6,125.000,4,5,0.000,0,1,4 142 | 1,29,37,1,13,145.000,5,0,0.000,1,4,4 143 | 1,42,44,1,2,99.000,2,21,0.000,0,3,3 144 | 1,20,34,1,2,22.000,4,0,0.000,1,3,4 145 | 2,72,60,1,21,31.000,1,38,1.000,0,2,2 146 | 1,2,36,0,3,25.000,1,6,0.000,1,1,3 147 | 3,46,25,0,5,57.000,5,0,0.000,1,2,1 148 | 2,39,51,1,21,41.000,5,20,0.000,0,6,4 149 | 3,24,25,1,1,20.000,3,1,0.000,1,2,2 150 | 3,59,43,1,4,101.000,2,22,0.000,1,5,2 151 | 1,65,37,0,8,56.000,2,15,0.000,0,1,2 152 | 2,50,27,1,5,22.000,2,5,0.000,1,3,1 153 | 1,57,37,0,11,108.000,4,9,0.000,1,1,3 154 | 3,61,48,0,14,115.000,4,20,0.000,1,1,4 155 | 3,34,54,0,31,53.000,4,22,0.000,0,1,4 156 | 3,38,53,0,13,242.000,4,12,0.000,0,1,4 157 | 1,3,24,1,2,20.000,2,3,0.000,1,5,3 158 | 2,4,47,0,5,123.000,4,11,0.000,0,1,1 159 | 2,15,35,0,0,34.000,1,12,0.000,0,1,1 160 | 1,25,29,1,8,31.000,4,3,0.000,1,5,2 161 | 3,21,44,0,25,47.000,1,12,0.000,1,2,3 162 | 3,50,37,1,11,48.000,3,8,0.000,1,2,3 163 | 2,65,64,1,22,13.000,2,5,1.000,1,3,3 164 | 2,59,52,1,18,78.000,1,25,0.000,0,2,1 165 | 3,29,26,0,7,34.000,1,7,0.000,1,2,3 166 | 1,60,46,0,17,81.000,5,9,0.000,1,1,2 167 | 1,71,45,1,14,86.000,2,25,0.000,0,2,2 168 | 3,38,27,1,0,26.000,4,2,0.000,0,4,4 169 | 3,16,34,1,5,51.000,2,8,0.000,1,5,1 170 | 3,7,31,0,10,59.000,3,10,0.000,0,1,1 171 | 1,59,53,0,21,112.000,4,16,0.000,0,1,2 172 | 2,22,56,1,14,57.000,4,28,0.000,0,2,4 173 | 1,18,48,1,20,41.000,1,2,0.000,1,2,1 174 | 2,64,46,1,19,168.000,2,26,0.000,1,2,4 175 | 2,7,33,0,4,29.000,4,4,0.000,1,3,1 176 | 3,65,58,0,13,167.000,2,14,0.000,1,2,2 177 | 3,7,24,0,5,29.000,2,3,0.000,0,3,1 178 | 1,64,55,0,28,104.000,1,26,0.000,1,1,3 179 | 3,42,22,1,0,46.000,4,0,0.000,0,2,4 180 | 3,33,26,0,5,46.000,4,1,0.000,1,3,4 181 | 1,3,33,0,8,51.000,4,0,0.000,1,1,1 182 | 2,69,42,1,11,65.000,2,18,0.000,0,2,4 183 | 2,9,33,0,1,48.000,2,13,0.000,0,2,1 184 | 3,22,34,1,1,46.000,3,1,0.000,0,4,2 185 | 2,53,63,1,15,43.000,2,18,0.000,0,2,2 186 | 3,71,53,0,30,110.000,1,34,0.000,0,1,2 187 | 3,24,26,0,2,32.000,3,2,0.000,0,3,1 188 | 3,12,22,1,1,18.000,3,0,0.000,1,2,1 189 | 1,25,39,1,1,26.000,4,0,0.000,0,4,2 190 | 3,63,28,1,9,34.000,2,10,0.000,1,6,3 191 | 1,33,54,1,18,57.000,4,4,0.000,1,3,3 192 | 2,57,54,1,20,21.000,2,0,0.000,1,3,1 193 | 3,13,33,0,7,38.000,3,10,0.000,1,1,4 194 | 1,13,21,0,2,19.000,3,0,0.000,1,1,2 195 | 1,71,39,0,2,40.000,3,17,0.000,0,1,3 196 | 3,18,69,1,11,58.000,3,8,0.000,0,2,4 197 | 1,17,51,1,10,95.000,2,15,0.000,1,2,1 198 | 2,22,43,0,4,114.000,2,19,0.000,0,1,1 199 | 2,65,65,1,27,128.000,3,24,0.000,0,2,3 200 | 3,7,40,0,9,33.000,4,2,0.000,1,1,1 201 | 3,72,66,0,30,460.000,4,41,0.000,1,1,4 202 | 1,35,43,0,12,224.000,3,17,0.000,0,1,1 203 | 1,24,39,0,2,122.000,4,12,0.000,0,3,4 204 | 1,64,24,1,0,23.000,3,1,0.000,1,5,2 205 | 2,63,63,1,19,191.000,3,27,0.000,0,2,2 206 | 1,38,31,1,7,187.000,4,6,0.000,0,3,4 207 | 2,29,37,1,9,119.000,3,12,0.000,0,3,1 208 | 1,72,69,1,49,44.000,2,44,1.000,1,2,3 209 | 1,10,27,1,2,15.000,3,2,0.000,1,2,4 210 | 3,72,64,0,41,674.000,4,37,0.000,1,1,3 211 | 3,52,23,1,4,33.000,4,0,0.000,1,2,3 212 | 3,13,55,0,3,250.000,2,35,0.000,1,2,1 213 | 1,3,33,0,6,64.000,4,0,0.000,0,2,1 214 | 3,39,24,1,2,26.000,2,4,0.000,0,4,1 215 | 2,28,29,0,4,23.000,3,5,0.000,0,2,2 216 | 2,46,42,0,9,52.000,4,7,0.000,0,3,2 217 | 2,62,46,0,27,200.000,3,9,0.000,1,1,1 218 | 2,23,33,1,7,28.000,1,4,0.000,0,2,3 219 | 3,23,25,1,5,19.000,5,0,0.000,0,3,4 220 | 2,72,61,1,39,76.000,3,21,0.000,0,2,4 221 | 3,56,60,0,26,70.000,2,20,0.000,0,1,1 222 | 1,8,30,0,1,34.000,2,9,0.000,1,1,3 223 | 3,3,36,0,7,34.000,5,3,0.000,1,3,4 224 | 3,37,37,0,7,172.000,3,13,0.000,0,1,1 225 | 3,37,53,1,13,48.000,1,10,0.000,0,3,3 226 | 2,6,30,0,9,45.000,2,2,0.000,1,1,1 227 | 1,48,47,0,9,72.000,3,13,0.000,0,5,4 228 | 2,43,43,1,3,55.000,4,18,0.000,1,4,4 229 | 2,21,29,0,7,40.000,4,2,0.000,1,4,1 230 | 3,6,33,0,12,26.000,3,0,0.000,0,1,3 231 | 2,69,51,0,18,96.000,1,33,0.000,0,4,3 232 | 1,22,33,0,9,54.000,1,10,0.000,0,1,3 233 | 1,28,40,1,1,47.000,2,9,0.000,1,3,3 234 | 1,71,47,1,23,142.000,1,30,0.000,0,2,2 235 | 2,7,37,0,5,57.000,4,1,0.000,0,1,1 236 | 1,63,29,0,10,20.000,3,7,0.000,0,2,4 237 | 2,53,57,0,25,37.000,1,7,0.000,0,1,2 238 | 2,50,52,1,17,36.000,4,16,0.000,0,4,4 239 | 2,39,31,1,6,24.000,4,2,0.000,0,2,4 240 | 3,16,19,0,0,21.000,2,0,0.000,1,1,1 241 | 3,43,51,1,15,186.000,3,25,0.000,0,2,4 242 | 3,44,31,0,12,30.000,1,7,0.000,1,3,1 243 | 2,72,66,1,43,96.000,2,16,0.000,1,2,4 244 | 3,61,76,0,39,15.000,1,29,1.000,0,1,3 245 | 3,43,21,1,1,25.000,1,4,0.000,0,4,2 246 | 3,24,39,0,11,49.000,3,7,0.000,1,1,1 247 | 3,64,45,0,4,38.000,2,7,0.000,1,1,3 248 | 2,64,38,0,0,29.000,2,2,0.000,0,2,2 249 | 1,24,26,0,7,55.000,2,7,0.000,1,1,1 250 | 3,33,55,1,21,64.000,3,10,0.000,1,3,1 251 | 2,33,33,1,12,42.000,4,7,0.000,1,5,2 252 | 1,2,40,0,9,134.000,4,2,0.000,0,1,1 253 | 2,42,48,1,22,82.000,1,21,0.000,1,5,4 254 | 1,52,54,1,5,153.000,5,10,0.000,0,2,2 255 | 1,45,66,0,43,144.000,2,13,0.000,1,1,2 256 | 3,3,25,0,4,22.000,4,0,0.000,0,2,4 257 | 2,37,53,1,16,35.000,1,16,0.000,1,4,3 258 | 2,28,57,0,33,82.000,4,22,0.000,1,1,2 259 | 3,40,63,1,9,61.000,1,22,0.000,0,2,1 260 | 2,37,33,0,1,102.000,2,12,0.000,0,1,4 261 | 3,7,27,0,5,31.000,3,4,0.000,0,5,4 262 | 1,71,56,0,23,170.000,1,30,0.000,1,1,4 263 | 2,19,46,0,11,26.000,2,0,0.000,1,1,1 264 | 2,61,40,0,15,85.000,3,15,0.000,1,1,3 265 | 3,70,53,1,16,183.000,2,35,0.000,1,2,2 266 | 2,60,55,1,13,67.000,1,18,0.000,1,2,1 267 | 3,48,45,1,5,42.000,1,11,0.000,1,2,1 268 | 1,8,63,1,1,31.000,1,9,0.000,0,2,3 269 | 2,58,58,1,10,96.000,2,17,0.000,0,4,3 270 | 1,16,24,1,5,24.000,3,2,0.000,0,5,3 271 | 2,33,33,0,4,53.000,2,14,0.000,1,1,1 272 | 2,39,25,0,4,18.000,1,0,0.000,1,1,3 273 | 1,62,51,0,10,43.000,4,19,0.000,0,2,2 274 | 1,50,34,0,10,104.000,3,9,0.000,0,1,4 275 | 2,48,55,0,31,26.000,1,1,0.000,0,1,1 276 | 3,31,62,0,5,56.000,2,26,0.000,1,1,3 277 | 2,65,33,1,9,49.000,2,6,0.000,0,5,2 278 | 1,36,58,0,34,80.000,1,21,0.000,1,1,2 279 | 1,61,46,0,5,318.000,3,18,0.000,1,1,3 280 | 2,48,42,0,21,35.000,4,5,0.000,0,1,3 281 | 3,35,45,0,17,29.000,1,6,0.000,0,1,1 282 | 1,51,28,0,8,43.000,5,1,0.000,0,5,4 283 | 2,49,51,1,5,80.000,5,3,0.000,0,2,4 284 | 1,35,39,1,11,30.000,3,2,0.000,1,3,3 285 | 1,58,70,0,2,9.000,3,24,1.000,1,1,2 286 | 1,23,35,1,0,23.000,2,1,0.000,0,5,3 287 | 2,26,39,1,15,58.000,4,9,0.000,1,2,3 288 | 1,38,37,1,1,52.000,1,20,0.000,1,4,4 289 | 1,35,66,1,44,14.000,3,12,1.000,1,2,3 290 | 2,37,50,0,9,110.000,2,21,0.000,1,1,3 291 | 2,48,43,1,1,60.000,2,12,0.000,0,2,2 292 | 3,13,43,1,1,123.000,3,21,0.000,0,5,4 293 | 1,72,63,1,29,15.000,5,31,1.000,0,2,3 294 | 1,30,27,0,4,47.000,4,2,0.000,1,2,2 295 | 1,24,43,0,14,138.000,4,11,0.000,1,1,3 296 | 2,25,51,1,20,359.000,3,29,0.000,0,2,4 297 | 2,50,52,0,30,214.000,3,22,0.000,0,1,4 298 | 2,28,40,0,7,64.000,1,19,0.000,0,1,3 299 | 3,34,51,0,8,50.000,4,14,0.000,0,1,1 300 | 1,48,32,0,2,88.000,3,9,0.000,0,1,4 301 | 2,42,36,1,8,86.000,2,15,0.000,1,2,2 302 | 3,67,65,1,30,17.000,1,29,1.000,1,2,3 303 | 1,17,41,1,7,25.000,2,17,0.000,0,3,3 304 | 1,29,38,1,19,46.000,2,6,0.000,0,2,2 305 | 3,66,61,0,26,361.000,1,40,0.000,1,1,2 306 | 2,1,21,0,1,18.000,3,0,0.000,1,1,1 307 | 1,28,33,1,3,14.000,4,1,0.000,0,3,4 308 | 1,15,35,1,5,34.000,3,8,0.000,0,2,2 309 | 2,12,64,0,13,9.000,2,6,1.000,1,1,3 310 | 3,48,49,0,18,39.000,2,5,0.000,1,1,4 311 | 2,59,25,1,4,18.000,3,2,0.000,0,4,1 312 | 3,8,26,1,1,25.000,4,1,0.000,0,2,1 313 | 1,45,52,0,15,46.000,5,8,0.000,1,1,2 314 | 1,25,39,1,8,94.000,4,11,0.000,1,4,2 315 | 1,21,44,1,4,26.000,3,2,0.000,0,2,4 316 | 2,17,68,1,1,335.000,1,35,0.000,1,3,4 317 | 2,41,45,0,14,25.000,3,6,0.000,0,1,1 318 | 1,13,45,1,3,99.000,3,8,0.000,1,2,3 319 | 2,1,26,1,3,30.000,2,7,0.000,1,5,1 320 | 3,3,31,1,4,35.000,2,2,0.000,1,3,4 321 | 1,23,27,1,2,51.000,5,1,0.000,1,2,1 322 | 3,26,42,0,20,49.000,3,13,0.000,0,1,4 323 | 1,37,51,1,15,54.000,1,15,0.000,0,3,3 324 | 3,5,30,0,3,46.000,4,7,0.000,0,1,2 325 | 1,32,27,1,3,91.000,4,1,0.000,1,3,4 326 | 3,12,22,0,1,23.000,4,0,0.000,0,2,1 327 | 1,72,54,1,6,345.000,1,32,0.000,0,2,3 328 | 3,3,25,1,2,45.000,4,0,0.000,1,2,1 329 | 1,54,43,0,12,53.000,2,12,0.000,0,1,3 330 | 2,26,26,0,2,14.000,2,6,0.000,1,1,4 331 | 3,14,36,0,13,67.000,5,4,0.000,1,1,1 332 | 3,22,51,0,27,49.000,3,1,0.000,0,1,1 333 | 3,65,47,1,18,37.000,1,15,0.000,1,4,1 334 | 3,26,45,0,18,33.000,1,9,0.000,0,1,1 335 | 1,20,37,0,5,46.000,3,10,0.000,1,4,4 336 | 2,67,60,1,32,93.000,1,21,0.000,0,4,2 337 | 1,34,55,0,2,48.000,2,11,0.000,0,1,1 338 | 1,37,54,1,35,183.000,2,22,0.000,0,3,3 339 | 2,22,23,1,3,16.000,1,5,0.000,0,4,1 340 | 2,5,69,0,0,11.000,2,14,1.000,0,1,1 341 | 1,28,31,1,0,42.000,4,5,0.000,1,3,3 342 | 1,20,41,1,7,67.000,4,14,0.000,1,4,1 343 | 3,72,41,0,20,181.000,4,12,0.000,0,1,4 344 | 2,30,43,0,18,53.000,4,6,0.000,0,1,1 345 | 3,71,52,0,17,172.000,2,32,0.000,0,1,4 346 | 2,56,44,0,24,87.000,2,19,0.000,0,1,3 347 | 1,8,29,1,2,21.000,5,0,0.000,0,2,1 348 | 2,32,65,0,6,9.000,2,30,1.000,1,1,2 349 | 2,46,47,0,22,144.000,3,22,0.000,1,2,3 350 | 3,60,45,1,23,117.000,4,11,0.000,0,3,4 351 | 2,42,32,1,0,59.000,4,4,0.000,1,6,4 352 | 3,71,60,1,39,508.000,4,35,0.000,0,4,4 353 | 2,72,63,1,40,222.000,2,43,1.000,1,2,3 354 | 3,45,66,0,11,87.000,3,25,1.000,1,1,1 355 | 3,5,31,0,4,34.000,1,9,0.000,0,1,3 356 | 2,17,38,1,19,18.000,4,0,0.000,1,4,2 357 | 2,70,55,0,12,65.000,3,24,0.000,0,1,3 358 | 3,57,57,1,19,130.000,3,27,0.000,1,2,2 359 | 1,20,33,0,13,44.000,3,8,0.000,1,1,2 360 | 1,72,52,1,21,63.000,1,24,0.000,0,3,3 361 | 3,32,50,0,28,57.000,4,20,0.000,0,1,3 362 | 3,22,57,1,7,264.000,1,35,0.000,1,2,3 363 | 2,6,24,0,2,28.000,1,5,0.000,0,2,3 364 | 3,32,23,1,2,26.000,3,1,0.000,1,6,4 365 | 1,38,33,0,3,38.000,4,0,0.000,0,1,2 366 | 1,53,35,0,15,59.000,3,5,0.000,1,1,3 367 | 3,37,76,0,38,117.000,4,21,0.000,1,1,1 368 | 1,47,35,1,13,70.000,4,9,0.000,1,2,4 369 | 3,69,59,0,37,104.000,2,13,0.000,0,1,3 370 | 3,63,56,0,17,71.000,3,18,0.000,1,1,1 371 | 1,61,34,1,0,70.000,3,7,0.000,1,3,4 372 | 1,56,38,1,8,56.000,4,10,0.000,0,2,4 373 | 3,40,57,1,18,68.000,1,23,0.000,0,2,3 374 | 2,61,45,0,21,80.000,2,13,0.000,1,1,2 375 | 2,16,26,0,7,21.000,4,1,0.000,0,6,4 376 | 2,7,43,0,4,69.000,3,5,0.000,1,1,1 377 | 3,20,31,1,10,39.000,2,3,0.000,1,2,1 378 | 2,35,38,0,7,90.000,2,18,0.000,0,4,2 379 | 1,60,37,1,12,64.000,5,1,0.000,1,2,2 380 | 2,47,25,0,2,28.000,2,6,0.000,0,1,4 381 | 2,33,28,1,3,24.000,2,0,0.000,1,6,1 382 | 3,12,34,0,2,44.000,2,7,0.000,0,1,1 383 | 1,10,31,0,0,32.000,4,7,0.000,1,2,2 384 | 2,69,33,1,9,52.000,4,9,0.000,1,3,3 385 | 1,23,20,0,1,15.000,3,0,0.000,1,1,2 386 | 1,53,31,0,10,46.000,3,6,0.000,0,2,2 387 | 2,60,57,1,14,107.000,2,33,0.000,1,2,3 388 | 2,33,33,0,7,56.000,2,11,0.000,0,1,1 389 | 3,67,40,1,21,40.000,4,7,0.000,1,4,4 390 | 3,15,38,1,5,56.000,3,13,0.000,0,3,3 391 | 3,64,31,1,5,46.000,2,7,0.000,1,4,2 392 | 2,46,49,1,13,51.000,2,21,0.000,0,2,1 393 | 2,72,60,1,33,12.000,1,20,1.000,0,3,4 394 | 1,61,43,1,6,34.000,5,6,0.000,0,3,4 395 | 3,54,27,0,3,27.000,2,6,0.000,1,5,4 396 | 3,28,36,0,3,42.000,3,7,0.000,1,1,3 397 | 2,28,38,0,8,119.000,5,2,0.000,0,3,4 398 | 2,32,43,0,13,146.000,1,27,0.000,1,1,3 399 | 2,52,46,0,17,51.000,2,13,0.000,1,3,2 400 | 1,48,62,0,33,86.000,1,22,1.000,1,1,3 401 | 3,52,42,1,17,27.000,3,8,0.000,1,2,3 402 | 1,37,43,0,8,38.000,3,9,0.000,0,1,2 403 | 1,41,52,0,26,928.000,3,29,0.000,0,3,3 404 | 2,43,27,1,3,21.000,2,1,0.000,1,3,2 405 | 3,67,60,0,24,24.000,1,6,0.000,1,1,3 406 | 3,61,52,1,30,91.000,1,25,0.000,1,2,1 407 | 3,70,39,1,20,38.000,2,8,0.000,1,4,3 408 | 3,44,32,1,1,96.000,4,5,0.000,1,4,4 409 | 3,46,45,0,12,96.000,3,17,0.000,0,1,2 410 | 3,39,34,0,2,54.000,3,5,0.000,0,1,1 411 | 2,39,59,0,20,1668.000,4,27,0.000,1,1,2 412 | 2,50,29,1,10,53.000,4,0,0.000,1,4,4 413 | 3,5,31,0,1,21.000,3,1,0.000,1,2,1 414 | 2,10,34,0,1,52.000,5,3,0.000,1,1,2 415 | 1,72,69,0,45,17.000,3,36,1.000,1,1,2 416 | 2,68,60,0,40,262.000,2,39,0.000,1,1,3 417 | 3,58,28,0,5,34.000,5,1,0.000,1,1,2 418 | 3,18,44,1,10,107.000,1,19,0.000,1,7,1 419 | 1,65,45,1,25,67.000,5,13,0.000,1,2,3 420 | 3,8,45,0,1,37.000,4,0,0.000,0,1,2 421 | 2,1,20,0,1,21.000,2,0,0.000,1,2,1 422 | 3,42,58,0,7,22.000,1,4,0.000,1,1,2 423 | 1,4,25,1,3,71.000,3,3,0.000,1,5,1 424 | 3,17,35,0,4,77.000,3,8,0.000,1,1,3 425 | 2,8,24,1,1,18.000,4,0,0.000,1,5,4 426 | 1,49,37,1,17,50.000,2,12,0.000,1,2,2 427 | 1,56,46,1,21,33.000,4,7,0.000,0,2,2 428 | 1,62,57,1,21,94.000,1,22,0.000,0,2,4 429 | 2,42,57,1,25,209.000,1,40,0.000,1,4,1 430 | 1,13,32,0,7,31.000,4,5,0.000,0,2,3 431 | 1,69,46,1,18,66.000,2,19,0.000,0,4,2 432 | 2,45,59,0,14,51.000,1,15,0.000,0,2,1 433 | 2,66,42,1,18,118.000,2,20,0.000,0,2,2 434 | 2,15,35,0,10,25.000,2,3,0.000,0,1,1 435 | 1,41,44,1,1,59.000,3,21,0.000,1,3,2 436 | 2,11,20,0,1,20.000,2,0,0.000,1,1,4 437 | 3,45,30,1,0,63.000,5,4,0.000,0,4,4 438 | 1,52,62,0,23,36.000,4,17,0.000,0,1,1 439 | 1,42,56,0,24,72.000,4,18,0.000,1,1,4 440 | 2,48,35,0,11,50.000,1,16,0.000,0,3,3 441 | 3,4,31,0,5,39.000,2,5,0.000,0,2,4 442 | 3,53,64,1,10,54.000,2,20,0.000,1,2,4 443 | 3,1,59,0,4,9.000,1,4,1.000,0,1,3 444 | 3,19,30,1,10,33.000,4,1,0.000,0,3,4 445 | 2,33,30,0,11,28.000,1,9,0.000,0,1,1 446 | 3,42,36,1,14,44.000,2,11,0.000,0,3,3 447 | 1,60,32,0,12,34.000,4,4,0.000,1,2,2 448 | 2,57,51,1,7,68.000,4,18,0.000,1,3,3 449 | 1,64,39,0,5,37.000,1,9,0.000,0,1,1 450 | 2,28,20,1,1,18.000,3,0,0.000,0,3,4 451 | 1,29,35,1,10,55.000,2,7,0.000,0,4,3 452 | 1,46,31,1,12,21.000,1,5,0.000,0,5,3 453 | 1,22,41,1,9,29.000,3,11,0.000,0,4,1 454 | 2,10,56,0,26,9.000,2,6,1.000,0,2,1 455 | 3,50,41,0,16,138.000,2,18,0.000,1,1,2 456 | 1,55,65,0,34,28.000,4,12,1.000,1,1,4 457 | 2,15,54,0,7,62.000,1,11,0.000,0,1,1 458 | 1,31,32,1,13,37.000,3,8,0.000,1,3,4 459 | 3,5,44,0,5,83.000,1,16,0.000,1,1,2 460 | 2,69,63,0,28,168.000,1,43,0.000,1,2,3 461 | 2,26,27,1,4,33.000,4,1,0.000,0,6,4 462 | 3,10,33,1,2,66.000,3,9,0.000,1,4,1 463 | 1,26,30,0,9,18.000,4,1,0.000,0,3,4 464 | 3,25,30,0,0,20.000,1,4,0.000,1,1,3 465 | 3,30,23,1,4,19.000,3,1,0.000,0,4,2 466 | 1,21,39,1,7,25.000,2,1,0.000,0,4,3 467 | 1,71,46,0,27,119.000,5,7,0.000,1,1,4 468 | 3,11,48,1,5,323.000,4,25,0.000,0,4,3 469 | 1,24,30,1,7,45.000,1,11,0.000,1,2,1 470 | 1,65,59,1,27,197.000,4,26,0.000,0,2,2 471 | 1,16,34,1,13,56.000,3,10,0.000,1,3,1 472 | 3,37,51,1,26,24.000,4,4,0.000,0,2,2 473 | 1,5,30,0,4,25.000,3,6,0.000,0,1,3 474 | 1,3,55,1,9,26.000,5,0,0.000,1,2,2 475 | 1,5,32,0,6,33.000,4,3,0.000,0,2,1 476 | 1,60,44,1,4,150.000,2,24,0.000,0,2,4 477 | 2,19,37,1,4,99.000,2,15,0.000,1,3,2 478 | 3,54,40,0,19,50.000,4,12,0.000,1,2,4 479 | 2,25,41,0,3,81.000,3,20,0.000,1,4,3 480 | 2,1,36,0,6,21.000,1,1,0.000,0,2,2 481 | 3,54,68,0,7,273.000,3,27,0.000,1,1,1 482 | 1,14,31,1,9,39.000,3,4,0.000,1,7,1 483 | 3,14,28,1,7,17.000,2,3,0.000,0,4,1 484 | 2,10,45,1,8,115.000,3,17,0.000,1,4,2 485 | 1,7,23,0,3,27.000,2,1,0.000,1,3,1 486 | 2,33,45,0,24,35.000,1,6,0.000,0,1,3 487 | 1,3,23,0,2,20.000,2,4,0.000,0,1,1 488 | 2,56,58,1,3,92.000,1,25,0.000,0,3,1 489 | 2,54,56,0,8,207.000,2,35,0.000,1,1,3 490 | 1,72,55,1,28,40.000,1,11,0.000,0,5,2 491 | 1,18,29,0,4,40.000,2,1,0.000,0,1,3 492 | 3,25,29,0,9,55.000,4,1,0.000,0,1,1 493 | 1,42,62,0,26,26.000,1,35,1.000,1,1,1 494 | 3,4,37,0,2,41.000,2,8,0.000,0,1,1 495 | 2,12,50,1,16,203.000,4,25,0.000,0,2,1 496 | 2,53,64,1,17,380.000,2,36,0.000,1,2,3 497 | 2,13,38,1,8,104.000,4,6,0.000,0,7,4 498 | 3,18,31,1,12,24.000,3,4,0.000,0,4,2 499 | 3,67,42,1,20,81.000,3,20,0.000,1,5,4 500 | 2,6,24,0,2,17.000,1,1,0.000,0,1,3 501 | 1,25,46,0,13,45.000,1,12,0.000,1,1,3 502 | 2,15,39,0,19,32.000,2,2,0.000,0,1,2 503 | 3,32,35,0,12,57.000,4,5,0.000,0,1,2 504 | 2,22,39,0,0,63.000,4,9,0.000,0,1,2 505 | 2,8,22,0,3,25.000,4,0,0.000,0,1,3 506 | 1,3,31,1,4,22.000,1,1,0.000,0,2,1 507 | 2,32,54,0,6,155.000,2,20,0.000,0,1,1 508 | 1,49,54,0,24,30.000,2,5,0.000,0,1,2 509 | 2,2,28,0,7,46.000,3,0,0.000,1,1,4 510 | 1,39,47,0,1,68.000,4,10,0.000,1,2,2 511 | 1,42,33,0,2,67.000,2,5,0.000,0,2,2 512 | 1,28,36,0,3,69.000,3,2,0.000,0,1,4 513 | 2,41,46,1,14,47.000,2,11,0.000,0,5,4 514 | 3,67,50,1,31,83.000,2,12,0.000,0,2,2 515 | 1,38,40,0,15,34.000,2,11,0.000,1,1,2 516 | 2,18,38,0,8,45.000,2,2,0.000,1,4,4 517 | 2,6,42,1,17,29.000,2,2,0.000,1,2,4 518 | 2,4,36,0,0,21.000,2,1,0.000,1,3,1 519 | 1,13,23,1,3,30.000,3,0,0.000,1,4,4 520 | 1,20,35,0,0,78.000,4,7,0.000,0,1,4 521 | 1,34,49,1,27,61.000,3,15,0.000,1,2,3 522 | 1,46,29,1,4,90.000,5,3,0.000,0,4,4 523 | 1,48,41,0,21,43.000,2,7,0.000,0,1,2 524 | 3,69,61,1,12,301.000,2,37,0.000,0,2,3 525 | 1,32,34,1,7,32.000,2,15,0.000,0,4,1 526 | 1,29,29,0,9,41.000,2,9,0.000,0,2,2 527 | 3,55,52,0,22,127.000,1,28,0.000,1,3,2 528 | 3,72,60,1,36,271.000,1,43,0.000,1,2,3 529 | 3,29,26,0,1,32.000,3,2,0.000,1,1,1 530 | 2,50,25,1,1,36.000,4,2,0.000,1,5,4 531 | 3,35,50,1,11,293.000,3,21,0.000,0,2,1 532 | 3,11,63,0,9,41.000,3,3,0.000,0,1,1 533 | 1,9,29,0,9,39.000,3,5,0.000,1,1,1 534 | 2,51,48,0,27,58.000,1,18,0.000,0,1,3 535 | 1,33,34,1,2,28.000,1,3,0.000,1,3,2 536 | 1,25,20,0,0,15.000,2,0,0.000,0,2,3 537 | 3,31,34,0,9,105.000,4,7,0.000,1,4,3 538 | 2,67,40,1,10,80.000,4,8,0.000,1,2,4 539 | 3,55,45,0,8,36.000,2,9,0.000,0,3,3 540 | 3,19,50,0,7,112.000,4,3,0.000,1,1,4 541 | 1,39,34,1,11,55.000,4,10,0.000,1,4,4 542 | 2,52,35,1,8,40.000,4,1,0.000,0,2,3 543 | 3,55,37,1,15,23.000,2,0,0.000,1,5,3 544 | 2,25,62,0,27,28.000,4,33,1.000,1,1,4 545 | 3,33,58,0,12,21.000,2,23,1.000,1,1,1 546 | 2,48,35,0,5,63.000,4,12,0.000,0,2,3 547 | 2,48,31,1,12,52.000,4,3,0.000,0,3,4 548 | 2,49,57,0,3,189.000,1,31,0.000,1,3,1 549 | 3,54,32,1,0,50.000,1,15,0.000,0,3,3 550 | 1,45,32,1,9,23.000,1,15,0.000,0,4,1 551 | 3,4,37,1,1,33.000,2,15,0.000,1,5,1 552 | 3,49,27,1,3,29.000,3,6,0.000,1,3,2 553 | 2,23,35,0,2,96.000,3,4,0.000,0,1,4 554 | 2,31,44,0,23,80.000,5,15,0.000,0,1,2 555 | 2,55,44,1,2,68.000,2,15,0.000,0,5,3 556 | 1,9,28,0,7,30.000,1,4,0.000,1,6,1 557 | 2,55,57,1,25,34.000,2,12,0.000,0,3,1 558 | 1,34,30,0,3,35.000,2,2,0.000,1,3,3 559 | 1,10,23,0,4,36.000,2,1,0.000,0,3,3 560 | 2,62,76,0,20,35.000,3,18,1.000,1,1,2 561 | 2,51,36,0,13,62.000,3,9,0.000,0,2,4 562 | 1,65,48,0,28,94.000,1,25,0.000,0,1,2 563 | 3,53,33,0,1,60.000,1,6,0.000,0,2,4 564 | 1,23,35,0,4,118.000,2,11,0.000,0,1,1 565 | 3,1,30,0,3,135.000,4,3,0.000,0,3,1 566 | 3,66,55,0,20,80.000,2,24,0.000,1,1,3 567 | 3,8,27,1,0,21.000,3,3,0.000,1,2,2 568 | 3,18,28,1,3,22.000,1,2,0.000,1,5,1 569 | 3,36,32,1,3,63.000,4,7,0.000,0,4,2 570 | 1,13,40,0,16,142.000,2,18,0.000,1,1,1 571 | 3,4,44,0,15,32.000,3,0,0.000,1,1,3 572 | 3,20,32,0,10,19.000,3,5,0.000,0,1,2 573 | 1,66,39,1,12,60.000,4,10,0.000,0,3,4 574 | 3,67,47,1,7,97.000,1,23,0.000,0,4,3 575 | 1,11,24,1,0,31.000,2,3,0.000,1,4,4 576 | 1,6,26,0,7,30.000,3,1,0.000,1,2,1 577 | 2,13,23,1,4,31.000,1,5,0.000,1,4,1 578 | 2,2,29,1,3,44.000,2,9,0.000,1,6,4 579 | 1,4,40,0,12,38.000,2,5,0.000,0,1,1 580 | 1,34,63,1,10,23.000,2,0,0.000,1,2,4 581 | 1,13,34,1,6,37.000,3,6,0.000,0,4,3 582 | 2,37,40,1,14,26.000,2,0,0.000,0,5,2 583 | 1,45,30,1,8,38.000,2,5,0.000,0,6,3 584 | 2,71,70,0,30,153.000,3,34,1.000,1,1,3 585 | 1,35,40,0,5,49.000,1,20,0.000,1,1,1 586 | 3,44,41,0,21,51.000,3,7,0.000,0,1,1 587 | 3,44,45,1,19,88.000,1,21,0.000,0,2,3 588 | 1,26,41,1,20,19.000,1,0,0.000,1,6,1 589 | 2,42,46,1,9,31.000,1,12,0.000,1,3,1 590 | 2,41,47,1,6,90.000,2,19,0.000,0,3,1 591 | 1,31,24,1,3,14.000,1,0,0.000,0,4,1 592 | 3,62,63,0,43,341.000,2,30,0.000,0,1,3 593 | 1,12,23,1,2,24.000,4,0,0.000,1,8,4 594 | 2,45,49,1,27,81.000,2,7,0.000,1,4,3 595 | 1,47,34,1,4,63.000,2,13,0.000,1,4,1 596 | 2,23,22,1,1,27.000,1,4,0.000,0,5,3 597 | 3,39,34,0,4,20.000,5,3,0.000,1,1,4 598 | 2,19,26,0,2,48.000,3,0,0.000,0,2,3 599 | 1,13,54,0,12,121.000,4,23,0.000,0,1,3 600 | 2,5,33,0,6,39.000,3,4,0.000,0,3,4 601 | 2,11,19,0,0,21.000,2,0,0.000,0,1,1 602 | 3,4,32,0,2,53.000,4,5,0.000,0,1,1 603 | 3,17,42,0,8,58.000,1,23,0.000,0,1,2 604 | 2,7,27,0,3,39.000,3,2,0.000,0,1,1 605 | 1,19,65,0,3,108.000,1,33,0.000,0,3,3 606 | 2,32,34,1,0,38.000,1,10,0.000,0,4,2 607 | 2,53,51,0,31,245.000,4,0,0.000,0,1,2 608 | 1,51,38,0,19,51.000,1,18,0.000,1,1,3 609 | 1,35,34,0,7,78.000,4,10,0.000,1,1,1 610 | 1,3,31,1,4,23.000,4,4,0.000,0,5,4 611 | 1,22,48,0,7,88.000,4,12,0.000,0,1,1 612 | 3,17,21,1,0,25.000,2,1,0.000,1,3,3 613 | 2,68,39,0,20,73.000,2,16,0.000,1,1,2 614 | 2,27,41,1,7,62.000,4,15,0.000,1,2,3 615 | 3,68,52,1,8,456.000,3,30,0.000,1,4,3 616 | 3,56,42,1,10,24.000,2,5,0.000,0,2,3 617 | 3,11,32,0,8,23.000,2,0,0.000,1,1,1 618 | 3,5,47,0,7,46.000,1,6,0.000,1,1,1 619 | 3,16,50,0,5,263.000,2,29,0.000,1,3,3 620 | 2,59,30,0,2,34.000,4,2,0.000,1,1,2 621 | 1,60,64,0,14,33.000,2,13,1.000,0,1,4 622 | 3,55,47,0,11,37.000,1,11,0.000,0,1,3 623 | 1,18,56,0,22,296.000,2,25,0.000,0,1,1 624 | 2,16,22,0,0,26.000,3,0,0.000,1,3,2 625 | 1,35,57,1,32,47.000,4,12,0.000,0,2,2 626 | 3,31,23,1,1,31.000,3,1,0.000,1,2,4 627 | 2,20,49,1,5,264.000,2,29,0.000,1,2,1 628 | 2,16,27,0,5,41.000,4,2,0.000,1,1,4 629 | 2,69,48,1,27,55.000,2,4,0.000,0,2,3 630 | 3,69,66,1,31,49.000,2,15,0.000,0,2,1 631 | 2,27,24,1,3,26.000,1,7,0.000,1,4,1 632 | 3,48,39,0,20,110.000,4,9,0.000,0,1,3 633 | 1,18,52,1,20,66.000,1,19,0.000,1,2,1 634 | 2,24,40,1,4,39.000,3,5,0.000,1,2,3 635 | 2,17,42,0,6,31.000,2,2,0.000,0,1,4 636 | 2,9,24,1,3,26.000,4,1,0.000,1,3,1 637 | 2,15,46,0,5,232.000,4,15,0.000,1,2,4 638 | 1,62,37,1,10,43.000,4,1,0.000,1,3,2 639 | 1,9,41,0,12,39.000,4,3,0.000,0,1,1 640 | 2,71,41,1,10,73.000,2,23,0.000,1,2,3 641 | 2,62,59,1,37,74.000,1,20,0.000,0,2,3 642 | 2,20,38,0,19,237.000,4,13,0.000,0,1,4 643 | 1,19,33,1,11,22.000,1,3,0.000,0,5,3 644 | 2,49,61,1,28,21.000,1,8,0.000,0,2,3 645 | 3,3,64,0,3,9.000,2,6,1.000,0,1,1 646 | 1,33,36,0,2,41.000,5,10,0.000,0,1,1 647 | 2,38,44,0,1,50.000,4,7,0.000,1,1,2 648 | 3,42,48,1,14,27.000,3,5,0.000,1,3,4 649 | 1,11,26,0,2,53.000,3,3,0.000,0,1,4 650 | 3,27,28,1,0,58.000,3,0,0.000,0,2,3 651 | 3,23,50,0,1,151.000,4,8,0.000,1,1,1 652 | 2,4,38,1,13,54.000,2,4,0.000,0,2,3 653 | 3,36,42,0,5,31.000,1,4,0.000,1,1,1 654 | 2,37,42,0,10,115.000,1,22,0.000,1,1,3 655 | 3,24,58,1,30,24.000,1,5,0.000,0,2,1 656 | 3,4,24,0,1,17.000,2,2,0.000,0,3,1 657 | 3,54,33,0,5,47.000,2,13,0.000,1,2,3 658 | 3,7,49,0,8,36.000,1,0,0.000,1,1,3 659 | 1,10,28,0,9,75.000,4,1,0.000,0,1,4 660 | 3,24,35,1,10,41.000,5,6,0.000,1,2,2 661 | 3,12,31,0,8,18.000,4,4,0.000,0,1,1 662 | 2,45,54,0,25,171.000,3,33,0.000,1,1,3 663 | 3,59,55,1,29,42.000,3,21,0.000,1,2,2 664 | 1,3,21,1,1,36.000,3,0,0.000,1,4,1 665 | 3,8,35,0,6,31.000,5,7,0.000,0,1,4 666 | 1,16,59,0,5,207.000,2,37,0.000,1,2,4 667 | 3,32,33,0,1,38.000,1,12,0.000,1,1,1 668 | 1,8,49,0,14,134.000,4,9,0.000,1,1,4 669 | 1,9,28,1,8,28.000,3,4,0.000,0,3,2 670 | 2,12,19,1,0,15.000,2,0,0.000,1,4,2 671 | 2,24,43,1,15,48.000,4,4,0.000,0,6,4 672 | 3,72,75,0,48,14.000,2,6,1.000,1,1,2 673 | 3,58,61,0,42,101.000,4,18,0.000,1,1,2 674 | 1,70,44,1,24,87.000,2,18,0.000,0,3,3 675 | 3,9,25,0,3,27.000,1,4,0.000,1,1,1 676 | 1,49,25,0,1,27.000,5,0,0.000,1,1,2 677 | 3,52,54,0,7,59.000,3,22,0.000,0,1,3 678 | 1,10,42,1,10,294.000,4,9,0.000,1,6,4 679 | 1,14,52,1,2,44.000,3,5,0.000,0,2,3 680 | 1,19,27,1,3,35.000,2,4,0.000,0,6,4 681 | 3,12,36,1,4,205.000,4,10,0.000,1,4,2 682 | 1,65,59,0,27,732.000,3,31,0.000,1,1,2 683 | 1,67,40,1,14,59.000,3,11,0.000,0,2,3 684 | 2,29,24,1,3,23.000,1,7,0.000,0,2,3 685 | 3,55,46,0,8,87.000,2,19,0.000,1,1,1 686 | 3,71,67,1,25,110.000,1,23,0.000,1,2,3 687 | 3,5,44,1,2,83.000,3,14,0.000,1,3,3 688 | 3,60,58,0,0,63.000,4,21,0.000,1,3,2 689 | 2,10,40,0,6,22.000,3,6,0.000,1,1,1 690 | 3,52,64,0,44,43.000,3,7,0.000,0,1,4 691 | 2,61,43,1,22,35.000,2,11,0.000,0,3,2 692 | 1,61,29,1,5,48.000,2,11,0.000,0,4,2 693 | 2,67,47,1,19,69.000,3,12,0.000,0,3,3 694 | 3,41,42,1,12,26.000,4,5,0.000,0,4,2 695 | 1,30,28,0,1,20.000,1,8,0.000,1,1,1 696 | 1,24,46,0,12,43.000,2,6,0.000,0,1,4 697 | 1,72,75,0,37,33.000,1,44,1.000,1,1,2 698 | 2,17,35,0,7,66.000,2,11,0.000,0,1,4 699 | 2,65,72,0,46,12.000,2,0,1.000,0,1,3 700 | 1,15,30,1,8,90.000,2,11,0.000,0,5,4 701 | 1,34,33,0,8,35.000,3,2,0.000,1,1,2 702 | 1,26,43,0,23,51.000,5,4,0.000,1,1,2 703 | 2,36,45,0,22,117.000,3,15,0.000,1,1,2 704 | 1,60,48,0,29,100.000,2,26,0.000,1,1,3 705 | 1,16,54,0,20,147.000,1,29,0.000,0,4,3 706 | 2,1,33,0,5,53.000,4,6,0.000,1,1,1 707 | 3,56,30,1,9,57.000,5,2,0.000,0,4,2 708 | 2,6,50,0,5,37.000,2,0,0.000,0,1,2 709 | 2,48,34,0,7,42.000,3,2,0.000,1,1,2 710 | 2,34,36,1,17,50.000,1,4,0.000,0,2,3 711 | 2,54,42,1,0,55.000,4,2,0.000,0,5,4 712 | 3,38,31,1,2,42.000,2,7,0.000,1,4,4 713 | 2,25,55,0,15,37.000,1,7,0.000,1,1,1 714 | 1,47,69,1,17,228.000,3,39,0.000,1,4,4 715 | 1,72,62,1,35,163.000,5,31,0.000,0,2,4 716 | 2,6,35,1,6,28.000,4,3,0.000,1,3,4 717 | 1,30,51,1,9,118.000,5,21,0.000,1,2,1 718 | 1,65,70,1,9,115.000,2,39,1.000,1,2,4 719 | 1,22,44,1,19,37.000,4,1,0.000,0,2,3 720 | 1,60,53,0,32,93.000,1,28,0.000,0,1,3 721 | 2,19,32,0,12,71.000,4,5,0.000,1,1,4 722 | 3,18,25,0,4,26.000,2,7,0.000,1,3,1 723 | 1,9,37,1,10,14.000,4,5,0.000,1,6,1 724 | 1,1,34,1,6,18.000,1,0,0.000,1,2,1 725 | 3,10,28,1,2,36.000,4,0,0.000,1,3,4 726 | 3,45,34,1,14,43.000,4,0,0.000,0,4,2 727 | 1,25,28,1,6,41.000,4,4,0.000,1,4,3 728 | 3,66,39,0,1,66.000,2,15,0.000,1,1,2 729 | 1,27,67,0,1,33.000,4,19,1.000,1,1,3 730 | 1,19,41,0,5,53.000,4,6,0.000,0,1,1 731 | 2,17,41,1,9,28.000,4,3,0.000,0,6,1 732 | 2,16,36,0,13,58.000,4,4,0.000,0,1,1 733 | 1,8,42,1,2,129.000,4,17,0.000,1,3,1 734 | 1,3,25,0,0,65.000,4,0,0.000,1,1,4 735 | 3,59,57,0,26,311.000,3,36,0.000,1,1,1 736 | 2,45,37,0,2,57.000,3,14,0.000,1,1,2 737 | 1,66,62,0,31,47.000,2,4,0.000,1,1,2 738 | 3,28,50,1,7,25.000,1,3,0.000,0,2,1 739 | 1,15,38,1,11,46.000,5,11,0.000,0,3,1 740 | 3,39,34,1,10,51.000,3,13,0.000,1,4,4 741 | 3,38,33,0,4,19.000,2,5,0.000,1,2,3 742 | 3,34,29,0,5,26.000,3,4,0.000,1,2,2 743 | 3,60,57,1,18,72.000,5,30,1.000,0,2,4 744 | 3,72,65,1,33,71.000,1,41,1.000,1,4,3 745 | 1,42,42,1,9,41.000,2,13,0.000,0,8,3 746 | 3,32,37,0,9,44.000,2,7,0.000,0,1,1 747 | 3,48,50,1,4,208.000,4,19,0.000,1,2,4 748 | 3,63,37,1,1,45.000,4,9,0.000,1,6,2 749 | 3,45,27,1,3,39.000,3,2,0.000,0,4,2 750 | 2,68,42,0,16,89.000,4,12,0.000,0,1,2 751 | 1,6,29,1,4,19.000,2,5,0.000,1,2,1 752 | 1,68,57,1,33,15.000,2,0,0.000,1,2,4 753 | 3,72,68,1,26,59.000,2,36,1.000,1,2,4 754 | 3,53,38,1,15,20.000,3,6,0.000,1,4,2 755 | 3,20,51,1,11,27.000,4,6,0.000,0,3,3 756 | 2,70,35,1,4,48.000,2,9,0.000,0,3,2 757 | 3,32,47,0,25,135.000,2,6,0.000,1,1,1 758 | 2,61,52,1,23,19.000,1,0,0.000,0,2,3 759 | 1,24,25,1,3,28.000,4,0,0.000,0,3,2 760 | 2,56,61,1,14,12.000,1,16,1.000,1,2,1 761 | 3,54,59,0,28,28.000,2,15,0.000,0,1,2 762 | 3,43,38,1,7,18.000,3,8,0.000,1,3,4 763 | 1,19,35,0,7,58.000,3,5,0.000,1,1,4 764 | 3,5,43,0,16,72.000,3,17,0.000,1,1,3 765 | 1,24,61,1,6,112.000,4,3,0.000,1,4,1 766 | 3,62,35,1,2,28.000,2,8,0.000,1,2,2 767 | 2,14,40,0,13,398.000,5,11,0.000,1,1,4 768 | 3,60,48,1,22,106.000,3,21,0.000,1,3,4 769 | 2,30,74,1,12,10.000,1,3,1.000,1,2,3 770 | 1,40,42,1,16,108.000,3,17,0.000,0,4,2 771 | 2,42,50,1,17,85.000,2,15,0.000,1,4,1 772 | 1,20,34,0,14,51.000,3,9,0.000,1,1,2 773 | 1,4,22,1,1,22.000,2,2,0.000,1,3,3 774 | 2,44,49,1,2,66.000,2,8,0.000,1,4,4 775 | 3,49,63,0,18,10.000,2,2,1.000,0,1,3 776 | 3,5,36,1,2,34.000,1,4,0.000,1,2,3 777 | 3,70,50,1,29,67.000,1,22,0.000,0,4,2 778 | 1,30,57,1,16,19.000,1,1,0.000,0,4,1 779 | 2,28,48,0,20,128.000,4,4,0.000,1,1,4 780 | 1,5,28,1,3,39.000,5,1,0.000,0,6,4 781 | 2,27,35,0,12,56.000,1,3,0.000,1,1,4 782 | 2,17,39,0,12,45.000,4,10,0.000,1,1,1 783 | 1,4,37,1,8,25.000,1,1,0.000,1,5,1 784 | 1,30,23,1,0,24.000,2,1,0.000,1,4,3 785 | 3,71,40,1,19,53.000,4,14,0.000,1,2,4 786 | 3,67,77,0,55,49.000,1,45,1.000,1,1,1 787 | 3,24,48,1,3,38.000,1,16,0.000,1,2,1 788 | 1,24,54,1,7,84.000,3,10,0.000,1,2,1 789 | 2,59,54,0,32,126.000,1,22,0.000,1,1,3 790 | 3,59,32,0,9,73.000,4,5,0.000,0,1,2 791 | 3,44,37,1,1,41.000,4,8,0.000,1,6,2 792 | 1,19,31,1,0,22.000,4,2,0.000,0,3,2 793 | 2,58,46,1,23,23.000,1,2,0.000,1,2,1 794 | 1,41,53,1,22,94.000,2,25,0.000,0,2,2 795 | 2,16,33,0,1,30.000,3,7,0.000,0,4,3 796 | 3,13,36,0,11,31.000,2,0,0.000,0,1,1 797 | 1,60,53,0,22,171.000,1,37,0.000,1,1,3 798 | 2,72,49,1,23,102.000,1,27,0.000,1,6,2 799 | 2,33,39,0,17,27.000,4,3,0.000,1,1,4 800 | 2,72,45,1,25,98.000,4,20,0.000,1,2,4 801 | 1,66,54,1,8,591.000,4,25,0.000,1,2,4 802 | 3,53,37,1,7,25.000,1,2,0.000,1,6,3 803 | 2,22,28,1,0,36.000,4,0,0.000,0,4,4 804 | 2,14,26,1,1,25.000,2,0,0.000,1,3,3 805 | 1,52,49,0,18,69.000,2,13,0.000,1,2,3 806 | 3,33,30,1,5,22.000,1,0,0.000,0,2,3 807 | 3,72,66,0,26,67.000,2,37,1.000,1,1,3 808 | 1,40,40,1,3,73.000,4,12,0.000,1,4,3 809 | 2,29,26,0,1,22.000,4,2,0.000,1,1,4 810 | 3,63,67,0,22,9.000,2,12,1.000,0,1,3 811 | 2,56,54,0,25,147.000,3,31,0.000,1,1,2 812 | 3,60,50,1,26,239.000,2,19,0.000,1,2,4 813 | 3,28,22,1,3,23.000,3,1,0.000,0,5,1 814 | 2,39,39,0,19,29.000,4,6,0.000,0,1,2 815 | 3,65,39,1,18,32.000,2,15,0.000,0,4,3 816 | 3,29,31,0,7,30.000,3,7,0.000,1,1,3 817 | 3,28,36,0,8,57.000,2,13,0.000,1,2,3 818 | 1,2,18,0,0,20.000,2,0,0.000,0,1,3 819 | 3,67,52,0,13,248.000,2,20,0.000,1,3,3 820 | 3,5,35,1,9,59.000,3,8,0.000,1,5,4 821 | 3,48,35,0,10,24.000,2,6,0.000,0,1,3 822 | 2,10,26,1,4,25.000,1,4,0.000,1,3,1 823 | 2,17,28,0,3,37.000,3,2,0.000,1,1,3 824 | 1,21,24,1,0,30.000,4,0,0.000,1,5,4 825 | 2,25,51,0,9,66.000,4,13,0.000,0,2,4 826 | 3,44,31,0,8,16.000,1,3,0.000,0,1,1 827 | 2,59,49,0,28,429.000,4,23,0.000,1,2,4 828 | 2,11,26,0,7,20.000,2,4,0.000,0,1,1 829 | 2,50,68,1,32,262.000,3,28,0.000,0,2,3 830 | 3,23,25,1,2,28.000,3,1,0.000,1,5,2 831 | 1,17,19,0,0,18.000,2,0,0.000,1,2,2 832 | 1,15,62,1,3,144.000,3,21,0.000,0,2,3 833 | 2,68,57,1,38,33.000,1,9,0.000,1,2,2 834 | 1,69,42,1,23,19.000,3,0,0.000,1,2,1 835 | 1,30,24,1,3,30.000,1,7,0.000,1,5,1 836 | 3,22,30,0,8,41.000,2,8,0.000,0,1,3 837 | 2,10,20,1,1,20.000,2,0,0.000,1,4,1 838 | 1,72,59,1,12,65.000,3,21,0.000,1,6,2 839 | 2,34,24,1,5,17.000,4,0,0.000,1,2,3 840 | 2,43,49,1,18,66.000,1,22,0.000,0,4,3 841 | 3,31,37,1,15,18.000,4,5,0.000,0,3,4 842 | 2,6,27,1,5,26.000,4,2,0.000,0,5,1 843 | 2,48,42,0,21,46.000,4,4,0.000,0,1,3 844 | 2,50,25,0,6,19.000,1,5,0.000,0,3,1 845 | 3,20,41,1,19,58.000,2,6,0.000,1,2,2 846 | 3,61,73,0,10,25.000,1,21,1.000,0,1,1 847 | 2,6,21,0,0,15.000,2,1,0.000,1,1,1 848 | 2,1,22,0,2,35.000,2,2,0.000,0,1,1 849 | 1,21,43,0,13,41.000,2,1,0.000,1,1,4 850 | 3,7,32,0,1,71.000,4,3,0.000,0,2,4 851 | 3,65,56,1,19,608.000,3,34,0.000,1,2,4 852 | 1,64,37,0,10,44.000,4,9,0.000,1,1,2 853 | 3,4,30,0,0,41.000,2,0,0.000,0,2,3 854 | 1,67,45,1,22,83.000,3,11,0.000,1,2,2 855 | 2,22,35,0,12,28.000,2,3,0.000,0,2,2 856 | 3,51,37,1,17,101.000,2,10,0.000,1,2,3 857 | 1,50,28,0,6,28.000,1,10,0.000,1,1,3 858 | 1,31,47,1,18,35.000,2,0,0.000,0,2,1 859 | 3,4,28,0,0,39.000,2,3,0.000,1,1,3 860 | 3,60,56,0,19,51.000,4,11,0.000,0,1,4 861 | 3,7,31,1,12,31.000,2,7,0.000,1,6,1 862 | 3,20,40,0,2,209.000,4,15,0.000,1,1,2 863 | 2,19,23,1,4,37.000,3,1,0.000,1,4,3 864 | 3,58,37,1,5,64.000,4,8,0.000,1,2,4 865 | 3,65,64,0,20,88.000,3,26,0.000,1,1,4 866 | 3,72,61,1,34,61.000,1,8,0.000,0,3,2 867 | 3,8,42,0,3,66.000,3,10,0.000,0,1,4 868 | 2,33,40,1,14,19.000,3,5,0.000,0,2,1 869 | 3,18,34,1,4,42.000,2,14,0.000,1,5,4 870 | 2,24,31,1,6,41.000,2,13,0.000,0,5,3 871 | 3,70,36,0,8,50.000,1,15,0.000,1,1,2 872 | 1,7,38,0,4,70.000,4,4,0.000,0,1,4 873 | 3,70,59,0,27,85.000,1,19,0.000,1,1,3 874 | 2,20,48,1,21,45.000,3,2,0.000,1,2,3 875 | 3,54,32,1,12,27.000,1,9,0.000,0,2,2 876 | 3,36,48,0,28,29.000,1,2,0.000,0,1,1 877 | 3,71,53,1,29,48.000,4,0,0.000,1,2,3 878 | 1,17,41,0,4,32.000,2,3,0.000,0,1,3 879 | 1,18,45,1,17,43.000,1,9,0.000,0,3,3 880 | 1,16,27,0,8,16.000,5,0,0.000,1,1,4 881 | 3,38,30,1,6,22.000,2,3,0.000,1,2,2 882 | 1,52,45,1,19,40.000,3,9,0.000,1,3,4 883 | 1,46,48,1,12,256.000,3,25,0.000,0,6,4 884 | 2,31,46,0,23,144.000,4,13,0.000,0,1,3 885 | 2,23,31,0,10,45.000,4,0,0.000,0,2,2 886 | 2,38,56,1,34,34.000,2,0,0.000,1,3,3 887 | 2,50,54,0,15,248.000,3,28,0.000,1,1,3 888 | 2,16,49,0,17,41.000,2,5,0.000,0,1,4 889 | 2,14,34,1,4,27.000,4,1,0.000,1,2,4 890 | 2,30,52,0,23,17.000,3,3,0.000,1,1,2 891 | 3,22,47,1,0,44.000,4,11,0.000,1,3,4 892 | 1,38,55,1,12,135.000,2,24,0.000,0,4,4 893 | 3,59,26,1,3,41.000,4,1,0.000,1,3,2 894 | 2,54,55,0,1,587.000,3,33,0.000,0,1,3 895 | 3,9,40,0,13,38.000,4,7,0.000,1,1,4 896 | 1,67,67,1,38,49.000,2,10,0.000,1,2,2 897 | 2,24,44,0,19,33.000,3,0,0.000,1,2,1 898 | 2,12,55,1,13,36.000,1,5,0.000,1,2,2 899 | 1,26,47,0,13,54.000,3,0,0.000,0,1,2 900 | 2,3,32,0,4,58.000,2,11,0.000,0,4,4 901 | 2,20,46,0,2,23.000,5,4,0.000,1,2,4 902 | 3,5,52,1,0,97.000,5,9,0.000,0,2,4 903 | 3,58,53,0,19,207.000,3,12,0.000,1,1,4 904 | 1,54,28,1,2,21.000,3,3,0.000,0,4,1 905 | 2,52,39,0,6,119.000,3,18,0.000,1,1,1 906 | 2,38,30,1,10,25.000,1,5,0.000,1,4,1 907 | 2,19,46,0,5,59.000,3,17,0.000,0,1,1 908 | 2,22,33,1,2,24.000,1,3,0.000,0,4,1 909 | 1,49,35,1,15,113.000,4,4,0.000,0,5,2 910 | 3,5,39,0,0,22.000,1,0,0.000,1,2,3 911 | 2,18,69,0,28,11.000,1,17,1.000,0,1,1 912 | 3,43,29,0,4,33.000,1,13,0.000,1,4,3 913 | 3,17,50,0,2,205.000,5,19,0.000,0,1,4 914 | 2,37,33,0,4,41.000,3,8,0.000,1,3,2 915 | 2,8,39,0,0,39.000,1,7,0.000,1,1,1 916 | 2,51,46,1,8,107.000,2,21,0.000,1,6,3 917 | 1,71,55,1,11,198.000,1,31,0.000,1,3,3 918 | 1,56,53,1,23,100.000,5,14,0.000,1,2,2 919 | 3,70,68,0,21,1131.000,2,45,0.000,0,1,3 920 | 1,62,53,0,14,360.000,3,26,0.000,1,2,3 921 | 1,50,52,1,22,162.000,2,27,0.000,1,2,4 922 | 3,1,31,0,3,29.000,5,2,0.000,1,3,1 923 | 3,15,26,0,1,38.000,4,1,0.000,0,2,4 924 | 3,72,55,1,24,82.000,3,25,0.000,1,2,2 925 | 2,69,47,1,10,159.000,2,24,0.000,0,4,3 926 | 2,32,44,0,10,201.000,2,24,0.000,0,1,3 927 | 1,19,25,1,6,27.000,4,0,0.000,1,2,3 928 | 2,42,54,0,19,57.000,3,10,0.000,1,1,4 929 | 3,51,49,0,29,45.000,1,16,0.000,0,1,1 930 | 2,32,22,1,2,17.000,2,2,0.000,1,4,3 931 | 3,26,55,1,13,61.000,1,26,0.000,0,2,3 932 | 2,39,44,1,14,418.000,4,18,0.000,0,2,4 933 | 2,72,53,0,12,152.000,2,32,0.000,1,1,4 934 | 2,4,39,0,11,38.000,4,1,0.000,0,2,4 935 | 2,54,50,1,5,151.000,4,20,0.000,0,3,3 936 | 1,34,40,1,21,23.000,4,9,0.000,1,3,4 937 | 1,20,25,1,4,33.000,4,0,0.000,1,3,1 938 | 3,14,49,0,4,26.000,1,8,0.000,0,1,1 939 | 1,61,59,0,26,89.000,1,40,1.000,1,1,3 940 | 2,4,24,0,2,25.000,4,0,0.000,0,1,1 941 | 3,16,48,0,25,59.000,2,13,0.000,0,2,3 942 | 1,13,41,0,9,55.000,2,12,0.000,1,3,1 943 | 2,53,37,1,18,25.000,3,6,0.000,1,2,2 944 | 2,58,36,1,13,39.000,2,8,0.000,1,7,4 945 | 2,25,38,1,19,56.000,1,19,0.000,1,2,3 946 | 1,71,61,1,19,155.000,3,30,0.000,0,2,3 947 | 1,22,33,1,13,119.000,5,0,0.000,1,4,4 948 | 1,46,48,1,15,64.000,3,20,0.000,0,2,3 949 | 3,66,50,1,2,333.000,5,24,0.000,0,2,2 950 | 1,69,58,1,29,26.000,2,7,0.000,1,3,4 951 | 2,38,49,1,3,33.000,4,11,0.000,0,4,2 952 | 3,16,35,0,7,46.000,3,3,0.000,0,1,1 953 | 3,44,39,1,8,41.000,2,8,0.000,0,4,4 954 | 1,50,50,0,24,31.000,1,8,0.000,0,1,3 955 | 3,10,23,0,1,29.000,4,0,0.000,1,1,4 956 | 2,7,41,0,6,94.000,2,16,0.000,0,1,4 957 | 3,71,48,0,25,288.000,3,19,0.000,0,1,1 958 | 3,16,39,1,5,31.000,1,4,0.000,1,5,1 959 | 3,6,20,0,0,25.000,2,0,0.000,1,2,3 960 | 2,46,55,0,24,269.000,2,35,0.000,0,1,3 961 | 3,51,49,1,13,203.000,3,27,0.000,0,4,3 962 | 3,5,34,0,12,22.000,2,2,0.000,0,2,1 963 | 2,49,54,1,23,25.000,2,1,0.000,1,4,1 964 | 3,49,51,1,14,222.000,5,16,0.000,0,2,2 965 | 1,26,30,1,4,76.000,3,7,0.000,0,2,1 966 | 3,22,39,0,4,55.000,2,17,0.000,1,1,4 967 | 3,51,45,1,4,46.000,3,6,0.000,0,2,4 968 | 1,19,27,1,7,56.000,4,1,0.000,0,4,4 969 | 1,9,36,0,1,79.000,3,11,0.000,1,5,3 970 | 3,15,25,1,0,51.000,3,2,0.000,1,2,1 971 | 2,57,63,0,19,61.000,1,27,0.000,0,1,3 972 | 3,48,45,0,15,41.000,1,10,0.000,1,2,1 973 | 2,9,53,1,20,50.000,2,13,0.000,1,2,1 974 | 1,61,52,0,21,82.000,1,18,0.000,0,1,3 975 | 3,72,53,0,34,108.000,1,23,0.000,0,1,3 976 | 3,57,60,0,20,14.000,2,27,1.000,1,1,3 977 | 2,57,51,0,10,46.000,1,13,0.000,0,1,2 978 | 1,37,60,1,0,46.000,1,20,0.000,0,3,3 979 | 3,45,51,0,13,146.000,4,16,0.000,0,2,1 980 | 3,45,39,1,15,36.000,1,11,0.000,1,5,1 981 | 2,55,44,0,24,83.000,1,23,0.000,1,1,3 982 | 2,24,27,0,2,17.000,3,3,0.000,0,5,2 983 | 3,1,20,0,1,18.000,3,0,0.000,0,1,1 984 | 2,34,23,1,3,24.000,1,7,0.000,1,4,3 985 | 3,14,37,1,5,48.000,3,1,0.000,1,4,1 986 | 3,6,32,0,10,47.000,1,10,0.000,0,1,3 987 | 3,24,30,0,0,25.000,4,5,0.000,0,2,4 988 | 2,3,26,1,6,59.000,4,0,0.000,1,3,4 989 | 1,4,30,0,1,45.000,4,6,0.000,0,3,4 990 | 1,72,40,1,19,163.000,4,15,0.000,0,2,2 991 | 1,44,56,0,4,63.000,2,6,0.000,1,1,3 992 | 1,50,43,0,6,27.000,3,4,0.000,0,1,3 993 | 2,61,50,1,16,190.000,2,22,0.000,1,2,2 994 | 1,34,52,1,2,106.000,2,19,0.000,0,2,3 995 | 3,26,54,1,30,26.000,3,1,0.000,1,2,4 996 | 1,15,46,1,17,63.000,5,1,0.000,0,2,4 997 | 3,10,39,0,0,27.000,3,0,0.000,1,3,1 998 | 1,7,34,0,2,22.000,5,5,0.000,1,1,1 999 | 3,67,59,0,40,944.000,5,33,0.000,1,1,4 1000 | 3,70,49,0,18,87.000,2,22,0.000,1,1,3 1001 | 3,50,36,1,7,39.000,3,3,0.000,1,3,2 1002 | --------------------------------------------------------------------------------