├── Data Scientist's Salary Prediction ├── Data Scientist's Salary Prediction.ipynb ├── README.md ├── glassdoor_jobs.csv └── readme-resources │ ├── corr1.png │ ├── data-scientist-salary-banner.png │ ├── infogain.png │ ├── jih.png │ ├── prediction.PNG │ ├── rating.png │ └── rating1.png ├── Diabetes Classification ├── Diabetes Classification.ipynb ├── README.md ├── dataset │ └── kaggle_diabetes.csv └── readme-resources │ └── diabetes-banner.png ├── First Innings Score Prediction - IPL ├── First Innings Score Prediction - IPL.ipynb ├── README.md ├── dataset │ └── ipl.csv └── readme-resources │ ├── consistent_teams.PNG │ ├── first-innings-banner.png │ └── prediction.PNG ├── Heart Disease Prediction ├── Heart Disease Prediction.ipynb └── heart.csv ├── Mall Customer Segmentation ├── Mall Customer Segmentation - PPT.pdf ├── Mall Customer Segmentation - Report.pdf ├── Mall Customer Segmentation.ipynb └── Mall_Customers.csv ├── Predicting Admission into UCLA ├── Predicting Admission into UCLA.ipynb └── admission_predict.csv ├── Predicting House Prices in Bengaluru ├── Bengaluru house price prediction.ipynb └── Bengaluru_House_Data.csv ├── README.md └── readme-resources ├── data-scientist-salary-banner.png └── machine-learning.png /Data Scientist's Salary Prediction/README.md: -------------------------------------------------------------------------------- 1 | ![DSSP](readme-resources/data-scientist-salary-banner.png) 2 | ![Dataset](https://img.shields.io/badge/Dataset-Ken_Jee-blue.svg) ![Python 3.6](https://img.shields.io/badge/Python-3.6-brightgreen.svg) ![library](https://img.shields.io/badge/Library-sklearn-orange.svg) 3 | 4 | ## Project Overview 5 | • Created a machine learning model that **estimates salary of data scientist based on the features like rating, company_founded, etc.**
6 | • Engineered features from the text of each job description to quantify the value companies put on python, excel, tableau and sql 7 | 8 | ## How will this project help? 9 | • This project **helps data scientist/analyst to negotiate their income for an existing or a new job** 10 | 11 | ## Resources Used 12 | • Packages: **pandas, numpy, sklearn, matplotlib, seaborn.**
13 | • Dataset by **Ken Jee**: https://github.com/PlayingNumbers/ds_salary_proj 14 | 15 | ## Exploratory Data Analysis (EDA) and Data Cleaning 16 | • **Removed unwanted columns**: 'Unnamed: 0'
17 | • **Plotted bargraphs and countplots** for numerical and categorical features respectively for EDA
18 | • **Numerical Features** (Rating, Founded): **Replaced NaN or -1 values with mean or meadian based on their distribution**
19 | ![rating](readme-resources/rating.png) ![rating1](readme-resources/rating1.png)
20 | • **Categorical Features: Replaced NaN or -1 values with 'Other'/'Unknown' category**
21 | • **Removed unwanted alphabet/special characters from Salary feature**
22 | • **Converted the Salary column into one scale** i.e from (per hour, per annum, employer provided salary) to (per annum) 23 | 24 | ## Feature Engineering 25 | • **Creating new features** from existing features e.g. **job_in_headquaters from (job_location, headquarters)**, etc.
26 | ![jih](readme-resources/jih.png)
27 | • Trimming columns i.e. **Trimming features having more than 10 categories to reduce the dimensionality**
28 | • **Handling ordinal and nominal categorical features**
29 | • Feature Selection using **information gain (mutual_info_regression) and correlation matrix**
30 | ![infogain](readme-resources/infogain.png)
31 | ![corr1](readme-resources/corr1.png)
32 | • Feature Scaling using **StandardScalar** 33 | 34 | ## Model Building and Evaluation 35 | Metric: Negative Root Mean Squared Error (NRMSE)
36 | • Multiple Linear Regression: -27.523
37 | • Lasso Regression: -27.993
38 | • **Random Forest: -17.637**
39 | • Gradient Boosting: -24.429
40 | • Voting (Random Forest + Gradient Boosting): -19.136
41 | _**Note: Evaluation scores are obtained using cross validation.**_ 42 | 43 | ## Model Prediction 44 | ![Prediction](readme-resources/prediction.PNG) 45 | -------------------------------------------------------------------------------- /Data Scientist's Salary Prediction/readme-resources/corr1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Data Scientist's Salary Prediction/readme-resources/corr1.png -------------------------------------------------------------------------------- /Data Scientist's Salary Prediction/readme-resources/data-scientist-salary-banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Data Scientist's Salary Prediction/readme-resources/data-scientist-salary-banner.png -------------------------------------------------------------------------------- /Data Scientist's Salary Prediction/readme-resources/infogain.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Data Scientist's Salary Prediction/readme-resources/infogain.png -------------------------------------------------------------------------------- /Data Scientist's Salary Prediction/readme-resources/jih.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Data Scientist's Salary Prediction/readme-resources/jih.png -------------------------------------------------------------------------------- /Data Scientist's Salary Prediction/readme-resources/prediction.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Data Scientist's Salary Prediction/readme-resources/prediction.PNG -------------------------------------------------------------------------------- /Data Scientist's Salary Prediction/readme-resources/rating.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Data Scientist's Salary Prediction/readme-resources/rating.png -------------------------------------------------------------------------------- /Data Scientist's Salary Prediction/readme-resources/rating1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Data Scientist's Salary Prediction/readme-resources/rating1.png -------------------------------------------------------------------------------- /Diabetes Classification/README.md: -------------------------------------------------------------------------------- 1 | ![DC](readme-resources/diabetes-banner.png) 2 | 3 | ## Deployed Web App 4 | If you want to view the deployed model, then go to following links mention below:
5 | • GitHub: _https://github.com/anujvyas/Diabetes-Prediction-Deployment_
6 | • Web App: _https://predicting-diabetes.herokuapp.com/_ 7 | -------------------------------------------------------------------------------- /Diabetes Classification/dataset/kaggle_diabetes.csv: -------------------------------------------------------------------------------- 1 | Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome 2 | 2,138,62,35,0,33.6,0.127,47,1 3 | 0,84,82,31,125,38.2,0.233,23,0 4 | 0,145,0,0,0,44.2,0.63,31,1 5 | 0,135,68,42,250,42.3,0.365,24,1 6 | 1,139,62,41,480,40.7,0.536,21,0 7 | 0,173,78,32,265,46.5,1.159,58,0 8 | 4,99,72,17,0,25.6,0.294,28,0 9 | 8,194,80,0,0,26.1,0.551,67,0 10 | 2,83,65,28,66,36.8,0.629,24,0 11 | 2,89,90,30,0,33.5,0.292,42,0 12 | 4,99,68,38,0,32.8,0.145,33,0 13 | 4,125,70,18,122,28.9,1.144,45,1 14 | 3,80,0,0,0,0,0.174,22,0 15 | 6,166,74,0,0,26.6,0.304,66,0 16 | 5,110,68,0,0,26,0.292,30,0 17 | 2,81,72,15,76,30.1,0.547,25,0 18 | 7,195,70,33,145,25.1,0.163,55,1 19 | 6,154,74,32,193,29.3,0.839,39,0 20 | 2,117,90,19,71,25.2,0.313,21,0 21 | 3,84,72,32,0,37.2,0.267,28,0 22 | 6,0,68,41,0,39,0.727,41,1 23 | 7,94,64,25,79,33.3,0.738,41,0 24 | 3,96,78,39,0,37.3,0.238,40,0 25 | 10,75,82,0,0,33.3,0.263,38,0 26 | 0,180,90,26,90,36.5,0.314,35,1 27 | 1,130,60,23,170,28.6,0.692,21,0 28 | 2,84,50,23,76,30.4,0.968,21,0 29 | 8,120,78,0,0,25,0.409,64,0 30 | 12,84,72,31,0,29.7,0.297,46,1 31 | 0,139,62,17,210,22.1,0.207,21,0 32 | 9,91,68,0,0,24.2,0.2,58,0 33 | 2,91,62,0,0,27.3,0.525,22,0 34 | 3,99,54,19,86,25.6,0.154,24,0 35 | 3,163,70,18,105,31.6,0.268,28,1 36 | 10,122,78,31,0,27.6,0.512,45,0 37 | 4,103,60,33,192,24,0.966,33,0 38 | 11,138,76,0,0,33.2,0.42,35,0 39 | 9,102,76,37,0,32.9,0.665,46,1 40 | 2,90,68,42,0,38.2,0.503,27,1 41 | 4,111,72,47,207,37.1,1.39,56,1 42 | 3,180,64,25,70,34,0.271,26,0 43 | 7,133,84,0,0,40.2,0.696,37,0 44 | 7,106,92,18,0,22.7,0.235,48,0 45 | 9,171,110,24,240,45.4,0.721,54,1 46 | 7,159,64,0,0,27.4,0.294,40,0 47 | 0,180,66,39,0,42,1.893,25,1 48 | 1,146,56,0,0,29.7,0.564,29,0 49 | 2,71,70,27,0,28,0.586,22,0 50 | 7,103,66,32,0,39.1,0.344,31,1 51 | 7,105,0,0,0,0,0.305,24,0 52 | 1,103,80,11,82,19.4,0.491,22,0 53 | 1,101,50,15,36,24.2,0.526,26,0 54 | 5,88,66,21,23,24.4,0.342,30,0 55 | 8,176,90,34,300,33.7,0.467,58,1 56 | 7,150,66,42,342,34.7,0.718,42,0 57 | 1,73,50,10,0,23,0.248,21,0 58 | 7,187,68,39,304,37.7,0.254,41,1 59 | 0,100,88,60,110,46.8,0.962,31,0 60 | 0,146,82,0,0,40.5,1.781,44,0 61 | 0,105,64,41,142,41.5,0.173,22,0 62 | 2,84,0,0,0,0,0.304,21,0 63 | 8,133,72,0,0,32.9,0.27,39,1 64 | 5,44,62,0,0,25,0.587,36,0 65 | 2,141,58,34,128,25.4,0.699,24,0 66 | 7,114,66,0,0,32.8,0.258,42,1 67 | 5,99,74,27,0,29,0.203,32,0 68 | 0,109,88,30,0,32.5,0.855,38,1 69 | 2,109,92,0,0,42.7,0.845,54,0 70 | 1,95,66,13,38,19.6,0.334,25,0 71 | 4,146,85,27,100,28.9,0.189,27,0 72 | 2,100,66,20,90,32.9,0.867,28,1 73 | 5,139,64,35,140,28.6,0.411,26,0 74 | 13,126,90,0,0,43.4,0.583,42,1 75 | 4,129,86,20,270,35.1,0.231,23,0 76 | 1,79,75,30,0,32,0.396,22,0 77 | 1,0,48,20,0,24.7,0.14,22,0 78 | 7,62,78,0,0,32.6,0.391,41,0 79 | 5,95,72,33,0,37.7,0.37,27,0 80 | 0,131,0,0,0,43.2,0.27,26,1 81 | 2,112,66,22,0,25,0.307,24,0 82 | 3,113,44,13,0,22.4,0.14,22,0 83 | 2,74,0,0,0,0,0.102,22,0 84 | 7,83,78,26,71,29.3,0.767,36,0 85 | 0,101,65,28,0,24.6,0.237,22,0 86 | 5,137,108,0,0,48.8,0.227,37,1 87 | 2,110,74,29,125,32.4,0.698,27,0 88 | 13,106,72,54,0,36.6,0.178,45,0 89 | 2,100,68,25,71,38.5,0.324,26,0 90 | 15,136,70,32,110,37.1,0.153,43,1 91 | 1,107,68,19,0,26.5,0.165,24,0 92 | 1,80,55,0,0,19.1,0.258,21,0 93 | 4,123,80,15,176,32,0.443,34,0 94 | 7,81,78,40,48,46.7,0.261,42,0 95 | 4,134,72,0,0,23.8,0.277,60,1 96 | 2,142,82,18,64,24.7,0.761,21,0 97 | 6,144,72,27,228,33.9,0.255,40,0 98 | 2,92,62,28,0,31.6,0.13,24,0 99 | 1,71,48,18,76,20.4,0.323,22,0 100 | 6,93,50,30,64,28.7,0.356,23,0 101 | 1,122,90,51,220,49.7,0.325,31,1 102 | 1,163,72,0,0,39,1.222,33,1 103 | 1,151,60,0,0,26.1,0.179,22,0 104 | 0,125,96,0,0,22.5,0.262,21,0 105 | 1,81,72,18,40,26.6,0.283,24,0 106 | 2,85,65,0,0,39.6,0.93,27,0 107 | 1,126,56,29,152,28.7,0.801,21,0 108 | 1,96,122,0,0,22.4,0.207,27,0 109 | 4,144,58,28,140,29.5,0.287,37,0 110 | 3,83,58,31,18,34.3,0.336,25,0 111 | 0,95,85,25,36,37.4,0.247,24,1 112 | 3,171,72,33,135,33.3,0.199,24,1 113 | 8,155,62,26,495,34,0.543,46,1 114 | 1,89,76,34,37,31.2,0.192,23,0 115 | 4,76,62,0,0,34,0.391,25,0 116 | 7,160,54,32,175,30.5,0.588,39,1 117 | 4,146,92,0,0,31.2,0.539,61,1 118 | 5,124,74,0,0,34,0.22,38,1 119 | 5,78,48,0,0,33.7,0.654,25,0 120 | 4,97,60,23,0,28.2,0.443,22,0 121 | 4,99,76,15,51,23.2,0.223,21,0 122 | 0,162,76,56,100,53.2,0.759,25,1 123 | 6,111,64,39,0,34.2,0.26,24,0 124 | 2,107,74,30,100,33.6,0.404,23,0 125 | 5,132,80,0,0,26.8,0.186,69,0 126 | 0,113,76,0,0,33.3,0.278,23,1 127 | 1,88,30,42,99,55,0.496,26,1 128 | 3,120,70,30,135,42.9,0.452,30,0 129 | 1,118,58,36,94,33.3,0.261,23,0 130 | 1,117,88,24,145,34.5,0.403,40,1 131 | 0,105,84,0,0,27.9,0.741,62,1 132 | 4,173,70,14,168,29.7,0.361,33,1 133 | 9,122,56,0,0,33.3,1.114,33,1 134 | 3,170,64,37,225,34.5,0.356,30,1 135 | 8,84,74,31,0,38.3,0.457,39,0 136 | 2,96,68,13,49,21.1,0.647,26,0 137 | 2,125,60,20,140,33.8,0.088,31,0 138 | 0,100,70,26,50,30.8,0.597,21,0 139 | 0,93,60,25,92,28.7,0.532,22,0 140 | 0,129,80,0,0,31.2,0.703,29,0 141 | 5,105,72,29,325,36.9,0.159,28,0 142 | 3,128,78,0,0,21.1,0.268,55,0 143 | 5,106,82,30,0,39.5,0.286,38,0 144 | 2,108,52,26,63,32.5,0.318,22,0 145 | 10,108,66,0,0,32.4,0.272,42,1 146 | 4,154,62,31,284,32.8,0.237,23,0 147 | 0,102,75,23,0,0,0.572,21,0 148 | 9,57,80,37,0,32.8,0.096,41,0 149 | 2,106,64,35,119,30.5,1.4,34,0 150 | 5,147,78,0,0,33.7,0.218,65,0 151 | 2,90,70,17,0,27.3,0.085,22,0 152 | 1,136,74,50,204,37.4,0.399,24,0 153 | 4,114,65,0,0,21.9,0.432,37,0 154 | 9,156,86,28,155,34.3,1.189,42,1 155 | 1,153,82,42,485,40.6,0.687,23,0 156 | 8,188,78,0,0,47.9,0.137,43,1 157 | 7,152,88,44,0,50,0.337,36,1 158 | 2,99,52,15,94,24.6,0.637,21,0 159 | 1,109,56,21,135,25.2,0.833,23,0 160 | 2,88,74,19,53,29,0.229,22,0 161 | 17,163,72,41,114,40.9,0.817,47,1 162 | 4,151,90,38,0,29.7,0.294,36,0 163 | 7,102,74,40,105,37.2,0.204,45,0 164 | 0,114,80,34,285,44.2,0.167,27,0 165 | 2,100,64,23,0,29.7,0.368,21,0 166 | 0,131,88,0,0,31.6,0.743,32,1 167 | 6,104,74,18,156,29.9,0.722,41,1 168 | 3,148,66,25,0,32.5,0.256,22,0 169 | 4,120,68,0,0,29.6,0.709,34,0 170 | 4,110,66,0,0,31.9,0.471,29,0 171 | 3,111,90,12,78,28.4,0.495,29,0 172 | 6,102,82,0,0,30.8,0.18,36,1 173 | 6,134,70,23,130,35.4,0.542,29,1 174 | 2,87,0,23,0,28.9,0.773,25,0 175 | 1,79,60,42,48,43.5,0.678,23,0 176 | 2,75,64,24,55,29.7,0.37,33,0 177 | 8,179,72,42,130,32.7,0.719,36,1 178 | 6,85,78,0,0,31.2,0.382,42,0 179 | 0,129,110,46,130,67.1,0.319,26,1 180 | 5,143,78,0,0,45,0.19,47,0 181 | 5,130,82,0,0,39.1,0.956,37,1 182 | 6,87,80,0,0,23.2,0.084,32,0 183 | 0,119,64,18,92,34.9,0.725,23,0 184 | 1,0,74,20,23,27.7,0.299,21,0 185 | 5,73,60,0,0,26.8,0.268,27,0 186 | 4,141,74,0,0,27.6,0.244,40,0 187 | 7,194,68,28,0,35.9,0.745,41,1 188 | 8,181,68,36,495,30.1,0.615,60,1 189 | 1,128,98,41,58,32,1.321,33,1 190 | 8,109,76,39,114,27.9,0.64,31,1 191 | 5,139,80,35,160,31.6,0.361,25,1 192 | 3,111,62,0,0,22.6,0.142,21,0 193 | 9,123,70,44,94,33.1,0.374,40,0 194 | 7,159,66,0,0,30.4,0.383,36,1 195 | 11,135,0,0,0,52.3,0.578,40,1 196 | 8,85,55,20,0,24.4,0.136,42,0 197 | 5,158,84,41,210,39.4,0.395,29,1 198 | 1,105,58,0,0,24.3,0.187,21,0 199 | 3,107,62,13,48,22.9,0.678,23,1 200 | 4,109,64,44,99,34.8,0.905,26,1 201 | 4,148,60,27,318,30.9,0.15,29,1 202 | 0,113,80,16,0,31,0.874,21,0 203 | 1,138,82,0,0,40.1,0.236,28,0 204 | 0,108,68,20,0,27.3,0.787,32,0 205 | 2,99,70,16,44,20.4,0.235,27,0 206 | 6,103,72,32,190,37.7,0.324,55,0 207 | 5,111,72,28,0,23.9,0.407,27,0 208 | 8,196,76,29,280,37.5,0.605,57,1 209 | 5,162,104,0,0,37.7,0.151,52,1 210 | 1,96,64,27,87,33.2,0.289,21,0 211 | 7,184,84,33,0,35.5,0.355,41,1 212 | 2,81,60,22,0,27.7,0.29,25,0 213 | 0,147,85,54,0,42.8,0.375,24,0 214 | 7,179,95,31,0,34.2,0.164,60,0 215 | 0,140,65,26,130,42.6,0.431,24,1 216 | 9,112,82,32,175,34.2,0.26,36,1 217 | 12,151,70,40,271,41.8,0.742,38,1 218 | 5,109,62,41,129,35.8,0.514,25,1 219 | 6,125,68,30,120,30,0.464,32,0 220 | 5,85,74,22,0,29,1.224,32,1 221 | 5,112,66,0,0,37.8,0.261,41,1 222 | 0,177,60,29,478,34.6,1.072,21,1 223 | 2,158,90,0,0,31.6,0.805,66,1 224 | 7,119,0,0,0,25.2,0.209,37,0 225 | 7,142,60,33,190,28.8,0.687,61,0 226 | 1,100,66,15,56,23.6,0.666,26,0 227 | 1,87,78,27,32,34.6,0.101,22,0 228 | 0,101,76,0,0,35.7,0.198,26,0 229 | 3,162,52,38,0,37.2,0.652,24,1 230 | 4,197,70,39,744,36.7,2.329,31,0 231 | 0,117,80,31,53,45.2,0.089,24,0 232 | 4,142,86,0,0,44,0.645,22,1 233 | 6,134,80,37,370,46.2,0.238,46,1 234 | 1,79,80,25,37,25.4,0.583,22,0 235 | 4,122,68,0,0,35,0.394,29,0 236 | 3,74,68,28,45,29.7,0.293,23,0 237 | 4,171,72,0,0,43.6,0.479,26,1 238 | 7,181,84,21,192,35.9,0.586,51,1 239 | 0,179,90,27,0,44.1,0.686,23,1 240 | 9,164,84,21,0,30.8,0.831,32,1 241 | 0,104,76,0,0,18.4,0.582,27,0 242 | 1,91,64,24,0,29.2,0.192,21,0 243 | 4,91,70,32,88,33.1,0.446,22,0 244 | 3,139,54,0,0,25.6,0.402,22,1 245 | 6,119,50,22,176,27.1,1.318,33,1 246 | 2,146,76,35,194,38.2,0.329,29,0 247 | 9,184,85,15,0,30,1.213,49,1 248 | 10,122,68,0,0,31.2,0.258,41,0 249 | 0,165,90,33,680,52.3,0.427,23,0 250 | 9,124,70,33,402,35.4,0.282,34,0 251 | 1,111,86,19,0,30.1,0.143,23,0 252 | 9,106,52,0,0,31.2,0.38,42,0 253 | 2,129,84,0,0,28,0.284,27,0 254 | 2,90,80,14,55,24.4,0.249,24,0 255 | 0,86,68,32,0,35.8,0.238,25,0 256 | 12,92,62,7,258,27.6,0.926,44,1 257 | 1,113,64,35,0,33.6,0.543,21,1 258 | 3,111,56,39,0,30.1,0.557,30,0 259 | 2,114,68,22,0,28.7,0.092,25,0 260 | 1,193,50,16,375,25.9,0.655,24,0 261 | 11,155,76,28,150,33.3,1.353,51,1 262 | 3,191,68,15,130,30.9,0.299,34,0 263 | 3,141,0,0,0,30,0.761,27,1 264 | 4,95,70,32,0,32.1,0.612,24,0 265 | 3,142,80,15,0,32.4,0.2,63,0 266 | 4,123,62,0,0,32,0.226,35,1 267 | 5,96,74,18,67,33.6,0.997,43,0 268 | 0,138,0,0,0,36.3,0.933,25,1 269 | 2,128,64,42,0,40,1.101,24,0 270 | 0,102,52,0,0,25.1,0.078,21,0 271 | 2,146,0,0,0,27.5,0.24,28,1 272 | 10,101,86,37,0,45.6,1.136,38,1 273 | 2,108,62,32,56,25.2,0.128,21,0 274 | 3,122,78,0,0,23,0.254,40,0 275 | 1,71,78,50,45,33.2,0.422,21,0 276 | 13,106,70,0,0,34.2,0.251,52,0 277 | 2,100,70,52,57,40.5,0.677,25,0 278 | 7,106,60,24,0,26.5,0.296,29,1 279 | 0,104,64,23,116,27.8,0.454,23,0 280 | 5,114,74,0,0,24.9,0.744,57,0 281 | 2,108,62,10,278,25.3,0.881,22,0 282 | 0,146,70,0,0,37.9,0.334,28,1 283 | 10,129,76,28,122,35.9,0.28,39,0 284 | 7,133,88,15,155,32.4,0.262,37,0 285 | 7,161,86,0,0,30.4,0.165,47,1 286 | 2,108,80,0,0,27,0.259,52,1 287 | 7,136,74,26,135,26,0.647,51,0 288 | 5,155,84,44,545,38.7,0.619,34,0 289 | 1,119,86,39,220,45.6,0.808,29,1 290 | 4,96,56,17,49,20.8,0.34,26,0 291 | 5,108,72,43,75,36.1,0.263,33,0 292 | 0,78,88,29,40,36.9,0.434,21,0 293 | 0,107,62,30,74,36.6,0.757,25,1 294 | 2,128,78,37,182,43.3,1.224,31,1 295 | 1,128,48,45,194,40.5,0.613,24,1 296 | 0,161,50,0,0,21.9,0.254,65,0 297 | 6,151,62,31,120,35.5,0.692,28,0 298 | 2,146,70,38,360,28,0.337,29,1 299 | 0,126,84,29,215,30.7,0.52,24,0 300 | 14,100,78,25,184,36.6,0.412,46,1 301 | 8,112,72,0,0,23.6,0.84,58,0 302 | 0,167,0,0,0,32.3,0.839,30,1 303 | 2,144,58,33,135,31.6,0.422,25,1 304 | 5,77,82,41,42,35.8,0.156,35,0 305 | 5,115,98,0,0,52.9,0.209,28,1 306 | 3,150,76,0,0,21,0.207,37,0 307 | 2,120,76,37,105,39.7,0.215,29,0 308 | 10,161,68,23,132,25.5,0.326,47,1 309 | 0,137,68,14,148,24.8,0.143,21,0 310 | 0,128,68,19,180,30.5,1.391,25,1 311 | 2,124,68,28,205,32.9,0.875,30,1 312 | 6,80,66,30,0,26.2,0.313,41,0 313 | 0,106,70,37,148,39.4,0.605,22,0 314 | 2,155,74,17,96,26.6,0.433,27,1 315 | 3,113,50,10,85,29.5,0.626,25,0 316 | 7,109,80,31,0,35.9,1.127,43,1 317 | 2,112,68,22,94,34.1,0.315,26,0 318 | 3,99,80,11,64,19.3,0.284,30,0 319 | 3,182,74,0,0,30.5,0.345,29,1 320 | 3,115,66,39,140,38.1,0.15,28,0 321 | 6,194,78,0,0,23.5,0.129,59,1 322 | 4,129,60,12,231,27.5,0.527,31,0 323 | 3,112,74,30,0,31.6,0.197,25,1 324 | 0,124,70,20,0,27.4,0.254,36,1 325 | 13,152,90,33,29,26.8,0.731,43,1 326 | 2,112,75,32,0,35.7,0.148,21,0 327 | 1,157,72,21,168,25.6,0.123,24,0 328 | 1,122,64,32,156,35.1,0.692,30,1 329 | 10,179,70,0,0,35.1,0.2,37,0 330 | 2,102,86,36,120,45.5,0.127,23,1 331 | 6,105,70,32,68,30.8,0.122,37,0 332 | 8,118,72,19,0,23.1,1.476,46,0 333 | 2,87,58,16,52,32.7,0.166,25,0 334 | 1,180,0,0,0,43.3,0.282,41,1 335 | 12,106,80,0,0,23.6,0.137,44,0 336 | 1,95,60,18,58,23.9,0.26,22,0 337 | 0,165,76,43,255,47.9,0.259,26,0 338 | 0,117,0,0,0,33.8,0.932,44,0 339 | 5,115,76,0,0,31.2,0.343,44,1 340 | 9,152,78,34,171,34.2,0.893,33,1 341 | 7,178,84,0,0,39.9,0.331,41,1 342 | 1,130,70,13,105,25.9,0.472,22,0 343 | 1,95,74,21,73,25.9,0.673,36,0 344 | 1,0,68,35,0,32,0.389,22,0 345 | 5,122,86,0,0,34.7,0.29,33,0 346 | 8,95,72,0,0,36.8,0.485,57,0 347 | 8,126,88,36,108,38.5,0.349,49,0 348 | 1,139,46,19,83,28.7,0.654,22,0 349 | 3,116,0,0,0,23.5,0.187,23,0 350 | 3,99,62,19,74,21.8,0.279,26,0 351 | 5,0,80,32,0,41,0.346,37,1 352 | 4,92,80,0,0,42.2,0.237,29,0 353 | 4,137,84,0,0,31.2,0.252,30,0 354 | 3,61,82,28,0,34.4,0.243,46,0 355 | 1,90,62,12,43,27.2,0.58,24,0 356 | 3,90,78,0,0,42.7,0.559,21,0 357 | 9,165,88,0,0,30.4,0.302,49,1 358 | 1,125,50,40,167,33.3,0.962,28,1 359 | 13,129,0,30,0,39.9,0.569,44,1 360 | 12,88,74,40,54,35.3,0.378,48,0 361 | 1,196,76,36,249,36.5,0.875,29,1 362 | 5,189,64,33,325,31.2,0.583,29,1 363 | 5,158,70,0,0,29.8,0.207,63,0 364 | 5,103,108,37,0,39.2,0.305,65,0 365 | 4,146,78,0,0,38.5,0.52,67,1 366 | 4,147,74,25,293,34.9,0.385,30,0 367 | 5,99,54,28,83,34,0.499,30,0 368 | 6,124,72,0,0,27.6,0.368,29,1 369 | 0,101,64,17,0,21,0.252,21,0 370 | 3,81,86,16,66,27.5,0.306,22,0 371 | 1,133,102,28,140,32.8,0.234,45,1 372 | 3,173,82,48,465,38.4,2.137,25,1 373 | 0,118,64,23,89,0,1.731,21,0 374 | 0,84,64,22,66,35.8,0.545,21,0 375 | 2,105,58,40,94,34.9,0.225,25,0 376 | 2,122,52,43,158,36.2,0.816,28,0 377 | 12,140,82,43,325,39.2,0.528,58,1 378 | 0,98,82,15,84,25.2,0.299,22,0 379 | 1,87,60,37,75,37.2,0.509,22,0 380 | 4,156,75,0,0,48.3,0.238,32,1 381 | 0,93,100,39,72,43.4,1.021,35,0 382 | 1,107,72,30,82,30.8,0.821,24,0 383 | 0,105,68,22,0,20,0.236,22,0 384 | 1,109,60,8,182,25.4,0.947,21,0 385 | 1,90,62,18,59,25.1,1.268,25,0 386 | 1,125,70,24,110,24.3,0.221,25,0 387 | 1,119,54,13,50,22.3,0.205,24,0 388 | 5,116,74,29,0,32.3,0.66,35,1 389 | 8,105,100,36,0,43.3,0.239,45,1 390 | 5,144,82,26,285,32,0.452,58,1 391 | 3,100,68,23,81,31.6,0.949,28,0 392 | 1,100,66,29,196,32,0.444,42,0 393 | 5,166,76,0,0,45.7,0.34,27,1 394 | 1,131,64,14,415,23.7,0.389,21,0 395 | 4,116,72,12,87,22.1,0.463,37,0 396 | 4,158,78,0,0,32.9,0.803,31,1 397 | 2,127,58,24,275,27.7,1.6,25,0 398 | 3,96,56,34,115,24.7,0.944,39,0 399 | 0,131,66,40,0,34.3,0.196,22,1 400 | 3,82,70,0,0,21.1,0.389,25,0 401 | 3,193,70,31,0,34.9,0.241,25,1 402 | 4,95,64,0,0,32,0.161,31,1 403 | 6,137,61,0,0,24.2,0.151,55,0 404 | 5,136,84,41,88,35,0.286,35,1 405 | 9,72,78,25,0,31.6,0.28,38,0 406 | 5,168,64,0,0,32.9,0.135,41,1 407 | 2,123,48,32,165,42.1,0.52,26,0 408 | 4,115,72,0,0,28.9,0.376,46,1 409 | 0,101,62,0,0,21.9,0.336,25,0 410 | 8,197,74,0,0,25.9,1.191,39,1 411 | 1,172,68,49,579,42.4,0.702,28,1 412 | 6,102,90,39,0,35.7,0.674,28,0 413 | 1,112,72,30,176,34.4,0.528,25,0 414 | 1,143,84,23,310,42.4,1.076,22,0 415 | 1,143,74,22,61,26.2,0.256,21,0 416 | 0,138,60,35,167,34.6,0.534,21,1 417 | 3,173,84,33,474,35.7,0.258,22,1 418 | 1,97,68,21,0,27.2,1.095,22,0 419 | 4,144,82,32,0,38.5,0.554,37,1 420 | 1,83,68,0,0,18.2,0.624,27,0 421 | 3,129,64,29,115,26.4,0.219,28,1 422 | 1,119,88,41,170,45.3,0.507,26,0 423 | 2,94,68,18,76,26,0.561,21,0 424 | 0,102,64,46,78,40.6,0.496,21,0 425 | 2,115,64,22,0,30.8,0.421,21,0 426 | 8,151,78,32,210,42.9,0.516,36,1 427 | 4,184,78,39,277,37,0.264,31,1 428 | 0,94,0,0,0,0,0.256,25,0 429 | 1,181,64,30,180,34.1,0.328,38,1 430 | 0,135,94,46,145,40.6,0.284,26,0 431 | 1,95,82,25,180,35,0.233,43,1 432 | 2,99,0,0,0,22.2,0.108,23,0 433 | 3,89,74,16,85,30.4,0.551,38,0 434 | 1,80,74,11,60,30,0.527,22,0 435 | 2,139,75,0,0,25.6,0.167,29,0 436 | 1,90,68,8,0,24.5,1.138,36,0 437 | 0,141,0,0,0,42.4,0.205,29,1 438 | 12,140,85,33,0,37.4,0.244,41,0 439 | 5,147,75,0,0,29.9,0.434,28,0 440 | 1,97,70,15,0,18.2,0.147,21,0 441 | 6,107,88,0,0,36.8,0.727,31,0 442 | 0,189,104,25,0,34.3,0.435,41,1 443 | 2,83,66,23,50,32.2,0.497,22,0 444 | 4,117,64,27,120,33.2,0.23,24,0 445 | 8,108,70,0,0,30.5,0.955,33,1 446 | 4,117,62,12,0,29.7,0.38,30,1 447 | 0,180,78,63,14,59.4,2.42,25,1 448 | 1,100,72,12,70,25.3,0.658,28,0 449 | 0,95,80,45,92,36.5,0.33,26,0 450 | 0,104,64,37,64,33.6,0.51,22,1 451 | 0,120,74,18,63,30.5,0.285,26,0 452 | 1,82,64,13,95,21.2,0.415,23,0 453 | 2,134,70,0,0,28.9,0.542,23,1 454 | 0,91,68,32,210,39.9,0.381,25,0 455 | 2,119,0,0,0,19.6,0.832,72,0 456 | 2,100,54,28,105,37.8,0.498,24,0 457 | 14,175,62,30,0,33.6,0.212,38,1 458 | 1,135,54,0,0,26.7,0.687,62,0 459 | 5,86,68,28,71,30.2,0.364,24,0 460 | 10,148,84,48,237,37.6,1.001,51,1 461 | 9,134,74,33,60,25.9,0.46,81,0 462 | 9,120,72,22,56,20.8,0.733,48,0 463 | 1,71,62,0,0,21.8,0.416,26,0 464 | 8,74,70,40,49,35.3,0.705,39,0 465 | 5,88,78,30,0,27.6,0.258,37,0 466 | 10,115,98,0,0,24,1.022,34,0 467 | 0,124,56,13,105,21.8,0.452,21,0 468 | 0,74,52,10,36,27.8,0.269,22,0 469 | 0,97,64,36,100,36.8,0.6,25,0 470 | 8,120,0,0,0,30,0.183,38,1 471 | 6,154,78,41,140,46.1,0.571,27,0 472 | 1,144,82,40,0,41.3,0.607,28,0 473 | 0,137,70,38,0,33.2,0.17,22,0 474 | 0,119,66,27,0,38.8,0.259,22,0 475 | 7,136,90,0,0,29.9,0.21,50,0 476 | 4,114,64,0,0,28.9,0.126,24,0 477 | 0,137,84,27,0,27.3,0.231,59,0 478 | 2,105,80,45,191,33.7,0.711,29,1 479 | 7,114,76,17,110,23.8,0.466,31,0 480 | 8,126,74,38,75,25.9,0.162,39,0 481 | 4,132,86,31,0,28,0.419,63,0 482 | 3,158,70,30,328,35.5,0.344,35,1 483 | 0,123,88,37,0,35.2,0.197,29,0 484 | 4,85,58,22,49,27.8,0.306,28,0 485 | 0,84,82,31,125,38.2,0.233,23,0 486 | 0,145,0,0,0,44.2,0.63,31,1 487 | 0,135,68,42,250,42.3,0.365,24,1 488 | 1,139,62,41,480,40.7,0.536,21,0 489 | 0,173,78,32,265,46.5,1.159,58,0 490 | 4,99,72,17,0,25.6,0.294,28,0 491 | 8,194,80,0,0,26.1,0.551,67,0 492 | 2,83,65,28,66,36.8,0.629,24,0 493 | 2,89,90,30,0,33.5,0.292,42,0 494 | 4,99,68,38,0,32.8,0.145,33,0 495 | 4,125,70,18,122,28.9,1.144,45,1 496 | 3,80,0,0,0,0,0.174,22,0 497 | 6,166,74,0,0,26.6,0.304,66,0 498 | 5,110,68,0,0,26,0.292,30,0 499 | 2,81,72,15,76,30.1,0.547,25,0 500 | 7,195,70,33,145,25.1,0.163,55,1 501 | 6,154,74,32,193,29.3,0.839,39,0 502 | 2,117,90,19,71,25.2,0.313,21,0 503 | 3,84,72,32,0,37.2,0.267,28,0 504 | 6,0,68,41,0,39,0.727,41,1 505 | 7,94,64,25,79,33.3,0.738,41,0 506 | 3,96,78,39,0,37.3,0.238,40,0 507 | 10,75,82,0,0,33.3,0.263,38,0 508 | 0,180,90,26,90,36.5,0.314,35,1 509 | 1,130,60,23,170,28.6,0.692,21,0 510 | 2,84,50,23,76,30.4,0.968,21,0 511 | 8,120,78,0,0,25,0.409,64,0 512 | 12,84,72,31,0,29.7,0.297,46,1 513 | 0,139,62,17,210,22.1,0.207,21,0 514 | 9,91,68,0,0,24.2,0.2,58,0 515 | 2,91,62,0,0,27.3,0.525,22,0 516 | 3,99,54,19,86,25.6,0.154,24,0 517 | 3,163,70,18,105,31.6,0.268,28,1 518 | 9,145,88,34,165,30.3,0.771,53,1 519 | 7,125,86,0,0,37.6,0.304,51,0 520 | 13,76,60,0,0,32.8,0.18,41,0 521 | 6,129,90,7,326,19.6,0.582,60,0 522 | 2,68,70,32,66,25,0.187,25,0 523 | 3,124,80,33,130,33.2,0.305,26,0 524 | 6,114,0,0,0,0,0.189,26,0 525 | 9,130,70,0,0,34.2,0.652,45,1 526 | 3,125,58,0,0,31.6,0.151,24,0 527 | 3,87,60,18,0,21.8,0.444,21,0 528 | 1,97,64,19,82,18.2,0.299,21,0 529 | 3,116,74,15,105,26.3,0.107,24,0 530 | 0,117,66,31,188,30.8,0.493,22,0 531 | 0,111,65,0,0,24.6,0.66,31,0 532 | 2,122,60,18,106,29.8,0.717,22,0 533 | 0,107,76,0,0,45.3,0.686,24,0 534 | 1,86,66,52,65,41.3,0.917,29,0 535 | 6,91,0,0,0,29.8,0.501,31,0 536 | 1,77,56,30,56,33.3,1.251,24,0 537 | 4,132,0,0,0,32.9,0.302,23,1 538 | 0,105,90,0,0,29.6,0.197,46,0 539 | 0,57,60,0,0,21.7,0.735,67,0 540 | 0,127,80,37,210,36.3,0.804,23,0 541 | 3,129,92,49,155,36.4,0.968,32,1 542 | 8,100,74,40,215,39.4,0.661,43,1 543 | 3,128,72,25,190,32.4,0.549,27,1 544 | 10,90,85,32,0,34.9,0.825,56,1 545 | 4,84,90,23,56,39.5,0.159,25,0 546 | 1,88,78,29,76,32,0.365,29,0 547 | 8,186,90,35,225,34.5,0.423,37,1 548 | 5,187,76,27,207,43.6,1.034,53,1 549 | 4,131,68,21,166,33.1,0.16,28,0 550 | 1,164,82,43,67,32.8,0.341,50,0 551 | 4,189,110,31,0,28.5,0.68,37,0 552 | 1,116,70,28,0,27.4,0.204,21,0 553 | 3,84,68,30,106,31.9,0.591,25,0 554 | 6,114,88,0,0,27.8,0.247,66,0 555 | 1,88,62,24,44,29.9,0.422,23,0 556 | 1,84,64,23,115,36.9,0.471,28,0 557 | 7,124,70,33,215,25.5,0.161,37,0 558 | 1,97,70,40,0,38.1,0.218,30,0 559 | 8,110,76,0,0,27.8,0.237,58,0 560 | 11,103,68,40,0,46.2,0.126,42,0 561 | 11,85,74,0,0,30.1,0.3,35,0 562 | 6,125,76,0,0,33.8,0.121,54,1 563 | 0,198,66,32,274,41.3,0.502,28,1 564 | 1,87,68,34,77,37.6,0.401,24,0 565 | 6,99,60,19,54,26.9,0.497,32,0 566 | 0,91,80,0,0,32.4,0.601,27,0 567 | 2,95,54,14,88,26.1,0.748,22,0 568 | 1,99,72,30,18,38.6,0.412,21,0 569 | 6,92,62,32,126,32,0.085,46,0 570 | 4,154,72,29,126,31.3,0.338,37,0 571 | 0,121,66,30,165,34.3,0.203,33,1 572 | 3,78,70,0,0,32.5,0.27,39,0 573 | 2,130,96,0,0,22.6,0.268,21,0 574 | 3,111,58,31,44,29.5,0.43,22,0 575 | 2,98,60,17,120,34.7,0.198,22,0 576 | 1,143,86,30,330,30.1,0.892,23,0 577 | 1,119,44,47,63,35.5,0.28,25,0 578 | 6,108,44,20,130,24,0.813,35,0 579 | 2,118,80,0,0,42.9,0.693,21,1 580 | 10,133,68,0,0,27,0.245,36,0 581 | 2,197,70,99,0,34.7,0.575,62,1 582 | 0,151,90,46,0,42.1,0.371,21,1 583 | 6,109,60,27,0,25,0.206,27,0 584 | 12,121,78,17,0,26.5,0.259,62,0 585 | 8,100,76,0,0,38.7,0.19,42,0 586 | 8,124,76,24,600,28.7,0.687,52,1 587 | 1,93,56,11,0,22.5,0.417,22,0 588 | 8,143,66,0,0,34.9,0.129,41,1 589 | 6,103,66,0,0,24.3,0.249,29,0 590 | 3,176,86,27,156,33.3,1.154,52,1 591 | 0,73,0,0,0,21.1,0.342,25,0 592 | 11,111,84,40,0,46.8,0.925,45,1 593 | 2,112,78,50,140,39.4,0.175,24,0 594 | 3,132,80,0,0,34.4,0.402,44,1 595 | 2,82,52,22,115,28.5,1.699,25,0 596 | 6,123,72,45,230,33.6,0.733,34,0 597 | 0,188,82,14,185,32,0.682,22,1 598 | 0,67,76,0,0,45.3,0.194,46,0 599 | 1,89,24,19,25,27.8,0.559,21,0 600 | 1,173,74,0,0,36.8,0.088,38,1 601 | 1,109,38,18,120,23.1,0.407,26,0 602 | 1,108,88,19,0,27.1,0.4,24,0 603 | 6,96,0,0,0,23.7,0.19,28,0 604 | 1,124,74,36,0,27.8,0.1,30,0 605 | 7,150,78,29,126,35.2,0.692,54,1 606 | 4,183,0,0,0,28.4,0.212,36,1 607 | 1,124,60,32,0,35.8,0.514,21,0 608 | 1,181,78,42,293,40,1.258,22,1 609 | 1,92,62,25,41,19.5,0.482,25,0 610 | 0,152,82,39,272,41.5,0.27,27,0 611 | 1,111,62,13,182,24,0.138,23,0 612 | 3,106,54,21,158,30.9,0.292,24,0 613 | 3,174,58,22,194,32.9,0.593,36,1 614 | 7,168,88,42,321,38.2,0.787,40,1 615 | 6,105,80,28,0,32.5,0.878,26,0 616 | 11,138,74,26,144,36.1,0.557,50,1 617 | 3,106,72,0,0,25.8,0.207,27,0 618 | 6,117,96,0,0,28.7,0.157,30,0 619 | 2,68,62,13,15,20.1,0.257,23,0 620 | 9,112,82,24,0,28.2,1.282,50,1 621 | 0,119,0,0,0,32.4,0.141,24,1 622 | 2,112,86,42,160,38.4,0.246,28,0 623 | 2,92,76,20,0,24.2,1.698,28,0 624 | 6,183,94,0,0,40.8,1.461,45,0 625 | 0,94,70,27,115,43.5,0.347,21,0 626 | 2,108,64,0,0,30.8,0.158,21,0 627 | 4,90,88,47,54,37.7,0.362,29,0 628 | 0,125,68,0,0,24.7,0.206,21,0 629 | 0,132,78,0,0,32.4,0.393,21,0 630 | 5,128,80,0,0,34.6,0.144,45,0 631 | 4,94,65,22,0,24.7,0.148,21,0 632 | 7,114,64,0,0,27.4,0.732,34,1 633 | 0,102,78,40,90,34.5,0.238,24,0 634 | 2,111,60,0,0,26.2,0.343,23,0 635 | 1,128,82,17,183,27.5,0.115,22,0 636 | 10,92,62,0,0,25.9,0.167,31,0 637 | 13,104,72,0,0,31.2,0.465,38,1 638 | 5,104,74,0,0,28.8,0.153,48,0 639 | 2,94,76,18,66,31.6,0.649,23,0 640 | 7,97,76,32,91,40.9,0.871,32,1 641 | 1,100,74,12,46,19.5,0.149,28,0 642 | 0,102,86,17,105,29.3,0.695,27,0 643 | 4,128,70,0,0,34.3,0.303,24,0 644 | 6,147,80,0,0,29.5,0.178,50,1 645 | 4,90,0,0,0,28,0.61,31,0 646 | 3,103,72,30,152,27.6,0.73,27,0 647 | 2,157,74,35,440,39.4,0.134,30,0 648 | 1,167,74,17,144,23.4,0.447,33,1 649 | 0,179,50,36,159,37.8,0.455,22,1 650 | 11,136,84,35,130,28.3,0.26,42,1 651 | 0,107,60,25,0,26.4,0.133,23,0 652 | 1,91,54,25,100,25.2,0.234,23,0 653 | 1,117,60,23,106,33.8,0.466,27,0 654 | 5,123,74,40,77,34.1,0.269,28,0 655 | 2,120,54,0,0,26.8,0.455,27,0 656 | 1,106,70,28,135,34.2,0.142,22,0 657 | 2,155,52,27,540,38.7,0.24,25,1 658 | 2,101,58,35,90,21.8,0.155,22,0 659 | 1,120,80,48,200,38.9,1.162,41,0 660 | 11,127,106,0,0,39,0.19,51,0 661 | 3,80,82,31,70,34.2,1.292,27,1 662 | 10,162,84,0,0,27.7,0.182,54,0 663 | 1,199,76,43,0,42.9,1.394,22,1 664 | 8,167,106,46,231,37.6,0.165,43,1 665 | 9,145,80,46,130,37.9,0.637,40,1 666 | 6,115,60,39,0,33.7,0.245,40,1 667 | 1,112,80,45,132,34.8,0.217,24,0 668 | 4,145,82,18,0,32.5,0.235,70,1 669 | 10,111,70,27,0,27.5,0.141,40,1 670 | 6,98,58,33,190,34,0.43,43,0 671 | 9,154,78,30,100,30.9,0.164,45,0 672 | 6,165,68,26,168,33.6,0.631,49,0 673 | 1,99,58,10,0,25.4,0.551,21,0 674 | 10,68,106,23,49,35.5,0.285,47,0 675 | 3,123,100,35,240,57.3,0.88,22,0 676 | 8,91,82,0,0,35.6,0.587,68,0 677 | 6,195,70,0,0,30.9,0.328,31,1 678 | 9,156,86,0,0,24.8,0.23,53,1 679 | 0,93,60,0,0,35.3,0.263,25,0 680 | 3,121,52,0,0,36,0.127,25,1 681 | 2,101,58,17,265,24.2,0.614,23,0 682 | 2,56,56,28,45,24.2,0.332,22,0 683 | 0,162,76,36,0,49.6,0.364,26,1 684 | 0,95,64,39,105,44.6,0.366,22,0 685 | 4,125,80,0,0,32.3,0.536,27,1 686 | 5,136,82,0,0,0,0.64,69,0 687 | 2,129,74,26,205,33.2,0.591,25,0 688 | 3,130,64,0,0,23.1,0.314,22,0 689 | 1,107,50,19,0,28.3,0.181,29,0 690 | 1,140,74,26,180,24.1,0.828,23,0 691 | 1,144,82,46,180,46.1,0.335,46,1 692 | 8,107,80,0,0,24.6,0.856,34,0 693 | 13,158,114,0,0,42.3,0.257,44,1 694 | 2,121,70,32,95,39.1,0.886,23,0 695 | 7,129,68,49,125,38.5,0.439,43,1 696 | 2,90,60,0,0,23.5,0.191,25,0 697 | 7,142,90,24,480,30.4,0.128,43,1 698 | 3,169,74,19,125,29.9,0.268,31,1 699 | 0,99,0,0,0,25,0.253,22,0 700 | 4,127,88,11,155,34.5,0.598,28,0 701 | 4,118,70,0,0,44.5,0.904,26,0 702 | 2,122,76,27,200,35.9,0.483,26,0 703 | 6,125,78,31,0,27.6,0.565,49,1 704 | 1,168,88,29,0,35,0.905,52,1 705 | 2,129,0,0,0,38.5,0.304,41,0 706 | 4,110,76,20,100,28.4,0.118,27,0 707 | 6,80,80,36,0,39.8,0.177,28,0 708 | 10,115,0,0,0,0,0.261,30,1 709 | 2,127,46,21,335,34.4,0.176,22,0 710 | 9,164,78,0,0,32.8,0.148,45,1 711 | 2,93,64,32,160,38,0.674,23,1 712 | 3,158,64,13,387,31.2,0.295,24,0 713 | 5,126,78,27,22,29.6,0.439,40,0 714 | 10,129,62,36,0,41.2,0.441,38,1 715 | 0,134,58,20,291,26.4,0.352,21,0 716 | 3,102,74,0,0,29.5,0.121,32,0 717 | 7,187,50,33,392,33.9,0.826,34,1 718 | 3,173,78,39,185,33.8,0.97,31,1 719 | 10,94,72,18,0,23.1,0.595,56,0 720 | 1,108,60,46,178,35.5,0.415,24,0 721 | 5,97,76,27,0,35.6,0.378,52,1 722 | 4,83,86,19,0,29.3,0.317,34,0 723 | 1,114,66,36,200,38.1,0.289,21,0 724 | 1,149,68,29,127,29.3,0.349,42,1 725 | 5,117,86,30,105,39.1,0.251,42,0 726 | 1,111,94,0,0,32.8,0.265,45,0 727 | 4,112,78,40,0,39.4,0.236,38,0 728 | 1,116,78,29,180,36.1,0.496,25,0 729 | 0,141,84,26,0,32.4,0.433,22,0 730 | 2,175,88,0,0,22.9,0.326,22,0 731 | 2,92,52,0,0,30.1,0.141,22,0 732 | 3,130,78,23,79,28.4,0.323,34,1 733 | 8,120,86,0,0,28.4,0.259,22,1 734 | 2,174,88,37,120,44.5,0.646,24,1 735 | 2,106,56,27,165,29,0.426,22,0 736 | 2,105,75,0,0,23.3,0.56,53,0 737 | 4,95,60,32,0,35.4,0.284,28,0 738 | 0,126,86,27,120,27.4,0.515,21,0 739 | 8,65,72,23,0,32,0.6,42,0 740 | 2,99,60,17,160,36.6,0.453,21,0 741 | 1,102,74,0,0,39.5,0.293,42,1 742 | 11,120,80,37,150,42.3,0.785,48,1 743 | 3,102,44,20,94,30.8,0.4,26,0 744 | 1,109,58,18,116,28.5,0.219,22,0 745 | 9,140,94,0,0,32.7,0.734,45,1 746 | 13,153,88,37,140,40.6,1.174,39,0 747 | 12,100,84,33,105,30,0.488,46,0 748 | 1,147,94,41,0,49.3,0.358,27,1 749 | 1,81,74,41,57,46.3,1.096,32,0 750 | 3,187,70,22,200,36.4,0.408,36,1 751 | 6,162,62,0,0,24.3,0.178,50,1 752 | 4,136,70,0,0,31.2,1.182,22,1 753 | 1,121,78,39,74,39,0.261,28,0 754 | 3,108,62,24,0,26,0.223,25,0 755 | 0,181,88,44,510,43.3,0.222,26,1 756 | 8,154,78,32,0,32.4,0.443,45,1 757 | 1,128,88,39,110,36.5,1.057,37,1 758 | 7,137,90,41,0,32,0.391,39,0 759 | 0,123,72,0,0,36.3,0.258,52,1 760 | 1,106,76,0,0,37.5,0.197,26,0 761 | 6,190,92,0,0,35.5,0.278,66,1 762 | 2,88,58,26,16,28.4,0.766,22,0 763 | 9,170,74,31,0,44,0.403,43,1 764 | 9,89,62,0,0,22.5,0.142,33,0 765 | 10,101,76,48,180,32.9,0.171,63,0 766 | 2,122,70,27,0,36.8,0.34,27,0 767 | 5,121,72,23,112,26.2,0.245,30,0 768 | 1,126,60,0,0,30.1,0.349,47,1 769 | 1,93,70,31,0,30.4,0.315,23,0 770 | 14,100,78,25,184,36.6,0.412,46,1 771 | 8,112,72,0,0,23.6,0.84,58,0 772 | 0,167,0,0,0,32.3,0.839,30,1 773 | 2,144,58,33,135,31.6,0.422,25,1 774 | 5,77,82,41,42,35.8,0.156,35,0 775 | 5,115,98,0,0,52.9,0.209,28,1 776 | 3,150,76,0,0,21,0.207,37,0 777 | 2,120,76,37,105,39.7,0.215,29,0 778 | 10,161,68,23,132,25.5,0.326,47,1 779 | 0,137,68,14,148,24.8,0.143,21,0 780 | 0,128,68,19,180,30.5,1.391,25,1 781 | 2,124,68,28,205,32.9,0.875,30,1 782 | 6,80,66,30,0,26.2,0.313,41,0 783 | 0,106,70,37,148,39.4,0.605,22,0 784 | 2,155,74,17,96,26.6,0.433,27,1 785 | 3,113,50,10,85,29.5,0.626,25,0 786 | 7,109,80,31,0,35.9,1.127,43,1 787 | 2,112,68,22,94,34.1,0.315,26,0 788 | 3,99,80,11,64,19.3,0.284,30,0 789 | 3,182,74,0,0,30.5,0.345,29,1 790 | 3,115,66,39,140,38.1,0.15,28,0 791 | 6,194,78,0,0,23.5,0.129,59,1 792 | 4,129,60,12,231,27.5,0.527,31,0 793 | 3,112,74,30,0,31.6,0.197,25,1 794 | 0,124,70,20,0,27.4,0.254,36,1 795 | 13,152,90,33,29,26.8,0.731,43,1 796 | 2,112,75,32,0,35.7,0.148,21,0 797 | 1,157,72,21,168,25.6,0.123,24,0 798 | 1,122,64,32,156,35.1,0.692,30,1 799 | 10,179,70,0,0,35.1,0.2,37,0 800 | 2,102,86,36,120,45.5,0.127,23,1 801 | 6,105,70,32,68,30.8,0.122,37,0 802 | 8,118,72,19,0,23.1,1.476,46,0 803 | 2,87,58,16,52,32.7,0.166,25,0 804 | 1,180,0,0,0,43.3,0.282,41,1 805 | 12,106,80,0,0,23.6,0.137,44,0 806 | 1,95,60,18,58,23.9,0.26,22,0 807 | 0,165,76,43,255,47.9,0.259,26,0 808 | 0,117,0,0,0,33.8,0.932,44,0 809 | 5,115,76,0,0,31.2,0.343,44,1 810 | 9,152,78,34,171,34.2,0.893,33,1 811 | 7,178,84,0,0,39.9,0.331,41,1 812 | 1,130,70,13,105,25.9,0.472,22,0 813 | 1,95,74,21,73,25.9,0.673,36,0 814 | 1,0,68,35,0,32,0.389,22,0 815 | 5,122,86,0,0,34.7,0.29,33,0 816 | 8,95,72,0,0,36.8,0.485,57,0 817 | 8,126,88,36,108,38.5,0.349,49,0 818 | 1,139,46,19,83,28.7,0.654,22,0 819 | 3,116,0,0,0,23.5,0.187,23,0 820 | 3,99,62,19,74,21.8,0.279,26,0 821 | 5,0,80,32,0,41,0.346,37,1 822 | 4,92,80,0,0,42.2,0.237,29,0 823 | 4,137,84,0,0,31.2,0.252,30,0 824 | 3,61,82,28,0,34.4,0.243,46,0 825 | 1,90,62,12,43,27.2,0.58,24,0 826 | 3,90,78,0,0,42.7,0.559,21,0 827 | 9,165,88,0,0,30.4,0.302,49,1 828 | 1,125,50,40,167,33.3,0.962,28,1 829 | 13,129,0,30,0,39.9,0.569,44,1 830 | 12,88,74,40,54,35.3,0.378,48,0 831 | 1,196,76,36,249,36.5,0.875,29,1 832 | 5,189,64,33,325,31.2,0.583,29,1 833 | 5,158,70,0,0,29.8,0.207,63,0 834 | 5,103,108,37,0,39.2,0.305,65,0 835 | 4,146,78,0,0,38.5,0.52,67,1 836 | 4,147,74,25,293,34.9,0.385,30,0 837 | 5,99,54,28,83,34,0.499,30,0 838 | 6,124,72,0,0,27.6,0.368,29,1 839 | 0,101,64,17,0,21,0.252,21,0 840 | 3,81,86,16,66,27.5,0.306,22,0 841 | 1,133,102,28,140,32.8,0.234,45,1 842 | 3,173,82,48,465,38.4,2.137,25,1 843 | 0,118,64,23,89,0,1.731,21,0 844 | 0,84,64,22,66,35.8,0.545,21,0 845 | 2,105,58,40,94,34.9,0.225,25,0 846 | 2,122,52,43,158,36.2,0.816,28,0 847 | 12,140,82,43,325,39.2,0.528,58,1 848 | 0,98,82,15,84,25.2,0.299,22,0 849 | 1,87,60,37,75,37.2,0.509,22,0 850 | 4,156,75,0,0,48.3,0.238,32,1 851 | 0,93,100,39,72,43.4,1.021,35,0 852 | 1,107,72,30,82,30.8,0.821,24,0 853 | 0,105,68,22,0,20,0.236,22,0 854 | 1,109,60,8,182,25.4,0.947,21,0 855 | 1,90,62,18,59,25.1,1.268,25,0 856 | 1,125,70,24,110,24.3,0.221,25,0 857 | 1,119,54,13,50,22.3,0.205,24,0 858 | 5,116,74,29,0,32.3,0.66,35,1 859 | 8,105,100,36,0,43.3,0.239,45,1 860 | 5,144,82,26,285,32,0.452,58,1 861 | 3,100,68,23,81,31.6,0.949,28,0 862 | 1,100,66,29,196,32,0.444,42,0 863 | 5,166,76,0,0,45.7,0.34,27,1 864 | 1,131,64,14,415,23.7,0.389,21,0 865 | 4,116,72,12,87,22.1,0.463,37,0 866 | 4,158,78,0,0,32.9,0.803,31,1 867 | 2,127,58,24,275,27.7,1.6,25,0 868 | 3,96,56,34,115,24.7,0.944,39,0 869 | 0,131,66,40,0,34.3,0.196,22,1 870 | 3,82,70,0,0,21.1,0.389,25,0 871 | 3,193,70,31,0,34.9,0.241,25,1 872 | 4,95,64,0,0,32,0.161,31,1 873 | 6,137,61,0,0,24.2,0.151,55,0 874 | 5,136,84,41,88,35,0.286,35,1 875 | 9,72,78,25,0,31.6,0.28,38,0 876 | 5,168,64,0,0,32.9,0.135,41,1 877 | 2,123,48,32,165,42.1,0.52,26,0 878 | 4,115,72,0,0,28.9,0.376,46,1 879 | 0,101,62,0,0,21.9,0.336,25,0 880 | 8,197,74,0,0,25.9,1.191,39,1 881 | 1,172,68,49,579,42.4,0.702,28,1 882 | 6,102,90,39,0,35.7,0.674,28,0 883 | 1,112,72,30,176,34.4,0.528,25,0 884 | 1,143,84,23,310,42.4,1.076,22,0 885 | 1,143,74,22,61,26.2,0.256,21,0 886 | 0,138,60,35,167,34.6,0.534,21,1 887 | 3,173,84,33,474,35.7,0.258,22,1 888 | 1,97,68,21,0,27.2,1.095,22,0 889 | 4,144,82,32,0,38.5,0.554,37,1 890 | 1,83,68,0,0,18.2,0.624,27,0 891 | 3,129,64,29,115,26.4,0.219,28,1 892 | 1,119,88,41,170,45.3,0.507,26,0 893 | 2,94,68,18,76,26,0.561,21,0 894 | 0,102,64,46,78,40.6,0.496,21,0 895 | 2,115,64,22,0,30.8,0.421,21,0 896 | 8,151,78,32,210,42.9,0.516,36,1 897 | 4,184,78,39,277,37,0.264,31,1 898 | 0,94,0,0,0,0,0.256,25,0 899 | 1,181,64,30,180,34.1,0.328,38,1 900 | 0,135,94,46,145,40.6,0.284,26,0 901 | 1,95,82,25,180,35,0.233,43,1 902 | 2,99,0,0,0,22.2,0.108,23,0 903 | 3,89,74,16,85,30.4,0.551,38,0 904 | 1,80,74,11,60,30,0.527,22,0 905 | 2,139,75,0,0,25.6,0.167,29,0 906 | 1,90,68,8,0,24.5,1.138,36,0 907 | 0,141,0,0,0,42.4,0.205,29,1 908 | 12,140,85,33,0,37.4,0.244,41,0 909 | 5,147,75,0,0,29.9,0.434,28,0 910 | 1,97,70,15,0,18.2,0.147,21,0 911 | 6,107,88,0,0,36.8,0.727,31,0 912 | 0,189,104,25,0,34.3,0.435,41,1 913 | 2,83,66,23,50,32.2,0.497,22,0 914 | 4,117,64,27,120,33.2,0.23,24,0 915 | 8,108,70,0,0,30.5,0.955,33,1 916 | 4,117,62,12,0,29.7,0.38,30,1 917 | 0,180,78,63,14,59.4,2.42,25,1 918 | 1,100,72,12,70,25.3,0.658,28,0 919 | 0,95,80,45,92,36.5,0.33,26,0 920 | 0,104,64,37,64,33.6,0.51,22,1 921 | 0,120,74,18,63,30.5,0.285,26,0 922 | 1,82,64,13,95,21.2,0.415,23,0 923 | 2,134,70,0,0,28.9,0.542,23,1 924 | 0,91,68,32,210,39.9,0.381,25,0 925 | 2,119,0,0,0,19.6,0.832,72,0 926 | 2,100,54,28,105,37.8,0.498,24,0 927 | 14,175,62,30,0,33.6,0.212,38,1 928 | 1,135,54,0,0,26.7,0.687,62,0 929 | 5,86,68,28,71,30.2,0.364,24,0 930 | 10,148,84,48,237,37.6,1.001,51,1 931 | 9,134,74,33,60,25.9,0.46,81,0 932 | 9,120,72,22,56,20.8,0.733,48,0 933 | 1,71,62,0,0,21.8,0.416,26,0 934 | 8,74,70,40,49,35.3,0.705,39,0 935 | 5,88,78,30,0,27.6,0.258,37,0 936 | 10,115,98,0,0,24,1.022,34,0 937 | 0,124,56,13,105,21.8,0.452,21,0 938 | 0,74,52,10,36,27.8,0.269,22,0 939 | 0,97,64,36,100,36.8,0.6,25,0 940 | 8,120,0,0,0,30,0.183,38,1 941 | 6,154,78,41,140,46.1,0.571,27,0 942 | 1,144,82,40,0,41.3,0.607,28,0 943 | 0,137,70,38,0,33.2,0.17,22,0 944 | 0,119,66,27,0,38.8,0.259,22,0 945 | 7,136,90,0,0,29.9,0.21,50,0 946 | 4,114,64,0,0,28.9,0.126,24,0 947 | 0,137,84,27,0,27.3,0.231,59,0 948 | 2,105,80,45,191,33.7,0.711,29,1 949 | 7,114,76,17,110,23.8,0.466,31,0 950 | 8,126,74,38,75,25.9,0.162,39,0 951 | 4,132,86,31,0,28,0.419,63,0 952 | 3,158,70,30,328,35.5,0.344,35,1 953 | 0,123,88,37,0,35.2,0.197,29,0 954 | 4,85,58,22,49,27.8,0.306,28,0 955 | 0,84,82,31,125,38.2,0.233,23,0 956 | 0,145,0,0,0,44.2,0.63,31,1 957 | 0,135,68,42,250,42.3,0.365,24,1 958 | 1,139,62,41,480,40.7,0.536,21,0 959 | 0,173,78,32,265,46.5,1.159,58,0 960 | 4,99,72,17,0,25.6,0.294,28,0 961 | 8,194,80,0,0,26.1,0.551,67,0 962 | 2,83,65,28,66,36.8,0.629,24,0 963 | 2,89,90,30,0,33.5,0.292,42,0 964 | 4,99,68,38,0,32.8,0.145,33,0 965 | 4,125,70,18,122,28.9,1.144,45,1 966 | 3,80,0,0,0,0,0.174,22,0 967 | 6,166,74,0,0,26.6,0.304,66,0 968 | 5,110,68,0,0,26,0.292,30,0 969 | 2,81,72,15,76,30.1,0.547,25,0 970 | 7,195,70,33,145,25.1,0.163,55,1 971 | 6,154,74,32,193,29.3,0.839,39,0 972 | 2,117,90,19,71,25.2,0.313,21,0 973 | 3,84,72,32,0,37.2,0.267,28,0 974 | 6,0,68,41,0,39,0.727,41,1 975 | 7,94,64,25,79,33.3,0.738,41,0 976 | 3,96,78,39,0,37.3,0.238,40,0 977 | 10,75,82,0,0,33.3,0.263,38,0 978 | 0,180,90,26,90,36.5,0.314,35,1 979 | 1,130,60,23,170,28.6,0.692,21,0 980 | 2,84,50,23,76,30.4,0.968,21,0 981 | 8,120,78,0,0,25,0.409,64,0 982 | 12,84,72,31,0,29.7,0.297,46,1 983 | 0,139,62,17,210,22.1,0.207,21,0 984 | 9,91,68,0,0,24.2,0.2,58,0 985 | 2,91,62,0,0,27.3,0.525,22,0 986 | 3,99,54,19,86,25.6,0.154,24,0 987 | 3,163,70,18,105,31.6,0.268,28,1 988 | 9,145,88,34,165,30.3,0.771,53,1 989 | 2,122,60,18,106,29.8,0.717,22,0 990 | 0,107,76,0,0,45.3,0.686,24,0 991 | 1,86,66,52,65,41.3,0.917,29,0 992 | 6,91,0,0,0,29.8,0.501,31,0 993 | 1,77,56,30,56,33.3,1.251,24,0 994 | 4,132,0,0,0,32.9,0.302,23,1 995 | 0,105,90,0,0,29.6,0.197,46,0 996 | 0,57,60,0,0,21.7,0.735,67,0 997 | 0,127,80,37,210,36.3,0.804,23,0 998 | 3,129,92,49,155,36.4,0.968,32,1 999 | 8,100,74,40,215,39.4,0.661,43,1 1000 | 3,128,72,25,190,32.4,0.549,27,1 1001 | 10,90,85,32,0,34.9,0.825,56,1 1002 | 4,84,90,23,56,39.5,0.159,25,0 1003 | 1,88,78,29,76,32,0.365,29,0 1004 | 8,186,90,35,225,34.5,0.423,37,1 1005 | 5,187,76,27,207,43.6,1.034,53,1 1006 | 4,131,68,21,166,33.1,0.16,28,0 1007 | 1,164,82,43,67,32.8,0.341,50,0 1008 | 4,189,110,31,0,28.5,0.68,37,0 1009 | 1,116,70,28,0,27.4,0.204,21,0 1010 | 3,84,68,30,106,31.9,0.591,25,0 1011 | 6,114,88,0,0,27.8,0.247,66,0 1012 | 1,88,62,24,44,29.9,0.422,23,0 1013 | 1,84,64,23,115,36.9,0.471,28,0 1014 | 7,124,70,33,215,25.5,0.161,37,0 1015 | 1,97,70,40,0,38.1,0.218,30,0 1016 | 8,110,76,0,0,27.8,0.237,58,0 1017 | 11,103,68,40,0,46.2,0.126,42,0 1018 | 11,85,74,0,0,30.1,0.3,35,0 1019 | 6,125,76,0,0,33.8,0.121,54,1 1020 | 0,198,66,32,274,41.3,0.502,28,1 1021 | 1,87,68,34,77,37.6,0.401,24,0 1022 | 6,99,60,19,54,26.9,0.497,32,0 1023 | 0,91,80,0,0,32.4,0.601,27,0 1024 | 2,95,54,14,88,26.1,0.748,22,0 1025 | 1,99,72,30,18,38.6,0.412,21,0 1026 | 6,92,62,32,126,32,0.085,46,0 1027 | 4,154,72,29,126,31.3,0.338,37,0 1028 | 0,121,66,30,165,34.3,0.203,33,1 1029 | 3,78,70,0,0,32.5,0.27,39,0 1030 | 2,130,96,0,0,22.6,0.268,21,0 1031 | 3,111,58,31,44,29.5,0.43,22,0 1032 | 2,98,60,17,120,34.7,0.198,22,0 1033 | 1,143,86,30,330,30.1,0.892,23,0 1034 | 1,119,44,47,63,35.5,0.28,25,0 1035 | 6,108,44,20,130,24,0.813,35,0 1036 | 2,118,80,0,0,42.9,0.693,21,1 1037 | 10,133,68,0,0,27,0.245,36,0 1038 | 2,197,70,99,0,34.7,0.575,62,1 1039 | 0,151,90,46,0,42.1,0.371,21,1 1040 | 6,109,60,27,0,25,0.206,27,0 1041 | 12,121,78,17,0,26.5,0.259,62,0 1042 | 8,100,76,0,0,38.7,0.19,42,0 1043 | 8,124,76,24,600,28.7,0.687,52,1 1044 | 1,93,56,11,0,22.5,0.417,22,0 1045 | 8,143,66,0,0,34.9,0.129,41,1 1046 | 6,103,66,0,0,24.3,0.249,29,0 1047 | 3,176,86,27,156,33.3,1.154,52,1 1048 | 0,73,0,0,0,21.1,0.342,25,0 1049 | 11,111,84,40,0,46.8,0.925,45,1 1050 | 2,112,78,50,140,39.4,0.175,24,0 1051 | 3,132,80,0,0,34.4,0.402,44,1 1052 | 2,82,52,22,115,28.5,1.699,25,0 1053 | 6,123,72,45,230,33.6,0.733,34,0 1054 | 0,188,82,14,185,32,0.682,22,1 1055 | 0,67,76,0,0,45.3,0.194,46,0 1056 | 1,89,24,19,25,27.8,0.559,21,0 1057 | 1,173,74,0,0,36.8,0.088,38,1 1058 | 1,109,38,18,120,23.1,0.407,26,0 1059 | 1,108,88,19,0,27.1,0.4,24,0 1060 | 6,96,0,0,0,23.7,0.19,28,0 1061 | 1,124,74,36,0,27.8,0.1,30,0 1062 | 7,150,78,29,126,35.2,0.692,54,1 1063 | 4,183,0,0,0,28.4,0.212,36,1 1064 | 1,124,60,32,0,35.8,0.514,21,0 1065 | 1,181,78,42,293,40,1.258,22,1 1066 | 1,92,62,25,41,19.5,0.482,25,0 1067 | 0,152,82,39,272,41.5,0.27,27,0 1068 | 1,111,62,13,182,24,0.138,23,0 1069 | 3,106,54,21,158,30.9,0.292,24,0 1070 | 3,174,58,22,194,32.9,0.593,36,1 1071 | 7,168,88,42,321,38.2,0.787,40,1 1072 | 6,105,80,28,0,32.5,0.878,26,0 1073 | 11,138,74,26,144,36.1,0.557,50,1 1074 | 3,106,72,0,0,25.8,0.207,27,0 1075 | 6,117,96,0,0,28.7,0.157,30,0 1076 | 2,68,62,13,15,20.1,0.257,23,0 1077 | 9,112,82,24,0,28.2,1.282,50,1 1078 | 0,119,0,0,0,32.4,0.141,24,1 1079 | 2,112,86,42,160,38.4,0.246,28,0 1080 | 2,92,76,20,0,24.2,1.698,28,0 1081 | 6,183,94,0,0,40.8,1.461,45,0 1082 | 0,94,70,27,115,43.5,0.347,21,0 1083 | 2,108,64,0,0,30.8,0.158,21,0 1084 | 4,90,88,47,54,37.7,0.362,29,0 1085 | 0,125,68,0,0,24.7,0.206,21,0 1086 | 0,132,78,0,0,32.4,0.393,21,0 1087 | 5,128,80,0,0,34.6,0.144,45,0 1088 | 4,94,65,22,0,24.7,0.148,21,0 1089 | 7,114,64,0,0,27.4,0.732,34,1 1090 | 0,102,78,40,90,34.5,0.238,24,0 1091 | 2,111,60,0,0,26.2,0.343,23,0 1092 | 1,128,82,17,183,27.5,0.115,22,0 1093 | 10,92,62,0,0,25.9,0.167,31,0 1094 | 13,104,72,0,0,31.2,0.465,38,1 1095 | 5,104,74,0,0,28.8,0.153,48,0 1096 | 2,94,76,18,66,31.6,0.649,23,0 1097 | 7,97,76,32,91,40.9,0.871,32,1 1098 | 1,100,74,12,46,19.5,0.149,28,0 1099 | 0,102,86,17,105,29.3,0.695,27,0 1100 | 4,128,70,0,0,34.3,0.303,24,0 1101 | 6,147,80,0,0,29.5,0.178,50,1 1102 | 4,90,0,0,0,28,0.61,31,0 1103 | 3,103,72,30,152,27.6,0.73,27,0 1104 | 2,157,74,35,440,39.4,0.134,30,0 1105 | 1,167,74,17,144,23.4,0.447,33,1 1106 | 0,179,50,36,159,37.8,0.455,22,1 1107 | 11,136,84,35,130,28.3,0.26,42,1 1108 | 0,107,60,25,0,26.4,0.133,23,0 1109 | 1,91,54,25,100,25.2,0.234,23,0 1110 | 1,117,60,23,106,33.8,0.466,27,0 1111 | 5,123,74,40,77,34.1,0.269,28,0 1112 | 2,120,54,0,0,26.8,0.455,27,0 1113 | 1,106,70,28,135,34.2,0.142,22,0 1114 | 2,155,52,27,540,38.7,0.24,25,1 1115 | 2,101,58,35,90,21.8,0.155,22,0 1116 | 1,120,80,48,200,38.9,1.162,41,0 1117 | 11,127,106,0,0,39,0.19,51,0 1118 | 3,80,82,31,70,34.2,1.292,27,1 1119 | 10,162,84,0,0,27.7,0.182,54,0 1120 | 1,199,76,43,0,42.9,1.394,22,1 1121 | 8,167,106,46,231,37.6,0.165,43,1 1122 | 9,145,80,46,130,37.9,0.637,40,1 1123 | 6,115,60,39,0,33.7,0.245,40,1 1124 | 1,112,80,45,132,34.8,0.217,24,0 1125 | 4,145,82,18,0,32.5,0.235,70,1 1126 | 10,111,70,27,0,27.5,0.141,40,1 1127 | 6,98,58,33,190,34,0.43,43,0 1128 | 9,154,78,30,100,30.9,0.164,45,0 1129 | 6,165,68,26,168,33.6,0.631,49,0 1130 | 1,99,58,10,0,25.4,0.551,21,0 1131 | 10,68,106,23,49,35.5,0.285,47,0 1132 | 3,123,100,35,240,57.3,0.88,22,0 1133 | 8,91,82,0,0,35.6,0.587,68,0 1134 | 6,195,70,0,0,30.9,0.328,31,1 1135 | 9,156,86,0,0,24.8,0.23,53,1 1136 | 0,93,60,0,0,35.3,0.263,25,0 1137 | 3,121,52,0,0,36,0.127,25,1 1138 | 2,101,58,17,265,24.2,0.614,23,0 1139 | 2,56,56,28,45,24.2,0.332,22,0 1140 | 0,162,76,36,0,49.6,0.364,26,1 1141 | 0,95,64,39,105,44.6,0.366,22,0 1142 | 4,125,80,0,0,32.3,0.536,27,1 1143 | 5,136,82,0,0,0,0.64,69,0 1144 | 2,129,74,26,205,33.2,0.591,25,0 1145 | 3,130,64,0,0,23.1,0.314,22,0 1146 | 1,107,50,19,0,28.3,0.181,29,0 1147 | 1,140,74,26,180,24.1,0.828,23,0 1148 | 1,144,82,46,180,46.1,0.335,46,1 1149 | 8,107,80,0,0,24.6,0.856,34,0 1150 | 13,158,114,0,0,42.3,0.257,44,1 1151 | 2,121,70,32,95,39.1,0.886,23,0 1152 | 7,129,68,49,125,38.5,0.439,43,1 1153 | 2,90,60,0,0,23.5,0.191,25,0 1154 | 7,142,90,24,480,30.4,0.128,43,1 1155 | 3,169,74,19,125,29.9,0.268,31,1 1156 | 0,99,0,0,0,25,0.253,22,0 1157 | 4,127,88,11,155,34.5,0.598,28,0 1158 | 4,118,70,0,0,44.5,0.904,26,0 1159 | 2,122,76,27,200,35.9,0.483,26,0 1160 | 6,125,78,31,0,27.6,0.565,49,1 1161 | 1,168,88,29,0,35,0.905,52,1 1162 | 2,129,0,0,0,38.5,0.304,41,0 1163 | 4,110,76,20,100,28.4,0.118,27,0 1164 | 6,80,80,36,0,39.8,0.177,28,0 1165 | 10,115,0,0,0,0,0.261,30,1 1166 | 2,127,46,21,335,34.4,0.176,22,0 1167 | 9,164,78,0,0,32.8,0.148,45,1 1168 | 2,93,64,32,160,38,0.674,23,1 1169 | 3,158,64,13,387,31.2,0.295,24,0 1170 | 5,126,78,27,22,29.6,0.439,40,0 1171 | 10,129,62,36,0,41.2,0.441,38,1 1172 | 0,134,58,20,291,26.4,0.352,21,0 1173 | 3,102,74,0,0,29.5,0.121,32,0 1174 | 7,187,50,33,392,33.9,0.826,34,1 1175 | 3,173,78,39,185,33.8,0.97,31,1 1176 | 10,94,72,18,0,23.1,0.595,56,0 1177 | 1,108,60,46,178,35.5,0.415,24,0 1178 | 5,97,76,27,0,35.6,0.378,52,1 1179 | 4,83,86,19,0,29.3,0.317,34,0 1180 | 1,114,66,36,200,38.1,0.289,21,0 1181 | 1,149,68,29,127,29.3,0.349,42,1 1182 | 5,117,86,30,105,39.1,0.251,42,0 1183 | 1,111,94,0,0,32.8,0.265,45,0 1184 | 4,112,78,40,0,39.4,0.236,38,0 1185 | 1,116,78,29,180,36.1,0.496,25,0 1186 | 0,141,84,26,0,32.4,0.433,22,0 1187 | 2,175,88,0,0,22.9,0.326,22,0 1188 | 2,92,52,0,0,30.1,0.141,22,0 1189 | 3,130,78,23,79,28.4,0.323,34,1 1190 | 8,120,86,0,0,28.4,0.259,22,1 1191 | 2,174,88,37,120,44.5,0.646,24,1 1192 | 2,106,56,27,165,29,0.426,22,0 1193 | 2,105,75,0,0,23.3,0.56,53,0 1194 | 4,95,60,32,0,35.4,0.284,28,0 1195 | 0,126,86,27,120,27.4,0.515,21,0 1196 | 8,65,72,23,0,32,0.6,42,0 1197 | 2,99,60,17,160,36.6,0.453,21,0 1198 | 1,102,74,0,0,39.5,0.293,42,1 1199 | 11,120,80,37,150,42.3,0.785,48,1 1200 | 3,102,44,20,94,30.8,0.4,26,0 1201 | 1,109,58,18,116,28.5,0.219,22,0 1202 | 9,140,94,0,0,32.7,0.734,45,1 1203 | 13,153,88,37,140,40.6,1.174,39,0 1204 | 12,100,84,33,105,30,0.488,46,0 1205 | 1,147,94,41,0,49.3,0.358,27,1 1206 | 1,81,74,41,57,46.3,1.096,32,0 1207 | 3,187,70,22,200,36.4,0.408,36,1 1208 | 6,162,62,0,0,24.3,0.178,50,1 1209 | 4,136,70,0,0,31.2,1.182,22,1 1210 | 1,121,78,39,74,39,0.261,28,0 1211 | 3,108,62,24,0,26,0.223,25,0 1212 | 0,181,88,44,510,43.3,0.222,26,1 1213 | 8,154,78,32,0,32.4,0.443,45,1 1214 | 1,128,88,39,110,36.5,1.057,37,1 1215 | 7,137,90,41,0,32,0.391,39,0 1216 | 0,123,72,0,0,36.3,0.258,52,1 1217 | 1,106,76,0,0,37.5,0.197,26,0 1218 | 6,190,92,0,0,35.5,0.278,66,1 1219 | 2,88,58,26,16,28.4,0.766,22,0 1220 | 9,170,74,31,0,44,0.403,43,1 1221 | 9,89,62,0,0,22.5,0.142,33,0 1222 | 10,101,76,48,180,32.9,0.171,63,0 1223 | 2,122,70,27,0,36.8,0.34,27,0 1224 | 5,121,72,23,112,26.2,0.245,30,0 1225 | 1,126,60,0,0,30.1,0.349,47,1 1226 | 1,93,70,31,0,30.4,0.315,23,0 1227 | 14,100,78,25,184,36.6,0.412,46,1 1228 | 8,112,72,0,0,23.6,0.84,58,0 1229 | 0,167,0,0,0,32.3,0.839,30,1 1230 | 2,144,58,33,135,31.6,0.422,25,1 1231 | 5,77,82,41,42,35.8,0.156,35,0 1232 | 5,115,98,0,0,52.9,0.209,28,1 1233 | 3,150,76,0,0,21,0.207,37,0 1234 | 2,120,76,37,105,39.7,0.215,29,0 1235 | 10,161,68,23,132,25.5,0.326,47,1 1236 | 0,137,68,14,148,24.8,0.143,21,0 1237 | 0,128,68,19,180,30.5,1.391,25,1 1238 | 2,124,68,28,205,32.9,0.875,30,1 1239 | 6,80,66,30,0,26.2,0.313,41,0 1240 | 0,106,70,37,148,39.4,0.605,22,0 1241 | 2,155,74,17,96,26.6,0.433,27,1 1242 | 3,113,50,10,85,29.5,0.626,25,0 1243 | 7,109,80,31,0,35.9,1.127,43,1 1244 | 2,112,68,22,94,34.1,0.315,26,0 1245 | 3,99,80,11,64,19.3,0.284,30,0 1246 | 3,182,74,0,0,30.5,0.345,29,1 1247 | 3,115,66,39,140,38.1,0.15,28,0 1248 | 6,194,78,0,0,23.5,0.129,59,1 1249 | 4,129,60,12,231,27.5,0.527,31,0 1250 | 3,112,74,30,0,31.6,0.197,25,1 1251 | 0,124,70,20,0,27.4,0.254,36,1 1252 | 13,152,90,33,29,26.8,0.731,43,1 1253 | 2,112,75,32,0,35.7,0.148,21,0 1254 | 1,157,72,21,168,25.6,0.123,24,0 1255 | 1,122,64,32,156,35.1,0.692,30,1 1256 | 10,179,70,0,0,35.1,0.2,37,0 1257 | 2,102,86,36,120,45.5,0.127,23,1 1258 | 6,93,50,30,64,28.7,0.356,23,0 1259 | 1,122,90,51,220,49.7,0.325,31,1 1260 | 1,163,72,0,0,39,1.222,33,1 1261 | 1,151,60,0,0,26.1,0.179,22,0 1262 | 0,125,96,0,0,22.5,0.262,21,0 1263 | 1,81,72,18,40,26.6,0.283,24,0 1264 | 2,85,65,0,0,39.6,0.93,27,0 1265 | 1,126,56,29,152,28.7,0.801,21,0 1266 | 1,96,122,0,0,22.4,0.207,27,0 1267 | 4,144,58,28,140,29.5,0.287,37,0 1268 | 3,83,58,31,18,34.3,0.336,25,0 1269 | 0,95,85,25,36,37.4,0.247,24,1 1270 | 3,171,72,33,135,33.3,0.199,24,1 1271 | 8,155,62,26,495,34,0.543,46,1 1272 | 1,89,76,34,37,31.2,0.192,23,0 1273 | 4,76,62,0,0,34,0.391,25,0 1274 | 7,160,54,32,175,30.5,0.588,39,1 1275 | 4,146,92,0,0,31.2,0.539,61,1 1276 | 5,124,74,0,0,34,0.22,38,1 1277 | 5,78,48,0,0,33.7,0.654,25,0 1278 | 4,97,60,23,0,28.2,0.443,22,0 1279 | 4,99,76,15,51,23.2,0.223,21,0 1280 | 0,162,76,56,100,53.2,0.759,25,1 1281 | 6,111,64,39,0,34.2,0.26,24,0 1282 | 2,107,74,30,100,33.6,0.404,23,0 1283 | 5,132,80,0,0,26.8,0.186,69,0 1284 | 0,113,76,0,0,33.3,0.278,23,1 1285 | 1,88,30,42,99,55,0.496,26,1 1286 | 3,120,70,30,135,42.9,0.452,30,0 1287 | 1,118,58,36,94,33.3,0.261,23,0 1288 | 1,117,88,24,145,34.5,0.403,40,1 1289 | 0,105,84,0,0,27.9,0.741,62,1 1290 | 4,173,70,14,168,29.7,0.361,33,1 1291 | 9,122,56,0,0,33.3,1.114,33,1 1292 | 3,170,64,37,225,34.5,0.356,30,1 1293 | 8,84,74,31,0,38.3,0.457,39,0 1294 | 2,96,68,13,49,21.1,0.647,26,0 1295 | 2,125,60,20,140,33.8,0.088,31,0 1296 | 0,100,70,26,50,30.8,0.597,21,0 1297 | 0,93,60,25,92,28.7,0.532,22,0 1298 | 0,129,80,0,0,31.2,0.703,29,0 1299 | 5,105,72,29,325,36.9,0.159,28,0 1300 | 3,128,78,0,0,21.1,0.268,55,0 1301 | 5,106,82,30,0,39.5,0.286,38,0 1302 | 2,108,52,26,63,32.5,0.318,22,0 1303 | 10,108,66,0,0,32.4,0.272,42,1 1304 | 4,154,62,31,284,32.8,0.237,23,0 1305 | 0,102,75,23,0,0,0.572,21,0 1306 | 9,57,80,37,0,32.8,0.096,41,0 1307 | 2,106,64,35,119,30.5,1.4,34,0 1308 | 5,147,78,0,0,33.7,0.218,65,0 1309 | 2,90,70,17,0,27.3,0.085,22,0 1310 | 1,136,74,50,204,37.4,0.399,24,0 1311 | 4,114,65,0,0,21.9,0.432,37,0 1312 | 9,156,86,28,155,34.3,1.189,42,1 1313 | 1,153,82,42,485,40.6,0.687,23,0 1314 | 8,188,78,0,0,47.9,0.137,43,1 1315 | 7,152,88,44,0,50,0.337,36,1 1316 | 2,99,52,15,94,24.6,0.637,21,0 1317 | 1,109,56,21,135,25.2,0.833,23,0 1318 | 2,88,74,19,53,29,0.229,22,0 1319 | 17,163,72,41,114,40.9,0.817,47,1 1320 | 4,151,90,38,0,29.7,0.294,36,0 1321 | 7,102,74,40,105,37.2,0.204,45,0 1322 | 0,114,80,34,285,44.2,0.167,27,0 1323 | 2,100,64,23,0,29.7,0.368,21,0 1324 | 0,131,88,0,0,31.6,0.743,32,1 1325 | 6,104,74,18,156,29.9,0.722,41,1 1326 | 3,148,66,25,0,32.5,0.256,22,0 1327 | 4,120,68,0,0,29.6,0.709,34,0 1328 | 4,110,66,0,0,31.9,0.471,29,0 1329 | 3,111,90,12,78,28.4,0.495,29,0 1330 | 6,102,82,0,0,30.8,0.18,36,1 1331 | 6,134,70,23,130,35.4,0.542,29,1 1332 | 2,87,0,23,0,28.9,0.773,25,0 1333 | 1,79,60,42,48,43.5,0.678,23,0 1334 | 2,75,64,24,55,29.7,0.37,33,0 1335 | 8,179,72,42,130,32.7,0.719,36,1 1336 | 6,85,78,0,0,31.2,0.382,42,0 1337 | 0,129,110,46,130,67.1,0.319,26,1 1338 | 5,143,78,0,0,45,0.19,47,0 1339 | 5,130,82,0,0,39.1,0.956,37,1 1340 | 6,87,80,0,0,23.2,0.084,32,0 1341 | 0,119,64,18,92,34.9,0.725,23,0 1342 | 1,0,74,20,23,27.7,0.299,21,0 1343 | 5,73,60,0,0,26.8,0.268,27,0 1344 | 4,141,74,0,0,27.6,0.244,40,0 1345 | 7,194,68,28,0,35.9,0.745,41,1 1346 | 8,181,68,36,495,30.1,0.615,60,1 1347 | 1,128,98,41,58,32,1.321,33,1 1348 | 8,109,76,39,114,27.9,0.64,31,1 1349 | 5,139,80,35,160,31.6,0.361,25,1 1350 | 3,111,62,0,0,22.6,0.142,21,0 1351 | 9,123,70,44,94,33.1,0.374,40,0 1352 | 7,159,66,0,0,30.4,0.383,36,1 1353 | 11,135,0,0,0,52.3,0.578,40,1 1354 | 8,85,55,20,0,24.4,0.136,42,0 1355 | 5,158,84,41,210,39.4,0.395,29,1 1356 | 1,105,58,0,0,24.3,0.187,21,0 1357 | 3,107,62,13,48,22.9,0.678,23,1 1358 | 4,109,64,44,99,34.8,0.905,26,1 1359 | 4,148,60,27,318,30.9,0.15,29,1 1360 | 0,113,80,16,0,31,0.874,21,0 1361 | 1,138,82,0,0,40.1,0.236,28,0 1362 | 0,108,68,20,0,27.3,0.787,32,0 1363 | 2,99,70,16,44,20.4,0.235,27,0 1364 | 6,103,72,32,190,37.7,0.324,55,0 1365 | 5,111,72,28,0,23.9,0.407,27,0 1366 | 8,196,76,29,280,37.5,0.605,57,1 1367 | 5,162,104,0,0,37.7,0.151,52,1 1368 | 1,96,64,27,87,33.2,0.289,21,0 1369 | 7,184,84,33,0,35.5,0.355,41,1 1370 | 2,81,60,22,0,27.7,0.29,25,0 1371 | 0,147,85,54,0,42.8,0.375,24,0 1372 | 7,179,95,31,0,34.2,0.164,60,0 1373 | 0,140,65,26,130,42.6,0.431,24,1 1374 | 9,112,82,32,175,34.2,0.26,36,1 1375 | 12,151,70,40,271,41.8,0.742,38,1 1376 | 5,109,62,41,129,35.8,0.514,25,1 1377 | 6,125,68,30,120,30,0.464,32,0 1378 | 5,85,74,22,0,29,1.224,32,1 1379 | 5,112,66,0,0,37.8,0.261,41,1 1380 | 0,177,60,29,478,34.6,1.072,21,1 1381 | 2,158,90,0,0,31.6,0.805,66,1 1382 | 7,119,0,0,0,25.2,0.209,37,0 1383 | 7,142,60,33,190,28.8,0.687,61,0 1384 | 1,100,66,15,56,23.6,0.666,26,0 1385 | 1,87,78,27,32,34.6,0.101,22,0 1386 | 0,101,76,0,0,35.7,0.198,26,0 1387 | 3,162,52,38,0,37.2,0.652,24,1 1388 | 4,197,70,39,744,36.7,2.329,31,0 1389 | 0,117,80,31,53,45.2,0.089,24,0 1390 | 4,142,86,0,0,44,0.645,22,1 1391 | 6,134,80,37,370,46.2,0.238,46,1 1392 | 1,79,80,25,37,25.4,0.583,22,0 1393 | 4,122,68,0,0,35,0.394,29,0 1394 | 3,74,68,28,45,29.7,0.293,23,0 1395 | 4,171,72,0,0,43.6,0.479,26,1 1396 | 7,181,84,21,192,35.9,0.586,51,1 1397 | 0,179,90,27,0,44.1,0.686,23,1 1398 | 9,164,84,21,0,30.8,0.831,32,1 1399 | 0,104,76,0,0,18.4,0.582,27,0 1400 | 1,91,64,24,0,29.2,0.192,21,0 1401 | 4,91,70,32,88,33.1,0.446,22,0 1402 | 3,139,54,0,0,25.6,0.402,22,1 1403 | 6,119,50,22,176,27.1,1.318,33,1 1404 | 2,146,76,35,194,38.2,0.329,29,0 1405 | 9,184,85,15,0,30,1.213,49,1 1406 | 10,122,68,0,0,31.2,0.258,41,0 1407 | 0,165,90,33,680,52.3,0.427,23,0 1408 | 9,124,70,33,402,35.4,0.282,34,0 1409 | 1,111,86,19,0,30.1,0.143,23,0 1410 | 9,106,52,0,0,31.2,0.38,42,0 1411 | 2,129,84,0,0,28,0.284,27,0 1412 | 2,90,80,14,55,24.4,0.249,24,0 1413 | 0,86,68,32,0,35.8,0.238,25,0 1414 | 12,92,62,7,258,27.6,0.926,44,1 1415 | 1,113,64,35,0,33.6,0.543,21,1 1416 | 3,111,56,39,0,30.1,0.557,30,0 1417 | 2,114,68,22,0,28.7,0.092,25,0 1418 | 1,193,50,16,375,25.9,0.655,24,0 1419 | 11,155,76,28,150,33.3,1.353,51,1 1420 | 3,191,68,15,130,30.9,0.299,34,0 1421 | 3,141,0,0,0,30,0.761,27,1 1422 | 4,95,70,32,0,32.1,0.612,24,0 1423 | 3,142,80,15,0,32.4,0.2,63,0 1424 | 4,123,62,0,0,32,0.226,35,1 1425 | 5,96,74,18,67,33.6,0.997,43,0 1426 | 0,138,0,0,0,36.3,0.933,25,1 1427 | 2,128,64,42,0,40,1.101,24,0 1428 | 0,102,52,0,0,25.1,0.078,21,0 1429 | 2,146,0,0,0,27.5,0.24,28,1 1430 | 10,101,86,37,0,45.6,1.136,38,1 1431 | 2,108,62,32,56,25.2,0.128,21,0 1432 | 3,122,78,0,0,23,0.254,40,0 1433 | 1,71,78,50,45,33.2,0.422,21,0 1434 | 13,106,70,0,0,34.2,0.251,52,0 1435 | 2,100,70,52,57,40.5,0.677,25,0 1436 | 7,106,60,24,0,26.5,0.296,29,1 1437 | 0,104,64,23,116,27.8,0.454,23,0 1438 | 5,114,74,0,0,24.9,0.744,57,0 1439 | 2,108,62,10,278,25.3,0.881,22,0 1440 | 0,146,70,0,0,37.9,0.334,28,1 1441 | 10,129,76,28,122,35.9,0.28,39,0 1442 | 7,133,88,15,155,32.4,0.262,37,0 1443 | 7,161,86,0,0,30.4,0.165,47,1 1444 | 2,108,80,0,0,27,0.259,52,1 1445 | 7,136,74,26,135,26,0.647,51,0 1446 | 5,155,84,44,545,38.7,0.619,34,0 1447 | 1,119,86,39,220,45.6,0.808,29,1 1448 | 4,96,56,17,49,20.8,0.34,26,0 1449 | 5,108,72,43,75,36.1,0.263,33,0 1450 | 0,78,88,29,40,36.9,0.434,21,0 1451 | 0,107,62,30,74,36.6,0.757,25,1 1452 | 2,128,78,37,182,43.3,1.224,31,1 1453 | 1,128,48,45,194,40.5,0.613,24,1 1454 | 0,161,50,0,0,21.9,0.254,65,0 1455 | 6,151,62,31,120,35.5,0.692,28,0 1456 | 2,146,70,38,360,28,0.337,29,1 1457 | 0,126,84,29,215,30.7,0.52,24,0 1458 | 14,100,78,25,184,36.6,0.412,46,1 1459 | 3,82,70,0,0,21.1,0.389,25,0 1460 | 3,193,70,31,0,34.9,0.241,25,1 1461 | 4,95,64,0,0,32,0.161,31,1 1462 | 6,137,61,0,0,24.2,0.151,55,0 1463 | 5,136,84,41,88,35,0.286,35,1 1464 | 9,72,78,25,0,31.6,0.28,38,0 1465 | 5,168,64,0,0,32.9,0.135,41,1 1466 | 2,123,48,32,165,42.1,0.52,26,0 1467 | 4,115,72,0,0,28.9,0.376,46,1 1468 | 0,101,62,0,0,21.9,0.336,25,0 1469 | 8,197,74,0,0,25.9,1.191,39,1 1470 | 1,172,68,49,579,42.4,0.702,28,1 1471 | 6,102,90,39,0,35.7,0.674,28,0 1472 | 1,112,72,30,176,34.4,0.528,25,0 1473 | 1,143,84,23,310,42.4,1.076,22,0 1474 | 1,143,74,22,61,26.2,0.256,21,0 1475 | 0,138,60,35,167,34.6,0.534,21,1 1476 | 3,173,84,33,474,35.7,0.258,22,1 1477 | 1,97,68,21,0,27.2,1.095,22,0 1478 | 4,144,82,32,0,38.5,0.554,37,1 1479 | 1,83,68,0,0,18.2,0.624,27,0 1480 | 3,129,64,29,115,26.4,0.219,28,1 1481 | 1,119,88,41,170,45.3,0.507,26,0 1482 | 2,94,68,18,76,26,0.561,21,0 1483 | 0,102,64,46,78,40.6,0.496,21,0 1484 | 2,115,64,22,0,30.8,0.421,21,0 1485 | 8,151,78,32,210,42.9,0.516,36,1 1486 | 4,184,78,39,277,37,0.264,31,1 1487 | 0,94,0,0,0,0,0.256,25,0 1488 | 1,181,64,30,180,34.1,0.328,38,1 1489 | 0,135,94,46,145,40.6,0.284,26,0 1490 | 1,95,82,25,180,35,0.233,43,1 1491 | 2,99,0,0,0,22.2,0.108,23,0 1492 | 3,89,74,16,85,30.4,0.551,38,0 1493 | 1,80,74,11,60,30,0.527,22,0 1494 | 2,139,75,0,0,25.6,0.167,29,0 1495 | 1,90,68,8,0,24.5,1.138,36,0 1496 | 0,141,0,0,0,42.4,0.205,29,1 1497 | 12,140,85,33,0,37.4,0.244,41,0 1498 | 5,147,75,0,0,29.9,0.434,28,0 1499 | 1,97,70,15,0,18.2,0.147,21,0 1500 | 6,107,88,0,0,36.8,0.727,31,0 1501 | 0,189,104,25,0,34.3,0.435,41,1 1502 | 2,83,66,23,50,32.2,0.497,22,0 1503 | 4,117,64,27,120,33.2,0.23,24,0 1504 | 8,108,70,0,0,30.5,0.955,33,1 1505 | 4,117,62,12,0,29.7,0.38,30,1 1506 | 0,180,78,63,14,59.4,2.42,25,1 1507 | 1,100,72,12,70,25.3,0.658,28,0 1508 | 0,95,80,45,92,36.5,0.33,26,0 1509 | 0,104,64,37,64,33.6,0.51,22,1 1510 | 0,120,74,18,63,30.5,0.285,26,0 1511 | 1,82,64,13,95,21.2,0.415,23,0 1512 | 2,134,70,0,0,28.9,0.542,23,1 1513 | 0,91,68,32,210,39.9,0.381,25,0 1514 | 2,119,0,0,0,19.6,0.832,72,0 1515 | 2,100,54,28,105,37.8,0.498,24,0 1516 | 14,175,62,30,0,33.6,0.212,38,1 1517 | 1,135,54,0,0,26.7,0.687,62,0 1518 | 5,86,68,28,71,30.2,0.364,24,0 1519 | 10,148,84,48,237,37.6,1.001,51,1 1520 | 9,134,74,33,60,25.9,0.46,81,0 1521 | 9,120,72,22,56,20.8,0.733,48,0 1522 | 1,71,62,0,0,21.8,0.416,26,0 1523 | 8,74,70,40,49,35.3,0.705,39,0 1524 | 5,88,78,30,0,27.6,0.258,37,0 1525 | 10,115,98,0,0,24,1.022,34,0 1526 | 0,124,56,13,105,21.8,0.452,21,0 1527 | 0,74,52,10,36,27.8,0.269,22,0 1528 | 0,97,64,36,100,36.8,0.6,25,0 1529 | 8,120,0,0,0,30,0.183,38,1 1530 | 6,154,78,41,140,46.1,0.571,27,0 1531 | 1,144,82,40,0,41.3,0.607,28,0 1532 | 0,137,70,38,0,33.2,0.17,22,0 1533 | 0,119,66,27,0,38.8,0.259,22,0 1534 | 7,136,90,0,0,29.9,0.21,50,0 1535 | 4,114,64,0,0,28.9,0.126,24,0 1536 | 0,137,84,27,0,27.3,0.231,59,0 1537 | 2,105,80,45,191,33.7,0.711,29,1 1538 | 7,114,76,17,110,23.8,0.466,31,0 1539 | 8,126,74,38,75,25.9,0.162,39,0 1540 | 4,132,86,31,0,28,0.419,63,0 1541 | 3,158,70,30,328,35.5,0.344,35,1 1542 | 0,123,88,37,0,35.2,0.197,29,0 1543 | 4,85,58,22,49,27.8,0.306,28,0 1544 | 0,84,82,31,125,38.2,0.233,23,0 1545 | 0,145,0,0,0,44.2,0.63,31,1 1546 | 0,135,68,42,250,42.3,0.365,24,1 1547 | 1,139,62,41,480,40.7,0.536,21,0 1548 | 0,173,78,32,265,46.5,1.159,58,0 1549 | 4,99,72,17,0,25.6,0.294,28,0 1550 | 8,194,80,0,0,26.1,0.551,67,0 1551 | 2,83,65,28,66,36.8,0.629,24,0 1552 | 2,89,90,30,0,33.5,0.292,42,0 1553 | 4,99,68,38,0,32.8,0.145,33,0 1554 | 4,125,70,18,122,28.9,1.144,45,1 1555 | 3,80,0,0,0,0,0.174,22,0 1556 | 6,166,74,0,0,26.6,0.304,66,0 1557 | 5,110,68,0,0,26,0.292,30,0 1558 | 2,81,72,15,76,30.1,0.547,25,0 1559 | 7,195,70,33,145,25.1,0.163,55,1 1560 | 6,154,74,32,193,29.3,0.839,39,0 1561 | 0,136,74,49,220,20.1,0.82,44,1 1562 | 0,126,84,29,215,30.7,0.52,24,0 1563 | 0,116,64,39,225,40.2,0.72,50,0 1564 | 2,142,94,59,177,38.3,0.62,63,1 1565 | 4,183,66,0,215,80.6,0.654,40,0 1566 | 1,100,62,0,0,64.4,0.152,36,0 1567 | 0,163,40,23,64,40.7,0.322,33,0 1568 | 6,139,84,37,0,50.7,0.32,50,1 1569 | 2,167,44,30,140,52.7,0.452,28,0 1570 | 3,162,0,110,215,48.7,0.52,24,0 1571 | 0,173,78,32,265,46.5,1.159,58,0 1572 | 4,99,72,17,0,25.6,0.294,28,0 1573 | 8,194,80,0,0,26.1,0.551,67,0 1574 | 2,83,65,28,66,36.8,0.629,24,0 1575 | 2,89,90,30,0,33.5,0.292,42,0 1576 | 4,99,68,38,0,32.8,0.145,33,0 1577 | 4,125,70,18,122,28.9,1.144,45,1 1578 | 3,80,0,0,0,0,0.174,22,0 1579 | 6,166,74,0,0,26.6,0.304,66,0 1580 | 5,110,68,0,0,26,0.292,30,0 1581 | 2,81,72,15,76,30.1,0.547,25,0 1582 | 7,195,70,33,145,25.1,0.163,55,1 1583 | 6,154,74,32,193,29.3,0.839,39,0 1584 | 0,136,74,49,220,20.1,0.82,44,1 1585 | 0,126,84,29,215,30.7,0.52,24,0 1586 | 0,116,64,39,225,40.2,0.72,50,0 1587 | 2,142,94,59,177,38.3,0.62,63,1 1588 | 4,183,66,0,215,80.6,0.654,40,0 1589 | 1,100,62,0,0,64.4,0.152,36,0 1590 | 0,163,40,23,64,40.7,0.322,33,0 1591 | 6,139,84,37,0,50.7,0.32,50,1 1592 | 2,167,44,30,140,52.7,0.452,28,0 1593 | 3,162,0,110,215,48.7,0.52,24,0 1594 | 7,178,84,0,0,39.9,0.331,41,1 1595 | 1,130,70,13,105,25.9,0.472,22,0 1596 | 1,95,74,21,73,25.9,0.673,36,0 1597 | 1,0,68,35,0,32,0.389,22,0 1598 | 5,122,86,0,0,34.7,0.29,33,0 1599 | 8,95,72,0,0,36.8,0.485,57,0 1600 | 8,126,88,36,108,38.5,0.349,49,0 1601 | 1,139,46,19,83,28.7,0.654,22,0 1602 | 3,116,0,0,0,23.5,0.187,23,0 1603 | 3,99,62,19,74,21.8,0.279,26,0 1604 | 5,0,80,32,0,41,0.346,37,1 1605 | 4,92,80,0,0,42.2,0.237,29,0 1606 | 4,137,84,0,0,31.2,0.252,30,0 1607 | 3,61,82,28,0,34.4,0.243,46,0 1608 | 1,90,62,12,43,27.2,0.58,24,0 1609 | 3,90,78,0,0,42.7,0.559,21,0 1610 | 9,165,88,0,0,30.4,0.302,49,1 1611 | 1,125,50,40,167,33.3,0.962,28,1 1612 | 13,129,0,30,0,39.9,0.569,44,1 1613 | 12,88,74,40,54,35.3,0.378,48,0 1614 | 1,196,76,36,249,36.5,0.875,29,1 1615 | 5,189,64,33,325,31.2,0.583,29,1 1616 | 5,158,70,0,0,29.8,0.207,63,0 1617 | 5,103,108,37,0,39.2,0.305,65,0 1618 | 4,146,78,0,0,38.5,0.52,67,1 1619 | 4,147,74,25,293,34.9,0.385,30,0 1620 | 5,99,54,28,83,34,0.499,30,0 1621 | 6,124,72,0,0,27.6,0.368,29,1 1622 | 0,101,64,17,0,21,0.252,21,0 1623 | 3,81,86,16,66,27.5,0.306,22,0 1624 | 1,133,102,28,140,32.8,0.234,45,1 1625 | 3,173,82,48,465,38.4,2.137,25,1 1626 | 0,118,64,23,89,0,1.731,21,0 1627 | 0,84,64,22,66,35.8,0.545,21,0 1628 | 2,105,58,40,94,34.9,0.225,25,0 1629 | 2,122,52,43,158,36.2,0.816,28,0 1630 | 12,140,82,43,325,39.2,0.528,58,1 1631 | 0,98,82,15,84,25.2,0.299,22,0 1632 | 1,87,60,37,75,37.2,0.509,22,0 1633 | 4,156,75,0,0,48.3,0.238,32,1 1634 | 0,93,100,39,72,43.4,1.021,35,0 1635 | 1,107,72,30,82,30.8,0.821,24,0 1636 | 0,105,68,22,0,20,0.236,22,0 1637 | 1,109,60,8,182,25.4,0.947,21,0 1638 | 1,90,62,18,59,25.1,1.268,25,0 1639 | 1,125,70,24,110,24.3,0.221,25,0 1640 | 1,119,54,13,50,22.3,0.205,24,0 1641 | 5,116,74,29,0,32.3,0.66,35,1 1642 | 8,105,100,36,0,43.3,0.239,45,1 1643 | 5,144,82,26,285,32,0.452,58,1 1644 | 3,100,68,23,81,31.6,0.949,28,0 1645 | 1,100,66,29,196,32,0.444,42,0 1646 | 5,166,76,0,0,45.7,0.34,27,1 1647 | 1,131,64,14,415,23.7,0.389,21,0 1648 | 4,116,72,12,87,22.1,0.463,37,0 1649 | 4,158,78,0,0,32.9,0.803,31,1 1650 | 2,127,58,24,275,27.7,1.6,25,0 1651 | 3,96,56,34,115,24.7,0.944,39,0 1652 | 0,131,66,40,0,34.3,0.196,22,1 1653 | 3,82,70,0,0,21.1,0.389,25,0 1654 | 3,193,70,31,0,34.9,0.241,25,1 1655 | 4,95,64,0,0,32,0.161,31,1 1656 | 6,137,61,0,0,24.2,0.151,55,0 1657 | 5,136,84,41,88,35,0.286,35,1 1658 | 9,72,78,25,0,31.6,0.28,38,0 1659 | 5,168,64,0,0,32.9,0.135,41,1 1660 | 2,123,48,32,165,42.1,0.52,26,0 1661 | 4,115,72,0,0,28.9,0.376,46,1 1662 | 0,101,62,0,0,21.9,0.336,25,0 1663 | 8,197,74,0,0,25.9,1.191,39,1 1664 | 1,172,68,49,579,42.4,0.702,28,1 1665 | 6,102,90,39,0,35.7,0.674,28,0 1666 | 1,112,72,30,176,34.4,0.528,25,0 1667 | 1,143,84,23,310,42.4,1.076,22,0 1668 | 1,143,74,22,61,26.2,0.256,21,0 1669 | 0,138,60,35,167,34.6,0.534,21,1 1670 | 3,173,84,33,474,35.7,0.258,22,1 1671 | 1,97,68,21,0,27.2,1.095,22,0 1672 | 4,144,82,32,0,38.5,0.554,37,1 1673 | 1,83,68,0,0,18.2,0.624,27,0 1674 | 3,129,64,29,115,26.4,0.219,28,1 1675 | 1,119,88,41,170,45.3,0.507,26,0 1676 | 2,94,68,18,76,26,0.561,21,0 1677 | 0,102,64,46,78,40.6,0.496,21,0 1678 | 2,115,64,22,0,30.8,0.421,21,0 1679 | 8,151,78,32,210,42.9,0.516,36,1 1680 | 4,184,78,39,277,37,0.264,31,1 1681 | 0,94,0,0,0,0,0.256,25,0 1682 | 1,181,64,30,180,34.1,0.328,38,1 1683 | 0,135,94,46,145,40.6,0.284,26,0 1684 | 1,95,82,25,180,35,0.233,43,1 1685 | 2,99,0,0,0,22.2,0.108,23,0 1686 | 3,89,74,16,85,30.4,0.551,38,0 1687 | 1,80,74,11,60,30,0.527,22,0 1688 | 1,173,74,0,0,36.8,0.088,38,1 1689 | 1,109,38,18,120,23.1,0.407,26,0 1690 | 1,108,88,19,0,27.1,0.4,24,0 1691 | 6,96,0,0,0,23.7,0.19,28,0 1692 | 1,124,74,36,0,27.8,0.1,30,0 1693 | 7,150,78,29,126,35.2,0.692,54,1 1694 | 4,183,0,0,0,28.4,0.212,36,1 1695 | 1,124,60,32,0,35.8,0.514,21,0 1696 | 1,181,78,42,293,40,1.258,22,1 1697 | 1,92,62,25,41,19.5,0.482,25,0 1698 | 0,152,82,39,272,41.5,0.27,27,0 1699 | 1,111,62,13,182,24,0.138,23,0 1700 | 3,106,54,21,158,30.9,0.292,24,0 1701 | 3,174,58,22,194,32.9,0.593,36,1 1702 | 7,168,88,42,321,38.2,0.787,40,1 1703 | 6,105,80,28,0,32.5,0.878,26,0 1704 | 11,138,74,26,144,36.1,0.557,50,1 1705 | 3,106,72,0,0,25.8,0.207,27,0 1706 | 6,117,96,0,0,28.7,0.157,30,0 1707 | 2,68,62,13,15,20.1,0.257,23,0 1708 | 9,112,82,24,0,28.2,1.282,50,1 1709 | 0,119,0,0,0,32.4,0.141,24,1 1710 | 2,112,86,42,160,38.4,0.246,28,0 1711 | 2,92,76,20,0,24.2,1.698,28,0 1712 | 6,183,94,0,0,40.8,1.461,45,0 1713 | 0,94,70,27,115,43.5,0.347,21,0 1714 | 2,108,64,0,0,30.8,0.158,21,0 1715 | 4,90,88,47,54,37.7,0.362,29,0 1716 | 0,125,68,0,0,24.7,0.206,21,0 1717 | 0,132,78,0,0,32.4,0.393,21,0 1718 | 5,128,80,0,0,34.6,0.144,45,0 1719 | 4,94,65,22,0,24.7,0.148,21,0 1720 | 7,114,64,0,0,27.4,0.732,34,1 1721 | 0,102,78,40,90,34.5,0.238,24,0 1722 | 2,111,60,0,0,26.2,0.343,23,0 1723 | 1,128,82,17,183,27.5,0.115,22,0 1724 | 10,92,62,0,0,25.9,0.167,31,0 1725 | 13,104,72,0,0,31.2,0.465,38,1 1726 | 5,104,74,0,0,28.8,0.153,48,0 1727 | 2,94,76,18,66,31.6,0.649,23,0 1728 | 7,97,76,32,91,40.9,0.871,32,1 1729 | 1,100,74,12,46,19.5,0.149,28,0 1730 | 0,102,86,17,105,29.3,0.695,27,0 1731 | 4,128,70,0,0,34.3,0.303,24,0 1732 | 6,147,80,0,0,29.5,0.178,50,1 1733 | 4,90,0,0,0,28,0.61,31,0 1734 | 3,103,72,30,152,27.6,0.73,27,0 1735 | 2,157,74,35,440,39.4,0.134,30,0 1736 | 1,167,74,17,144,23.4,0.447,33,1 1737 | 0,179,50,36,159,37.8,0.455,22,1 1738 | 11,136,84,35,130,28.3,0.26,42,1 1739 | 0,107,60,25,0,26.4,0.133,23,0 1740 | 1,91,54,25,100,25.2,0.234,23,0 1741 | 1,117,60,23,106,33.8,0.466,27,0 1742 | 5,123,74,40,77,34.1,0.269,28,0 1743 | 2,120,54,0,0,26.8,0.455,27,0 1744 | 1,106,70,28,135,34.2,0.142,22,0 1745 | 2,155,52,27,540,38.7,0.24,25,1 1746 | 2,101,58,35,90,21.8,0.155,22,0 1747 | 1,120,80,48,200,38.9,1.162,41,0 1748 | 11,127,106,0,0,39,0.19,51,0 1749 | 3,80,82,31,70,34.2,1.292,27,1 1750 | 10,162,84,0,0,27.7,0.182,54,0 1751 | 1,199,76,43,0,42.9,1.394,22,1 1752 | 8,167,106,46,231,37.6,0.165,43,1 1753 | 9,145,80,46,130,37.9,0.637,40,1 1754 | 6,115,60,39,0,33.7,0.245,40,1 1755 | 1,112,80,45,132,34.8,0.217,24,0 1756 | 4,145,82,18,0,32.5,0.235,70,1 1757 | 10,111,70,27,0,27.5,0.141,40,1 1758 | 6,98,58,33,190,34,0.43,43,0 1759 | 9,154,78,30,100,30.9,0.164,45,0 1760 | 6,165,68,26,168,33.6,0.631,49,0 1761 | 1,99,58,10,0,25.4,0.551,21,0 1762 | 10,68,106,23,49,35.5,0.285,47,0 1763 | 3,123,100,35,240,57.3,0.88,22,0 1764 | 8,91,82,0,0,35.6,0.587,68,0 1765 | 6,195,70,0,0,30.9,0.328,31,1 1766 | 9,156,86,0,0,24.8,0.23,53,1 1767 | 0,93,60,0,0,35.3,0.263,25,0 1768 | 3,121,52,0,0,36,0.127,25,1 1769 | 2,101,58,17,265,24.2,0.614,23,0 1770 | 2,56,56,28,45,24.2,0.332,22,0 1771 | 0,162,76,36,0,49.6,0.364,26,1 1772 | 0,95,64,39,105,44.6,0.366,22,0 1773 | 4,125,80,0,0,32.3,0.536,27,1 1774 | 5,136,82,0,0,0,0.64,69,0 1775 | 2,129,74,26,205,33.2,0.591,25,0 1776 | 3,130,64,0,0,23.1,0.314,22,0 1777 | 1,107,50,19,0,28.3,0.181,29,0 1778 | 1,140,74,26,180,24.1,0.828,23,0 1779 | 1,144,82,46,180,46.1,0.335,46,1 1780 | 8,107,80,0,0,24.6,0.856,34,0 1781 | 13,158,114,0,0,42.3,0.257,44,1 1782 | 2,121,70,32,95,39.1,0.886,23,0 1783 | 7,129,68,49,125,38.5,0.439,43,1 1784 | 2,90,60,0,0,23.5,0.191,25,0 1785 | 7,142,90,24,480,30.4,0.128,43,1 1786 | 3,169,74,19,125,29.9,0.268,31,1 1787 | 0,99,0,0,0,25,0.253,22,0 1788 | 4,127,88,11,155,34.5,0.598,28,0 1789 | 4,118,70,0,0,44.5,0.904,26,0 1790 | 2,122,76,27,200,35.9,0.483,26,0 1791 | 6,125,78,31,0,27.6,0.565,49,1 1792 | 1,168,88,29,0,35,0.905,52,1 1793 | 2,129,0,0,0,38.5,0.304,41,0 1794 | 4,110,76,20,100,28.4,0.118,27,0 1795 | 6,80,80,36,0,39.8,0.177,28,0 1796 | 10,115,0,0,0,0,0.261,30,1 1797 | 2,127,46,21,335,34.4,0.176,22,0 1798 | 9,164,78,0,0,32.8,0.148,45,1 1799 | 2,93,64,32,160,38,0.674,23,1 1800 | 3,158,64,13,387,31.2,0.295,24,0 1801 | 5,126,78,27,22,29.6,0.439,40,0 1802 | 10,129,62,36,0,41.2,0.441,38,1 1803 | 0,134,58,20,291,26.4,0.352,21,0 1804 | 3,102,74,0,0,29.5,0.121,32,0 1805 | 7,187,50,33,392,33.9,0.826,34,1 1806 | 3,173,78,39,185,33.8,0.97,31,1 1807 | 10,94,72,18,0,23.1,0.595,56,0 1808 | 1,108,60,46,178,35.5,0.415,24,0 1809 | 5,97,76,27,0,35.6,0.378,52,1 1810 | 4,83,86,19,0,29.3,0.317,34,0 1811 | 1,114,66,36,200,38.1,0.289,21,0 1812 | 1,149,68,29,127,29.3,0.349,42,1 1813 | 5,117,86,30,105,39.1,0.251,42,0 1814 | 1,111,94,0,0,32.8,0.265,45,0 1815 | 4,112,78,40,0,39.4,0.236,38,0 1816 | 1,116,78,29,180,36.1,0.496,25,0 1817 | 0,141,84,26,0,32.4,0.433,22,0 1818 | 2,175,88,0,0,22.9,0.326,22,0 1819 | 2,92,52,0,0,30.1,0.141,22,0 1820 | 3,130,78,23,79,28.4,0.323,34,1 1821 | 8,120,86,0,0,28.4,0.259,22,1 1822 | 2,174,88,37,120,44.5,0.646,24,1 1823 | 2,106,56,27,165,29,0.426,22,0 1824 | 2,105,75,0,0,23.3,0.56,53,0 1825 | 4,95,60,32,0,35.4,0.284,28,0 1826 | 0,126,86,27,120,27.4,0.515,21,0 1827 | 8,65,72,23,0,32,0.6,42,0 1828 | 2,99,60,17,160,36.6,0.453,21,0 1829 | 1,102,74,0,0,39.5,0.293,42,1 1830 | 11,120,80,37,150,42.3,0.785,48,1 1831 | 3,102,44,20,94,30.8,0.4,26,0 1832 | 1,109,58,18,116,28.5,0.219,22,0 1833 | 9,140,94,0,0,32.7,0.734,45,1 1834 | 13,153,88,37,140,40.6,1.174,39,0 1835 | 12,100,84,33,105,30,0.488,46,0 1836 | 1,147,94,41,0,49.3,0.358,27,1 1837 | 1,81,74,41,57,46.3,1.096,32,0 1838 | 3,187,70,22,200,36.4,0.408,36,1 1839 | 6,162,62,0,0,24.3,0.178,50,1 1840 | 4,136,70,0,0,31.2,1.182,22,1 1841 | 1,121,78,39,74,39,0.261,28,0 1842 | 3,108,62,24,0,26,0.223,25,0 1843 | 0,181,88,44,510,43.3,0.222,26,1 1844 | 8,154,78,32,0,32.4,0.443,45,1 1845 | 1,128,88,39,110,36.5,1.057,37,1 1846 | 7,137,90,41,0,32,0.391,39,0 1847 | 0,123,72,0,0,36.3,0.258,52,1 1848 | 1,106,76,0,0,37.5,0.197,26,0 1849 | 6,190,92,0,0,35.5,0.278,66,1 1850 | 2,88,58,26,16,28.4,0.766,22,0 1851 | 9,170,74,31,0,44,0.403,43,1 1852 | 9,89,62,0,0,22.5,0.142,33,0 1853 | 10,101,76,48,180,32.9,0.171,63,0 1854 | 2,122,70,27,0,36.8,0.34,27,0 1855 | 5,121,72,23,112,26.2,0.245,30,0 1856 | 1,126,60,0,0,30.1,0.349,47,1 1857 | 10,122,78,31,0,27.6,0.512,45,0 1858 | 4,103,60,33,192,24,0.966,33,0 1859 | 11,138,76,0,0,33.2,0.42,35,0 1860 | 9,102,76,37,0,32.9,0.665,46,1 1861 | 2,90,68,42,0,38.2,0.503,27,1 1862 | 4,111,72,47,207,37.1,1.39,56,1 1863 | 3,180,64,25,70,34,0.271,26,0 1864 | 7,133,84,0,0,40.2,0.696,37,0 1865 | 7,106,92,18,0,22.7,0.235,48,0 1866 | 9,171,110,24,240,45.4,0.721,54,1 1867 | 7,159,64,0,0,27.4,0.294,40,0 1868 | 0,180,66,39,0,42,1.893,25,1 1869 | 1,146,56,0,0,29.7,0.564,29,0 1870 | 2,71,70,27,0,28,0.586,22,0 1871 | 7,103,66,32,0,39.1,0.344,31,1 1872 | 7,105,0,0,0,0,0.305,24,0 1873 | 1,103,80,11,82,19.4,0.491,22,0 1874 | 1,101,50,15,36,24.2,0.526,26,0 1875 | 5,88,66,21,23,24.4,0.342,30,0 1876 | 8,176,90,34,300,33.7,0.467,58,1 1877 | 7,150,66,42,342,34.7,0.718,42,0 1878 | 1,73,50,10,0,23,0.248,21,0 1879 | 7,187,68,39,304,37.7,0.254,41,1 1880 | 0,100,88,60,110,46.8,0.962,31,0 1881 | 0,146,82,0,0,40.5,1.781,44,0 1882 | 0,105,64,41,142,41.5,0.173,22,0 1883 | 2,84,0,0,0,0,0.304,21,0 1884 | 8,133,72,0,0,32.9,0.27,39,1 1885 | 5,44,62,0,0,25,0.587,36,0 1886 | 2,141,58,34,128,25.4,0.699,24,0 1887 | 7,114,66,0,0,32.8,0.258,42,1 1888 | 5,99,74,27,0,29,0.203,32,0 1889 | 0,109,88,30,0,32.5,0.855,38,1 1890 | 2,109,92,0,0,42.7,0.845,54,0 1891 | 1,95,66,13,38,19.6,0.334,25,0 1892 | 4,146,85,27,100,28.9,0.189,27,0 1893 | 2,100,66,20,90,32.9,0.867,28,1 1894 | 5,139,64,35,140,28.6,0.411,26,0 1895 | 13,126,90,0,0,43.4,0.583,42,1 1896 | 4,129,86,20,270,35.1,0.231,23,0 1897 | 1,79,75,30,0,32,0.396,22,0 1898 | 1,0,48,20,0,24.7,0.14,22,0 1899 | 7,62,78,0,0,32.6,0.391,41,0 1900 | 5,95,72,33,0,37.7,0.37,27,0 1901 | 0,131,0,0,0,43.2,0.27,26,1 1902 | 2,112,66,22,0,25,0.307,24,0 1903 | 3,113,44,13,0,22.4,0.14,22,0 1904 | 2,74,0,0,0,0,0.102,22,0 1905 | 7,83,78,26,71,29.3,0.767,36,0 1906 | 0,101,65,28,0,24.6,0.237,22,0 1907 | 5,137,108,0,0,48.8,0.227,37,1 1908 | 2,110,74,29,125,32.4,0.698,27,0 1909 | 13,106,72,54,0,36.6,0.178,45,0 1910 | 2,100,68,25,71,38.5,0.324,26,0 1911 | 15,136,70,32,110,37.1,0.153,43,1 1912 | 1,107,68,19,0,26.5,0.165,24,0 1913 | 1,80,55,0,0,19.1,0.258,21,0 1914 | 4,123,80,15,176,32,0.443,34,0 1915 | 7,81,78,40,48,46.7,0.261,42,0 1916 | 4,134,72,0,0,23.8,0.277,60,1 1917 | 2,142,82,18,64,24.7,0.761,21,0 1918 | 6,144,72,27,228,33.9,0.255,40,0 1919 | 2,92,62,28,0,31.6,0.13,24,0 1920 | 1,71,48,18,76,20.4,0.323,22,0 1921 | 6,93,50,30,64,28.7,0.356,23,0 1922 | 1,122,90,51,220,49.7,0.325,31,1 1923 | 1,163,72,0,0,39,1.222,33,1 1924 | 1,151,60,0,0,26.1,0.179,22,0 1925 | 0,125,96,0,0,22.5,0.262,21,0 1926 | 1,81,72,18,40,26.6,0.283,24,0 1927 | 2,85,65,0,0,39.6,0.93,27,0 1928 | 1,126,56,29,152,28.7,0.801,21,0 1929 | 1,96,122,0,0,22.4,0.207,27,0 1930 | 4,144,58,28,140,29.5,0.287,37,0 1931 | 3,83,58,31,18,34.3,0.336,25,0 1932 | 0,95,85,25,36,37.4,0.247,24,1 1933 | 3,171,72,33,135,33.3,0.199,24,1 1934 | 8,155,62,26,495,34,0.543,46,1 1935 | 1,89,76,34,37,31.2,0.192,23,0 1936 | 4,76,62,0,0,34,0.391,25,0 1937 | 7,160,54,32,175,30.5,0.588,39,1 1938 | 4,146,92,0,0,31.2,0.539,61,1 1939 | 5,124,74,0,0,34,0.22,38,1 1940 | 5,78,48,0,0,33.7,0.654,25,0 1941 | 4,97,60,23,0,28.2,0.443,22,0 1942 | 4,99,76,15,51,23.2,0.223,21,0 1943 | 0,162,76,56,100,53.2,0.759,25,1 1944 | 6,111,64,39,0,34.2,0.26,24,0 1945 | 2,107,74,30,100,33.6,0.404,23,0 1946 | 5,132,80,0,0,26.8,0.186,69,0 1947 | 0,113,76,0,0,33.3,0.278,23,1 1948 | 1,88,30,42,99,55,0.496,26,1 1949 | 3,120,70,30,135,42.9,0.452,30,0 1950 | 1,118,58,36,94,33.3,0.261,23,0 1951 | 1,117,88,24,145,34.5,0.403,40,1 1952 | 0,105,84,0,0,27.9,0.741,62,1 1953 | 4,173,70,14,168,29.7,0.361,33,1 1954 | 9,122,56,0,0,33.3,1.114,33,1 1955 | 3,170,64,37,225,34.5,0.356,30,1 1956 | 8,84,74,31,0,38.3,0.457,39,0 1957 | 2,96,68,13,49,21.1,0.647,26,0 1958 | 2,125,60,20,140,33.8,0.088,31,0 1959 | 0,100,70,26,50,30.8,0.597,21,0 1960 | 0,93,60,25,92,28.7,0.532,22,0 1961 | 0,129,80,0,0,31.2,0.703,29,0 1962 | 5,105,72,29,325,36.9,0.159,28,0 1963 | 3,128,78,0,0,21.1,0.268,55,0 1964 | 5,106,82,30,0,39.5,0.286,38,0 1965 | 2,108,52,26,63,32.5,0.318,22,0 1966 | 10,108,66,0,0,32.4,0.272,42,1 1967 | 4,154,62,31,284,32.8,0.237,23,0 1968 | 0,102,75,23,0,0,0.572,21,0 1969 | 9,57,80,37,0,32.8,0.096,41,0 1970 | 2,106,64,35,119,30.5,1.4,34,0 1971 | 5,147,78,0,0,33.7,0.218,65,0 1972 | 2,90,70,17,0,27.3,0.085,22,0 1973 | 1,136,74,50,204,37.4,0.399,24,0 1974 | 4,114,65,0,0,21.9,0.432,37,0 1975 | 9,156,86,28,155,34.3,1.189,42,1 1976 | 1,153,82,42,485,40.6,0.687,23,0 1977 | 8,188,78,0,0,47.9,0.137,43,1 1978 | 7,152,88,44,0,50,0.337,36,1 1979 | 2,99,52,15,94,24.6,0.637,21,0 1980 | 1,109,56,21,135,25.2,0.833,23,0 1981 | 2,88,74,19,53,29,0.229,22,0 1982 | 17,163,72,41,114,40.9,0.817,47,1 1983 | 4,151,90,38,0,29.7,0.294,36,0 1984 | 7,102,74,40,105,37.2,0.204,45,0 1985 | 0,114,80,34,285,44.2,0.167,27,0 1986 | 2,100,64,23,0,29.7,0.368,21,0 1987 | 0,131,88,0,0,31.6,0.743,32,1 1988 | 6,104,74,18,156,29.9,0.722,41,1 1989 | 3,148,66,25,0,32.5,0.256,22,0 1990 | 4,120,68,0,0,29.6,0.709,34,0 1991 | 4,110,66,0,0,31.9,0.471,29,0 1992 | 3,111,90,12,78,28.4,0.495,29,0 1993 | 6,102,82,0,0,30.8,0.18,36,1 1994 | 6,134,70,23,130,35.4,0.542,29,1 1995 | 2,87,0,23,0,28.9,0.773,25,0 1996 | 1,79,60,42,48,43.5,0.678,23,0 1997 | 2,75,64,24,55,29.7,0.37,33,0 1998 | 8,179,72,42,130,32.7,0.719,36,1 1999 | 6,85,78,0,0,31.2,0.382,42,0 2000 | 0,129,110,46,130,67.1,0.319,26,1 2001 | 2,81,72,15,76,30.1,0.547,25,0 2002 | -------------------------------------------------------------------------------- /Diabetes Classification/readme-resources/diabetes-banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Diabetes Classification/readme-resources/diabetes-banner.png -------------------------------------------------------------------------------- /First Innings Score Prediction - IPL/README.md: -------------------------------------------------------------------------------- 1 | ![FISP](readme-resources/first-innings-banner.png) 2 | ![Dataset](https://img.shields.io/badge/Dataset-Shivam_Mitra-blue.svg) ![Python](https://img.shields.io/badge/Python-3.6-brightgreen.svg) ![sklearn](https://img.shields.io/badge/Library-sklearn-orange.svg) 3 | 4 | ## Project Overview 5 | • Created a model that predicts the score (in terms of range) of IPL matches
6 | • Optimized Multiple-Linear, Decision Tree, Random Forest, and AdaBoost regression models using GridsearchCV 7 | 8 | ## How will this project help? 9 | • This project is for the fantasy cricket fans out there, helping them to earn extra fantasy points for Dream11 IPL 2020 10 | 11 | ## Resources Used 12 | • Packages: pandas, numpy, sklearn, matplotlib, seaborn
13 | • Dataset by **Shivam Mitra**: https://github.com/codophobia/CricketScorePredictor 14 | 15 | ## Data Cleaning and Preprocessing 16 | • **Removing unwanted columns**
17 | • **Keeping only consistent teams**
18 | ![ct](readme-resources/consistent_teams.PNG)
19 | • **Removing the first 5 overs data in every match**
20 | • **Converting the column 'date' from string into datetime object**
21 | • **Handling categorical features** 22 | 23 | ## Model Building and Evaluation 24 | Evaluation metric: Root Mean Squared Error (RMSE)
25 | • Multiple Linear Regression - 15.843
26 | • Decision Tree - 23.044
27 | • Random Forest - 18.171
28 | • **Adaptive Boosting (AdaBoost) - 15.798** 29 | 30 | ## Model Prediction 31 | ![pred](readme-resources/prediction.PNG)
32 | 33 | ## Future Scope 34 | • Add columns in dataset of top batsmen and bowlers of all the teams.
35 | • Add columns that consists of striker and non-striker's strike rates.
36 | • Implement this problem statement using Artificial Neural Network (ANN).
37 | 38 | ## Deployed Web App 39 | If you want to view the deployed model, then follow the links mentioned below:
40 | • GitHub: _https://github.com/anujvyas/IPL-First-Innings-Score-Prediction-Deployment_
41 | • Web App: _https://ipl-first-innings-score.herokuapp.com/_ 42 | -------------------------------------------------------------------------------- /First Innings Score Prediction - IPL/readme-resources/consistent_teams.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/First Innings Score Prediction - IPL/readme-resources/consistent_teams.PNG -------------------------------------------------------------------------------- /First Innings Score Prediction - IPL/readme-resources/first-innings-banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/First Innings Score Prediction - IPL/readme-resources/first-innings-banner.png -------------------------------------------------------------------------------- /First Innings Score Prediction - IPL/readme-resources/prediction.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/First Innings Score Prediction - IPL/readme-resources/prediction.PNG -------------------------------------------------------------------------------- /Heart Disease Prediction/heart.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1 10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1 17 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1 18 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1 19 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1 20 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1 21 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1 22 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1 23 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1 24 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1 25 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1 26 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1 27 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1 28 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1 29 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1 30 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1 31 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1 32 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1 33 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1 34 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1 35 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1 36 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1 37 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1 38 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1 39 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1 40 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1 41 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1 45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1 47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1 49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1 50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1 51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1 52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1 57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1 59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1 60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1 61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1 62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1 63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1 64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1 65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1 66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1 67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1 71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1 72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1 74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1 75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1 76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1 80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1 81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1 83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1 84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1 85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1 89 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1 90 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1 91 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1 92 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1 93 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1 94 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1 95 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1 96 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1 97 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1 98 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1 99 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1 100 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1 101 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1 102 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1 103 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1 104 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1 105 | 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55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /Mall Customer Segmentation/Mall Customer Segmentation - PPT.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Mall Customer Segmentation/Mall Customer Segmentation - PPT.pdf -------------------------------------------------------------------------------- /Mall Customer Segmentation/Mall Customer Segmentation - Report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/Mall Customer Segmentation/Mall Customer Segmentation - Report.pdf -------------------------------------------------------------------------------- /Mall Customer Segmentation/Mall_Customers.csv: -------------------------------------------------------------------------------- 1 | CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100) 2 | 1,Male,19,15,39 3 | 2,Male,21,15,81 4 | 3,Female,20,16,6 5 | 4,Female,23,16,77 6 | 5,Female,31,17,40 7 | 6,Female,22,17,76 8 | 7,Female,35,18,6 9 | 8,Female,23,18,94 10 | 9,Male,64,19,3 11 | 10,Female,30,19,72 12 | 11,Male,67,19,14 13 | 12,Female,35,19,99 14 | 13,Female,58,20,15 15 | 14,Female,24,20,77 16 | 15,Male,37,20,13 17 | 16,Male,22,20,79 18 | 17,Female,35,21,35 19 | 18,Male,20,21,66 20 | 19,Male,52,23,29 21 | 20,Female,35,23,98 22 | 21,Male,35,24,35 23 | 22,Male,25,24,73 24 | 23,Female,46,25,5 25 | 24,Male,31,25,73 26 | 25,Female,54,28,14 27 | 26,Male,29,28,82 28 | 27,Female,45,28,32 29 | 28,Male,35,28,61 30 | 29,Female,40,29,31 31 | 30,Female,23,29,87 32 | 31,Male,60,30,4 33 | 32,Female,21,30,73 34 | 33,Male,53,33,4 35 | 34,Male,18,33,92 36 | 35,Female,49,33,14 37 | 36,Female,21,33,81 38 | 37,Female,42,34,17 39 | 38,Female,30,34,73 40 | 39,Female,36,37,26 41 | 40,Female,20,37,75 42 | 41,Female,65,38,35 43 | 42,Male,24,38,92 44 | 43,Male,48,39,36 45 | 44,Female,31,39,61 46 | 45,Female,49,39,28 47 | 46,Female,24,39,65 48 | 47,Female,50,40,55 49 | 48,Female,27,40,47 50 | 49,Female,29,40,42 51 | 50,Female,31,40,42 52 | 51,Female,49,42,52 53 | 52,Male,33,42,60 54 | 53,Female,31,43,54 55 | 54,Male,59,43,60 56 | 55,Female,50,43,45 57 | 56,Male,47,43,41 58 | 57,Female,51,44,50 59 | 58,Male,69,44,46 60 | 59,Female,27,46,51 61 | 60,Male,53,46,46 62 | 61,Male,70,46,56 63 | 62,Male,19,46,55 64 | 63,Female,67,47,52 65 | 64,Female,54,47,59 66 | 65,Male,63,48,51 67 | 66,Male,18,48,59 68 | 67,Female,43,48,50 69 | 68,Female,68,48,48 70 | 69,Male,19,48,59 71 | 70,Female,32,48,47 72 | 71,Male,70,49,55 73 | 72,Female,47,49,42 74 | 73,Female,60,50,49 75 | 74,Female,60,50,56 76 | 75,Male,59,54,47 77 | 76,Male,26,54,54 78 | 77,Female,45,54,53 79 | 78,Male,40,54,48 80 | 79,Female,23,54,52 81 | 80,Female,49,54,42 82 | 81,Male,57,54,51 83 | 82,Male,38,54,55 84 | 83,Male,67,54,41 85 | 84,Female,46,54,44 86 | 85,Female,21,54,57 87 | 86,Male,48,54,46 88 | 87,Female,55,57,58 89 | 88,Female,22,57,55 90 | 89,Female,34,58,60 91 | 90,Female,50,58,46 92 | 91,Female,68,59,55 93 | 92,Male,18,59,41 94 | 93,Male,48,60,49 95 | 94,Female,40,60,40 96 | 95,Female,32,60,42 97 | 96,Male,24,60,52 98 | 97,Female,47,60,47 99 | 98,Female,27,60,50 100 | 99,Male,48,61,42 101 | 100,Male,20,61,49 102 | 101,Female,23,62,41 103 | 102,Female,49,62,48 104 | 103,Male,67,62,59 105 | 104,Male,26,62,55 106 | 105,Male,49,62,56 107 | 106,Female,21,62,42 108 | 107,Female,66,63,50 109 | 108,Male,54,63,46 110 | 109,Male,68,63,43 111 | 110,Male,66,63,48 112 | 111,Male,65,63,52 113 | 112,Female,19,63,54 114 | 113,Female,38,64,42 115 | 114,Male,19,64,46 116 | 115,Female,18,65,48 117 | 116,Female,19,65,50 118 | 117,Female,63,65,43 119 | 118,Female,49,65,59 120 | 119,Female,51,67,43 121 | 120,Female,50,67,57 122 | 121,Male,27,67,56 123 | 122,Female,38,67,40 124 | 123,Female,40,69,58 125 | 124,Male,39,69,91 126 | 125,Female,23,70,29 127 | 126,Female,31,70,77 128 | 127,Male,43,71,35 129 | 128,Male,40,71,95 130 | 129,Male,59,71,11 131 | 130,Male,38,71,75 132 | 131,Male,47,71,9 133 | 132,Male,39,71,75 134 | 133,Female,25,72,34 135 | 134,Female,31,72,71 136 | 135,Male,20,73,5 137 | 136,Female,29,73,88 138 | 137,Female,44,73,7 139 | 138,Male,32,73,73 140 | 139,Male,19,74,10 141 | 140,Female,35,74,72 142 | 141,Female,57,75,5 143 | 142,Male,32,75,93 144 | 143,Female,28,76,40 145 | 144,Female,32,76,87 146 | 145,Male,25,77,12 147 | 146,Male,28,77,97 148 | 147,Male,48,77,36 149 | 148,Female,32,77,74 150 | 149,Female,34,78,22 151 | 150,Male,34,78,90 152 | 151,Male,43,78,17 153 | 152,Male,39,78,88 154 | 153,Female,44,78,20 155 | 154,Female,38,78,76 156 | 155,Female,47,78,16 157 | 156,Female,27,78,89 158 | 157,Male,37,78,1 159 | 158,Female,30,78,78 160 | 159,Male,34,78,1 161 | 160,Female,30,78,73 162 | 161,Female,56,79,35 163 | 162,Female,29,79,83 164 | 163,Male,19,81,5 165 | 164,Female,31,81,93 166 | 165,Male,50,85,26 167 | 166,Female,36,85,75 168 | 167,Male,42,86,20 169 | 168,Female,33,86,95 170 | 169,Female,36,87,27 171 | 170,Male,32,87,63 172 | 171,Male,40,87,13 173 | 172,Male,28,87,75 174 | 173,Male,36,87,10 175 | 174,Male,36,87,92 176 | 175,Female,52,88,13 177 | 176,Female,30,88,86 178 | 177,Male,58,88,15 179 | 178,Male,27,88,69 180 | 179,Male,59,93,14 181 | 180,Male,35,93,90 182 | 181,Female,37,97,32 183 | 182,Female,32,97,86 184 | 183,Male,46,98,15 185 | 184,Female,29,98,88 186 | 185,Female,41,99,39 187 | 186,Male,30,99,97 188 | 187,Female,54,101,24 189 | 188,Male,28,101,68 190 | 189,Female,41,103,17 191 | 190,Female,36,103,85 192 | 191,Female,34,103,23 193 | 192,Female,32,103,69 194 | 193,Male,33,113,8 195 | 194,Female,38,113,91 196 | 195,Female,47,120,16 197 | 196,Female,35,120,79 198 | 197,Female,45,126,28 199 | 198,Male,32,126,74 200 | 199,Male,32,137,18 201 | 200,Male,30,137,83 202 | -------------------------------------------------------------------------------- /Predicting Admission into UCLA/Predicting Admission into UCLA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "# Importing essential libraries\n", 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "from matplotlib import pyplot as plt\n", 13 | "%matplotlib inline" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "# Loading the dataset\n", 23 | "df = pd.read_csv('admission_predict.csv')" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "## Exploring the dataset" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 3, 36 | "metadata": {}, 37 | "outputs": [ 38 | { 39 | "data": { 40 | "text/plain": [ 41 | "(500, 9)" 42 | ] 43 | }, 44 | "execution_count": 3, 45 | "metadata": {}, 46 | "output_type": "execute_result" 47 | } 48 | ], 49 | "source": [ 50 | "# Returns number of rows and columns of the dataset\n", 51 | "df.shape" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 4, 57 | "metadata": {}, 58 | "outputs": [ 59 | { 60 | "data": { 61 | "text/html": [ 62 | "
\n", 63 | "\n", 76 | "\n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | "
Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1232410744.04.58.8710.76
2331610433.03.58.0010.72
3432211033.52.58.6710.80
4531410322.03.08.2100.65
\n", 154 | "
" 155 | ], 156 | "text/plain": [ 157 | " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", 158 | "0 1 337 118 4 4.5 4.5 9.65 \n", 159 | "1 2 324 107 4 4.0 4.5 8.87 \n", 160 | "2 3 316 104 3 3.0 3.5 8.00 \n", 161 | "3 4 322 110 3 3.5 2.5 8.67 \n", 162 | "4 5 314 103 2 2.0 3.0 8.21 \n", 163 | "\n", 164 | " Research Chance of Admit \n", 165 | "0 1 0.92 \n", 166 | "1 1 0.76 \n", 167 | "2 1 0.72 \n", 168 | "3 1 0.80 \n", 169 | "4 0 0.65 " 170 | ] 171 | }, 172 | "execution_count": 4, 173 | "metadata": {}, 174 | "output_type": "execute_result" 175 | } 176 | ], 177 | "source": [ 178 | "# Returns the first x number of rows when head(num). Without a number it returns 5\n", 179 | "df.head()" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 5, 185 | "metadata": {}, 186 | "outputs": [ 187 | { 188 | "data": { 189 | "text/html": [ 190 | "
\n", 191 | "\n", 204 | "\n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \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 | "
Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
49549633210854.54.09.0210.87
49649733711755.05.09.8710.96
49749833012054.55.09.5610.93
49849931210344.05.08.4300.73
49950032711344.54.59.0400.84
\n", 282 | "
" 283 | ], 284 | "text/plain": [ 285 | " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", 286 | "495 496 332 108 5 4.5 4.0 9.02 \n", 287 | "496 497 337 117 5 5.0 5.0 9.87 \n", 288 | "497 498 330 120 5 4.5 5.0 9.56 \n", 289 | "498 499 312 103 4 4.0 5.0 8.43 \n", 290 | "499 500 327 113 4 4.5 4.5 9.04 \n", 291 | "\n", 292 | " Research Chance of Admit \n", 293 | "495 1 0.87 \n", 294 | "496 1 0.96 \n", 295 | "497 1 0.93 \n", 296 | "498 0 0.73 \n", 297 | "499 0 0.84 " 298 | ] 299 | }, 300 | "execution_count": 5, 301 | "metadata": {}, 302 | "output_type": "execute_result" 303 | } 304 | ], 305 | "source": [ 306 | "# Returns the first x number of rows when tail(num). Without a number it returns 5\n", 307 | "df.tail()" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": 6, 313 | "metadata": {}, 314 | "outputs": [ 315 | { 316 | "data": { 317 | "text/plain": [ 318 | "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n", 319 | " 'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n", 320 | " dtype='object')" 321 | ] 322 | }, 323 | "execution_count": 6, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "# Returns an object with all of the column headers\n", 330 | "df.columns" 331 | ] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "execution_count": 7, 336 | "metadata": {}, 337 | "outputs": [ 338 | { 339 | "name": "stdout", 340 | "output_type": "stream", 341 | "text": [ 342 | "\n", 343 | "RangeIndex: 500 entries, 0 to 499\n", 344 | "Data columns (total 9 columns):\n", 345 | "Serial No. 500 non-null int64\n", 346 | "GRE Score 500 non-null int64\n", 347 | "TOEFL Score 500 non-null int64\n", 348 | "University Rating 500 non-null int64\n", 349 | "SOP 500 non-null float64\n", 350 | "LOR 500 non-null float64\n", 351 | "CGPA 500 non-null float64\n", 352 | "Research 500 non-null int64\n", 353 | "Chance of Admit 500 non-null float64\n", 354 | "dtypes: float64(4), int64(5)\n", 355 | "memory usage: 35.3 KB\n" 356 | ] 357 | } 358 | ], 359 | "source": [ 360 | "# Returns basic information on all columns\n", 361 | "df.info()" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 8, 367 | "metadata": {}, 368 | "outputs": [ 369 | { 370 | "data": { 371 | "text/html": [ 372 | "
\n", 373 | "\n", 386 | "\n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | "
countmeanstdmin25%50%75%max
Serial No.500.0250.50000144.4818331.00125.7500250.50375.25500.00
GRE Score500.0316.4720011.295148290.00308.0000317.00325.00340.00
TOEFL Score500.0107.192006.08186892.00103.0000107.00112.00120.00
University Rating500.03.114001.1435121.002.00003.004.005.00
SOP500.03.374000.9910041.002.50003.504.005.00
LOR500.03.484000.9254501.003.00003.504.005.00
CGPA500.08.576440.6048136.808.12758.569.049.92
Research500.00.560000.4968840.000.00001.001.001.00
Chance of Admit500.00.721740.1411400.340.63000.720.820.97
\n", 502 | "
" 503 | ], 504 | "text/plain": [ 505 | " count mean std min 25% 50% \\\n", 506 | "Serial No. 500.0 250.50000 144.481833 1.00 125.7500 250.50 \n", 507 | "GRE Score 500.0 316.47200 11.295148 290.00 308.0000 317.00 \n", 508 | "TOEFL Score 500.0 107.19200 6.081868 92.00 103.0000 107.00 \n", 509 | "University Rating 500.0 3.11400 1.143512 1.00 2.0000 3.00 \n", 510 | "SOP 500.0 3.37400 0.991004 1.00 2.5000 3.50 \n", 511 | "LOR 500.0 3.48400 0.925450 1.00 3.0000 3.50 \n", 512 | "CGPA 500.0 8.57644 0.604813 6.80 8.1275 8.56 \n", 513 | "Research 500.0 0.56000 0.496884 0.00 0.0000 1.00 \n", 514 | "Chance of Admit 500.0 0.72174 0.141140 0.34 0.6300 0.72 \n", 515 | "\n", 516 | " 75% max \n", 517 | "Serial No. 375.25 500.00 \n", 518 | "GRE Score 325.00 340.00 \n", 519 | "TOEFL Score 112.00 120.00 \n", 520 | "University Rating 4.00 5.00 \n", 521 | "SOP 4.00 5.00 \n", 522 | "LOR 4.00 5.00 \n", 523 | "CGPA 9.04 9.92 \n", 524 | "Research 1.00 1.00 \n", 525 | "Chance of Admit 0.82 0.97 " 526 | ] 527 | }, 528 | "execution_count": 8, 529 | "metadata": {}, 530 | "output_type": "execute_result" 531 | } 532 | ], 533 | "source": [ 534 | "# Returns basic statistics on numeric columns\n", 535 | "df.describe().T" 536 | ] 537 | }, 538 | { 539 | "cell_type": "code", 540 | "execution_count": 9, 541 | "metadata": {}, 542 | "outputs": [ 543 | { 544 | "data": { 545 | "text/plain": [ 546 | "Serial No. int64\n", 547 | "GRE Score int64\n", 548 | "TOEFL Score int64\n", 549 | "University Rating int64\n", 550 | "SOP float64\n", 551 | "LOR float64\n", 552 | "CGPA float64\n", 553 | "Research int64\n", 554 | "Chance of Admit float64\n", 555 | "dtype: object" 556 | ] 557 | }, 558 | "execution_count": 9, 559 | "metadata": {}, 560 | "output_type": "execute_result" 561 | } 562 | ], 563 | "source": [ 564 | "# Returns different datatypes for each columns (float, int, string, bool, etc.)\n", 565 | "df.dtypes" 566 | ] 567 | }, 568 | { 569 | "cell_type": "code", 570 | "execution_count": 10, 571 | "metadata": {}, 572 | "outputs": [ 573 | { 574 | "data": { 575 | "text/plain": [ 576 | "Serial No. False\n", 577 | "GRE Score False\n", 578 | "TOEFL Score False\n", 579 | "University Rating False\n", 580 | "SOP False\n", 581 | "LOR False\n", 582 | "CGPA False\n", 583 | "Research False\n", 584 | "Chance of Admit False\n", 585 | "dtype: bool" 586 | ] 587 | }, 588 | "execution_count": 10, 589 | "metadata": {}, 590 | "output_type": "execute_result" 591 | } 592 | ], 593 | "source": [ 594 | "# Returns true for a column having null values, else false\n", 595 | "df.isnull().any()" 596 | ] 597 | }, 598 | { 599 | "cell_type": "code", 600 | "execution_count": 11, 601 | "metadata": { 602 | "scrolled": true 603 | }, 604 | "outputs": [ 605 | { 606 | "data": { 607 | "text/html": [ 608 | "
\n", 609 | "\n", 622 | "\n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | " \n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | "
Serial No.GRETOEFLUniversity RatingSOPLORCGPAResearchProbability
0133711844.54.59.6510.92
1232410744.04.58.8710.76
2331610433.03.58.0010.72
3432211033.52.58.6710.80
4531410322.03.08.2100.65
\n", 700 | "
" 701 | ], 702 | "text/plain": [ 703 | " Serial No. GRE TOEFL University Rating SOP LOR CGPA Research \\\n", 704 | "0 1 337 118 4 4.5 4.5 9.65 1 \n", 705 | "1 2 324 107 4 4.0 4.5 8.87 1 \n", 706 | "2 3 316 104 3 3.0 3.5 8.00 1 \n", 707 | "3 4 322 110 3 3.5 2.5 8.67 1 \n", 708 | "4 5 314 103 2 2.0 3.0 8.21 0 \n", 709 | "\n", 710 | " Probability \n", 711 | "0 0.92 \n", 712 | "1 0.76 \n", 713 | "2 0.72 \n", 714 | "3 0.80 \n", 715 | "4 0.65 " 716 | ] 717 | }, 718 | "execution_count": 11, 719 | "metadata": {}, 720 | "output_type": "execute_result" 721 | } 722 | ], 723 | "source": [ 724 | "# Renaming the columns with appropriate names\n", 725 | "df = df.rename(columns={'GRE Score': 'GRE', 'TOEFL Score': 'TOEFL', 'LOR ': 'LOR', 'Chance of Admit ': 'Probability'})\n", 726 | "df.head()" 727 | ] 728 | }, 729 | { 730 | "cell_type": "markdown", 731 | "metadata": {}, 732 | "source": [ 733 | "## Data Visualization" 734 | ] 735 | }, 736 | { 737 | "cell_type": "code", 738 | "execution_count": 12, 739 | "metadata": {}, 740 | "outputs": [ 741 | { 742 | "data": { 743 | "image/png": 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\n", 744 | "text/plain": [ 745 | "
" 746 | ] 747 | }, 748 | "metadata": { 749 | "needs_background": "light" 750 | }, 751 | "output_type": "display_data" 752 | } 753 | ], 754 | "source": [ 755 | "# Visualizing the feature GRE\n", 756 | "fig = plt.hist(df['GRE'], rwidth=0.7)\n", 757 | "plt.title(\"Distribution of GRE Scores\")\n", 758 | "plt.xlabel('GRE Scores')\n", 759 | "plt.ylabel('Count')\n", 760 | "plt.show()" 761 | ] 762 | }, 763 | { 764 | "cell_type": "code", 765 | "execution_count": 13, 766 | "metadata": { 767 | "scrolled": true 768 | }, 769 | "outputs": [ 770 | { 771 | "data": { 772 | "image/png": "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\n", 773 | "text/plain": [ 774 | "
" 775 | ] 776 | }, 777 | "metadata": { 778 | "needs_background": "light" 779 | }, 780 | "output_type": "display_data" 781 | } 782 | ], 783 | "source": [ 784 | "# Visualizing the feature TOEFL\n", 785 | "fig = plt.hist(df['TOEFL'], rwidth=0.7)\n", 786 | "plt.title('Distribution of TOEFL Scores')\n", 787 | "plt.xlabel('TOEFL Scores')\n", 788 | "plt.ylabel('Count')\n", 789 | "plt.show()" 790 | ] 791 | }, 792 | { 793 | "cell_type": "code", 794 | "execution_count": 14, 795 | "metadata": {}, 796 | "outputs": [ 797 | { 798 | "data": { 799 | "image/png": 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\n", 800 | "text/plain": [ 801 | "
" 802 | ] 803 | }, 804 | "metadata": { 805 | "needs_background": "light" 806 | }, 807 | "output_type": "display_data" 808 | } 809 | ], 810 | "source": [ 811 | "# Visualizing the feature TOEFL\n", 812 | "fig = plt.hist(df['University Rating'], rwidth=0.7)\n", 813 | "plt.title('Distribution of University Rating')\n", 814 | "plt.xlabel('University Rating')\n", 815 | "plt.ylabel('Count')\n", 816 | "plt.show()" 817 | ] 818 | }, 819 | { 820 | "cell_type": "code", 821 | "execution_count": 15, 822 | "metadata": { 823 | "scrolled": true 824 | }, 825 | "outputs": [ 826 | { 827 | "data": { 828 | "image/png": "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\n", 829 | "text/plain": [ 830 | "
" 831 | ] 832 | }, 833 | "metadata": { 834 | "needs_background": "light" 835 | }, 836 | "output_type": "display_data" 837 | } 838 | ], 839 | "source": [ 840 | "# Visualizing the feature TOEFL\n", 841 | "fig = plt.hist(df['SOP'], rwidth=0.7)\n", 842 | "plt.title('Distribution of SOP')\n", 843 | "plt.xlabel('SOP Rating')\n", 844 | "plt.ylabel('Count')\n", 845 | "plt.show()" 846 | ] 847 | }, 848 | { 849 | "cell_type": "code", 850 | "execution_count": 16, 851 | "metadata": {}, 852 | "outputs": [ 853 | { 854 | "data": { 855 | "image/png": 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\n", 856 | "text/plain": [ 857 | "
" 858 | ] 859 | }, 860 | "metadata": { 861 | "needs_background": "light" 862 | }, 863 | "output_type": "display_data" 864 | } 865 | ], 866 | "source": [ 867 | "# Visualizing the feature TOEFL\n", 868 | "fig = plt.hist(df['LOR'], rwidth=0.7)\n", 869 | "plt.title('Distribution of LOR Rating')\n", 870 | "plt.xlabel('LOR Rating')\n", 871 | "plt.ylabel('Count')\n", 872 | "plt.show()" 873 | ] 874 | }, 875 | { 876 | "cell_type": "code", 877 | "execution_count": 17, 878 | "metadata": {}, 879 | "outputs": [ 880 | { 881 | "data": { 882 | "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAWVUlEQVR4nO3de7RkZX3m8e8jLSCicmsRQTgwEm/MaEyLCo5RIVGDAt7JMtIYDMNyNCom2l5yMeNENI4xMZM4jDggKqKoA4o3RPCyVLRBjVxMQGgQQWhQRJBRWn/zx94N5XnP6VN9uvepc5rvZ61aVfv27t/b1VXP2W/t2pWqQpKkUfeYdAGSpMXHcJAkNQwHSVLDcJAkNQwHSVLDcJAkNQwHLbgk707yF5uprT2T3Jpkq376vCQv2Rxt9+19OsnKzdXeRuz3zUluTPKjhd63BIaDNrMka5LcnuRnSW5O8tUkxya58/9aVR1bVf9tzLYO3tA6VXV1VW1fVb/aDLX/dZL3T2v/6VV18qa2vZF1PAh4NfDwqnrALOvcN8k7k1zdh+Pl/fQuI+sckeT8JLcluaF//NIk6ZeflOSX/fY/TnJ2kodO289RSSrJ84fssxYfw0FDeGZV3QfYCzgeeC1w4ubeSZJlm7vNRWIv4KaqumGmhUm2Bs4BHgE8DbgvcABwE7B/v86rgX8A/g54ALArcCxwILD1SHNvq6rtgT2AG4CTpu1uJfDj/l53J1XlzdtmuwFrgIOnzdsf+DWwXz99EvDm/vEuwCeBm+nehL5M90fLKf02twO3Aq8BpoACjgauBr40Mm9Z3955wFuAbwA/Bc4AduqXPQm4ZqZ66d5kfwnc0e/vOyPtvaR/fA/gjcBVdG+k7wPu1y9bX8fKvrYbgTds4N/pfv32a/v23ti3f3Df51/3dZw0w7YvAa4Htt9A27cBz5njubrzeeinDwFuHZneq6/jOcA6YNdJ///ytnA3jxw0uKr6BnAN8J9nWPzqftlyur9uX99tUi+ie5N9ZnXDRm8b2eZ3gYcBT51ll0cCfww8kO5N7R/HqPEzwN8Cp/X7e+QMqx3V354M7ANsD/zTtHWeADwEOAj4yyQPm2WX76J7E9+n78+RwIur6vPA04Fr+zqOmmHbg4HPVNWts7T9eGAbumAcS5LtgRcC3xqZfSSwuqo+ClzaL9fdhOGghXItsNMM8+8AdgP2qqo7qurLVTXXBb/+uqpuq6rbZ1l+SlVdVFW3AX8BPH/9B9ab6IXAO6rqiv6N+XXAEdOGt95UVbdX1XeA7wBNyPS1vAB4XVX9rKrWAP8DeNGYdewMXLeB5bsAN1bVupF9frX/DOj2JE8cWffPktwMXE4XdkeNLDsS+GD/+IM4tHS3YjhooexON2w03d/RvTF9LskVSVaN0dYPNmL5VcA96d4wN9UD+/ZG215Gd8Sz3ujZRT+ne8Odbhe6cf/pbe0+Zh030QXqhpbvMhpaVXVAVe3QLxt93b+9qnaoqgdU1aFV9X2AJAcCewMf6tf7IPAfkzxqzBq1xBkOGlySx9C98X1l+rL+L+dXV9U+wDOB45IctH7xLE3OdWTxoJHHe9IdndxINw6/3UhdW9ENZ43b7rV04/Cjba+jG//fGDf2NU1v64djbv954KlJ7j3L8q8BvwAO28i6Rq0EAny7P532/H7+kZvQppYQw0GD6U+3fAbdX5/vr6rvzrDOM5I8uD+98hbgV/0Nujfdfeax6z9K8vAk2wF/A5xe3amu/w5sm+SQJPek+xB4m5HtrgemRk+7neZU4FVJ9u7H6Nd/RrFulvVn1NfyYeC/J7lPkr2A44D3b3jLO51Cd3T00SQPTXKPJDsneX2SP6iqm4E3Af+c5LlJtu/XeRQwW6DcKcm2wPOBY4BHjdxeDrxwCz5LTCMMBw3hE0l+RvcG9gbgHcCLZ1l3X7q/hG+l+4v3n6vqvH7ZW4A39mPlf7YR+z+F7kycHwHbAn8KUFU/BV4KvIfur/Tb6D4MX+8j/f1NSS6cod339m1/CbgS+H90b5jz8fJ+/1fQHVF9sG9/TlX1C7oPpb8HnE0Xqt+gG646v1/nbXSB8xq6M6uuB/4X3WnFX51jF4fTnTH1vqr60fob3enIW9Gd2aUtXOb+7E+SdHfjkYMkqWE4SJIahoMkqWE4SJIaS+KUtF122aWmpqYmXYYkLSkXXHDBjVW1fO41W0siHKampli9evWky5CkJSXJVXOvNTOHlSRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJjSXxDWlpKZladdYg7a45/pBB2pVm4pGDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKmxbNIFSNpyTK06a5B21xx/yCDtanYeOUiSGoaDJKkxaDgkeVWSi5NclOTUJNsm2TvJ+UkuS3Jakq2HrEGStPEGC4ckuwN/Cqyoqv2ArYAjgLcCf19V+wI/AY4eqgZJ0vwMPay0DLhXkmXAdsB1wFOA0/vlJwOHD1yDJGkjDXa2UlX9MMnbgauB24HPARcAN1fVun61a4DdZ9o+yTHAMQB77rnnUGVqC+VZM9KmGXJYaUfgMGBv4IHAvYGnz7BqzbR9VZ1QVSuqasXy5cuHKlOSNIMhh5UOBq6sqrVVdQfwMeAAYId+mAlgD+DaAWuQJM3DkOFwNfC4JNslCXAQcAlwLvDcfp2VwBkD1iBJmofBwqGqzqf74PlC4Lv9vk4AXgscl+RyYGfgxKFqkCTNz6CXz6iqvwL+atrsK4D9h9yvJGnT+A1pSVLDcJAkNQwHSVLDcJAkNQwHSVLDcJAkNQwHSVLDcJAkNQwHSVLDcJAkNQa9fIakheXvWGhz8chBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktQwHCRJDcNBktRYNukCtGWaWnXWIO2uOf6QQdqV9JsGPXJIskOS05N8L8mlSR6fZKckZye5rL/fccgaJEkbb+hhpX8APlNVDwUeCVwKrALOqap9gXP6aUnSIjJYOCS5L/BE4ESAqvplVd0MHAac3K92MnD4UDVIkuZnyCOHfYC1wP9J8q0k70lyb2DXqroOoL+//0wbJzkmyeokq9euXTtgmZKk6YYMh2XAo4F/qarfBm5jI4aQquqEqlpRVSuWL18+VI2SpBkMGQ7XANdU1fn99Ol0YXF9kt0A+vsbBqxBkjQPg4VDVf0I+EGSh/SzDgIuAc4EVvbzVgJnDFWDJGl+hv6ew8uBDyTZGrgCeDFdIH04ydHA1cDzBq5BkrSRBg2Hqvo2sGKGRQcNuV9J0qbx8hmSpIbhIElqGA6SpIbhIElqjBUOSQ4cZ54kacsw7pHDu8acJ0naAmzwVNYkjwcOAJYnOW5k0X2BrYYsTJI0OXN9z2FrYPt+vfuMzL8FeO5QRUmSJmuD4VBVXwS+mOSkqrpqgWqSJE3YuN+Q3ibJCcDU6DZV9ZQhipIkTda44fAR4N3Ae4BfDVeOJGkxGDcc1lXVvwxaiSRp0Rj3VNZPJHlpkt2S7LT+NmhlkqSJGffIYf3vL/z5yLyi+ylQSdIWZqxwqKq9hy5EkrR4jBUOSY6caX5VvW/zliNJWgzGHVZ6zMjjbel+rOdCwHCQpC3QuMNKLx+dTnI/4JRBKpIkTdx8L9n9c2DfzVmIJGnxGPczh0/QnZ0E3QX3HgZ8eKiiJEmTNe5nDm8febwOuKqqrhmgHknSIjDWsFJ/Ab7v0V2ZdUfgl0MWJUmarHF/Ce75wDeA5wHPB85P4iW7JWkLNe6w0huAx1TVDQBJlgOfB04fqjBJ0uSMe7bSPdYHQ++mjdhWkrTEjHvk8JkknwVO7adfAHxqmJIkSZM2129IPxjYtar+PMmzgScAAb4GfGAB6pMkTcBcQ0PvBH4GUFUfq6rjqupVdEcN7xy6OEnSZMwVDlNV9a/TZ1bVarqfDJUkbYHmCodtN7DsXpuzEEnS4jFXOHwzyZ9Mn5nkaOCCYUqSJE3aXGcrvRL4eJIXclcYrAC2Bp41ZGGSpMnZYDhU1fXAAUmeDOzXzz6rqr4weGWSpIkZ9/cczgXOHbgWSdIiMfi3nJNsleRbST7ZT++d5PwklyU5LcnWQ9cgSdo4C3EJjFcAl45MvxX4+6raF/gJcPQC1CBJ2giDhkOSPYBDgPf00wGewl0X7DsZOHzIGiRJG2/oI4d3Aq8Bft1P7wzcXFXr+ulrgN0HrkGStJEGC4ckzwBuqKrR70NkhlVrhnkkOSbJ6iSr165dO0iNkqSZDXnkcCBwaJI1wIfohpPeCeyQZP1ZUnsA1860cVWdUFUrqmrF8uXLByxTkjTdYOFQVa+rqj2qago4AvhCVb2Q7pTY9b8itxI4Y6gaJEnzM4kf7HktcFySy+k+gzhxAjVIkjZg3B/72SRVdR5wXv/4CmD/hdivpLuPqVVnDdLumuMPGaTdxc6f+pQkNQwHSVLDcJAkNQwHSVLDcJAkNQwHSVLDcJAkNQwHSVJjQb4Ep8nwS0GS5ssjB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSw3CQJDUMB0lSY9mkC5CkpW5q1VmDtLvm+EMGaXccHjlIkhqGgySpMVg4JHlQknOTXJrk4iSv6OfvlOTsJJf19zsOVYMkaX6GPHJYB7y6qh4GPA74r0keDqwCzqmqfYFz+mlJ0iIyWDhU1XVVdWH/+GfApcDuwGHAyf1qJwOHD1WDJGl+FuQzhyRTwG8D5wO7VtV10AUIcP9Ztjkmyeokq9euXbsQZUqSeoOHQ5LtgY8Cr6yqW8bdrqpOqKoVVbVi+fLlwxUoSWoMGg5J7kkXDB+oqo/1s69Pslu/fDfghiFrkCRtvCHPVgpwInBpVb1jZNGZwMr+8UrgjKFqkCTNz5DfkD4QeBHw3STf7ue9Hjge+HCSo4GrgecNWIMkaR4GC4eq+gqQWRYfNNR+JUmbzm9IS5IahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqTHk7znc7U2tOmuQdtccf8gg7UrSeh45SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqWE4SJIahoMkqTGRcEjytCT/luTyJKsmUYMkaXbLFnqHSbYC/ifwe8A1wDeTnFlVlwyxv6lVZw3RLGuOP2SQdiVpMZjEkcP+wOVVdUVV/RL4EHDYBOqQJM0iVbWwO0yeCzytql7ST78IeGxVvWzaescAx/STDwH+bUEL7ewC3DiB/Q5hS+mL/Vh8tpS+bIn92Kuqls+nkQUfVgIyw7wmoarqBOCE4cuZXZLVVbVikjVsLltKX+zH4rOl9MV+/KZJDCtdAzxoZHoP4NoJ1CFJmsUkwuGbwL5J9k6yNXAEcOYE6pAkzWLBh5Wqal2SlwGfBbYC3ltVFy90HWOa6LDWZral9MV+LD5bSl/sx4gF/0BakrT4+Q1pSVLDcJAkNe724ZDkIUm+PXK7Jckrp62TJP/YX+7jX5M8elL1bsiYfXlSkp+OrPOXk6p3Q5K8KsnFSS5KcmqSbact3ybJaf1zcn6SqclUumFj9OOoJGtHno+XTKrWDUnyir4PF0//P9UvXyqvkbn6sWhfH0nem+SGJBeNzNspydlJLuvvd5xl25X9OpclWTnWDqvKW3+j+4D8R3RfHBmd/wfAp+m+o/E44PxJ17oJfXkS8MlJ1zdH7bsDVwL36qc/DBw1bZ2XAu/uHx8BnDbpuufZj6OAf5p0rXP0Yz/gImA7upNYPg/sO22dRf8aGbMfi/b1ATwReDRw0ci8twGr+sergLfOsN1OwBX9/Y794x3n2t/d/shhmoOA71fVVdPmHwa8rzpfB3ZIstvCl7dRZuvLUrEMuFeSZXQv5unfhTkMOLl/fDpwUJKZvmA5aXP1Yyl4GPD1qvp5Va0Dvgg8a9o6S+E1Mk4/Fq2q+hLw42mzR18HJwOHz7DpU4Gzq+rHVfUT4GzgaXPtz3D4TUcAp84wf3fgByPT1/TzFrPZ+gLw+CTfSfLpJI9YyKLGUVU/BN4OXA1cB/y0qj43bbU7n5P+hf5TYOeFrHMuY/YD4Dn9UMzpSR40w/JJuwh4YpKdk2xHd5Qwvc6l8BoZpx+wyF8f0+xaVdcB9Pf3n2GdeT03hkOv/0LeocBHZlo8w7xFew7wHH25kG6o6ZHAu4D/u5C1jaMfNz0M2Bt4IHDvJH80fbUZNl1Uz8mY/fgEMFVV/4lumONkFpmquhR4K91fnJ8BvgOsm7baon8+xuzHon99zMO8nhvD4S5PBy6squtnWLbULvkxa1+q6paqurV//Cngnkl2WegC53AwcGVVra2qO4CPAQdMW+fO56Qfsrkf7SH3pM3Zj6q6qap+0U/+b+B3FrjGsVTViVX16Kp6It2/82XTVlkSr5G5+rFEXh+jrl8/fNff3zDDOvN6bgyHu/whsw/DnAkc2Z+R8Ti64YHrFq60jTZrX5I8YP3YfJL96f4P3LSAtY3jauBxSbbraz0IuHTaOmcC68+6eC7wheo/fVtE5uzHtHH5Q6cvXyyS3L+/3xN4Nu3/ryXxGpmrH0vk9TFq9HWwEjhjhnU+C/x+kh37o9nf7+dt2KQ/gV8MN7oPCm8C7jcy71jg2P5x6H6g6PvAd4EVk655E/ryMuBiukPqrwMHTLrmWfrxJuB7dOPEpwDbAH8DHNov35Zu2Oxy4BvAPpOueZ79eMvI83Eu8NBJ1zxLP74MXNLXedAM/6+WxGtkjH4s2tcHXZBdB9xBdzRwNN3nbOfQHQGdA+zUr7sCeM/Itn/cv1YuB148zv68fIYkqeGwkiSpYThIkhqGgySpYThIkhqGgySpYThII/rz3D+U5PtJLknyqSS/lWTfJJ/s51+Q5NwkT+y3Gb2y6iVJ/mRam2ck+dpkeiTNj+Eg9fovP30cOK+q/kNVPRx4PbArcBZwQj//d4CXA/uMbH5aVT2K7qqef5tk177NHeiupLlDkr0XrjfSpjEcpLs8Gbijqt69fkZVfRv4LeBrVXXmyPyLquqk6Q1U1Q10XwTbq5/1HLrrJ32I7mKI0pJgOEh32Q+4YIb5j6C7INuckuxDd0RxeT9r/aVMTu0fS0vCskkXIC01ST4O7Av8e1U9u5/9giRPAH4B/Jeq+nE/tPRg4CtVVUnWJdmvqi6apWlp0fDIQbrLxcx8VdSL6T43AKCqnkX3C247jaxzWlU9qqoeW1Uf7+e9gO6Xt65MsgaYwqElLRGGg3SXLwDbjJ5tlOQxdENEByY5dGTd7cZo7w+Bp1XVVFVN0QWP4aAlwXCQetVdhfJZwO/1p6xeDPw13bXvnwEcm+SK/rTUNwJvnq2tJFPAnnRX9lzf/pXALUkeO1QfpM3Fq7JKkhoeOUiSGoaDJKlhOEiSGoaDJKlhOEiSGoaDJKlhOEiSGv8f7w8y12Ry1J8AAAAASUVORK5CYII=\n", 883 | "text/plain": [ 884 | "
" 885 | ] 886 | }, 887 | "metadata": { 888 | "needs_background": "light" 889 | }, 890 | "output_type": "display_data" 891 | } 892 | ], 893 | "source": [ 894 | "# Visualizing the feature TOEFL\n", 895 | "fig = plt.hist(df['CGPA'], rwidth=0.7)\n", 896 | "plt.title('Distribution of CGPA')\n", 897 | "plt.xlabel('CGPA')\n", 898 | "plt.ylabel('Count')\n", 899 | "plt.show()" 900 | ] 901 | }, 902 | { 903 | "cell_type": "code", 904 | "execution_count": 18, 905 | "metadata": {}, 906 | "outputs": [ 907 | { 908 | "data": { 909 | "image/png": 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\n", 910 | "text/plain": [ 911 | "
" 912 | ] 913 | }, 914 | "metadata": { 915 | "needs_background": "light" 916 | }, 917 | "output_type": "display_data" 918 | } 919 | ], 920 | "source": [ 921 | "# Visualizing the feature TOEFL\n", 922 | "fig = plt.hist(df['Research'], rwidth=0.7)\n", 923 | "plt.title('Distribution of Research Papers')\n", 924 | "plt.xlabel('Research')\n", 925 | "plt.ylabel('Count')\n", 926 | "plt.show()" 927 | ] 928 | }, 929 | { 930 | "cell_type": "markdown", 931 | "metadata": {}, 932 | "source": [ 933 | "## Data Cleaning" 934 | ] 935 | }, 936 | { 937 | "cell_type": "code", 938 | "execution_count": 19, 939 | "metadata": {}, 940 | "outputs": [ 941 | { 942 | "data": { 943 | "text/html": [ 944 | "
\n", 945 | "\n", 958 | "\n", 959 | " \n", 960 | " \n", 961 | " \n", 962 | " \n", 963 | " \n", 964 | " \n", 965 | " \n", 966 | " \n", 967 | " \n", 968 | " \n", 969 | " \n", 970 | " \n", 971 | " \n", 972 | " \n", 973 | " \n", 974 | " \n", 975 | " \n", 976 | " \n", 977 | " \n", 978 | " \n", 979 | " \n", 980 | " \n", 981 | " \n", 982 | " \n", 983 | " \n", 984 | " \n", 985 | " \n", 986 | " \n", 987 | " \n", 988 | " \n", 989 | " \n", 990 | " \n", 991 | " \n", 992 | " \n", 993 | " \n", 994 | " \n", 995 | " \n", 996 | " \n", 997 | " \n", 998 | " \n", 999 | " \n", 1000 | " \n", 1001 | " \n", 1002 | " \n", 1003 | " \n", 1004 | " \n", 1005 | " \n", 1006 | " \n", 1007 | " \n", 1008 | " \n", 1009 | " \n", 1010 | " \n", 1011 | " \n", 1012 | " \n", 1013 | " \n", 1014 | " \n", 1015 | " \n", 1016 | " \n", 1017 | " \n", 1018 | " \n", 1019 | " \n", 1020 | " \n", 1021 | " \n", 1022 | " \n", 1023 | " \n", 1024 | " \n", 1025 | " \n", 1026 | " \n", 1027 | " \n", 1028 | " \n", 1029 | "
GRETOEFLUniversity RatingSOPLORCGPAResearchProbability
033711844.54.59.6510.92
132410744.04.58.8710.76
231610433.03.58.0010.72
332211033.52.58.6710.80
431410322.03.08.2100.65
\n", 1030 | "
" 1031 | ], 1032 | "text/plain": [ 1033 | " GRE TOEFL University Rating SOP LOR CGPA Research Probability\n", 1034 | "0 337 118 4 4.5 4.5 9.65 1 0.92\n", 1035 | "1 324 107 4 4.0 4.5 8.87 1 0.76\n", 1036 | "2 316 104 3 3.0 3.5 8.00 1 0.72\n", 1037 | "3 322 110 3 3.5 2.5 8.67 1 0.80\n", 1038 | "4 314 103 2 2.0 3.0 8.21 0 0.65" 1039 | ] 1040 | }, 1041 | "execution_count": 19, 1042 | "metadata": {}, 1043 | "output_type": "execute_result" 1044 | } 1045 | ], 1046 | "source": [ 1047 | "# Removing the serial no, column\n", 1048 | "df.drop('Serial No.', axis='columns', inplace=True)\n", 1049 | "df.head()" 1050 | ] 1051 | }, 1052 | { 1053 | "cell_type": "code", 1054 | "execution_count": 20, 1055 | "metadata": {}, 1056 | "outputs": [ 1057 | { 1058 | "data": { 1059 | "text/plain": [ 1060 | "GRE 0\n", 1061 | "TOEFL 0\n", 1062 | "University Rating 0\n", 1063 | "SOP 0\n", 1064 | "LOR 0\n", 1065 | "CGPA 0\n", 1066 | "Research 0\n", 1067 | "Probability 0\n", 1068 | "dtype: int64" 1069 | ] 1070 | }, 1071 | "execution_count": 20, 1072 | "metadata": {}, 1073 | "output_type": "execute_result" 1074 | } 1075 | ], 1076 | "source": [ 1077 | "# Replacing the 0 values from ['GRE','TOEFL','University Rating','SOP','LOR','CGPA'] by NaN\n", 1078 | "df_copy = df.copy(deep=True)\n", 1079 | "df_copy[['GRE','TOEFL','University Rating','SOP','LOR','CGPA']] = df_copy[['GRE','TOEFL','University Rating','SOP','LOR','CGPA']].replace(0, np.NaN)\n", 1080 | "df_copy.isnull().sum()" 1081 | ] 1082 | }, 1083 | { 1084 | "cell_type": "markdown", 1085 | "metadata": {}, 1086 | "source": [ 1087 | "## Model Building" 1088 | ] 1089 | }, 1090 | { 1091 | "cell_type": "code", 1092 | "execution_count": 21, 1093 | "metadata": {}, 1094 | "outputs": [], 1095 | "source": [ 1096 | "# Splitting the dataset in features and label\n", 1097 | "X = df_copy.drop('Probability', axis='columns')\n", 1098 | "y = df_copy['Probability']" 1099 | ] 1100 | }, 1101 | { 1102 | "cell_type": "code", 1103 | "execution_count": 22, 1104 | "metadata": {}, 1105 | "outputs": [], 1106 | "source": [ 1107 | "# Using GridSearchCV to find the best algorithm for this problem\n", 1108 | "from sklearn.model_selection import GridSearchCV\n", 1109 | "from sklearn.linear_model import LinearRegression\n", 1110 | "from sklearn.linear_model import Lasso\n", 1111 | "from sklearn.svm import SVR\n", 1112 | "from sklearn.tree import DecisionTreeRegressor\n", 1113 | "from sklearn.ensemble import RandomForestRegressor\n", 1114 | "from sklearn.neighbors import KNeighborsRegressor" 1115 | ] 1116 | }, 1117 | { 1118 | "cell_type": "code", 1119 | "execution_count": 23, 1120 | "metadata": {}, 1121 | "outputs": [ 1122 | { 1123 | "data": { 1124 | "text/html": [ 1125 | "
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modelbest_parametersscore
0linear_regression{'normalize': True}0.810802
1lasso{'alpha': 1, 'selection': 'random'}0.215088
2svr{'gamma': 'scale'}0.654099
3decision_tree{'criterion': 'mse', 'splitter': 'random'}0.586808
4random_forest{'n_estimators': 15}0.768831
5knn{'n_neighbors': 20}0.722961
\n", 1187 | "
" 1188 | ], 1189 | "text/plain": [ 1190 | " model best_parameters score\n", 1191 | "0 linear_regression {'normalize': True} 0.810802\n", 1192 | "1 lasso {'alpha': 1, 'selection': 'random'} 0.215088\n", 1193 | "2 svr {'gamma': 'scale'} 0.654099\n", 1194 | "3 decision_tree {'criterion': 'mse', 'splitter': 'random'} 0.586808\n", 1195 | "4 random_forest {'n_estimators': 15} 0.768831\n", 1196 | "5 knn {'n_neighbors': 20} 0.722961" 1197 | ] 1198 | }, 1199 | "execution_count": 23, 1200 | "metadata": {}, 1201 | "output_type": "execute_result" 1202 | } 1203 | ], 1204 | "source": [ 1205 | "# Creating a function to calculate best model for this problem\n", 1206 | "def find_best_model(X, y):\n", 1207 | " models = {\n", 1208 | " 'linear_regression': {\n", 1209 | " 'model': LinearRegression(),\n", 1210 | " 'parameters': {\n", 1211 | " 'normalize': [True,False]\n", 1212 | " }\n", 1213 | " },\n", 1214 | " \n", 1215 | " 'lasso': {\n", 1216 | " 'model': Lasso(),\n", 1217 | " 'parameters': {\n", 1218 | " 'alpha': [1,2],\n", 1219 | " 'selection': ['random', 'cyclic']\n", 1220 | " }\n", 1221 | " },\n", 1222 | " \n", 1223 | " 'svr': {\n", 1224 | " 'model': SVR(),\n", 1225 | " 'parameters': {\n", 1226 | " 'gamma': ['auto','scale']\n", 1227 | " }\n", 1228 | " },\n", 1229 | " \n", 1230 | " 'decision_tree': {\n", 1231 | " 'model': DecisionTreeRegressor(),\n", 1232 | " 'parameters': {\n", 1233 | " 'criterion': ['mse', 'friedman_mse'],\n", 1234 | " 'splitter': ['best', 'random']\n", 1235 | " }\n", 1236 | " },\n", 1237 | " \n", 1238 | " 'random_forest': {\n", 1239 | " 'model': RandomForestRegressor(criterion='mse'),\n", 1240 | " 'parameters': {\n", 1241 | " 'n_estimators': [5,10,15,20]\n", 1242 | " }\n", 1243 | " },\n", 1244 | " \n", 1245 | " 'knn': {\n", 1246 | " 'model': KNeighborsRegressor(algorithm='auto'),\n", 1247 | " 'parameters': {\n", 1248 | " 'n_neighbors': [2,5,10,20]\n", 1249 | " }\n", 1250 | " }\n", 1251 | " }\n", 1252 | " \n", 1253 | " scores = []\n", 1254 | " for model_name, model_params in models.items():\n", 1255 | " gs = GridSearchCV(model_params['model'], model_params['parameters'], cv=5, return_train_score=False)\n", 1256 | " gs.fit(X, y)\n", 1257 | " scores.append({\n", 1258 | " 'model': model_name,\n", 1259 | " 'best_parameters': gs.best_params_,\n", 1260 | " 'score': gs.best_score_\n", 1261 | " })\n", 1262 | " \n", 1263 | " return pd.DataFrame(scores, columns=['model','best_parameters','score'])\n", 1264 | " \n", 1265 | "find_best_model(X, y)" 1266 | ] 1267 | }, 1268 | { 1269 | "cell_type": "markdown", 1270 | "metadata": {}, 1271 | "source": [ 1272 | "#### Since the Linear Regression algorithm has the highest accuracy, the model selected for this problem is Linear Regression." 1273 | ] 1274 | }, 1275 | { 1276 | "cell_type": "code", 1277 | "execution_count": 24, 1278 | "metadata": {}, 1279 | "outputs": [ 1280 | { 1281 | "name": "stdout", 1282 | "output_type": "stream", 1283 | "text": [ 1284 | "Highest Accuracy : 81.0%\n" 1285 | ] 1286 | } 1287 | ], 1288 | "source": [ 1289 | "# Using cross_val_score for gaining highest accuracy\n", 1290 | "from sklearn.model_selection import cross_val_score\n", 1291 | "scores = cross_val_score(LinearRegression(normalize=True), X, y, cv=5)\n", 1292 | "print('Highest Accuracy : {}%'.format(round(sum(scores)*100/len(scores)), 3))" 1293 | ] 1294 | }, 1295 | { 1296 | "cell_type": "code", 1297 | "execution_count": 25, 1298 | "metadata": {}, 1299 | "outputs": [ 1300 | { 1301 | "name": "stdout", 1302 | "output_type": "stream", 1303 | "text": [ 1304 | "400 100\n" 1305 | ] 1306 | } 1307 | ], 1308 | "source": [ 1309 | "# Splitting the dataset into train and test samples\n", 1310 | "from sklearn.model_selection import train_test_split\n", 1311 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=5)\n", 1312 | "print(len(X_train), len(X_test))" 1313 | ] 1314 | }, 1315 | { 1316 | "cell_type": "code", 1317 | "execution_count": 26, 1318 | "metadata": {}, 1319 | "outputs": [ 1320 | { 1321 | "data": { 1322 | "text/plain": [ 1323 | "0.821478736588966" 1324 | ] 1325 | }, 1326 | "execution_count": 26, 1327 | "metadata": {}, 1328 | "output_type": "execute_result" 1329 | } 1330 | ], 1331 | "source": [ 1332 | "# Creating Linear Regression Model\n", 1333 | "model = LinearRegression(normalize=True)\n", 1334 | "model.fit(X_train, y_train)\n", 1335 | "model.score(X_test, y_test)" 1336 | ] 1337 | }, 1338 | { 1339 | "cell_type": "markdown", 1340 | "metadata": {}, 1341 | "source": [ 1342 | "#### Predicting the values using our trained model" 1343 | ] 1344 | }, 1345 | { 1346 | "cell_type": "code", 1347 | "execution_count": 28, 1348 | "metadata": { 1349 | "scrolled": true 1350 | }, 1351 | "outputs": [ 1352 | { 1353 | "name": "stdout", 1354 | "output_type": "stream", 1355 | "text": [ 1356 | "Chance of getting into UCLA is 92.855%\n" 1357 | ] 1358 | } 1359 | ], 1360 | "source": [ 1361 | "# Prediction 1\n", 1362 | "# Input in the form : GRE, TOEFL, University Rating, SOP, LOR, CGPA, Research\n", 1363 | "print('Chance of getting into UCLA is {}%'.format(round(model.predict([[337, 118, 4, 4.5, 4.5, 9.65, 0]])[0]*100, 3)))" 1364 | ] 1365 | }, 1366 | { 1367 | "cell_type": "code", 1368 | "execution_count": 29, 1369 | "metadata": {}, 1370 | "outputs": [ 1371 | { 1372 | "name": "stdout", 1373 | "output_type": "stream", 1374 | "text": [ 1375 | "Chance of getting into UCLA is 73.627%\n" 1376 | ] 1377 | } 1378 | ], 1379 | "source": [ 1380 | "# Prediction 2\n", 1381 | "# Input in the form : GRE, TOEFL, University Rating, SOP, LOR, CGPA, Research\n", 1382 | "print('Chance of getting into UCLA is {}%'.format(round(model.predict([[320, 113, 2, 2.0, 2.5, 8.64, 1]])[0]*100, 3)))" 1383 | ] 1384 | } 1385 | ], 1386 | "metadata": { 1387 | "kernelspec": { 1388 | "display_name": "Python 3", 1389 | "language": "python", 1390 | "name": "python3" 1391 | }, 1392 | "language_info": { 1393 | "codemirror_mode": { 1394 | "name": "ipython", 1395 | "version": 3 1396 | }, 1397 | "file_extension": ".py", 1398 | "mimetype": "text/x-python", 1399 | "name": "python", 1400 | "nbconvert_exporter": "python", 1401 | "pygments_lexer": "ipython3", 1402 | "version": "3.7.4" 1403 | } 1404 | }, 1405 | "nbformat": 4, 1406 | "nbformat_minor": 2 1407 | } 1408 | -------------------------------------------------------------------------------- /Predicting Admission into UCLA/admission_predict.csv: -------------------------------------------------------------------------------- 1 | Serial No.,GRE Score,TOEFL Score,University Rating,SOP,LOR ,CGPA,Research,Chance of Admit 2 | 1,337,118,4,4.5,4.5,9.65,1,0.92 3 | 2,324,107,4,4,4.5,8.87,1,0.76 4 | 3,316,104,3,3,3.5,8,1,0.72 5 | 4,322,110,3,3.5,2.5,8.67,1,0.8 6 | 5,314,103,2,2,3,8.21,0,0.65 7 | 6,330,115,5,4.5,3,9.34,1,0.9 8 | 7,321,109,3,3,4,8.2,1,0.75 9 | 8,308,101,2,3,4,7.9,0,0.68 10 | 9,302,102,1,2,1.5,8,0,0.5 11 | 10,323,108,3,3.5,3,8.6,0,0.45 12 | 11,325,106,3,3.5,4,8.4,1,0.52 13 | 12,327,111,4,4,4.5,9,1,0.84 14 | 13,328,112,4,4,4.5,9.1,1,0.78 15 | 14,307,109,3,4,3,8,1,0.62 16 | 15,311,104,3,3.5,2,8.2,1,0.61 17 | 16,314,105,3,3.5,2.5,8.3,0,0.54 18 | 17,317,107,3,4,3,8.7,0,0.66 19 | 18,319,106,3,4,3,8,1,0.65 20 | 19,318,110,3,4,3,8.8,0,0.63 21 | 20,303,102,3,3.5,3,8.5,0,0.62 22 | 21,312,107,3,3,2,7.9,1,0.64 23 | 22,325,114,4,3,2,8.4,0,0.7 24 | 23,328,116,5,5,5,9.5,1,0.94 25 | 24,334,119,5,5,4.5,9.7,1,0.95 26 | 25,336,119,5,4,3.5,9.8,1,0.97 27 | 26,340,120,5,4.5,4.5,9.6,1,0.94 28 | 27,322,109,5,4.5,3.5,8.8,0,0.76 29 | 28,298,98,2,1.5,2.5,7.5,1,0.44 30 | 29,295,93,1,2,2,7.2,0,0.46 31 | 30,310,99,2,1.5,2,7.3,0,0.54 32 | 31,300,97,2,3,3,8.1,1,0.65 33 | 32,327,103,3,4,4,8.3,1,0.74 34 | 33,338,118,4,3,4.5,9.4,1,0.91 35 | 34,340,114,5,4,4,9.6,1,0.9 36 | 35,331,112,5,4,5,9.8,1,0.94 37 | 36,320,110,5,5,5,9.2,1,0.88 38 | 37,299,106,2,4,4,8.4,0,0.64 39 | 38,300,105,1,1,2,7.8,0,0.58 40 | 39,304,105,1,3,1.5,7.5,0,0.52 41 | 40,307,108,2,4,3.5,7.7,0,0.48 42 | 41,308,110,3,3.5,3,8,1,0.46 43 | 42,316,105,2,2.5,2.5,8.2,1,0.49 44 | 43,313,107,2,2.5,2,8.5,1,0.53 45 | 44,332,117,4,4.5,4,9.1,0,0.87 46 | 45,326,113,5,4.5,4,9.4,1,0.91 47 | 46,322,110,5,5,4,9.1,1,0.88 48 | 47,329,114,5,4,5,9.3,1,0.86 49 | 48,339,119,5,4.5,4,9.7,0,0.89 50 | 49,321,110,3,3.5,5,8.85,1,0.82 51 | 50,327,111,4,3,4,8.4,1,0.78 52 | 51,313,98,3,2.5,4.5,8.3,1,0.76 53 | 52,312,100,2,1.5,3.5,7.9,1,0.56 54 | 53,334,116,4,4,3,8,1,0.78 55 | 54,324,112,4,4,2.5,8.1,1,0.72 56 | 55,322,110,3,3,3.5,8,0,0.7 57 | 56,320,103,3,3,3,7.7,0,0.64 58 | 57,316,102,3,2,3,7.4,0,0.64 59 | 58,298,99,2,4,2,7.6,0,0.46 60 | 59,300,99,1,3,2,6.8,1,0.36 61 | 60,311,104,2,2,2,8.3,0,0.42 62 | 61,309,100,2,3,3,8.1,0,0.48 63 | 62,307,101,3,4,3,8.2,0,0.47 64 | 63,304,105,2,3,3,8.2,1,0.54 65 | 64,315,107,2,4,3,8.5,1,0.56 66 | 65,325,111,3,3,3.5,8.7,0,0.52 67 | 66,325,112,4,3.5,3.5,8.92,0,0.55 68 | 67,327,114,3,3,3,9.02,0,0.61 69 | 68,316,107,2,3.5,3.5,8.64,1,0.57 70 | 69,318,109,3,3.5,4,9.22,1,0.68 71 | 70,328,115,4,4.5,4,9.16,1,0.78 72 | 71,332,118,5,5,5,9.64,1,0.94 73 | 72,336,112,5,5,5,9.76,1,0.96 74 | 73,321,111,5,5,5,9.45,1,0.93 75 | 74,314,108,4,4.5,4,9.04,1,0.84 76 | 75,314,106,3,3,5,8.9,0,0.74 77 | 76,329,114,2,2,4,8.56,1,0.72 78 | 77,327,112,3,3,3,8.72,1,0.74 79 | 78,301,99,2,3,2,8.22,0,0.64 80 | 79,296,95,2,3,2,7.54,1,0.44 81 | 80,294,93,1,1.5,2,7.36,0,0.46 82 | 81,312,105,3,2,3,8.02,1,0.5 83 | 82,340,120,4,5,5,9.5,1,0.96 84 | 83,320,110,5,5,4.5,9.22,1,0.92 85 | 84,322,115,5,4,4.5,9.36,1,0.92 86 | 85,340,115,5,4.5,4.5,9.45,1,0.94 87 | 86,319,103,4,4.5,3.5,8.66,0,0.76 88 | 87,315,106,3,4.5,3.5,8.42,0,0.72 89 | 88,317,107,2,3.5,3,8.28,0,0.66 90 | 89,314,108,3,4.5,3.5,8.14,0,0.64 91 | 90,316,109,4,4.5,3.5,8.76,1,0.74 92 | 91,318,106,2,4,4,7.92,1,0.64 93 | 92,299,97,3,5,3.5,7.66,0,0.38 94 | 93,298,98,2,4,3,8.03,0,0.34 95 | 94,301,97,2,3,3,7.88,1,0.44 96 | 95,303,99,3,2,2.5,7.66,0,0.36 97 | 96,304,100,4,1.5,2.5,7.84,0,0.42 98 | 97,306,100,2,3,3,8,0,0.48 99 | 98,331,120,3,4,4,8.96,1,0.86 100 | 99,332,119,4,5,4.5,9.24,1,0.9 101 | 100,323,113,3,4,4,8.88,1,0.79 102 | 101,322,107,3,3.5,3.5,8.46,1,0.71 103 | 102,312,105,2,2.5,3,8.12,0,0.64 104 | 103,314,106,2,4,3.5,8.25,0,0.62 105 | 104,317,104,2,4.5,4,8.47,0,0.57 106 | 105,326,112,3,3.5,3,9.05,1,0.74 107 | 106,316,110,3,4,4.5,8.78,1,0.69 108 | 107,329,111,4,4.5,4.5,9.18,1,0.87 109 | 108,338,117,4,3.5,4.5,9.46,1,0.91 110 | 109,331,116,5,5,5,9.38,1,0.93 111 | 110,304,103,5,5,4,8.64,0,0.68 112 | 111,305,108,5,3,3,8.48,0,0.61 113 | 112,321,109,4,4,4,8.68,1,0.69 114 | 113,301,107,3,3.5,3.5,8.34,1,0.62 115 | 114,320,110,2,4,3.5,8.56,0,0.72 116 | 115,311,105,3,3.5,3,8.45,1,0.59 117 | 116,310,106,4,4.5,4.5,9.04,1,0.66 118 | 117,299,102,3,4,3.5,8.62,0,0.56 119 | 118,290,104,4,2,2.5,7.46,0,0.45 120 | 119,296,99,2,3,3.5,7.28,0,0.47 121 | 120,327,104,5,3,3.5,8.84,1,0.71 122 | 121,335,117,5,5,5,9.56,1,0.94 123 | 122,334,119,5,4.5,4.5,9.48,1,0.94 124 | 123,310,106,4,1.5,2.5,8.36,0,0.57 125 | 124,308,108,3,3.5,3.5,8.22,0,0.61 126 | 125,301,106,4,2.5,3,8.47,0,0.57 127 | 126,300,100,3,2,3,8.66,1,0.64 128 | 127,323,113,3,4,3,9.32,1,0.85 129 | 128,319,112,3,2.5,2,8.71,1,0.78 130 | 129,326,112,3,3.5,3,9.1,1,0.84 131 | 130,333,118,5,5,5,9.35,1,0.92 132 | 131,339,114,5,4,4.5,9.76,1,0.96 133 | 132,303,105,5,5,4.5,8.65,0,0.77 134 | 133,309,105,5,3.5,3.5,8.56,0,0.71 135 | 134,323,112,5,4,4.5,8.78,0,0.79 136 | 135,333,113,5,4,4,9.28,1,0.89 137 | 136,314,109,4,3.5,4,8.77,1,0.82 138 | 137,312,103,3,5,4,8.45,0,0.76 139 | 138,316,100,2,1.5,3,8.16,1,0.71 140 | 139,326,116,2,4.5,3,9.08,1,0.8 141 | 140,318,109,1,3.5,3.5,9.12,0,0.78 142 | 141,329,110,2,4,3,9.15,1,0.84 143 | 142,332,118,2,4.5,3.5,9.36,1,0.9 144 | 143,331,115,5,4,3.5,9.44,1,0.92 145 | 144,340,120,4,4.5,4,9.92,1,0.97 146 | 145,325,112,2,3,3.5,8.96,1,0.8 147 | 146,320,113,2,2,2.5,8.64,1,0.81 148 | 147,315,105,3,2,2.5,8.48,0,0.75 149 | 148,326,114,3,3,3,9.11,1,0.83 150 | 149,339,116,4,4,3.5,9.8,1,0.96 151 | 150,311,106,2,3.5,3,8.26,1,0.79 152 | 151,334,114,4,4,4,9.43,1,0.93 153 | 152,332,116,5,5,5,9.28,1,0.94 154 | 153,321,112,5,5,5,9.06,1,0.86 155 | 154,324,105,3,3,4,8.75,0,0.79 156 | 155,326,108,3,3,3.5,8.89,0,0.8 157 | 156,312,109,3,3,3,8.69,0,0.77 158 | 157,315,105,3,2,2.5,8.34,0,0.7 159 | 158,309,104,2,2,2.5,8.26,0,0.65 160 | 159,306,106,2,2,2.5,8.14,0,0.61 161 | 160,297,100,1,1.5,2,7.9,0,0.52 162 | 161,315,103,1,1.5,2,7.86,0,0.57 163 | 162,298,99,1,1.5,3,7.46,0,0.53 164 | 163,318,109,3,3,3,8.5,0,0.67 165 | 164,317,105,3,3.5,3,8.56,0,0.68 166 | 165,329,111,4,4.5,4,9.01,1,0.81 167 | 166,322,110,5,4.5,4,8.97,0,0.78 168 | 167,302,102,3,3.5,5,8.33,0,0.65 169 | 168,313,102,3,2,3,8.27,0,0.64 170 | 169,293,97,2,2,4,7.8,1,0.64 171 | 170,311,99,2,2.5,3,7.98,0,0.65 172 | 171,312,101,2,2.5,3.5,8.04,1,0.68 173 | 172,334,117,5,4,4.5,9.07,1,0.89 174 | 173,322,110,4,4,5,9.13,1,0.86 175 | 174,323,113,4,4,4.5,9.23,1,0.89 176 | 175,321,111,4,4,4,8.97,1,0.87 177 | 176,320,111,4,4.5,3.5,8.87,1,0.85 178 | 177,329,119,4,4.5,4.5,9.16,1,0.9 179 | 178,319,110,3,3.5,3.5,9.04,0,0.82 180 | 179,309,108,3,2.5,3,8.12,0,0.72 181 | 180,307,102,3,3,3,8.27,0,0.73 182 | 181,300,104,3,3.5,3,8.16,0,0.71 183 | 182,305,107,2,2.5,2.5,8.42,0,0.71 184 | 183,299,100,2,3,3.5,7.88,0,0.68 185 | 184,314,110,3,4,4,8.8,0,0.75 186 | 185,316,106,2,2.5,4,8.32,0,0.72 187 | 186,327,113,4,4.5,4.5,9.11,1,0.89 188 | 187,317,107,3,3.5,3,8.68,1,0.84 189 | 188,335,118,5,4.5,3.5,9.44,1,0.93 190 | 189,331,115,5,4.5,3.5,9.36,1,0.93 191 | 190,324,112,5,5,5,9.08,1,0.88 192 | 191,324,111,5,4.5,4,9.16,1,0.9 193 | 192,323,110,5,4,5,8.98,1,0.87 194 | 193,322,114,5,4.5,4,8.94,1,0.86 195 | 194,336,118,5,4.5,5,9.53,1,0.94 196 | 195,316,109,3,3.5,3,8.76,0,0.77 197 | 196,307,107,2,3,3.5,8.52,1,0.78 198 | 197,306,105,2,3,2.5,8.26,0,0.73 199 | 198,310,106,2,3.5,2.5,8.33,0,0.73 200 | 199,311,104,3,4.5,4.5,8.43,0,0.7 201 | 200,313,107,3,4,4.5,8.69,0,0.72 202 | 201,317,103,3,2.5,3,8.54,1,0.73 203 | 202,315,110,2,3.5,3,8.46,1,0.72 204 | 203,340,120,5,4.5,4.5,9.91,1,0.97 205 | 204,334,120,5,4,5,9.87,1,0.97 206 | 205,298,105,3,3.5,4,8.54,0,0.69 207 | 206,295,99,2,2.5,3,7.65,0,0.57 208 | 207,315,99,2,3.5,3,7.89,0,0.63 209 | 208,310,102,3,3.5,4,8.02,1,0.66 210 | 209,305,106,2,3,3,8.16,0,0.64 211 | 210,301,104,3,3.5,4,8.12,1,0.68 212 | 211,325,108,4,4.5,4,9.06,1,0.79 213 | 212,328,110,4,5,4,9.14,1,0.82 214 | 213,338,120,4,5,5,9.66,1,0.95 215 | 214,333,119,5,5,4.5,9.78,1,0.96 216 | 215,331,117,4,4.5,5,9.42,1,0.94 217 | 216,330,116,5,5,4.5,9.36,1,0.93 218 | 217,322,112,4,4.5,4.5,9.26,1,0.91 219 | 218,321,109,4,4,4,9.13,1,0.85 220 | 219,324,110,4,3,3.5,8.97,1,0.84 221 | 220,312,104,3,3.5,3.5,8.42,0,0.74 222 | 221,313,103,3,4,4,8.75,0,0.76 223 | 222,316,110,3,3.5,4,8.56,0,0.75 224 | 223,324,113,4,4.5,4,8.79,0,0.76 225 | 224,308,109,2,3,4,8.45,0,0.71 226 | 225,305,105,2,3,2,8.23,0,0.67 227 | 226,296,99,2,2.5,2.5,8.03,0,0.61 228 | 227,306,110,2,3.5,4,8.45,0,0.63 229 | 228,312,110,2,3.5,3,8.53,0,0.64 230 | 229,318,112,3,4,3.5,8.67,0,0.71 231 | 230,324,111,4,3,3,9.01,1,0.82 232 | 231,313,104,3,4,4.5,8.65,0,0.73 233 | 232,319,106,3,3.5,2.5,8.33,1,0.74 234 | 233,312,107,2,2.5,3.5,8.27,0,0.69 235 | 234,304,100,2,2.5,3.5,8.07,0,0.64 236 | 235,330,113,5,5,4,9.31,1,0.91 237 | 236,326,111,5,4.5,4,9.23,1,0.88 238 | 237,325,112,4,4,4.5,9.17,1,0.85 239 | 238,329,114,5,4.5,5,9.19,1,0.86 240 | 239,310,104,3,2,3.5,8.37,0,0.7 241 | 240,299,100,1,1.5,2,7.89,0,0.59 242 | 241,296,101,1,2.5,3,7.68,0,0.6 243 | 242,317,103,2,2.5,2,8.15,0,0.65 244 | 243,324,115,3,3.5,3,8.76,1,0.7 245 | 244,325,114,3,3.5,3,9.04,1,0.76 246 | 245,314,107,2,2.5,4,8.56,0,0.63 247 | 246,328,110,4,4,2.5,9.02,1,0.81 248 | 247,316,105,3,3,3.5,8.73,0,0.72 249 | 248,311,104,2,2.5,3.5,8.48,0,0.71 250 | 249,324,110,3,3.5,4,8.87,1,0.8 251 | 250,321,111,3,3.5,4,8.83,1,0.77 252 | 251,320,104,3,3,2.5,8.57,1,0.74 253 | 252,316,99,2,2.5,3,9,0,0.7 254 | 253,318,100,2,2.5,3.5,8.54,1,0.71 255 | 254,335,115,4,4.5,4.5,9.68,1,0.93 256 | 255,321,114,4,4,5,9.12,0,0.85 257 | 256,307,110,4,4,4.5,8.37,0,0.79 258 | 257,309,99,3,4,4,8.56,0,0.76 259 | 258,324,100,3,4,5,8.64,1,0.78 260 | 259,326,102,4,5,5,8.76,1,0.77 261 | 260,331,119,4,5,4.5,9.34,1,0.9 262 | 261,327,108,5,5,3.5,9.13,1,0.87 263 | 262,312,104,3,3.5,4,8.09,0,0.71 264 | 263,308,103,2,2.5,4,8.36,1,0.7 265 | 264,324,111,3,2.5,1.5,8.79,1,0.7 266 | 265,325,110,2,3,2.5,8.76,1,0.75 267 | 266,313,102,3,2.5,2.5,8.68,0,0.71 268 | 267,312,105,2,2,2.5,8.45,0,0.72 269 | 268,314,107,3,3,3.5,8.17,1,0.73 270 | 269,327,113,4,4.5,5,9.14,0,0.83 271 | 270,308,108,4,4.5,5,8.34,0,0.77 272 | 271,306,105,2,2.5,3,8.22,1,0.72 273 | 272,299,96,2,1.5,2,7.86,0,0.54 274 | 273,294,95,1,1.5,1.5,7.64,0,0.49 275 | 274,312,99,1,1,1.5,8.01,1,0.52 276 | 275,315,100,1,2,2.5,7.95,0,0.58 277 | 276,322,110,3,3.5,3,8.96,1,0.78 278 | 277,329,113,5,5,4.5,9.45,1,0.89 279 | 278,320,101,2,2.5,3,8.62,0,0.7 280 | 279,308,103,2,3,3.5,8.49,0,0.66 281 | 280,304,102,2,3,4,8.73,0,0.67 282 | 281,311,102,3,4.5,4,8.64,1,0.68 283 | 282,317,110,3,4,4.5,9.11,1,0.8 284 | 283,312,106,3,4,3.5,8.79,1,0.81 285 | 284,321,111,3,2.5,3,8.9,1,0.8 286 | 285,340,112,4,5,4.5,9.66,1,0.94 287 | 286,331,116,5,4,4,9.26,1,0.93 288 | 287,336,118,5,4.5,4,9.19,1,0.92 289 | 288,324,114,5,5,4.5,9.08,1,0.89 290 | 289,314,104,4,5,5,9.02,0,0.82 291 | 290,313,109,3,4,3.5,9,0,0.79 292 | 291,307,105,2,2.5,3,7.65,0,0.58 293 | 292,300,102,2,1.5,2,7.87,0,0.56 294 | 293,302,99,2,1,2,7.97,0,0.56 295 | 294,312,98,1,3.5,3,8.18,1,0.64 296 | 295,316,101,2,2.5,2,8.32,1,0.61 297 | 296,317,100,2,3,2.5,8.57,0,0.68 298 | 297,310,107,3,3.5,3.5,8.67,0,0.76 299 | 298,320,120,3,4,4.5,9.11,0,0.86 300 | 299,330,114,3,4.5,4.5,9.24,1,0.9 301 | 300,305,112,3,3,3.5,8.65,0,0.71 302 | 301,309,106,2,2.5,2.5,8,0,0.62 303 | 302,319,108,2,2.5,3,8.76,0,0.66 304 | 303,322,105,2,3,3,8.45,1,0.65 305 | 304,323,107,3,3.5,3.5,8.55,1,0.73 306 | 305,313,106,2,2.5,2,8.43,0,0.62 307 | 306,321,109,3,3.5,3.5,8.8,1,0.74 308 | 307,323,110,3,4,3.5,9.1,1,0.79 309 | 308,325,112,4,4,4,9,1,0.8 310 | 309,312,108,3,3.5,3,8.53,0,0.69 311 | 310,308,110,4,3.5,3,8.6,0,0.7 312 | 311,320,104,3,3,3.5,8.74,1,0.76 313 | 312,328,108,4,4.5,4,9.18,1,0.84 314 | 313,311,107,4,4.5,4.5,9,1,0.78 315 | 314,301,100,3,3.5,3,8.04,0,0.67 316 | 315,305,105,2,3,4,8.13,0,0.66 317 | 316,308,104,2,2.5,3,8.07,0,0.65 318 | 317,298,101,2,1.5,2,7.86,0,0.54 319 | 318,300,99,1,1,2.5,8.01,0,0.58 320 | 319,324,111,3,2.5,2,8.8,1,0.79 321 | 320,327,113,4,3.5,3,8.69,1,0.8 322 | 321,317,106,3,4,3.5,8.5,1,0.75 323 | 322,323,104,3,4,4,8.44,1,0.73 324 | 323,314,107,2,2.5,4,8.27,0,0.72 325 | 324,305,102,2,2,2.5,8.18,0,0.62 326 | 325,315,104,3,3,2.5,8.33,0,0.67 327 | 326,326,116,3,3.5,4,9.14,1,0.81 328 | 327,299,100,3,2,2,8.02,0,0.63 329 | 328,295,101,2,2.5,2,7.86,0,0.69 330 | 329,324,112,4,4,3.5,8.77,1,0.8 331 | 330,297,96,2,2.5,1.5,7.89,0,0.43 332 | 331,327,113,3,3.5,3,8.66,1,0.8 333 | 332,311,105,2,3,2,8.12,1,0.73 334 | 333,308,106,3,3.5,2.5,8.21,1,0.75 335 | 334,319,108,3,3,3.5,8.54,1,0.71 336 | 335,312,107,4,4.5,4,8.65,1,0.73 337 | 336,325,111,4,4,4.5,9.11,1,0.83 338 | 337,319,110,3,3,2.5,8.79,0,0.72 339 | 338,332,118,5,5,5,9.47,1,0.94 340 | 339,323,108,5,4,4,8.74,1,0.81 341 | 340,324,107,5,3.5,4,8.66,1,0.81 342 | 341,312,107,3,3,3,8.46,1,0.75 343 | 342,326,110,3,3.5,3.5,8.76,1,0.79 344 | 343,308,106,3,3,3,8.24,0,0.58 345 | 344,305,103,2,2.5,3.5,8.13,0,0.59 346 | 345,295,96,2,1.5,2,7.34,0,0.47 347 | 346,316,98,1,1.5,2,7.43,0,0.49 348 | 347,304,97,2,1.5,2,7.64,0,0.47 349 | 348,299,94,1,1,1,7.34,0,0.42 350 | 349,302,99,1,2,2,7.25,0,0.57 351 | 350,313,101,3,2.5,3,8.04,0,0.62 352 | 351,318,107,3,3,3.5,8.27,1,0.74 353 | 352,325,110,4,3.5,4,8.67,1,0.73 354 | 353,303,100,2,3,3.5,8.06,1,0.64 355 | 354,300,102,3,3.5,2.5,8.17,0,0.63 356 | 355,297,98,2,2.5,3,7.67,0,0.59 357 | 356,317,106,2,2,3.5,8.12,0,0.73 358 | 357,327,109,3,3.5,4,8.77,1,0.79 359 | 358,301,104,2,3.5,3.5,7.89,1,0.68 360 | 359,314,105,2,2.5,2,7.64,0,0.7 361 | 360,321,107,2,2,1.5,8.44,0,0.81 362 | 361,322,110,3,4,5,8.64,1,0.85 363 | 362,334,116,4,4,3.5,9.54,1,0.93 364 | 363,338,115,5,4.5,5,9.23,1,0.91 365 | 364,306,103,2,2.5,3,8.36,0,0.69 366 | 365,313,102,3,3.5,4,8.9,1,0.77 367 | 366,330,114,4,4.5,3,9.17,1,0.86 368 | 367,320,104,3,3.5,4.5,8.34,1,0.74 369 | 368,311,98,1,1,2.5,7.46,0,0.57 370 | 369,298,92,1,2,2,7.88,0,0.51 371 | 370,301,98,1,2,3,8.03,1,0.67 372 | 371,310,103,2,2.5,2.5,8.24,0,0.72 373 | 372,324,110,3,3.5,3,9.22,1,0.89 374 | 373,336,119,4,4.5,4,9.62,1,0.95 375 | 374,321,109,3,3,3,8.54,1,0.79 376 | 375,315,105,2,2,2.5,7.65,0,0.39 377 | 376,304,101,2,2,2.5,7.66,0,0.38 378 | 377,297,96,2,2.5,2,7.43,0,0.34 379 | 378,290,100,1,1.5,2,7.56,0,0.47 380 | 379,303,98,1,2,2.5,7.65,0,0.56 381 | 380,311,99,1,2.5,3,8.43,1,0.71 382 | 381,322,104,3,3.5,4,8.84,1,0.78 383 | 382,319,105,3,3,3.5,8.67,1,0.73 384 | 383,324,110,4,4.5,4,9.15,1,0.82 385 | 384,300,100,3,3,3.5,8.26,0,0.62 386 | 385,340,113,4,5,5,9.74,1,0.96 387 | 386,335,117,5,5,5,9.82,1,0.96 388 | 387,302,101,2,2.5,3.5,7.96,0,0.46 389 | 388,307,105,2,2,3.5,8.1,0,0.53 390 | 389,296,97,2,1.5,2,7.8,0,0.49 391 | 390,320,108,3,3.5,4,8.44,1,0.76 392 | 391,314,102,2,2,2.5,8.24,0,0.64 393 | 392,318,106,3,2,3,8.65,0,0.71 394 | 393,326,112,4,4,3.5,9.12,1,0.84 395 | 394,317,104,2,3,3,8.76,0,0.77 396 | 395,329,111,4,4.5,4,9.23,1,0.89 397 | 396,324,110,3,3.5,3.5,9.04,1,0.82 398 | 397,325,107,3,3,3.5,9.11,1,0.84 399 | 398,330,116,4,5,4.5,9.45,1,0.91 400 | 399,312,103,3,3.5,4,8.78,0,0.67 401 | 400,333,117,4,5,4,9.66,1,0.95 402 | 401,304,100,2,3.5,3,8.22,0,0.63 403 | 402,315,105,2,3,3,8.34,0,0.66 404 | 403,324,109,3,3.5,3,8.94,1,0.78 405 | 404,330,116,4,4,3.5,9.23,1,0.91 406 | 405,311,101,3,2,2.5,7.64,1,0.62 407 | 406,302,99,3,2.5,3,7.45,0,0.52 408 | 407,322,103,4,3,2.5,8.02,1,0.61 409 | 408,298,100,3,2.5,4,7.95,1,0.58 410 | 409,297,101,3,2,4,7.67,1,0.57 411 | 410,300,98,1,2,2.5,8.02,0,0.61 412 | 411,301,96,1,3,4,7.56,0,0.54 413 | 412,313,94,2,2.5,1.5,8.13,0,0.56 414 | 413,314,102,4,2.5,2,7.88,1,0.59 415 | 414,317,101,3,3,2,7.94,1,0.49 416 | 415,321,110,4,3.5,4,8.35,1,0.72 417 | 416,327,106,4,4,4.5,8.75,1,0.76 418 | 417,315,104,3,4,2.5,8.1,0,0.65 419 | 418,316,103,3,3.5,2,7.68,0,0.52 420 | 419,309,111,2,2.5,4,8.03,0,0.6 421 | 420,308,102,2,2,3.5,7.98,1,0.58 422 | 421,299,100,3,2,3,7.42,0,0.42 423 | 422,321,112,3,3,4.5,8.95,1,0.77 424 | 423,322,112,4,3.5,2.5,9.02,1,0.73 425 | 424,334,119,5,4.5,5,9.54,1,0.94 426 | 425,325,114,5,4,5,9.46,1,0.91 427 | 426,323,111,5,4,5,9.86,1,0.92 428 | 427,312,106,3,3,5,8.57,0,0.71 429 | 428,310,101,3,3.5,5,8.65,1,0.71 430 | 429,316,103,2,2,4.5,8.74,0,0.69 431 | 430,340,115,5,5,4.5,9.06,1,0.95 432 | 431,311,104,3,4,3.5,8.13,1,0.74 433 | 432,320,112,2,3.5,3.5,8.78,1,0.73 434 | 433,324,112,4,4.5,4,9.22,1,0.86 435 | 434,316,111,4,4,5,8.54,0,0.71 436 | 435,306,103,3,3.5,3,8.21,0,0.64 437 | 436,309,105,2,2.5,4,7.68,0,0.55 438 | 437,310,110,1,1.5,4,7.23,1,0.58 439 | 438,317,106,1,1.5,3.5,7.65,1,0.61 440 | 439,318,110,1,2.5,3.5,8.54,1,0.67 441 | 440,312,105,2,1.5,3,8.46,0,0.66 442 | 441,305,104,2,2.5,1.5,7.79,0,0.53 443 | 442,332,112,1,1.5,3,8.66,1,0.79 444 | 443,331,116,4,4.5,4.5,9.44,1,0.92 445 | 444,321,114,5,4.5,4.5,9.16,1,0.87 446 | 445,324,113,5,4,5,9.25,1,0.92 447 | 446,328,116,5,4.5,5,9.08,1,0.91 448 | 447,327,118,4,5,5,9.67,1,0.93 449 | 448,320,108,3,3.5,5,8.97,1,0.84 450 | 449,312,109,2,2.5,4,9.02,0,0.8 451 | 450,315,101,3,3.5,4.5,9.13,0,0.79 452 | 451,320,112,4,3,4.5,8.86,1,0.82 453 | 452,324,113,4,4.5,4.5,9.25,1,0.89 454 | 453,328,116,4,5,3.5,9.6,1,0.93 455 | 454,319,103,3,2.5,4,8.76,1,0.73 456 | 455,310,105,2,3,3.5,8.01,0,0.71 457 | 456,305,102,2,1.5,2.5,7.64,0,0.59 458 | 457,299,100,2,2,2,7.88,0,0.51 459 | 458,295,99,1,2,1.5,7.57,0,0.37 460 | 459,312,100,1,3,3,8.53,1,0.69 461 | 460,329,113,4,4,3.5,9.36,1,0.89 462 | 461,319,105,4,4,4.5,8.66,1,0.77 463 | 462,301,102,3,2.5,2,8.13,1,0.68 464 | 463,307,105,4,3,3,7.94,0,0.62 465 | 464,304,107,3,3.5,3,7.86,0,0.57 466 | 465,298,97,2,2,3,7.21,0,0.45 467 | 466,305,96,4,3,4.5,8.26,0,0.54 468 | 467,314,99,4,3.5,4.5,8.73,1,0.71 469 | 468,318,101,5,3.5,5,8.78,1,0.78 470 | 469,323,110,4,4,5,8.88,1,0.81 471 | 470,326,114,4,4,3.5,9.16,1,0.86 472 | 471,320,110,5,4,4,9.27,1,0.87 473 | 472,311,103,3,2,4,8.09,0,0.64 474 | 473,327,116,4,4,4.5,9.48,1,0.9 475 | 474,316,102,2,4,3.5,8.15,0,0.67 476 | 475,308,105,4,3,2.5,7.95,1,0.67 477 | 476,300,101,3,3.5,2.5,7.88,0,0.59 478 | 477,304,104,3,2.5,2,8.12,0,0.62 479 | 478,309,105,4,3.5,2,8.18,0,0.65 480 | 479,318,103,3,4,4.5,8.49,1,0.71 481 | 480,325,110,4,4.5,4,8.96,1,0.79 482 | 481,321,102,3,3.5,4,9.01,1,0.8 483 | 482,323,107,4,3,2.5,8.48,1,0.78 484 | 483,328,113,4,4,2.5,8.77,1,0.83 485 | 484,304,103,5,5,3,7.92,0,0.71 486 | 485,317,106,3,3.5,3,7.89,1,0.73 487 | 486,311,101,2,2.5,3.5,8.34,1,0.7 488 | 487,319,102,3,2.5,2.5,8.37,0,0.68 489 | 488,327,115,4,3.5,4,9.14,0,0.79 490 | 489,322,112,3,3,4,8.62,1,0.76 491 | 490,302,110,3,4,4.5,8.5,0,0.65 492 | 491,307,105,2,2.5,4.5,8.12,1,0.67 493 | 492,297,99,4,3,3.5,7.81,0,0.54 494 | 493,298,101,4,2.5,4.5,7.69,1,0.53 495 | 494,300,95,2,3,1.5,8.22,1,0.62 496 | 495,301,99,3,2.5,2,8.45,1,0.68 497 | 496,332,108,5,4.5,4,9.02,1,0.87 498 | 497,337,117,5,5,5,9.87,1,0.96 499 | 498,330,120,5,4.5,5,9.56,1,0.93 500 | 499,312,103,4,4,5,8.43,0,0.73 501 | 500,327,113,4,4.5,4.5,9.04,0,0.84 502 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Projects 2 | ![Dataset](https://img.shields.io/badge/Dataset-Kaggle-blue.svg) ![Python 3.6](https://img.shields.io/badge/Python-3.6-brightgreen.svg) ![NLTK](https://img.shields.io/badge/Library-sklearn-orange.svg) 3 | 4 | ![ML](readme-resources/machine-learning.png) 5 | 6 | ## Why this repository? 7 | • The main purpose of making this repository is to keep all my Machine Learning projects at one place, hence keeping my GitHub clean!
8 | • It looks good, isn't it? 9 | 10 | ## Overview 11 | • This repository consists of all my Machine Learning projects.
12 | • Datasets are provided in each of the folders above, and the solution to the problem statements as well. 13 | 14 | ## Algorithms used 15 | **Regression:**
16 | • _Linear Regression_
17 | • _Multiple-Linear Regression_
18 | • _Logistic Regression_
19 | • _Polynomial Regression_
20 | • _Lasso and Ridge Regression (L1 & L2 Regularization)_
21 | • _Elastic-Net Regression_ 22 | 23 | **Classification:**
24 | • _K-Nearest Neighbours_
25 | • _Support Vector Machine_
26 | • _Naive Bayes_
27 | • _Decision Tree_
28 | 29 | **Clustering:**
30 | • _K-Means_
31 | 32 | **Ensemble:**
33 | • _Random Forest_
34 | • _Adaptive Boosting (AdaBoost)_
35 | • _Extreme Gradient Boosting (XGBoost)_
36 | • _Voting (Hard/Soft)_
37 | 38 | **Do ⭐ the repository, if it helped you in anyway.** 39 | -------------------------------------------------------------------------------- /readme-resources/data-scientist-salary-banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/readme-resources/data-scientist-salary-banner.png -------------------------------------------------------------------------------- /readme-resources/machine-learning.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anujvyas/Machine-Learning-Projects/6c39c0616e01b4603f21c5072b348b0719d2e87b/readme-resources/machine-learning.png --------------------------------------------------------------------------------