Cricket Team Selection using Distant envelop algorithm (DEA) in python

 

Cricket Team Selection using Distant envelop algorithm (DEA)

 

 PROJECT ID: PYTHON07

 

PROJECT NAME: Cricket Team Selection using Distant envelop algorithm (DEA)

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

This paper suggests a new method for cricket team selection using data envelopment analysis (DEA). We propose a DEA formulation for evaluation of cricket players in different capabilities using multiple outputs. This evaluation determines efficient and inefficient cricket players and ranks them on the basis of DEA scores. The ranking can be used to choose the required number of players for a cricket team in each cricketing capability. A real dataset, Indian Premier League 4 (IPL 2011), cricket players having various capabilities is used to choose the best cricket team. The proposed method has the advantage of considering multiple factors related to the performance of players in multiple capabilities collected from IPL 4 and aggregates their scores using a linear programming DEA model. This DEA Aggregation gives the scores of players objectively instead of using subjective computations. The proposed DEA method can be used to form a national cricket team from several clubs or a team of top cricketers.

Data Set Description

21) Winning_Team: This is the class attribute i.e. the winning team

1. We have listed 15 players in playerid.csv file

2. We are collecting one year average value of batsman, bowler, all rounder

3. We are pre-processing data and storeing the corresponding csv files

4. DEA algorithm is used

input: name, X values (features), Y value (Average)

output: theta for player

5. According to the output theta value, we are getting the 11 players selected

 

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

CONCLUSION

The model which is used to predict the results of the matches was built successfully with an accuracy rate of about 60% to 70%. The list of attributes was cut down to 10 important ones out of the 21 attributes available in the data set by using the attribute selection algorithms. The 4 data mining algorithms that were performed on the model were J48, Random Forest, Naïve Bayes and KNN. The prediction results were better when K-Fold Cross Validation method was used as compared to the Percentage Split. The accuracy of the Random Forest algorithm was the best with 71.08%. Although the accuracy was between 60% and 70% but it was still low because of the fact that the total number of instances in the data set was 574 and the total number of classes were 11. We need at least 100 instances per class to identify the patterns in the data set and perform a prediction with a high accuracy rate. So, with 11 classes in the data set we needed at least 1100 instances to perform a prediction with a high accuracy rate. Since, the data set consisted of 574 instances; in future it may improve the accuracy with more number of instances in the data set because with a larger number of instances the model will have the flexibility to deduce better rules and identify more patterns in the data set as compared to with a lesser number of instances.

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

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