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college_guide

A college guide website for freshers

covid-analysis

gcp_datawarehouse_management

powerbi_adventureworks

quality_movie_data_analysis

sales_data_projection

sleek--themes

student-database

A website with the facility of accessing a student database to perform all basic functionality of adding new student, deleting a record, updating a record, etc.

team_strategizing_using_machinelearning

Team strategizing is an important aspect which requires critical analysis to ensure a desirable near-optimum performance. The key to solve this issue is by tapping the available talent within the team which at times, can be elusive. With increasing competition, a talented team, with an ineffective and outdated scouting strategy, may have to face unfavourable results. In this paper, we have conducted research in the domain of Sports, specifically Soccer. Strategy considered in the research is centred around deciding the line-up of a team by assessing the skillset of the players. A total of 7 machine learning models namely Logistic Regression, KNN, SVM, Naïve Bayes, Decision Tree, Random Forest and ANN have been used to make predict the position a particular player will perform best at. The accuracy of the said models has been analysed for comparative analysis.