Hashwanth Gogineni

Data Science Skills

Artificial Intelligence	Statistical Modeling	Text Analytics
Big Data	Digital Analytics	Visualization
Python	R	Tableau

Domain Skills

Retail

Baseball Sports Analytics

Category: Domain Usecases

Sub-Category: Sports

Use-case Type: Structured Data Predictions

Tags: algorithm,artificial intelligence,ball,baseball,baseball sports analytics,bases,bat,batting,boosting,bowling,catboost,entertainment,fans,fielding,game,gradient boosting,ground,machine learning,play,players,prediction,Running,runs,sport,sports,structured data,supervised learning,teammates,Teams

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Note: Certain Model Inferences can take long time for the first run (Warmup) and would get faster with the subsequent inferences. We thank you for your patience.

Use Case Summary

Baseball is a bat-and-ball game played between two opposing teams, typically of nine players each, that take turns batting and fielding.
The project aims to predict total runs scored by baseball players.
The dataset utilised here was collected from the Lahman Baseball Database and contains 4535 rows of data for a select sample of players from 1960 to 2004.
'r2_score' has been used to check the model's performance.