This project focuses on predicting the market values of FIFA soccer players based on their in-game attributes. The dataset used is sourced from FIFA 2019, containing information such as overall rating, finishing, short passing, dribbling, ball control, vision, position, and value. The primary objective is to develop a statistical model that accurately predicts player values.
This project focuses on cleaning and standardizing the Nashville Housing dataset using SQL queries. The main tasks involve standardizing date formats, populating missing property address data, breaking down address columns into individual components, changing categorical values, and removing duplicates and unused columns.
Tableau Dashboards Showing relationship between two quantitative variables using SLR, and more.
This project involves the exploration of Covid-19 data using SQL queries, incorporating various SQL skills such as joins, common table expressions (CTEs), temporary tables, window functions, aggregate functions, and creating views.
This project involves exploring and analyzing movie data using Python with libraries such as NumPy, Pandas, Seaborn, and Matplotlib. The analysis includes data cleaning, visualization, and correlation assessments between various movie features.