Applied statistical inference project analyzing mileage differences between manual and automatic vehicles using data preprocessing, EDA, Shapiro–Wilk test, Levene’s test, independent t-test (Welch), Mann–Whitney U test, Cohen’s d, and 95% CIs in Python (Pandas, NumPy, SciPy, Matplotlib, Seaborn).

This repository demonstrates a complete data science workflow—from identifying data anomalies to deploying a validated predictive model. The core of the analysis centers on how Marketing Spend and Promotional Strategies interact to drive Daily Revenue.

Beginner-level data analysis on birthweight and maternal smoking using Python

Proces budowy modelu regresji liniowej na podstawie danych ekonomicznych