Repository Description

This repository contains a comprehensive data analysis project focused on NBA player datasets. The project demonstrates the entire workflow from data cleaning to in-depth analysis, with insights drawn from various visualizations and statistical methods. Key steps include:

Data Cleaning: Addressing missing values, correcting data inconsistencies, and preparing the dataset for further analysis.

Exploratory Data Analysis (EDA): Performing an in-depth analysis of the NBA player dataset, including the distribution of key variables, identifying patterns, and uncovering insights about player performance.

Visualizations: Utilizing graphical representations to showcase distributions, correlations, and trends across different variables.

Conclusions: Drawing insights based on the analysis to understand player performance, team dynamics, and other relevant metrics.

Additionally, a slide-show presentation is included in this repository, summarizing the entire workflow, analysis, and key takeaways in a concise format.

git clone https://github.com/semyonsw/Data-Analytics-Project.git

pip install -r requirements.txt