Description

This exercise explores the provided dataset, performs data cleaning and exploratory data analysis and produces visual insights to support conclusions.

Scope

• Data preprocessing
• Exploratory Data Analysis (EDA)
• Basic modeling
• Visual storytelling with seaborn and matplotlib, as well as Power BI

Analysis

Approach

Data cleaning, feature engineering, statistical summaries, visual exploration and storytelling.

Tools

Analysis and code were implemented in Python (Jupyter Notebook).

Visualizations

• Notebook plots: Charts and figures generated inline in the notebook.
• Power BI: Additional interactive dashboards and visualizations were created in Power BI to explore and present the results.

Requirements

• Python: 3.13+ (recommended)
• Python libraries: pandas, numpy, matplotlib, seaborn
• Power BI: Power BI Desktop (for opening/viewing .pbix dashboard files)

How to Run

1. Open and run the sprint8_1_lb.ipynb notebook in Jupyter or VS Code.
2. To view interactive dashboards, open the provided Power BI .pbix file in Power BI Desktop.

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