Data Analysis Assignment Completion

I’m working on a programming-focused college assignment that sits squarely in the data analysis / machine-learning space. The core requirement is to take a supplied dataset, clean it thoroughly, run the appropriate exploratory and statistical analyses, and extract clear, defensible insights that I can present in class. Here’s the flow I have in mind: • Receive the raw dataset from me (CSV or Excel). • Use Python—pandas, NumPy, and, where helpful, scikit-learn or SciPy—to handle missing values, correct data types, remove outliers, and generally prepare the data for analysis. • Perform exploratory data analysis: descriptive statistics, correlations, and any hypothesis tests that reveal meaningful patterns. • Summarise findings in both code comments and a concise written summary (Markdown or Word) that I can adapt for my final report. Plots created in Matplotlib or Seaborn are welcome to illustrate key points. Acceptance criteria – Clean, well-commented code notebook (Jupyter or .py script) that runs without error on my machine. – A short, clear insight report highlighting the most significant trends and what they imply. – Visuals that directly support the written conclusions. If you’re comfortable with reproducible, well-documented analysis and can turn this around on a student-friendly timeline, let’s get started.