Python Data Science Prediction Pipeline

I’m looking for a complete, end-to-end data science workflow that starts with pulling structured data from APIs and ends with a predictive model I can trust and iterate on. You’ll write clean Python code (Pandas, NumPy, Scikit-learn) that ingests the data, handles missing or inconsistent values, performs robust preprocessing, and walks through exploratory data analysis with clear, insightful visualizations using Matplotlib or Seaborn.

Once the data foundation is solid, build and evaluate several machine-learning models focused on prediction, document why each algorithm was chosen, and compare their performance with the usual metrics. I value transparency, so every notebook or script must be well commented, and a short, readable methodology report should explain your decisions, highlight key insights, and suggest where the model—and the underlying data collection—could be improved.

Deliverables
• Python code/notebooks, fully commented
• Cleaned and feature-engineered dataset (saved locally)
• Visualizations and EDA narrative
• Model files plus evaluation summary
• PDF or Markdown report with insights and improvement ideas

If this sounds straightforward to you and you can communicate findings clearly, let’s get started.