I have a clean but still-unexplored tabular dataset and I want to build a full regression workflow in Python, moving from raw data right through to a well-documented predictive model suite. Scope • Data preparation – handle missing values, treat obvious outliers or wrong entries, normalise numeric features and one-hot-encode categoricals. • Exploratory data analysis – visual and statistical insight that will guide feature engineering choices. • Feature engineering – create any additional variables that improve signal. • Model development – train and compare Linear, Polynomial, Ridge, Lasso and ElasticNet regression models (scikit-learn preferred). Feel free to suggest hyper-parameter tuning techniques such as GridSearchCV if it tightens performance. • Evaluation – report standard metrics (R², MAE, RMSE) on a withheld test set and include diagnostic plots. • Documentation – keep the code in clear, reproducible notebooks or scripts with inline comments and a short write-up explaining findings, model selection rationale and next steps. Deliverables 1. Fully-annotated Python code / Jupyter notebook. 2. A concise EDA report with visuals. 3. Comparative model performance table and plots. 4. Recommendations based on the results. I already have the data ready to share. If you thrive on pandas, NumPy, scikit-learn, seaborn or matplotlib and enjoy polishing regression models, I’d love to collaborate and see how far we can push predictive accuracy.