Predictive Machine Learning Model Development
Budget: ₹600 – ₹1,500 INR
The project centers on full-cycle machine learning model development with a clear focus on predictive analytics. I will provide the raw data and business context; your role is to translate that information into a production-ready model that can accurately forecast the target variable and deliver actionable insights.
Scope of work
• Clean and prepare the dataset I share, selecting the most relevant features for prediction.
• Experiment with appropriate supervised algorithms—feel free to leverage Python, scikit-learn, XGBoost, LightGBM, TensorFlow or similar libraries, choosing whichever brings the best balance of accuracy and interpretability.
• Validate performance using sound cross-validation techniques and present key metrics (MAE, RMSE, or another measure that matches the target type).
• Package the final model along with well-commented code and a concise technical report outlining data prep steps, feature engineering, algorithm selection rationale, and reproducible results.
Deliverables
– Fully trained, tested predictive model file
– Source code / notebooks with comments
– Brief report (PDF or Markdown) covering methodology, evaluation metrics, and instructions for re-training
Acceptance criteria
A minimum 10 % uplift over a naïve baseline, clear documentation, and the ability for me to run the provided code end-to-end on my machine without modification.
Communication can happen through GitHub, Google Colab, or another shared environment—whichever lets us iterate quickly and keep version control tidy.
Scope of work
• Clean and prepare the dataset I share, selecting the most relevant features for prediction.
• Experiment with appropriate supervised algorithms—feel free to leverage Python, scikit-learn, XGBoost, LightGBM, TensorFlow or similar libraries, choosing whichever brings the best balance of accuracy and interpretability.
• Validate performance using sound cross-validation techniques and present key metrics (MAE, RMSE, or another measure that matches the target type).
• Package the final model along with well-commented code and a concise technical report outlining data prep steps, feature engineering, algorithm selection rationale, and reproducible results.
Deliverables
– Fully trained, tested predictive model file
– Source code / notebooks with comments
– Brief report (PDF or Markdown) covering methodology, evaluation metrics, and instructions for re-training
Acceptance criteria
A minimum 10 % uplift over a naïve baseline, clear documentation, and the ability for me to run the provided code end-to-end on my machine without modification.
Communication can happen through GitHub, Google Colab, or another shared environment—whichever lets us iterate quickly and keep version control tidy.