Time-Series Regression Model Development
Budget: ₹12,500 – ₹37,500 INR
I need a hands-on data scientist to turn a set of time-stamped records into a working regression model that can reliably forecast future values. The project’s primary objective is to develop a predictive model—specifically, a regression solution built on time series data.
Here is what I will provide:
• A cleaned, well-structured dataset (CSV) that includes the historical target variable and supporting features.
• A short brief on the business context and the key performance metric I care about.
What I expect from you:
• Exploratory analysis that highlights trends, seasonality, outliers, and any useful lags or engineered features.
• A robust regression model suited to time-series behaviour (ARIMA, Prophet, XGBoost, LSTM, or similar—feel free to justify the best choice).
• Code written in Python using familiar libraries such as Pandas, NumPy, Scikit-learn (plus statsmodels, TensorFlow, or PyTorch if needed).
• Clear evaluation with train/validation splits, back-testing, and accuracy metrics (MAPE, RMSE, or another metric we agree on).
• Concise visualisations—matplotlib / seaborn for the notebook and, if helpful, a lightweight Power BI or Jupyter dashboard so stakeholders see how the predictions track actuals.
• A brief report or notebook markdown explaining assumptions, feature engineering steps, model interpretation, and how the results translate into actionable insights.
Deliverables will be:
1. The complete, reproducible Python code (notebook or .py).
2. Saved model artefacts ready for deployment.
3. Visual output illustrating forecast versus actual performance.
4. A short hand-off document so the business team can rerun or extend the work.
If this scope matches your expertise in Python, SQL, machine learning, statistics, and data visualisation, let’s get started—I’m ready to move quickly once we align on approach and timeline.
Here is what I will provide:
• A cleaned, well-structured dataset (CSV) that includes the historical target variable and supporting features.
• A short brief on the business context and the key performance metric I care about.
What I expect from you:
• Exploratory analysis that highlights trends, seasonality, outliers, and any useful lags or engineered features.
• A robust regression model suited to time-series behaviour (ARIMA, Prophet, XGBoost, LSTM, or similar—feel free to justify the best choice).
• Code written in Python using familiar libraries such as Pandas, NumPy, Scikit-learn (plus statsmodels, TensorFlow, or PyTorch if needed).
• Clear evaluation with train/validation splits, back-testing, and accuracy metrics (MAPE, RMSE, or another metric we agree on).
• Concise visualisations—matplotlib / seaborn for the notebook and, if helpful, a lightweight Power BI or Jupyter dashboard so stakeholders see how the predictions track actuals.
• A brief report or notebook markdown explaining assumptions, feature engineering steps, model interpretation, and how the results translate into actionable insights.
Deliverables will be:
1. The complete, reproducible Python code (notebook or .py).
2. Saved model artefacts ready for deployment.
3. Visual output illustrating forecast versus actual performance.
4. A short hand-off document so the business team can rerun or extend the work.
If this scope matches your expertise in Python, SQL, machine learning, statistics, and data visualisation, let’s get started—I’m ready to move quickly once we align on approach and timeline.