You will get An End-to-End Sales Forecasting & Revenue Optimization Platform
Project details
I build ML pipelines to predict sales and optimize revenue from your historical data. Using XGBoost, Random Forest, and Linear Regression, I identify the best model (R² > 0.60) and explain key drivers via SHAP analysis.
DELIVERABLES:
• Custom sales forecasting from transaction/store data
• 3-model comparison with time-based train/test split
• SHAP explainability revealing promotion, seasonality, and competition impact
• 8 visualizations (trends, patterns, model performance)
• Interactive Streamlit dashboard for scenario exploration
• Revenue optimization insights (promotion ROI, peak periods)
IDEAL FOR: Retail chains, e-commerce platforms, FMCG companies, restaurants, any business with historical sales data.
REQUIREMENTS: Your sales data (CSV/Excel with dates, store IDs, products, promotions, revenue) or transaction logs.
TECH: Python | XGBoost | SHAP | Streamlit | Plotly | scikit-learn | Pandas
See my Rossmann portfolio: 1M+ transactions, 1,115 stores, 62% variance explained (R²=0.62), €1,866 RMSE.
DELIVERABLES:
• Custom sales forecasting from transaction/store data
• 3-model comparison with time-based train/test split
• SHAP explainability revealing promotion, seasonality, and competition impact
• 8 visualizations (trends, patterns, model performance)
• Interactive Streamlit dashboard for scenario exploration
• Revenue optimization insights (promotion ROI, peak periods)
IDEAL FOR: Retail chains, e-commerce platforms, FMCG companies, restaurants, any business with historical sales data.
REQUIREMENTS: Your sales data (CSV/Excel with dates, store IDs, products, promotions, revenue) or transaction logs.
TECH: Python | XGBoost | SHAP | Streamlit | Plotly | scikit-learn | Pandas
See my Rossmann portfolio: 1M+ transactions, 1,115 stores, 62% variance explained (R²=0.62), €1,866 RMSE.
Machine Learning Tools
MLflow, Python, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$500
|
Standard
$600
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 3 |
Number of Scenarios | 1 | 2 | 4 |
Number of Graphs/Charts | 3 | 8 | 12 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$250 - $700
Additional Revision
+$200
Additional Model Variation
(+ 1 Day)
+$300
Additional Scenario
+$125
Additional Graph/Chart
+$100
Revenue Optimization Algorithm
+$500
Live Training Session
+$350
3-Month Support Retainer
+$900About Ebingiye Nelvin
Data Scientist | Python | PySpark | ML | Streamlit Dashboard
Port Harcourt, Nigeria - 3:32 pm local time
I help businesses, organizations, and teams turn complex data into clear, actionable insights especially for geospatial, environmental, and data-intensive projects.
Whether you're working with business data, climate datasets, or large-scale structured data, I can help you analyze it, model it, and present it in a way that drives better decisions.
🔹 What I can help you with:
Data cleaning, analysis, and visualization (Python, Pandas, SQL)
Machine learning models (classification, regression)
Large-scale data processing using PySpark
Geospatial and spatial analysis (GIS, remote sensing)
Interactive dashboards (Streamlit, Plotly)
Automation and data pipelines
🔹 Proven Work:
✔ Renewable Energy Site Selection System
Built a machine learning model with 91.25% accuracy to identify optimal solar and wind farm locations.
Developed a dashboard for real-time analysis across 400+ sites.
✔ Urban Heat Island Detection System (PySpark + Dashboard)
Built a scalable pipeline to analyze temperature and environmental data.
Delivered an interactive dashboard to identify heat hotspots and support planning decisions.
🔹 What you’ll get:
✔ Clear, decision-ready insights
✔ Interactive dashboards
✔ Clean, reliable code
✔ Fast communication and delivery
I don’t just analyze data, I help you understand it and use it to make better decisions.
Steps for completing your project
After purchasing the project, send requirements so Ebingiye Nelvin can start the project.
Delivery time starts when Ebingiye Nelvin receives requirements from you.
Ebingiye Nelvin works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset