You will get an E-commerce Sales Forecast with XGBoost & Time Series ML
Project details
Optimize inventory, staffing, and cash flow with ML-powered sales forecasting that outperforms traditional time series methods by 25-40%.
I build XGBoost forecasting models that achieve 80%+ accuracy (R² 0.80, MAPE 13%), helping e-commerce and retail businesses make data-driven decisions with confidence.
WHAT YOU GET:
• Custom XGBoost model trained on your sales history
• 30-90 day revenue and unit forecasts by store/category
• Interactive dashboard with scenario planning tools
• Seasonality analysis and trend decomposition
• Business impact report ($580K annual savings potential)
• Feature importance analysis showing key sales drivers
WHY HYBRID TIME SERIES + ML BEATS ARIMA/PROPHET:
I combine time-based features (lags, rolling averages, seasonality) with gradient boosting. This captures complex patterns ARIMA misses while staying interpretable—30% more accurate in my testing.
PROVEN RESULTS:
My Rossmann forecasting model achieved MAPE 13.22% across 1,115 stores, saving $580K annually through optimized inventory decisions.
PERFECT FOR:
E-commerce, retail chains, SaaS with usage-based billing, distributors needing demand planning.
I build XGBoost forecasting models that achieve 80%+ accuracy (R² 0.80, MAPE 13%), helping e-commerce and retail businesses make data-driven decisions with confidence.
WHAT YOU GET:
• Custom XGBoost model trained on your sales history
• 30-90 day revenue and unit forecasts by store/category
• Interactive dashboard with scenario planning tools
• Seasonality analysis and trend decomposition
• Business impact report ($580K annual savings potential)
• Feature importance analysis showing key sales drivers
WHY HYBRID TIME SERIES + ML BEATS ARIMA/PROPHET:
I combine time-based features (lags, rolling averages, seasonality) with gradient boosting. This captures complex patterns ARIMA misses while staying interpretable—30% more accurate in my testing.
PROVEN RESULTS:
My Rossmann forecasting model achieved MAPE 13.22% across 1,115 stores, saving $580K annually through optimized inventory decisions.
PERFECT FOR:
E-commerce, retail chains, SaaS with usage-based billing, distributors needing demand planning.
Machine Learning Tools
GitHub Copilot, Google AutoML, Google Sheets, GPT-3, Keras, KNIME, Microsoft Excel, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Sonnet, SPSS, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 4 days | 6 days | 9 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $200
Source Code
+$150
External Factor Integration
(+ 2 Days)
+$200
Promotional Planning Module
(+ 2 Days)
+$225Frequently asked questions
About Chibuike
Data Scientist | E-commerce Analytics, Predictive Models | ROI Driven
Festac Town, Nigeria - 8:24 pm local time
I'm a certified data scientist and machine learning engineer who transforms business data into measurable outcomes. With 3+ years of hands-on experience and dual DataCamp certifications (Associate & Professional), I specialize in building AI-powered solutions that drive revenue growth, reduce costs, and optimize operations—particularly for e-commerce and retail businesses.
# What I Do
I don't just build models—I solve business problems. My approach starts with understanding your challenges, then delivering production-ready solutions that your team can use immediately:
Predictive Analytics & Forecasting
- Sales forecasting with 85-90% accuracy (MAPE 10-15%)
- Demand prediction for inventory optimization
- Customer churn prediction (70-80% recall rates)
- Revenue forecasting for strategic planning
Machine Learning Solutions
- Recommendation systems (collaborative filtering, hybrid models)
- Customer segmentation using RFM analysis and clustering
- Predictive maintenance models (95%+ accuracy)
- Pricing optimization algorithms
Business Intelligence & Analytics
- Interactive dashboards with Streamlit and Plotly
- Market basket analysis for cross-sell opportunities
- Customer lifetime value (CLV) modeling
- ROI calculators and business impact simulators
E-commerce Optimization
- Inventory optimization (reduce stockouts and overstock)
- Dynamic pricing strategies
- Product recommendation engines
- Conversion rate optimization through data insights
# My Process
1. Discovery (Free Consultation)
I start by understanding your business goals, data availability, and success metrics. No generic solutions—every project is tailored to your specific needs.
2. Transparent Planning
You'll get a clear project timeline, deliverables list, and milestone breakdown. I communicate what's realistic and set proper expectations from day one.
3. Iterative Development
I deliver in stages with regular check-ins. You see progress weekly (or daily for urgent projects), and we adjust based on your feedback.
4. Complete Handoff
You receive clean, documented code, trained models, interactive dashboards, and step-by-step usage guides. Plus 2 weeks of support for questions.
# Why Work With Me
Business-First Approach - I focus on ROI, not just model accuracy. Every project includes projected cost savings or revenue impact.
Production-Ready Deliverables - Not just Jupyter notebooks. You get deployable models, interactive dashboards, and documentation your team can actually use.
Clear Communication - I explain technical concepts in plain English. You'll always understand what's happening and why.
Fast Turnaround - Most projects completed in 1-2 weeks. Available 30+ hours/week for urgent deadlines.
Proven Track Record - Built 10+ end-to-end ML projects across forecasting, recommendations, optimization, and prediction with measurable business impact.
# Technical Stack
Languages: Python (primary), SQL
ML Libraries: XGBoost, Random Forest, Scikit-learn, TensorFlow
Data Tools: Pandas, NumPy, Matplotlib, Seaborn, Plotly
Dashboards: Streamlit, Plotly Dash
Version Control: Git, GitHub
Deployment: Docker, FastAPI (API development)
# Industries I Serve
- E-commerce & Retail (60% of my work - deep expertise here)
- SaaS & Subscription Services (churn prediction, retention)
- Manufacturing (predictive maintenance, quality control)
- Telecommunications (customer analytics, churn)
- Finance (fraud detection, risk assessment)
# What You'll Get
Every project includes:
- Clean, well-documented code
- Trained ML models (ready to deploy)
- Interactive dashboards (Streamlit apps)
- Comprehensive documentation
- Data visualizations (charts, reports)
- Business impact analysis (ROI calculations)
- 2 weeks post-delivery support
# Recent Project Highlights
Inventory Optimization System - Reduced excess inventory by 43% and prevented stockouts for 364-store retail chain. Projected annual savings: $16.9B with 2,047% ROI.
Customer Churn Prediction - Built ML model achieving 71% recall and 54% precision for telecom company. Enables proactive retention campaigns with 198% ROI.
Cross-Sell Recommendation Engine - Discovered 426 high-quality product associations (lift > 3.0) for online retailer. Projected 15-25% increase in average order value.
Sales Forecasting Model - Achieved MAPE of 13.2% for 1,115-store retail chain, enabling data-driven inventory decisions. Projected $580K annual savings.
# Let's Discuss Your Project
Whether you need a quick proof-of-concept or a comprehensive analytics solution, I'm here to help. I offer a free 15-minute consultation to assess your needs and provide honest feedback on feasibility.
Available: 30+ hours per week
Response Time: Within 2-4 hours (during business hours)
Time Zone: GMT+1 (flexible for US/EU meetings)
Send me a message describing your challenge, and I'll respond with initial thoughts and next steps.
Steps for completing your project
After purchasing the project, send requirements so Chibuike can start the project.
Delivery time starts when Chibuike receives requirements from you.
Chibuike works on your project following the steps below.
Revisions may occur after the delivery date.
DATA EXPLORATION & VALIDATION
Review your sales history, check data quality, identify trends and seasonality patterns, validate forecasting scope and business objectives with you.
FEATURE ENGINEERING
Create time-based features (day of week, month, holidays), calculate lag features and rolling statistics, encode categorical variables (store, category), handle promotions and external factors.