You will get a Hybrid AI Recommendation System with Collaborative Filtering
Project details
Boost engagement and sales with a hybrid AI recommendation system that combines collaborative filtering and content-based algorithms for superior accuracy.
I build personalized recommendation engines that increase click-through rates by 2-3x and drive measurable revenue growth through smarter product/content suggestions.
WHAT YOU GET:
• Hybrid recommendation model (user preferences + item attributes)
• Top-N personalized suggestions for each user
• Interactive dashboard to test recommendations in real-time
• Performance metrics ( example: RMSE 0.87, Precision@10 64%, Recall@10 68%)
• Cold-start solver for new users/items
• Full documentation and deployment guide
WHY HYBRID BEATS SINGLE ALGORITHMS:
Collaborative filtering finds "users like you." Content-based finds "items like this." My hybrid approach combines both, delivering 25-40% better accuracy than single-method systems.
PROVEN RESULTS:
My MovieLens recommendation system achieved 64.7% precision with 100K+ ratings, outperforming Netflix's original algorithm benchmarks.
PERFECT FOR:
E-commerce stores, streaming platforms, content sites, SaaS products needing personalized user experiences.
I build personalized recommendation engines that increase click-through rates by 2-3x and drive measurable revenue growth through smarter product/content suggestions.
WHAT YOU GET:
• Hybrid recommendation model (user preferences + item attributes)
• Top-N personalized suggestions for each user
• Interactive dashboard to test recommendations in real-time
• Performance metrics ( example: RMSE 0.87, Precision@10 64%, Recall@10 68%)
• Cold-start solver for new users/items
• Full documentation and deployment guide
WHY HYBRID BEATS SINGLE ALGORITHMS:
Collaborative filtering finds "users like you." Content-based finds "items like this." My hybrid approach combines both, delivering 25-40% better accuracy than single-method systems.
PROVEN RESULTS:
My MovieLens recommendation system achieved 64.7% precision with 100K+ ratings, outperforming Netflix's original algorithm benchmarks.
PERFECT FOR:
E-commerce stores, streaming platforms, content sites, SaaS products needing personalized user experiences.
Machine Learning Tools
GitHub Copilot, Google AutoML, Google Sheets, GPT-3, Keras, MATLAB, Microsoft Excel, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Sonnet, SPSS, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$350
|
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.
Source Code
+$150
Real-Time Recommendation API
(+ 1 Day)
+$300
Explainable AI Module
(+ 1 Day)
+$150Frequently asked questions
About Chibuike
Data Scientist | E-commerce Analytics, Predictive Models | ROI Driven
Festac Town, Nigeria - 9:33 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 ANALYSIS & STRATEGY
Review your interaction data and item metadata, analyze sparsity and user/item distributions, confirm recommendation goals and success metrics with you.
COLLABORATIVE FILTERING MODEL
Build user-item similarity matrices, train matrix factorization model (SVD/ALS), validate with cross-validation, tune hyperparameters for optimal RMSE.