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Data Engineering and Automation
AI Agents | LLMs, Computer Vision & Full-Stack Dev
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Software Engineer
Data Engineer | Python, PostgreSQL, Docker & REST APIs
I just engineered and deployed a live Online Retail Executive Dashboard analyzing over $10.6M+ in transaction data.
Role: Data Analyst / BI Engineer
Tools Used: Python, Pandas, NumPy, MySQL, SQLAlchemy, PyMySQL, Streamlit, Plotly, Jupyter Notebook
Project Overview:
Engineered and deployed an interactive executive analytics application designed to process and visualize over $10.6M+ in raw online retail transaction data across 19k+ customer orders. The system automates key performance metric tracking, sales seasonality analysis, peak shopping hour detection, and customer lifetime value (LTV) segmentation.
PNG+ 1
Key Architecture & Execution Steps:
Database Architecture & Management: Structured relational database schemas and executed SQL queries via MySQL, SQLAlchemy, and PyMySQL to handle high-volume retail transactional records efficiently.
Data Pipeline & Transformation: Processed, cleaned, and aggregated raw order histories using Pandas and NumPy, handling missing values and calculating key financial KPIs.
Interactive BI Dashboard (Streamlit & Plotly): Built a responsive web dashboard featuring dynamic Plotly visualizations for monthly seasonality, hourly trend analyses, and top revenue-generating products.
Customer Segmentation & LTV Analysis: Isolated top VIP buyers driving significant revenue chunks to assist marketing and management in customer retention strategy.
Business Impact & Key Insights:
• Revenue Aggregation: Automated real-time tracking across $10.67M total revenue and 19k+ orders.
• Operational Efficiency: Identified peak shopping hours (12:00 PM – 2:00 PM) to inform targeted promotional schedules and server load planning.
• VIP Customer Targeting: Extracted lifetime value metrics to highlight high-value accounts responsible for major revenue share.
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An automated data engineering pipeline and interactive Streamlit dashboard for analyzing laptop market prices and specifications.
Role: Data Analyst / Python Web Developer
Tools Used: Python, Streamlit, Pandas, Data Cleaning, Data Normalization
Project Overview
Transformed a chaotic, uncleaned dataset of laptop technical specifications and market prices into an optimized, interactive analytics tool with automated data quality workflows.
Key Architecture & Execution Steps
1. Data Ingestion & Cleaning: Built automated Pandas routines to parse dirty text, handle structural anomalies, normalize formatting, and resolve missing values.
2. Pipeline Automation: Packaged the cleaning architecture into modular, reusable Python functions.
3. Web Interface & Analytics: Developed a responsive Streamlit UI with dynamic sidebar filters, real-time KPI metrics, and OS price benchmarking charts.
4. Performance & Cloud Deployment: Implemented @st.cache_data for ultra-fast queries and deployed the live app via Streamlit Cloud from GitHub.
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Automated Multi-Currency Data Cleaning & Normalization Web Application
Role: Data Analyst / Python Web Developer
Tools Used: Python, Streamlit, Pandas, Data Cleaning, Data Normalization
Project Overview:
Developed an interactive Python web application using Streamlit that automates the extraction, cleaning, and normalization of raw multi-currency transaction datasets.
Key Architecture & Execution Steps:
Raw Data Ingestion: Uploaded unformatted raw transaction records containing mixed currency symbols (e.g., ₦, ,, (.00)) and dates.
Interactive UI Configuration: Designed custom user dropdowns enabling users to select target currency and date columns for automated processing.
Data Sanitization Engine: Processed dirty strings into clean numeric float values using Pandas regex transformation pipelines (raw_total_clean).
Standardization & Export: Outputted standardized ISO-formatted sanitized data tables with immediate downloadable CSV capabilities.
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Live Cryptocurrency REST API & Containerized PostgreSQL Ingestion Pipeline
Role: Data Engineer / Backend Developer
Tools Used: Python, PostgreSQL, Docker, Pandas, SQLAlchemy, REST APIs
Project Overview:
Designed and deployed a production-ready Python data pipeline that ingests live market data from the CoinGecko REST API and loads structured records into a containerized PostgreSQL database.
Key Architecture & Execution Steps:
1. Extraction: Fetched real-time price and volume data using Python's requests library.
2. Transformation: Cleaned and formatted raw JSON payloads into structured Pandas DataFrames, adding automated ingestion timestamps.
3. Containerized Database Setup: Configured a local PostgreSQL database running inside a Docker container via Docker Compose.
4. Modern Database Connection: Connected Python directly to PostgreSQL using SQLAlchemy and the modern psycopg (v3) driver.
5. Security: Managed secrets securely using environment variables (.env).
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Connecting code with intelligence
Data Scientist, Python, ML Engineer & AI Developer
Full-Stack Data Specialist | Automation & Predictive
Full-Stack Data Specialist | Automation & Predictive
AI Student Success Intelligence Platform
A twelve‑module analytics platform analyzed 50,000 learners across six countries to predict dropout, model engagement, and simulate interventions, finding that a composite Student Engagement Index (SEI)—built from Time Commitment, Academic Quality, Platform Activity, and Social Learning—is the strongest predictor of dropout (behavior beats demographics), an ensemble of XGBoost/LightGBM/CatBoost achieved 99.72% AUC and F1 = 0.9522, risk tiers were highly precise (Low Risk = 0.0% dropout; Critical Risk = 99.7%), multi‑dimensional “Full Interventions” produced the largest simulated risk reductions, and correcting a data‑leakage issue (attendance proxy) was essential to preserve model integrity.
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Crypto Market Intelligence & Alpha Signal Engine
An end-to-end Colab pipeline ingests market feeds, engineers 43 signals, detects regimes (KMeans/HMM/GMM), models 24‑hour alpha with a Random Forest (ROC‑AUC 0.7714), flags anomalies (Isolation Forest/Autoencoders), and backtests strategies; key findings: the SELL signal is highly precise (only 5.14% of SELLs rose next day), anomalies are often bullish (36.87% up vs 25.49% normal), price‑level context and regime probabilities drive predictions, and the model favors low‑volatility, defensive assets during downturns.
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Global Retail Intelligence System: Product Success Prediction and Strategic Market Analysis
A multi-stage ML pipeline analyzed 44,888 Adidas SKUs using XGBoost and Random Forest to predict product success, demand trajectories, and stockout risk, finding that subcategory is the dominant success driver (~6× more explanatory than price, discount, or geography), the Success Classifier reached 94.3% accuracy and the Stockout Risk model 0.99 ROC‑AUC, 42.5% of products carry markdowns with deep discounts (≥30%) often eroding margins, 323 high-performing SKUs are under‑distributed and present near‑term expansion opportunities, the Budget tier outperforms Premium/Luxury in conversion to high performers, and 653 SKUs were flagged as high demand with elevated stockout risk requiring urgent replenishment.
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AI-Driven Global Smartphone Sales Strategy Optimizer
An end-to-end ML project used four years of global sales data and 132,000+ simulations to optimize pricing across 52 countries, identifying the exact product, channel, and price to maximize profit. Key findings: the “Discount Myth”—discounting has almost no effect on volume but erodes margins; switching from blanket 20% discounts to AI‑optimized pricing yields a 15.1% revenue gain (about $73,993 preserved per simulation). The B2B channel is optimal in 90% of markets. The production XGBoost model achieves 99.73% accuracy, and ultra‑premium products (notably the Samsung Neo QLED 8K) consistently generate the highest revenue.
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Power BI Expert | Turning Raw Data into Actionable Insights
Power BI Expert | Turning Raw Data into Actionable Insights
Data Analytics Project | BlinkIT Grocery Sales Analysis
Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈
📊 What I worked on:
Analyzed sales performance across product categories, outlet types, sizes, and locations
Identified top-performing item categories and customer preferences
Studied the impact of outlet size, establishment year, and visibility on sales
Converted raw data into actionable insights and business recommendations
💡 Key Insights:
Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales
FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz
)
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I’m excited to share my latest Data Analytics project where I analyzed Customer Shopping Behavior to uncover trends in sales, demographics, and purchasing habits.This project was a great exercise in building a complete data pipeline.
The Tech Stack:
Python (Pandas & NumPy): Used in Jupyter Notebooks for initial data exploration and statistical analysis.
PostgreSQL: Utilized for rigorous data cleaning, querying, and structuring the dataset for analysis.
Power BI: Built an interactive dashboard to visualize key metrics like seasonal trends, subscription impacts, and category performance.
Project Highlights:
✅ Data Cleaning: Leveraged PostgreSQL and Pandas to handle missing values and standardize categorical data.
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Venezuela’s Oil Reserves Analysis Dashboard 📊
I am excited to share my latest data analytics project focusing on the energy sector. Using a dataset covering 23 reservoirs, I designed a comprehensive dashboard to track production capacity and resource distribution in Venezuela.
Key Features: ✅ Real-time Metrics: Tracking the 390.50 Billion total barrels in reserve. ✅ Granular Analysis: Production capacity breakdown by reservoir name and oil grade (Extra Heavy to Light). ✅ Stakeholder Mapping: Visualizing the production sum by major operators.
Tools Used: [pandas(jupyter notebook)/mysql(for analysis and cleaning of data), Power BI / Excel(for building dashboard)]
I’d love to hear your thoughts or feedback on the dashboard design!
must visit my github for full project -( https://lnkd.in/dYKdZ8J2
)
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Data Analytics Project | BlinkIT Grocery Sales Analysis
Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈
📊 What I worked on:
Analyzed sales performance across product categories, outlet types, sizes, and locations
Identified top-performing item categories and customer preferences
Studied the impact of outlet size, establishment year, and visibility on sales
Converted raw data into actionable insights and business recommendations
💡 Key Insights:
Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales
Medium-sized outlets outperform others in overall revenue
FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz
)
Feedback and suggestions are always welcome!
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