Everything I've built, in detail.

A data analytics project that analyzes FoodExpress delivery operations to uncover insights on customer satisfaction, delivery performance, and business growth.

Cleaned and analyzed delivery, customer, and order data using Python (Pandas, NumPy), statistical analysis, and EDA, with visualizations created using Matplotlib and Seaborn.

• Analyzed delivery performance, revenue, customer behavior, and promotions.
• Identified delivery time as the strongest driver of customer satisfaction.
• Compared cuisine, city, and VIP customer performance.
• Produced a business intelligence report with actionable insights.

A data analytics project that examines relationships between vehicle specifications and fuel efficiency to uncover performance trade-offs, helping manufacturers and businesses make data-driven product, pricing, and customer targeting decisions.

The project cleans vehicle data, performs exploratory and correlation analysis, and visualizes relationships between MPG, horsepower, weight, cylinders, displacement, and transmission to identify the factors affecting fuel efficiency.

• Conducted data cleaning, statistical analysis, and visualizations to uncover key factors affecting fuel efficiency.
• Identified the trade-off between horsepower, vehicle weight, and MPG through correlation and comparative analysis.
• Delivered actionable business insights using Python, Pandas, NumPy, Matplotlib, and Seaborn to support product strategy and customer segmentation.

Analyzed an e-commerce A/B test using Excel to determine whether a redesigned landing page increased conversion rates. Applied statistical testing and visual analysis to deliver a data-driven recommendation on page deployment.

Processed A/B test data in Excel, cleaned and segmented user records, analyzed conversion metrics with Pivot Tables, visualized performance trends, and performed a two-proportion Z-test to evaluate whether differences in conversion rates were statistically significant.

• Cleaned and validated A/B testing data to ensure reliable statistical analysis.
• Performed Pivot Table analysis and a two-proportion z-test to evaluate conversion rate differences with statistical confidence.
• Built clear visualizations and translated statistical findings into actionable business recommendations on landing page deployment.

Analyzed hotel reservation data using Excel to uncover booking trends, cancellation behavior, guest demographics, and revenue drivers, delivering an interactive dashboard that supports occupancy planning, retention strategies, and operational decision-making.

Raw booking data was cleaned and transformed in Excel using formulas, PivotTables, and calculated fields. Interactive dashboards with slicers and charts visualized KPIs including bookings, cancellations, guest segments, seasonal demand, ADR, and revenue for business insights.

• Cleaned and structured hotel booking data, handling missing values and creating analytical metrics for accurate reporting.
• Built an interactive Excel dashboard with PivotTables, PivotCharts, KPIs, and slicers for dynamic exploration.
• Identified booking trends, cancellation patterns, seasonal demand, and revenue opportunities to support data-driven business decisions.

A data analytics project that examines 10,000 e-commerce customer transactions from Bangladesh to uncover purchasing patterns, return drivers, and satisfaction factors, enabling data-driven decisions for retention, logistics, inventory, and subscription growth.

Raw customer, order, payment, and delivery data were cleaned and integrated, followed by exploratory data analysis, statistical testing, correlation analysis, customer segmentation, and business KPI evaluation to identify behavioral trends and generate actionable recommendations.

• Analyzed 10,000 customer records across demographics, purchasing behavior, delivery, payments, and satisfaction metrics.
• Combined EDA, statistical analysis, correlation studies, and segmentation to identify key drivers of returns, retention, and customer satisfaction.
• Converted analytical findings into business recommendations for inventory planning, delivery optimization, payment experience, and subscription conversion.

A comprehensive data analysis and visualization project that transforms user tracking data into interactive dashboards using Google Sheets, helping uncover behavioral patterns, engagement trends, and actionable insights for data-driven decision-making.

Raw user tracking data is imported into Google Sheets, where formulas, pivot tables, filters, and charts are used to clean, aggregate, and analyze key metrics. Interactive dashboards visualize user behavior, trends, and performance, enabling quick exploration and insight generation.

• Designed an interactive Google Sheets dashboard with dynamic charts, pivot tables, and filters for exploratory analysis.
• Performed data cleaning, transformation, and KPI calculation using spreadsheet formulas to ensure accurate reporting.
• Identified user behavior trends, engagement patterns, and performance metrics, presenting insights through clear visualizations for informed decision-making.

Built an interactive Power BI dashboard to analyze car sales performance, profitability, customer activity, discounts, and sales channels, enabling stakeholders to monitor KPIs, compare year-over-year trends, and support data-driven business decisions.

Processed and modeled sales data using Power Query and a relational data model. Created DAX measures for revenue, profit, customers, orders, discounts, and YoY growth, then designed interactive dashboards with slicers, KPI cards, and trend visualizations.

• Developed dynamic DAX measures for sales, profit, customer count, discounts, and year-over-year KPI comparison.
• Built an optimized data model with Power Query transformations for accurate, high-performance reporting.
• Designed interactive dashboards with filters, drill-downs, and channel-wise visualizations for actionable business insights.

A data analytics project that examines retail sales and inventory data to uncover demand patterns, optimize stock management, reduce stockouts and overstocking, and support smarter inventory decisions through exploratory analysis and predictive insights.

Retail sales and inventory data are cleaned, transformed, and analyzed using EDA, statistical techniques, customer/product segmentation, clustering, demand forecasting, and inventory performance metrics. Interactive visualizations reveal trends, seasonality, and optimization opportunities.

• Built a complete inventory analytics pipeline from raw sales data to actionable business insights.
• Identified seasonal demand, high-performing products, and inventory turnover patterns using statistical analysis.
• Applied customer and product segmentation with clustering to uncover purchasing behaviors.
• Generated forecasting and inventory optimization recommendations to minimize stockouts, reduce overstocking, and improve supply chain efficiency.

A healthcare analytics project that examines demographic, lifestyle, and clinical data to identify factors associated with diabetes, prediabetes, and non-diabetes, enabling data-driven insights into disease risk, health patterns, and preventive care.

Collected and cleaned patient health records, performed exploratory data analysis, statistical comparisons, correlation analysis, and visualizations to evaluate relationships between diabetes status and health indicators including BMI, blood pressure, cholesterol, smoking, alcohol use, and physical activity.

• Performed comprehensive EDA on demographic, lifestyle, and medical variables to uncover health patterns across diabetic, prediabetic, and non-diabetic groups.
• Applied statistical analysis and correlation techniques to identify significant associations between diabetes status and risk factors such as BMI, blood pressure, cholesterol, smoking, and alcohol consumption.
• Developed clear visualizations and actionable insights to support evidence-based healthcare decision-making and preventive intervention strategies.

A comprehensive supply chain analytics project that analyzes manufacturing, supplier, warehouse, and transportation operations to identify inefficiencies, optimize inventory flow, improve warehouse productivity, and support data-driven operational decisions.

Built using Python, Pandas, NumPy, SQL, and visualization libraries to clean and analyze supply chain data, evaluate supplier performance, monitor manufacturing efficiency, optimize warehouse operations, and assess transportation metrics through interactive dashboards and KPIs.

• Designed an end-to-end analytics pipeline covering procurement, manufacturing, warehousing, inventory, and transportation performance.
• Developed KPI dashboards to evaluate supplier reliability, warehouse productivity, production efficiency, delivery performance, and inventory health.
• Generated actionable recommendations to reduce operational bottlenecks, optimize logistics costs, improve supplier management, and enhance overall supply chain efficiency.

Analyzes retail transaction data to uncover products frequently purchased together, enabling inventory optimization, targeted promotions, and effective cross-selling strategies based on real-world customer purchasing behavior.

Retail transaction records are cleaned and transformed into basket data, followed by market basket analysis using association rule mining (Apriori) to identify product relationships. The insights are visualized to support inventory planning, product placement, and promotional decisions.

• Built a complete data preprocessing pipeline to convert raw retail transactions into market basket datasets suitable for association rule mining.
• Applied the Apriori algorithm to discover frequent itemsets and generate high-confidence association rules using support, confidence, and lift metrics.
• Produced actionable visualizations highlighting product affinities to improve inventory management, store layout, cross-selling, and promotional campaigns.

Built an end-to-end SQL analytics project to evaluate marketing campaign performance, identify profitable campaign strategies, and measure customer engagement using a social media advertising dataset.

Campaign and customer interaction data were analyzed in MySQL using SQL queries, CTEs, aggregations, joins, window functions, and segmentation logic. The workflow measured investment, profit, engagement, email opt-out behavior, and conversion trends across campaign types, channels, demographics, and campaign timing.

• Designed optimized SQL queries to evaluate campaign profitability, ROI trends, and high-performing campaign-channel combinations.
• Performed customer segmentation by age, gender, and campaign timing to identify factors influencing conversions and engagement.
• Built analytical reports covering investment vs. profit, engagement rates, email opt-out behavior, and profitable campaign pairs to support data-driven marketing decisions.

Analyzed the U.S. Airbnb Open Data dataset from Kaggle using SQL to uncover pricing, availability, location, and host insights that support data-driven decisions in the travel industry.

The Kaggle U.S. Airbnb Open Data dataset was cleaned and preprocessed in Jupyter Notebook, imported into MS SQL Server, and analyzed using SQL queries to examine pricing, listings, host performance, availability, and geographic trends for business insights.

• Cleaned and transformed raw Airbnb data in Jupyter Notebook by handling missing values, duplicates, and inconsistent formats.
• Designed SQL queries in MS SQL Server to analyze pricing, room types, availability, host activity, review patterns, and location-based performance.
• Generated actionable insights that reveal market trends, popular destinations, and factors influencing Airbnb listings and customer preferences.