Walmart Sales Analysis Interactive Dashboard Built with SQL Python Streamlit

🚀 Instacart Customer Behavior Analysis | SQL + Python I recently completed an end-to-end data analytics project analyzing customer purchasing behavior using the Instacart dataset. The goal was simple: understand how customers shop, what they buy, and what drives repeat purchases. 📊 What I worked on: • Cleaned and processed raw CSV datasets using Python (Pandas) • Engineered key features like basket size and weekday vs weekend behavior • Designed a relational schema and loaded data into PostgreSQL • Wrote SQL queries to analyze product demand, order trends, and reorder patterns • Built visualizations to communicate insights clearly 📈 Key insights: • Fresh produce and essential grocery items dominate product demand • Customer activity peaks during daytime hours • Clear behavioral differences between weekday and weekend orders • Certain departments show high reorder rates → strong customer loyalty 🛠 Tech stack: SQL (PostgreSQL), Python (Pandas), Matplotlib, Seaborn 💡 This project strengthened my ability to: • Work with large datasets • Perform feature engineering • Combine SQL + Python for analysis • Translate data into business insights 📌 GitHub Repo: https://lnkd.in/gqD2h4_E #DataAnalytics #SQL #Python #PostgreSQL #BusinessIntelligence #DataProjects #EDA #AnalyticsPortfolio

🚀 Excited to share my latest project: Retail Sales Analysis using Python In this project, I worked on a real-world Superstore dataset to analyze sales performance and generate insights. 🔹 Tools & Libraries: Python (pandas, matplotlib, seaborn) 📊 Key Analysis: Sales and profit by category Top-performing states Data cleaning and EDA 📈 Key Insights: Technology category generated the highest sales California is the top-performing state Profit varies significantly across categories 🔗 GitHub Repository: https://lnkd.in/gQijwCnG #DataAnalytics #Python #DataAnalysis #SQL #Portfolio #Learning

 Automating E-commerce Data Extraction with Python Today, I focused on the first phase of an E-commerce Market Intelligence project: building a robust data extraction pipeline. Instead of manual data entry or using static files, I developed a Python script to interface directly with a REST API. This allows for the automated retrieval of real-time product data, ensuring the analysis is based on the most current market information. By automating the 'Collection' phase, I’m now ready to focus on the 'Analysis' phase—identifying stock risks and pricing trends through SQL and Power BI. #DataAnalytics #python #APIIntegration

E-commerce Data Analysis using Python I worked on a small data analysis project to understand how e-commerce data can be used to derive business insights. 📊 What the dataset includes: Orders and customers Product categories Regions Payment methods Order status (Completed / Cancelled / Returned) What I analyzed: Revenue distribution across categories Monthly sales trends Top customers based on spend Cancellation rate Region-wise performance 💡 Some observations: A few categories contribute a major portion of revenue Sales patterns vary across time periods Cancellation rates are not uniform across regions ⚙️ Tools used: Python (Pandas) Jupyter Notebook Project link: https://lnkd.in/gKUYb88x #DataAnalytics #Python #Pandas #DataProjects #Ecommerce #DataAnalyst

Built an end-to-end Customer Behavior Analytics Dashboard using Python, MySQL, and Power BI. Cleaned and transformed raw data, performed EDA, executed SQL queries, and visualized key insights like revenue trends, customer segments, and purchase behavior. Github link: https://lnkd.in/eafrA__f #DataAnalytics #PowerBI #SQL #Python #DataVisualization

🚀 Turning SQL data into insights with Python Ever wondered which products truly drive sales? I built a quick pipeline using SQL Server + Python (pandas, matplotlib) to query, clean, and visualize product performance. 📊 The chart below shows Laptops leading sales at $1200+, while lifestyle items like books trail far behind. This highlights how tech products dominate consumer spending compared to everyday goods. 🔧 Tools used: PyCharm, pandas, matplotlib, pyodbc 🎯 Skills showcased: database connection, data wrangling, visualization I’m exploring more ways to connect SQL data with Python visualizations. 👉 What’s your go-to tool for analytics and storytelling #Data Analytics #Python #SQL #Visualization #LinkedIn Project

I recently worked on a Python data analysis project where I explored retail sales data (Blinkit dataset) to understand business trends and customer behavior. Instead of just focusing on coding, I tried to approach it from a business perspective — asking questions like: Which products are driving the most revenue? How do sales vary across different city tiers? What factors impact overall performance? Some interesting things I found: Low-fat products contribute a major share of sales Tier 3 cities are generating the highest revenue Medium-sized outlets perform better than small and large ones A few categories like fruits and snacks dominate overall sales I used Python (Pandas, Matplotlib, Seaborn) to clean the data, analyze it, and create visualizations. This project really helped me understand how to turn raw data into insights that can actually support decisions. Sharing it here — would love your feedback! 🔗 GitHub: https://lnkd.in/g8DSY3xs

Just completed a Sales Data Analysis project using the Superstore dataset. 🔍 Key insights: • Sales are growing but highly seasonal (Q4 spikes) • Higher discounts → lower profits • Furniture category shows low profitability • Few products drive major losses 💡 Takeaway: Optimizing discount strategy can significantly improve margins. 🔗 https://lnkd.in/dM79iYw9 #DataAnalytics #Python #SQL #Pandas #BigQuery

Built a forecasting and BI dashboard for e-commerce operations. It combines sales forecasting, cash flow projections, ROAS tracking, and marketplace data into one decision-support tool. Stack: Python, Streamlit, Pandas, SQLAlchemy, Prophet, Plotly GitHub: https://lnkd.in/gRp6MM-a

This was my first Data Analysis project with Python. 🐍 Goal: clean and prepare customer data from a retail store for business analysis. Tools I used: → String manipulation: strip(), replace(), split() → Data type conversion → Error handling with try/except → List sorting and total spend calculation → Automated report generation with f-strings It looks basic. And it is. But this is exactly what happens before any real analysis: messy data that needs to be cleaned up first. I'll be uploading it to GitHub soon. This is the first of several projects I'll be sharing here as I move forward in my learning journey. Have you worked with data cleaning before? What was the hardest part at the beginning? #Python #DataAnalysis #DataCleaning #Bootcamp #LearningInPublic