Projects

Profitability Analysis for a Retail Business

Key Insights

✔ Analyzed 500+ retail sales transactions using SQL & Power BI, identifying key revenue-driving products and regions.
✔ Designed and implemented profitability dashboards in Power BI, enabling real-time tracking of sales and profit margins.
✔ Optimized product pricing strategy by identifying low-margin high-sales products, leading to a 10% potential increase in profitability.
✔ Developed a SQL-based ETL pipeline for sales data, automating data extraction, cleaning, and transformation.
✔ Provided data-driven recommendations to improve store performance, increasing revenue in underperforming locations by 15%.

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Customer Segmentation Analysis

Key Insights

✔ Conducted Customer Segmentation Analysis using RFM modeling & K-Means clustering on 300+ customer profiles.

✔ Built a Python-based ML pipeline to classify customers into 3 high-value segments, improving targeted marketing strategies.

✔ Developed interactive dashboards in Power BI, visualizing customer demographics, spending patterns, and retention trends.

✔ Recommended personalized marketing strategies that could potentially increase customer retention rates by 20%.

✔ Automated customer segmentation analysis, reducing manual effort by 80%, improving decision-making speed for business teams.

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Stock Price Prediction

(Machine Learning)

Project Review

Data Analytics

GitHub Link

Sales Analysis with MySQL

Problems Addressed

Identifying high-performing product lines.
Understanding sales trends and customer behavior.
Optimizing sales strategies based on data insights.

Approach Used: Feature Engineering & Exploratory Data Analysis (EDA).

Data Collection & Data Cleaning:
Feature Engineering :
Exploratory Data Analysis (EDA)

Insights from Walmart Sales Analysis:

Walmart shops are in Naypyitaw, Mandalay, and Yangon.
There are six unique product lines: Food & Beverages, Health & Beauty, Sports & Travel, Fashion Accessories, Home & Lifestyle, and Electronics Accessories.
The available payment methods are credit cards, e-wallets, and cash, with credit cards being the most common.
The Food & Beverages product line generates the highest revenue.
The two types of customers are members and normal, with members contributing the most revenue.

Naypyitaw has the highest tax percentage at 16.9%.

Strategy for Business Improvement:

Customer Offers: Increase offers for credit card users.
Member Benefits: Develop exclusive promotions for permanent members.
Product Line Enhancement: Improve and market other product lines.
Tax Optimization: Explore strategies to optimize tax-related expenses in Naypyitaw.

Data Analysis With Python

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Diwali Sales Data Analysis

The project aim is to get valuable insights and an overview of total revenue, total quantity sold, and average price per unit for the given period.

Steps Involved Data Analysis

Data Collection & Data Cleaning
Exploratory Data Analysis (EDA)
Data Visualization
Extracting Insights

Insights: We have done the EDA process on Gender, Age group, state, marital status, occupation, and product category.

Conclusion: Most are bought by married women age group 26-35 years from Up, Maharashtra, & Karnataka. Working in IT, healthcare and aviation is more like buying products from the food, clothing, & electronics categories.

OFFICE ANALYTICS WITH POWER BI

OFFICE ANALYTICS

The purpose of this project is to gain insights into the working preferences of employees and to optimize workplace strategies accordingly.