Project Details

The aim was to support strategic decision-making at various organizational levels by focusing on key performance indicators (KPIs) and profitability.

Key reports included:
Top 10 Products Report: Analyzed the best-performing products based on revenue and profitability.
Market Performance vs Target: Compared actual sales and market share against pre-set targets.
Customer Performance Report: Examined key customer segments, including revenue contributions and purchasing patterns.
P & L Yearly Report: Provided a high-level overview of annual financial performance.
P & L by Country: Broke down profit and loss by geographic region.
P & L by Month: Offered monthly financial performance insights.
P & L for Markets: Analyzed market-specific profitability.
GM % by Quarters: Provided a quarterly analysis of gross margin percentage

Project Details

Project Title: Business Insights with MySQL and Power BI

This project involves transforming raw business data using MySQL and visualizing it in Power BI to provide actionable insights across various domains.

Key Business Insights:
Finance View: Analyzes financial data to track revenue, expenses, profit margins, and overall financial health.
Sales View: Provides a breakdown of sales performance, identifying top-selling products, key customers, and sales trends over time.
Marketing View: Assesses marketing efforts by evaluating customer acquisition, campaign effectiveness, and ROI.
Supply Chain View: Examines supply chain efficiency, including inventory management, supplier reliability, and lead times.
Executive View: A high-level summary of key performance indicators (KPIs) across finance, sales, marketing, and supply chain for strategic decision-making.
Tools Used:

MySQL
Power BI

Live Interactive Dashboard

Project Details

This project involves data analysis using MySQL, focusing on financial analytics, customer/product performance, market ranking, and supply chain efficiency. Key features include gross and net sales analysis, automated monthly reports, top customers, products, and markets identification, and forecast accuracy improvement. Technologies used include MySQL for database management and SQL for programming. Key findings revealed insights into financial trends, product performance, and market efficiency, while automated updates improved supply chain forecast accuracy.

Project Details

The Hospitality Domain Analysis project employs Python and Jupyter Notebooks to analyze data from the hospitality industry, focusing on hotel bookings. It consists of two main sections: data exploration and insights generation.

In the exploration phase, the project identifies unique property IDs, calculates total bookings per property, finds dates with bookings exceeding capacity, and lists properties with the highest capacities.

In the insights generation phase, it determines the average occupancy rate by room category, analyzes city-wise occupancy rates, compares weekday versus weekend occupancy, assesses June occupancy by city, and calculates revenue per city based on bookings and occupancy.

This analysis provides valuable insights into booking patterns and occupancy trends, helping stakeholders make informed decisions.

Project Details

Expense Management System
This project is designed to manage and track expenses efficiently, utilizing a FastAPI backend and a Streamlit frontend.

Project Structure:

frontend/: Streamlit application code for user interaction.
backend/: FastAPI server for handling requests, connecting with databases, and processing data.
tests/: Test cases for both the frontend and backend to ensure functionality.
requirements.txt: Contains a list of all necessary Python packages for running the project.
Setup Instructions:

Clone the repository:
bash
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git clone https://github.com/J1089/expense-management-system.git
cd expense-management-system

Install dependencies:

bash
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pip install -r requirements.txt
Run the FastAPI server:
bash
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uvicorn server.server:app --reload
Run the Streamlit app:
bash
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streamlit run frontend/app.py

Project Details

Analyzed Zomato's restaurant data for key metrics like city trends, ratings, table bookings, and online delivery services.
Explored restaurant openings, costs, and customer preferences over time.
Created a data model in Excel, including a calendar table with Year, Month, and Financial Quarters And Build Dashboard.
Converted "Average cost for 2" to USD.
Categorized restaurants by city, country, ratings, and price.
Tracked KPIs for table bookings and online delivery percentages.
Built visualizations in Power BI and Tableau to analyze cuisines, cities, and ratings.
Used MySQL for data validation and large dataset handling.
Faced challenges in data conversion (XLSX to CSV) and MySQL performance issues.
Results showed growing markets, moderate customer satisfaction (3-4 ratings), and low service availability (bookings and deliveries).

Live Interactive Dashboard