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Retail Analysis: Detailed Description

This project demonstrates an end-to-end ETL (Extract, Transform, Load) pipeline for retail order data using Kaggle API, Python, and SQL Server. The goal is to extract data from Kaggle, clean and process it using Python, load it into SQL Server, and perform insightful data analysis using SQL queries.

Project Flow

ETL Pipeline Structure:

• Extract: Use the Kaggle API to download a retail dataset and store the raw data locally in CSV format.
• Transform: Use Python and Pandas to clean and preprocess the data, handle null values, correct data types, and perform feature engineering if required.
• Load: Load the cleaned dataset into SQL Server using a Python-based connection (e.g., pyodbc or sqlalchemy).
• Analyze: Write and execute SQL queries to analyze key business metrics such as total revenue, top-selling products, monthly sales trends, and customer purchase behavior.

Project Structure

Retail_ETL_Analysis/

• ETL_pipeline.ipynb # Jupyter Notebook for entire ETL process
• ETL_pipeline_structure.png # Visual flowchart of the pipeline
• README.md # Project documentation
• SQLQuery.sql # SQL script for data analysis
• orders.csv # Raw dataset (downloaded from Kaggle)

Tools & Technologies

• Python (Jupyter Notebook)
• Pandas
• Kaggle API
• SQL Server
• SQL
• pyodbc/sqlalchemy

Key Insights Derived

• Identify best-selling categories and products.
• Track monthly or regional sales trends.
• Detect outliers or unusual customer behavior.
• Suggest business strategies based on data-driven decisions.

Getting Started

Clone the repository:

git clone https://github.com/yourusername/Retail_ETL_Analysis.git
cd Retail_ETL_Analysis

Download the dataset using Kaggle API:

• Place your kaggle.json API key file in the working directory.
• Use the following command in the notebook or terminal: kaggle datasets download -d

Run the notebook:

• Open ETL_pipeline.ipynb in Jupyter Notebook or VS Code.
• Follow the steps to extract, transform, and load the data.

Run SQL Queries:

• Open SQLQuery.sql in SQL Server Management Studio (SSMS) or another SQL environment.
• Execute the queries to analyze the data.

Author

Vrushabh Gillarkar

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My Financial Dashboard: Detailed Description

Project Objective: "To design and implement a comprehensive Power BI dashboard , utilizing engineering financial data from 2022 to 2024 imported via CSV files from MS Excel. This project aims to deliver dynamic visualizations and in-depth analysis of financial performance indicators, enabling real-time insights, trend tracking, and data-driven decision-making to optimize financial management within the engineering domain."

DataSet Used

DataSet

Question (KPIs)

• Where / How do I spend?
• Where / How do I save?
• Do I Save / Spend according to I Earn?
• What are the Detailed Statement?
• Which expense category contributes the most to costs?
• What percentage of income is being saved, and how does it vary month-over-month?
• What Component show potential for cost reduction?

Dashboard Interaction : View Dashboard

Process

1. Collect and Prepare Data: Gather financial data (2022–2024) in CSV format, clean and validate it.
2. Import Data: Import the CSV files into Power BI and transform data as needed using Power Query.
3. Model Data: Create relationships and calculate metrics like expense%, saving%, and income change MOM.
4. Design Dashboard: Add visuals:
   • Pie chart for expense%.
   • Line chart for saving% trends.
   • KPI cards for total savings and expenses.
   • Line chart for income change MOM.
5. Analyze Trends: Identify trends, patterns, and areas for improvement in income, savings, and expenses.
6. Generate Insights: Highlight actionable recommendations based on key financial metrics.
7. Test and Deploy: Validate the dashboard, gather feedback, and publish for stakeholder use.
8. Maintain and Update: Regularly update with new data for ongoing insights.

Project Insight

• Expense Focus: High expenses in specific categories highlight areas for cost-saving.
• Savings Growth: Savings percentage improves over time, showing better financial management.
• Income Trends: Positive MOM income growth indicates financial stability.
• Seasonal Costs: Spikes in expenses suggest seasonal or operational cost drivers.
• Yearly Gains: Year-over-year performance shows increasing savings and controlled expenses.
• Anomalies: Identify irregular income or expense patterns for further investigation.
• Budget Gaps: Highlight deviations between planned and actual financial performance.

Final Conclusion

The dashboard highlights strong income growth, improved savings, and controlled expenses, with opportunities for cost optimization. It provides actionable insights for better budget management and financial decision-making, ensuring ongoing stability and growth.

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Walmart Data Analysis: SQL + Python Project (ETL) Detailed Description

Project Overview & Project Pipeline

This project is an end-to-end data analysis solution designed to extract critical business insights from Walmart sales data. We utilize Python for data processing and analysis, SQL for advanced querying, and structured problem-solving techniques to solve key business questions. The project is ideal for data analysts looking to develop skills in data manipulation, SQL querying, and data pipeline creation.

Project Steps

1. Set Up the Environment: Tools Used: Visual Studio Code (VS Code), Python, SQL (MySQL and PostgreSQL). Goal: Create a structured workspace within VS Code and organize project folders for smooth development and data handling.
2. Set Up Kaggle API: Obtain your Kaggle API token from Kaggle by navigating to your profile settings and downloading the JSON file. Configure Kaggle: Place the downloaded kaggle.json file in your local .kaggle folder. Use the command kaggle datasets download -d <dataset-path> to pull datasets directly into your project.
3. Download Walmart Sales Data: Data Source: Use the Kaggle API to download the Walmart sales datasets from Kaggle. Dataset Link: Walmart Sales Dataset. Storage: Save the data in the data/ folder for easy reference and access.
4. Install Required Libraries and Load Data: Libraries: Install necessary Python libraries using: pip install pandas numpy sqlalchemy mysql-connector-python psycopg2. Loading Data: Read the data into a Pandas DataFrame for initial analysis and transformations.
5. Explore the Data: Goal: Conduct an initial data exploration to understand data distribution, check column names, types, and identify potential issues. Analysis: Use functions like .info(), .describe(), and .head() to get a quick overview of the data structure and statistics.
6. Data Cleaning: Remove Duplicates: Identify and remove duplicate entries to avoid skewed results. Handle Missing Values: Drop rows or columns with missing values if they are insignificant; fill values where essential. Fix Data Types: Ensure all columns have consistent data types (e.g., dates as datetime, prices as float). Currency Formatting: Use .replace() to handle and format currency values for analysis. Validation: Check for any remaining inconsistencies and verify the cleaned data.
7. Feature Engineering: Create New Columns: Calculate the Total Amount for each transaction by multiplying unit_price by quantity and adding this as a new column. Enhance Dataset: Adding this calculated field will streamline further SQL analysis and aggregation tasks.
8. Load Data into MySQL and PostgreSQL: Set Up Connections: Connect to MySQL and PostgreSQL using sqlalchemy and load the cleaned data into each database. Table Creation: Set up tables in both MySQL and PostgreSQL using Python SQLAlchemy to automate table creation and data insertion. Verification: Run initial SQL queries to confirm that the data has been loaded accurately.
9. SQL Analysis: Complex Queries and Business Problem Solving: Business Problem-Solving: Write and execute complex SQL queries to answer critical business questions, such as: Revenue trends across branches and categories. Identifying best-selling product categories. Sales performance by time, city, and payment method. Analyzing peak sales periods and customer buying patterns. Profit margin analysis by branch and category. Documentation: Keep clear notes of each query's objective, approach, and results.
10. Project Publishing and Documentation: Documentation: Maintain well-structured documentation of the entire process in Markdown or a Jupyter Notebook. Project Publishing: Publish the completed project on GitHub or any other version control platform, including: The README.md file (this document). Jupyter Notebooks (if applicable). SQL query scripts. Data files (if possible) or steps to access them.

Requirements

• Python 3.8+
• SQL Databases: MySQL, PostgreSQL
• Python Libraries: pandas, numpy, sqlalchemy, mysql-connector-python, psycopg2
• Kaggle API Key (for data downloading)

Getting Started

Clone the repository:

git clone <repo-url>

Install Python libraries:

pip install -r requirements.txt

Set up your Kaggle API, download the data, and follow the steps to load and analyze.

Project Structure

|-- data/                     # Raw data and transformed data
|-- sql_queries/              # SQL scripts for analysis and queries
|-- notebooks/                # Jupyter notebooks for Python analysis
|-- README.md                 # Project documentation
|-- requirements.txt          # List of required Python libraries
|-- main.py                   # Main script for loading, cleaning, and processing data

Results and Insights

• Sales Insights: Key categories, branches with highest sales, and preferred payment methods.
• Profitability: Insights into the most profitable product categories and locations.
• Customer Behavior: Trends in ratings, payment preferences, and peak shopping hours.

Future Enhancements

• Integration with a dashboard tool (e.g., Power BI or Tableau) for interactive visualization.
• Additional data sources to enhance analysis depth.
• Automation of the data pipeline for real-time data ingestion and analysis.

Acknowledgments

• Data Source: Kaggle's Walmart Sales Dataset
• Inspiration: Walmart's business case studies on sales and supply chain optimization.

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Diwali Sales Analysis using EDA Detailed Description

Project Overview

This project is an Exploratory Data Analysis (EDA) of a Diwali sales dataset. The main goal is to understand customer behavior, product performance, and sales trends to help businesses make better marketing decisions during the Diwali festival season.

About the Project

In this analysis, we explore:

• Customer demographics (age, gender, marital status, etc.)
• Popular product categories
• Spending habits during Diwali
• Top-performing states and cities
• Sales trends and insights for marketing strategies

Tools & Libraries Used

• Python 🐍
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

Dataset

The dataset includes details like:

• Customer ID
• Gender
• Age
• State
• Marital Status
• Product Category
• Purchase Amount

Note: Dataset is assumed to be a sample collected during the Diwali sales period.

Key Insights

• Married women aged 26–35 years from Uttar Pradesh, Maharashtra, and Karnataka are more likely to buy products.
• Most purchased product categories: Clothing, Electronics, and Home Decor.
• The average purchase amount varies significantly by state and age group.
• Businesses should target young to middle-aged buyers during Diwali promotions.

How to Use

Clone this repository.

git clone https://github.com/VrushabhGillarkar/Diwali-Sales-Analysis-using-EDA.git

Open the .ipynb file in Jupyter Notebook.

Run the cells to view data cleaning, visualization, and insights.

Visuals

This project includes several charts and graphs such as:

• Bar plots for product categories and state-wise sales
• Pie charts for gender-wise spending
• Histograms for age group analysis

Conclusion

This EDA project gives useful business insights for boosting sales during Diwali. It helps understand customer segments, product preferences, and spending patterns.

Future Scope

• Use machine learning for sales prediction
• Add more time-series data for better trend analysis
• Personalize marketing strategies based on customer segments

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ShareSphere: A Resource Sharing and Ride-Sharing Community Platform

ShareSphere is a comprehensive community platform designed specifically for college students, facilitating both resource sharing and ride-sharing within the campus environment. This project aims to create a seamless and efficient system for students to exchange academic resources (notes, books, etc.) and coordinate carpools for daily commutes or weekend trips, thereby optimizing petrol usage and fostering a collaborative community.

Key Features:

• Resource Sharing: Students can easily post requests for or offer various academic resources, connecting with peers who have what they need or can lend a hand.
• Ride-Sharing: A dedicated module for college campus ride-sharing, allowing students to find or offer rides, reducing transportation costs and environmental impact.
• User-friendly UI: Built with a focus on intuitive navigation and a clean, modern design for a smooth user experience.
• Community Building: Fosters interaction and cooperation among students, creating a stronger campus community.

Technologies Used:

• Backend: Python, Django
• Frontend: HTML, CSS, JavaScript (with a focus on modern UI/UX)
• Database: (Mention your database, e.g., PostgreSQL, SQLite, MySQL)

Demo Video:

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Student Attendance Tracking System

This project is a web-based system designed to streamline the process of managing student attendance for educational institutions. It provides an intuitive interface for instructors to mark attendance and for students to track their attendance records, ensuring transparency and efficiency.

Key Features:

• Easy Attendance Marking: Instructors can quickly mark daily attendance for their classes.
• Student Record View: Students can access their attendance history and current status.
• Reporting: Generate attendance reports for analysis and record-keeping.
• User-friendly UI: Designed with a clean and responsive user interface for ease of use.

Technologies Used:

• Frontend: HTML, CSS, JavaScript
• Backend: Python (or other suitable language for web apps)
• Database: (Mention your database, e.g., SQLite, PostgreSQL, MySQL)

Demo Video:

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