🎯 Anudip Foundation Data Analytics Projects

A comprehensive portfolio of data analytics projects developed under the Anudip Foundation's Data Analytics Training Programme

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📋 Overview

This repository showcases a collection of data analytics projects developed as part of the Anudip Foundation's Data Analytics Training Programme. These projects demonstrate proficiency in data cleaning, exploratory analysis, and visualization, addressing real-world challenges across various industries through data-driven solutions.

🛠️ Technical Skills Demonstrated

📊 Data Analysis & Visualization Excel: Advanced data manipulation and analysis Power BI: Interactive dashboards and reports Python Libraries: Pandas, Matplotlib, Seaborn Statistical Analysis: Descriptive and inferential statistics

🔍 Data Analytics & Processing Data Cleaning: Preprocessing and transformation Exploratory Data Analysis: Pattern identification Data Visualization: Creating meaningful charts and graphs Business Intelligence: Dashboard development and reporting

🎯 Featured Projects

💰 1. Personal Expenditure Analysis

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Objective: Analyze personal financial data to optimize savings and achieve financial goals

Case Study: Nitin, a graphic designer with ₹15,000 monthly income, seeks to purchase a scooter but struggles with savings due to untracked expenses.

Key Features:

• 📈 Expense pattern analysis
• 🔍 Spending category breakdown
• 💡 Savings optimization recommendations
• 📊 Financial goal planning
• 🎯 Budget allocation strategies

Technologies Used: Python, Pandas, Matplotlib, Excel

Deliverables:

• Interactive Excel dashboard
• Python analysis scripts
• Comprehensive financial report
• Actionable recommendations

🚨 2. UPI Transaction Analysis

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Objective: Analyze UPI transaction patterns to identify trends and insights

Key Features:

• 🔍 Transaction pattern analysis
• 📊 Statistical analysis of payment behaviors
• 📈 Trend identification and visualization
• 🛡️ Risk assessment through data analysis
• 💹 Transaction volume and frequency analysis

Technologies Used: Python, Pandas, Matplotlib, Seaborn

Key Insights:

• Transaction volume trends
• Peak usage patterns
• Category-wise spending analysis
• Geographic distribution of transactions