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The projects in this repository were developed as part of the Anudip Foundation's data analytics training programme. They showcase essential skills in data cleaning, exploratory analysis, visualisation, and machine learning. Each project focuses on solving real-world problems from various industries, providing valuable insights through data-driven

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Anudip Foundation Data Analytics Projects

Anudip Foundation

This repository contains various projects developed as part of the Anudip Foundation's Data Analytics Training Programme. The projects showcase essential skills in data cleaning, exploratory analysis, visualization, and machine learning. Each project is designed to solve real-world problems across different industries, offering valuable insights through data-driven approaches.

Topics Covered

  • Excel: Data analysis and manipulation using Excel tools.
  • Data Analytics: General data analysis and exploration techniques.
  • Data Analysis: Advanced data analysis methods, including cleaning and transformation.
  • Data Analyst: Project work focused on analytical skills for data professionals.
  • Power BI: Visualization and reporting using Power BI tools.
  • Python for Data Analysis: Python-based analysis, including libraries like Pandas, Matplotlib, Seaborn, and Scikit-learn.
  • UPI Fraud Detection: A project aimed at detecting fraud in UPI transactions.
  • Project on Expenditure: Analyzing expenditure data for insights and forecasting.

Key Projects

1. Expenditure Analysis

This project involves analyzing expenditure data to uncover patterns and insights, with the goal of improving financial decision-making. Nitin is a graphic designer with a monthly income of Rs 15,000. He wants to buy a scooter for daily commutes. Currently, he hasn't been able to save due to expenses. I will analyze his financial data to identify areas for improvement and create a plan to achieve his financial goals.

2. UPI Fraud Detection

This project focuses on detecting fraud in UPI (Unified Payments Interface) transactions using machine learning techniques. The model analyzes transaction data to identify patterns indicative of fraudulent activity.

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The projects in this repository were developed as part of the Anudip Foundation's data analytics training programme. They showcase essential skills in data cleaning, exploratory analysis, visualisation, and machine learning. Each project focuses on solving real-world problems from various industries, providing valuable insights through data-driven

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