Welcome to my Data Analytics Portfolio! This collection of projects showcases my skills and experience in data analysis, using various tools and techniques. Each project within this portfolio is a complete Jupyter Notebook that I've used to explore, analyze, and visualize data in different domains. Through these projects, I aim to highlight my ability to extract valuable insights, solve complex business problems, and communicate findings effectively.

This comprehensive project provides businesses with the ability to understand their customers better, segment them based on various characteristics and behaviors, and develop effective strategies for marketing, sales, customer service, and overall growth. Key components include:

• Hierarchical Customer Segmentation Analysis
• Managerial Approach to Customer Segmentation
• Customer Scoring and Behavior Analysis
• Calculating and Analyzing Customer Lifetime Value

In this project, I performed a comprehensive analysis of the COVID-19 pandemic using data from Worldometers. The analysis includes data cleaning, visualization (time series plots, bar charts, choropleth maps), and statistical techniques (correlation analysis, regression analysis, clustering analysis) to provide insights into the spread and impact of the virus.

This project explores the historical daily gold price data from 2008 to 2021 using a Gaussian Hidden Markov Model (HMM). The goal was to analyze the changes in gold prices, identify different volatility regimes, and gain insights into the gold market dynamics for potential investment and risk management applications.

This project aims to provide an up-to-date understanding of the current job market for data science roles. It involves extracting and analyzing job listing data from major platforms like Monster, SimplyHired, and Indeed. The analysis identifies the key skills and technologies currently in high demand for data science roles.

In this project, I performed a comprehensive analysis of a financial fraud dataset to gain insights into the characteristics and patterns of fraudulent transactions. The analysis involved exploratory data analysis, feature engineering, model development and evaluation (using algorithms like logistic regression, decision trees, and gradient boosting), and model optimization for fraud detection.

To explore the projects in detail, clone the repository and navigate to the respective project directories. Each project is well-documented with a Jupyter Notebook that guides you through the process step-by-step.

This portfolio showcases my ability to tackle complex data analytics challenges, leverage various techniques and technologies, and deliver insightful solutions. I am passionate about extracting meaningful insights from data and using them to drive informed decision-making and business growth.

Feel free to explore the projects and provide any feedback or suggestions. Let's collaborate and create impactful data-driven solutions together!