DATA SCIENCE PORTFOLIO

INTRODUCTION

Welcome to my Data Science Portfolio, a curated showcase of my journey and accomplishments in the realm of data science. Here, you will find a selection of projects that exemplify my expertise in extracting actionable insights from complex data sets across various industries. These projects illustrate my proficiency in statistical analysis, machine learning, and data visualization, applied to solve real-world problems and support strategic decision-making.

Machine Learning

1. Predicting CO2 Emissions by Vehicles

   • Overview: In response to growing environmental concerns, this project aims to categorize vehicles into energy classes and predict their CO2 emissions using advanced machine learning and deep learning techniques.
   • Tools & Techniques: Utilized Python for data manipulation, and applied machine learning algorithms such as Random Forest and XGBoost alongside deep learning frameworks to model emissions accurately.
   • Results: Developed a model that predicts CO2 emissions with a Random Forest achieving an R² value of 99.7%, highlighting fuel consumption and electric range as significant predictors.
   • Impact: The findings could be utilized by policymakers and automotive manufacturers to develop greener vehicles and by consumers making informed choices.

2. Product Delivery Prediction

   • Objective: The goal was to create predictive models that estimate delivery times for e-commerce transactions to enhance customer service and logistical operations.
   • Tools & Techniques: Employed regression analysis techniques in Python, analyzing factors that impact delivery timelines.
   • Results: Achieved high predictive accuracy, allowing the e-commerce company to optimize their supply chain and improve customer satisfaction.

3. Heart Disease Prediction

   • Objective: This project explores the application of machine learning to predict an individual’s risk of developing heart disease, thus aiding early intervention strategies.
   • Tools & Techniques: Implemented Logistic Regression and Decision Trees in Python to analyze medical data and predict health outcomes.
   • Results: The models provided are highly accurate and could be part of a health analytics tool used in clinical settings to assess patient risk now.

4. Telco Customer Churn Analysis

   • Objective: Aimed to understand and predict customer churn in the telecommunications sector, enhancing retention strategies.
   • Tools & Techniques: Applied survival analysis and machine learning to model customer behavior and predict churn.
   • Results: The developed models accurately identify high-risk customers, informing targeted retention campaigns.

5. Titanic Survival Prediction

   • Objective: Leveraging historical data to predict survival rates of passengers aboard the Titanic, providing insights into factors that influenced survival.
   • Tools & Techniques: Used logistic regression to develop a predictive model based on passenger data like age, class, and gender.
   • Results: The model accurately predicts survival probabilities, offering valuable lessons for safety and risk management.

Data Analysis & Visualization

1. Amazon Products Analysis - Insights into the Indian E-Commerce Market

   • Objective: Analyze customer reviews and product data from Amazon to identify key factors influencing consumer satisfaction and purchasing decisions.
   • Tools & Techniques: Conducted text analysis and sentiment analysis using Python to extract insights from customer feedback.
   • Results: Uncovered critical insights into customer preferences and market trends, supporting strategic business decisions for product placements and promotions.

2. Flights Punctuality Analysis (Power BI)

   • Objective: This project aimed to analyze and visualize the punctuality of flights to improve airline and airport services.
   • Tools & Techniques: Utilized Power BI to create interactive dashboards that offer a detailed view of punctuality trends influenced by various factors.
   • Results: The dashboards serve as a decision-support tool for airport and airline operations, enhancing on-time performance and passenger satisfaction.

CONCLUSION

Through these projects, I have not only honed my technical skills but also developed a keen analytical mindset that is critical in the field of data science. I am committed to continuous learning and applying my knowledge to tackle new challenges. Please visit my GitHub Page for more insights and detailed project descriptions.

Contacts

For any inquiries or collaboration requests, please reach out via email at [email protected] or connect with me on LinkedIn.

Contributing

Contributions to this projects are more than welcome! Please review the CONTRIBUTING.md file for details on our code of conduct, and the process for submitting pull requests to me.

License

This project is licensed under the MIT License - see the LICENSE.md) file for details.