ML and Data Science Academic Projects

Context and Objective

These projects were developed as part of my academic journey in Data Science. They focused on solving real-world problems through advanced data analysis, modeling, and visualization techniques. Each project aimed to apply machine learning algorithms to extract valuable insights and provide practical solutions to specific business challenges. Additionally, I am currently in the process of obtaining the IBM AI Engineering Professional Certificate, which is enhancing my skills in AI and deep learning models.

Projects and Contributions

  1. Customer Segmentation and Personalized Recommendations I implemented clustering techniques and collaborative filtering to segment customers on an e-commerce platform. By identifying customer segments, I was able to provide more personalized product recommendations, which improved customer experience and increased conversion rates. Key Contribution: Developed a system that dynamically adjusted recommendations based on customer behavior, increasing the relevance of products shown to specific customer segments.

  2. Predicting Buying Behaviors in E-Commerce In this project, I analyzed historical booking data from British Airways to predict customer purchasing behaviors. Using machine learning algorithms, I identified the most important features influencing purchasing decisions and built a predictive model to generate personalized recommendations for users. Key Contribution: Leveraged historical data and user characteristics to improve the relevance of recommendations, enhancing the user experience and boosting conversion rates.

  3. Sentiment Analysis and Natural Language Processing (NLP) I conducted a sentiment analysis on customer reviews for British Airways using NLP techniques. By analyzing text data from reviews posted on a web portal, I identified emotional patterns and key opinions about the customer experience. This analysis provided actionable insights to improve customer satisfaction. Key Contribution: Applied advanced NLP techniques to extract meaningful insights from customer feedback, allowing for data-driven decisions to enhance service quality.

Results and Achievements

  • Customer Segmentation: Achieved more accurate customer segmentation, resulting in a more personalized recommendation system that improved conversion rates.
  • Buying Behavior Prediction: Developed a predictive model with a high accuracy rate, providing deeper insights into customer behavior and enhancing the effectiveness of marketing strategies.
  • Sentiment Analysis: Successfully identified customer sentiment trends, enabling British Airways to implement targeted strategies to address customer concerns and improve overall satisfaction.
  • IBM AI Engineering Certificate (in progress): This certification is equipping me with skills in building deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), allowing me to further enhance my ability to tackle complex AI-driven projects.

Challenges and Lessons Learned

One of the key challenges was managing large datasets and ensuring data quality through cleaning and transformation processes. The IBM AI Engineering program is also providing valuable insights into optimizing deep learning models and tackling complex AI tasks.