During my third year of college, I completed an internship where I was introduced to the fundamentals of Machine Learning through online training sessions.

As part of the internship, I worked on two key projects that allowed me to apply these concepts in practical scenarios.

For the first project, I worked with a team to develop a Random Forest-based Movie Recommendation System.

We began by scraping data from the IMDb website, followed by preprocessing it using NumPy and Pandas. The model was trained based on movie genres to generate personalized recommendations for viewers.

To better understand the data and optimize model training, we used Seaborn for visualization, analyzing the distribution of key parameters to enhance performance.

In the second project, we developed a tweet categorization application using Sentiment Analysis to categorize tweets as good or negative, hence improving the company's social media analytical skills.

We utilized the TF-IDF (Term Frequency-Inverse Document Frequency) method to convert text data into numerical features, which improved the accuracy of our sentiment analysis model.

This project not only strengthened my understanding of Natural Language Processing but also gave me hands-on experience in building machine learning models for real-world applications.