1. Time series Forecasting

This project begins with an in-depth analysis, identifying key patterns in the data. It involves applying specific regression models like ARIMA, Logistics, XGBoosted, CatBoosted, LGBMRegressor, Random Forest (Best F1 score 87%) and deep learning models such as LSTM, followed by a comparative analysis of their performance. The project concludes with actionable insights and predictions about future trends in [specific field/domain], derived from the model results.

2. Graph Neural Networks

Summary

Graph Neural Networks (GNNs) are adept at uncovering hidden patterns in complex network data. This project applied GNNs to [specific case study], demonstrating their effectiveness in interpreting intricate relationships, such as molecular interactions in medicine or social connections in digital platforms. The project highlights GNNs' ability to perceive the broader structure of data, providing insights that are not apparent in isolated data points.

3. Natural Language Model

Summary

This project involved text mining using Multinomial Naive Bayes and BERT models. While both models showed high accuracy, BERT excelled with a 93% accuracy rate, outperforming Naive Bayes' 85%. The project entailed fine-tuning Naive Bayes with grid search optimization and leveraging a pre-trained BERT model, optimized for specific parameters. The analysis also delved into BERT's multi-layer multi-headed attention mechanism, offering deeper insights into token relationships and suggesting potential applications in Customer service and review evaluations.

4. Recommendation System

Summary

In this project, we're creating a personalized movie recommendation system. We'll explore three types of recommender systems:

1. Demographic Filtering: Offers general recommendations to all users based on movie popularity and genre. It's a broad approach that doesn't account for individual preferences.

2. Content-Based Filtering: Recommends movies similar to ones you've liked before, using details like genre, director, and actors. It's tailored to your specific tastes in movie characteristics.

3. Collaborative Filtering: Suggests movies based on the preferences of users with similar tastes to yours, focusing on shared likes rather than movie content.

Our goal is to make your movie-watching experience more enjoyable and tailored to your personal preferences, without getting bogged down in technical complexities.