Predict emotions (happiness, anger, sadness) from WhatsApp chat data using machine learning and deep learning models. Includes text normalization, vectorization (TF-IDF, BoW, Word2Vec, GloVe), and model evaluation.

🗨️ This repository contains a collection of notebooks and resources for various NLP tasks using different architectures and frameworks.

This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.

The repository focuses on developing a comprehensive business opportunity analysis system that uses geospatial data, sentiment analysis, and topic modeling. The objective is to leverage these techniques to identify and evaluate potential business opportunities in area of interest.

The repository focuses on developing a comprehensive business opportunity analysis system that uses geospatial data, sentiment analysis, and topic modeling. The objective is to leverage these techniques to identify and evaluate potential business opportunities in area of interest.

Master thesis project - a hybrid Neural Network-Decision Tree system and dataset for classical music form recognition and analysis.

Repo for Implementing Research Papers & Projects related to Machine Learning

A bangla chatbot using bidirectional lstm

The Sea Ice Extent of 5 Arctic and Antarctic regions is forecasted using CNN+LSTM, Bidirectional LSTM and Standalone LSTM.

This project employs a deep neural network architecture for the classification of toxic comments, utilizing the Kaggle competition dataset from the Jigsaw Toxic Comment Classification Challenge.

Sentiment analysis on IMDB movie reviews using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells for binary classification of positive and negative sentiments.

The primary objective of this project is to develop a robust system capable of accurately classifying patient conditions solely based on their reviews. By leveraging advanced NLP techniques, the project aims to streamline the categorization process and provide valuable insights into patient health status.

Repo containing Channel Quality Indicator (CQI) data from real car routes in Greece. It contains a reproducable notebook with the implementation of a Bidirectional LSTM Neural Network for real-time CQI forecasting in heterogeneous ultra-dense beyond-5G networks.

This project implement basic OCR for Vietnamese from scratch with Pytorch, using CNN and BidirectionalLSTM

Detect various kind of data of badminton matches using Bidirectional LSTM || AI Cup 2023 - Teaching Computers to Watch Badminton Matches (5th place) / 教電腦看羽球 (第五名)

Sequence-to-Sequence Generative Model for Sequential Recommender System

Implementation of Handwritten Text Recognition Systems using TensorFlow

Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution

Text Emotion System Sentiment Analysis (TESSA) is an open-source project focused on sentiment analysis. 💬

I crafted a robust sentiment analysis model featuring Bidirectional LSTM layers and embeddings. Leveraging a Sequential model structure and fine-tuning with 'sparse_categorical_crossentropy' loss and 'adam' optimizer, the implementation excels in capturing contextual nuances for precise emotion classification in natural language data.