Project aims to forecast potato prices in India using LSTM, KNN, and Random Forest Regression, integrating historical data on prices, regional stats, and rainfall patterns. Targeting agricultural stakeholders for informed decision-making.

This project implements an Artificial Music Generator using LSTM (Long Short-Term Memory) networks, a type of recurrent neural network (RNN). The system generates music character by character based on a given input dataset.

Common machine learning algorithm implementations

This is a repository of AI projects that i did during my master's degree in computer science.

Deep Learning in python

Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

Generating Shakespearean Text with LSTM Neural Networks: This project uses LSTM networks to generate text in the style of William Shakespeare. It explores the intersection of literature and AI by mimicking the rich linguistic nuances and poetic depth of Shakespearean prose and poetry.

This repository contains notes, slides, labs, assignments and projects for the Deep Learning Specialization by DeepLearning.AI and Coursera.

We utilize LSTM networks to forecast Microsoft Corporation's stock prices. We gather comprehensive historical data, preprocess it, construct LSTM models, train and evaluate them, and provide future price predictions.

Developing a PyTorch-based solution for predicting future values in financial time series data, leveraging RNNs and GRUs as part of the M3 competition for time series forecasting.

This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.

Stringlifier is on Opensource ML Library for detecting random strings in raw text. It can be used in sanitising logs, detecting accidentally exposed credentials and as a pre-processing step in unsupervised ML-based analysis of application text data.

Stock Trend Prediction with LSTM is a powerful tool designed to empower users with insights into the dynamic world of stock market trends. Leveraging cutting-edge technologies such as Long Short-Term Memory (LSTM) networks and real-time data from Yahoo Finance, this project enables users to forecast future price movements of stocks with precision.

This repository features notebooks and datasets for predicting Tesla (TSLA) stock prices using LSTM models. Explore historical data, forecast trends, and gain insights into TSLA's market movements.

Natural Language Processing | BRACU

End-2-end speech synthesis with recurrent neural networks

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

A project dedicated to identifying the best classifier for clickbait detection, which includes the dataset and the associated research paper. Classifiers tested include classic ML models (MNB, LR, SVM), ensembles (RF, GB) and DL models (LSTM and Transformers), among others.

Remaining Useful Life Prediction