Python
A Novel Integrated Approach for Stock Prediction Based on Modal Decomposition Technology and Machine Learning
Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches
Efficient Machine Learning Approach For Crime Detection In India
The goal of this project is to create a reliable model for predicting droughts in regions that are vulnerable to them. Using Indian rainfall data, the project applies ARIMA and SARIMAX models to forecast droughts. The project aims to support better planning and response strategies, helping communities prepare for and mitigate the effects of droughts.
Predicting Market Performance Using Machine and Deep Learning Techniques
The aim of this study is to evaluate the effectiveness of various machine learning and deep learning algorithms, including LSTM networks, ARIMA models, and traditional machine learning techniques, for forecasting market prices. We analyze the performance of these models on stock historical datasets and compare their predictive accuracy to determine the most suitable approach for real-time market analysis. This research seeks to provide insights into the predictability of markets and support informed decision-making for investors
