RainfallPredict
Predicting rainfall patterns using Indonesia's climate data for sustainable solutions.
- Master Project by Bharath Reddy Jakkidi
RainfallPredict focuses on leveraging Indonesia’s climate dataset to develop predictive models for rainfall forecasting. This project aims to provide actionable insights for sustainable planning, agriculture, and disaster preparedness by analyzing rainfall patterns and their implications.
Project Overview
The project is dedicated to building predictive models for rainfall using the Indonesia Climate Dataset from Kaggle. This dataset offers extensive historical climate data, including temperature, humidity, rainfall, and wind metrics. RainfallPredict demonstrates how machine learning can address climate-related challenges and support sustainable development.
Objectives
- Rainfall Prediction: Create robust models to forecast daily rainfall based on historical weather data.
- Data Exploration: Analyze and preprocess climate data for effective feature selection and engineering.
- Practical Applications: Provide insights that support agricultural planning, flood prevention, and resource management.
- Skill Development: Enhance expertise in predictive modeling and machine learning through real-world environmental data.