Machine Learning Approaches for Accurate Rainfall Prediction and prepardeness
Objective
The primary objective of this project is to evaluate and compare multiple machine learning algorithms for their effectiveness in predicting rainfall patterns based on historical weather data. By utilizing a dataset comprising diverse climatic features—such as temperature, humidity, wind speed, and atmospheric pressure—the project aims to identify the algorithms that yield the highest accuracy in forecasting rainfall events.
Abstract
Rainfall prediction is the one of the important techniques to predict the climatic conditions in any country. This paper proposes a rainfall prediction model using Naive Bayes, Decision Trees, SVM, Logistic Regression, ANN, and LSTM. The input data is having multiple meteorological parameters and to predict the rainfall in more precise. The accuracy, correlation are the parameters used to validate the proposed model. From the results, the proposed machine learning model provides better results than the other algorithms in the literature.
KEYWORDS : Naive Bayes, Decision Trees, SVM, Logistic Regression, ANN, and LSTM enhance rainfall prediction through machine learning.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE & SOFTWARE REQUIREMENTS:
1. Processor : I3/Intel Processor window 7+
2. RAM : 4GB (min)
3. Server side Script : Python 3.6+
4. IDE : Jupyter notebook
5. Libraries Used : Sklearn, Pandas, matplotlib, seaborn
6. Dataset : Rain fall data from Kaggle /UCI repository
Demo Video
Request for Video
Paper PublishingRequest Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
