Weather Forecasting Using Deep Learning Techniques
Objective
The objective of weather forecasting using deep learning techniques is to leverage the power of neural networks and machine learning algorithms to accurately predict future weather conditions based on historical data and other relevant environmental factors. The primary goal is to improve the accuracy and reliability of weather predictions, leading to better preparedness and decision-making for individuals, businesses, and governments.
Abstract
The objective of this project is to develop a robust weather forecasting model using deep learning techniques. Leveraging historical weather data and machine learning algorithms, including logistic regression, naive Bayes, CNNs, and MLPs, the aim is to accurately predict weather types. The project seeks to achieve high prediction accuracy, demonstrating the potential of data mining methods for enhancing weather forecasting capabilities.
Keywords: Cnn, Mlp, demonstrating, weather, high prediction accuracy.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SOFTWARE REQUIREMENS
Operating System : Windows 7/8/10
Server side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries : Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy
IDE/Workbench : PyCharm, VSCode, Jypyter NoteBook
Technology : Python 3.6+
Server Deployment : Xampp Server
Database : MySQL
HARDWARE REQUIREMENTS
PROCESSOR - I5/Intel Processor
Hard Disk - 160GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - Any
RAM - 8GB
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.
