Paper PublishingMachine Learning based Rainfall Prediction
Objective
This paper proposes a rainfall prediction model using machine learning models such as Multiple Linear Regression (MLR) for considered dataset. The input data is having multiple meteorological parameters and to predict the rainfall in more precise.
Abstract
Rainfall prediction is the one of the important techniques to predict the climatic conditions in any country. This application proposes a rainfall prediction model using Multiple Linear Regression (MLR) for Indian dataset. The input data is having multiple meteorological parameters and to predict the rainfall in more precise. The Mean Square Error (MSE), 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: Multiple Linear Regression, Rainfall, Prediction, Machine Learning, LSTM, RNN.
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 SPECIFICATIONS:
- Technology: Machine Learning
- Libraries: Pandas, Numpy, Matplotlib, Sklearn.
- Version: Python 3.6+
- IDE: Jupiter notebook
HARDWARE SPECIFICATIONS:
- RAM: 8GB, 64-bit os.
- Processor: I3/Intel processor
- Hard Disk Capacity: 128 GB +
Learning Outcomes
- Scope of Real Time Application Scenarios
- Objective of the project.
- How Internet Works.
- What is a search engine and how browser can work.
- What type of technology versions are used.
- Use of HTML , and CSS on UI Designs.
- Data Parsing Front-End to Back-End.
- Working Procedure.
- Introduction to basic technologies used for.
- How project works.
- Input and Output modules.
- Frame work use.
- What is rainfall prediction.
- What are climatic conditions.
- Datasets properties.
- What is machine learning.
- Machine learning algorithms.
- Data preprocessing techniques.
- What is supervised learning.
- What is unsupervised learning.
- What is multi linear regression.
- What is mean square error.
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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