Prediction of Flood in Bangladesh using k-Nearest Neighbors Algorithm

Prediction of Flood in Bangladesh using k-Nearest Neighbors Algorithm

₹5,500.00
Product Code: Python - Machine Learning
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Product Description

Aim:

           Analyze the flood data from database for flood prediction.

Abstract:                           

          The unusual rainfall and global climate change has led to floods in different parts of the world. Floods are one of the worst affecting natural phenomena which causes heavy damage to property, infrastructure and most importantly human life. .This paper presents the Flood prediction and Rainfall analysis using Machine Learning .The main goal of employing this application is to prevent immediate impacts of flood .This application can be easily used by the common people or government to predict the occurrence of flood beforehand . Nowadays, machine learning (ML) methods are highly contributed in the advancement of prediction systems. These methods are providing better performance as well as cost effective solutions. The advancement in the information technology, the need for easy accessibility of large cloud storage and processing power is available. Data mining technologies helps us to provide reference for decision makers as summarized information even from the large amount of data. Among many data mining techniques, classification is a widely used one. Past studies proposed many techniques that could be applied to classification, such as random forest, neural networks, Bayesian classifiers. We use multiple algorithms such as Logistic Regression, Naive Bayes, K-Nearest Neighbors and Random Forest.

Proposed System:


           The aim of this project is to get all the flood data and from a dataset containing yearly flood data. By providing real time input to different models of machine learning, those are Logistic Regression, Naive Bayes, K-Nearest Neighbors and Random forest. To overcome the fallback in the existing system we propose a machine learning based system to increase the efficiency and accuracy. In our project, Random Forest algorithm gives 94% accuracy level compared with other algorithms. Finally, we are predicting the result via data visualization.

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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.