https://github.com/teja-1403/coursera-machine-learning-with-python-honors

This project involves building a classifier to predict rainfall for the next day based on weather data from the Australian Government's Bureau of Meteorology. Various machine learning techniques such as Linear Regression, KNN, Decision Trees, Logistic Regression, and SVM were implemented and evaluated.
https://github.com/teja-1403/coursera-machine-learning-with-python-honors

classification hierarchical-clustering machine-learning regression scikit-learn scipy

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This project involves building a classifier to predict rainfall for the next day based on weather data from the Australian Government's Bureau of Meteorology. Various machine learning techniques such as Linear Regression, KNN, Decision Trees, Logistic Regression, and SVM were implemented and evaluated.

• Host: GitHub
• URL: https://github.com/teja-1403/coursera-machine-learning-with-python-honors
• Owner: teja-1403
• License: mit
• Created: 2025-01-02T04:35:37.000Z (8 days ago)
• Default Branch: main
• Last Pushed: 2025-01-02T04:43:54.000Z (8 days ago)
• Last Synced: 2025-01-02T05:26:32.111Z (8 days ago)
• Topics: classification, hierarchical-clustering, machine-learning, regression, scikit-learn, scipy
• Language: Jupyter Notebook
• Homepage: https://www.coursera.org/learn/machine-learning-with-python/supplement/F0Ugl/project-scenario
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Coursera: Machine Learning with Python: Final Project

This project involves building a classifier to predict rainfall for the next day based on weather data from the Australian Government's Bureau of Meteorology. Various machine learning techniques such as Linear Regression, KNN, Decision Trees, Logistic Regression, and SVM were implemented and evaluated using key metrics such as Accuracy Score, Jaccard Index, F1-Score, LogLoss, Mean Absolute Error, Mean Squared Error, and R2-Score.

**Project Scenario:**

This is an Honors Project. In this project, you will complete a notebook where you will build a classifier to predict whether there will be rain the following day.

For your project, you will use a rainfall dataset from the Australian Government's Bureau of Meteorology, clean the data, and apply different classification algorithms to the data. Alternatively, you can download your data from [here](http://www.bom.gov.au/climate/dwo/). 

You are expected to use the following algorithms to build your models:

1.  Linear Regression

2.  KNN

3.  Decision Trees

4.  Logistic Regression

5.  SVM

The results are reported as the accuracy of each classifier, using the following metrics when these are applicable:

1. Accuracy Score

2. Jaccard Index

3. F1-Score

4. LogLoss

5. Mean Absolute Error

6. Mean Squared Error

7. R2-Score

Notebook URL: Alternatively, you can work on your local machine or any other environment of choice by downloading this link: [Final Assignment](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%206/ML0101EN_SkillUp_FinalAssignment.jupyterlite.ipynb).

There are a total of 53 points for 9 tasks in this final project. Your final assignment will be graded by your peers who are also completing this assignment within the same session.  You need to submit the following item for peer-reviews.

a.ipynb file of your Jupyter Notebook

This final project will be graded by your peers who are completing this course during the same session. This project is worth 53 points, broken down as follows:

1.     Splitting the dataset into training and testing data for regression (3 marks)

2.     Building and training a model using Linear Regression and calculating evaluation metrics (8 marks)

3.     Creating a final regression report/table of evaluation metrics (3 marks)

4.     Building and training a model using KNN and calculating evaluation metrics (8 marks)

5.     Building and training a model using Decision Trees and calculating evaluation metrics (8 marks)

6.     Building and training a model using Logistic Regression and calculating evaluation metrics (9 marks)

7.     Building and training a model using SVM and calculating evaluation metrics (8 marks)

8.     Creating a final classification report/table of evaluation metrics (3 marks)

You will also be asked to upload your Jupyter Notebook that you have created. You will receive marks for displaying your notebook. Your peers will review your Jupyter Notebook and evaluate Your Responses to each Question in the Assignment.