Coursera ML Final Assignment (MK)
Overview:
This repository contains the final assignment for the "Machine Learning with Python" course offered by IBM on Coursera. The project focuses on predicting Australian weather using various machine learning algorithms implemented in a single Colab notebook.
Dataset:
The dataset used in this project contains historical Australian weather data, including features such as Date, MinTemp, MaxTemp, Rainfall, Evaporation, Sunshine, WindGustDir, WindGustSpeed, WindDir9am, WindDir3pm ... Humidity9am, Humidity3pm, Pressure9am, Pressure3pm, Cloud9am, Cloud3pm, Temp9am, Temp3pm, RainToday. The target variable is RainTomorrow.
Project Structure:
Coursera_projects/ Directory containing the dataset file (weather_Data.csv). Coursera ML Final Assignment(MK).ipynb: Colab notebook containing code for data preprocessing, model training, and evaluation. README.md: This file, providing an overview of the project.
Algorithms Implemented:
The Colab notebook Coursera ML Final Assignment(MK).ipynb includes the implementation of the following machine learning algorithms for weather prediction:
- Linear Regression
- K Nearest Neighbors (KNN)
- Logistic Regression
- Decision Tree
- Support Vector Machine (SVM)
Usage:
Clone this repository to your local machine. Open and execute the colab notebook Coursera ML Final Assignment(MK).ipynb. Follow the instructions within the notebook to run each algorithm and evaluate its performance.
Dependencies:
Python 3.x, Google colab Notebook, NumPy, Pandas, Scikit-learn,
Install the required packages using pip install -r requirements.txt.
License:
This project is licensed under the MIT License. See the LICENSE file for more details.
Author:
M. Karthika