Weather Prediction Using Machine Learning — Australia Weather Dataset

This project focuses on building a machine learning system to predict weather conditions using the Australian Weather Dataset (WeatherAUS). The notebook includes data cleaning, visualization, feature selection, and training multiple ML models to compare accuracy and performance.

1. Dataset

The dataset contains historical weather data collected across multiple Australian locations.

Includes features such as temperature, humidity, pressure, rainfall, wind speed, cloud cover, and more.

The target variable typically predicts whether it will rain tomorrow (binary classification).

2. Preprocessing Steps

Handling missing values

Converting categorical values into numerical encoding

Splitting dataset into features (X) and labels (y)

Normalizing/standardizing numerical columns

Preparing the data for machine learning models

3. Exploratory Data Analysis (EDA)

Visualizing variable distributions

Checking correlations between weather features

Identifying patterns that influence rainfall

Plotting graphs for temperature, humidity, rainfall, etc.

4. Machine Learning Models Used

The project trains and evaluates multiple ML models, including:

Logistic Regression

Random Forest Classifier

Decision Trees

Support Vector Machine (SVM)

Naive Bayes

Each model is tested on unseen data to measure prediction accuracy.

5. Model Evaluation

The notebook includes evaluation using:

Accuracy

Precision

Recall

F1-score

Confusion Matrix

Classification Report

These metrics help identify which algorithm performs best for weather prediction.

6. Summary

This project demonstrates how machine learning can be applied to climate data for predictive weather analysis. It includes a full pipeline from raw dataset → preprocessing → EDA → ML training → evaluation.

The system can be extended for real-time forecasting and integrated into smart agriculture or climate monitoring applications.