The goal is to predict whether it will rain the next day or not, based on historical weather data from Sydney. The dataset includes features such as temperature, humidity, pressure, and previous rainfall data, which can be utilized to build predictive models.

Approach :

Collecting the data and understanding the dataset.

Data Preprocessing • Handle missing values • Convert categorical variables to numeric

Exploratory Data Analysis (EDA): Analyze the dataset to identify patterns, correlations, and outliers with visualizations.

Feature selection of variables.

Choose several Classification models to train and evaluate, including Logistic Regression, Decision Tree Classifier and Ensemble Methods such as Random Forests, Bagging and Boosting.

Split the data into training and testing sets. Train the models and evaluate their performance using metrics like accuracy, precision, recall and confusion matrix.

There are several classification models to train and evaluate, like 'Logistic Regression', 'KNN', 'Decision Tree Classifier' and Ensemble Methods such as 'Random Forests', 'Bagging' and 'Boosting'.