The script starts by importing necessary libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, XGBoost, and Imbalanced-learn.
The dataset is loaded using Pandas from a CSV file named 'weatherHistory.csv'. It's then displayed briefly using the head() method.
info(): Provides information about the dataset, including the data types and number of non-null values in each column.
Handling missing values: Calculating the percentage of missing values for each column,Dropping rows with missing values.
Correlation heatmap using Seaborn's heatmap() function.
Defining features (independent variables) and target (dependent variable),Splitting the dataset into training and testing sets using train_test_split().
Training two classifiers: Logistic Regression and XGBoost.
Making predictions on the test set.Calculating accuracy scores using accuracy_score() from Scikit-learn.Generating confusion matrices using confusion_matrix() from Scikit-learn.Displaying classification reports using classification_report() from Scikit-learn.