Flood Affected Prediction

In this notebook, various steps were undertaken to analyze and preprocess a dataset for predicting flood-affected areas. Initially, essential libraries such as numpy, pandas, seaborn, and matplotlib were imported, followed by loading the dataset from the Kaggle input directory. The dataset was explored by displaying the first few rows and checking the data types and general information. A heatmap was generated to check for missing values, confirming there were none. The data was further described to identify potential outliers in specific columns. For data preprocessing, Label Encoding was applied to convert categorical columns (protocoltype, service, flag) into numerical format, and the original categorical columns were subsequently dropped. The processed features were then combined with the target column. Additionally, visualizations were created for each column to understand the data distribution better. This comprehensive approach ensured the dataset was ready for further analysis and model building.