Exploratory Data Analysis and Model Evaluation for Flood Probability Prediction
In this project, we conduct an exploratory data analysis (EDA) on a dataset containing various features related to flood events. The primary objective is to predict the probability of flooding using machine learning models. We start by identifying numerical features and visualizing their distributions to gain insights into the data. Subsequently, we prepare the data for modeling, split it into training and testing sets, and standardize the features using StandardScaler.
We begin by selecting numerical columns from the dataset and creating histograms to visualize their distributions. The target variable, 'FloodProbability', is highlighted with a distinct color for clarity. This analysis provides valuable insights into the distribution and spread of numerical features, essential for understanding the data's characteristics.
For flood probability prediction, we employ two machine learning models: Linear Regression and LightGBM Regressor. Each model is trained using a pipeline that includes feature scaling and the respective regression algorithm. We conduct 5-fold cross-validation to evaluate model performance, using R-squared as the evaluation metric. The mean and standard deviation of R-squared scores are reported for each model, providing a comprehensive assessment of their predictive capabilities.
The results of our analysis and model evaluation demonstrate the feasibility of predicting flood probabilities using machine learning techniques. By leveraging numerical features and employing robust regression algorithms, our models exhibit promising performance in estimating flood probabilities. These findings contribute to the advancement of flood prediction methodologies, offering valuable insights for disaster preparedness and risk management efforts.
The predicted flood probabilities are incorporated into a submission dataframe and saved as a CSV file ('submission.csv'). This file serves as our final submission, encapsulating the predictions generated by our machine learning models.
This description highlights the key steps involved in the machine learning project, from data exploration to model training and submission preparation, emphasizing the importance of each stage in the predictive modeling process.