Shamoo100/Lagos-Flood-Prediction

A Machine Learning Approach to Predicting Floods In Lagos Nigeria # Lagos-Flood-Prediction A Machine Learning Approach to Predicting Floods In Lagos Nigeria Flood Prediction Using Gradient Boosting Model This project predicts flood events using historical weather data from Lagos, Nigeria. The prediction is performed using Gradient Boosting models. The aim is to predict the next date when a flood event will occur based on a series of input dates. ## Project Structure - **Data**: - `lagos_weather_data_2002_to_2024.csv`: Historical weather data used for training the models. - `Lagos 2024-07-06 to 2024-12-31.csv`: Weather data used for making future predictions. - **Code**: - `flood_prediction.py`: Main script containing data preprocessing, model training, and prediction functions. - `README.md`: Project documentation. ## Requirements - Python 3.7+ - Libraries: - pandas - numpy - scikit-learn - matplotlib You can install the required libraries using the following command: ```sh pip install pandas numpy scikit-learn matplotlib pip install tensorflow pip install pandas pip install numpy pip install matplotlip ``` ## Data Cleaning and Preprocessing: -Handle missing values. -Convert date columns to datetime format. -Ensure numeric columns are in the correct format. ## Feature Engineering: -Create additional features if necessary, such as rolling averages or lag features. -Encode categorical variables if any. ## Exploratory Data Analysis (EDA): -Visualize the distribution of key variables. -Explore correlations between features and the target variable (flooding events). -Identify any seasonal patterns or trends. ## Model Building: -Split the data into training and testing sets. -Select appropriate models for time series prediction or classification (e.g., Logistic Regression, Random Forest, Gradient Boosting, etc.). -Train and validate the models using cross-validation. -Evaluate the models based on performance metrics (e.g., accuracy, precision, recall, F1 score). ## Model Evaluation and Selection: -Compare t …