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kingsleyosunkwo/Lagos-Flood-Prediction--HNG-Task-2-

Domain:

climate

Record type:

project
Creator:
kin
Host:
This project aims to develop a model for predicting flood events in Lagos, Nigeria. By analyzing historical data on rainfall, water levels, and other relevant factors, the model will attempt to identify patterns and trends that can forecast future flood risks. # Lagos-Flood-Prediction This project aims to develop a model for predicting flood events in Lagos, Nigeria. By analyzing historical data on rainfall, water levels, and other relevant factors, the model will attempt to identify patterns and trends that can forecast future flood risks. ## Lagos Flood Prediction- (HNG Task 2) --- ### Introduction Flooding is a significant issue in Lagos, Nigeria, causing damage to infrastructure, displacement of residents, and economic losses. Predicting floods accurately can help in preparing and mitigating these adverse effects. This project aims to predict flood occurrences in Lagos using historical weather data and machine learning techniques. ### Tools Used The following tools were employed for this analysis: 1. Spreadsheet: For data gathering, cleaning, and Labelling. 2. Python: The primary programming language used for data analysis and model building. 3. Pandas: For data manipulation and cleaning within the python environment. 4. NumPy: For numerical operations. 5. Matplotlib and Seaborn: For data visualisation. 6. Scikit-learn: For machine learning model implementation. 7. Joblib: For saving and loading the machine learning model. 8. Jupyter Notebook: The environment for interactive data analysis and model building. ### Methodology 1. Data Collection: Historical weather data for Lagos was collected, including features such as temperature, humidity, precipitation, wind speed, and solar radiation. This was obtained from a dataset from Kaggle. The dataset has the following columns: 1. datetime: The date of the record. 2. tempmax: Maximum temperature recorded on that day (°C). 3. tempmin: Minimum temperature recorded on that day (°C). 4. temp: Average temperature of the day (°C). 5. feelslikemax: Maximum 'feels like' temperature recorded on that day (°C). 6. feelslikemin: Minimum 'feels like' temperature recorded on that day (°C). 7. feelslike: Average 'feels like' temperature of the day (°C). 8. dew: Dew point (°C), the …

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