Rainfall Prediction using 7 Popular Models

Context and Methodology

Research Domain/Project:

This dataset is part of a machine learning project focused on predicting rainfall, a critical task for sectors like agriculture, water resource management, and disaster prevention. The project employs machine learning algorithms to forecast rainfall occurrences based on historical weather data, including features like temperature, humidity, and pressure.

Purpose:

The primary goal of the dataset is to train multiple machine learning models to predict rainfall and compare their performances. The insights gained will help identify the most accurate models for real-world predictions of rainfall events.

Creation Process:

The dataset is derived from various historical weather observations, including temperature, humidity, wind speed, and pressure, collected by weather stations across Australia. These observations are used as inputs for training machine learning models. The dataset is publicly available on platforms like Kaggle and is often used in competitions and research to advance predictive analytics in meteorology.

Technical Details

Dataset Structure:

The dataset consists of weather data from multiple Australian weather stations, spanning various time periods. Key features include:

Temperature
Humidity
Wind Speed
Pressure
Rainfall (target variable)
These features are tracked for each weather station over different times, with the goal of predicting rainfall.

Software Requirements:

Python: The primary programming language for data analysis and machine learning.
scikit-learn: For implementing machine learning models.
XGBoost, LightGBM, and CatBoost: Popular libraries for building more advanced ensemble models.
Matplotlib/Seaborn: For data visualization.
These libraries and tools help in data manipulation, modeling, evaluation, and visualization of results.
DBRepo Authorization: Required to access datasets via the DBRepo API for dataset retrieval.

Additional Resources

Model Comparison Charts: The project includes output charts comparing the performance of seven popular machine learning models.
Trained Models (.pkl files): Pre-trained models are saved as .pkl files for reuse without retraining.
Documentation and Code: A Jupyter notebook guides through the process of data analysis, model training, and evaluation.