Rainfall prediction classifiers This project involves building a Rainfall Prediction Classifier using machine learning techniques.
Objective:
- Predict whether it will rain tomorrow based on historical weather data.
Key Steps:
- Data Collection: Collect weather data with features like temperature, humidity, pressure, wind, and rainfall.
- Data Preprocessing: Clean the data and convert categorical fields into numerical formats suitable for modeling.
- Feature Selection: Identify the most relevant features that influence rainfall.
- Model Training: Use machine learning algorithms such as Logistic Regression and Random Forest classifiers to train predictive models.
- Model Evaluation: Evaluate models on metrics like accuracy and true positive rate using a test dataset.
- Model Selection: Choose the best-performing model for rainfall prediction.
- Deployment: Export the model for practical use in predicting next-day rainfall.
Technologies:
- Python, pandas, scikit-learn for model building and evaluation.
- Optionally Jupyter Notebook or code editors like VS Code.
- GitHub for version control and submission.
Outcome:
- A functional classifier that accurately predicts rainfall, aiding meteorological analyses and planning.
This project teaches practical skills in data science workflow, classification algorithms, and performance evaluation.