Rainfall prediction is a critical task in weather analytics that supports agriculture planning, water resource management, and disaster preparedness.
In this project, a machine learning based approach is used to predict whether rainfall will occur based on historical weather conditions.
The goal is to build a reliable classification model using structured weather data and follow a complete end to end machine learning workflow.
Project Objectives
The main objectives of this project are:
Analyze historical weather data
Perform data cleaning and feature selection
Understand relationships between weather parameters
Handle class imbalance in rainfall data
Train a machine learning classification model
Optimize the model using hyperparameter tuning
Evaluate model performance using reliable metrics
Libraries and Tools Used
The following libraries and tools are used throughout the project:
Python as the core programming language
Pandas for data loading, manipulation, and preprocessing
NumPy for numerical computations
Matplotlib and Seaborn for data visualization and exploratory analysis
Scikit learn for model training, resampling, evaluation, and hyperparameter tuning
Dataset Description
The dataset contains daily weather observations collected over a period of time.
Key characteristics of the dataset:
Each row represents a single day of weather data
Features include temperature, humidity, pressure, wind speed, cloud cover, and sunshine
The target variable represents rainfall occurrence
The dataset is structured and suitable for supervised learning
Data Cleaning and Preprocessing
Before training the model, the dataset is carefully prepared.
The preprocessing steps include:
Inspecting the dataset structure and data types
Checking for missing or inconsistent values
Removing highly correlated features using correlation analysis
Reducing multicollinearity to improve model stability
Preparing the final feature set for training
Exploratory Data Analysis
Exploratory Data Analysis is performed to understand the behavior of different weather parameters.
Key EDA steps include:
Analyzing feature distributions using visualizations
Studying correlations between temperature, humidity, pressure, and rainfall
Identifying patterns that influence rainfall occurrence
Using insights from EDA to guide feature selection
Handling Class Imbalance
Rainfall datasets often have more non rainy days than rainy days, leading to class imbalance.
To address this issue:
The majority class is identified
Downsampling is applied to balance the dataset
Both classes are given equal importance during training
This prevents the model from becoming biased toward non rainy predictions
Model Selection
A Random Forest Classifier is chosen for rainfall prediction due to its strong performance on tabular data.
Reasons for choosing Random Forest:
Handles non linear relationships effectively
Reduces overfitting through ensemble learning
Performs well without extensive feature scaling
Provides stable and reliable predictions
Hyperparameter Tuning
To improve model performance, hyperparameter tuning is performed using GridSearchCV.
This process involves:
Testing multiple combinations of model parameters
Using cross validation to evaluate each combination
Selecting the parameter set that generalizes best
Avoiding underfitting and overfitting
Model Evaluation
The optimized model is evaluated using multiple metrics.
Evaluation steps include:
Measuring accuracy on unseen test data
Analyzing classification metrics for better insight
Using cross validation scores to confirm consistency
Ensuring the model performs reliably across different data splits
Conclusion
This project demonstrates a complete and structured machine learning pipeline for rainfall prediction.
By combining proper data preprocessing, exploratory analysis, class balancing, ensemble modeling, and hyperparameter tuning, the system produces reliable predictions.
The same workflow can be extended to other weather forecasting and environmental analytics problems.