aniketraut16/rain-predictor

This repository contains my first Machine Learning project: a Rain Predictor. The project involves predicting rainfall based on weather data, focusing on cloud cover and humidity. It includes data gathering, preprocessing, model training, and evaluation steps. The primary aim was to gain hands-on experience with the ML workflow.

Rain Predictor

Overview

This repository contains a simple Machine Learning project aimed at predicting whether it will rain based on weather data. The project serves as a learning exercise to understand the end-to-end process of building an ML model.

Project Workflow

1. Data Collection: The dataset was sourced from Kaggle and includes various weather features.
2. Feature Selection: Chose cloud cover and humidity as the key features for prediction.
3. Data Filtering: Cleaned and prepared the data for model training.
4. Data Splitting: Divided the data into training and testing sets.
5. Data Scaling: Scaled the training data to ensure values are between 0 and 1.
6. Model Training: Employed the Logistic Regression algorithm to train the model.
7. Model Testing: Evaluated the model, achieving an accuracy of 79%.
8. Model Export: Saved the trained model to a .pkl file.

Repository Structure

• data/: Contains the rainfall.csv dataset.
• notebooks/: Includes the Jupyter notebook with detailed analysis and model training steps.
• results/: Holds the exported model file (model.pkl).

Purpose and Future Work

The primary purpose of this project was to gain practical experience with the ML project workflow rather than to deliver a highly optimized solution. Future work will focus on understanding the rationale behind algorithm choices, improving model accuracy, and refining data preprocessing steps.