weather forecasting

This project is a machine learning model designed to predict weather conditions based on historical data.

Using Python with libraries such as scikit-learn, pandas, and NumPy, the model processes and analyzes weather datasets to forecast parameters like temperature, humidity, or rainfall.

Key Features:

Data cleaning and preprocessing of historical weather records.

Training a Random Forest model for accurate predictions.

Evaluation and optimization to improve forecasting accuracy.

Clear, well-documented Python code and visualizations of the results.

Outcome:

A practical and scalable tool that demonstrates how machine learning can be applied to real-world forecasting problems.