Weather-Prediction-using-Regression

This project demonstrates the application of Ridge Regression for weather prediction. The model utilizes historical weather data to predict future weather conditions, focusing on parameters such as precipitation, average temperature, and minimum temperature.

Project Overview

• Data Source: The dataset weather_data.csv is used, which contains weather observations indexed by date. The dataset is also added to this repository.
• Model Used: Ridge Regression, a type of linear regression that includes a regularization term to prevent overfitting.
• Steps Involved:
  1. Data Loading: Importing and preprocessing the weather dataset.
  2. Exploratory Data Analysis: Visualizing and understanding the dataset's features.
  3. Model Training: Training the Ridge Regression model on the preprocessed data.
  4. Prediction: Using the trained model to predict weather parameters for future dates.
  5. Evaluation: Assessing the model's performance using relevant metrics.