Weather Prediction using Linear Regression

This is an extensive and well-documented implementation of a linear regression model using both univariate and multivariate approaches to predict the next day's maximum temperature based on various features. The provided code covers:

Data Import and Preparation:

Loading the dataset and extracting relevant features and target variables. Splitting the dataset into training and test sets.

Univariate Regression:

Training a model using one feature (Present_Tmax) to predict the next day's temperature. Implementing gradient descent for parameter optimization. Visualizing the results with plots.

Feature Analysis:

Calculating and visualizing the correlation between different features and the target variable (Next_Tmax).

Improving Performance:

Exploring the effect of different features on the model's performance.

Multivariate Regression:

Extending the model to include multiple features. Using gradient descent for parameter optimization. Visualizing the cost over epochs.

Comparison with All Features:

Training a model using all available features. Comparing the final cost values between models trained with a subset of features and all features.

Analysis:

Suggesting experiments with different learning rates and train-test split ratios to observe their impact on model performance.