Gradient Descent

- Exploratory Data Analysis (EDA):

- Perform basic EDA to understand the dataset.

- Visualize relationships between features and the target variable.

- Data Preprocessing:

- Handle missing values (if any).

- Scale features as necessary for gradient descent.

- Model Implementation:

- Implement gradient descent from scratch for linear regression.

- Compare results with scikit-learn's implementation of linear regression.

- Hyperparameter Tuning:

- Experiment with different learning rates and observe their effects.

- Include visualizations of cost function convergence.