This notebook implements the Gradient descent algorithm to fit a linear regression model. Various machine learning techniques are explored as part of this exercise. A rough outline is as follows:
- Data Input
- EDA
- Linear Regression using Gradient Descent
- Linear Regression Cost Function
- Linear Regression Gradient Function
- Gradient Descent Function
- Fitting Linear Regression Parameters
- Learning Rate Tuning
- Parameter Interpretation and Visualization
- Polynomial Regression
- Model Evaluation: Cross-validation
Note: The Notebook may not open on GitHub. Please download the files to explore. Thanks!
Dataset info: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition. Accessed from UCI Machine Learning repository (https://archive.ics.uci.edu/ml/datasets/Auto+MPG)