Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

• Implemented linear regression and k nearest neighbors algorithm with gradient descent optimization to make an optimal model for predicting house prices using the Seattle King County dataset.
• Performed feature engineering and selection using lasso and ridge penalties to eliminate features which had little or no impact on the residual sum of squares error.

1. Simple Linear Regression
2. Multiple Linear Regression
3. Multiple Linear Regression with Gradient Descent Optimization
4. Polynomial Regression
5. Ridge Regression
6. Ridge Regression with Gradient Descent Optimization
7. Lasso Regression
8. Nearest Neighbor Regression

Python

Anaconda, Graphlab Create Installation guide

Jupyter notebook (IPython)

1. Fork this repository to have your own copy
2. Clone your copy on your local system
3. Install necessary packages

This repository does not contain optimal machine learning models! It only assesses various models that can be built using different machine learning algorithms (either implemented or used directly from Graphlab Create package) to perform different tasks.