pierogio/ml_regressions

Application of Machine Learning Regression to Housing Data.

ML_regressions

What is this project about.

After conducting an exploratory data analysis (EDA) and thorough data visualization, we are poised to leverage the power of supervised machine learning to predict housing prices. The goal is to compare the performance of these algorithms and visualize the results. Through the application of machine learning, our objective is to draw accurate conclusions about the process.

Algorithms Used and Compared

In our analysis, we've employed and compared the following machine learning algorithms:

• Linear Regression
• Ridge Regression
• k-Nearest Neighbors (KNN)
• Random Forest