Here are few key algorithms implementation in R

- Linear Regression
- Logistic Regression
- Decision Trees
- Market Basket Analysis
- Sentiment Analysis
- ClusteringÂ

Cheers!

Here are few key algorithms implementation in R

- Linear Regression
- Logistic Regression
- Decision Trees
- Market Basket Analysis
- Sentiment Analysis
- ClusteringÂ

Cheers!

Linear regression is one of the most fundamental machine learning technique in Python. For more on linear regression fundamentals click here. In this blog, we will build a regression model to predict house prices by looking into independent variables such as crime rate, % lower status population, quality of schools etc. We will be leveraging Scikit-learn library and in built data set called “Boston”.

Let’s now jump onto how to build a multiple linear regression model in Python.

You can see from the above metrics that overall this plain vanilla regression model is doing a decent job. However, it can be significantly improved upon by either doing feature engineering such as binning, multicollinearity and heteroscedasticity fixes etc. or by leveraging more robust techniques such as Elastic Net, Ridge Regression or SGD Regression, Non Linear models.

Cheers!