Multicollinearity is a concept relevant to all the input data that is used in a Machine learning Algorithm. This has to be understood well, to prepare the input data for many Machine Learning Algorithms. Let us understand what it means, why should we be interested in it and what action do we need to take about it. What is MultiCollinearity? Starting from first principles, Wikipedia says "Collinearity is a linear association between two explanatory variables. Two variables are

Outliers in data analysis and data preparation are to be considered in specific ways so that the data that is fed to a machine learning algorithm is good. As it is well known, "Garbage in, garbage out". There are times when Outliers can completely skew the results and predictions and cause very poor performance of the algorithms. So, let us see what is an outlier and what are the types of outliers? How do you deal with them, with some examples? What is an Outlier? the dictio

Feature Scaling is a very important aspect of data preparation for many Machine Learning Algorithms. Let us understand what is feature scaling, why it is important, and when it will be used. NOTE: For those who are just getting initiated into ML jargon, all the data or variables that are prepared and used as inputs to an ML algorithm are called features. Why Feature Scaling? Feature scaling is all about making things comparable. Ensuring one feature does not numerically domin

Last week, in Part 1, I walked through all the preliminary steps to be done before you can build a Linear Regression model. This week we will build the model itself. A brief refresher about the problem I had taken you through: We have data of a Bike Sharing System in a particular city and we want to use Multi-Linear Regression to make predictions and also derive insights into what are the factors that impact the number of bikes hired. The complete data and code are available