In this notebook, we worked on dealing with cleaning the data, missing values, univariate, bivariate and multivariate analysis. Additionally, few APIs were created to make sure that if the same piece of code is needed to be used several times over the course of the analysis, there is no need to repeat 10 lines of code again and again. Instead, the job can be accomplished using just one line of code.

Bivariate/Multivariate statisical testing methods like ANOVA and Chi Squared testing were employed to assist in making decisions on how to replace the missing values.

Missing No library was used to understand relationship between missingness of data.

Machine learning techniques like K-Nearest Neighbours were tweaked and used to help in cleaning the data and replacing categorical variables.