mde: Missing Data Explorer

An abstract missing value imputation library. EasyImputer employs the right kind of imputation technique based on the statistics of missing data.

Implementing different aspects of Machine learning in this Repository. Contributions are welcome

Categorical Binary Feature encoding script

Decision Tree is a supervised learning algorithm. The given problem is a classification problem. The data set consists of various predictors and a target variable - Outcome. Objective is to predict whether a person is diabetic or non-diabetic.

DMI Class implements the DMI imputation algorithm for imputing missing values in a dataset from Rahman, M. G., and Islam, M. Z. (2013): Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques

EDI uses two layers/steps of imputation namely the Early-Imputation step and the Advanced-Imputation step.

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.

Explored the dataset of a company that specializes in the reselling of used and refurbished devices. The objective of this project was to determine the future price of used phones and identify the factors that significantly influence them using a linear regression model with python

Classification model that will help the bank improve its services so that customers do not renounce their credit cards

Prevention and handling of missing data