tsrobprep - an R package for robust preprocessing of time series data. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001084

Coronavirus tweets NLP - Text Classification mini-project work for Data Science course, FCSE, Skopje

This repository contains a collection of Jupyter Notebook files for various feature engineering techniques, including missing value handling, encoding, transformation, imbalanced dataset, and outlier detection. Each notebook provides practical examples of methods for handling the corresponding problem.

(OLD VERSION - 1.0) - MVLS v1.0 is a function for R software to impute missing values in longitudinal dataset. R package.

Create and save .csv file with replaced categorical and non-categorical missing values

Missing value imputation using KNN.

This project aims to generate insights from the sample datasets which are provided.The interest is mainly about gaining insights regarding click-out distribution and click-through rates (CTR).

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

📶In this repository, we will do feature engineering with Python.

Python package for data cleaning and missing value treatment

Decision tree algorithm with management of missing attribute values in training examples

The need for missing value imputation is of extreme importance in big data applications as data volumes tend to grow exponentially and their data structures change rapidly.

Modeling of strength of high performance concrete using Machine Learning

The task was given by Innomatics to get the internship in Data Science

Data Mining and Machine Learning in datasets

A workaround to missing values using machine learning imputation techniques

Cricket World Cup dataset (1975 - Present) a detailed Exploratory Data Analysis, applying various statistical and data visualization techniques.

Class project for 6.830 database systems

Framework to test missing data imputation techniques