Imputation of missing values in tables.

Implementation of Data Preprocessing techniques such as handling missing values, noise smoothing, PCA, etc.

Repository containing the implementation of the models and experiments in the paper "Missing value imputation in Food Composition Data with Denoising Autoencoders"

Analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn (usage-based churn) and identify the main indicators of churn.

A machine learning project to predict loan defaults in a German bank's customer base. Using the German Credit Risk dataset, it explores key factors contributing to defaults and trains models like Random Forest, GBM, and XGBoost. Includes EDA, data processing, hyperparameter tuning, and model evaluation.

I visualized electric power consumption (kWh/capita) for 8 countries (2001–2014) using Pandas/Matplotlib. Line plots show China's +200% surge (1,200→3,600); bar (2008) ranks Canada #1 (16k); pies highlight China's share rise (16%→23%).

kDMI employs two levels of horizontal partitioning (based on a decision tree and k-NN algorithm) of a data set, in order to find the records that are very similar to the one with missing value/s. Additionally, it uses a novel approach to automatically find the value of k for each record.

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

Prevention and handling of missing data

SiMI imputes numerical and categorical missing values by making an educated guess based on records that are similar to the record having a missing value. Using the similarity and correlations, missing values are then imputed. To achieve a higher quality of imputation some segments are merged together using a novel approach.

Predicting the City Cycle Fuel Consumption in MPG of a Car. A Classification Problem

This project is based on the Indian and Southeast Asian market. Analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

This repository demonstrates data cleaning with a layoffs dataset. It covers handling missing values, detecting outliers, and encoding categorical data, using visualizations like boxplots and distplots to enhance data quality. Check out the code to see these techniques in action.

This repository focuses on practical feature engineering techniques for machine learning. Learn to handle missing values, balance datasets, perform interpolation, encode variables, and explore data relationships using summary statistics and visualizations. Perfect for boosting model performance with smarter data prep.

Exploratory data analysis on ICE retail gaming store.

MissNoMore is a Python-based missing value imputation tool designed to handle CSV datasets with missing data.

This repository provides a guide on handling missing values in Python, covering identification methods, imputation techniques (mean, median, mode, fill, interpolation), advanced methods (KNN, multiple imputation), and best practices. It includes practical examples for both numerical and categorical data.

In this repository, we intend to extract data from the mentioned dataset and display everything that seems interesting.

FIMUS imputes numerical and categorical missing values by using a data set’s existing patterns including co-appearances of attribute values, correlations among the attributes and similarity of values belonging to an attribute.