Missing value imputation using Gaussian copula

House Price Prediction

This project predicts wind turbine failure using numerous sensor data by applying classification based ML models that improves prediction by tuning model hyperparameters and addressing class imbalance through over and under sampling data. Final model is productionized using a data pipeline

This repository commits to the application of biostatistics knowledge on clinical, randomized trials and observational studies.

Predicting missing pairwise preferences from similarity features in group decision making and group recommendation system

Missing value imputation in methylation data R package

Python framework for explainable omics analysis

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

EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

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

perform Principal Component Analysis (PCA) using R languge

This file provides full practice of data preprocessing methods and techniques using different types of libraries.

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Analyzing Gender-Based Spending Patterns: A Comprehensive Study of Walmart Inc. Customers

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.

This project analyzes road accident data using MS Excel to identify trends, patterns, and contributing factors to accidents. Through data visualization techniques and statistical analysis, it provides insights that can inform safety measures and policy decisions, aiming to enhance road safety and reduce accident rates.

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.

Perform regression analysis to predict credit limits using machine learning methods, employing techniques such as feature encoding, scaling, selection, and multicollinearity handling to preprocess data.

End-to-end data preprocessing techniques using Python, including handling missing values, feature encoding, scaling, and outlier detection.

Librería para la imputación de valores faltantes de datasets

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.

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

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.

This project analyzes coffee sales data using Excel, featuring an interactive dashboard for visualizing sales trends and customer preferences. The analysis aids in decision-making by highlighting key metrics and insights, enabling better inventory management and marketing strategies for coffee-related businesses.

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.

Calculating Hypothetical credit score using extensive credit related information

R project for comparing different Missing Value Imputation (MVI)* approaches across three datasets.

Course project involving modeling imputation bias for survey data through bootstrapping various missing data scenarios & imputation methods. In partial fulfillment of the requirements in Stat 142: Introduction to Computational Statistics.

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

In this repo, I explored insurance customer data with Python, focusing on EDA. I cleaned, preprocessed, and analyzed a synthetic dataset, covering statistics, distributions, relationships, and segmentation. Refer the Looker Dashboard for insights via link-