High-quality data is essential for building reliable machine learning models. Raw datasets often contain missing values, outliers, duplicates, inconsistent formats, and unstructured categorical variables. These issues reduce model accuracy and lead to biased predictions. This paper presents a simple, reproducible, and universally applicable data cleaning framework using Python, Pandas, and Scikit-Learn. The framework includes structured steps for handling missing values, detecting outliers, removing duplicates, encoding categorical variables, and scaling numerical features. Conceptual figures are provided to illustrate each stage of the cleaning pipeline. The proposed workflow is suitable for beginners, students, and independent researchers, and can be applied to any tabular dataset. This paper serves as a practical reference for improving dataset quality before machine learning model development.
Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…
Abstract The rapid growth of data-intensive applications has necessitated the development of scalable and efficient architectures for cloud-based machine learning and data analysis. This study proposes a scalable, distributed, and fault-tolerant architecture designed to address t…
Proceedings of the Scientific Conference "The 13th International Scientific and Practical Online Conference of Young Scientists and Students ‘Contemporary Problems of Automation and Control’".The conference talk presents a structured approach to preparing and analyzing financial…
Litchi is a high-value fruit crop traditionally cultivated in regions with favorable climatic and soil conditions. Expanding litchi cultivation into non-traditional areas such as the Malwa region of Madhya Pradesh requires accurate identification of suitable locations to minimize…
This repository contains the R code used in the paper "Bayesian Additive Regression Trees for Circular Data: A Machine Learning Framework" by Talal Kurdi and Saralees Nadarajah. The code implements Bayesian Additive Regression Trees (BART) methods for regression with circular dat…
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…