DataBuddy: No-Code Data Science Tool

Data analysis and machine learning are increasingly essential in various industries; however, the complexity of existing tools creates barriers for non-technical users. This paper presents DataBuddy, a no-code tool designed to automate data analysis and machine learning processes through an intuitive graphical interface. DataBuddy integrates Python libraries like Pandas, Matplotlib, Seaborn, and Scikit-Learn, providing features such as automated exploratory data analysis (EDA), dynamic visualizations, and machine learning model training — all without requiring programming knowledge. The tool allows users to upload datasets, select columns, visualize data, and train predictive models through simple checkboxes and dropdown menus. Performance metrics and analysis reports are generated automatically and saved in organized folders, ensuring accessibility and efficiency. DataBuddy empowers users from diverse backgrounds to derive insights, make data-driven decisions, and build machine learning models with minimal effort. The results demonstrate the tool’s effectiveness in simplifying complex workflows, reducing analysis time, and bridging the gap between technical and non-technical users. Despite the growing importance of data analysis and machine learning across industries, many existing tools remain inaccessible to non-technical users due to their reliance on coding expertise. DataBuddy is a no-code tool that overcomes these challenges by providing a user-friendly graphical interface that allows users to perform data preprocessing, exploratory data analysis (EDA), visualization, and machine learning tasks without writing any code. The platform integrates Python libraries such as Pandas, Matplotlib, Seaborn, and Scikit-Learn to automate complex processes while offering simplicity through dropdowns, checkboxes, and guided workflows. Users can upload datasets, manipulate and analyze data, and train predictive models with minimal effort. DataBuddy aims to empower professionals from non-technical backgrounds to make informed, data-driven decisions with ease, significantly reducing the time and resources needed for comprehensive analysis.