Describe Your Problem
Enter your machine learning problem or dataset goals below and click 'Analyze & Recommend' to generate server-side Gemini AI model suggestions.
How this works
- Type or paste your project goal or problem description in plain text (up to 750 words).
- Click "Analyze & Recommend" to send your problem statement to backend Gemini AI.
- You will automatically navigate to Model Guidance to review recommended model algorithms.
Model Guidance
Review AI model evaluations and click 'Make this simpler' to ELI5 technical jargon into plain language, or hover terms for definitions.
How this works
- Review the top recommended machine learning algorithms selected specifically for your problem statement.
- Click "Make this simpler" repeatedly — each click makes the explanation noticeably simpler down to an ELI5 child analogy.
- Hover over highlighted technical terms (dotted purple underline) for instant glossary definitions.
Dataset Cleaning Engine
Upload up to 3 raw tabular datasets simultaneously, select outlier cleaning methods (IQR vs Z-Score), and download processed files in their original formats.
How this works
- Select up to 3 dataset files (.csv, .json, .tsv, .parquet, .txt) in the 40% opaque light yellow upload cards below.
- Choose outlier detection method on the right bar: IQR (Interquartile Range) or Z-Score (standard deviations). Hover for 20-word explanations.
- Click "Clean Dataset" or "Clean All 3 Datasets" to generate step-by-step audit reports and download cleaned files in original format.
Dataset Supply
Browse curated machine learning datasets below and click 'Download' to stream raw files directly to your device.
How this works
- Search or filter curated machine learning datasets from the supply library.
- Click "Download" to stream raw dataset files straight to your computer.
- New datasets are curated and added to the supply library directly by developers.
Testing Framework
Select a model estimator (or upload your custom model file) and click 'Execute Testing Pipeline' to evaluate performance and view accuracy/R² metrics.
How this works
- Optionally upload your own custom model artifact (.py, .js, .pkl, .html) using the box below.
- Select an estimator algorithm and specify the target task type (Classification vs. Regression).
- Click "Execute Testing Pipeline" to run model evaluation and inspect metrics and logs.
You can input models of types - .py/.js/.pkl/.html
Training Framework
Upload a trained model file, configure cross-validation folds, then click 'Start Model Training' to execute model training and inspect performance logs.
How this works
- Optionally upload your own custom model artifact (.py, .js, .pkl, .html).
- Choose cross-validation split strategy (5-fold vs 10-fold CV).
- Click "Start Model Training" to execute fitting loops and monitor execution progress.
You can input models of types - .py/.js/.pkl/.html
User Profile & Activity
Manage your account profile details and view historical machine learning problem statements entered in the Describe box.
User Name
About DataDealers
Unified AI/ML Data Engineering & Model Guidance Platform — Interdisciplinary Project Report (CSE-8)
Platform Overview
Data Dealers is a unified, beginner-accessible web platform designed to guide engineering students, hackathon participants, and early-career developers through every phase of the AI/ML project lifecycle. It combines structured learning material, automated dataset cleaning, intelligent model recommendation, and testing into one seamless guided workflow.
Core System Modules
Provides easy-to-understand guides covering fundamental AI data concepts, labeling conventions, and common pitfalls, creating an educational foundation for beginner practitioners.
Enables users to quickly discover, filter, and stream curated, AI-ready datasets tailored to their specific project goals, eliminating tedious search across general-purpose repositories.
Accepts plain-language project objectives and data characteristics to recommend optimal machine learning algorithms, input data formats, and training strategies.
Automatically cleans raw uploaded datasets by handling missing values and outlier detection via IQR or Z-Score statistical methods, generating structured, ready-to-train files.
Offers interactive model testing, custom model artifact evaluation, and k-fold cross-validation execution while surfacing execution logs and precision metrics.