Our Services

Two Ways to Build Better ML

Use AutoTrain ML for no-code model training, or work directly with MLXpert for custom engineering support.

SaaS Platform

AutoTrain ML

No-Code ML Solution

A no-code platform focused on developing machine learning models. Upload data, configure preprocessing, and deploy — no Python, no infrastructure setup needed.

Starter

Free forever

Basic

$9.90 /mo

Advanced

$17.90 /mo

✓ Upload CSV / Excel datasets (up to 500 MB)
✓ Auto model selection & hyperparameter tuning
✓ Classification, Regression & Clustering
✓ Visual evaluation charts & metrics
✓ One-click model export & API deployment

Start Building — It's Free

1-on-1 Consultation

MLXpert

ML & Software Consultant

A dedicated consultant to help you build ML models, systems, and full software solutions for any use case. Send an email to get a free consultation.

Private & Custom

Tailored pricing · scope-based

✓ Dedicated 1-on-1 sessions with ML engineers
✓ Custom model strategy & architecture design
✓ Advanced NLP, Computer Vision & time-series
✓ Code review, debugging & performance tuning
✓ Full project documentation & handoff

Contact an Expert

Built for General & Modern Users

No Python Required

Eliminate the coding barrier. Build complex ML models with zero lines of code, focusing purely on your data insights and business outcomes.

Comprehensive Methods

Full visibility into model parameters and metrics. We handle the complex mathematics, you see the results clearly and make informed decisions.

Product Oriented

Designed for deployment. Export your models immediately and integrate them into production apps effortlessly with one-click deployment.

Experience the AutoTrain ML Workflow

Upload Data

Simply drag & drop your CSV file. We instantly analyze the schema and detect feature types automatically.

Configure Target

Select what you want to predict. AutoTrain ML suggests the best problem type (Classification/Regression).

Auto-Train

Our engine tests multiple algorithms (Random Forest, XGBoost, etc.) and tunes them to find the best model.

Real-World Use Cases

Browse all use cases

Breast Cancer Detection with Dimensionality Reduction

Leverage Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to enhance breast cancer detection accuracy. Using the Wisconsin breast cancer dataset, this study demonstrates how dimensionality reduction techniques improve prediction accuracy and model performance, addressing a critical health challenge for women worldwide.

🏥 Healthcare

📄 View Research Paper

Obesity Risk & Meal Planning

Predict obesity risk based on physical characteristics and eating habits, then generate personalized meal plans. Prevents PCOS, cardiovascular disease, diabetes, and cancers using ML algorithms (GB, XGBoost, RF, SVM). Achieves 98.11% accuracy for early intervention and dietary guidance.

🏥 Healthcare

📄 View Research Paper

Student Learning Analytics

Monitor and assess student behavior patterns using K-means clustering on teacher and student attributes. Identify teaching effectiveness factors (speech quality, preparation, time management) that correlate with student performance to improve e-learning systems. Discovers key behavioral clusters for personalized learning interventions.

🎓 Education

📄 View Research Paper

Stock Selection Decision Support Systems

Utilize simplex-centroid mixture design and artificial neural networks to optimize stock selection models. This approach addresses limitations of traditional weighted scoring models by identifying optimal weight combinations for stock-picking concepts, meeting diverse investor preferences, and outperforming S&P 500 during both training and testing periods.

📈 Finance

📄 View Research Paper

Real Estate Valuation Models

Leverage the Quantitative Comparative Approach to build real estate valuation models. This innovative method estimates correction coefficients objectively, overcoming the subjectivity of traditional comparative approaches. Empirical results demonstrate higher accuracy compared to classical Hedonic price approaches, regression analysis, and neural networks.

🏠 Real Estate

📄 View Research Paper

Open Datasets

Browse dataset catalog

Iris Dataset

Classic multivariate flower classification dataset with 150 samples from three Iris species. Four features measured: sepal length/width and petal length/width in centimeters. Perfect for beginners learning ML classification.

View Dataset

California Housing

20,640 census block records with eight numeric features and median house values. A practical regression dataset for comparing linear, ensemble, and boosting models.

View Dataset

Breast Cancer Wisconsin

Diagnostic dataset for tumor classification (malignant vs benign). Essential for healthcare ML applications using Support Vector Machines and other classification algorithms.

View Dataset

Gold Price Forecasting

Five years of daily gold futures market data (OHLCV) with technical indicators. Includes moving averages, RSI, MACD, and Bollinger Bands for robust time-series forecasting models.

View Dataset

Obesity Classification

2,111 records from Mexico, Peru, and Colombia with 17 attributes for obesity level classification. Seven classes: Insufficient Weight to Obesity Type III. 77% synthetically generated, 23% real user data.

View Dataset

Student Performance

Student academic data with 30+ attributes including demographics, family background, lifestyle, and grades. Perfect for predicting final grades and identifying at-risk students early.

View Dataset

Plans & Pricing

Simple, Scalable Pricing

Choose the right plan for your training workflow. Start free, then scale when your projects grow.

Free

For first experiments and lightweight datasets

Free

Forever Free

Get Started

Basic

For active projects that need server-side training

$9.90

Per Month

Choose Basic

Advanced

For large-scale training and professional output

$17.90

Per Month

Go Advanced

consultant

MLXpert

Full-service ML Expert just for you

Custom

Contact Email

Send Email

AutoTrain ML Plan Comparison

Detailed capability differences across Free, Basic, and Advanced plans.

Feature	Free	Basic	Advanced
Dataset Slots	25 datasets	100 datasets	250 datasets
Training per Day	20 jobs/day	75 jobs/day	200 jobs/day
Storage	1 GB	4 GB	10 GB
Maximum Upload Size	500 MB	750 MB	1 GB
Worker Capacity	1 worker	2 workers	4 workers
Supported Algorithms	Decision Tree, K-Means, Simple Linear Regression, SMA	Free + Logistic Regression, Random Forest, KNN, Hierarchical, DBSCAN, MLR, Polynomial/Tree/Forest Regression, Naive Forecast, Exponential Smoothing	All classification, clustering, regression, and time-series methods
Compute Engine	AutoTrain Engine	AutoTrain Engine	AutoTrain Engine
Visualization Tools	Basic visualizations	Advanced visualizations	Professional visualizations
Hyperparameter Optimization	Not available	Standard optimization	Advanced optimization
Cross-Validation	Not available	✔ Available	✔ Available
Feature Importance Analysis	Not available	✔ Available	✔ Available
Export Trained Model	Not available	✔ Available	✔ Available
Model Testing Tabs	1 tab	3 tabs	10 tabs
Inference API Endpoints	Not available	3 endpoints	10 endpoints
Testing Usage	Unlimited dashboard tests	Unlimited while active	Unlimited while active
After Subscription Expiry	Free testing tab remains available	Paid testing slots and APIs are sealed	Paid testing slots and APIs are sealed
Custom Report Configuration	Not available	Not available	✔ Available

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Two Ways to Build Better ML