Data Upload Guide

Supported Formats:

• CSV: Comma-separated values with headers
• Excel: .xlsx files (first sheet)
• JSON: Structured arrays or objects

Data Requirements:

• Clear column headers
• Consistent data types
• Maximum 100MB file size
• Minimum 50 rows for training

Model Building

Available Algorithms:

• Regression: Linear, Decision Tree, Random Forest
• Classification: Logistic, SVM, Neural Networks
• Clustering: K-Means, Hierarchical
• Dimensionality Reduction: PCA

Configuration Tips:

• Start with simple algorithms
• Use default parameters initially
• Ensure proper target column selection

Training Models

Our platform supports comprehensive training for various machine learning algorithms with automated optimization and monitoring.

Regression Models

• Linear Regression: Simple linear relationships, fast training (1-2 minutes)
• Decision Tree Regression: Non-linear patterns, interpretable results (2-5 minutes)
• Random Forest Regression: Ensemble method, robust predictions (5-10 minutes)
• SVM Regression: Complex patterns, kernel-based learning (3-15 minutes)

Classification Models

• Logistic Regression: Binary/multi-class classification (2-5 minutes)
• Support Vector Machines: High-dimensional data, kernel methods (5-20 minutes)
• Random Forest Classifier: Ensemble learning, feature importance (5-15 minutes)
• Neural Networks: Deep patterns, backpropagation learning (10-30 minutes)

Clustering Models

• K-Means Clustering: Partitioning clusters, centroid-based (3-8 minutes)
• Hierarchical Clustering: Dendrogram-based, agglomerative approach (5-15 minutes)

Dimensionality Reduction

• Principal Component Analysis (PCA): Linear dimensionality reduction (2-10 minutes)
• t-SNE: Non-linear visualization, perplexity-based (10-30 minutes)

Deep Learning Models

• Neural Networks (MLP): Multi-layer perceptrons, dense connections (15-45 minutes)
• Convolutional Neural Networks (CNN): Image processing, convolution layers (30-120 minutes)
• Recurrent Neural Networks (RNN/LSTM): Sequential data, memory cells (20-90 minutes)

Training Process

1. Data Preprocessing:
   • Missing value imputation
   • Feature scaling and normalization
   • Categorical encoding
   • Outlier detection and handling
2. Data Splitting:
   • Training set (70%)
   • Validation set (15%)
   • Test set (15%)
3. Model Training:
   • Hyperparameter optimization
   • Cross-validation
   • Early stopping for neural networks
   • Regularization techniques
4. Model Evaluation:
   • Performance metrics calculation
   • Confusion matrix generation
   • Feature importance analysis
   • Model comparison

Monitoring Features

• Real-time Progress: Live training progress with percentage completion
• Training Logs: Detailed logging of each training step
• Resource Usage: CPU/GPU utilization monitoring
• Error Handling: Automatic error detection and recovery
• Queue Management: Multiple model training with priority queuing

Training Controls

• Start Training: Begin model training with selected parameters
• Pause/Resume: Pause training and resume from checkpoint
• Stop Training: Terminate training and save current model state
• Batch Training: Train multiple models simultaneously

Best Practices

• Start with simple models before complex ones
• Ensure adequate training data (minimum 100 samples per feature)
• Use cross-validation for robust performance estimation
• Monitor for overfitting using validation curves
• Save model checkpoints during long training sessions

Model Evaluation

Classification Metrics:

• Accuracy, Precision, Recall
• F1-Score, ROC-AUC
• Confusion Matrix

Regression Metrics:

• R² Score, MSE, RMSE
• Mean Absolute Error

Model Deployment

Deployment Options:

• AWS, GCP, Azure
• REST API endpoints
• Auto-scaling
• Health monitoring

API Usage:

POST /predict
{
  "features": [1.0, 2.0, 3.0]
}
                                

🔌 API Reference

Endpoints:

• GET /health - Health check
• POST /predict - Make predictions
• GET /info - Model information
• POST /batch-predict - Batch predictions

Authentication:

Include API key in headers:

Authorization: Bearer YOUR_API_KEY

Troubleshooting

Common Issues:

• Upload fails: Check file format and size
• Training errors: Verify data quality
• Low accuracy: Try different algorithms
• Deployment issues: Check configuration

Performance Tips:

• Clean your data before upload
• Start with simple models
• Use feature selection
• Monitor resource usage