Dynamic AutoML: Comprehensive Solution for Diverse Data Tasks
Dynamic AutoML is a versatile platform designed to streamline various data tasks, including CSV analysis, LSTM modeling, and image classification and detection. Our platform offers advanced features and capabilities to empower developers in handling diverse datasets efficiently.
Dynamic AutoML: A Closer Look
CSV Dataset Analysis and LazyPredict Model
Dynamic Dataset Architecture - The architecture of CSV datasets is dynamically determined by analyzing their structure, including the number of records, columns, and their types (textual, numeric, date). This dynamic approach ensures that our platform can handle a wide range of dataset formats without requiring manual configuration.
| Sno | X_columns | Y_columns | Datasets | Lazy Predicts (Y / N) | LP Model and max accuracy | Initial ANN loss | Final ANN loss | Percentage loss |
|---|---|---|---|---|---|---|---|---|
| 1 | age, anamía, creatine_phospokinase | death_event | Heart Failure | No | Null | 0.28 | 0.2 | 26.60% |
| 2 | power_consumed, weather_index, holiday_index, humidity | maximum-temperature | Electricity | Yes | AdaBoost - 91.3% | Null | Null | Null |
| 3 | tweet | label | Twitter Tweets | Yes | XGBoost - 93.1% | Null | Null | Null |
| 4 | cap-shape, cap-surface, habitat, veil-color | class | Mushrooms | Yes | Null | 0.36 | 0.24 | 35.10% |
| 5 | age, bmi, children, smoker | charges | Insurance | Yes | Random Forest - 85.1% | Null | Null | Null |
| 6 | age, gender, polyuria, sudden-weight-loss, weakness | class | Diabetes | Yes | Decision Tree - 95.1% | Null | Null | Null |
| 7 | customer-ID, credit-score | IsActiveMember | Bank Churning | No | Null | 0.36 | 0.24 | 31.70% |
LazyPredict Model Implementation - Our LazyPredict model is implemented from scratch to provide developers with a comprehensive tool for model selection and comparison. By automating the process of evaluating multiple models with various configurations, developers can quickly identify the most suitable model for their specific task, saving time and effort.
Image Classification and Detection
Automatic Model Training - Our platform simplifies the process of image classification by automating model training. Developers can upload a zip folder containing images organized into folders as classes. The platform then trains models using this data, enabling accurate classification of new images based on their content.
| Sno | Dataset Name | Accuracy | Precision | Recall | Intersection over Union | Dice Coefficient |
|---|---|---|---|---|---|---|
| 1 | Dogs vs Cats | 90% | 86% | 88% | 89% | 83% |
| 2 | Medical Images | 95% | 90% | 91% | 85% | 87% |
| 3 | Autonomous Driving | 85% | 84% | 87% | 86% | 87% |
| 4 | Satellite Imagery | 91% | 89% | 90% | 88% | 85% |
| 5 | Histopathology | 93% | 88% | 91% | 90% | 83% |
| 6 | Semantic Segmentation Benchmark | 85% | 89% | 86% | 86% | 88% |
| 7 | Lung Nodule Detection | 91% | 88% | 91% | 85% | 86% |
| 8 | Plant Disease Identification | 92% | 82% | 87% | 78% | 80% |
Dynamic Image Segmentation - Using techniques like YOLO, our platform dynamically determines classes from datasets such as COCO128, enabling precise image segmentation. This capability allows developers to identify and isolate specific objects within images, opening up possibilities for applications such as medical image analysis, autonomous vehicles, and more.
LSTM Model Training
Dynamic Architecture Determination - Similar to CSV datasets, the architecture of LSTM models is dynamically determined based on the dataset’s characteristics. This approach ensures that the model architecture is optimized for the specific task and dataset, leading to improved performance and adaptability.
| Sno | Testing Loss Tabulation | X Columns | Y Columns | Initial Loss | Final Loss | Decrease in Loss | Percentage Decrease |
|---|---|---|---|---|---|---|---|
| 1 | Tesla Dataset | Date year, Date_month, Open, High, Low | Close | 1,745,438.13 | 475.4735 | 1,745,362.66 | 99.9728% |
| 2 | Traffic Dataset | Date year, Date_month | Vehicles | 952.4327 | 6.8296 | 945.6031 | 99.2838% |
| 3 | Air Passengers | Date year, Date_month | Passengers | 96,211.27 | 51,782.60 | 44,428.63 | 46.1366% |
| 4 | Panama Electricity | datetime year, datetime_month, T2M_toc | T2M_san | 204.77 | 6.1987 | 198.5713 | 96.9723% |
| 5 | Google Train | Date year, Open, High, Volume | Close | 180,635.13 | 9,386.0029 | 171,249.13 | 94.8127% |
| 6 | Apple | Date year, Date_month, Open, High, Volume | Close | 27,494.8 | 3,015.0009 | 24,479.8 | 89.0341% |
| 7 | Netflix | Date year, Date_month, Open, High | Close | 187,213.42 | 22,841.346 | 164,616.19 | 87.8039% |
| 8 | London Bike | timestamp_year, timestamp_month, wind_speed | hum | 5,447.62 | 3,242.8624 | 2,204.76 | 40.4717% |
| 9 | Electricity_dah | date_year, Date_month, date_day | temp | 8,542,182.42 | 1,325,564.88 | 7,216,617.54 | 84.4874% |
| 10 | LSTM-Multivariate pollution | date_day, date_hour, wnd_spd, pressedev, pollution | temp | 300,186.5 | 173.9029 | 126.2786 | 42.076% |
Streamlined Model Training - Our platform streamlines the process of LSTM model training by automatically tuning hyperparameters based on dataset properties. This automation reduces the manual effort required for hyperparameter optimization, allowing developers to focus on model experimentation and refinement.
Usage
- Upload Data: Upload CSV files or image datasets to start your analysis and model training.
- Dataset Exploration: Explore dataset properties, perform preprocessing tasks, and visualize data distributions.
- Model Training: Choose appropriate models (LSTM, image classifiers, etc.) and train them using automated processes.
- Model Evaluation: Evaluate model performance using metrics and visualizations provided by the platform.
- Deployment: Download trained models for deployment or integrate them directly into your applications.
Access: Access the platform directly here.
Source Code
For more details, visit the source code on GitHub.
How It Helps Developers
Dynamic AutoML offers a range of features designed to streamline the development process and empower developers in handling diverse data tasks