AutoNexus is a secure, multi-user, no-code AutoML web application built with Streamlit and powered by a Firebase backend. It enables users to create an account, upload datasets, perform a full end-to-end machine learning workflow, and save their results. The platform guides users from data cleaning and exploratory data analysis to advanced model tuning and interpretation using SHAP and LIME.
AutoNexus provides a comprehensive suite of tools for any data scientist, from beginner to expert.
- 🔐 Secure User Authentication: Full login, signup, and password reset functionality powered by Firebase.
- ☁️ Cloud Storage: (Coming Soon) Each user's datasets are stored in their own secure cloud folder.
- Conversational Guidance: Get help and explanations at any step of the workflow.
- Context-Aware: The assistant, powered by Google's Gemini models, understands the user's current dataset and progress in the app.
- Data Interpretation: Ask questions in plain English about your data, models, and results.
- 📁 CSV Dataset Upload: Upload datasets for analysis.
- 🧹 Data Cleaning: A suite of tools to handle duplicates, rename columns, standardize text, and more.
- 📊 Exploratory Data Analysis (EDA): Generate interactive plots (histograms, scatter plots, heatmaps) and statistical summaries to understand the data.
- 🔬 Advanced Preprocessing:
- Handle missing values with various strategies (mean, median, mode).
- Model-Based Feature Selection using Random Forest to identify the most predictive features.
- Statistical feature selection (ANOVA, Mutual Information).
- ⚖️ Class Imbalance Handling: Correctly apply SMOTE, Random OverSampling, or Random UnderSampling to the training data to improve model performance on skewed datasets.
- 🤖 Model Training: Train a variety of classification and regression models, including:
- Logistic/Linear Regression, Ridge, Lasso
- Random Forest, Decision Trees
- XGBoost
- ⚙️ Advanced Hyperparameter Tuning:
- Go beyond default settings with automated tuning.
- Choose between GridSearchCV (exhaustive) and RandomizedSearchCV (fast and efficient) to find the best parameters for your model.
- 📈 Robust Evaluation:
- Utilizes a proper Train/Validate/Test split for reliable performance metrics.
- View detailed classification reports, confusion matrices, and key regression metrics (R², MAE, RMSE).
- 🧠 Model Interpretability: Understand why your model makes its predictions.
- SHAP: Global and local feature importance plots.
- LIME: Explain individual predictions on a case-by-case basis.
- 💾 Download Artifacts: Download the final, trained model (
.pkl) for offline use or deployment.
Click here to try AutoNexus on Streamlit Cloud
- Frontend: Streamlit
- Backend Services: Firebase (Authentication, Cloud Storage), FastAPI (Planned)
- Data Science: Pandas, Scikit-learn, Imbalanced-learn
- Modeling: XGBoost, InterpretML
- Explainability: SHAP, LIME
Srilekha Tirumala Vinjamoori MS in Information Systems | Business Analytics | UTA
MIT License