README.md

metadata

title: ML Data Analysis Studio
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.50.0
python_version: '3.11'
app_file: app.py
pinned: false
license: mit

🤖 ML Data Analysis Studio

An interactive machine-learning playground. Upload a dataset in almost any format, clean it, train models, inspect the generated code, and download full analysis reports — no coding required.

Features

• 💬 ML Assistant — chat about ML models (random forests, SVM, overfitting, metrics, model selection...) and get guided through the analysis workflow.
• 📊 Data upload & preprocessing — supports CSV, TSV, Excel, JSON, and Parquet, including hard-to-read files (auto-detects delimiter and encoding, skips malformed rows, fixes messy headers). Automatic profiling (missing values, duplicates, dtypes), then configurable cleaning: mean/median imputation or row-dropping, duplicate removal, IQR outlier clipping, one-hot encoding, and standard or min-max scaling. Placeholder values ('N/A', '?', '-'), numeric-looking text ('$1,234', '45%'), and empty/constant columns are handled automatically.
• 🧠 Model training — regression (Linear, Decision Tree, Random Forest, Gradient Boosting, SVR, KNN) and classification (Logistic, Decision Tree, Random Forest, Gradient Boosting, SVC, KNN). Auto-suggests the task type from your target column. Shows metrics, an actual-vs-predicted / confusion-matrix plot, and feature importance.
• 👨‍💻 Code viewer — dropdown accordions reveal the exact standalone Python (pandas + scikit-learn) equivalent to every preprocessing and training step, ready to copy.
• 📥 Downloads — one-click export of a PDF report, a self-contained HTML report, and the cleaned dataset as CSV.

How to use

1. Data & Preprocessing — upload your file, review the automatic profile, pick your cleaning options, and run preprocessing.
2. Model Training — choose a target column, task, and model, then train and review the metrics and charts.
3. Reports & Downloads — export a PDF/HTML report of the whole analysis plus the cleaned dataset.

Note: uploaded data is processed in temporary storage for your session only — it is not collected or kept.