ds_toolbox
Go to the application¶
Overview¶
A Streamlit data‑science toolbox that lets users upload CSV/Excel files, clean missing values and outliers, visualize data with various plots, and train a regression model.
Main functionalities¶
- Upload CSV/Excel file via the Streamlit uploader.
- Data cleaning: impute missing values (mean, median, mode) and remove IQR outliers.
- Interactive visualisation: histogram, boxplot, scatter plot, heatmap using Plotly.
- Train a regression model with automatic evaluation metrics.
Technologies & skills¶
- Python
- Streamlit
- Pandas
- Plotly
- Scikit‑learn
Project Report¶
- Modular component architecture (data_loader, data_cleaner, visualizer, model_trainer).
- Data ingestion and preprocessing pipeline.
- Interactive charts rendered with Plotly in Streamlit.
- Simple regression training routine returning metrics.
Sample photos¶
Application usage¶
- Upload a CSV or Excel file.
- Use the “Data Cleaning” expander to impute missing values and remove outliers.
- Select visualisation type in the “Visualisation” section and view interactive charts.
- Choose target column in the “Modeling (Regression)” section, run training, and review metrics.
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