Building Interactive Predictive Machine Learning WorkFlow with Streamlit

A Guide to Visualize Data Analysis and Interact with Your Machine Learning Predictive Models Using Steamlit

In this post, you will explore how to unlock the power of Streamlit, an open-source Python library that empowers you to develop interactive interfaces for data exploration and predictive modeling. You will develop an interactive and engaging interface for data exploration and predictive modeling. The interface will

• Select the CSV file, load, view, and analyze the data
• Identify important features employing tools like Random Forest Regressor, XGBoost, and LIME to pinpoint the features that most significantly boost the performance of the predictive models.
• Develop predictive models using techniques like Random Forest, Linear Regression, and XGBoost. A key focus will be on fine-tuning model hyperparameters to see how they alter predictions on test data.

This immersive experience will deepen your understanding of model building and equip you with the knowledge to transform it into an interactive and user-friendly experience using Streamlit.

Streamlit

Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.