Machine Learning Basics | Model Deployment using Streamlit | UK Used Car Price Prediction Part 2

✅Topics Covered:

1. Introduction to Model Improvement
- Recap of the baseline model built using Linear Regression in Part 1.
- Overview of strategies to enhance model performance.

2. Improving Model Performance Using XGBoost
- Building the #xgboost model:
- Hyperparameter tuning to #optimizeperformance.
- Training the model on the dataset.
- Evaluating the model using appropriate metrics.
- Comparison of XGBoost with the baseline Linear Regression model.

3. Feature Selection
- Techniques for #featureselection to improve model accuracy.
- Using feature importance scores mutual info regression for feature selection.
- Refining the feature set to include the most significant variables.

4. Decision Tree Regressor Model
- Building and training the Decision Tree Regressor model.
- Comparing the performance of the Decision Tree model with XGBoost and Linear Regression.

5. **Saving the Winning Model
- Identifying the best-performing model (XGBoost in this case).
- Saving the trained XGBoost model as a pickle file for future use.

6. Building a User Interface for Predictions Using #streamlit
- Step-by-step guide to setting up a Streamlit application.
- Creating a #userinterface for the UK Used Car Price Prediction model:
- Building interactive widgets for user input (e.g., car make, model, year, mileage).
- Displaying prediction results using the saved XGBoost model.
- Deploying the Streamlit application for real-world use.

✅Highlights:
- Comprehensive guide to improving model performance using XGBoost and feature selection.
- Introduction to Decision Tree Regressor and its application in predicting used car prices.
- Step-by-step instructions for saving the winning model as a pickle file.
- Practical implementation of building and deploying a user interface using Streamlit.

✅About the Instructor:

By the end of this class, you’ll have a solid understanding of how to enhance model performance, save the best model, and deploy it using Streamlit. This session will provide a complete, end-to-end experience of executing a machine learning project and deploying it for real-world use.

Join us and continue your journey to mastering the basics of machine learning. Happy learning!

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