agi-app-sklearn-pipeline

Create a mini-application that leverages the 'agi-app-sklearn-pipeline' package to predict housing prices based on various features like square footage, number of bedrooms, location, etc. This application will serve as a simple yet powerful demonstration of how machine learning models can be built, evaluated, and used to make predictions using the provided package.

The application should include the following steps:
1. Data Collection: Use a publicly available dataset such as the Boston Housing Dataset from sklearn.datasets.
2. Preprocessing: Clean and preprocess the data to ensure it is suitable for training a machine learning model. This may involve handling missing values, scaling features, and encoding categorical variables.
3. Model Training: Utilize the 'agi-app-sklearn-pipeline' package to create a scikit-learn pipeline that includes preprocessing steps and a regression model (e.g., Linear Regression, Decision Tree, Random Forest).
4. Model Evaluation: Evaluate the trained model using appropriate metrics (e.g., Mean Absolute Error, R^2 Score). The package should automatically generate a report detailing these metrics.
5. Prediction: Allow users to input new housing data and receive a predicted price along with an explanation of the prediction (e.g., which features had the most impact on the price).
6. Visualization: Implement visualizations to help understand the relationship between different features and the predicted housing prices.

Additional Features:
- Include a user-friendly interface for inputting new housing data.
- Provide explanations for the importance of each feature in predicting housing prices.
- Allow users to compare predictions made by different models included in the pipeline.
- Save and load models to/from disk to allow persistent use of the best-performing model.

This project aims to showcase the capabilities of the 'agi-app-sklearn-pipeline' package while also providing a practical tool for predicting housing prices.