Custom Model Predictor

This code implements a Streamlit application for custom machine learning model training and evaluation for regression tasks. Users can upload a CSV dataset, preprocess the data, select features and target variables, choose a prediction algorithm, and train and evaluate the model.

Getting Started

1. Ensure you have Python installed on your system, along with the following libraries:
   • numpy
   • pandas
   • matplotlib
   • streamlit
2. Clone or download this repository.
3. Open a terminal or command prompt and navigate to the project directory.
4. Run the application using streamlit run main.py.

Code Structure

• preprocess_data.py: Contains functions to preprocess data, including handling date columns and encoding categorical features.
• train_test_split_custom.py: Implements a custom function for splitting data into training and testing sets.
• linear_regression.py: Provides a linear regression implementation.
• ridge_regression.py: Implements Ridge regression with an alpha parameter for regularization.
• lasso_regression.py: Implements Lasso regression with an alpha parameter for regularization.
• gradient_boosting_regression.py: Implements Gradient Boosting regression with hyperparameters for number of estimators and learning rate.
• decision_tree_regression.py: Implements a basic Decision Tree regression model.
• r2_score.py: Defines a function to calculate the R-squared score for regression evaluation.
• mean_squared_error.py: Defines a function to calculate the Mean Squared Error (MSE) for regression evaluation.
• main.py: The main script that builds the Streamlit application.

Using the App

1. Upload a CSV dataset using the file uploader.
2. The application displays a preview of the data.
3. Preprocessed data is displayed after feature engineering.
4. Select features and target columns for prediction.
5. Choose a prediction task from the dropdown menu (for informational purposes only).
6. Select a machine learning algorithm from the available options:
   • Linear Regression
   • Ridge Regression
   • Lasso Regression
   • Gradient Boosting Regression
   • Decision Tree Regression
7. Click the "Train and Evaluate" button.
8. The app displays the chosen algorithm and evaluation metrics, including R-squared and Mean Squared Error (MSE).
9. A scatter plot visualizes the actual vs. predicted values.