Model Enhancements

[highlyprofitable108]

To upgrade and enhance the existing modeling capabilities, I'll make the following additions:

1. Adding More Models: Introduce more regression models to choose from, which can be specified in the configuration.
2. SHAP Integration: Add SHAP-based interpretability to understand feature importances better.
3. Hyperparameter Tuning: Expand GridSearchCV to use more hyperparameters for tuning.
4. Refactoring: Refactor the code for better modularity and ease of adding more features.

Let's start:

Upgrade Strategy Guide

1. Add More Models:

You currently have the RandomForestRegressor. I'll add support for GradientBoostingRegressor and LinearRegression for demonstration.

#### Step 1.1: Additional Model Imports

Add the necessary imports:

```python
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import GradientBoostingRegressor

Step 1.2: Model Training Methods

Expand the train_model method to include:

elif model_type == "linear_regression":
    return self.train_linear_regression(X, y)
elif model_type == "gradient_boosting":
    return self.train_gradient_boosting(X, y)

And add the respective training methods:

def train_linear_regression(self, X, y):
    model = LinearRegression()
    model.fit(X, y)
    return model

def train_gradient_boosting(self, X, y):
    # Use GridSearch or other optimization techniques here as necessary
    model = GradientBoostingRegressor()
    model.fit(X, y)
    return model

### 2. SHAP Integration:

```markdown
#### Step 2.1: Install SHAP

Ensure you've installed SHAP:

```bash
pip install shap

Step 2.2: SHAP Interpretation

After training your model, use SHAP to explain predictions:

import shap

def compute_shap_values(self, model, X):
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)
    return shap_values, explainer

def visualize_shap_summary(self, shap_values, X, feature_columns):
    shap.summary_plot(shap_values, X, feature_names=feature_columns)

Invoke these methods in your main after training and evaluating the model.

### 3. Hyperparameter Tuning:

```markdown
#### Step 3.1: Expanding Hyperparameters for RandomForest

Modify the `train_random_forest` method to include more hyperparameters for tuning:

```python
param_grid = {
    'n_estimators': [50, 100, 150],
    'max_depth': [None, 10, 20],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4]
}

### 4. Refactoring:

```markdown
#### Step 4.1: Modularizing the Code

- Split the code into smaller methods to handle different tasks. For example, a separate method for data scaling and another for saving the model.
- Use more helper methods to avoid redundant code, especially in the data preprocessing steps.
- Consider using OOP principles to create more classes to handle specific tasks.

This is a high-level guide that will help in upgrading and enhancing your modeling capabilities. Actual implementation might need more detailed code depending on the specific requirements and dataset.

[highlyprofitable108]

Added a bunch of currently untested models and ensembles

Need to review param grid inputs

[highlyprofitable108]

modeling and eda methods are organized

[highlyprofitable108]

Retrain, save, load functionality

Retrain on new data, updated results, other....

[highlyprofitable108]

Lots of stuff added. Combined model and eda too.

Classes have a lot of untested base methods for later.