1. Data Cleaning: Handle missing values and ensure data consistency.
2. Exploratory Data Analysis (EDA): Understand the relationships between yield and other variables.
3. Feature Engineering: Create new features if necessary.
4. Modeling: Build predictive models to optimize yield.

1. OC1: Shows a moderate positive correlation with yield.
2. Temp11: Shows a moderate negative correlation with yield.
3. PressureC1: Shows a moderate positive correlation with yield.
4. FlowC1: Shows a moderate positive correlation with yield.
5. TempC1: Shows a moderate negative correlation with yield.

• Mean Squared Error (MSE): 0.709
• R-squared (R²): 0.349

1. Feature Engineering:
   • Create new features based on domain knowledge. For example, interaction terms between temperature and pressure might be significant.
   • Use polynomial features to capture non-linear relationships.
2. Hyperparameter Tuning:
   • Perform hyperparameter tuning on the RandomForestRegressor model using techniques like Grid Search or Random Search to find the best parameters.
3. Advanced Modeling Techniques:
   • Experiment with other machine learning models such as Gradient Boosting Machines (GBM), XGBoost, or Neural Networks.
   • Use ensemble methods to combine predictions from multiple models.
4. Cross-Validation:
   • Use cross-validation techniques to ensure the model's robustness and avoid overfitting.
5. Data Augmentation:
   • If possible, collect more data to improve the model's performance.
   • Use synthetic data generation techniques to augment the dataset.
6. Domain-Specific Adjustments:
   • Consult with domain experts to understand the underlying processes better and incorporate their insights into the model.
7. Regular Monitoring and Updating:
   • Continuously monitor the model's performance and update it with new data to maintain its accuracy.

• Mean Squared Error (MSE): 0.881
• R-squared (R²): 0.190

1. Feature Importance:
   • The features are listed on the y-axis in descending order of importance. The most important feature is at the top.
2. SHAP Values:
   • The x-axis represents the SHAP values, which indicate the impact of each feature on the model's output. A positive SHAP value means the feature increases the prediction, while a negative SHAP value means it decreases the prediction.
3. Color Coding:
   • Each dot represents a single prediction (row) from the dataset. The color of the dot indicates the value of the feature from low (blue) to high (red).
4. Distribution of SHAP Values:
   • The spread of the dots along the x-axis shows the distribution of the SHAP values for each feature. A wider spread indicates a higher impact on the model's predictions.
5. Interaction Effects:
   • The plot can also reveal interaction effects between features. For example, if a feature has a high SHAP value only when another feature is at a certain level, this interaction can be observed in the plot.

Executive Summary: Yield Prediction Analysis for Distillation Tower

Overview

Data Summary

• Dataset: The dataset contains various process variables recorded over time, including temperatures, pressures, and flow rates.
• Total Features: 24 (excluding the date and yield columns)
• Target Variable: Yield

Key Findings

Correlation Analysis

• A correlation matrix was generated to understand the relationships between different process variables.
• Key observations include strong correlations between certain temperature and flow variables, indicating potential areas for process optimization.

Model Performance

• Model Used: XGBoost Regressor
• Mean Squared Error (MSE): 0.881
• R-squared (R²): 0.190

Feature Importance

1. FlowC1 (Importance: 0.275)
2. Temp1 (Importance: 0.181)
3. TempC2 (Importance: 0.078)
4. FlowC9 (Importance: 0.072)
5. TempC3 (Importance: 0.070)

SHAP Analysis

• SHAP (SHapley Additive exPlanations) values were used to interpret the model's predictions.
• The SHAP summary plot provides a detailed view of the impact of each feature on the model's output.
• Key insights include the significant influence of FlowC1 and Temp1 on yield predictions, with FlowC1 having the highest impact.

Recommendations

1. Focus on Key Variables: Given the importance of FlowC1 and Temp1, process engineers should prioritize monitoring and optimizing these variables to improve yield.
2. Further Model Optimization: Consider additional feature engineering and model tuning to improve the predictive performance.
3. Regular Monitoring: Implement real-time monitoring of key variables to ensure they remain within optimal ranges.

Visualizations

• Correlation Matrix: Provides an overview of relationships between process variables.
• Feature Importances: Highlights the most influential features for yield prediction.
• SHAP Summary Plot: Offers detailed interpretability of the model's predictions.

Conclusion