Machine-Learning-Foundations-for-Product-Managers-Project

Machine learning project predicting the energy output of a power plant using environmental data (temperature, pressure, humidity, vacuum). Linear Regression and Random Forest models were compared, with Random Forest achieving better accuracy. Key steps include data exploration, model building, cross-validation, and performance evaluation.

Steps for Power Plant Energy Output Prediction Project

• Step 1: Problem Understanding

• Define the problem: Predict energy output based on environmental conditions.
• Identify target variable (PE) and features (Temperature, Pressure, Humidity, Vacuum).

• Step 2: Data Exploration

• Load the dataset and check for missing values.
• Perform descriptive statistics to understand the data range.
• Visualize relationships using pairplots and correlation heatmaps.

• Step 3: Feature Selection

• Select key features (Temperature, Pressure, Humidity, Vacuum) based on correlations.

• Step 4: Data Splitting

• Split the data into training (80%) and test (20%) sets.

• Step 5: Model Selection

• Choose two models: Linear Regression and Random Forest.
• Train both models using cross-validation.

• Step 6: Model Evaluation

• Use Mean Absolute Error (MAE) to evaluate the models.
• Compare MAE scores of Linear Regression and Random Forest.

• Step 7: Final Model Selection

• Select the Random Forest model due to its lower MAE.

• Step 8: Test Set Evaluation

• Test the final model on the test set and check MAE performance.

• Step 9: Visualization

• Visualize feature importance and plot predictions vs actual values.
• Analyze residuals for model fit.

• Step 10: Conclusion

• Summarize the results: Random Forest model performs better with an MAE of 2.47.