Multi-Machine-Learning-Model

The comparison of multiple Machine Learning models refers to training, evaluating, and analyzing the performance of different algorithms on the same dataset to identify which model performs best for a specific predictive task. By comparing multiple models, we aim to select the most effective algorithm that offers the optimal balance of accuracy, complexity, and performance for their specific problem.

DataSet

The dataset consists of 414 entries and 7 columns, with no missing values. Here’s a brief overview of the columns:

• Transaction date: The date of the house sale (object type, which suggests it might need conversion or extraction of useful features like year, month, etc.).
• House age: The age of the house in years (float).
• Distance to the nearest MRT station: The distance to the nearest mass rapid transit station in meters (float).
• Number of convenience stores: The number of convenience stores in the living circle on foot (integer).
• Latitude: The geographic coordinate that specifies the north-south position (float).
• Longitude: The geographic coordinate that specifies the east-west position (float).
• House price of unit area: Price of the house per unit area (float), which is likely our target variable for prediction.

Model Training and Comparison

• Linear Regression: A good baseline model for regression tasks.
• Decision Tree Regressor: To see how a simple tree-based model performs.
• Random Forest Regressor: An ensemble method to improve upon the decision tree’s performance.
• Gradient Boosting Regressor: Another powerful ensemble method for regression.

Evaluation Metrics

• Mean Absolute Error
• R-squared

Result

Model			MAE	        R2 Score
LinearRegression	9.748246	0.529615
Decision Tree	        11.760342	0.204962
Random Forest	        9.887601	0.509547
Gradient Boosting	10.000117	0.476071

Conclusion

• Linear Regression has the lowest MAE (9.75) and the highest R² (0.53), making it the best-performing model among those evaluated. It suggests that, despite its simplicity, Linear Regression is quite effective for this dataset.
• Decision Tree Regressor shows the highest MAE (11.76) and the lowest R² (0.20), indicating it may be overfitting to the training data and performing poorly on the test data.
• Random Forest Regressor and Gradient Boosting Regressor have similar MAEs (9.89 and 10.00, respectively) and R² scores (0.51 and 0.48, respectively), performing slightly worse than the Linear Regression model but better than the Decision Tree.