Results, Assessment, and Conclusion

After building and evaluating the K-Nearest Neighbors (KNN) regression model, the final step is to interpret the results and understand how effectively the model predicts stock prices. Since stock prices are continuous values, regression evaluation metrics such as MAE, MSE, RMSE, and R² Score are used to measure prediction accuracy. Visualizations further help compare actual stock prices with predicted values and identify how well the model captures market trends.

In this module, we will review the outputs generated during the project, interpret the visualizations, evaluate the regression model, test our understanding with multiple-choice questions, and conclude the project with future enhancements and key takeaways.

After completing the implementation, the Stock Price Prediction system produces several outputs that help evaluate the effectiveness of the KNN regression model.

The project successfully:

• Loaded and analyzed the historical stock market dataset.
• Performed exploratory data analysis (EDA).
• Created engineered features from stock price data.
• Optimized the KNN model using GridSearchCV.
• Predicted stock closing prices.
• Evaluated the regression model using multiple performance metrics.
• Compared actual and predicted stock prices through visualizations.

These outputs provide a comprehensive understanding of how well the model forecasts stock prices based on historical market data.