Result and Conclusion

Model Results and Visualization

1. Logistic Regression Performance

• Confusion Matrix: The confusion matrix for logistic regression shows that the model accurately classifies most instances, with very few misclassifications. The majority of “rain” and “snow” cases were correctly identified.

• Accuracy: The logistic regression model achieved a high accuracy of approximately 99.8%, making it highly effective for this binary classification task.

• Decision Boundary Plot: Using only two features, temperature and humidity, we visualized the model’s decision boundary. This plot clearly shows how logistic regression separates “rain” and “snow” classes, with most points aligning well within their predicted regions, reflecting the model’s strong performance in distinguishing between the two classes.

2. Multinomial Naïve Bayes Performance

• Confusion Matrix: The confusion matrix for Naïve Bayes highlights challenges in correctly classifying “snow.” The model classified nearly all points as “rain,” indicating it struggled with the feature distributions of the dataset.

• Accuracy: The Naïve Bayes model reached an accuracy of approximately 88.8%, significantly lower than logistic regression, suggesting that it may not be well-suited for this dataset’s characteristics.

3. Accuracy Comparison

• The accuracy comparison chart visually reinforces that logistic regression performed substantially better than Naïve Bayes for this dataset, making it the more suitable model for this weather classification task.

Key Insights and Learnings

1. Logistic Regression’s Suitability for Weather Prediction

• Logistic regression provided highly accurate predictions for precipitation type. This indicates that temperature, humidity, wind speed, and other environmental features are strong indicators for binary weather classification when a linear decision boundary suffices.

2. Limitations of Multinomial Naïve Bayes

• Naïve Bayes, particularly the multinomial variant, did not perform as well, largely due to its assumptions of feature independence and the need for non-negative scaled features. Its lower accuracy and confusion matrix reveal that it is less effective with continuous weather data that has interdependent features like temperature and humidity.

3. Future Directions

• To improve weather classification models further, we could explore more complex algorithms (e.g., support vector machines or decision trees) or integrate additional features such as time-based variables or atmospheric pressure patterns.

• Non-linear models may capture more intricate relationships in weather data, potentially improving the accuracy of predictions in more complex scenarios.

Conclusion

This analysis demonstrates that logistic regression is a robust method for binary classification in weather prediction tasks involving clear, linear separations between classes. The results underscore the importance of choosing an algorithm that aligns with the data characteristics. This study provides a strong foundation for understanding precipitation patterns and offers avenues for future exploration to enhance predictive accuracy.