Conclusion

Few Conclusions which can be drawn from this analysis:

1. The Naive Bayes classifier can effectively predict the total fare amount based on the selected features. The accuracy of this classifier has been evaluated and found to be high.

2. The selected features have a significant impact on the total fare amount. For example, trip distance, tip amount and total trip time count have a positive correlation with the total fare amount, while payment type and extra charges have a negative correlation.

3. The classification of the total fare amount into High/Medium/Low can provide useful insights for taxi companies and customers. For example, if a trip is classified as High fare, customers may be willing to consider other transportation options or choose a different route to reduce the fare.

4. The Naive Bayes classifier can be used to identify patterns and trends in the data that can inform business decisions. For example, if a certain payment type is associated with lower fare amounts, taxi companies may want to consider offering incentives to encourage customers to use that payment method.