cab_fare_prediction_model
predict the cab fare data using the historical data
Project report completed successfully for and submitted for review
Project report preparation for
Refactoring the ML python code for
Libre office setup done for preparing project report
Model evaluated with the unseen data successfully in R and the final output pushed to the repository
Evaluated the model and predicted the fare amount for in R.
Implemented the random forest model for in R
Developed a decision tree regressor model for in R
Developed linear regression model in R for cab_fare_prediction
Pushed R file to cab-fare-rental-prediction
Feature scaling is completed successfully for cab_fare_prediction in R
Firing up...selected the necessary features for model development in R
Completed Univariate and Bivariate analysis for the cab_fare_data and interesting things rolled out...
Feature Engineering completed successfully for the cab fare prediction data:)
Pushed 1 change to cab-fare-rental-prediction
Completed outlier analysis for cab_fare_prediction in R
Completed missing value analysis for cab_fare_prediction in R
Pushed 1 change to cab-fare-rental-prediction
Predicted the unseen data with the Random forest model created successfully in Python
Implemented the Random Forest Regressor model for cab fare prediction and turns out to be a better model
Implemented Decision tree regressor model for cab fare prediction
Implemented Linear model and calculated metrics for the cab fare prediction
Have completed feature_scaling in cab_fare_prediction_model
