Pinned Repositories
Advance_analytic
1/2/3 Way ANOVA | Residual analysis | Tukey’s HSD Test
BMTC-Ridership-prediction-at-bus-stop-level
The most important of electronic ticketing; not because it makes things easier for the conductor or passenger, but because it results in a gold mine of data,that can go a long way in solving the city’s transport problems.Ticketing data in-principle is used by transport corporations (transcos) to identify how many tickets were sold, how many passengers boarded, and what denomination tickets they purchased. In the older, punched ticket system used by BMTC once tickets were sold, conductors would log the serial number of the ticket on top of the bundle at fixed intervals. The difference in serial numbers would indicate the number of tickets sold. However, data such which stage the passenger travelled would be unavailable. Two passengers travelling the same distance but at different points would pay the same. E-ticketing solves that issue to a great extent. It logs the bus stop where the passenger boarded and disembarked, how many passengers travelled on which stage and more. Further, with smart cards, even passes can be tracked, thus giving the operator a clearer picture of the total passenger count.
Data-Analysis-with-R
Using gglot2, tidyr, dplyr, ggmap, choroplethr, shiny, logistic regression, clustering models and more
Data-mining-Decision-models
Data mining-Decision models
datacamp-community-tutorials
Tutorials for DataCamp (www.datacamp.com)
gentfg-machine-learning
All the Machine Learning Tutorials as part of the GEN Technology Focus Group
MachineLearningSamples-ChurnPrediction
MachineLearningSamples-ChurnPrediction
Matrice-Hiring-Assignment---ML
MG-222-2018-Assignment
Credit Scoring and Its Applications
MG-286-PROJECT-MANAGEMENT-
Revanthgh's Repositories
Revanthgh/Advance_analytic
1/2/3 Way ANOVA | Residual analysis | Tukey’s HSD Test
Revanthgh/BMTC-Ridership-prediction-at-bus-stop-level
The most important of electronic ticketing; not because it makes things easier for the conductor or passenger, but because it results in a gold mine of data,that can go a long way in solving the city’s transport problems.Ticketing data in-principle is used by transport corporations (transcos) to identify how many tickets were sold, how many passengers boarded, and what denomination tickets they purchased. In the older, punched ticket system used by BMTC once tickets were sold, conductors would log the serial number of the ticket on top of the bundle at fixed intervals. The difference in serial numbers would indicate the number of tickets sold. However, data such which stage the passenger travelled would be unavailable. Two passengers travelling the same distance but at different points would pay the same. E-ticketing solves that issue to a great extent. It logs the bus stop where the passenger boarded and disembarked, how many passengers travelled on which stage and more. Further, with smart cards, even passes can be tracked, thus giving the operator a clearer picture of the total passenger count.
Revanthgh/Data-Analysis-with-R
Using gglot2, tidyr, dplyr, ggmap, choroplethr, shiny, logistic regression, clustering models and more
Revanthgh/Data-mining-Decision-models
Data mining-Decision models
Revanthgh/datacamp-community-tutorials
Tutorials for DataCamp (www.datacamp.com)
Revanthgh/gentfg-machine-learning
All the Machine Learning Tutorials as part of the GEN Technology Focus Group
Revanthgh/MachineLearningSamples-ChurnPrediction
MachineLearningSamples-ChurnPrediction
Revanthgh/Matrice-Hiring-Assignment---ML
Revanthgh/MG-222-2018-Assignment
Credit Scoring and Its Applications
Revanthgh/MG-286-PROJECT-MANAGEMENT-