Pinned Repositories
application1
Bike-Sharing-Analysis
From a company's perspective, identifying the expected bike demand in a specific area, within a specific time frame, can significantly increase revenue and customer satisfaction. Moreover, bike relocation can be optimized to further reduce operational costs. From a user's perspective, probably the most important factor is bike availability in the shortest wait time, which we can easily see aligning with the company's interests. In this chapter, we will analyze bike sharing data from Capital Bikeshare in Washington, D.C., USA, for the period between January 1, 2011, and December 31, 2012. The data is aggregated on an hourly basis. This means that no initial and final locations of the individual rides are available, but only the total number of rides per hour. Nevertheless, additional meteorological information is available in the data, which could serve as a driving factor for identifying the total number of requests for a specific time frame (bad weather conditions could have a substantial impact on bike sharing demand)
data-science
data indexing, data grouping and aggregation, data cleaning and preparation, data, data structure, data wrangling, essential functions and time series analysis
data-science-
data indexing
Dog-breeds-prediction-Using-Supervised-Learning-
EPA
My websites
face-mask-detection-application
Identifying-Course-Combinations-and-Recommending-Courses
Pan-card-tampering
Predicting-Airfare-on-new-roots
Roythescientist's Repositories
Roythescientist/application1
Roythescientist/Bike-Sharing-Analysis
From a company's perspective, identifying the expected bike demand in a specific area, within a specific time frame, can significantly increase revenue and customer satisfaction. Moreover, bike relocation can be optimized to further reduce operational costs. From a user's perspective, probably the most important factor is bike availability in the shortest wait time, which we can easily see aligning with the company's interests. In this chapter, we will analyze bike sharing data from Capital Bikeshare in Washington, D.C., USA, for the period between January 1, 2011, and December 31, 2012. The data is aggregated on an hourly basis. This means that no initial and final locations of the individual rides are available, but only the total number of rides per hour. Nevertheless, additional meteorological information is available in the data, which could serve as a driving factor for identifying the total number of requests for a specific time frame (bad weather conditions could have a substantial impact on bike sharing demand)
Roythescientist/data-science
data indexing, data grouping and aggregation, data cleaning and preparation, data, data structure, data wrangling, essential functions and time series analysis
Roythescientist/data-science-
data indexing
Roythescientist/Dog-breeds-prediction-Using-Supervised-Learning-
Roythescientist/EPA
My websites
Roythescientist/Eradicate-Poverty-Africa
Roythescientist/face-mask-detection-application
Roythescientist/Identifying-Course-Combinations-and-Recommending-Courses
Roythescientist/myweb
Roythescientist/Pan-card-tampering
Roythescientist/personal-website
Roythescientist/Predicting-Airfare-on-new-roots
Roythescientist/project-on-global-data-stores-in-2016
The aim of this project is to analyse the data with respect to the sales and profit after filtering some of the columns
Roythescientist/project-on-olympics-data
Aim of this project for the Olympics data is to analyze with the following details: The data has the first four rows with non tabular form, so read it by skipping these rows initially while loading it
Roythescientist/Roythescientist
Config files for my GitHub profile.
Roythescientist/First-app-using-flutter
simple flutter dart app
Roythescientist/HSI-Interview