Fikru Abraham

Fikru Abraham


Goodness of Fit of a Survival Regression Model


Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as customer churn in a marketing perspective, a death in biological organisms, failure in mechanical systems. Generally, it involves the modeling of time to event data; in this context, death/failure/churn is taken as an “event” in the survival analysis literature — usually, only a single event occurs for each subject, after which the customer, organism or mechanism is dead or broken or churned[1]. Survival analysis attempts to answer specific questions, such as:

  • What proportion of a population will survive beyond a particular timeframe?Of those that manage to survive, at what rate will they fail or die?Can multiple causes of failure or death can be taken into account?How do particular characteristics or circumstances increase or decrease the probability of survival?

This article will give us a fundamental overview of Cox’s proportional hazard model, hazard & hazard ratio and ‘Lifelines’ python package. It then details the key focus, i.e. measuring the goodness of fit of the survival regression model.

#machine-learning #python

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Goodness of Fit of a Survival Regression Model
Meryem Rai

Meryem Rai


Fitness App Development: How to Create a Best Fitness App?

There’s a large body of research about the necessity of fitness to have great outcomes for mental health, cardiovascular health, longevity, etc. The rise of sedentary lifestyles makes it vital for people to add fitness routines to their daily schedules. Thus, fitness videos and apps have become popular making fitness accessible. Especially during the 2020-21 pandemic, sports and fitness studios were able to reach clients only through fitness apps. As a fitness instructor, creating a fitness streaming app development gives you credibility, direct connection to clients, and independence. Read on to find out how you can build a fitness app.

Why create the best fitness app?

Technology is evolving at a fast pace. After internet speeds picked up, video streaming websites leveraged it; also smartphones and tablets became ubiquitous, allowing everyone to watch videos on the go. Finally, since 2014, wearables became popular purchases and interest in fitness apps shot up.

Online fitness platform gyms have successfully combined the offline and online experience. VPlayed fitness app building service offers core features for this. It is scalable and easy to integrate while carrying your brand name.

Engaging services to be provided by fitness apps
Fitness apps have more features than ever and you should consider adding the following to yours:

Diet plan

This feature tracks the food consumed by the user, the nutritional breakup, and may also suggest complete meals based on preferences. There can also be a water intake tracker.

Activity tracking

This feature is crucial especially when the app can integrate with wearables. They use smartphone features to calculate distance traveled, steps taken, heart rate, and even the timings and duration of the activity, throughout the day.

Top Key features in online fitness App
After seeing the top functions users appreciate, it’s time to see what a fitness app’s features should be.

Personal Account

Users should be able to log in especially if you need to collect user data to make the app experience more personalized.

Third-Party Device Connectivity

Users will want their wearables and other devices like tablets and smartwatches to be connected, requiring IoT hybrid solutions.

User Activity Tracking

Since fitness apps are expected to track users’ progress over time, the activity-tracking feature is crucial. The data can be gathered globally, i.e over a period or locally, i.e from session to session.

How to monetize your fitness app?

There are a few different ways to monetize your fitness video content . Choose one based on the number of users and the potential for scalability.

Subscription-based Video on Demand
SVOD platform in this model people sign up for packages that give them access to some or all of the app content for a fixed period. The more features you offer, the more you can earn from this option.

Pay Per View

This option is useful for live-streaming fitness classes to new users who want to test the app. You may also combine it with SVOD.

Advertisement-based Video on Demand

Here your app content is available in exchange for viewers watching ads. This option works out well if you have enough users and infrequent uploads.

Coupons & Promotions

Having coupons and promotions in addition to one or more of the above monetization options helps attract new users or gets old users to sign up for more features.

Create the perfect on demand fitness streaming services just like Apple Fitness Plus, Peloton, CorePower on Demand, Sona Fitness, DailyBurn, Practice with Clara, YogaGlo and lots more for you to target like-minded fitness excersiers and generate best money.


Many top fitness experts have established trademark routines and launched fitness streaming platforms to control their revenue streams. Choosing a white-label fitness app development solution saves the trouble of hiring multiple teams and gives you all the fitness app documentation. Options like VPlayed give you good tech support as well, freeing you to focus on making unique content.

#fitness app #fitness app development #how to create fitness app #fitness streaming #online fitness platform

Bella Garvin

Bella Garvin


Fitness App Development

Orbit Edge is one of the leading fitness app development companies that designs a healthcare app with a wide range of health related solutions. Team Orbit Edge focuses on quality so that users can easily navigate through the app interface. Dedicated developers provide their 100% to deliver a secure app that has come up with all the essential compliances.

#fitness app development #fitness app development company #fitness app development services #hire fitness app developers #build fitness app

Angela  Dickens

Angela Dickens


Regression: Linear Regression

Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics. Since it is very important to understand the background of any algorithm you want to implement, this could pose a challenge to people with a non-mathematical background as the maths can sap your motivation by slowing you down.

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In this article, we would be discussing linear and logistic regression and some regression techniques assuming we all have heard or even learnt about the Linear model in Mathematics class at high school. Hopefully, at the end of the article, the concept would be clearer.

**Regression Analysis **is a statistical process for estimating the relationships between the dependent variables (say Y) and one or more independent variables or predictors (X). It explains the changes in the dependent variables with respect to changes in select predictors. Some major uses for regression analysis are in determining the strength of predictors, forecasting an effect, and trend forecasting. It finds the significant relationship between variables and the impact of predictors on dependent variables. In regression, we fit a curve/line (regression/best fit line) to the data points, such that the differences between the distances of data points from the curve/line are minimized.

#regression #machine-learning #beginner #logistic-regression #linear-regression #deep learning

5 Regression algorithms: Explanation & Implementation in Python

Take your current understanding and skills on machine learning algorithms to the next level with this article. What is regression analysis in simple words? How is it applied in practice for real-world problems? And what is the possible snippet of codes in Python you can use for implementation regression algorithms for various objectives? Let’s forget about boring learning stuff and talk about science and the way it works.

#linear-regression-python #linear-regression #multivariate-regression #regression #python-programming

Elton  Bogan

Elton Bogan


Polynomial Regression — The “curves” of a linear model

The most glamorous part of a data analytics project/report is, as many would agree, the one where the Machine Learning algorithms do their magic using the data. However, one of the most overlooked part of the process is the preprocessing of data.

A lot more significant effort is put into preparing the data to fit a model on rather than tuning the model to fit the data better. One such preprocessing technique that we intend to disentangle is Polynomial Regression.

#data-science #machine-learning #polynomial-regression #regression #linear-regression