1597588560

TensorFlow Probability uses structural time series models to conduct time series forecasting. In particular, this library allows for a “scenario analysis” form of modelling — whereby various forecasts regarding the future are made.

Structural time series modelling takes the inherent characteristics of the time series into account when making forecasts. This includes factors such as the **local linear trend**, **seasonal**, **residual** and **autoregressive** components. The greater the variation surrounding these components — the more uncertain the forecast.

The examples illustrated in this article use the template from the Structural Time Series modeling in TensorFlow Probability tutorial, of which the original authors (Copyright 2019 The TensorFlow Authors) have made available under the Apache 2.0 license.

For this example, a structural time series model is built in TensorFlow Probability to forecast air passenger data. The data is sourced from San Francisco Open Data: Air Traffic Passenger Statistics.

In particular, passenger numbers for United Airlines from February 2014 — June 2020 are analysed. The specific segment of passengers analysed are enplaned, domestic, departing from Terminal 3 at Boarding Area E.

Here is a visual overview of the time series:

#tensorflow #time-series-forecasting #data-science #time-series-analysis #machine-learning #data analysis

1597588560

TensorFlow Probability uses structural time series models to conduct time series forecasting. In particular, this library allows for a “scenario analysis” form of modelling — whereby various forecasts regarding the future are made.

Structural time series modelling takes the inherent characteristics of the time series into account when making forecasts. This includes factors such as the **local linear trend**, **seasonal**, **residual** and **autoregressive** components. The greater the variation surrounding these components — the more uncertain the forecast.

The examples illustrated in this article use the template from the Structural Time Series modeling in TensorFlow Probability tutorial, of which the original authors (Copyright 2019 The TensorFlow Authors) have made available under the Apache 2.0 license.

For this example, a structural time series model is built in TensorFlow Probability to forecast air passenger data. The data is sourced from San Francisco Open Data: Air Traffic Passenger Statistics.

In particular, passenger numbers for United Airlines from February 2014 — June 2020 are analysed. The specific segment of passengers analysed are enplaned, domestic, departing from Terminal 3 at Boarding Area E.

Here is a visual overview of the time series:

#tensorflow #time-series-forecasting #data-science #time-series-analysis #machine-learning #data analysis

1619668822

Customer segmentation is a key consideration for any business.

While it may be tempting to look at sales data in isolation, doing so can overlook the fact that different customer segments have different spending patterns, and this means that sales data can vary widely across different groups.

In this regard, using a standard linear regression to quantify the impact of different features on sales can be misleading, as different groups can **exist within each feature** that impact sales in a different way. Therefore, a mechanism is needed to model structured linear relationships in an appropriate way.

TensorFlow Probability will be employed for this purpose, using the **JointDistributionCoroutine** and **Markov Chain Monte Carlo** modules. The below analysis is taken from the Linear Mixed Effects Models template, available from the TensorFlow Probability guide.

The original hotel booking datasets and research by Antonio et al. can be found here: Hotel Booking Demand Datasets (2019).

#tensorflow-probability #statistics #data-scienc #tensorflow #machine-learning

1620418260

Introduction

A number is said to be the perfect number if the sum of its proper divisors (not including the number itself) is equal to the number.

To get a better idea let’s consider an example, proper divisors of 6 are 1, 2, 3. Now the sum of these divisors is equal to 6 (1+2+3=6), so 6 is said to be a perfect number. Whereas if we consider another number like 12, proper divisors of 12 are 1, 2, 3, 4, 6. Now the sum of these divisors is not equal to 12, so 12 is not a perfect number.

Programming in Python is relatively simpler and more fun when compared to other languages because of its simpler syntax, good readability. Now that we are clear with the concept of perfect number let’s write a python program to check if a number is a perfect number or not. Let’s build a python code for checking if the given user input is a perfect number or not and explore the fun in coding with python.

#data science #how to check if a number is perfect #perfect number #perfect number in python #perfect number program in python #python

1619607900

Introduction

A number is said to be the perfect number if the sum of its proper divisors (not including the number itself) is equal to the number.

To get a better idea let’s consider an example, proper divisors of 6 are 1, 2, 3. Now the sum of these divisors is equal to 6 (1+2+3=6), so 6 is said to be a perfect number. Whereas if we consider another number like 12, proper divisors of 12 are 1, 2, 3, 4, 6. Now the sum of these divisors is not equal to 12, so 12 is not a perfect number.

Programming in Python is relatively simpler and more fun when compared to other languages because of its simpler syntax, good readability. Now that we are clear with the concept of perfect number let’s write a python program to check if a number is a perfect number or not. Let’s build a python code for checking if the given user input is a perfect number or not and explore the fun in coding with python.

#data science #how to check if a number is perfect #perfect number #perfect number in python #perfect number program in python #python

1623228736

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

**Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.**

**2. Do the course questions in parallel in PyCharm.**

**…**

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing