Ilene  Jerde

Ilene Jerde


Deep Learning Models in Keras - Exploratory Data Analysis (EDA)


Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications.

In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them.

Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries and frameworks like TensorFlowKeras, and PyTorch are built in Python.

In this article, we’ll be performing Exploratory Data Analysis (EDA) on a dataset before Data Preprocessing and finally, building a Deep Learning Model in Keras and evaluating it.

Why Keras?

Keras is a deep learning API built on top of TensorFlow. TensorFlow is an end-to-end machine learning platform that allows developers to create and deploy machine learning models. TensorFlow was developed and used by Google; though it released under an open-source license in 2015.

Keras provides a high-level API for TensorFlow. It makes it really easy to build different types of machine learning models while taking the benefits of TensorFlow’s infrastructure and scalability.

It allows you to define, compile, train, and evaluate deep learning models using simple and concise syntax as we will see later in this series.

Keras is very powerful; it is the most used machine learning tool by top Kaggle champions in the different competitions held on Kaggle.

#python #keras #tensorflow #machine learning #ai #artificial intelligence #deep learning

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Deep Learning Models in Keras - Exploratory Data Analysis (EDA)
Aketch  Rachel

Aketch Rachel


Exploratory Data Analysis in Few Seconds

EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.

EDA is a time taking process as we need to make visualizations between different features using libraries like Matplot, seaborn, etc.

There is a way to automate this process by a single line of code using the library Pandas Visual Analysis.

About Pandas Visual Analysis

  1. It is an open-source python library used for Exploratory Data Analysis.
  2. It creates an interactive user interface to visualize datasets in Jupyter Notebook.
  3. Visualizations created can be downloaded as images from the interface itself.
  4. It has a selection type that will help to visualize patterns with and without outliers.


  1. Installation
  2. 2. Importing Dataset
  3. 3. EDA using Pandas Visual Analysis

Understanding Output

Let’s understand the different sections in the user interface :

  1. Statistical Analysis: This section will show the statistical properties like Mean, Median, Mode, and Quantiles of all numerical features.
  2. Scatter Plot-It shows the Distribution between 2 different features with the help of a scatter plot. you can choose features to be plotted on the X and Y axis from the dropdown.
  3. Histogram-It shows the distribution between 2 Different features with the help of a Histogram.

#data-analysis #machine-learning #data-visualization #data-science #data analysis #exploratory data analysis

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Marget D

Marget D


Top Deep Learning Development Services | Hire Deep Learning Developer

View more:

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Exploratory Data Analysis


Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see. You’ll first search for the tickets on multiple websites on multiple airline service providers. You will then compare the cost of the tickets with the services they are providing. Is there free WiFi available? Are breakfast and lunch complimentary? Is the overall rating of the airlines better than the others?

Whatever measures you will take from thinking about buying a ticket and finding the best ticket option for you and booking it is called “Data Analysis”. The formal definition of Exploratory Data Analysis can be given as:

Exploratory Data Analysis (EDA) refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypotheses and to check assumptions with the help of summary statistics and graphical representations.


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Types of Data (Image by author)

  • **Dichotomous Variable: **A dichotomous variable is a variable that takes only one out of two possible values when measured. For eg. Gender: male/female.
  • **Polynomic Variable: **A polynomic variable is a variable that has multiple values to choose from. For eg. Educational Qualifications: Uneducated/ Undergraduate/ Postgraduate/ Doctoral, etc.
  • **Discrete Variable: **Discrete variables are countable variables. For eg. your bank balance, no. of employees in an organization, etc.
  • **Continuous Variable: **A continuous variable is a variable that has an infinite no. of possible values. Any kind of measure is a continuous variable. For eg. Temperature is a continuous variable. The temperature of a particular area can be described as 30 °C, 30.2 °C, 30.22 °C, 30.221 °C, and so on.

#exploratory-data-analysis #data-science #data-analysis #analytics #machine-learning #data analysis

Gerhard  Brink

Gerhard Brink


How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

  • Building/collecting data
  • Cleaning/filtering data
  • Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different