As a data science disciple, I used to work more in jupyter notebook, conducting EDA, designing and developing the model, and presenting the result on the notebook only. Sometimes, I tried to connect the Flask model and give it a working view for the audience, but connecting and developing a model with the front end seems time-consuming and tedious. Then I started exploring alternative and came to Streamlit, which is one of the great ways to share machine learning/data science models, analyses, and visualization without worrying much about the front end at all.

“Streamlit is the next step in ML…

Organized by Graphic Hunters

COVID-19 has created us to bound us within the four walls, surfing the internet has been a window to explore the world. A few weeks ago, I studied a blog from Mollie Pettit at Nightingale, and I came to know about the S-H-O-W conference.

One of the most vital characteristics — or blessing, I must say — which distinguishes humans from other species on this planet is the process of learning. For Learning attending conferences, sharing a platform with great enthusiastic experts is worth the shot, so I started digging more into the conference’s landing page. I applied for the…

Getting Started, Design Psych.

A year back, I got an opportunity to read Human-Computer Interaction(HCI) at my university. There, I came to know about various human psyche principles presented in the domain. I was so excited to explore multiple such principles from the view of the Data Visualization. Data visualization is a high bandwidth connection between data on a computer system and the human brain, facilitated by visual communication so by understanding how different psyche principles influence human behavior; we can design our products (Viz, Graphic, UI/UX, and so on.) to elicit specific responses and actions from our users. …

1 + 1 = 3

A few months back, while I was surfing Instagram, I saw a comment in a post, with a remark, “You have used data-ink ratio in good order.” I immediately started exploring the term “data-ink” and realized that it was coined by “Edward Tufte.” I had read his name on some of the research papers a few months ago on the Human-Computer Interaction course. As I determined the connection between the author’s name and the term data-ink ratio, I began to explore it and started implementing it on my data visualization journey.

This blog is dedicated to those who want to…

An Approach with Artificial Intelligence Concepts

TL;DR Understanding spam or ham classifier from the aspect of Artificial Intelligence concepts, work with various classification algorithms, and select high accuracy producing algorithms and develop the Python Flask App.

The blog is a series of the blog post, if you haven’t read the theoretical Artificial Intelligence concept of spam or ham classifier and have not worked with algorithms in jupyter notebook, please explore it at:

We have covered in part 1 & 2

• Theoretical AI Concept Regarding Spam or Ham Classifier
• Classification Algorithms
• Exploring Data Source
• Data Preparation
• Exploratory Data Analysis

We will cover here in Part 3

• Naïve…

An Approach with Artificial Intelligence Concepts

TL;DR Understanding spam or ham classifier from the aspect of Artificial Intelligence concepts, work with various classification algorithms, and select high accuracy producing algorithms and develop the Python Flask App.

The blog is a series of the blog post, if you haven’t read the theoretical Artificial Intelligence concept of spam or ham classifier, please take a ten-minute read at:

We have covered in Part 1

• Theoretical AI Concept Regarding Spam or Ham Classifier
• Classification Algorithms

We will cover here in Part 2

• Exploring Data Source
• Data Preparation
• Exploratory Data Analysis

We will cover in next Part 3

• Naïve Bayes Behind…

An Approach with Artificial Intelligence Concepts

TL;DR Understanding spam or ham classifier from the aspect of Artificial Intelligence concepts, work with various classification algorithms, and select high accuracy producing algorithm and develop the Python Flask App for SMS: spam or ham detector.

Recently I came across this dataset, where I needed to analyze the sales recording of digital products. I got the dataset of having almost 572000 rows and 12 columns. I was so excited to work on such big data. With great enthusiasm, I gave a quick view of data, and I found the same name repeatedly taking different rows. (Ah! Much time for data cleaning!).

On some product column, it contains iphone while on other it was written Iphone, or iphone 7 + and iphone 7 Plus, and for some regular customer list, some have Pushpa Yadav while on other…