In this article will going to see how we can implement different types of graph/charts in python using Seaborn Library- based on matplotlib. If you dont have basic intituition about different types of graphs/charts/plots why they are used how they are used go throught the below mentioned article to have a clear insight before implementing, It is always suggested to build a foundation strong.

For installation and enviroment setup please go through the article mentioned below:

After setting our enviroment and installing packages we can start with our implementation of various graphs using seaborn with python.

  1. Structure of code

Seaborn…


In this article will going to see how we can implement different types of graph/charts in python using matplotlib. If you dont have basic intituition about different types of graphs/charts/plots why they are used how they are used go throught the below mentioned article to have a clear insight before implementing, It is always suggested to build a foundation strong.

For installation and enviroment setup please go through the article mentioned below:

After setting our enviroment and installing packages we can start with our implementation of various graphs using matplotlib with python.

  1. Structure of code

Matplotlib has specific code which…


In this part we will have a glimpse of the types of graphs/chart which are used to visualize the data and information obtained from these graphs/charts.

When managing numbers in insights, consolidating information representation is necessary for making a lucid and justifiable rundown of dataset. It doesn’t make any difference if it’s a huge or little dataset, envisioning information utilizing diagrams and graphs will contribute to a great extent to your crowd understanding the message.

There are, in any case, various sorts of diagrams and outlines utilized in information perception and it is some of the time precarious picking which…


This Part is the continuation of the Deploying AI models Part-3 , where we deployed Iris classification model using Decision Tree Classifier. You can skip the training part if you have read the Part-3 of this series. In this article, we will use Flask as the front end to our web application to deploy the trained model for classification on Heroku platform with the help of docker.

Note: If you have followed my Model Deployement series from starting you can skip the section 1.

Article: Deploying AI models Part-3

1.Iris Model Web application using Flask.

1.1. Packages

The following packages were used to create the application.

1.1.1. Numpy


Photo by Richy Great on Unsplash

In this article we will see how we can deploy out html or static codes in Github — is repository where we can store our codes and manage it via Git-CLI or Git GUI.

For articles related to the AL/ML model deployment, please visit to the following articles:

1. How to Deploy AI Models? — Part 1

2. How to Deploy AI Models? — Part 2 Setting up the Github For Herolu and Streamlit

3. Deploy AI models -Part 3 using Flask and Json

4. How to Deploy AI models ? Part 4- Deploying Web-application on Heroku via Github

5. How to Deploy AI models ? Part 5- Deploying Web-application on Heroku-CLI

1. Github

Git is a version control system which automatically tracks and keeps records of the changes which have been performed on the repository (local or remote) over the time. Below image summarizes the workflow of the git with local repository and remote repository.


Python is a scripting programming language which has an ample amount of support from open source libraries. Visualization is the technique which is used in many fields, especially in Data Analytics, Data Science, Data Analysis, Machine Learning, Deep Learning and various other fields. In this series, we will discuss the libraries which are widely used in visualization, which are mentioned below along with different plotting styles such as a bar graph, histogram and many others:

  1. Matplotlib
  2. Seaborn
  3. GGplot
  4. Bokeh
  5. Pygal
  6. Altair
  7. Plotly
  8. Folium

In this article, let us start by setting up our environment i.e. Installing the above mention libraries…


This Part is the continuation of the Deploying AI models , where we deployed Iris classification model using Decision Tree Classifier, had a glance of version control i.e. Git. In this article, we will see how we can deploy data science as well as Machine Learning application easily with the help of open source library streamlit.

1. Digit Classification Model Web application using Streamlit.

1.1. Packages

The following packages were used to create the application.

1.1.1. Numpy, pandas

1.1 .2. Streamlit

  1. 1.3. Dataset, SVC, Model Selection from sklearn
Big Data Jobs

1.2. Dataset

The dataset is used to train the model is of digit dataset composed of 10 classes i.e. label is 10 from 0–9…


This Part is the continuation of the Deploying AI models Part-3 , where we deployed Iris classification model using Decision Tree Classifier. You can skip the training part if you have read the Part-3 of this series. In this article, we will have a glimpse of Flask which will be used as the front end to our web application to deploy the trained model for classification on Heroku platform.

1.Iris Model Web application using Flask.

1.1. Packages

The following packages were used to create the application.

1.1.1. Numpy

1.1 .2. Flask, Request, render_template from flask

  1. 1.3. Dataset, Ensemble, Model Selection from sklearn
Big Data Jobs

1.2. Dataset

The dataset is used to train…


This Part is the continuation of the Deploying AI models Part-3 , where we deployed Iris classification model using Decision Tree Classifier. You can skip the training part if you have read the Part-3 of this series. In this article, we will have a glimpse of Flask which will be used as the front end to our web application to deploy the trained model for classification on Heroku platform.

1.Iris Model Web application using Flask.

1.1. Packages

The following packages were used to create the application.

1.1.1. Numpy

1.1 .2. Flask, Request, render_template from flask

  1. 1.3. Dataset, Ensemble, Model Selection from sklearn

1.2. Dataset

The dataset is used to train…


We have seen in the earlier part of the AI model deployment how to set up the directory as well as the git. In this part we will build the foundation of how we can deploy model locally and in the next article we deploy the model in Heroku and Streamlit, In this section we will focus how to deploy model using Json .

1.Iris Model deployment using JSON.

1.1. Packages

The following packages were used to create the application.

1.1.1. Numpy

1.1 .2. Flask, Request, Jsonfy, render_template from flask

  1. 1.3. Dataset, Ensemble, Model Selection from sklearn
Big Data Jobs

1.2. Dataset

Dataset…

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