I'm a

Hi, my name is ChandanaEswari

A Software Engineer with 3+ Years of experience in industry in building end to end products.

About Me

Hello! I'm ChandanaEswari.

I am hardworking, enthusiastic, and passionate at what I do. With my firm knowledge in statistics, data science, machine learning, and software development, I create machine learning models and engineer data science techniques which helps in creating state of the art data products for a business or research.

Here are a few technologies I've been working with recently:

  • Tensor Flow
  • Python
  • CNN


SEPT 2021 - Present

Software Engineer

Add Matter

March 2020 - May 2021

Software Engineer


Mar 2018 - Feb 2020

Machine Learning Framework

  • Tensor Flow
  • Scikit Learn
  • FB prophet
  • Keras

Big Data Tools

  • Kafka
  • Spark
  • HDFS
  • Neo4j

Programming Language

  • Python
  • C#
  • C++
  • SQL

Data Visualization

  • Matplotlib
  • PyPlot
  • Tableau
  • SAP Crystal Report


  • Microsoft Azure
  • AWS
  • GCP


  • Object Oriented Programing
  • Design Patterns


In free time I spent time in learning new aspects in data science and software indusrty.

Phishing Detector Using URL NLP Features

  • Beat 250+ participants in shell hacks 2019, by building models that fit the problem set.
  • Achived 94.89% accuracy in detection in new phishing or fraudulent websites using URL feature and NLP, host based feature.

Recommendation Engine

  • Recommendation systems is extremely important given the huge demand for personalized content of modern consumers. Which helps companies to make more money.
  • Used various cosine similarlity, TfidfVectorizer and NLTK.

Save Money In Airline Industry

  • Failure prediction is a major topic in predictive maintenance in many industries. Airlines are particularly interested in predicting equipment failures in advance so that they can enhance operations and reduce flight delays.

Boston Housing Price Prediction

  • The dataset used in this project comes from the UCI Machine Learning Repository. This data was collected in 1978 and each of the 506 entries represents aggregate information about 14 features of homes from various suburbs located in Boston.


My research contribution to feild of Data Science/Machine Learning/Deep Learning.

Learning-based models to detect runtime phishing activities using URLs

    In the world of internet to save a home users from phishing attacks I have developed a Deep Learning based Phishing attach detector. Which has a potential to Save Industry from Data Breach

A Quick Start of Time Series Forecasting : Using Facebook Prophet

    This article is to gives an overview of how to use the facebook prophet forecasting framework.