J_sai_subrahmanyam_Resume

1. JANAPATI SAI SUBRAHMANYAM
Associate Software Engineer
Phone: 9943633909, Email: janapati.saisubrahmanyam@gmail.com
Linkedin :- https://www.linkedin.com/in/sai-subrahmanyam-janapati
Blog :- https://knowledgengg.wordpress.com/
Kaggle :- https://www.kaggle.com/saisubrahmanyam/kernels
OBJECTIVE
To be involved in the type of work where I can utilize skill and creativity within the system, that effectively contributes to
the growth of organization.
ABSTRACT
• 1.5 years of work experience in Data Science and Data Warehouse.
• Presently working at Accenture as an Automation Engineer for Data Warehousing and Reporting project.
• Worked on SQL, Python, Dash, Informatica.
• Mostly my daily work includes tools like SQL and pandas to clean data, Dash/Flask for Reporting and automating
data pipelines and SKLEARN for training productionable models.
• Currently working on Auto-Admin Project. Our task is to automate server or DB admin process using AI/NLP.
EDUCATION | ACADEMIC QUALIFICATION
Examination
B. Tech.
University/School
Karunya University
Location
Coimbatore
Year
2018
CGPA/Percent
8.01
ECE
CERTIFICATIONS
• Post-Graduation Program in Big Data Analytics and Optimization from INSOFE
• Introduction to Machine Learning by Andrue NG
• Intro to AI from Microsoft
• Python A-Z for Data Science by Kirill Ermakov.
SOFTWARE SKILLS
• Languages: Python, R, SQL.
• DataScience Tools: Keras, SkLearn, Pandas, NumPy, Tableau, Dash, Plotly, ggPlot, NLTK, Spacy, Flask.
• BigData/Database Tools: PySPARK, Informatica, IBM Netezza.
• Operating Systems: Windows, Raspbian, Ubuntu.
DATASCIENCE PROJECTS
1. Web App for Reporting and Data Analysis:
• We have built this application for Data analysts, to create the reports and analyze data without writing any queries.
• Using this tool any one can create any Machine Learning model, just by clicking on it. We have created a feature to
also allow user to write his own algorithm.
• After any model is trained users can create a productionable ready to deploy Machine Learning API just by clicking
a button.
• User can generate reports and can also create data pipeline or transformations to prepare data.
• We have used DASH/Flask as web application Framework, CSS for styling, Plotly for Graphs and Reporting,
Pandas/Pandas_SQL/Netezza SQL for ETL and Data preparation, Feather and HDF4 along with Netezza DB for
storing data, Sklearn for Machine learning, TensorFlow, TF-Keras for Deep Learning.
2. Benchmarking the error metrics of any structured data
• This application accepts any kind of structured data as input from a web-based UI. It will do all the basic
Preprocessing in the backend and will prompt the user to select the Machine learning and Deep Learning models

2. that he/she want to run their data benchmarking on.
• After this process user will be displayed with a Dashboards of all the error Metrics. These metrics can act as a
benchmark or a reference one so that user can enhance or tune the models or data to improve the metrics.
• I have used Flask for web interactions, Sklearn for Machine learning, TensorFlow and Keras for Deep Learning,
Html and Css for Web UI.
3. Clinical Data Analysis on predicting Heart Stroke:
• Build a classification model for predicting the heart condition on various qualitative and quantitativemeasures.
4. Bankruptcy Prediction:
• This is an unbalanced data-set. Tried various techniques to deal the unbalance problem. Finally used XG-Boost
model and achieved a F1 score of 0.75.
5. Sentiment analysis on IMDB Reviews:
• Build a Deep Learning LSTM model on the IMDB Review Corpus and achieved 87.59% Accuracy.
IoT PROJECTS
• Solar car: (Dec 2016 - Mar 2017)
We have designed and developed a single seater car, powered by the solar cells. My role included the design and
integration of all on-board electronic sensors.
• Smart Streets: (Mar 2017 – July 2017)
Developed a Raspberry Pie based vehicle tracking and Street light control system for our college. This model will
track the vehicles that are entering the college premises and notify the guard if any unauthorized vehicle enters.
This process is done by validating the License Plate on the vehicle. I have used KNN for Character recognition.
CORPORATE EXPERIENCE
Accenture, Bangalore | May 2018 – till date
• Successfully automated the testing process that saved lot of time and resources.
• Successfully Analyzed the insurance data, that has to be reported to the government and build a Machine
Learning model to detect the errors in it before it is being reported by the Government (ISO). Which reduced the
reported error rate from 32% to 4%. This saved a lot of costs to the client.
• Build 5 dashboards to forecast the historical data loads such as financial, claims, etc., This made the work
of Business Analyst a bit easier.
• Build 6 mappings in Informatica which have very minimal issues.
CO-CURRICULAR ACTIVITES
• Conducted a two-day workshop on developing multipurpose robot using Arduino, for our department’s annual
Technological Fest.
• Conducted a one-day Workshop on basics of Arduino.
• Participated in a National Level Conclave on IoT for Smart City.
AWARDS AND ACHIEVEMENTS
• Runner up in Machine Learning Hackathon conducted at INSOFE
• Winner in the Project Expo-2017 for the project Smart Streets
• Finalists in Electric Solar Vehicle Championship conducted by ISIE
• Runner up in the Pravega Aero carnival 2016, by IISC Bangalore.
RESEARCH INTEREST
• Machine Learning, Deep Learning and IoT.
PERSONAL INTEREST’S
• Reading blogs | playing badminton | computer games | Trying new things that makes my work simpler.