KRISHNAVENI K DATA SCIENCE/DATA ENGINEER

adyhs7@r.postjobfree.com 978-***-**** linkedin.com/in/krishnaveni-kanagaraju github.com/KRISHNAVENI2802 SUMMARY

IT-professional with 3 years of experience in Data science,Machine Learning,Data Analysis. Key items of experience include:

Expertise in programming languages like: Python,R Programming and web application frameworks like: Flask.

Expertise in predictive modeling,data mining,data catalogue,data profiling,data validation.

Expertise in building pipeline for analyzing and building model according to dataset with suitable algorithms.

Leveraging analytical skills and strong attention to detail in order to deliver proper solution.

Worked on solutions involving data stores MySQL,PostgreSql,Big Query,snowflake.

Strong in solving problems of data structures and algorithms. EDUCATIONAL QUALIFICATION

Qualification : M.E in Computer Science Specialization in Big Data Analytics (2017-2019) College : College Of Engineering,Anna University,Guindy,Chennai Qualification : B.E Electronics and Communication Engineering (2013-2017) College : Jeppiaar Maamallan Engineering College,Sriperumbudhur WORK EXPERIENCE/INTERNSHIP

Organization : Pipecandy Junior Data Scientist/Data Engineer Oct 2021-Present Organization : Optisol business Solution Machine Learning Engineer/Data Analyst Feb2020-Sep2021 Organization : Idodeli Junior Data Architect Sep2019-Jan2020 PROJECTS

Developed Interactive Dashboards using Tableau to provide real-time insights to stakeholders.Built visualizations with drill down capabilities to allow users to interact with data in more depth.

Experiencing in using snowflake for managing data warehousing and data lake solutions.Improved query performance by optimizing snowflake virtual warehouses and dbt models.

WebSales Estimation - Analysis of web sales data to identify trends,patterns and opportunities.Developed sales forecasting models using regression analysis, timeseries analysis and machine learning algorithm. Conducted data cleaning,preprocessing,feature engineering to improve model accuracy.

ABSA - Implemented aspect-based sentiment analysis algorithms to identify sentiment towards specific product features using NLP-architect and pyabsa in Amazon review data.Conducted data profiling and cleansing,annotation to ensure accuracy and completeness of sentiment data.

Entity ID Generation - Developed domain validation rules using regular expressions,Fuzzy logics and lookup tables and tried poc using rule based algorithm on ecommerce data.

Shopify Data Analysis - Implemented Data Analytics solution to support business decisions,product analysis,product assortment,pricing and promotion.Conducted Market and competitive analysis to identify trends and opportunities. Project Name :Clinical Trials Document Analysis /Named Entity Recognition Technology : Python, Spacy, Gensim, NLTK, BERT, Pytorch, TensorFlow Description :Getting an approval for clinical trial involves preparation of protocol document and varied supporting documents for review board, Currently,reviewing documents for accuracy and completeness is manual, time consuming and potentially error prone process due to medical terminologies mismatch and conceptual variance. The intent of this project is to automate the document analysis to improve efficiency and reduce errors.

Built a custom NLP pipeline to parse different documents involved in a clinical trial like protocol documents, consent forms etc.

Extracted relevant sections of the different documents.Example :Adverse symptoms section of protocol document and that of consent forms.

Developed a model to tag medicine name and diseases name in the medical document which had to get approval from FDA data obtained from trial and error methods of medical reports.

SKILLS

Predictive Analytics

-- Recommendation

-- NLP

-- Clustering

-- Classification

-- Time Series Forecasting

Data Annotation

Data Visualization

Problem Solving

Analytical Thinker

Individual Contributor

TOOLS & PLATFORM

Database : Microsoft SQL server, PostgreSql,

MongoDB

AWS services : Lambda, GLUE, SNS,

SSM,EC2, Crawler, Athena

Reporting Tools : Tableau Desktop, Tableau

Server.

Scheduling : Airflow, Cloudera Oozie, AWS

Cloud Watch.

Big Data Technologies : Apache Spark,

Map Reduce,HIve,Hadoop

Streaming Platform : Kafka

CERTIFICATION

Coursera

Machine Learning

Introduction to statistics

Udemy

Data Augmentation in NLP

NLP with Transformers

Data Science and machine learning

Learn DBT from Scratch

Snowflake - The complete masterclass

Tableau server and Tableau online for

Data Analysts

edx

Introduction to Artificial intelligence with

python

Project Name : Idodeli -Time Series Forecasting

Technology : Python,Big Query,data studio,MLkit,Arima,Prophet,LSTM Description : Forcasting the number of delivery persons,cost per order.

Used Big Query to store data and fetch data for analyzing.

Forecasting the delivery count and person required for the next week.

Analysis the data to improve the pay for the delivery person which also helps to improve company revenue depends on the delivery count and distance covered by delivery person.

PROGRAMMING

Python Libraries : Pandas,Numpy,Scikit

Learn,basics of TensorFlow,Keras,NLTK,Bert

R Programming Packages: dplry,tidyr

Pyspark

Project Name : TC Global Recommendation Engine

Technology :Flask,RecommendationAlgorithm(KNN),AWS,PostgreSql,Fliar,Fuzzy,Git,Lamda,EC2 Description :TC Global is a leading global ed, learning and investment services platform with a substantial and diversified base of consumers that include students,professionals,universities.

Analysis 30 years students legacy data and came up with useful insights that helps business.

want to build Recommendation engine with 30 years data and 2 lakh university details data.

Delivering the Flask APIs to do NLP task and Recommendation engine .

Created Web Services using python-flask and used Postgresql to store and process.

Created recommenders based on Content-based Filtering and Knowledge based filtering. Project Name :Data Migration and ETL process in Databricks Technology : Python, SQL, PostgreSQL, Pandas, Databricks Delta lake. Description : The project scope is to migrate data from PostgreSQL to Databricks Delta lake house to PostgreSQL. Population of data is done for analyzing the financial check of the sales and improving with the legacy data.

Created Schema for the tables in Databricks Delta table.

Using Databricks Notebook and python, connection is established between PostgreSQL and Databricks.

Data migration is done by querying the DB in PostgreSQL and populated in Databricks Delta lake house platform.

Transformation process is done with the Notebook and loaded to the destination PostgreSQL DB. Project Name : Review Analysis

Technology : Naive Bayes, Scrapy, MongoDB

Description : Physicians reviews are scrapped from online directory listing services like google, yelp, facebook, RateMDSetc., AnNLP Machine Learning Classifier is trained to analyze and score the reviews.The output of this analysis is stored in SQL and exposed as APIs for the UI team to build dashboards for the stakeholders.

Built a web scrapping pipelines using Selenium and Scrapy to extract the reviews from the online directories.

Trained an NLP classifier based on Naive Bayes algorithm to generate sentiment scores on the reviews.

Created a custom NLP pipeline extract entities (Doctors, Nurses, Assistants) and their attributes(Positive and Negative) from the review text. Machine Learning Projects

Developed an email classification model using machine learning techniques to categorize emails based on their content or purpose.

Detection of motorcyclists with or without helmet in traffic surveillance video.

Developed an analytical model using R and R shiny dashboard,used fuzzy logic to aggregate names of similar products.

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