Koushik Reddy Kondapally

502-***-**** adlqxp@r.postjobfree.com Linkedin

SUMMARY

Graduate student in Data Science from University of North Texas looking for full-time opportunities in the field related to my skills.

EDUCATION

Master of science in Data Science GPA: 3.9/4

University of North Texas, Denton, Texas August 2019-May 2021 Bachelor of Technology in Computer Science GPA: 9/10 CVR college of engineering, India July 2015-May 2019 TECHNICAL SKILLS

Programming Languages: Python, Java, R, SQL, PySpark. Machine Learning: Supervised and Unsupervised Learning, Text Mining, Natural Language processing, Optimization, Classification, Regularization, K-NN, SVM, Naïve Bayes, Random Forest, CNN, RNN, TensorFlow.

Statistical Skills: Descriptive and Inferential Statistics, Hypothesis testing, z-test, t-test, F-test, ANOVA, A/B Testing, OLS, GLM, Linear, Logistic.

ETL Tools: Pentaho kettle, Schema Workbench, Report Designer. Visualization Tools: Tableau, Power BI.

Version Control Systems: GIT

WORK EXPERINCE:

Research Assistant at University of North Texas Jan 2021-april 2021

• Implementing ML classification by sentimental analysis using to predict the most trending hashtags by the day, finding whether the given tweet has positive sentiment or negative sentiment and trying to improve the model precision to 90%.

• Developing an efficient and reliable Data pipeline to pull the Data from Twitter and process the data for further data analysis and predictive modelling. Graduate Assistant at University of North Texas Jan 2020-Dec 2020 Utilized AWS S3 for storing data in the cloud.

• Involved in maintaining the Database and upgrading employer’s software.

• Troubleshooted and resolved network related issues.

• Configured NTP server to update the local Time in the university workstations. Data Analyst Intern at GGK Technologies July 2018-june 2019

• Expertise in generating graphs using MS Excel Pivot tables. Extracted data from existing data source, Developing and executing departmental reports for performance and response purposes by using SQL, MS Excel.

• Designed and developed weekly, monthly reports related to the marketing and financial departments using SQL.

• Worked on data profiling, data analysis and validating the reports send to third party. ACADEMIC PROJECTS:

Detecting Abusive Comments in Social Commentary

Brief Abstract: Recognizing on the web misuse, offending conduct or cyberbullying has been an inclining topic over every social stage for various years. Computerized frameworks to distinguish "savages" and individuals who affront have neglected to be totally secure. It influences the greater part of youthful web-based media clients around the world, experiencing delayed and additionally planned advanced provocation. The exploration work centers around approaches to recognize and group tormenting in the content by breaking down and trying different things with various. In this study, it focuses to identify and classify bullying in the text. By AUC ROC metric by implementing in Support Vector Machines and Random Forest algorithms.

Fake Job Detection

Brief Abstract: Predict the job is Fake or Not by using NLTK, Spark ML lib, Spark, Text Analytics. Predicted job posting is real or fake using Naive Bayes, Logistic regression ML models, generated visualizations and performed data cleaning operations using text analytics methods Tokenization, stop words, Remover, stemming on data frames Utilized Databricks platform to run ML models optimally, evaluated model with an accuracy of 96%.

Spam Message Detection

Brief Abstract: Built Content based filters using Machine Learning techniques applied to a set of pre-classified messages. These so-called Bayesian filters are very accurate according to recent statistics, and their applicability to SMS spam seems immediate. Bayesian filters automatically induce or learn a spam classifier from a set of manually classified examples of spam and legitimate (or ham) messages (the training collection). Analysis of Sample Sales Data

Brief Abstract: Applied data mining techniques such as Linear Regression, Decision Tree on data set from the automobile industry which is growing rapidly day by day across the globe due to huge transportation need occurred for the human beings for travelling purpose and many other sources also to get insight of the data. Traffic Signs Recognition

Brief Abstract: Traffic-sign recognition is a technology that enables a vehicle to recognize the signs mounted on the road. In the field of pattern recognition in general, the development of TSR systems includes the use of computer vision techniques, which could be considered fundamental. We are going to successfully classify the traffic signs classifier with 95% accuracy in this Python project and we will visualize how our accuracy and losses change over time. Detecting COVID-19 with Chest X-ray:

Brief Abstract: Classified the Kaggle radiography Chest X-Ray dataset of 3000 scans by building ResNet-18 Convolutional Neural Network model and optimized it using the gradient descent technique into three categories Normal, Viral Pneumonia and COVID-19. Developed an interactive dashboard using Microsoft Power BI tool, which displays the count of confirmed, recovered and deaths which can be filtered in two dimensions based on country and date.

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