Machine Learning Computer Science

DAVOOD RABBANI SHAIK

817-***-**** • *********************@*****.*** • //www.linkedin.com/in/davood-rabbani-shaik-20855a281/ • github.com/shaikdr1 • Arlington, Texas

SUMMARY

Extracted and presented actionable insights from large datasets, aiding strategic decision-making processes. Identified and analyzed trends within data to support predictive analysis and enhance forecasting accuracy using Tableau. EDUCATION

M.Sc, Computer Science and Engineering May 2024

University of Texas at Arlington, Arlington, TX 3.7 GPA Relevant coursework: Intelligent Systems/Robotics, Big Data Management/Databases, and Software Engineering B.Tech, Computer Science and Engineering May 2022

Acharya Nagarjuna University, Guntur, A.P, INDIA 7.69 GPA Relevant coursework: Data Structure, Web Technologies, Microprocessors, and Artificial Intelligence TECHNICAL SKILLS

Data Analysis and visualisation: Tableau, Seaborn, Matplotlib, Numpy, Pandas, Regression, Decision Tree ETL and Reporting Tools: Apache Spark, Apache Airflow, Kafka, Shell Programming and IDE’s: Python, C, Java Script, R, Visual Studio Code, IntelliJ IDEA, Jupyter Notebook, Java Big Data and Machine Learning: Scala, Hadoop, TensorFlow, Pytorch, Databricks, Pyspark, Scikit-learn Cloud Technologies: AWS, Azure, Kubernetes, Docker Database: MySQL, MongoDB, PostgreSQL

ACADEMIC PROJECTS

Predicting Stock and Stock Price Index movement using Machine Learning Algorithms Collaborated in a team of three to implement diverse Machine Learning Techniques on stocks and stock indexes.

• Evaluated 10 years of historical data and computed 10 technical parameters using stock data

• The Random Forest achieved an accuracy of 82.5, while the Polynomial Kernel attained 80.5 accuracy, and the RBF Kernel achieved 70.12 accuracy with the Support Vector Classifier Real-time Sign Language Detection

Supervised a quartet in designing and developing a model for detecting Indian Sign Language.

• The tool was trained on 42,000 images. RGB images were converted into black-and-white images

• Applied Convolutional Neural Networks (CNNs) and Data Generators, utilizing various CNN architectures and optimizers to train the model and select the best one Matrix Multiplication using Hadoop and Scala

Implemented a scalable data processing pipeline using Apache Spark in Scala.

• In Hadoop, tasks undergo splitting into map and reduce phases, which are then executed across a distributed cluster of nodes. This approach enables parallel processing

• In Scala, tasks are performed using the Resilient Distributed Dataset concept, which is applied to large datasets Implementing different Machine Learning Algorithms. Led team of four in implementing and evaluating multiple machine learning methodologies.

• Completed assignments utilizing several machine learning algorithms including K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, K-means, Hierarchical Clustering, Apriori and FP-growth Algorithms

• Preprocessed datasets to enhance data quality and prepare for analysis. Utilized Seaborn and Matplotlib to create insightful visualizations, effectively communicating data trends and patterns CERTIFICATIONS

Tableau Business Intelligence Analyst Professional Certificate (Tableau) Machine Learning with Python (IBM)

Data Analysis with python (IBM)

ETL and Data Pipelines with Shell,Airflow and Kafka (IBM)

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