Karthikeyan A-Cryptocurrency Price Prediction using Machine Learning Algorithms
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Experience
Cryptocurrency Price Prediction using Machine Learning Algorithms
- Designed, implemented, and evaluated multiple machine learning models (e.g., regression, time series, neural networks) to forecast cryptocurrency prices, incorporating data preprocessing, feature engineering, and model optimization for improved predictive accuracy.
- Performed in-depth data exploration and visualization on large cryptocurrency datasets, using tools like Python and libraries such as Pandas and Matplotlib to identify trends and patterns.
Advanced Research Methodologies – Adversarial Machine Learning
- Conducted an in-depth study on applying adversarial machine learning techniques to enhance malware detection and classification, outlining background, objectives, methodology, and anticipated findings in a 15-page presentation for the Advanced Research Methodologies course.
- Designed a structured research plan including thesis statement, source origins, anticipated challenges, recommendations, and references, focusing on the role of adversarial machine learning in cybersecurity, presented as part of a collaborative academic project.
Cloud Computing - Netflix Data Visualization Project using Amazon QuickSight
- Developed and customized dashboards using a Netflix dataset stored in an S3 bucket with filters to generate bar graphs comparing release years and types using Amazon QuickSight.
- Integrated an S3 bucket with QuickSight for data analysis by successfully connecting via a manifest.json file with the appropriate URI, enabling seamless data import and visualization of CSV-based Netflix show data.
Data Visualization - Most Streamed Spotify Songs 2023
- Conducted exploratory data analysis on the Spotify 2023 dataset to identify missing values (e.g., 5.25% in Shazam charts, 9.97% in key) and data types to prepare for visualization and trend analysis in music attributes like danceability and energy.
- Computed missing value percentages and structured data on song features (e.g., streams, playlists across platforms) to uncover patterns in popularity, release dates, and audio characteristics for a comprehensive music streaming study.
Machine Learning – Weather Prediction
- Implemented a Random Forest model for weather prediction in Google Colab using scikit-learn to forecast weather conditions based on historical data features like temperature, humidity, pressure, and wind speed from a CSV dataset.
- Uploaded and analyzed a weather prediction CSV file in Pandas, displaying data frames to examine patterns in variables such as date, time, and environmental metrics to prepare for model training.
Data Analytics - Hybrid Recommendation System
- Designed and implemented a hybrid recommendation system for Netflix movies and TV shows using content-based and collaborative filtering techniques, integrating machine learning models and NLP with NLTK to enhance recommendation accuracy.
- Conducted exploratory data analysis on a Netflix dataset, visualizing key patterns (e.g., genre distribution, top directors) using Matplotlib and Seaborn, executing data cleaning, and applying feature engineering techniques including target-guided encoding and binary feature creation for cast, directors, and genres, achieving high accuracy, precision, and recall scores across multiple models.
Data Engineering - Titanic Project
- Designed and implemented a data pipeline in GCP using Apache Airflow to load and process the Titanic dataset into BigQuery, creating user-managed service accounts and enabling APIs for seamless data integration and management.
- Established a connection between BigQuery and Looker Studio to build a comprehensive dashboard, enabling effective data exploration and visualization of the Titanic dataset insights.
- Configured and monitored Directed Acyclic Graphs (DAGs) in Airflow to orchestrate data processing tasks, ensuring efficient execution and validation of data loaded into BigQuery from public datasets.
Automation Engineer
CommScope
- Designed and developed an infrastructure and manpower resource allocation website for creating projects, release names, and builds using web technologies.
- Redesigned and developed a tool called IHCDTS (Integrated-Home Continuous Development Test System) to track Wi-Fi test chambers in the lab, allowing users to view chamber status and generate test reports.
- Performed detailed inspection of cable modems, routers, and gateways, developing and implementing new test cases and analyzing the performance and quality of existing ones.
- Analyzed automation test scripts and performed mobile app testing using the Appium tool.
- Achieved 80% test coverage, reducing product defects by 25% and increasing test coverage effectiveness by 90% by streamlining existing test cases and developing new ones.
Industry Experience
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Experienced in Telecommunication, Information Technology, Banking and Finance, and Media and Entertainment.
Business Area Experience
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Experienced in Information Technology, Quality Assurance, Business Intelligence, Finance, and Research and Development.
Skills
- Ai Productivity Tools: Replit, Gemini, Copilot
- Data Science: Tableau, Powerbi, Tensorflow, Pandas, Numpy, Scikit-Learn
- Cloud: Docker
- Programming Languages: C, Python, Shell Script, Javascript
- Ci/Cd: Maven, Jenkins, Github
- Platform: Linux, Window, Macos
Languages
Education
University of Europe for Applied Sciences
Data Science · Data Science · Potsdam, Germany
Visvesvaraya Technological University
Bachelor of Engineering, Information Science · Information Science · Bengaluru, India
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Experience
Global Experience
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