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
See where this freelancer has spent most of their professional time.
Experienced in Telecommunication, Information Technology, Banking and Finance, and Media and Entertainment.
Business area experience
See which departments and functions this freelancer has contributed to most.
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
Statistics
Experience
Global experience
Expertise
Qualifications
Profile
Frequently asked questions
Have questions? Find more information here.
Karthikeyan is based in Berlin, Germany.
Karthikeyan speaks the following languages: English (Advanced), German (Elementary).
Karthikeyan has at least 4 years of experience. During this time, Karthikeyan has worked in at least 8 different roles and for 1 company. The average length of individual experience is 5 months. Note that Karthikeyan may not have shared all experience and actually has more experience.
Based on recent experience, Karthikeyan would be well-suited for roles such as: Cryptocurrency Price Prediction using Machine Learning Algorithms, Advanced Research Methodologies – Adversarial Machine Learning, Cloud Computing - Netflix Data Visualization Project using Amazon QuickSight.
Karthikeyan's most recent position is Cryptocurrency Price Prediction using Machine Learning Algorithms.
In recent years, Karthikeyan has worked for CommScope.
Karthikeyan is most experienced in industries like Telecommunication, Information Technology, and Media and Entertainment. Karthikeyan also has some experience in Banking and Finance.
Karthikeyan is most experienced in business areas like Information Technology, Quality Assurance, and Business Intelligence. Karthikeyan also has some experience in Research and Development and Finance.
Karthikeyan holds a Master in Data Science from University of Europe for Applied Sciences and a Bachelor in Information Science from Visvesvaraya Technological University.
The availability of Karthikeyan needs to be confirmed.
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Average rates for similar positions
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The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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