Ravikant Yadav

Data Analyst and Freelance Data Scientist with expertise in Python, Data Science, and Artificial Intelligence. Proven track record in developing end-to-end analytical solutions that leverage real-world datasets to inform business strategies. Proficient in identifying complex data patterns, creating predictive models, and implementing data-driven decisions. Engaged in cutting-edge fields such as Ethical Hacking, AI-driven Cybersecurity, and Android Development, focusing on transforming data into actionable insights.

• Emotion recognition: Developed an AI-powered web application to recognize emotions from text input using OpenAI's NLP models, and integrated Flask to create a lightweight API for real-time emotion analysis, and optimized API calls and response handling for seamless user interaction
• Fraud Detection Model: Developed a fraud detection model for analyzing historical transaction data to identify patterns and anomalies, and implemented features such as transaction amount, location, and user behavior. Achieved a high accuracy rate in detecting fraudulent activities, utilized real-time monitoring to provide instant alerts for suspicious transactions, reduced financial losses, and improved security for the organization
• Bull Detection System: A developed detection system capable of identifying moving bulls, outlining boundaries, and eliminating text from video using the YOLO model trained on over 6,000 annotated images with Roboflow.
• AI-powered fraud detection: Working on the design and development of a robust, scalable application that protects users from fraud, scams, phishing attacks, and data breaches by integrating leading third-party APIs. Over time, the application should evolve toward using our own AI models for cost efficiency, faster detection, and better data ownership, with integrated Truecaller APIs, etc.
• Desktop Assistant: Alex leverages artificial intelligence to understand user commands and streamline daily operations, such as opening applications, managing files, setting reminders, and retrieving information
• Music web application: Developed a dynamic music web application using the Django framework with Python, enabling users to stream, search, and manage their favorite tracks in an intuitive interface
• Brain Tumor Detection (VGG-16): Implemented a brain tumor detection model using the VGG-16 convolutional neural network architecture, aimed at classifying MRI brain scans with high accuracy, and leveraged transfer learning techniques to fine-tune the pre-trained VGG-16 model on a labeled medical imaging dataset.

Coordinator (Rise Club), PGDAV College, University of Delhi (DU)

• Collaborate with a team of researchers to conduct cutting-edge research in time series analysis, deep learning, machine learning, adaptive hypermedia, green computing, and data science
• Train junior researchers and students, providing guidance on research methodology and data analysis.

• English
• Hindi