I'm a fourth-year B.Tech student specializing in Artificial Intelligence and Machine Learning with a strong interest in building intelligent systems that solve real-world problems.

My current focus is on machine learning, data science, software engineering, and applied AI. I enjoy taking projects from data collection and preprocessing all the way to model development, evaluation, and deployment.

🎓 Fourth-Year B.Tech Student in Artificial Intelligence & Machine Learning

🌍 Based in Kerala, India

💻 Aspiring AI-ML Engineer

📊 Interested in Data Science, Machine Learning, Deep Learning, and Predictive Analytics

⚽ Football Enthusiast

🌱 Currently learning:

• Audio Signal Processing
• Feature Engineering
• Time-Series Analysis
• XGBoost
• Machine Learning Pipelines
• Deep Learning Fundamentals
• Linux & System Programming

• Python
• C
• C++
• Java
• SQL

• Scikit-Learn
• XGBoost
• Pandas
• NumPy
• Matplotlib
• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Model Evaluation
• Time-Series Analysis

• Audio Processing
• FFT
• STFT
• Spectrogram Analysis
• MFCC Feature Extraction
• Rainfall Prediction Systems

• FastAPI
• Streamlit
• HTML
• CSS
• JavaScript
• REST APIs

• Git
• GitHub
• VS Code
• Linux
• Jupyter Notebook
• Kaggle
• Google Colab

A machine learning project that predicts rainfall intensity using environmental audio recordings.

Key Concepts:

• Audio Signal Processing
• Feature Extraction
• Spectrograms
• MFCCs
• Time-Series Analysis
• XGBoost
• Rainfall Prediction

Current Work:

• Data Cleaning
• Exploratory Data Analysis
• Audio Feature Engineering
• Model Development

An AI-driven crowd analytics system designed to estimate and forecast pedestrian footfall at choke-point entry locations using computer vision and time-series forecasting techniques.

Key Concepts:

Computer Vision Crowd Counting Object Detection & Tracking Time-Series Forecasting ARIMA Modeling Data Analysis & Visualization Predictive Analytics

Features:

Automated Crowd Counting from Video Streams Footfall Trend Analysis Crowd Forecasting at Entry Points Data Aggregation and Time-Series Modeling Real-World Application for Crowd Management and Public Safety

Technologies Used:

Python OpenCV Pandas NumPy Matplotlib Scikit-Learn ARIMA

Machine learning models built using historical FIFA World Cup datasets to predict tournament outcomes and team performances.

Features:

• Historical Data Analysis
• Feature Engineering
• Predictive Modeling
• Sports Analytics

• Machine Learning
• Deep Learning
• Audio Analytics
• Time-Series Forecasting
• XGBoost
• Linux System Programming
• Data Structures & Algorithms
• Software Engineering Best Practices

📧 Email: amarnathdj56@gmail.com

💼 LinkedIn: https://linkedin.com/in/amarnath-dj-b710abc