Arjun Nair

An AI professional with a solid foundation in Python programming and hands-on experience in developing machine learning models. Notable projects include a Breast Cancer Detection ANN and a Spam Email Classifier. Passionate about applying AI to solve real-world problems, with a focus on enhancing model performance and driving innovation. Eager to contribute technical skills as an AI Engineer.

Spam Email Classifier using Naive Bayes, 11/01/24, 12/31/24: Created a Streamlit application to identify spam emails using a Naive Bayes model. The app accepts user inputs, encodes features, and provides a prediction on whether an email is spam. Included functionality to train a model or load an existing one, with test accuracy of approximately 96%. This project highlights skills in machine learning, feature engineering, and interactive app development. 

Breast Cancer Detection using ANN, 09/01/24, 10/31/24: Designed a predictive model to identify breast cancer presence based on patient medical data. The data was preprocessed using pandas for organization and StandardScaler to normalize features, ensuring consistent input for the model. Built and trained an Artificial Neural Network (ANN) using TensorFlow and Keras, incorporating ReLU and sigmoid activation functions to optimize classification performance. The model was fine-tuned with the Adam optimizer and binary cross-entropy loss function, achieving a test accuracy of approximately 96% on unseen data. Evaluated the outcomes through confusion matrix analysis and visualized model performance trends using matplotlib and seaborn. This project showcases strong capabilities in machine learning, data preprocessing, and result interpretation to address healthcare challenges. 

Text Summarizer using NLP, 08/01/24, 08/31/24: Built a Streamlit-based app for text summarization using TF-IDF and cosine similarity. Tokenized input text, ranked sentences by importance, and extracted top sentences to generate a summary. The app allows users to adjust the summary length, showcasing skills in NLP and interactive app development. 

Image Compression using K-Means, 06/01/24, 06/30/24: Implemented an image compression method using K-Means clustering to reduce file size while maintaining visual quality. Processed image data with OpenCV and applied clustering to group similar colors, replacing each pixel with its cluster center. Reduced the color palette to k=8 and visualized the compression results using matplotlib. This project highlights skills in image processing and unsupervised learning.

• Date of Birth: 06/09/04
• Gender: Male

Python Development, Programming, Part Time, Computer Operating, Artificial Intelligence, SQL, Natural Language Processing, Deep Learning, Neural Networks, English, Both, Hindi, Both, Malayalam, Both

• Machine Learning: Infosys Springboard, June 2024
• Advance Your Skills in Deep Learning and Neural Networks :LinkedIn Learning, July 2024
• Advance Your Skills in Natural Language Processing: LinkedIn Learning, July 2024