Naga Nischala Chinnam

Motivated data science professional with a Master’s in Computer Science. Developed machine learning models for classifying news articles using advanced NLP techniques, ensuring data accuracy and reliability. Adapts quickly to new technologies and communicates insights effectively, ready to contribute to innovative projects in technology-driven environments.

• Linux Essentials Certificate - Cisco Academy
• Python for Data Science - Coursera
• Java Programming Basics - Udemy

EEG-Based Emotion Clustering using Unsupervised Learning

• Fine-tuned a MiniBatch K-Means model for EEG-based emotion clustering using PCA and hyperparameter optimization to improve cluster stability and convergence.
• Developed an end-to-end unsupervised learning pipeline for preprocessing and analyzing high-dimensional, noisy EEG signals.
• Applied AI-driven pattern recognition techniques to extract latent emotional features and explored deep learning–based representation learning.
  Tools: Python, Pandas, NumPy, Scikit-learn, MiniBatch K-Means, PCA, Matplotlib, Jupyter Notebook

Fake News Detection using Machine Learning

• Developed a machine learning model to classify news articles as real or fake using natural language processing techniques.
• Performed text preprocessing, tokenization, and TF-IDF vectorization to convert unstructured text into numerical features.
• Trained and evaluated classification models to improve prediction accuracy and reliability.
  Tools: Python, Pandas, NumPy, Scikit-learn, NLTK, TF-IDF Vectorizer, Matplotlib, Jupyter Notebook