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Ruthik Raj Nataraja's Projects

alzheimer-s-disease-detection- icon alzheimer-s-disease-detection-

Distinguishing four Alzheimer's disease stages (Mild Demented, Moderate Demented, Non-Demented, Very Mild Demented) from brain MRI scans using a Convolutional Neural Network (CNN) with transfer learning, specifically leveraging VGG architecture. The model was trained using TensorFlow and Keras.

elon-musk-tweet-analysis- icon elon-musk-tweet-analysis-

Computed word frequencies for years 2017-2022. Showed the top 10 words for each year by the highest word frequency value and generated bigram network graphs for each year.

f1-championship-forecast icon f1-championship-forecast

Build a deep learning model like Neural Network to predict F1 championship winners by analyzing historical data and driver features. The model will identify complex patterns and relationships between variables for more accurate predictions.

fcaebook icon fcaebook

Collected and processed Facebook Marketplace data using Talend, then integrated it into a PostgreSQL database. This allowed for in-depth analysis, providing insights on trends, demographics, and revenue.

human-activity-monitoring-dataset icon human-activity-monitoring-dataset

Applying NVG and HVG Calculating average degree, network diameter, and average path length as time series features for human activity data for 15 individuals, each conducted six different activities and recorded with a separate sensor.

pvae icon pvae

code for "Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders".

text-to-sql-generator icon text-to-sql-generator

This project creates a text-to-SQL system with Seq2SeqTrainer for training. Evaluation, including ROUGE scores, shows the model's proficiency in translating natural language queries to SQL. Our approach highlights potential in enhancing NLP models for specific tasks, emphasizing natural language understanding in query generation.

world-indicator-dataset icon world-indicator-dataset

Implement a Machine Learning algorithm to analyze and cluster the real-world dataset using sci-kit python libraries and validate using the silhouette coefficient and other external and internal validation to confirm the analytics.

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