My Portfolio
Covid Data Analysis
Created a Tableau dashboard using COVID-19 data, with SQL for data cleaning. The dashboard presents insightful pandemic trends and stats, simplifying complex information for users.The project involved a two-fold approach: data preparation and visualization. Initially, I employed SQL to establish a connection with the data source and meticulously clean the data, ensuring it was free from inconsistencies and ready for analysis. I leveraged the features of Tableau to craft a user-friendly and interactive dashboard. This platform enabled users to explore and comprehend critical insights, including trends, patterns, and statistical information associated with the COVID-19 pandemic.
Tableau Public
Landslide Prediction Network
A landslide forecasting system was developed using Neural Networks for accurate predictions. The team worked together to curate datasets from sources such as NASA and IMD, which included critical parameters that impact landslide occurrences. Leadership and effective communication were essential in guiding and motivating team members towards the successful development of a Multi-layered Perceptron model in C++. The model employed a bagging-based ensemble technique to improve the accuracy of the predictions. The combination of technical skills and qualities such as teamwork, leadership, and communication helped ensure the success of the project.
Virtual Assistant
A client-side web application was developed using Flask, which integrated OPEN AI’s advanced natural language understanding model into a virtual assistant. The team utilized Langchain to deliver precise and efficient responses to student queries, enhancing the virtual assistant’s overall effectiveness. The team worked together using agile methodologies to develop the product, with a focus on testing and debugging to ensure high-quality results. The team’s strong communication and collaboration skills helped them to work effectively together, while their leadership and product development skills helped guide the project towards success. These qualities, combined with their technical skills, helped ensure the successful development of the virtual assistant.
Brain-Tumor Classification
Brain-tumor classification using a CNN model is a powerful tool for accurately diagnosing brain tumors. In this project, we augmented a limited and class imbalanced dataset to improve the performance of the model. By using data augmentation techniques, we were able to increase the size of the dataset and balance the distribution of classes, which helped the model to learn more effectively. The CNN model was then trained on this augmented dataset and was able to perform classification with an accuracy of more than 90%. This high level of accuracy demonstrates the effectiveness of using data augmentation techniques and a CNN model for brain-tumor classification.
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Edge Detection Application
In this project, OpenCV and Tkinter were used to create an application that helps visualize different edge detection techniques. These techniques included the canny edge detection algorithm, laplacian edge detection, prewitt edge detection, sobel edge detection, and the robinson mask operator. The application provided a user-friendly interface for users to experiment with these techniques and see their results in real-time. This allowed for a better understanding of how each technique works and their respective strengths and weaknesses. Overall, the project was successful in providing a valuable tool for visualizing and comparing different edge detection techniques..
Read moreCertifications
Google Advanced Data Analytics Professional Certification
The Certification had 7 courses:
DeepLearning.AI Deep Learning Specialization
The Certification had 5 courses:
DeepLearning.AI TensorFlow: Advanced Techniques Specialization
The Certification had 4 courses:
- Custom Models, Layers, and Loss Functions with TensorFlow
- and Distributed Training with TensorFlow (In Progress)
- Computer Vision with TensorFlow (In Progress)
- Deep Learning with TensorFlow (In Progress)