As a data scientist, I have applied my knowledge and skills to devise unique solutions tailored to address diverse problems encountered by clients.
Pre-Covid and Post-Covid Twitter sentiment analysis
The main objective of this project is to examine the sentiment on Twitter during two distinct periods: before and after the Covid-19 outbreak. By utilizing advanced techniques in natural language processing, we delve into a large volume of Twitter data to extract meaningful findings regarding public sentiment and its changes over time. Our analysis aims to comprehend the shifts in attitudes, emotions, and opinions expressed on social media prior to and following the Covid-19 pandemic. By illuminating these sentiments, we gain a deeper comprehension of the social and emotional consequences of the global crisis, enabling informed decision-making and proactive responses in future situations.
Image Forgery Localization with Deep Learning
This focuses on image forgery localization using deep learning techniques and the generation of masks based on the predicted results. With the rise of digital manipulation and the proliferation of fake images, it has become crucial to develop robust methods for detecting and localizing forged regions within images. By leveraging the power of deep learning algorithms, we have trained a model to accurately identify areas that have been tampered with. Furthermore, we have developed a mechanism to generate masks that precisely outline the forged regions, visually representing the detected manipulations. Our project aims to contribute to the fight against image forgery by providing an efficient and reliable tool for forensic analysis and verification of authenticity.
Text summarization with PageRank
The objective of this project is to improve the text summarization process through the combination of the cosine distance and PageRank algorithms. By using the cosine distance, we assess the semantic similarities among sentences, ensuring that the summary effectively captures the essential meaning of the original text. Additionally, by incorporating the PageRank algorithm, we assign importance scores to sentences based on their centrality, giving priority to crucial information in the summary. This approach enables users to efficiently understand and digest extensive amounts of text by generating concise and coherent summaries. The ultimate goal of this project is to enhance productivity and accessibility across various industries, making information more manageable and actionable.
Website Clustering and Classification with ML
This project focuses on clustering webpages using word vectors and classifying new pages based on their content. By leveraging advanced natural language processing techniques, we convert webpage text into word vectors, which capture the semantic meaning and relationships between words. Through clustering algorithms, we group similar webpages together based on their word vector representations, enabling the efficient organization and navigation of related content. Additionally, we develop a classification model trained on the clustered data, allowing us to classify new webpages into their respective clusters or categories. This project aims to improve information retrieval and categorization systems, facilitating better content management and enhancing the user experience in web browsing.
Multi-class weather recognition from still image with Deep Learning
This project focuses on the development of a deep-learning model for multi-class weather recognition from still images. By utilizing advanced convolutional neural networks (CNNs) and image processing techniques, we have created an accurate and efficient system capable of categorizing images into various weather conditions. This technology has numerous applications, ranging from automated weather monitoring to enhancing image search and retrieval systems. Our project aims to provide a valuable tool for weather analysis and enable more informed decision-making in various industries that rely on weather information.