Mehrab Mashrafi

Topics Covered: Data Scraping Data Preprocessing Data Cleaning Deep Learning Computer Vision Machine Learning

The Indian subcontinent is blessed with a diverse range of mammal species, each contributing to the region's rich biodiversity. However, wildlife conservation faces significant challenges, including the need for efficient species identification, behavior understanding, and habitat preservation. Our project addresses these challenges by harnessing AI technology to aid in wildlife conservation efforts.

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Topics Covered: Data Scraping Data Preprocessing Data Cleaning Data Segmentation GIS Tableau Market Analysis Data Analysis

This was a comprehensive data analysis project that involved various stages. We initiated by scraping data from the web, specifically from PropertyFinder.ae. After gathering the data, we meticulously cleaned and organized it using Python. Additionally, we conducted segmentation to enhance our analysis by categorizing the data in various ways. To further enrich our insights, we incorporated GIS (Geographic Information System) data for more in-depth analysis. Finally, we wrapped up the project by creating interactive dashboards to visualize our findings, making the data easily accessible and comprehensible for presentation purposes.

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Topics Covered: Data Collection Data Preprocessing Image Augmentation Transfer Learning Computer Vision Machine Learning

In an era where artificial intelligence (AI) has revolutionized numerous domains, we find ourselves presented with a unique opportunity. What if we could harness the power of AI to enhance our ability to classify and identify birds within this rich tapestry of avian life?

To answer that, we collected images of bird species from the web, encompassing all 42 orders within the Aves class. After preprocessing the data, we trained a model using ResNet101 transfer learning.

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Topics Covered: Data Scraping Data Preprocessing Data Cleaning Deep Learning NLP (Natural Language Processing) Machine Learning Transfer Learning Language Model

How much do you know about fan made stories? Did you know about the immense world of fan community where non-professional and semi-professional writers alike showcase their writing ability. Well, I found out about it and thought, why not utilize these data to make a NLP classification model that can learn pattern from fan made stories.

This project showcases the implementation of multi-label text classification using Hugging Face Transformers and Fastai. It involves training a deep learning model to predict multiple genre labels for text descriptions. Key features include web scraping with Selenium Webdriver, data extraction from Royal Road (a fan-made stories website), utilization of pre-trained transformer models, Fastai's text processing capabilities, performance analysis metrics like F1 score and ROC AUC, and detailed code explanations.

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