https://github.com/gabya06/gabya06.github.io
This repo serves as a hub for my current and past data science projects.
https://github.com/gabya06/gabya06.github.io
classification data-science ensemble-learning forecasting machine-learning nlp-machine-learning python sentiment-analysis webscraping
Last synced: 2 months ago
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This repo serves as a hub for my current and past data science projects.
- Host: GitHub
- URL: https://github.com/gabya06/gabya06.github.io
- Owner: Gabya06
- Created: 2024-08-11T23:47:33.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-14T15:31:02.000Z (over 1 year ago)
- Last Synced: 2025-09-10T01:23:05.039Z (11 months ago)
- Topics: classification, data-science, ensemble-learning, forecasting, machine-learning, nlp-machine-learning, python, sentiment-analysis, webscraping
- Language: Jupyter Notebook
- Homepage: https://gabya06.github.io/
- Size: 12.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
## Welcome to my data science portfolio!
## 📚 Projects
| **Project Title** | **Category** | **Tools** | **Description** |
|----------------------------------------------------------|--------------------------|------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| **Reddit API for NYC Apartment Posts** | Data Science | **Reddit API**, **Pandas**, **SQL** | Analyzing **real estate data** from Reddit posts to explore apartment listings in NYC using the Reddit API and **data analysis** techniques with **Pandas**. |
| **Sentiment Analysis on Glassdoor Reviews** | NLP, Data Science | **TextBlob**, **Pandas**, **NLP** | Performing sentiment analysis on **employee reviews** from Glassdoor using **NLP** and **TextBlob**, providing insights into company culture and employee sentiment. |
| **Mastering Interactive Data Visualizations with Streamlit** | Data Science, Visualization | **Streamlit**, **Python**, **Pandas** | Building **interactive data visualizations** using **Streamlit**, allowing for dynamic exploration of datasets and insights. |
| **RoBERTa Model for Reddit Posts Sentiment Analysis** | NLP, Machine Learning | **RoBERTa**, **Transformers**, **PyTorch**, **Hugging Face** | Fine-tuning the **RoBERTa** transformer model to perform **sentiment classification** on Reddit posts, categorizing content into positive, negative, or neutral. |
| **Predicting Movie Genres using Machine Learning** | Machine Learning | **Scikit-learn**, **Python**, **Pandas**, **ML Models** | Using **machine learning models** to predict movie genres based on **textual descriptions**, applying techniques like **feature extraction** and **classification algorithms**. |
| **Ensemble Models for Price Forecasting** | Machine Learning | **XGBoost**, **Scikit-learn**, **Python**, **Pandas** | Building **ensemble machine learning models** to predict price trends from historical data, improving forecasting accuracy with techniques like **XGBoost**. |
| **BART Text Summarization** | NLP, Machine Learning | **BART**, **Transformers**, **PyTorch**, **Hugging Face** | Implementing **BART** for **text summarization** tasks, fine-tuning it to generate concise summaries of long-form text, showcasing applications in content extraction and document understanding. |
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### 🛠️ Tools & Technologies
| **Category** | **Tools & Technologies** |
|------------------------|-----------------------------------------------------------------------------------------------------------------|
| **Languages** | Python, SQL |
| **Machine Learning Libraries** | **PyTorch**, **TensorFlow**, **Hugging Face** |
| **Natural Language Processing** | **RoBERTa**, **BART**, **TextBlob**, **spaCy** |
| **Cloud & MLOps** | **AWS** (SageMaker, EC2, Lambda), **Docker**, **CI/CD** |
| **Data Processing** | **Pandas**, **NumPy**, **PySpark** |
| **Visualization** | **Streamlit**, **Tableau**, **Looker Studio** |
| **Forecasting** | **Ensemble Methods**, **LSTM** |