Dhiren Vazirani - Data Scientist Portfolio

Hey there! I'm Dhiren Vazirani, a dedicated Data Scientist with a passion for turning data into insights. Below are some of the exciting projects I've worked on:

Cryptocurrency Sentiment Analysis

Objective: Developed a sentiment analysis platform focusing on cryptocurrency discussions on Twitter to provide real-time insights into the community's sentiment.

Technologies:

Tweepy for Twitter API access NLTK for data preprocessing TensorFlow for building and training the sentiment analysis neural network Flask for API deployment

Key Features: Dynamic data collection from Twitter for up-to-date sentiment analysis. Robust data preprocessing pipeline ensuring the quality of input data. Neural network model designed to capture nuanced sentiment patterns in cryptocurrency-related text. Thorough model evaluation using diverse metrics for performance assessment. Practical deployment through a Flask API, enabling real-time sentiment predictions.

Outcomes Successful creation of a tool for monitoring and understanding sentiment trends within the cryptocurrency community. Showcase of proficiency in Python, machine learning, and deployment strategies.

This projct involves sentiment analysis on tweets related to various cryptocurrencies. Users can get an overview of sentiment (positive, negative, or neutral) based on what people are tweeting. The project utilizes AWS services like Elasticache and S3, with EC2 and auto-scaling to handle demand effectively.

Movie Recommendation System (MRS)

Key Features:

Objective: Engineered a Python-based Movie Recommendation System utilizing collaborative filtering and matrix factorization techniques.

Technologies: Collaborative Filtering Algorithms (User-Based, Item-Based) Matrix Factorization (SVD) Python, NumPy, pandas User Interface Development (Flask/Django)

Key Features: Personalized movie recommendations through advanced collaborative filtering. Efficient data handling and processing using NumPy and pandas. Developed an intuitive user interface for a seamless user experience. Showcased expertise in recommendation system algorithms and user interface development.

MRS is a comprehensive movie recommendation system with a Django-based web application. The model is trained on a large movie dataset from Kaggle, ensuring robust recommendations. The application is deployed on Render Free Cloud, providing users with a seamless experience.

Want to Explore More?

Feel free to check out the individual repositories for each project:

1. CryptoCrowd Repository (opens in a new tab)
2. Movie Recommendation System (MRS) Repository (opens in a new tab)

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