Data Science Projects

Emotion Detection using CNN

This project showcases an AI-driven model designed to detect emotions from images, leveraging deep learning techniques. It uses a Convolutional Neural Network (CNN) to analyze facial expressions and predict emotions such as happiness, sadness, anger, and surprise. The model was trained on a large dataset of labeled facial images, allowing it to learn and identify subtle differences between various emotional states. Users can upload images to receive real-time emotion predictions, demonstrating the application of AI in understanding human emotions through visual data.

Features:

• Real-time Emotion Detection: Upload an image to receive an automated emotion prediction instantly.

• AI Techniques: Utilizes CNN for image processing and emotion classification.

• Dataset: Trained on a diverse facial image dataset to ensure high accuracy across different expressions.

• Applications: Useful for mental health tools, customer service analysis, and interactive entertainment.

Fake News Classification using NLTK

This project highlights a machine learning-based model that classifies news articles as real or fake, using natural language processing. Built with the Natural Language Toolkit (NLTK), it processes textual data to detect patterns and markers commonly associated with fake news. The model was trained on a large dataset of labeled news articles, enabling it to distinguish between factual reporting and misinformation. Users can input text or upload articles to check their authenticity, showcasing the model's effectiveness in combating the spread of fake news.

Features:

• Real-time Fake News Detection: Input news text to receive an automated classification of real or fake.

• AI Techniques: Employs NLTK for natural language processing and feature extraction.

• Dataset: Trained on an extensive set of labeled news articles for robust performance.

• Applications: Useful for media companies, social media platforms, and fact-checking tools.

Image Caption Generator

Image Caption Generator
This project showcases an AI-powered model that generates descriptive captions for images. Leveraging deep learning techniques, it combines a Convolutional Neural Network (CNN) to process the visual data and a Recurrent Neural Network (RNN) for generating coherent text. The model was trained on a large dataset of labeled images to create natural language captions that accurately describe the content of new images. Users can upload images to see how the model interprets and captions them, demonstrating the potential of AI in visual understanding and language generation.

Features:

• Real-time Image Captioning: Upload an image to receive an automated caption instantly.

• AI Techniques: Uses CNN for image processing and RNN for language generation.

• Dataset: Trained on a comprehensive image dataset for high accuracy.

• Applications: Useful for accessibility tools, social media tagging, and visual search enhancement.

eda_visualizer Library in PyPl

This Python library provides a powerful tool for Exploratory Data Analysis (EDA), helping data scientists visualize and analyze datasets with ease. EDA_Visualizer offers a range of functions to create common visualizations, identify trends, and detect anomalies in data, making it a valuable addition to the data science workflow. Available on PyPI, this library enables users to quickly understand dataset characteristics through intuitive, well-designed visuals.

Features:

• Automated EDA Visualizations: Generate a variety of EDA visuals with simple function calls.

• Data Analysis Techniques: Includes support for univariate, bivariate, and multivariate analysis.

• Accessibility: Easily installable via PyPI for seamless integration into Python projects.

• Applications: Ideal for data preprocessing, feature engineering, and data quality assessments.