I am Karan Mishra

Detailed showcase of data science projects including data analysis, model building, and visualization techniques used.

Explore projects that involve complex data manipulations, feature engineering, and predictive modeling.

This project involves developing and fine-tuning a sentiment analysis model using advanced techniques like deep learning and natural language processing.

Key features include sentiment classification, feature extraction, and model evaluation metrics.

A modern implementation of the classic Tic Tac Toe game. Features include a user-friendly interface, real-time game updates, and an AI opponent.

Technology stack includes HTML, CSS, JavaScript, and some game theory algorithms for the AI opponent.

A tool for analyzing feedback data to extract insights and trends. Includes sentiment analysis, keyword extraction, and feedback categorization.

Built with Python and uses libraries such as Pandas, NLTK, and Matplotlib for data processing and visualization.

An interactive tool for accessing and managing feedback through voice commands. Supports features such as searching, filtering, and retrieving feedback entries.

Implemented with speech recognition and natural language processing technologies.

A plugin designed for accessing location services in Cordova applications. Provides functionality for retrieving geolocation data and handling location-related events.

Developed using JavaScript and Cordova APIs for seamless integration into mobile apps.

This project focuses on applying advanced sentiment analysis techniques to analyze and interpret emotions in textual data. Utilizes machine learning algorithms and NLP methods for precise sentiment classification.

Features include deep learning models, feature extraction, and real-time sentiment tracking.

Developing data science models for various applications, including predictive analytics and machine learning. Emphasizes model training, evaluation, and deployment strategies.

Involves using tools like Python, R, and various ML libraries to build robust and accurate models.

A tool designed for creating interactive and engaging visualizations. Allows users to explore data through dynamic charts and graphs.

Utilizes libraries like D3.js and Plotly to provide an immersive data exploration experience.

This project focuses on enhancing existing machine learning models through optimization and fine-tuning techniques. Aims to improve model accuracy and efficiency.

Includes algorithm tuning, hyperparameter optimization, and performance evaluation.