Hello! I'm a computer science graduate from KIIT University Bhubaneswar with a passion for Java Core, Python, data analysis, data science, and cloud computing.

🚀 About Me: 🔭 I’m currently working on various projects involving data analysis and machine learning. 🌱 I’m continuously learning and exploring new technologies in cloud computing and data science. 👯 I’m looking to collaborate on open-source projects and innovative tech solutions. 💬 Ask me about Java, Python, data analysis, and cloud computing. 📫 How to reach me: E-mail | LinkedIn 🛠️ Technologies & Tools: Programming Languages: Java, Python Data Science & Analysis: Pandas, NumPy, Matplotlib, Scikit-learn Cloud Computing: AWS, Google Cloud Platform Version Control: Git, GitHub

• ABM Intern at Highradius
• Virtual Software Engineer Experience at JPMorgan Chase & Co
• AWS Semester 1 Cloud Certification Click
• Mentee, Microsoft Engage 2022 at [Microsoft]

1. IBM DATA SCIENCE - CAPSTONE PROJECT: The IBM_Final_Capstone project predicts whether the SpaceX Falcon 9 first stage will land successfully. The project involves data collection using SpaceX API and web scraping, data wrangling, exploratory data analysis with SQL, geospatial analytics using Folium, and creating an interactive dashboard with Plotly Dash. It culminates in predictive analysis using machine learning models to forecast landing outcomes. The project showcases skills in data science methodologies, data visualization, and machine learning. GitHub repository

2. Movie-Mate: The Movie Mate project is a movie recommendation system built using Streamlit. It provides personalized movie recommendations based on user interests. The system leverages content-based, collaborative filtering, and hybrid recommendation approaches. It uses cosine similarity for model building and incorporates data analysis techniques to create a robust recommendation engine. The project includes a web application interface for users to interact with and receive movie suggestions. GitHub repository.

3. The FaceMask Detection using CNNThe Face Mask Detection Using CNN project aims to detect whether individuals are wearing face masks using Convolutional Neural Networks (CNN). The model is trained on a dataset with images of people both with and without face masks. The architecture includes multiple convolutional layers, max-pooling layers, and fully connected layers. The project involves training the model, evaluating its performance, and making predictions on new images. Key dependencies include TensorFlow, Keras, OpenCV, and NumPy. GitHub repository.

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