Aspiring and detail-oriented beginner in Python development and Data Analytics with foundational skills in Python, Excel, and SQL for data handling, cleaning, and visualization. Experienced in creating simple da…
Aspiring and detail-oriented beginner in Python development and Data Analytics with foundational skills in Python, Excel, and SQL for data handling, cleaning, and visualization. Experienced in creating simple dashboards using Power BI and completing academic projects in data preparation, visualizations, and reporting. Eager to learn new tools, strengthen technical expertise, and apply analytical thinking to real-world tasks.
<p>Gained extra knowledge in this domain<br>
Done some small-scale projects</p>
<p>Applied Python (Pandas, Matplotlib) and Excel to handle, analyze, and visualize datasets<br>
Improved data quality through cleaning and preprocessing for accurate analysis</p>
<p>Applied Python (Pandas, Matplotlib) and Excel to handle, analyze, and visualize datasets., Improved data quality through cleaning and preprocessing for accurate analysis.</p>
<p>- Developed a non-invasive Fingerprint Based Blood Group Detection </p><p> System using VGG16 deep learning model with transfer learning, </p><p> achieving 87.41% validation accuracy across 8 blood group classes </p><p> (A+, A-, B+, B-, AB+, AB-, O+, O-) trained on 6,011 images </p><p> at 8Queens Private Limited.</p><p></p><p>- Built a Flask REST API backend with Bootstrap 5 web interface </p><p> for real-time fingerprint upload, blood group prediction with </p><p> confidence scores, and complete donor registration and management </p><p> system.</p><p></p><p>- Integrated MongoDB database using PyMongo for persistent storage </p><p> of donor records and real-time blood bank stock monitoring with </p><p> low-stock alert detection for all 8 blood groups.</p><p></p><p>- Implemented automated SMS notification system using Twilio REST </p><p> API that sends alerts to registered donors when blood stock falls </p><p> to or below 3 units, with runtime credential configuration and </p><p> live API verification.</p><p></p><p>- Preprocessed fingerprint images using OpenCV with resize to </p><p> 224×224, normalization, and data augmentation techniques, and </p><p> deployed the trained VGG16 model on a Flask server with demo </p><p> mode fallback for UI testing without model file.</p>
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