Sep 2025 – Nov 2025 · 2 mos · Remote

• Selected for Batch 3 of the Infosys Springboard Virtual Internship 6.0.
• Worked on CliniScan – AI-Enabled Lung Abnormality Detection using Chest X-ray Images as part of Infosys Springboard Virtual Internship 6.0 (B3), gaining practical exposure to healthcare AI, medical imaging, and machine learning.
• Applied machine learning and deep learning techniques for medical image analysis, supporting early detection of lung abnormalities.
• Gained hands-on experience in AI-driven healthcare solutions, data preprocessing, model evaluation, and performance analysis.
• Enhanced technical, research, communication, and collaboration skills through structured learning, continuous assessments, and mentor-guided feedback

Aug 2025 – Sep 2025 · 1 mo · Remote

• Selected for the IBM SkillsBuild 6-Week Internship Program in collaboration with AICTE and Edunet Foundation.
• Gaining hands-on experience in front-end web development technologies
• Working on a real-world project under mentor guidance
• Learning through the IBM SkillsBuild platform with 80,000+ curated courses
• Attending masterclasses led by industry experts and technical sessions

Jun 2025 – Jul 2025 · 1 mo · Remote

• Completed a 4-week internship on Azure Artificial Intelligence From 11 June 2025 to 11 July 2025, a Microsoft initiative Implemented by Edunet Foundation in collaboration with (All India Council for Technical Education (AICTE).
• Gained practical experience in AI & ML fundamentals, supervised & unsupervised learning.
• Worked on Deep Learning, Neural Networks, and Computer Vision using Azure Custom Vision.
• Explored Generative AI and Microsoft Copilot for NLP and content generation.
• Built a real-world project from concept to demo using Azure AI Services.
• Learned principles of Ethical AI & Responsible Innovation for impactful solutions.

Jul 2025 – Aug 2025 · 1 mo

• Delivered 4+ end-to-end ML projects covering data preprocessing, model building, evaluation, and visualization using Python, Scikit-learn, Pandas, Matplotlib, and Keras.
• Traffic Sign Recognition: Trained a custom CNN on the 50K+ image GTSRB dataset, applying image preprocessing and achieving high accuracy; evaluated with a confusion matrix.
• Student Score Prediction: Built a regression model to predict scores from study hours; performed EDA and visualized predictions for 25+ test cases.
• Customer Segmentation: Applied K-Means clustering on 200+ mall customer records, optimized cluster count, and visualized segments in 2D.
• Loan Approval Prediction: Developed a binary classification model to predict approvals; processed 600+ loan applications, handled missing data, and improved recall & F1-score with imbalance handling techniques.

Jul 2025 – Jul 2025 · 0 mo

• Executed 4+ NLP projects involving text preprocessing, classification, and named entity recognition using Python, scikit-learn, NLTK, and spaCy.
• Sentiment Analysis: Classified 50K+ IMDb/Amazon reviews using Logistic Regression, TF-IDF, and CountVectorizer; tested Naive Bayes and visualized top word frequencies.
• News Category Classification: Built multiclass classifiers (Logistic Regression, Random Forest) on the 120K+ record AG News dataset, applying TF-IDF and feature engineering.
• Fake News Detection: Trained SVM and Logistic Regression models to classify 20K+ news articles as fake or real, achieving high F1-scores.
• Named Entity Recognition: Applied rule-based and statistical NER on the CoNLL-003 dataset (~35K sentences), visualized outputs with displaCy, and compared model performance.

Jun 2025 – Jul 2025 · 1 mo

• Built a Fake News Detection System achieving 99.58% accuracy using Decision Tree and TF-IDF text features.
• Processed 5K+ news articles and evaluated 4 ML models to identify the most effective.
• Developed real-time prediction functionality for instant news verification.
• Created a Credit Card Fraud Detection model using anomaly detection and supervised learning, improving fraud identification on 284K+ transactions.
• Applied class imbalance handling and feature engineering to boost model precision and reduce false positives.

Jun 2025 – Jul 2025 · 1 mo · Remote

• Gained hands-on experience in developing and applying AI/ML models.
• Worked on real-world problem statements and implemented machine learning solutions.
• Strengthened skills in Python, data analysis, and model evaluation.
• Recognized as one of the Best Performers of the internship program.