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mohit User

@itzmohit06

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AI/ML student building Python models, including an MNIST classifier that reached 98.90% CNN accuracy.

India

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What I'm looking for

I'm interested in applying AI/ML techniques to practical problems.

On my Handwritten Digit Classification project, I built and evaluated CNN and RNN models on MNIST. The CNN reached 98.90% accuracy, and I improved the RNN from 93.66% to 96.52% before selecting the CNN using accuracy and confusion matrices.

I also built a Cats vs Dogs image classifier from scratch with three convolutional layers, image preprocessing, and data augmentation. It achieved 89.08% accuracy on the test set.

For my Loan Approval Prediction Model, I evaluated Logistic Regression, KNN, and Naive Bayes after preprocessing the data and engineering features. I used precision, recall, and F1-score to assess performance, achieving 81% precision.

I'm studying Artificial Intelligence and Machine Learning at PES Modern College of Engineering. My other projects include an OpenCV image editor and SmartCart, an unsupervised customer segmentation pipeline using PCA and clustering; I'm interested in applying AI/ML techniques to practical problems.

Experience

Work history, roles, and key accomplishments

AI/ML Intern

PES Modern College of Engineering

Hands-on experience developing machine learning and deep learning projects using Python, Scikit-learn, and PyTorch. Skilled in data preprocessing, exploratory data analysis, feature engineering, model training, and evaluation.