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Machine Learning project developed under the faculty guidance during the NSUT Hydrophonics Traning Program to predict plant weight and height using Python, Pandas, NumPy, Scikit-learn
A beginner-friendly machine learning project using Python to classify Iris flowers (Setosa, Versicolor, and Virginica) based on sepal & petal measurements. The dataset, introduced by R.A. Fisher (1936), is widely used for ML model evaluation.
Log message classifier using TF-IDF + Logistic Regression to categorize server/app logs into auth_failure, normal, null_pointer, and timeout - 99.6% test accuracy, 96.9% on unseen data.
This project explores the US Accidents (2016 - 2023) dataset from Kaggle. The goal is to understand patterns in road accidents across the United States using data visualization and exploratory analysis techniques.