Data professional specializing in Python, SQL, Tableau, data analysis, automation, machine learning, and data visualization.
@AyswaryaLinkedinThis project involves predicting the steering angle of a self-driving car using CNN based on NVIDIA’s architecture. The dataset consists of front-view images, preprocessed and fed into a model with convolutional layers. The model is trained using the Adam optimizer, with dropout and L2 regularization to improve performance, aiming to minimize the mean squared error
This project builds an apparel recommendation system using Amazon product data, applying machine learning techniques to analyze product features and suggest similar apparel items based on user preferences.
This project focuses on classifying genetic mutations using clinical text data, aiming to predict the category of each mutation by applying advanced machine learning models to analyze patterns in the data.
This project uses a Convolutional Neural Network (CNN) to classify handwritten digits from the MNIST dataset, applying deep learning techniques to achieve high accuracy in digit recognition tasks.
This project applies Stochastic Gradient Descent (SGD) for linear regression on the Boston housing dataset, comparing a custom SGD implementation with Scikit-learn’s SGD Regressor and Linear Regression models to evaluate model performance.
This project clusters classroom project proposals from DonorsChoose.org to predict approval likelihood, using clustering algorithms such as K-Means, Agglomerative Clustering, and DBSCAN, along with feature engineering techniques.
This project implements a DenseNet Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset, leveraging techniques such as batch normalization, data augmentation, and Adam optimizer to improve model performance.