Aniket Patil's repositories
Stock_Price_Prediction
In this project two models are build a Multivariate CNN-LSTM model using keras and tensorflow, ARIMA model, and FbProphet. In multivariate CNN-LSTM five feature are given as a input to the model and output as Closing price. Forecasted for the next 30 days. the dataset has been collected from Yahoo finance.
TumorNet-GradCAM
This repository explores the fascinating world of brain tumor classification using cutting-edge Convolutional Neural Networks (CNNs) and eXplainable Artificial Intelligence (XAI) techniques.
ETH-Price-Prediction
This project seeks to predict the price of ETH based on historical ETH prices and Ethereum blockchain data such as gas usage.
Skin-Cancer-Lesion-Segmentation
Automatic Skin Lesion Segmentation using SegNet, a Deep Learning architecture with certain extra requirements. Keeping the pre- and post-processing of the photos to a minimum is the secondary goal. The dermoscopic pictures in the PH2 dataset, which are part of the limited amount of images used to train the proposed model, were manually segmented.
CCA-Assignment
This repository contains my solutions and assignments for the Cloud Computing course.
Emoji-Prediction
BERT, a well-liked pretrained language model, was utilised to offer a transformer-based strategy for emoji prediction. Additionally, BERT was refined using a sizable corpus of text (tweets) that had both text (tweets) and emojis in order to predict the ideal emoji for a particular text.
MiDaS-PyTorch
MiDaS is a deep learning model for monocular depth estimation, which means it can estimate the depth of objects in a single 2D image.
Phonebook-App-AWS-Terraform
Phonebook Application (Python Flask) deployed on AWS Application using Terraform
Term-Deposit-Subscription-
This project involves the analysis of bank marketing campaigns and the prediction of term deposit subscriptions. By utilizing machine learning techniques, we aim to uncover patterns in customer behavior and predict whether a client will subscribe to a term deposit based on various features.