Arpita Satsangi's Projects
a special repository
Prediction on whether customers will make additional audiobook purchases from a specific website or not.
The dataset used in the development of the method was the open-access Stroke Prediction dataset. It is used to predict whether a patient is likely to get stroke based on the input parameters like age, various diseases, bmi, average glucose level and smoking status. K-nearest neighbor and random forest algorithm are used in the dataset.
Chatbots: rule-based and retrieval-based
This project involved querying and ordering COVID-19 data at a global and regional level, focusing on cases, deaths, and percentages. Additionally, I integrated COVID-19 cases data with vaccination records, calculating total vaccinations, population percentages, and creating temporary tables and views for visualization.
Starter repo for task 1 of the JPMC software engineering program
Problem Statement- To perform object detection using machine learning (viola jones) and deep learning(faster R-CNN) on PASCAL VOC 2007.
This C++ project implements a console-based library management system using Object-Oriented Programming, featuring encapsulation, inheritance, polymorphism, and abstraction to handle general and genre-specific book details.
Designed and developed a graphical calculator application using Python and tkinter, enabling users to perform arithmetic operations and evaluate mathematical expressions. It has user-friendly features such as responsive button clicks, a clear input field option, and robust error handling for invalid expressions.
course on Udemy
Perform object detection on images and videos using the YOLO (You Only Look Once) model with OpenCV. Easily integrate real-time object recognition into the computer vision applications.
Implemented a spam detection project using Python, employing data cleaning, exploratory data analysis, and text preprocessing techniques. Trained and evaluated Naive Bayes models, achieving a notable 97% accuracy and 97.35% precision with the Bernoulli Naive Bayes classifier.
It is built to assist supermarkets in calculating and displaying bills, as well as serving customers more quickly and efficiently. This software solution includes an effective and simple user interface to assist employees in bill calculating and customer service.
Build a Python program for the classic Tic-Tac-Toe game. This project involves functions for board display, player input, marker placement, win checking, random player selection, and game continuation. The game runs in a loop, enabling players to enjoy multiple rounds and decide if they want to play again.
A Time Series Approach for Predicting Hourly Consumption Using XGBoost. This project explores patterns in energy consumption through feature-rich time series forecasting, providing accurate predictions and insights.