Ninad Patil's Projects
This project is a comprehensive data analysis initiative aimed at extracting valuable insights from bank card usage data. The tools and techniques includes Python, Excel, Tableau, web scraping, pandas. It centers around understanding and visualizing trends and patterns in credit and debit card usage across multiple banks.
This project aims to recognize characters within CAPTCHA images using deep learning techniques. CAPTCHA, or Completely Automated Public Turing test to tell Computers and Humans Apart, is a challenge-response test commonly used in computing to determine whether or not the user is human.
This project aims to tackle the growing challenge of analyzing emotions conveyed through text, particularly in the context of the vast amount of comments generated daily on social media platforms and reviews from websites.
The Heart Disease Detection Analysis aims to create a predictive model for identifying individuals at risk of heart disease. Using a dataset with attributes like age, sex, and health metrics, the project focuses on distinguishing patients with and without heart disease.
This comprehensive analysis delves into the performance and characteristics of the hospital's emergency room over the past year. By scrutinizing key metrics and patient demographics, this study aims to provide valuable insights for optimizing patient care, resource allocation, and overall operational efficiency.
Explore NLP tasks with Python using NLTK, SpaCy & scikit-learn: Tokenization, Normalization, NER, POS tagging, Encoding, Word embedding.
The "Online Store Sales Analysis" interactive dashboard offers comprehensive insights into the sales performance and customer behavior of the online store for the year 2022. This user-friendly dashboard provides a visually appealing and intuitive interface to explore key sales metrics and customer trends.
This project aims to predict property insurance premiums using machine learning techniques. The dataset consists of various features related to insurance policies and properties. The goal is to build models that can accurately predict the annual premium based on these features.
An overview of how to perform Sales Market Basket Analysis using PySpark, focusing on the steps from data preprocessing to association rule mining. It is a method used by retailers to uncover patterns in customer purchasing behavior, involves analyzing the items that customers frequently buy together and associations between products