ABHISHEK ANGADI's Projects
This project focuses on analyzing sentiment in Amazon reviews using Natural Language Processing (NLP) techniques and extracting keywords for review visualization using WordCloud. By harnessing the power of NLP and keyword extraction, with 94% accuracy classified reviews as positive or negative feedback, using RandomForestClassifier.
This project aims to analyze the Black Friday sales data to gain insights into customer behavior, popular products, and purchase trends during the Black Friday sale. The dataset used for this analysis contains information about customer demographics, product categories, and purchase amounts.
It employs various data science and machine learning techniques to provide personalized book recommendations, enhancing the reading experience for users. Building a book recommendation system requires a combination of data processing, machine learning expertise, and a deep understanding of user preferences.
The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. So,this system is used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.
Dynamic Pricing is a strategy that harnesses data science to adjust prices of products or services in real-time. By analyzing market demand, customer behavior, demographics, and competitor pricing, companies can optimize revenue by setting flexible prices. This article guides you through creating a data-driven Dynamic Pricing Strategy using Python.
Market size analysis for electric vehicles involves a multi-step process that includes defining the market scope, collecting and preparing data, analytical modelling, and communicating findings through visualization and reporting.
Handwritten Digit Recognition using Logistic Regression
Discovering insights from Turkiye student evaluations using Python. This project covers clustering techniques to analyze and understand student feedback. By using Clustering algorithm, classified the Bad, Neutral and Good feedback given by students from Clusters formed.