special Vector Vector Product

GitHub Repo https://github.com/WClampitt1/Fall_2017_Math_Research_Paper

WClampitt1/Fall_2017_Math_Research_Paper

Research and explanation of the vector cross product and how it is a special use case of the exterior product in three dimensions.

GitHub Repo https://github.com/red0078/shopper_prediction

red0078/shopper_prediction

The dataset is publicly available at the UC Irvine Machine Learning Repository. here. BRIEF DESCRIPTION: The dataset consists of feature vectors belonging to 12,330 (online) sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period. Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping. The 'Revenue' attribute will be my target values for this machine learning project. The project should be able to predict whether an online shopper (web user) will purchase a product from an online store, thus it is a classification problem.

GitHub Repo https://github.com/Benjamimmoreira/DealSpotter-AI-Autonomous-Bargain-Hunting-Agents

Benjamimmoreira/DealSpotter-AI-Autonomous-Bargain-Hunting-Agents

This project uses cloud-based agents, machine learning models, and vector databases to find and notify users about online deals. Modal powers remote agent execution, ChromaDB stores product data, and Gradio provides a user interface. Agents collaborate to predict prices and send real-time push notifications for special bargains

GitHub Repo https://github.com/akhilvydyula/Amazon-fine-food-reviews-sentiment-analysis

akhilvydyula/Amazon-fine-food-reviews-sentiment-analysis

Amazon_fine_food_review Introduction The Amazon Fine Food Reviews dataset consists of 568,454 food reviews. This dataset consists of a single CSV file, Reviews.csv Data Set Click here to get the dataset. Review.csv - 251MB Dataset statistics Number of reviews 568,454 Number of users 256,059 Number of products 74,258 Users with > 50 reviews 260 Median no. of words per review 56 Timespan Oct 1999 - Oct 2012 Data Fields Explanation Id - Unique row number ProductId - unique identifier for the product UserId - unqiue identifier for the user ProfileName HelpfulnessNumerator - number of users who found the review helpful HelpfulnessDenominator - number of users who indicated whether they found the review helpful Score - rating between 1 and 5 Time - timestamp for the review Summary - brief summary of the review Text - text of the review EDA Objective Analysing the data & plot the required graphs to show that these conclusions are true: a. Positive reviews are very common. b. Positive reviews are shorter. c. Longer reviews are more helpful. d. Despite being more common and shorter, positive reviews are found more helpful. e. Frequent reviewers are more discerning in their ratings, write longer reviews, and write more helpful reviews Note: This notebook is highly inspired from the Exploratory visualization of Amazon fine food reviews by Rob Castellano. Model Building STEP-1: Copy the data in Pandas DataFrame and drop unwanted columns. STEP-2: Text Preprocessing. a. Converting to lower-case. b. Removing HTML Tags. c. Removing Special Characters. d. Removing Stop Words. e. Stemming (Snowball Stemming) STEP-3: Vectorizing out Data Set STEP-4: Building and evaluating the model a. Naive Bayes b. Logistic Regression - with L1 and L2 regularizors c. Linear SVM d. RBF Kernel SVM

GitHub Repo https://github.com/priyanshuuu9555/Mini-Project-1-Basics-of-NLP-Text-Cleaning-Vectorization

priyanshuuu9555/Mini-Project-1-Basics-of-NLP-Text-Cleaning-Vectorization

In the digital age, customer reviews provide valuable insights into user experiences with products and services. However, raw text data often contains noise such as special characters, stopwords, and inconsistent word forms, making it difficult to analyze directly.