Machine Learning Project which predict the discounts on the products avalaible on Amazon and Flipkart This is the capstone project of Summer Analytics, a primer course on Data Science, conducted by Consulting and Analytics Club of IIT Guwahati. Description Artificial Intelligence is an integral part of all major e-commerce companies today. Today's online retail platforms are heavily powered by algorithms and applications that use AI. Machine learning is used in a variety of ways, from inventory control and quality assurance in the warehouse to product recommendations and sales demographics on the website. Let’s say you want to create a promotional campaign for an e-commerce store and offer discounts to customers in the hopes that this might increase your sales. You have been provided descriptions of products on Amazon and Flipkart, including details like product title, ratings, reviews, and actual prices. In this challenge, you will predict discounted prices of the listed products based on their ratings and actual prices.

Restaurant Business Analytics for price, discount etc prediction with people counting web app

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Discount analytics and price prediction for the MELI Marketplace

A comprehensive collection of Excel sheets for advanced financial analysis. Explore Discounted Cash Flow (DCF) valuation, Monte Carlo simulation for stock price prediction, and Black-Scholes option pricing models. Easily calculate intrinsic values, simulate stock price scenarios, and estimate option prices using industry-standard methodologies.

Implementation of the PVD-B algorithm for dynamic pricing with volume discounts. Features synthetic dataset generation with seasonality, Bayesian Linear Regression for demand prediction, Thompson Sampling for optimization, and threshold-based pricing strategies to maximize e-commerce profits with limited transaction data.

This is a repo of a machine learning demand forecasting application that predicts product demand based on key business factors such as pricing, discounts, inventory levels, promotions, competitor pricing, and product category. The system uses the XGBoost regression algorithm for accurate demand prediction and is deployed through Streamlit

InsightMart is a full-stack dashboard that helps e-commerce sellers understand how pricing, discounts, customer ratings, and sales trends influence product performance. It also includes a lightweight machine learning prediction module that forecasts future sales based on product attributes.

Better consumer than better productivity and better the economy. The rapid development of the Internet, the capacity of users to create contents has created vigorous online communities that deliver a mammon of product information. However, the high volume of reviews that are usually makes it difficult for manufacturer and seller to examine the quality of product by people opinion over products sales. Currently, product various factors impact the product’s sales. We advance further to analyze the sarcasm reviews which would be ironic or sarcastic that impact the sales with sentiment, readability, and product price, discount, reviewCount and listprice. Here, readability means total strength count of characters in reviews, which would impact the product’s sales Rank. The current research impact was encounter by regression model without being considering deep learning model and deep feedforward networks. Further, we extend the existing work and analyze the sarcasm and sentiment about the products with all factors with the augment of deep learning model using tensor flow. Hypothesis: Do sarcasm in reviews impact the sales with all other products’ characteristics more than sentiments? Hypothesis: Do sentiment or credibility alone impact the products’ characteristics? The deep learning model has huge contribution in big data, following the similar trends we will analyze the sentiments, sarcasm of products’ reviews using RNN a deep learning model, mxnet deep learning model of feedforward and regression analysis using LM model. The prediction of sales rank over these product characteristics and reviews characteristics will help to understand the consumer behavior in detail to improve the productivity and product marketing. Particularly we will analyze the sarcastic reviews separately and in comparison, with sentiment of reviews of the three various categories but here we have chosen IKEA store particularly HOME AND FURNITURE category. Product outcomes: 1- Distributor, store and Consumer can see his behavior impact of products’ sales. 2- The product will allow the researcher to seek a way to see better impact of people opinions in clear vision about product’s sales.

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dataset of flipkart is used

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Discount Price Prediction

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Flipkart And Amazon Discounted Price Prediction

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RESUME PROJECT

AI-powered price & discount prediction app

Model Prediction of Watch Price Discounts

Mobile Discount Price Prediction using ML model (Predictive Modeling) Deployed with a Streamlit app.

AI based prediction for success of product based on price, discount and product's description and review (optional)