Building a model for prediction on Black Friday sales dataset
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Updated
Jun 22, 2021 - HTML
Building a model for prediction on Black Friday sales dataset
Scraping retail stores to extract product prices and monitor them (web scraping + Pandas + Streamlit app)
This project is a Chrome Extension to avoid compulsive shopping in Black Friday time.
It is a price drop monitor for Amazon products.
Proposta de solução para ajudar lojas online a aumentarem suas taxas de conversão durante a Black Friday 2023. Esse projeto faz parte do 4º Hackathon promovido pela Deco.cx em Novembro de 2023.
This Capstone Project summarizes our preliminary (Exploratory Data Analysis EDA) analysis on the dataset, the Black Friday Dataset from Amazon. It shared some insightful results from the EDA and descriptive statistics. Further, this paper identifies a set of analyses used to answer the business questions on the dataset and justifies those findings.
Extracted real-time data from Twitter with Apache NIFI and then converted the resulting json file into a sentiment analysis graphs with Spark
Landing Page Black Friday 2023 SOUQ with form
EDA-REGRESSION-CLASSIFICATION-WITH-BALCK-FRIDAY-DATASET
Predicting Black Friday sales involves building a model that forecasts the sales volume or revenue on the Black Friday shopping day.
* My solution to Analytics Vidya's Black friday Sales prediction Problem
Landing Page Future Friday 2023 IDA with form
The dataset here is a sample of the transactions made in a retail store.
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