# How I Built a Retail Demand Forecasting App with Python and Streamlit
Okparaji Wisdom developed a retail demand forecasting app called DemandForecast AI using Python and Streamlit. The app predicts weekly product demand for 20 retail products across four categories, addressing issues of stockouts and overstock. It utilizes a synthetic dataset and employs linear regression models to analyze demand patterns and promotional impacts.
- ▪The app forecasts weekly demand for 20 retail products in categories such as Electronics and Fashion.
- ▪It supports forecast horizons ranging from 4 to 26 weeks and models demand spikes during Nigerian festivities.
- ▪The dataset consists of 3,140 weekly records generated with realistic business logic, including seasonality and trend.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3950314) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Okparaji Wisdom Posted on May 25 # How I Built a Retail Demand Forecasting App with Python and Streamlit #datascience #machinelearning #python #showdev By Okparaji Wisdom | Data Scientist | Nigeria Retailers in Nigeria lose millions of naira every year to two problems: stockouts (shelves go empty, customers leave) and overstock (too much inventory, capital tied up, goods expire). Both are avoidable with data.
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