Article
REGIONAL SALES ANALYSIS AND PREDICTION
The Regional Sales Analysis and Prediction system is designed to analyze historical sales data across different geographical regions and predict future sales performance. Regional sales can vary due to factors such as customer demand, product category, pricing, seasonality, marketing activities, and regional market conditions. Analyzing these variations helps organizations identify high-performing regions and improve sales planning. The proposed system collects historical sales information such as region, sales amount, quantity sold, product category, order date, profit, and customer details. The collected data is cleaned and preprocessed before performing exploratory data analysis. Statistical analysis and visualization techniques are used to identify regional sales trends, compare performance between regions, and determine factors influencing sales. Machine-learning algorithms such as Linear Regression, Random Forest, XGBoost, and other suitable forecasting models can be used to predict future regional sales. Recent research shows that machine-learning models can incorporate multiple input factors for sales forecasting, while regional and spatial information can further improve sales prediction and market analysis. The prediction results can be evaluated using metrics such as MAE, RMSE, MAPE, and R² to determine model performance. An interactive dashboard can display regionwise sales, profit, sales trends, top-performing regions, and predicted future sales. This allows managers to make data-driven decisions regarding inventory, marketing, resource allocation, and sales strategies.
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