🛍️ Retail Sales Forecasting Dashboard

A comprehensive web application for retail sales forecasting and inventory optimization using machine learning models.

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📊 Overview

This application provides retail businesses with powerful sales forecasting capabilities using time series analysis and machine learning.
It helps optimize inventory management, identify sales trends, and make data-driven decisions through an intuitive Flask-based web interface.

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✨ Features

🏠 Dashboard Overview

• Key performance indicators and metrics
• Store distribution analytics
• Recent sales performance tracking
• Quick access to all features

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🏪 Store Analysis

• Store type distribution visualization
• Size analysis and statistics
• Performance metrics by store type
• Comparative analytics

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📈 Sales Trends

• Historical sales trend visualization
• Store type comparison over time
• Monthly seasonal patterns
• Interactive time series charts

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🔮 Sales Forecasting

• 12-week sales predictions using Prophet and ARIMA models
• Confidence interval visualization
• Model performance comparison
• Interactive forecast charts

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📦 Inventory Recommendations

• Optimal inventory level calculations
• Safety stock and reorder point recommendations
• Seasonal adjustment factors
• Store-type specific strategies

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🛠️ Technology Stack

Backend

• Flask – Web framework
• Pandas – Data manipulation
• NumPy – Numerical computing
• Matplotlib – Data visualization

Machine Learning

• Prophet – Time series forecasting
• ARIMA – Statistical forecasting model
• Scikit-learn – Model evaluation metrics

Frontend

• Bootstrap 5 – Responsive UI framework
• Chart.js – Interactive charts
• Font Awesome – Icons

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📋 Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

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Prepare Data Files

Ensure you have the following files in the project root:

stores.csv – Store information (provided)

(Optional) Pre-trained model files from Jupyter notebook

🚀 Installation

Clone the Repository

git clone https://github.com/pragyan2004/retail-sales-forecasting.git
cd retail-sales-forecasting

Prepare Data Files

Ensure you have the following files in the project root:

stores.csv – Store information (provided)

(Optional) Pre-trained model files from Jupyter notebook

🎯 Usage

1. Start the Application

python app.py

2. Access the Dashboard

Open your browser and go to:

http://localhost:5000

3. Navigate Through Pages

Home – Dashboard overview with key metrics

Stores – Store distribution and analysis

Sales Trends – Historical sales patterns

Forecast – Future sales predictions

Inventory – Stock optimization recommendations

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📊 Data Processing Pipeline

Data Loading & Preprocessing

Load store information from CSV

Generate synthetic sales data (if needed)

Handle missing values and validation

Time Series Analysis

Weekly aggregation of sales

Seasonal decomposition

Trend identification

Stationarity testing

Model Training

Prophet and ARIMA models

Evaluate with MAE, RMSE, MAPE

Forecasting & Visualization

12-week predictions

Confidence intervals

Interactive charts

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🧠 Machine Learning Models

Prophet Model

Handles seasonality and holidays

Robust to missing data

Automatic changepoint detection

ARIMA Model

Statistical time series analysis

Handles trend and seasonality

Configurable parameters (p, d, q)

Model Evaluation Metrics

MAE – Mean Absolute Error

RMSE – Root Mean Square Error

MAPE – Mean Absolute Percentage Error

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📈 Business Insights

Inventory Optimization

Safety stock and reorder point recommendations

Seasonal adjustments

Store-type strategies

Sales Predictions

Weekly forecasts

Confidence intervals

Trend and seasonality insights

Store Performance

Type-based analytics

Size correlation analysis

Performance benchmarking

Growth opportunities