🍔 Fast Food Sales Prediction & Customer Segmentation

📖 Overview

Analyze and predict sales for a fast food restaurant using linear regression and K-Means clustering to enhance inventory and sales strategies.

📊 Features

• Data Preprocessing: Clean and prepare sales data.
• Visualization: Insights on orders by day, time, and customer demographics.
• Customer Segmentation: Cluster customers based on purchasing behavior.
• Sales Prediction: Predict item sales using linear regression.

📅 Dataset

• Source: Fast Food Sales Dataset
• Key Columns:
  • order_id: Unique identifier for each order
  • date: Date of the order
  • item_name: Name of the food item
  • item_type: Type of item (e.g., Fastfood, Beverages)
  • item_price: Price of the item
  • quantity: Quantity sold
  • transaction_amount: Total transaction amount
  • transaction_type: Type of transaction (e.g., Cash, Online)
  • gender: Gender of the customer
  • time_of_sale: Time when the order was placed

🚀 Getting Started

Prerequisites

Install the following libraries:

pip install pandas numpy matplotlib seaborn scikit-learn statsmodels