🛍️ Online Retail Shop Prediction & Forecasting App

This project presents a Retail Shop Prediction App that analyzes and forecasts retail sales using historical data. Developed using Python and interactive visualization tools, it provides a user-friendly interface to help retailers make data-driven decisions.

📌 Project Overview

The notebook implements:

• Data preprocessing and exploratory data analysis

• Sales trend visualization

• Machine learning model for prediction

• Time series forecasting for future sales

• Interactive interface for exploring predictions

🧰 Technologies Used

• Python

• Pandas, NumPy

• Matplotlib, Seaborn

• Scikit-learn

• Facebook Prophet (or other forecasting library, if applicable)

• Streamlit / Gradio (if used for UI)

🚀 Features

• 📊 Sales Data Analysis: Visualizes key metrics and trends.

• 🤖 Prediction Model: Uses regression/classification to predict outcomes.

• 🔮 Forecasting Engine: Projects future sales using time-series modeling.

• 🧑‍💼 User Input: Interactive input options for exploring scenarios.

🔍 Dataset

The dataset used in this project downloaded from Kaggle, it includes online retail records with various features like Customer_id, product_name, price, payment method etc.

⚙️ Requirements

Install the following packages before running the notebook:

pip install pandas numpy matplotlib seaborn scikit-learn xgboost