Sales Prediction using Advertising Data

📊 Project Overview

This project predicts product sales based on advertising spend across different media channels (TV, Radio, Newspaper). It demonstrates a complete machine learning workflow from data exploration to model deployment.

🎯 Problem Statement

A company wants to understand how their advertising budget across different media channels affects sales. Using historical data, we build a predictive model to forecast sales and optimize advertising spend allocation.

📁 Dataset

The dataset contains 200 observations with 4 variables:

• TV: Advertising spend on TV (in thousands of dollars)
• Radio: Advertising spend on Radio (in thousands of dollars)
• Newspaper: Advertising spend on Newspaper (in thousands of dollars)
• Sales: Product sales (in thousands of units)

🔧 Technologies Used

• Python 3.8+
• Pandas & NumPy (Data manipulation)
• Matplotlib & Seaborn (Visualization)
• Scikit-learn (Machine Learning)

📈 Key Features

• Data exploration and visualization
• Correlation analysis
• Linear Regression model
• Model evaluation metrics (R², RMSE)
• Sales prediction function

🚀 Getting Started

Prerequisites

pip install -r requirements.txt