📊 Sales Prediction Using Python

A machine learning project that predicts product sales based on advertising spend across TV, Radio, and Newspaper channels.

Built as part of my CodeAlpha Data Science Internship

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📌 Project Overview

Detail	Description
Objective	Predict sales based on advertising budget allocation
Dataset	200 entries, 4 features
Models Used	Linear Regression, Random Forest, Gradient Boosting
Best Model	Gradient Boosting / Random Forest (highest R²)
Tools	Python, Pandas, Scikit-learn, Matplotlib, Seaborn

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📂 Project Structure

├── Sales_Prediction.ipynb       # Main Jupyter Notebook
├── Advertising.csv              # Dataset
├── requirements.txt             # Python dependencies
└── README.md                    # Project documentation

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📊 Dataset Features

Feature	Description
TV	Advertising spend on TV (in $)
Radio	Advertising spend on Radio (in $)
Newspaper	Advertising spend on Newspaper (in $)
Sales	Product sales (Target variable)

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🔧 Workflow

1. Data Loading — Read CSV using Pandas
2. Exploratory Data Analysis — Distribution plots, correlation heatmap, scatter plots
3. Feature Analysis — Investigated impact of each advertising channel on sales
4. Model Training — Trained Linear Regression, Random Forest, and Gradient Boosting models
5. Model Evaluation — Compared using MAE, RMSE, and R² Score
6. Business Insights — Identified most effective advertising channels

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📈 Results

Model	MAE	RMSE	R² Score
Linear Regression	~1.2	~1.6	~0.90
Random Forest	~0.6	~0.9	~0.97
Gradient Boosting	~0.7	~0.9	~0.97

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🔑 Key Business Insights

• TV advertising has the strongest impact on sales (correlation ~0.78)
• Radio advertising has a moderate positive impact (correlation ~0.58)
• Newspaper advertising has the weakest effect (~0.23) — least cost-effective
• Recommendation: Prioritize TV and Radio budgets, reduce Newspaper spend
• Advertising spend explains over 90% of sales variation

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🚀 How to Run

1. Clone the repository

   git clone https://github.com/kinzaemannn/CodeAlpha-Sales-Prediction.git
   cd CodeAlpha-Sales-Prediction

2. Install dependencies

   pip install -r requirements.txt

3. Open the notebook

   jupyter notebook Sales_Prediction.ipynb

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🛠️ Technologies Used

• Python 3.x
• Pandas — Data manipulation
• NumPy — Numerical operations
• Matplotlib & Seaborn — Data visualization
• Scikit-learn — ML models and evaluation

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📝 License

This project is for educational purposes as part of the CodeAlpha Data Science Internship.

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