Marketing Campaign Analysis

This project performs customer segmentation and prediction using the Kaggle dataset 'Customer Personality Analysis'.

Project Summary

This project analyzes customer behavior and segments them effectively based on their demographic data, spending habits, and response to marketing campaigns.

Dataset

The dataset includes customer demographic data, spending habits, and response to marketing campaigns.

Key Steps

1. Data Cleaning & Feature Engineering: Dates were parsed, age and total spending were calculated, and categorical variables were encoded.
2. Exploratory Data Analysis:
   • Age distribution was analyzed.
   • Income vs. total spending was visualized.
3. Predictive Modeling:
   • Linear Regression: Predicted Total_Spend using demographic and campaign data.
   • Decision Tree Classifier: Predicted Response (likelihood of purchase).
4. Customer Segmentation:
   • Used KMeans clustering on key features like income, spending, and age.
   • Visualized clusters for strategic marketing insights.

Insights

• Younger customers tend to spend more on certain categories.
• Income and total spending are positively correlated.
• Distinct customer segments were identified using clustering.

Usage

1. Download the dataset from Kaggle: https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis
2. Install the required libraries: bash pip install kagglehub pandas numpy matplotlib seaborn scikit-learn
3. Run the Jupyter notebook to perform the analysis.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License.