Analyzing Customer Satisfaction through Demographic and Product Based Modeling

📊 Project: Comprehensive analysis of customer satisfaction based on a synthetic dataset incorporating various demographic features.

✨ This project utilizes statistical methods and machine learning techniques to explore the relationships between customer demographics and satisfaction scores.

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📈 Methodology Highlights

• Descriptive Statistics:

  • Analyzing data distributions, central tendencies (mean, median), and variability (standard deviation) to understand the dataset.

• Hypothesis Testing:

  • t-tests used to assess the significance of differences in satisfaction scores based on different customer segments.

• Correlation Analysis:

  • Exploring the relationships between demographic features and customer satisfaction using correlation metrics.

• Regression Modeling:

  • Applied to model the impact of demographics and product type on customer satisfaction scores.

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🤖 Machine Learning Models

• Logistic Regression:

  • Used to predict binary outcomes (e.g., satisfied vs unsatisfied) based on customer demographics.

• Decision Tree:

  • Visualizes the decision-making process for predicting customer satisfaction and allows for easy interpretability.

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🎯 Key Findings

• Significant Differences:

  • Discovered significant differences in satisfaction scores across various product types.

• Effective Predictive Power:

  • Demographic features such as age, gender, and income are strong predictors of customer satisfaction.

• Data-Driven Insights:

  • This project demonstrates how leveraging data-driven insights can enhance customer experience and inform better decision-making.

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📚 Impact

• Improved Customer Experience:

  • Companies can use this analysis to identify satisfaction drivers and optimize their offerings for specific demographic groups.

• Informed Decision Making:

  • Helps businesses tailor their products, services, and marketing efforts based on actionable insights derived from customer data.

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💡 Tech Stack

• Programming Language: Python
• Libraries Used: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Machine Learning Techniques: Logistic Regression, Decision Trees
• Statistical Methods: Descriptive Statistics, Hypothesis Testing, Correlation, Regression