Learn how to conduct customer segmentation analysis and predict consumer behaviour using machine learning
What you will learn
Learn how to conduct customer segmentation analysis using k means clustering
Learn how to build customer spending prediction model using decision tree regressor
Learn how to build customer churn prediction model using support vector machine
Learn the basic fundamentals of customer segmentation analytics, technical challenges and limitations in customer analytics, and its use cases in marketing
Learn about predictive customer analytics workflow. This section covers data collection, feature selection, model selection, model training, and prediction
Learn how to segment customer by age and gender
Learn how to segment customer by education level
Learn how to calculate average customer spending by country
Learn how to find correlation between purchase frequency and customer spending
Learn how to find correlation between customer income and customer spending
Learn how to conduct feature importance analysis using random forest
Learn how to evaluate model accuracy and performance using k fold cross validation method
Learn how to deploy machine learning model and create user interface using Gradio
Learn how to handle class imbalance with synthetic minority oversampling technique
Learn about factors that influence consumer behaviour, such as psychological, economic, social, technology, personal, and culture
Learn how to clean dataset by removing missing values and duplicates
Learn how to find and download customer spending data from Kaggle
Why take this course?
🎓 Customer Segmentation Analysis & Predict Consumer Behaviour 🌟
Course Overview:
Embark on a transformative learning journey with our “Customer Segmentation Analysis & Predicting Consumer Behaviour” course! This project-based program is designed for enthusiasts and professionals aiming to refine their data science acumen towards customer analytics. By combining theoretical knowledge with practical applications, you will master the art of dividing large customer groups into segments to tailor effective marketing strategies.
What You Will Learn:
🔥 Fundamentals of Customer Segmentation Analysis:
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- Real-world applications and importance in marketing
- Understanding machine learning models
- Navigating technical challenges and limitations in customer analytics
✅ Predictive Customer Analytics Workflow:
- Data collection and preparation
- Feature engineering and model training
- Evaluation and deployment of predictive models
- Addressing the factors that influence consumer behaviour across Psychological, Economic, Social, Technology, Personal, and Cultural aspects
📊 Hands-On Project:
- Setting up Google Colab IDE for your analysis
- Downloading a customer segmentation dataset from Kaggle
- Data visualization and pattern recognition
- Segmenting customers using K-means clustering based on shared characteristics
- Identifying key factors with Random Forest feature importance analysis
- Predicting spending scores with a Decision Tree Regressor
- Forecasting customer churn with a Support Vector Machine (SVM)
- Utilizing K-fold cross validation for model evaluation
Why This Course?
Understanding Customer Segmentation Analysis allows you to tailor marketing strategies to specific needs, ultimately driving up conversion rates. By Predicting Consumer Behaviour with Machine Learning, you can forecast trends and make informed decisions, optimizing resources and enhancing customer satisfaction. This course will equip you with the skills to deliver meaningful experiences and build stronger relationships, ensuring a sustained competitive advantage in your industry.
Key Outcomes:
- Mastering the fundamentals of customer segmentation analytics
- Grasping the end-to-end predictive customer analytics workflow
- Identifying and addressing factors influencing consumer decisions
- Acquiring data literacy through real-world datasets from Kaggle
- Performing data cleaning to ensure dataset integrity
- Segmenting customers by demographics such as age, gender, and education level
- Analyzing customer spending patterns across different countries
- Discovering correlations between purchase frequency, customer spending, and income
- Leveraging Random Forest for feature importance analysis
- Applying K-means clustering for customer segmentation
- Building and fine-tuning predictive models for customer spending and churn
- Handling class imbalance with synthetic minority oversampling technique (SMOTE)
- Evaluating model performance using k-fold cross validation
- Deploying machine learning models with Gradio for real-world application
Join us now to transform data into actionable insights and elevate your career to new heights! 🚀
