Machine Learning Python: Predicting Cross-Sell Opportunities in Insurance

🎯 Objective: Crafted a predictive model for a leading insurance company to identify potential customers interested in purchasing vehicle insurance based on their previous health insurance history.

Key Achievements

🔍 Data Mastery:

• Explored a diverse dataset with unique buyer attributes.
• Leveraged insights from gender, age, driving license status, region-specific details, and more.

🌟 Challenging Task Conquered:

• Successfully predicted buyer responses to sales proposals.
• Contributed to optimizing cross-selling strategies for the business.

💡 Innovative Approaches:

• Implemented advanced techniques to handle imbalanced class distribution.
• Ensured model accuracy and reliability, setting it apart in the field.

Detailed Project Workflow

1. Data Preprocessing:

• Cleansed and prepared the dataset by normalizing features and handling missing values, ensuring the data was suitable for analysis.
• Engineered new features to enrich the dataset, providing deeper insights for the predictive models.

2. Customer Clustering:

• Applied clustering algorithms to segment the customer base into distinct groups based on their attributes. This step helped in understanding diverse customer behaviors and preferences.
• Utilized techniques like K-means and hierarchical clustering to identify and categorize customer segments effectively.

3. Target Variable Definition through Cluster Analysis:

• Analyzed the resulting clusters to determine patterns and characteristics that define potential buyers of vehicle insurance.
• Established a target variable for the predictive modeling based on cluster insights, focusing on those most likely to convert.

4. Implementation of Machine Learning Models:

• 4a Naive Bayes Bernoulli: Tested for binary classification based on features converted into binary format.
• 4b Naive Bayes Gaussian: Employed for features with a normal distribution, assessing the likelihood of purchasing based on statistical probabilities.
• 4c SVC Kernel Linear: Applied for linearly separable data, maximizing the margin between classes.
• 4d SVC Kernel Polynomial: Used to model more complex relationships through higher-dimensional spaces.
• 4e SVC Kernel Sigmoid: Explored for its ability to model nonlinear relationships similar to neural network behavior.
• 4f SVC Kernel Gaussian (RBF): Best for handling non-linear separation in data through transformation into higher dimensions.
• 4g Neural Network: Configured to learn through layers of interconnected nodes, capturing intricate patterns in large data sets.
• 4h Nearest Neighbors: Implemented to classify based on the proximity to the nearest data points, useful for small datasets.

Your Experience Journey

📊 Key Dataset Properties:

• Unique buyer identifiers
• Gender and age insights
• Driving license status
• Region-specific details
• Vehicle insurance history
• Vehicle age and damage indicators
• Annual premium information
• Sales channel anonymized codes
• Customer vintage (loyalty) metrics

🔮 Your Impact:

• Directly influenced the success of the company by delivering insights that transformed cross-selling strategies.
• Sharpened machine learning skills while working on a project with tangible business outcomes.

Explore My Code

🔗 GitHub Repository: Dive into the codebase, witness the journey of crafting a robust predictive model, and understand the innovative techniques employed. Discover how diverse machine learning algorithms tackle the challenge of predicting customer behavior and optimizing cross-sell strategies in the insurance sector.