You are working for a new-age insurance company and employ multiple outreach plans to sell term insurance to your customers. Telephonic marketing campaigns still remain one of the most effective ways to reach out to people; however, they incur a lot of cost. Hence, it is important to identify the customers that are most likely to convert beforehand so that they can be specifically targeted via call. We are given the historical marketing data of the insurance company and are required to build an ML model that will predict if a client will subscribe to the insurance.
The historical sales data.
Features:
- age (numeric)
- job: type of job
- marital: marital status
- educational_qual: education status
- call_type: contact communication type
- day: last contact day of the month (numeric)
- mon: last contact month of the year
- dur: last contact duration, in seconds (numeric)
- num_calls: number of contacts performed during this campaign and for this client
- prev_outcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")
Output variable (desired target):
- y - has the client subscribed to the insurance?
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Data Preprocessing: The first step involves cleaning and transforming the data to make it suitable for modeling. This includes handling missing values, encoding categorical variables, and scaling numerical features.
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Feature Engineering: Additional features may be created or existing features may be modified to enhance the predictive power of the model. This step involves extracting meaningful information from the raw data.
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Model Selection: Various machine learning algorithms such as logistic regression, decision trees, random forests, and gradient boosting may be evaluated to determine the best performing model for the task.
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Model Training: The selected model is trained on the preprocessed data using techniques such as cross-validation to ensure robustness and generalizability.
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Model Evaluation: The trained model is evaluated using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score to assess its performance.
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Deployment: Once the model has been trained and evaluated, it can be deployed to make predictions on new data. This involves integrating the model into existing systems or applications.
This project aims to create a machine learning model for predicting customer conversion to streamline telephonic outreach. The model leverages features such as age, job type, marital status, educational qualification, contact communication type, and past campaign outcomes to predict whether a client will subscribe to insurance. By prioritizing clients with a higher likelihood of conversion, the model optimizes telephonic campaigns, reducing costs and improving efficiency.