AlmaBetter Verfied Project - AlmaBetter School
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Insurance is a contract, represented by a policy, in which an individual or entity receives financial protection or reimbursement against losses from an insurance company.
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This EDA will use Python libraries, matplotlib, and Seaborn to examine the Subscribed health insurance customers dataset through visualizations and graphs.
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The dataset is of Subscribed Health insurance customers from insurance companies, contains information such as Age, Gender, Driving Licence, Region Etc.
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Machine learning has a wide range of applications in our organization. Prediction and analysis has long been the most well-known application of machine learning, which fuels our sale prediction.
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We're also utilizing machine learning to assist in designing our Sale strategies and Campaign programmes by identifying traits that lead to successful content.
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We utilize it to help Company's to rapidly expand their reach to customers with appropriate data driven decisions.
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We can also employ machine learning to improve Service and Customer retention, Target oriented promotions.
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Our major goal in this project is to identify Customers who are interested in purchasing vehcle insurance based on subscribed health insurance data of the company.
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The objective was to anticipate Customers who are interested in purchasing vehcle insurance.
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Exploratory Data Analysis is done on the dataset to get the insights from the information however the principal invalid qualities are taken care of. Likewise, some hypothesis testing was additionally performed from the experiences from EDA.
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After that Response variable is our objective variable must be highlighted where Analysis activities are performed on it and after that visualization has done for it to understand hidden insights.
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From that point forward, all that was left was to track down the important factors and feature encoding and fit our models by creating various features, and further, the model is assessed utilizing the metrics.
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id - A unique id for each customer.
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Gender - Gender details of the health insurance owner.
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Age - Age details of the health insurance owner.
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Driving_License - Whether the customer has a driving license or Not.
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Region Code - Region with code details of the health insurance owner.
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Previously_Insured -Whether the customer previously_Insured or Not.
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Vehicle_Age - Age of vehicle of the health insurance owner.
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Vehicle_Damage - Whether the customer Vehicle Damaged or Not.
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Annual Premium - Annual Premium amount details of a Customer.
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Policy_Sales_Channel - Policy Sales Channel shows us,the number of the sales channel.
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Response - Response of the customer to buying vehicle insurance.
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This dataset consists information Subscribed Health insurance customers from insurance company.
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The dataset is provided by the insurance company.
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They are expanding their services to vehicle insurance from Health insurance.
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The task was to predict and build the model to understand the factors and customers who are interested in vehicle insurance.
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It will be interesting to explore what all other insights can be obtained from the same dataset.
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Integrating the factors affecting the purchase and insights from the data, will help companie to identify strategies and expand there reach to customer.
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Our main goal in this project was to determine different factors based on response, which we have done.
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After fitting different models, we did hyperparameter tunig for better results.
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which we evaluated using the different evaluation metrics.
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Comparing the ROC curve we concluded that the Random Forest model performs better..
- Ppt Presentation Link:-https://drive.google.com/file/d/14PD5IhbpLJai9u_5pkwRvRfJJ8qJpgya/view?usp=sharing