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Insurance Cost Prediction

The 'Insurance Cost Prediction' project aims to utilize machine learning to predict individual insurance costs based on factors like age, gender, and lifestyle. Its objectives include developing a predictive model, enhancing risk assessment for insurance companies, improving efficiency in premium estimation, understanding key cost factors, and optimizing the model's accuracy. The project involves data preprocessing, feature engineering, model training, and evaluation for practical applications.

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Eyob
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5 views1 page

Insurance Cost Prediction

The 'Insurance Cost Prediction' project aims to utilize machine learning to predict individual insurance costs based on factors like age, gender, and lifestyle. Its objectives include developing a predictive model, enhancing risk assessment for insurance companies, improving efficiency in premium estimation, understanding key cost factors, and optimizing the model's accuracy. The project involves data preprocessing, feature engineering, model training, and evaluation for practical applications.

Uploaded by

Eyob
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Title: Insurance Cost Prediction

Group members:

BAHAR ABDI.……………………..T/4877/14

DEREJE MIHIRETU……….……….2557/14

EYOB ASSEFA …………..…..………2729/14

KENU KEFYALEW …………..…….2887/14

NETSANET HAILE ……….….…… 3605/14

NIGIST MIHRET……….…….……. 3611/14

Description

The "Insurance Cost Prediction" project aims to leverage machine learning techniques to predict
individual insurance costs based on various factors such as age, gender, BMI, region, smoking
habits, and more. By analyzing these factors, the project seeks to create an accurate and robust
predictive model that can assist insurance companies in assessing risks and setting premiums.
The project combines data preprocessing, feature engineering, model training, and evaluation to
deliver a practical solution for real-world applications.

Objectives

1. Develop a Predictive Model: Build a machine learning model capable of accurately


predicting insurance costs using relevant demographic and lifestyle data.

2. Enhance Risk Assessment: Provide a data-driven approach for insurance companies to


assess individual risk profiles effectively.

3. Improve Efficiency: Reduce the manual effort required to estimate insurance premiums
by automating the process.

4. Understand Key Factors: Identify and analyze the most significant factors influencing
insurance costs.

5. Model Optimization: Experiment with different algorithms and hyperparameter tuning


to improve the model's accuracy and generalizability.

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