This project is insurance pricing. As many of us are aware, health insurance costs depend on factors like our health condition, marital status, the number of children we have, where we live, our income, whether we smoke, and even our BMI. Renewing insurance can be stressful because we're not sure which company to choose and how much they'll charge us. We often worry about ending up paying more than necessary.
To help people understand which factors affect their insurance payments, I've created a personalized insurance price recommender. By selecting your specific details, such as age, gender, number of children, BMI, smoking status, and location, you can get an approximate yearly insurance cost tailored to you.
After evaluating multiple ML models such us Linear Regression, Decision Tree, SVM and many more, i decided to stick with Random forest. Here are the key metrics that led me to choose the Random Forest model as the top performer:
You will find below following:
Please select your feature on below streamlit app and press "Predict" it will give you the insurance cost
The bank's marketing department aims to boost personal loan sales among its current customers by introducing a tailored loan offer. To achieve this, they have requested the analytics team to provide a thorough analysis and prediction of the anticipated loan acceptance rate among current customers. Additionally, they seek to identify specific customer profiles likely to embrace this offering. NOTE: This work was done with the group on Data mining course.
Through running comprehensive analysis, including Exploratory Data Analysis (EDA) and examining variable correlations, we aim to identify the key factors that influence customers' decisions to accept the personal loan offer. Subsequently, we will develop a logistic regression model to predict the likelihood of customers accepting the proposed personal loan.
You will find below following:
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