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## Insurance Claim Prediction Machine Learning Project with Source Code

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## Latest Machine Learning Project with Source Code

insurance claim prediction machine learning. Insurance companies are extremely interested in the prediction of the future. Accurate prediction gives a chance to reduce financial loss for the company. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Furthermore, because of the payment errors, processing the claims again accounts for a significant portion of administrative costs.

## Dataset :

This dataset contains 7 features as shown below:

age: age of the policyholder
sex: gender of policyholder (female=0, male=1)
BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 25
steps: average walking steps per day of the policyholder
children: number of children/dependents of the policyholder
smoker: smoking state of policyholder (non-smoke=0;smoker=1)
region: the residential area of the policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3)
charges: individual medical costs billed by health insurance.

## Installation Steps :-

1. Install Python 3.7.0
2. Install all dependencies cmd -python -m pip install –-user -r requirements.txt
3. Finally run cmd - python app.py

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## Medical Insurance Cost Prediction Project in Python Flask

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## Medical Insurance Cost Prediction using Random Forest Regressor.

To predict things have been never so easy. I used to wonder how Insurance amount is charged normally. So, in the mean time I came across this dataset and thought of working on it! Using this I wanted to know how few features determine our insurance amount!

## Features

1. Exploring the dataset
2. Converting Categorical values to Numerical
3. Plotting Heatmap to see dependency of Dependent valeu on Independent features
4. Data Visualization (Plots of feature vs feature)
5. Plotting Skew and Kurtosis
6. Data Preparation
7. Prediction using Linear Regression
8. Prediction using SVR
9. Prediction using Ridge Regressor
10. Prediction using Random Forest Regressor
11. Performing Hyper tuning for above mentioned models
12. Plotting Graph for all Models to compare performance
13. Preparing model for deployment