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
14. Deployed model using Flask

Context

Univariant plottings

###Person with charges based on sex,Region and Smoking habit

###Relationship between Multiple features

###Accuracy

🔑 Results

Model gave 86% accuracy for Medical Insurance Amount Prediction using Random Forest Regressor

Analysis in Flask

This webpage shows medical insurance of AN INDIVIDUAL

📁 Dataset

The dataset used can be downloaded here (Kaggle) - Click to Download