Insurance-cost-predictor

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

📁 Dataset

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