Problem Statement: Customers need to find the right insurance plan by identifying the optimal prices to quote based on the coverage, health records. Data Selection: Claim’s data & patient generated health data are selected to determine the patient disease, medical & claim history. All internal & external data is considered which will helps us to determine the right plan for the patient. Data Preparation: It includes sorting, structuring, preprocessing & filling the missing data points (if any). Training Data Set & Testing Data Set will be prepared. Development Development & modelling: A machine learning technique is chosen. Right algorithms are selected. Technology stack: Machine Learning, Pandas, TensorFlow & Torch/PyTorch , python Collaboration: Rapid iteration & validation of Trained models are done so that the business goal is achieved. Testing: Here the model is tested to check how well the model works on the data it has never seen. Validation In this final stage initial insights are presented & results will be evaluated. Recommendations will be issued for deployment strategy. All possible recommendations will be evaluated & validated.