Medical Cost Predictive Analysis

[sitamgithub-MSIT]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Medical Cost Predictive Analysis :red_circle: Aim : Predict the medical insurance charge cost of individuals based on BMI, sex, smoker status, regions, and other features. :red_circle: Dataset : Kaggle Dataset :red_circle: Approach : I will use 3–4 algorithms to implement the models and compare all the algorithms to find the best-fit algorithm for the model by checking the accuracy scores. Also, I will add an exploratory data analysis before creating model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name : Sitam Meur
• GitHub Profile Link : https://github.com/sitamgithub-MSIT
• Participant ID (If not, then put NA) :
• Approach for this Project :
  1. Import necessary libraries like numpy, pandas, scikit-learn, etc.
  2. Performing the necessary pre-processing, encoding, scaling, etc.
  3. Creating all the required functions. Probably some visualization utility functions and others.
  4. Split the data into test and training datasets.
  5. Train the model on linear regression, xg-boost, and other algorithms.
  6. Checking the accuracy, like the r2 score and others. At the end, label all the accuracy of all models in a table and do a comparative analysis between them.
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) KWOC Participant

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[abhisheks008]

Issue assigned to you. Make sure you merge the PR before the event deadline. @sitamgithub-MSIT