## Kidney Stone Prediction

Kidney Stones are hard deposits that's composed of minerals and acid salts which sticks together in concentrated urine. They can be painful when passing through the urinary tract.

The most common symptom is severe pain usually in the side of the abdomen that's often associated with nausea. Treatment includes pain relievers and drinking lots of water to help pass the stone. Medical procedures may be required to remove or break up larger stones.

**Dataset**

You can access the kidney stone dataset here in [Kaggle](https://www.kaggle.com/datasets/utkarshxy/kidney-stone-data)

**Goal**

The goal of this problem statement is to create a prediction model which will predict the success rate of kidney stone operation based on the stone's size and type of treatment.

**What Have I Done**

1. Imported all the required libraries
2. Processed Data Cleaning and demonstrated Data Visualization with insights
3. Finally implemented Prediction Modelling :
   
    - KNN Algorithm
    - Logistic Regression
    - Random Forest Classifier
    - Decision Tree Classifier
    - Support Vector Machine Classifier
    - AdaBoost Classifier
    - Gradient Boosting Classifier
    - Gaussian Naive Bayes Classifier
    - MLP Classifier
      
4. Atlast, compared the results for better conclusion

**Model Results**

I had applied different algorithms to check the accuracy score of the models. Keeping a glance, all the models reverted with approximately same score of 82.86. We can apply any of the models / algorithms to deploy the final model. Though the outcome is accurate enough, I suggest to go with logstic regression sufficiently.
