1. The primary objective of this project is to conduct a comprehensive analysis of kidney stone prediction, focusing on understanding the underlying factors that contribute to kidney stone formation and predicting the likelihood of kidney stone occurrence in patients.
   
2. The project aims to analyze various urinary characteristics of patients in order to uncover patterns and insights related to kidney stone formation. By examining features such as urine gravity, pH, osmolality, conductivity, urea, and calcium levels, the goal is to gain a deeper understanding of the physiological factors associated with kidney stone risk.
   

1. 📚 Importing Libraries: - To perform Data Manipulation,Visualization & Model Building.
   
2. ⏳ Loading Dataset: - Load the dataset into a suitable data structure using pandas.
   
3. 🧠 Basic Understaning of Data: - Generate basic informations about the data.
   
4. 📊 Exploatory Data Analysis: - To identify trends, patterns, and relationships among the variabels.
   
5. 📈 Feature Engineering: - To create new relevant features for model building.
   
6. 🍀 Statistical Analysis: - To perform hypothesis testing to understand the feature importance.
   
7. ⚙️ Data Preprocessing: - To transform data for creating more accurate & robust model.
   
8. 🎯 Model building:- To build predictive models, using various algorithms.
   
9. ⚡️ Model evaluation: - To analyze the Model performance using metrics.
   
10. 🎈 Conclusion: - Conclude the project by summarizing the key findings.
    

• The model demonstrates strong performance with an accuracy of 95% on training data and 88% on testing data, indicating a solid fit to the dataset.
• Remarkably, the model achieves consistent recall, precision, and F1 score values, suggesting a perfect balance between effectively identifying positive cases and minimizing false positives.

• 💻 Python
• 💻 HTML
• 🐼 Pandas
• 📊 Matplotlib
• 📈 Seaborn
• 📈 Statistics
• 🤖 Scikit-learn
• 🧠 Machine Learning
• 📓 Jupyter Notebook
• 🔗 GitHub

• The project has reached completion, successfully meeting the predefined goals and purposes.
• All project objectives have been accomplished, including end-to-end execution from data collection and preprocessing to model development and evaluation.

Contributions are welcome! If you have any suggestions, bug fixes, or feature additions, please open an issue or submit a pull request.

For any questions or inquiries, please contact [email protected] or you can contact me on LinkedIn.

Thank you for checking out my repository! I hope you find the projects and code provided helpful and informative. If you have any questions or suggestions, please feel free to reach out.😊