Added thyroid prediction ML code

thyroid prediction/.ipynb_checkpoints/readme-checkpoint.md

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+# **Thyroid Disease Detection using Machine Learning**  
+
+## **GOAL**  
+The goal of this project is to develop a machine learning model that can accurately predict  recurrence of well differentiated thyroid cancer. The project involves **data preprocessing, feature selection, and classification models** to identify the best approach for thyroid disease detection.  
+
+Dataset can be downloaded from [here](https://www.kaggle.com/sudhanshu8897/thyroid-disease-dataset).  
+
+---
+
+## **MODELS USED**  
+- Logistic Regression  
+- Support Vector Machine (SVM)  
+- Decision Trees  
+- Random Forest  
+
+---
+
+## **LIBRARIES NEEDED**  
+- numpy  
+- pandas  
+- seaborn  
+- matplotlib  
+- scikit-learn  
+
+---
+
+## **STEPS BEING FOLLOWED**  
+
+1. Load the dataset  
+2. Import libraries  
+3. Data Visualization  
+4. Data Preprocessing (handling missing values, outliers, and scaling)  
+5. Splitting data into training and testing sets
+6. Feature Selection Techniques:  
+   - Principal Component Analysis (PCA)   
+   - Mutual Information  
+   - Recursive Feature Elimination with Cross-Validation (RFECV)
+7. Model training using different classification algorithms  
+8. Model evaluation using accuracy, precision, recall, F1-score, and AUC-ROC  
+9. Comparison of models based on performance and training time  
+
+---
+
+## **CONCLUSION : Best Accuracy of Each Model**  
+
+By using Logistic Regression:  
+```  
+Accuracy: 95.41%
+Feature Selection Technique:PCA  
+```  
+
+By using Random Forest:  
+```  
+Accuracy: 98.26%
+Feature Selection Technique:MutualInfo
+```   
+
+By using Support Vector Machine (SVM):  
+```  
+Accuracy: 95.11%
+Feature Selection Technique:PCA
+``` 
+
+By using Decision Trees:  
+```  
+Accuracy: 93.91%
+Feature Selection Technique:MutualInfo
+``` 
+
+---