https://github.com/darshan12345678910/implementation-of-decision_tree_svm_rnf-ml-algorithm

Implementation of Decision trees, Random Forest classifiers
https://github.com/darshan12345678910/implementation-of-decision_tree_svm_rnf-ml-algorithm

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Implementation of Decision trees, Random Forest classifiers

• Host: GitHub
• URL: https://github.com/darshan12345678910/implementation-of-decision_tree_svm_rnf-ml-algorithm
• Owner: darshan12345678910
• Created: 2024-02-01T09:16:16.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-02-01T09:31:34.000Z (10 months ago)
• Last Synced: 2024-02-01T10:34:00.727Z (10 months ago)
• Language: Jupyter Notebook
• Size: 389 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Healthcare Dataset Analysis and Stroke Prediction

## Project Overview:

This project focuses on the analysis and prediction of strokes using a healthcare dataset. Stroke is a critical health event, and predicting its occurrence can aid in early intervention and preventive measures. The dataset includes diverse information about individuals, encompassing demographic details, health conditions, and lifestyle factors.

## Key Steps:

### 1. Data Exploration and Preprocessing:

- **Loading Data:** The dataset is loaded using Pandas from a CSV file, and preliminary exploration is performed.

- **Data Cleaning:** Handling missing values in the 'bmi' column and dropping unnecessary columns ('id' and 'avg_glucose_level').

### 2. Feature Engineering and Visualization:

- **Label Encoding:** Converting categorical variables to numeric using `LabelEncoder`.

- **Outlier Detection:** Visualizing outliers in the 'bmi' column through boxplots.

- **Correlation Analysis:** Creating a heatmap to understand feature relationships.

- **Distribution Visualization:** Plotting histograms for the distribution of 'bmi'.

### 3. Handling Imbalanced Data:

- **SMOTE Technique:** Applying Synthetic Minority Over-sampling Technique (SMOTE) for oversampling to address class imbalance in the 'stroke' variable.

### 4. Machine Learning Models:

- **Decision Tree Classifier:** Implementing a Decision Tree model to predict strokes.

- **Support Vector Machine (SVM):** Utilizing SVM with a linear kernel for prediction.

- **Random Forest Classifier:** Implementing an ensemble method, the Random Forest Classifier.

### 5. Model Evaluation and Interpretation:

- **Data Splitting:** Splitting the data into training and testing sets for model evaluation.

- **Model Evaluation Metrics:** Assessing model performance using accuracy, confusion matrix, and classification reports.

- **Visualizing Decision Trees:** Displaying the decision tree structure for interpretability.

### 6. Usage and Contribution:

- **Code Usage:** Providing instructions for running the code and exploring the results.

- **Acknowledgments:** Crediting data sources and contributors.

- **License Information:** Specifying the project's license for use and modification.

## Next Steps:

- Further optimization and fine-tuning of models based on performance metrics.

- Exploration of additional features or external datasets for enhanced prediction.

- Deployment of the predictive model for real-time stroke risk assessment.

## Acknowledgments:

- The dataset used in this analysis is publicly available in kaggle