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https://github.com/27ahmad/medicine-recommendation-system
This project aims to create a medicine recommendation system based on symptoms provided by the user. The system is built using machine learning models trained on a dataset of symptoms and their corresponding diagnoses. The frontend is designed using Bootstrap for an intuitive user interface.
https://github.com/27ahmad/medicine-recommendation-system
bootstrap machine-learning medicine-applications pandas recommendation-system scikit-learn
Last synced: about 4 hours ago
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This project aims to create a medicine recommendation system based on symptoms provided by the user. The system is built using machine learning models trained on a dataset of symptoms and their corresponding diagnoses. The frontend is designed using Bootstrap for an intuitive user interface.
- Host: GitHub
- URL: https://github.com/27ahmad/medicine-recommendation-system
- Owner: 27ahmad
- Created: 2024-05-15T16:28:07.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-12-05T11:06:16.000Z (about 1 month ago)
- Last Synced: 2024-12-05T12:19:34.629Z (about 1 month ago)
- Topics: bootstrap, machine-learning, medicine-applications, pandas, recommendation-system, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 124 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Medicine Recommendation System 🏥
## Project Overview
This project aims to create a medicine recommendation system based on symptoms provided by the user. The system is built using machine learning models trained on a dataset of symptoms and their corresponding diagnoses. The frontend is designed using Bootstrap for an intuitive user interface and the backend is built in flask.
## Table of Contents
- [Project Overview](#project-overview)
- [Dataset](#dataset)
- [Preprocessing](#preprocessing)
- [Model Training](#model-training)
- [Evaluation](#evaluation)
- [Frontend](#frontend)
- [Usage](#usage)
- [Dependencies](#dependencies)
- [Contributing](#contributing)
- [License](#license)
## Dataset
The dataset contains various symptoms and the corresponding prognosis (diagnosis). It includes 4920 instances and 133 columns, where each column represents a symptom (binary value) and the prognosis.
## Preprocessing
1. **Loading the Dataset:**
```python
df = pd.read_csv("E:\\Medicine Recommendation System\\datasets\\Training.csv")
```
2. **Splitting the Dataset:**
```python
X = df.drop('prognosis', axis=1)
Y = df['prognosis']
le = LabelEncoder()
le.fit(Y)
Y = le.transform(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=20)
```
## Model Training
Several machine learning models were trained and evaluated to find the best-performing model:
- Support Vector Classifier (SVC)
- RandomForestClassifier
- GradientBoostingClassifier
- KNeighborsClassifier
- MultinomialNB
Example code for training and evaluating the models:
```python
models = {
"SVC": SVC(kernel='linear'),
"RandomForest": RandomForestClassifier(n_estimators=100, random_state=42),
"GradientBoosting": GradientBoostingClassifier(n_estimators=100, random_state=42),
"KNN": KNeighborsClassifier(n_neighbors=5),
"MultinomialNB": MultinomialNB()
}
for model_name, model in models.items():
model.fit(X_train, Y_train)
predictions = model.predict(X_test)
accuracy = accuracy_score(Y_test, predictions)
cm = confusion_matrix(Y_test, predictions)
print(f"{model_name} Accuracy: {accuracy}")
print(f"{model_name} Confusion Matrix:")
print(np.array2string(cm, separator=', '))
```
## Evaluation
All models achieved an accuracy of 100% on the test set. The confusion matrices also indicate perfect predictions for all classes.
## Frontend
The frontend is built using Bootstrap to provide a user-friendly interface. Users can input their symptoms, and the system will predict the possible diagnosis.
## Usage
1. **Training the Model:**
```python
svc = SVC(kernel='linear')
svc.fit(X_train, Y_train)
pickle.dump(svc, open('svc.pkl', 'wb'))
```
2. **Loading the Model:**
```python
svc = pickle.load(open('svc.pkl', 'rb'))
ypredict = svc.predict(X_test)
print(f"Predicted Label : {ypredict}")
print(f"Actual Label: {Y_test}")
```
3. **Running the App:**
- Run main.py to run the flask application
## Dependencies
- pandas
- numpy
- scikit-learn
- pickle
- Bootstrap (for the frontend)
- Flask
## Contributing
We welcome contributions! Please fork the repository and submit pull requests.
## License
This project is licensed under the MIT License - see the LICENSE file for details.