sklearn joblib medical tabular-regression license:mit region:us
---
library_name: sklearn
license: mit
metrics:
- accuracy
pipeline_tag: tabular-regression
tags:
- medical
---


# Logistic Regression Diabetes Prediction Model

## Instructions for Users

### DiabeticLogistic Model

This model predicts the likelihood of diabetes based on medical data using logistic regression.

### Dataset

The model is trained on a dataset with features including:
- Glucose
- BloodPressure
- SkinThickness
- Insulin
- BMI
- DiabetesPedigreeFunction
- Age

### Preprocessing

Features are normalized using `StandardScaler`.

### Usage

#### Downloading the Model

```python
!pip install pandas scikit-learn joblib huggingface_hub

from huggingface_hub import hf_hub_download
import joblib
import pandas as pd

# Your Hugging Face token
token = "put your token here"

# Download the model and scaler from the Hugging Face Hub using the token
model_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="logistic_regression_model.joblib", use_auth_token=token)
scaler_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="scaler.joblib", use_auth_token=token)

# Load the model and scaler
model = joblib.load(model_path)
scaler = joblib.load(scaler_path)

# Example data
data = pd.DataFrame({
    'Pregnancies': [6, 1],
    'Glucose': [148, 85],
    'BloodPressure': [72, 66],
    'SkinThickness': [35, 29],
    'Insulin': [0, 0],
    'BMI': [33.6, 26.6],
    'DiabetesPedigreeFunction': [0.627, 0.351],
    'Age': [50, 31]
})

# Normalize the data
data_scaled = scaler.transform(data)

# Make predictions
predictions = model.predict(data_scaled)
print("Predictions:", predictions)
```

#### Fine-Tuning the Model

To fine-tune the model, follow these steps:

##### Load the Model and Data

```python
from huggingface_hub import hf_hub_download
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import joblib

# Your Hugging Face token
token = "put your token here"

# Download the model and scaler from the Hugging Face Hub using the token
model_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="logistic_regression_model.joblib", use_auth_token=token)
scaler_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="scaler.joblib", use_auth_token=token)

# Load the model and scaler
model = joblib.load(model_path)
scaler = joblib.load(scaler_path)

# Load your dataset
data = pd.read_csv('/content/Healthcare-Diabetes.csv')

# Drop the 'Id' column if it exists
if 'Id' in data.columns:
    data = data.drop(columns=['Id'])

X = data.drop(columns=['Outcome'])
y = data['Outcome']

# Normalize the features
X_scaled = scaler.transform(X)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
```

##### Fine-Tune the Model

```python
# Fine-tune the model
model.fit(X_train, y_train)

# Evaluate the fine-tuned model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Fine-tuned Accuracy: {accuracy:.2f}')
```

##### Save the Fine-Tuned Model

```python
joblib.dump(model, 'fine_tuned_logistic_regression_model.joblib')
```

