Upload 4 files

app.py

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+# Import the libraries
+
+
+
+# Run the training script placed in the same directory as app.py
+# The training script will train and persist a linear regression
+# model with the filename 'model.joblib'
+
+
+
+
+# Load the freshly trained model from disk
+
+
+# Prepare the logging functionality
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="-----------",  # provide a name "insurance-charge-mlops-logs" for the repo_id
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+# Define the predict function which will take features, convert to dataframe and make predictions using the saved model
+# the functions runs when 'Submit' is clicked or when a API request is made
+
+
+    # While the prediction is made, log both the inputs and outputs to a  log file
+    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
+    # access
+
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'age': age,
+                    'bmi': bmi,
+                    'children': children,
+                    'sex': sex,
+                    'smoker': smoker,
+                    'region': region,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
+
+    return prediction[0]
+
+
+
+# Set up UI components for input and output
+
+
+
+# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+
+
+# Launch with a load balancer
+demo.queue()
+demo.launch(share=False)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a389766ae43175dfcf9d32ddadb5925fe5521ca1b5875de9af11fbe4754616df
+size 3849

requirements.txt

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+scikit-learn==1.3.2

train.py

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+import pandas as pd
+import joblib
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.model_selection import train_test_split, RandomizedSearchCV
+from sklearn.linear_model import Ridge
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+# Read the uploaded file
+df = pd.read_csv('/insurance (1).csv')
+
+# Define the target variable
+y = df['charges']
+
+# Define the feature columns
+numerical_columns = ['age', 'bmi', 'children']
+categorical_columns = ['sex', 'smoker', 'region']
+
+# Define feature matrix X
+X = df[numerical_columns + categorical_columns]
+
+# Split the data
+Xtrain, Xtest, ytrain, ytest = train_test_split(
+    X, y,
+    test_size=0.2,
+    random_state=42
+)
+
+# Create a column transformer for preprocessing
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), numerical_columns),  # Standard scaling for numerical columns
+        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_columns)  # One-hot encoding for categorical columns
+    ]
+)
+
+# Create a Ridge regression model pipeline
+ridge_pipeline = Pipeline([
+    ('preprocessor', preprocessor),
+    ('ridge', Ridge())
+])
+
+# Define a parameter distribution for hyperparameter tuning
+param_distribution = {
+    'ridge__alpha': [0.001, 0.01, 0.1, 0.5, 1, 5, 10]
+}
+
+# Perform hyperparameter tuning using RandomizedSearchCV
+random_search = RandomizedSearchCV(ridge_pipeline, param_distribution, n_iter=5, cv=5)
+random_search.fit(Xtrain, ytrain)
+
+# Model evaluation for testing set
+y_pred = random_search.best_estimator_.predict(Xtest)
+
+mae = mean_absolute_error(ytest, y_pred)
+mse = mean_squared_error(ytest, y_pred)
+r2 = r2_score(ytest, y_pred)
+
+print("The model performance for the testing set")
+print("--------------------------------------")
+print('MAE is {}'.format(mae))
+print('MSE is {}'.format(mse))
+print('R2 score is {}'.format(r2))
+
+# Save the best model
+saved_model_path = "model.joblib"
+joblib.dump(random_search.best_estimator_, saved_model_path)