Automating with MLflow and BentoML

Flask App for ML Prediction

Flask App for ML Prediction

from flask import Flask, render_template, request
import pandas as pd
import requests
import base64
import io

app = Flask(__name__)

# Route for the home page
@app.route('/')
def index():
    return render_template('index.html')

# Route to handle the CSV file upload and prediction
@app.route('/predict', methods=['POST'])
def predict():
    file_data = request.form.get('file')
    
    # Decode the Base64 encoded file content
    decoded_file = base64.b64decode(file_data.split(',')[1])

    # Read the decoded content into a DataFrame
    df = pd.read_csv(io.StringIO(decoded_file.decode('utf-8')))

    # Separate the 'claim_id' column if it exists
    if 'claim_id' in df.columns:
        claim_ids = df['claim_id']
        df = df.drop(columns=['claim_id'])
    else:
        claim_ids = None

    # Send the DataFrame to the BentoML service
    response = requests.post(
        'http://127.0.0.1:3000/predict',  # BentoML endpoint
        json=df.to_dict(orient='records')
    )

    # Get predictions from the response
    predictions = response.json()['predictions']

    # Add predictions to the DataFrame
    df['Prediction'] = predictions

    # Reattach the 'claim_id' column to the DataFrame
    if claim_ids is not None:
        df['claim_id'] = claim_ids

    # Reorder columns to have 'claim_id' first
    if 'claim_id' in df.columns:
        df = df[['claim_id'] + [col for col in df.columns if col != 'claim_id']]

    # Render the DataFrame as an HTML table
    return render_template('result.html', tables=[df.to_html(classes='data', header="true")])

if __name__ == '__main__':
    app.run(debug=True, port=5005)

README

README

Here is the converted `README.md` file for your project:

```markdown
# Health Claims Fraud Detection Project

This project involves building a Flask web application that uses an Isolation Forest model to detect potentially fraudulent health claims. It leverages BentoML for model serving and MLflow for experiment tracking.

## Setup Instructions

### 1. Environment Setup

- Load the bash profile and set up the virtual environment:

```bash
source ~/.bash_profile
virtualenv venv
source venv/bin/activate
```

- Install required dependencies:

```bash
pip3 install -r requirements.txt
```

### 2. BentoML and Model Management

- List BentoML models:

```bash
bentoml models list
```

- Run the synthetic data generator script:

```bash
python3 synthetic_health_claims.py
```

- Train and evaluate the Isolation Forest model:

```bash
python3 isolation_model.py
```

### 3. MLflow for Experiment Tracking

- Start the MLflow UI to track experiments:

```bash
mlflow ui
```

### 4. Run the Flask Web Application

- Open a new terminal window, source the environment, and run the Flask app:

```bash
source ~/.bash_profile
source venv/bin/activate
python3 flask_app.py
```

### 5. Register Model and Serve with BentoML

- In another terminal, run the following commands to register and serve the model with BentoML:

```bash
source ~/.bash_profile
source venv/bin/activate
python3 isolation_model.py
python3 register_model.py
bentoml serve service.py --reload
```

## Additional Notes

- Ensure that all commands are executed in the correct order for proper setup and functioning of the application.
- Use BentoML to serve models and integrate them with the Flask app for real-time fraud detection.
- The MLflow UI helps to track experiments and evaluate model performance.
```

This `README.md` file gives a clear step-by-step guide for setting up and running your project. Let me know if you'd like any additional adjustments!

Model Registration Script

Model Registration Script

import bentoml
import pickle

# Load the model from the downloaded PKL file using pickle
model_path = "model.pkl"  # Replace with your actual path

with open(model_path, 'rb') as model_file:  # Open in binary mode
    model = pickle.load(model_file)

# Save the model to BentoML
bento_model = bentoml.sklearn.save_model("health_insurance_anomaly_detector", model)

print(f"Model registered with BentoML: {bento_model}")

Python Requirements

Python Requirements

mlflow
pandas
numpy
bentoml

ML Service

ML Service

import bentoml
from bentoml.io import JSON, PandasDataFrame

# Load the registered model
model_runner = bentoml.sklearn.get("health_insurance_anomaly_detector:latest").to_runner()

# Create a BentoML Service
svc = bentoml.Service("health_insurance_anomaly_detection_service", runners=[model_runner])

# Define an API endpoint for prediction
@svc.api(input=PandasDataFrame(), output=JSON())
def predict(data):
    # Make predictions
    predictions = model_runner.predict.run(data)
    # Return predictions as JSON
    return {"predictions": predictions.tolist()}

Synthetic Claims Generator

Synthetic Claims Generator

import pandas as pd
import numpy as np

# Set random seed for reproducibility
np.random.seed(42)

# Generate synthetic data
num_samples = 1000
data = {
    'claim_id': np.arange(1, num_samples + 1),
    'claim_amount': np.random.normal(1000, 250, num_samples),
    'num_services': np.random.randint(1, 10, num_samples),
    'patient_age': np.random.randint(18, 90, num_samples),
    'provider_id': np.random.randint(1, 50, num_samples),
    'days_since_last_claim': np.random.randint(0, 365, num_samples),
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Introduce some anomalies (e.g., very high claim amounts)
num_anomalies = 50
anomalies = {
    'claim_id': np.arange(num_samples + 1, num_samples + num_anomalies + 1),
    'claim_amount': np.random.normal(10000, 2500, num_anomalies),  # Much higher amounts
    'num_services': np.random.randint(10, 20, num_anomalies),
    'patient_age': np.random.randint(18, 90, num_anomalies),
    'provider_id': np.random.randint(1, 50, num_anomalies),
    'days_since_last_claim': np.random.randint(0, 365, num_anomalies),
}

df_anomalies = pd.DataFrame(anomalies)

# Combine normal data with anomalies
df = pd.concat([df, df_anomalies]).reset_index(drop=True)

# Shuffle the dataset
df = df.sample(frac=1).reset_index(drop=True)

# Save the data to CSV
df.to_csv('synthetic_health_claims.csv', index=False)

print("Synthetic data generated and saved to 'synthetic_health_claims.csv'.")

Claim Test

Claim Test

import requests
import pandas as pd

# Define 10 different test inputs
test_data = [
    {"claim_amount": 1000, "num_services": 2, "patient_age": 30, "provider_id": 1, "days_since_last_claim": 100},
    {"claim_amount": 2000, "num_services": 5, "patient_age": 45, "provider_id": 2, "days_since_last_claim": 200},
    {"claim_amount": 15000, "num_services": 10, "patient_age": 50, "provider_id": 3, "days_since_last_claim": 300},
    {"claim_amount": 500, "num_services": 1, "patient_age": 25, "provider_id": 4, "days_since_last_claim": 10},
    {"claim_amount": 7500, "num_services": 8, "patient_age": 60, "provider_id": 5, "days_since_last_claim": 50},
    {"claim_amount": 2500, "num_services": 3, "patient_age": 35, "provider_id": 6, "days_since_last_claim": 120},
    {"claim_amount": 9000, "num_services": 15, "patient_age": 70, "provider_id": 7, "days_since_last_claim": 180},
    {"claim_amount": 400, "num_services": 2, "patient_age": 22, "provider_id": 8, "days_since_last_claim": 365},
    {"claim_amount": 11000, "num_services": 6, "patient_age": 55, "provider_id": 9, "days_since_last_claim": 250},
    {"claim_amount": 600, "num_services": 4, "patient_age": 40, "provider_id": 10, "days_since_last_claim": 30},
]

# Convert to DataFrame
df_test = pd.DataFrame(test_data)

# Make the prediction request
response = requests.post("http://127.0.0.1:3000/predict", json=df_test.to_dict(orient="records"))

# Check the response
if response.status_code == 200:
    predictions = response.json()["predictions"]
    for i, prediction in enumerate(predictions):
        print(f"Test Case {i+1}: Prediction: {prediction}")
else:
    print(f"Error: {response.status_code} - {response.text}")

Flask App v2

Flask App v2

from flask import Flask, render_template, request, redirect, url_for
import pandas as pd
import requests
import base64
import io
import matplotlib.pyplot as plt
import os

app = Flask(__name__)

# Route for the home page
@app.route('/')
def index():
    return render_template('index.html')

# Route to handle the CSV file upload and prediction
@app.route('/predict', methods=['POST'])
def predict():
    file_data = request.form.get('file')
    
    # Decode the Base64 encoded file content
    decoded_file = base64.b64decode(file_data.split(',')[1])

    # Read the decoded content into a DataFrame
    df = pd.read_csv(io.StringIO(decoded_file.decode('utf-8')))

    # Separate the 'claim_id' column if it exists
    if 'claim_id' in df.columns:
        claim_ids = df['claim_id']
        df = df.drop(columns=['claim_id'])
    else:
        claim_ids = None

    # Send the DataFrame to the BentoML service
    response = requests.post(
        'http://127.0.0.1:3000/predict',  # BentoML endpoint
        json=df.to_dict(orient='records')
    )

    # Get predictions from the response
    predictions = response.json()['predictions']

    # Add predictions to the DataFrame
    df['Prediction'] = predictions

    # Reattach the 'claim_id' column to the DataFrame
    if claim_ids is not None:
        df['claim_id'] = claim_ids

    # Reorder columns to have 'claim_id' first
    if 'claim_id' in df.columns:
        df = df[['claim_id'] + [col for col in df.columns if col != 'claim_id']]

    # Save the DataFrame to a session file for visualization
    df.to_csv('session_data.csv', index=False)

    # Render the DataFrame as an HTML table with a link to visualize
    return render_template('result.html', tables=[df.to_html(classes='data', header="true")])

# Route to handle the visualization
@app.route('/visualize')
def visualize():
    # Load the session data
    df = pd.read_csv('session_data.csv')

    # Create a pie chart based on the 'Prediction' column
    prediction_counts = df['Prediction'].value_counts()
    plt.figure(figsize=(8, 8))
    plt.pie(prediction_counts, labels=prediction_counts.index, autopct='%1.1f%%', startangle=140)
    plt.title('Prediction Distribution')

    # Save the pie chart as an image
    if not os.path.exists('static'):
        os.makedirs('static')
    chart_path = 'static/prediction_pie_chart.png'
    plt.savefig(chart_path)
    plt.close()

    # Render the visualization page with the pie chart
    return render_template('visualize.html', chart_path=chart_path)

if __name__ == '__main__':
    app.run(debug=True, port=5005)

Templates

index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Health Insurance Anomaly Detection</title>
    <style>
        .drag-area {
            border: 2px dashed #ccc;
            border-radius: 10px;
            width: 100%;
            height: 200px;
            text-align: center;
            padding: 20px;
            font-size: 20px;
            color: #333;
        }
        .drag-area.hover {
            border-color: #333;
            background-color: #f0f0f0;
        }
        .drag-area p {
            margin: 30px 0;
        }
        .drag-area button {
            margin-top: 10px;
        }
    </style>
</head>
<body>
    <h1>Upload CSV File</h1>
    <div class="drag-area" id="drag-area">
        <p>Drag & Drop your CSV file here or click to upload</p>
        <input type="file" id="fileInput" name="file" style="display: none;" accept=".csv">
        <button onclick="document.getElementById('fileInput').click();">Browse Files</button>
    </div>
    <form id="uploadForm" action="/predict" method="post" enctype="multipart/form-data">
        <input type="hidden" name="file" id="hiddenFileInput">
    </form>

    <script>
        const dragArea = document.getElementById('drag-area');
        const fileInput = document.getElementById('fileInput');
        const hiddenFileInput = document.getElementById('hiddenFileInput');
        const uploadForm = document.getElementById('uploadForm');

        dragArea.addEventListener('dragover', (e) => {
            e.preventDefault();
            dragArea.classList.add('hover');
        });

        dragArea.addEventListener('dragleave', () => {
            dragArea.classList.remove('hover');
        });

        dragArea.addEventListener('drop', (e) => {
            e.preventDefault();
            dragArea.classList.remove('hover');
            const file = e.dataTransfer.files[0];
            handleFile(file);
        });

        fileInput.addEventListener('change', (e) => {
            const file = e.target.files[0];
            handleFile(file);
        });

        function handleFile(file) {
            if (file && file.type === 'text/csv') {
                const reader = new FileReader();
                reader.readAsDataURL(file);
                reader.onload = () => {
                    hiddenFileInput.value = reader.result;
                    uploadForm.submit();
                };
            } else {
                alert('Please upload a valid CSV file.');
            }
        }
    </script>
</body>
</html>

result.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Prediction Results</title>
    <style>
        table {
            width: 100%;
            border-collapse: collapse;
        }
        table, th, td {
            border: 1px solid black;
        }
        th, td {
            padding: 10px;
            text-align: left;
        }
    </style>
</head>
<body>
    <h1>Prediction Results</h1>
    {% for table in tables %}
        {{ table|safe }}
    {% endfor %}
    <a href="/">Go Back</a>
</body>
</html>

visualize.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Visualization</title>
</head>
<body>
    <h1>Prediction Distribution</h1>
    <img src="{{ url_for('static', filename='prediction_pie_chart.png') }}" alt="Prediction Pie Chart">
    <br><br>
    <a href="/">Go Back</a>
</body>
</html>

ML Model for Insurance Claims

Initial Setup Script

Initial Setup Script

Automating with MLflow and BentoML

1. Develope the Model
2. Store the experiments
3. Finalize the survey

ML-Model to accelerate insurance claims

What advantages it brings when we use ML-Model

1. Faster Processing - ML Models can automate tasks like claim validation and fraud detection, significantly reducing the processing time.
2. Improved Accuracy - ML models can analyze vast amounts of data to identify patterns and anomalies human might miss, leading to fewer errors.
3. Increased efficiency - Automation frees up human adjusters to focus on complex or high value claims


Speed, Accuracy and Efficiency

1. Reduced Costs
2. Enhanced Customer Satisfaction
3. Better Risk Assessment

Claims Dataset

Claims Dataset

claim_id,claim_amount,num_services,patient_age,provider_id,days_since_last_claim
1008,12064.315907367947,11,32,2,33
346,1058.0124843394092,3,80,49,75
264,743.9030896664275,4,49,29,228
695,1148.275314492096,9,77,27,38
15,568.7705418717418,4,56,27,185
31,849.5733469426508,8,70,43,141
999,857.2052525543008,9,25,4,146
773,1158.1019347638803,4,27,10,277
147,690.7623222804796,2,74,18,208
507,851.9015189402827,6,67,15,360
847,1072.2921609769546,8,70,38,29
418,1028.9186585732148,9,83,2,238
951,939.6909855359175,4,60,45,187
876,1448.639465879447,1,74,20,10
29,849.8403275202987,7,38,22,75
952,1088.0138491285743,6,37,14,153
160,1164.1384021584574,4,60,46,165
462,1008.8158879929322,9,62,11,106
196,1096.329344932209,9,86,15,238
933,637.996524895939,9,50,44,315
24,643.8129534466358,6,42,16,295
592,880.5856383087208,5,24,19,45
1036,8214.38472236602,10,30,38,275
818,1222.4076989058592,9,21,19,29
217,806.793696365607,9,46,19,295
604,1338.9094647012378,6,38,46,103
661,856.5844982799409,2,76,28,363
137,804.1866769159408,3,55,40,75
972,829.7370856312798,2,23,23,110
351,1077.7268913995013,9,34,15,314
245,699.9258982360559,6,55,21,145
67,981.9974696049165,2,78,26,65
607,806.5527002241067,8,66,5,334
400,1309.4540779933654,6,36,15,271
912,1054.7875819159847,1,51,17,34
684,1122.3436403069795,3,62,30,297
379,1547.450733304418,5,65,30,70
943,1162.5502944896652,8,82,11,0
930,983.562434731753,5,60,15,332
995,749.5949975262711,2,46,28,113
184,1120.6181038107964,4,63,18,19
138,919.4846209485811,6,23,11,277
469,867.3747130973682,2,33,30,94
360,793.1922641119193,8,36,1,253
456,801.781791934414,4,89,37,272
858,1117.853889096601,4,55,38,28
839,1246.080599619146,9,21,31,199
542,717.5732863355955,7,32,8,77
948,827.0229825296889,8,81,15,305
540,855.7770326192128,5,61,18,158
23,1016.882051171981,2,75,35,303
358,927.8353402699655,8,34,28,11
66,1339.0600071427057,2,70,19,88
430,560.3151283942215,6,88,37,277
838,750.4114898153023,8,41,14,43
467,1153.5416750108564,8,34,45,326
770,1453.1121394992322,9,84,32,86
315,1326.3697017885822,7,47,10,333
133,734.4240715684738,5,51,5,246
698,687.2216059036742,6,48,9,266
1010,13492.403951144599,12,80,13,5
585,1045.4665637646237,3,55,5,205
646,931.8191075630824,7,42,17,57
136,1387.483601254385,7,89,34,344
666,1026.6075569229743,3,45,2,72
486,644.4365726005815,8,47,38,330
946,732.4788084184619,1,23,9,109
1037,9541.281892503732,19,29,36,111
1044,9064.850876021788,11,40,47,190
626,872.1960808922037,5,71,21,277
643,935.1021621590988,8,54,22,19
628,968.5532699750879,1,46,5,218
47,884.8403072600531,6,47,30,282
571,1004.6045947973879,1,59,22,95
281,1028.379336312812,6,36,44,246
239,834.5533838079031,9,62,49,363
560,894.7538797949343,1,30,15,313
227,1016.0700047738657,3,44,25,286
791,1107.4045547831465,4,75,25,70
613,1069.992156579955,1,70,42,272
923,1033.242418536719,3,81,21,174
305,994.774601508963,3,21,28,280
428,1171.5128649996097,3,25,8,74
1026,7491.9645240248965,11,59,24,89
742,680.1057583160746,2,21,11,234
837,1387.6251232035193,5,41,26,69
893,1266.6686723972884,7,27,24,223
6,941.4657607627049,9,32,5,268
986,710.4088277018644,2,85,2,147
290,844.3251200448515,9,81,42,17
353,1214.4149058005048,4,36,43,78
944,975.2060340551543,6,86,34,265
916,1069.7553814425848,7,23,32,76
201,1089.4468400870708,4,64,1,43
326,696.4528468030669,3,34,48,208
252,1229.4654867636941,4,70,41,150
50,559.2399611593165,1,31,3,162
141,1056.8649836510324,5,78,11,110
800,1007.4390348739358,9,39,15,298
640,676.2303069840959,5,76,15,217
737,1064.0074335784689,4,35,8,10
412,718.8394770405328,7,84,31,179
725,1042.7163595319855,6,28,34,207
142,1326.7856885706071,1,29,16,320
516,1188.847806456439,5,25,12,362
277,1049.7649238933675,4,82,21,290
419,1294.8242960159566,9,58,49,334
345,882.2404235954193,4,18,1,297
545,382.08887496817766,2,66,38,14
497,740.6884614183859,4,40,21,83
269,889.9888783257541,9,51,32,322
842,1168.704873041651,2,74,8,18
115,951.9097588047193,8,43,26,239
484,1050.8659089668058,2,82,32,305
478,1077.812538635884,3,84,26,105
396,882.7060869738237,1,65,18,325
261,768.2673821054792,4,80,30,145
1024,7496.603751923622,18,30,47,188
614,718.6277381754059,4,63,20,82
288,1147.079301621144,6,66,43,269
489,1421.7854087681412,3,38,38,194
21,1366.4121922303884,3,84,34,283
432,490.1919555599749,4,24,45,299
60,1243.8862817805898,3,88,41,131
863,1386.8763003325155,1,85,32,270
102,894.8386693086602,5,26,17,191
845,1041.613052053264,7,40,32,253
941,679.9239003322764,3,66,44,277
493,1019.3420769119045,9,74,30,143
662,863.285264689902,2,21,26,203
896,1162.1774718974107,4,65,45,118
1016,8870.077525963776,12,33,35,173
316,1005.2509604081897,5,59,40,354
297,1224.8999688583126,4,48,42,82
333,1018.9511395484316,9,51,24,330
857,791.9111067239428,5,24,32,246
648,986.4262833705469,1,72,46,128
7,1394.8032038768479,6,52,28,307
153,829.9938196053773,7,66,3,189
109,1064.387597680691,3,57,47,236
727,1004.6084832663483,9,87,38,88
859,861.9442389297571,9,78,8,160
100,941.3532166562131,2,50,48,202
62,953.5852558340457,4,36,2,175
752,662.0788485959166,3,82,13,285
716,1275.8254705041304,6,79,42,323
37,1052.215898751189,6,23,20,117
1032,8175.147635244881,18,28,35,221
437,1407.1538863928229,8,54,37,105
602,769.4586689555937,6,71,24,282
881,1631.7331064684054,9,71,45,92
1027,12299.263233067913,18,78,41,175
420,1016.8796203525272,1,41,9,147
231,817.4083420707158,2,33,45,279
320,967.4642364080788,3,23,29,265
55,1257.7498806239878,4,20,9,174
202,1140.1961315920587,2,66,38,359
794,680.8128560449226,7,27,12,290
808,880.3128445841307,9,43,26,46
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939,1060.9501784427996,7,46,47,101
625,1398.2966566598493,6,29,11,94
128,858.425567599307,6,21,47,102
634,893.279732513145,8,33,22,342
306,1029.3318458271956,4,43,24,236
784,1152.646436320956,3,34,31,235
485,810.9123136789242,2,34,49,170
291,947.9694374106812,8,86,14,112
22,943.5559248783661,9,21,25,209
757,1123.3294752202223,1,26,22,92
445,673.8826248737867,2,18,6,188
597,932.5312661766571,7,87,25,309
340,859.0688617990063,7,63,49,143
93,824.4867265306619,4,67,9,191
94,918.0844633505579,2,38,45,39
568,747.9784222706489,8,38,30,335
601,1189.247154161338,5,71,23,108
974,836.8440051744022,3,74,22,231
357,995.3717160019025,5,22,25,62
1039,10116.928577102117,10,69,30,213
386,1037.5984466190519,4,32,22,274
310,949.4518368915265,5,68,47,205
347,637.978914625669,9,41,17,187
392,1122.9797928766263,5,64,15,310
14,521.6799388355505,2,64,49,82
550,849.0037033210449,9,34,43,124
921,1268.407937464943,3,64,13,121
765,1287.3183315714189,6,72,2,180
704,647.8846759091101,9,50,47,274
927,860.2695381821029,9,49,17,135
477,1144.1392407639416,7,56,17,198
1046,10097.886479532812,15,81,8,311
502,1477.3541601175327,5,71,7,265
82,1089.2781428779367,7,19,49,362
98,1065.2638180449724,9,84,32,179
349,820.388944686891,1,28,25,245
889,1147.6637076730774,6,76,1,250
334,830.7095721219721,1,70,26,271
236,1158.4797555795028,2,52,36,44
79,1022.9401941338756,3,85,45,276
446,1167.4181372075095,2,53,29,338
546,800.7761861323808,4,87,48,11
1048,8486.636491556797,19,46,28,34
781,1044.4252502333134,5,84,17,307
753,756.0316867556668,4,60,32,347
884,1261.040219267268,3,33,20,133
222,533.1837018520629,3,68,4,345
256,878.9414817834372,6,84,17,3
928,1094.30296876613,4,85,40,342
701,869.3192448702401,9,22,37,10
352,1368.839054237388,7,41,29,282
488,729.6129990964012,7,30,3,11
984,988.0721609645051,5,64,49,196
866,903.074610016004,2,58,3,171
361,1129.836628560293,2,81,29,42
973,1210.160887247181,5,22,6,136
559,1098.6130355945743,9,38,31,127
702,1262.2523064592224,2,69,42,293
387,1085.43899394429,3,44,6,105
591,1017.8915593048481,4,25,29,198
776,607.4438200273859,1,44,24,168
247,881.263672209761,5,38,6,220
804,884.4311778237393,6,28,32,145
562,1518.8501996613595,1,41,13,149
271,1360.3183222665289,7,45,47,138
718,1037.5754403654694,5,30,29,357
663,991.8116824460004,5,84,30,211
892,1126.8185077768244,1,28,17,166
415,1083.078502994898,9,59,27,37
447,1091.649561524212,3,52,17,94
116,1075.3868355834031,1,69,19,1
611,1163.5914140885145,2,61,4,286
372,1124.4995728113624,3,58,2,117
3,1161.922134525173,8,34,17,298
224,596.821032202587,1,45,27,351
209,1128.761921576512,2,36,10,354
383,469.0260689225483,7,50,2,284
165,1240.8440323110804,5,79,43,13
575,1207.3513952958722,3,58,24,293
690,1111.06582787151,9,82,7,76
843,719.3194946094253,5,66,25,100
172,796.0474287586404,2,38,9,240
967,830.9019244851826,3,70,32,91
464,1053.4949776835556,9,33,8,128
1,1124.1785382528083,7,65,29,217
283,1396.504204036338,8,77,30,250
616,1032.3052954938069,9,54,25,189

Isolation Forest Model

Isolation Forest Model

import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.model_selection import train_test_split
import mlflow
import mlflow.sklearn

# Load the synthetic data
df = pd.read_csv('synthetic_health_claims.csv')

mlflow.set_tracking_uri("http://127.0.0.1:5001")

# Features to use for the model
features = ['claim_amount', 'num_services', 'patient_age', 'provider_id', 'days_since_last_claim']

# Split the data into training and test sets
X_train, X_test = train_test_split(df[features], test_size=0.2, random_state=42)

# Set up MLflow
mlflow.set_experiment("Health Insurance Claim Anomaly Detection")

with mlflow.start_run():
    # Train the Isolation Forest model
    model = IsolationForest(n_estimators=100, contamination=0.05, random_state=42)
    model.fit(X_train)

    # Predict on the test set
    y_pred_train = model.predict(X_train)
    y_pred_test = model.predict(X_test)
    
    # Convert predictions to anomaly scores (-1 is anomaly, 1 is normal)
    anomaly_score_train = (y_pred_train == -1).astype(int)
    anomaly_score_test = (y_pred_test == -1).astype(int)

    # Log parameters
    mlflow.log_param("n_estimators", 100)
    mlflow.log_param("contamination", 0.05)
    
    # Log metrics
    train_anomaly_percentage = anomaly_score_train.mean() * 100
    test_anomaly_percentage = anomaly_score_test.mean() * 100
    
    mlflow.log_metric("train_anomaly_percentage", train_anomaly_percentage)
    mlflow.log_metric("test_anomaly_percentage", test_anomaly_percentage)

    # Log the model
    mlflow.sklearn.log_model(model, "model")

    print(f"Train Anomaly Percentage: {train_anomaly_percentage:.2f}%")
    print(f"Test Anomaly Percentage: {test_anomaly_percentage:.2f}%")
    print("Model and metrics logged to MLflow.")

Create MLflow Experiment

Download Model PKL File

Trained Model File

Serve MLModel in BentoML

Deploy Flask Web App

Model Predictions in UI

ML Model Final Output

Second Demo Script

Second Demo Script

pip3 install -r requirements.txt

Synthetic Healh Claims - Generate random claims .. both normal and abnormal claims and generates csv

(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ python3.12 ./synthetic_health_claims.py
Synthetic data generated and saved to 'synthetic_health_claims.csv'.
(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$


Setup MLflow server and run the ML Experiment:(usually we setup mlflow server in k8s as a micro-service)
----------------------------------------------------------------------------------------------------------->
(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ mlflow ui --port 5001
[2025-03-06 06:34:38 +0800] [8053] [INFO] Starting gunicorn 23.0.0
[2025-03-06 06:34:38 +0800] [8053] [INFO] Listening at: http://127.0.0.1:5001 (8053)
[2025-03-06 06:34:38 +0800] [8053] [INFO] Using worker: sync
[2025-03-06 06:34:38 +0800] [8054] [INFO] Booting worker with pid: 8054
[2025-03-06 06:34:38 +0800] [8055] [INFO] Booting worker with pid: 8055
[2025-03-06 06:34:38 +0800] [8056] [INFO] Booting worker with pid: 8056
[2025-03-06 06:34:38 +0800] [8057] [INFO] Booting worker with pid: 8057


So next step is to train and evaluate a ML model - In this we are going to use a model called Isolation Forest Model.
isolation_model.py


(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ python3.12 ./isolation_model.py
2025/03/06 06:37:59 INFO mlflow.tracking.fluent: Experiment with name 'Health Insurance Claim Anomaly Detection' does not exist. Creating a new experiment.
2025/03/06 06:38:01 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.
Train Anomaly Percentage: 5.00%
Test Anomaly Percentage: 4.29%
Model and metrics logged to MLflow.
🏃 View run bright-deer-511 at: http://127.0.0.1:5001/#/experiments/362845262008705571/runs/accfbfcfd6fb4b21a730589340cc8a0b
🧪 View experiment at: http://127.0.0.1:5001/#/experiments/362845262008705571
(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$

Now experiment  got deployed to mlflow server ..download model.pkl file and service it using BentoML

So Register the Model and setup BentoML for serving ML Models:

Register ML Model:
------------------------->

(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ python3.12 ./register_model.py
Model registered with BentoML: Model(tag="health_insurance_anomaly_detector:c4gg5ih2csj46prc")
(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$

(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ bentoml models list
 Tag                                                 Module           Size      Creation Time
 health_insurance_anomaly_detector:c4gg5ih2csj46prc  bentoml.sklearn  1.39 MiB  2025-03-06 06:49:26
 house_price_model_v2:v2nt7nxzjcngcprc               bentoml.sklearn  1.56 KiB  2025-03-05 06:33:23
 house_price_model:ymkym6xziwjagprc                  bentoml.sklearn  1.23 KiB  2025-03-05 06:12:29
(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$

So Model has been registered in the BentoML artifact repository.
So we have two places where the model artifacts are present.
  1. One is our mlflow itself and then
  2. In the BentoML
So the difference is in the mlflow you might have 50 models, Because we may run 50 experiments, but in the BentoML list you only have the model that you will be using for the serving purpose.

Lets create the service file which will be used by BentoML to serve the model.

(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ bentoml serve ./service.py --reload
/Users/bharathkumardasaraju/external/learn-ml-ops/.venv/lib/python3.12/site-packages/bentoml/io.py:7: BentoMLDeprecationWarning: `bentoml.io` is deprecated since BentoML v1.4 and will be removed in a future version. Please upgrade to new style IO types instead.
  warn_deprecated(
/Users/bharathkumardasaraju/external/learn-ml-ops/.venv/lib/python3.12/site-packages/bentoml/_internal/models/model.py:367: BentoMLDeprecationWarning: `get_runnable` is deprecated since BentoML v1.4 and will be removed in a future version. Use `get_service` instead.
  self._runnable = self.info.imported_module.get_runnable(self)
/Users/bharathkumardasaraju/external/learn-ml-ops/.venv/lib/python3.12/site-packages/bentoml/_internal/models/model.py:354: BentoMLDeprecationWarning: `Runner` is deprecated since BentoML v1.4 and will be removed in a future version. Please upgrade to new style services.
  return Runner(
/Users/bharathkumardasaraju/external/learn-ml-ops/6.Automating-with-MLflow-and-BentoML/service.py:8: BentoMLDeprecationWarning: `bentoml.Service` is deprecated since BentoML v1.4 and will be removed in a future version. Please upgrade to @bentoml.service().
  svc = bentoml.Service("health_insurance_anomaly_detection_service", runners=[model_runner])
2025-03-06T07:00:22+0800 [INFO] [cli] Environ for worker 0: set CPU thread count to 8
2025-03-06T07:00:22+0800 [INFO] [cli] Prometheus metrics for HTTP BentoServer from "./service.py" can be accessed at http://localhost:3000/metrics.
2025-03-06T07:00:22+0800 [INFO] [cli] Starting production HTTP BentoServer from "./service.py" listening on http://0.0.0.0:3000 (Press CTRL+C to quit)
2025-03-06T07:03:21+0800 [INFO] [api_server:health_insurance_anomaly_detection_service:6] 127.0.0.1:58576 (scheme=http,method=GET,path=/,type=,length=) (status=200,type=text/html; charset=utf-8,length=2945) 1.389ms (trace=4773d94d4e03db1de1df3e9ceb6d55c0,span=a3f21653cb495b4c,sampled=0,service.name=health_insurance_anomaly_detection_service)
2025-03-06T07:03:22+0800 [INFO] [api_server:health_insurance_anomaly_detection_service:6] 127.0.0.1:58583 (scheme=http,method=GET,path=/docs.json,type=,length=) (status=200,type=application/json,length=4825) 17.838ms (trace=5ea37cd2a918ebf85d74672993177891,span=42c03758840366d0,sampled=0,service.name=health_insurance_anomaly_detection_service)


So Now we have /predict endpoint  - /predict endpoint has to be used by any framework(flask, fastapi etc..) which is going to be connect with this /predict endpoint pass the data and it is going to get a prediction.

So we have to build a web-app used by our insurance claim agents, where we can upload the csv file and /predict endpoint gonna predict is valid claim process it or not.


SO Develop Python Flask Web-app to connect to BentoML for Online Serving:
--------------------------------------------------------------------------------->

(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ python3.12 ./flask_app.py
 * Serving Flask app 'flask_app'
 * Debug mode: on
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:5005
Press CTRL+C to quit
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 846-305-491
127.0.0.1 - - [06/Mar/2025 07:13:55] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [06/Mar/2025 07:13:55] "GET /favicon.ico HTTP/1.1" 404 -



Now upload csv file to the flask web-app and see the prediction results immediately...

So in the backgroud.. flask be able to actually calls BentoML /predict api endpoint....
so this BentoML /predict endpoint had the ML-Model present and that ML-Model got triggered.. it gives the prediction with results 1 or -1


upgraded flask app

(.venv) bharathkumardasaraju@6.Automating-with-MLflow-and-BentoML$ python3.12 ./v2_app.py
Matplotlib is building the font cache; this may take a moment.
 * Serving Flask app 'v2_app'
 * Debug mode: on
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:5005
Press CTRL+C to quit
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 846-305-491

Session Data

Session Data

claim_id,claim_amount,num_services,patient_age,provider_id,days_since_last_claim,Prediction
1008,12064.315907367947,11,32,2,33,-1
346,1058.0124843394092,3,80,49,75,1
264,743.9030896664275,4,49,29,228,1
695,1148.275314492096,9,77,27,38,1
15,568.7705418717418,4,56,27,185,1
31,849.5733469426508,8,70,43,141,1
999,857.2052525543008,9,25,4,146,1
773,1158.1019347638803,4,27,10,277,1
147,690.7623222804796,2,74,18,208,1
507,851.9015189402827,6,67,15,360,1
847,1072.2921609769546,8,70,38,29,1
418,1028.9186585732148,9,83,2,238,1
951,939.6909855359175,4,60,45,187,1
876,1448.639465879447,1,74,20,10,1
29,849.8403275202987,7,38,22,75,1
952,1088.0138491285743,6,37,14,153,1
160,1164.1384021584574,4,60,46,165,1
462,1008.8158879929322,9,62,11,106,1
196,1096.329344932209,9,86,15,238,1
933,637.996524895939,9,50,44,315,1
24,643.8129534466358,6,42,16,295,1
592,880.5856383087208,5,24,19,45,1
1036,8214.38472236602,10,30,38,275,-1
818,1222.4076989058592,9,21,19,29,1
217,806.793696365607,9,46,19,295,1
604,1338.9094647012378,6,38,46,103,1
661,856.5844982799409,2,76,28,363,1
137,804.1866769159408,3,55,40,75,1
972,829.7370856312798,2,23,23,110,1
351,1077.7268913995013,9,34,15,314,1
245,699.9258982360559,6,55,21,145,1
67,981.9974696049164,2,78,26,65,1
607,806.5527002241067,8,66,5,334,1
400,1309.4540779933654,6,36,15,271,1
912,1054.787581915985,1,51,17,34,1
684,1122.3436403069795,3,62,30,297,1
379,1547.450733304418,5,65,30,70,1
943,1162.5502944896652,8,82,11,0,1
930,983.562434731753,5,60,15,332,1
995,749.5949975262711,2,46,28,113,1
184,1120.6181038107964,4,63,18,19,1
138,919.4846209485812,6,23,11,277,1
469,867.3747130973682,2,33,30,94,1
360,793.1922641119193,8,36,1,253,1
456,801.781791934414,4,89,37,272,1
858,1117.853889096601,4,55,38,28,1
839,1246.080599619146,9,21,31,199,1
542,717.5732863355955,7,32,8,77,1
948,827.0229825296889,8,81,15,305,1
540,855.7770326192128,5,61,18,158,1
23,1016.882051171981,2,75,35,303,1
358,927.8353402699656,8,34,28,11,1
66,1339.0600071427057,2,70,19,88,1
430,560.3151283942215,6,88,37,277,1
838,750.4114898153023,8,41,14,43,1
467,1153.5416750108564,8,34,45,326,1
770,1453.1121394992322,9,84,32,86,1
315,1326.3697017885822,7,47,10,333,1
133,734.4240715684738,5,51,5,246,1
698,687.2216059036742,6,48,9,266,1
1010,13492.4039511446,12,80,13,5,-1
585,1045.4665637646235,3,55,5,205,1
646,931.8191075630824,7,42,17,57,1
136,1387.483601254385,7,89,34,344,1
666,1026.6075569229745,3,45,2,72,1
486,644.4365726005815,8,47,38,330,1
946,732.4788084184619,1,23,9,109,1
1037,9541.281892503732,19,29,36,111,-1
1044,9064.850876021788,11,40,47,190,-1
626,872.1960808922037,5,71,21,277,1
643,935.1021621590988,8,54,22,19,1
628,968.553269975088,1,46,5,218,1
47,884.8403072600531,6,47,30,282,1
571,1004.604594797388,1,59,22,95,1
281,1028.379336312812,6,36,44,246,1
239,834.5533838079031,9,62,49,363,1
560,894.7538797949343,1,30,15,313,1
227,1016.0700047738655,3,44,25,286,1
791,1107.4045547831463,4,75,25,70,1
613,1069.992156579955,1,70,42,272,1
923,1033.242418536719,3,81,21,174,1
305,994.774601508963,3,21,28,280,1
428,1171.5128649996095,3,25,8,74,1
1026,7491.964524024896,11,59,24,89,-1
742,680.1057583160746,2,21,11,234,1
837,1387.6251232035193,5,41,26,69,1
893,1266.6686723972884,7,27,24,223,1
6,941.4657607627048,9,32,5,268,1
986,710.4088277018644,2,85,2,147,1
290,844.3251200448515,9,81,42,17,1
353,1214.4149058005048,4,36,43,78,1
944,975.2060340551544,6,86,34,265,1
916,1069.7553814425848,7,23,32,76,1
201,1089.4468400870708,4,64,1,43,1
326,696.4528468030669,3,34,48,208,1
252,1229.465486763694,4,70,41,150,1
50,559.2399611593165,1,31,3,162,1
141,1056.8649836510324,5,78,11,110,1
800,1007.4390348739358,9,39,15,298,1
640,676.2303069840959,5,76,15,217,1
737,1064.0074335784689,4,35,8,10,1
412,718.8394770405328,7,84,31,179,1
725,1042.7163595319855,6,28,34,207,1
142,1326.7856885706071,1,29,16,320,1
516,1188.847806456439,5,25,12,362,1
277,1049.7649238933675,4,82,21,290,1
419,1294.8242960159566,9,58,49,334,1
345,882.2404235954193,4,18,1,297,1
545,382.08887496817766,2,66,38,14,1
497,740.6884614183859,4,40,21,83,1
269,889.9888783257541,9,51,32,322,1
842,1168.704873041651,2,74,8,18,1
115,951.9097588047192,8,43,26,239,1
484,1050.8659089668058,2,82,32,305,1
478,1077.812538635884,3,84,26,105,1
396,882.7060869738237,1,65,18,325,1
261,768.2673821054792,4,80,30,145,1
1024,7496.603751923622,18,30,47,188,-1
614,718.6277381754059,4,63,20,82,1
288,1147.079301621144,6,66,43,269,1
489,1421.7854087681412,3,38,38,194,1
21,1366.4121922303884,3,84,34,283,1
432,490.1919555599749,4,24,45,299,1
60,1243.8862817805898,3,88,41,131,1
863,1386.8763003325157,1,85,32,270,1
102,894.8386693086602,5,26,17,191,1
845,1041.613052053264,7,40,32,253,1
941,679.9239003322764,3,66,44,277,1
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342,1060.921802872978,8,61,7,363,1
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713,863.7702282977626,4,23,12,266,1
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593,1119.744956436598,8,53,43,287,1
390,855.7740860843992,8,70,40,164,1
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482,761.1148898498935,2,70,23,18,1
935,1110.003612513333,2,51,23,332,1
975,888.4541416963051,9,45,38,219,1
132,1017.1407437015068,9,83,46,161,1
130,874.1310864709502,2,62,47,185,1
503,650.3581065452147,7,69,22,297,1
442,991.8263129764769,9,25,28,158,1
848,1613.8250349777236,1,79,28,248,1
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939,1060.9501784427996,7,46,47,101,1
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784,1152.646436320956,3,34,31,235,1
485,810.9123136789242,2,34,49,170,1
291,947.9694374106812,8,86,14,112,1
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757,1123.3294752202223,1,26,22,92,1
445,673.8826248737867,2,18,6,188,1
597,932.5312661766573,7,87,25,309,1
340,859.0688617990063,7,63,49,143,1
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568,747.9784222706489,8,38,30,335,1
601,1189.247154161338,5,71,23,108,1
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357,995.3717160019024,5,22,25,62,1
1039,10116.928577102117,10,69,30,213,-1
386,1037.598446619052,4,32,22,274,1
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765,1287.3183315714189,6,72,2,180,1
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753,756.0316867556668,4,60,32,347,1
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222,533.1837018520629,3,68,4,345,1
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701,869.3192448702401,9,22,37,10,1
352,1368.839054237388,7,41,29,282,1
488,729.6129990964012,7,30,3,11,1
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973,1210.160887247181,5,22,6,136,1
559,1098.6130355945745,9,38,31,127,1
702,1262.2523064592224,2,69,42,293,1
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591,1017.891559304848,4,25,29,198,1
776,607.4438200273859,1,44,24,168,1
247,881.263672209761,5,38,6,220,1
804,884.4311778237393,6,28,32,145,1
562,1518.8501996613595,1,41,13,149,1
271,1360.3183222665289,7,45,47,138,1
718,1037.5754403654694,5,30,29,357,1
663,991.8116824460004,5,84,30,211,1
892,1126.8185077768244,1,28,17,166,1
415,1083.078502994898,9,59,27,37,1
447,1091.649561524212,3,52,17,94,1
116,1075.3868355834031,1,69,19,1,1
611,1163.5914140885145,2,61,4,286,1
372,1124.4995728113624,3,58,2,117,1
3,1161.922134525173,8,34,17,298,1
224,596.821032202587,1,45,27,351,1
209,1128.761921576512,2,36,10,354,1
383,469.0260689225483,7,50,2,284,1
165,1240.8440323110804,5,79,43,13,1
575,1207.3513952958722,3,58,24,293,1
690,1111.06582787151,9,82,7,76,1
843,719.3194946094253,5,66,25,100,1
172,796.0474287586404,2,38,9,240,1
967,830.9019244851826,3,70,32,91,1
464,1053.4949776835556,9,33,8,128,1
1,1124.1785382528085,7,65,29,217,1
283,1396.504204036338,8,77,30,250,1
616,1032.3052954938069,9,54,25,189,1

Third Demo Script

Third Demo Script

root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜  python3 synthetic_health_claims.py
Synthetic data generated and saved to 'synthetic_health_claims.csv'.

root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜


root@controlplane ~ ➜  mlflow ui --host 0.0.0.0 --port 3500
[2025-03-05 18:44:25 -0500] [35657] [INFO] Starting gunicorn 23.0.0
[2025-03-05 18:44:25 -0500] [35657] [INFO] Listening at: http://0.0.0.0:3500 (35657)
[2025-03-05 18:44:25 -0500] [35657] [INFO] Using worker: sync
[2025-03-05 18:44:25 -0500] [35664] [INFO] Booting worker with pid: 35664
[2025-03-05 18:44:25 -0500] [35665] [INFO] Booting worker with pid: 35665
[2025-03-05 18:44:25 -0500] [35666] [INFO] Booting worker with pid: 35666
[2025-03-05 18:44:25 -0500] [35667] [INFO] Booting worker with pid: 35667



Train and evaluate the Isolation Forest model by running
python3 isolation_model.py


You will need to create a Python script to download the model artifact /root/assets/05-project/model.pkl for local use.

MLflow UI host: http://localhost:3500
This is an example format of artifact URI: mlflow-artifacts:/324575258297482099/f877ad1ef20e4b35a44346cd28cf9e2b/artifacts/model/model.pkl
Reference this documentation to create the script:
https://mlflow.org/docs/latest/python_api/mlflow.artifacts.html
https://mlflow.org/docs/latest/python_api/mlflow.set_tracking_uri.html


root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜  python3 isolation_model.py
2025/03/05 18:46:31 INFO mlflow.tracking.fluent: Experiment with name 'Health Insurance Claim Anomaly Detection' does not exist. Creating a new experiment.
2025/03/05 18:46:36 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.
Train Anomaly Percentage: 5.00%
Test Anomaly Percentage: 4.29%
Model and metrics logged to MLflow.
🏃 View run dashing-swan-340 at: http://127.0.0.1:3500/#/experiments/981188833218800573/runs/ca50c2fee6194da689f5c05702d2091e
🧪 View experiment at: http://127.0.0.1:3500/#/experiments/981188833218800573

root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜


mlflow-artifacts:/981188833218800573/ca50c2fee6194da689f5c05702d2091e/artifacts/model
mlflow-artifacts:/981188833218800573/ca50c2fee6194da689f5c05702d2091e/artifacts/model/model.pkl


root@controlplane ~/assets/05-project via 🐍 v3.12.3 ✖ python3 ./download_model_pkl.py
Downloading artifacts: 100%|███████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 92.84it/s]
Artifact downloaded to: /tmp/tmpz1e2smm6/model.pkl

root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜



root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜  python3 ./register_model.py
Model registered with BentoML: Model(tag="health_insurance_anomaly_detector:kxs7et72dwhhi4wj")

root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜  bentoml models list
 Tag                                                 Module           Size      Creation Time
 health_insurance_anomaly_detector:kxs7et72dwhhi4wj  bentoml.sklearn  1.39 MiB  2025-03-05 18:55:37

root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜


Now - Start the BentoML service for model predictions.



root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜  bentoml serve service.py --reload
/usr/local/lib/python3.12/dist-packages/bentoml/io.py:7: BentoMLDeprecationWarning: `bentoml.io` is deprecated since BentoML v1.4 and will be removed in a future version. Please upgrade to new style IO types instead.
  warn_deprecated(
/usr/local/lib/python3.12/dist-packages/bentoml/_internal/models/model.py:367: BentoMLDeprecationWarning: `get_runnable` is deprecated since BentoML v1.4 and will be removed in a future version. Use `get_service` instead.
  self._runnable = self.info.imported_module.get_runnable(self)
/usr/local/lib/python3.12/dist-packages/bentoml/_internal/models/model.py:354: BentoMLDeprecationWarning: `Runner` is deprecated since BentoML v1.4 and will be removed in a future version. Please upgrade to new style services.
  return Runner(
/root/assets/05-project/service.py:8: BentoMLDeprecationWarning: `bentoml.Service` is deprecated since BentoML v1.4 and will be removed in a future version. Please upgrade to @bentoml.service().
  svc = bentoml.Service("health_insurance_anomaly_detection_service", runners=[model_runner])
2025-03-05T18:56:55-0500 [INFO] [cli] Environ for worker 0: set CPU thread count to 16
2025-03-05T18:56:55-0500 [INFO] [cli] Prometheus metrics for HTTP BentoServer from "service.py" can be accessed at http://localhost:3000/metrics.
2025-03-05T18:56:55-0500 [INFO] [cli] Starting production HTTP BentoServer from "service.py" listening on http://0.0.0.0:3000 (Press CTRL+C to quit)


Now run the webapp --

root@controlplane ~/assets/05-project via 🐍 v3.12.3 ➜  python3 flask_app.py
 * Serving Flask app 'flask_app'
 * Debug mode: on
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on all addresses (0.0.0.0)
 * Running on http://127.0.0.1:5005
 * Running on http://192.168.163.25:5005
Press CTRL+C to quit
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 686-296-505

Supporting Folders

• mlruns/ – MLflow run artifacts

• mlartifacts/ – Model artifacts

• templates/ – HTML templates

3.demo Outputs

MLflow Experiments

Script to Download Model

Script to Download Model

import mlflow

# Set the MLflow tracking URI
mlflow.set_tracking_uri("http://localhost:3500")

# Define the artifact URI as a string (ensure it's correctly formatted)
artifact_uri = "mlflow-artifacts:/981188833218800573/ca50c2fee6194da689f5c05702d2091e/artifacts/model/model.pkl"

# Download the artifact
local_path = mlflow.artifacts.download_artifacts(artifact_uri=artifact_uri)

print(f"Artifact downloaded to: {local_path}")

BentoML Serving

Flask App UI

File Server UI

Predicted ML Results

3.demo → labs → assets → 05-project

Flask App v2

Flask App Version 2

from flask import Flask, render_template, request, redirect, url_for
import pandas as pd
import requests
import base64
import io
import matplotlib.pyplot as plt
import os

app = Flask(__name__)

# Route for the home page
@app.route('/')
def index():
    return render_template('index.html')

# Route to handle the CSV file upload and prediction
@app.route('/predict', methods=['POST'])
def predict():
    file_data = request.form.get('file')
    
    # Decode the Base64 encoded file content
    decoded_file = base64.b64decode(file_data.split(',')[1])

    # Read the decoded content into a DataFrame
    df = pd.read_csv(io.StringIO(decoded_file.decode('utf-8')))

    # Separate the 'claim_id' column if it exists
    if 'claim_id' in df.columns:
        claim_ids = df['claim_id']
        df = df.drop(columns=['claim_id'])
    else:
        claim_ids = None

    # Send the DataFrame to the BentoML service
    response = requests.post(
        'http://127.0.0.1:3000/predict',  # BentoML endpoint
        json=df.to_dict(orient='records')
    )

    # Get predictions from the response
    predictions = response.json()['predictions']

    # Add predictions to the DataFrame
    df['Prediction'] = predictions

    # Reattach the 'claim_id' column to the DataFrame
    if claim_ids is not None:
        df['claim_id'] = claim_ids

    # Reorder columns to have 'claim_id' first
    if 'claim_id' in df.columns:
        df = df[['claim_id'] + [col for col in df.columns if col != 'claim_id']]

    # Save the DataFrame to a session file for visualization
    df.to_csv('session_data.csv', index=False)

    # Render the DataFrame as an HTML table with a link to visualize
    return render_template('result.html', tables=[df.to_html(classes='data', header="true")])

# Route to handle the visualization
@app.route('/visualize')
def visualize():
    # Load the session data
    df = pd.read_csv('session_data.csv')

    # Create a pie chart based on the 'Prediction' column
    prediction_counts = df['Prediction'].value_counts()
    plt.figure(figsize=(8, 8))
    plt.pie(prediction_counts, labels=prediction_counts.index, autopct='%1.1f%%', startangle=140)
    plt.title('Prediction Distribution')

    # Save the pie chart as an image
    if not os.path.exists('static'):
        os.makedirs('static')
    chart_path = 'static/prediction_pie_chart.png'
    plt.savefig(chart_path)
    plt.close()

    # Render the visualization page with the pie chart
    return render_template('visualize.html', chart_path=chart_path)

if __name__ == '__main__':
    app.run(debug=True, port=5005)

Test Claim

Test Script for Health Claim

import requests
import pandas as pd

# Define 10 different test inputs
test_data = [
    {"claim_amount": 1000, "num_services": 2, "patient_age": 30, "provider_id": 1, "days_since_last_claim": 100},
    {"claim_amount": 2000, "num_services": 5, "patient_age": 45, "provider_id": 2, "days_since_last_claim": 200},
    {"claim_amount": 15000, "num_services": 10, "patient_age": 50, "provider_id": 3, "days_since_last_claim": 300},
    {"claim_amount": 500, "num_services": 1, "patient_age": 25, "provider_id": 4, "days_since_last_claim": 10},
    {"claim_amount": 7500, "num_services": 8, "patient_age": 60, "provider_id": 5, "days_since_last_claim": 50},
    {"claim_amount": 2500, "num_services": 3, "patient_age": 35, "provider_id": 6, "days_since_last_claim": 120},
    {"claim_amount": 9000, "num_services": 15, "patient_age": 70, "provider_id": 7, "days_since_last_claim": 180},
    {"claim_amount": 400, "num_services": 2, "patient_age": 22, "provider_id": 8, "days_since_last_claim": 365},
    {"claim_amount": 11000, "num_services": 6, "patient_age": 55, "provider_id": 9, "days_since_last_claim": 250},
    {"claim_amount": 600, "num_services": 4, "patient_age": 40, "provider_id": 10, "days_since_last_claim": 30},
]

# Convert to DataFrame
df_test = pd.DataFrame(test_data)

# Make the prediction request
response = requests.post("http://127.0.0.1:3000/predict", json=df_test.to_dict(orient="records"))

# Check the response
if response.status_code == 200:
    predictions = response.json()["predictions"]
    for i, prediction in enumerate(predictions):
        print(f"Test Case {i+1}: Prediction: {prediction}")
else:
    print(f"Error: {response.status_code} - {response.text}")

Synthetic Health Claims Generator

Generate Synthetic Claims

import pandas as pd
import numpy as np

# Set random seed for reproducibility
np.random.seed(42)

# Generate synthetic data
num_samples = 1000
data = {
    'claim_id': np.arange(1, num_samples + 1),
    'claim_amount': np.random.normal(1000, 250, num_samples),
    'num_services': np.random.randint(1, 10, num_samples),
    'patient_age': np.random.randint(18, 90, num_samples),
    'provider_id': np.random.randint(1, 50, num_samples),
    'days_since_last_claim': np.random.randint(0, 365, num_samples),
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Introduce some anomalies (e.g., very high claim amounts)
num_anomalies = 50
anomalies = {
    'claim_id': np.arange(num_samples + 1, num_samples + num_anomalies + 1),
    'claim_amount': np.random.normal(10000, 2500, num_anomalies),  # Much higher amounts
    'num_services': np.random.randint(10, 20, num_anomalies),
    'patient_age': np.random.randint(18, 90, num_anomalies),
    'provider_id': np.random.randint(1, 50, num_anomalies),
    'days_since_last_claim': np.random.randint(0, 365, num_anomalies),
}

df_anomalies = pd.DataFrame(anomalies)

# Combine normal data with anomalies
df = pd.concat([df, df_anomalies]).reset_index(drop=True)

# Shuffle the dataset
df = df.sample(frac=1).reset_index(drop=True)

# Save the data to CSV
df.to_csv('synthetic_health_claims.csv', index=False)

print("Synthetic data generated and saved to 'synthetic_health_claims.csv'.")

ML Service

Service Layer

import bentoml
from bentoml.io import JSON, PandasDataFrame

# Load the registered model
model_runner = bentoml.sklearn.get("health_insurance_anomaly_detector:latest").to_runner()

# Create a BentoML Service
svc = bentoml.Service("health_insurance_anomaly_detection_service", runners=[model_runner])

# Define an API endpoint for prediction
@svc.api(input=PandasDataFrame(), output=JSON())
def predict(data):
    # Make predictions
    predictions = model_runner.predict.run(data)
    # Return predictions as JSON
    return {"predictions": predictions.tolist()}

Python Requirements

Requirements File

mlflow
pandas
numpy
bentoml

Register Model Script

Model Registration

import bentoml
import pickle

# Load the model from the downloaded PKL file using pickle
model_path = "model.pkl"  # Replace with your actual path

with open(model_path, 'rb') as model_file:  # Open in binary mode
    model = pickle.load(model_file)

# Save the model to BentoML
bento_model = bentoml.sklearn.save_model("health_insurance_anomaly_detector", model)

print(f"Model registered with BentoML: {bento_model}")

README

Project Documentation

Here is the converted `README.md` file for your project:

```markdown
# Health Claims Fraud Detection Project

This project involves building a Flask web application that uses an Isolation Forest model to detect potentially fraudulent health claims. It leverages BentoML for model serving and MLflow for experiment tracking.

## Setup Instructions

### 1. Environment Setup

- Load the bash profile and set up the virtual environment:

```bash
source ~/.bash_profile
virtualenv venv
source venv/bin/activate
```

- Install required dependencies:

```bash
pip3 install -r requirements.txt
```

### 2. BentoML and Model Management

- List BentoML models:

```bash
bentoml models list
```

- Run the synthetic data generator script:

```bash
python3 synthetic_health_claims.py
```

- Train and evaluate the Isolation Forest model:

```bash
python3 isolation_model.py
```

### 3. MLflow for Experiment Tracking

- Start the MLflow UI to track experiments:

```bash
mlflow ui
```

### 4. Run the Flask Web Application

- Open a new terminal window, source the environment, and run the Flask app:

```bash
source ~/.bash_profile
source venv/bin/activate
python3 flask_app.py
```

### 5. Register Model and Serve with BentoML

- In another terminal, run the following commands to register and serve the model with BentoML:

```bash
source ~/.bash_profile
source venv/bin/activate
python3 isolation_model.py
python3 register_model.py
bentoml serve service.py --reload
```

## Additional Notes

- Ensure that all commands are executed in the correct order for proper setup and functioning of the application.
- Use BentoML to serve models and integrate them with the Flask app for real-time fraud detection.
- The MLflow UI helps to track experiments and evaluate model performance.
```

This `README.md` file gives a clear step-by-step guide for setting up and running your project. Let me know if you'd like any additional adjustments!

Isolation Model

Isolation Forest Implementation

import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.model_selection import train_test_split
import mlflow
import mlflow.sklearn

# Load the synthetic data
df = pd.read_csv('synthetic_health_claims.csv')

mlflow.set_tracking_uri("http://127.0.0.1:3500")

# Features to use for the model
features = ['claim_amount', 'num_services', 'patient_age', 'provider_id', 'days_since_last_claim']

# Split the data into training and test sets
X_train, X_test = train_test_split(df[features], test_size=0.2, random_state=42)

# Set up MLflow
mlflow.set_experiment("Health Insurance Claim Anomaly Detection")

with mlflow.start_run():
    # Train the Isolation Forest model
    model = IsolationForest(n_estimators=100, contamination=0.05, random_state=42)
    model.fit(X_train)

    # Predict on the test set
    y_pred_train = model.predict(X_train)
    y_pred_test = model.predict(X_test)
    
    # Convert predictions to anomaly scores (-1 is anomaly, 1 is normal)
    anomaly_score_train = (y_pred_train == -1).astype(int)
    anomaly_score_test = (y_pred_test == -1).astype(int)

    # Log parameters
    mlflow.log_param("n_estimators", 100)
    mlflow.log_param("contamination", 0.05)
    
    # Log metrics
    train_anomaly_percentage = anomaly_score_train.mean() * 100
    test_anomaly_percentage = anomaly_score_test.mean() * 100
    
    mlflow.log_metric("train_anomaly_percentage", train_anomaly_percentage)
    mlflow.log_metric("test_anomaly_percentage", test_anomaly_percentage)

    # Log the model
    mlflow.sklearn.log_model(model, "model")

    print(f"Train Anomaly Percentage: {train_anomaly_percentage:.2f}%")
    print(f"Test Anomaly Percentage: {test_anomaly_percentage:.2f}%")
    print("Model and metrics logged to MLflow.")

Synthetic Claims Dataset

Dataset - Synthetic Claims

claim_id,claim_amount,num_services,patient_age,provider_id,days_since_last_claim
1008,12064.315907367947,11,32,2,33
346,1058.0124843394092,3,80,49,75
264,743.9030896664276,4,49,29,228
695,1148.275314492096,9,77,27,38
15,568.7705418717418,4,56,27,185
31,849.5733469426508,8,70,43,141
999,857.2052525543008,9,25,4,146
773,1158.1019347638803,4,27,10,277
147,690.7623222804796,2,74,18,208
507,851.9015189402827,6,67,15,360
847,1072.2921609769546,8,70,38,29
418,1028.9186585732148,9,83,2,238
951,939.6909855359175,4,60,45,187
876,1448.639465879447,1,74,20,10
29,849.8403275202987,7,38,22,75
952,1088.0138491285743,6,37,14,153
160,1164.1384021584574,4,60,46,165
462,1008.8158879929322,9,62,11,106
196,1096.329344932209,9,86,15,238
933,637.996524895939,9,50,44,315
24,643.8129534466358,6,42,16,295
592,880.5856383087208,5,24,19,45
1036,8214.38472236602,10,30,38,275
818,1222.4076989058592,9,21,19,29
217,806.793696365607,9,46,19,295
604,1338.9094647012378,6,38,46,103
661,856.5844982799409,2,76,28,363
137,804.1866769159408,3,55,40,75
972,829.7370856312798,2,23,23,110
351,1077.7268913995013,9,34,15,314
245,699.9258982360559,6,55,21,145
67,981.9974696049165,2,78,26,65
607,806.5527002241067,8,66,5,334
400,1309.4540779933654,6,36,15,271
912,1054.7875819159847,1,51,17,34
684,1122.3436403069795,3,62,30,297
379,1547.450733304418,5,65,30,70
943,1162.5502944896652,8,82,11,0
930,983.562434731753,5,60,15,332
995,749.5949975262711,2,46,28,113
184,1120.6181038107964,4,63,18,19
138,919.4846209485811,6,23,11,277
469,867.3747130973682,2,33,30,94
360,793.1922641119193,8,36,1,253
456,801.781791934414,4,89,37,272
858,1117.853889096601,4,55,38,28
839,1246.080599619146,9,21,31,199
542,717.5732863355954,7,32,8,77
948,827.0229825296889,8,81,15,305
540,855.7770326192128,5,61,18,158
23,1016.882051171981,2,75,35,303
358,927.8353402699654,8,34,28,11
66,1339.0600071427057,2,70,19,88
430,560.3151283942213,6,88,37,277
838,750.4114898153023,8,41,14,43
467,1153.5416750108564,8,34,45,326
770,1453.1121394992322,9,84,32,86
315,1326.3697017885822,7,47,10,333
133,734.4240715684738,5,51,5,246
698,687.2216059036743,6,48,9,266
1010,13492.403951144599,12,80,13,5
585,1045.4665637646237,3,55,5,205
646,931.8191075630824,7,42,17,57
136,1387.483601254385,7,89,34,344
666,1026.6075569229743,3,45,2,72
486,644.4365726005815,8,47,38,330
946,732.4788084184619,1,23,9,109
1037,9541.281892503732,19,29,36,111
1044,9064.850876021788,11,40,47,190
626,872.1960808922037,5,71,21,277
643,935.1021621590988,8,54,22,19
628,968.5532699750879,1,46,5,218
47,884.8403072600531,6,47,30,282
571,1004.6045947973879,1,59,22,95
281,1028.379336312812,6,36,44,246
239,834.553383807903,9,62,49,363
560,894.7538797949343,1,30,15,313
227,1016.0700047738657,3,44,25,286
791,1107.4045547831465,4,75,25,70
613,1069.992156579955,1,70,42,272
923,1033.242418536719,3,81,21,174
305,994.774601508963,3,21,28,280
428,1171.5128649996097,3,25,8,74
1026,7491.9645240248965,11,59,24,89
742,680.1057583160746,2,21,11,234
837,1387.6251232035193,5,41,26,69
893,1266.6686723972884,7,27,24,223
6,941.4657607627048,9,32,5,268
986,710.4088277018643,2,85,2,147
290,844.3251200448516,9,81,42,17
353,1214.4149058005048,4,36,43,78
944,975.2060340551543,6,86,34,265
916,1069.7553814425848,7,23,32,76
201,1089.4468400870708,4,64,1,43
326,696.4528468030669,3,34,48,208
252,1229.4654867636941,4,70,41,150
50,559.2399611593165,1,31,3,162
141,1056.8649836510324,5,78,11,110
800,1007.4390348739363,9,39,15,298
640,676.2303069840959,5,76,15,217
737,1064.0074335784689,4,35,8,10
412,718.8394770405328,7,84,31,179
725,1042.7163595319855,6,28,34,207
142,1326.785688570607,1,29,16,320
516,1188.847806456439,5,25,12,362
277,1049.7649238933675,4,82,21,290
419,1294.8242960159566,9,58,49,334
345,882.2404235954193,4,18,1,297
545,382.0888749681777,2,66,38,14
497,740.6884614183859,4,40,21,83
269,889.9888783257541,9,51,32,322
842,1168.704873041651,2,74,8,18
115,951.9097588047193,8,43,26,239
484,1050.8659089668058,2,82,32,305
478,1077.812538635884,3,84,26,105
396,882.7060869738239,1,65,18,325
261,768.2673821054793,4,80,30,145
1024,7496.603751923622,18,30,47,188
614,718.6277381754059,4,63,20,82
288,1147.0793016211442,6,66,43,269
489,1421.7854087681412,3,38,38,194
21,1366.4121922303884,3,84,34,283
432,490.1919555599749,4,24,45,299
60,1243.8862817805898,3,88,41,131
863,1386.8763003325155,1,85,32,270
102,894.8386693086602,5,26,17,191
845,1041.613052053264,7,40,32,253
941,679.9239003322764,3,66,44,277
493,1019.3420769119045,9,74,30,143
662,863.285264689902,2,21,26,203
896,1162.1774718974107,4,65,45,118
1016,8870.077525963776,12,33,35,173
316,1005.2509604081897,5,59,40,354
297,1224.8999688583126,4,48,42,82
333,1018.9511395484316,9,51,24,330
857,791.9111067239428,5,24,32,246
648,986.4262833705469,1,72,46,128
7,1394.8032038768479,6,52,28,307
153,829.9938196053773,7,66,3,189
109,1064.387597680691,3,57,47,236
727,1004.6084832663483,9,87,38,88
859,861.9442389297571,9,78,8,160
100,941.3532166562132,2,50,48,202
62,953.5852558340457,4,36,2,175
752,662.0788485959166,3,82,13,285
716,1275.8254705041304,6,79,42,323
37,1052.215898751189,6,23,20,117
1032,8175.147635244881,18,28,35,221
437,1407.153886392823,8,54,37,105
602,769.4586689555937,6,71,24,282
881,1631.7331064684054,9,71,45,92
1027,12299.263233067913,18,78,41,175
420,1016.8796203525272,1,41,9,147
231,817.4083420707159,2,33,45,279
320,967.4642364080788,3,23,29,265
55,1257.7498806239878,4,20,9,174
202,1140.1961315920587,2,66,38,359
794,680.8128560449227,7,27,12,290
808,880.3128445841306,9,43,26,46
678,1438.0676108559057,9,76,22,110
421,1515.1869812204968,3,52,28,8
888,910.1769773032352,8,49,33,54
860,1158.2329544388776,7,20,8,234
744,1006.5227625527084,3,76,8,25
365,1172.5359979277782,6,64,44,291
190,621.2881938285338,9,89,22,248
203,1270.7628107938192,6,64,38,316
868,1083.6141974967563,7,30,49,258
578,1192.7162984717418,7,88,36,364
712,975.8217220324019,4,86,39,262
235,1535.9860223313315,8,29,5,200
48,1264.280556554729,1,42,34,112
645,938.5642339785139,3,24,47,192
111,520.3071961752396,9,63,4,348
272,641.0344622051401,7,54,48,298
782,666.1639103224747,3,37,1,160
584,1567.6732144510988,1,26,16,230
809,1313.9390313933802,4,36,27,250
849,840.565003937171,3,58,30,32
911,923.0554412617498,8,74,18,28
603,1217.4014800264151,8,82,11,342
475,1411.2419283753209,6,48,15,180
761,856.0905434405565,9,59,38,216
760,1175.0774698522478,1,45,18,109
38,510.08246903005613,2,40,27,72
605,1103.3587258059251,1,30,34,255
303,1186.8234012808155,9,84,19,214
885,1170.4728724065778,7,76,13,24
225,882.0170335526416,1,20,42,236
174,1085.287993704161,7,76,21,162
466,944.7576001166942,2,57,40,3
856,857.3134265626309,2,57,22,331
91,1024.2693873370101,7,28,10,359
563,1217.781175857923,2,28,49,35
17,746.7922199163941,4,37,12,354
96,634.1212629669703,6,65,42,137
766,824.2058937185282,4,89,31,63
799,1007.0795940326153,8,34,15,122
1038,5825.499617259132,12,54,7,223
689,1177.7399920508728,2,45,43,298
1034,11432.052513815663,12,30,49,327
877,870.5971752409571,5,64,39,67
806,922.7069691328402,4,68,40,15
170,811.5659589106276,5,58,33,282
28,1093.924504586418,3,80,38,183
299,1203.21552970974,8,67,11,174
173,980.7245726464739,1,68,18,244
688,906.2947981126006,3,85,46,25
517,1125.2292969060952,6,29,33,282
374,1239.8177065213017,7,29,45,174
774,1243.1386124066826,2,26,40,336
249,1441.3635600702742,7,35,35,97
243,1126.2468197451142,4,40,33,285
63,723.4162564984929,5,64,29,182
163,1289.648894751851,8,66,9,261
409,1030.0739079279747,2,48,40,220
230,1169.899437233669,8,20,25,135
53,830.7694999235104,4,65,27,43
792,1051.9219217907778,6,43,48,177
853,840.6532181733705,9,87,45,349
195,1043.2952314627955,1,62,28,62
619,1120.2523079341784,7,31,32,211
350,946.6382120720382,6,68,3,129
998,1160.2107153167524,2,74,37,202
332,775.6864071285421,7,30,39,53
673,914.3281014799128,3,32,11,170
836,1058.55368313413,7,53,13,236
36,694.7890875072444,2,26,38,249
80,503.1077713497768,8,85,28,69
870,1502.5511346915873,1,67,29,170
95,901.9729617169606,7,86,46,241
483,598.3884199356069,9,56,18,295
335,1243.7799333544378,7,68,17,240
449,871.5332706658266,4,80,23,287
822,1352.336860046395,8,58,18,65
623,1470.5061241187584,8,20,18,284
938,1177.0891118248383,2,76,9,326
746,818.5640467116336,3,60,30,28
600,1094.325123261213,5,45,19,260
724,1185.8160235056787,5,67,44,87
403,1001.3109249295458,8,84,42,29
981,1196.4500396627081,9,36,35,279
476,937.7409901109054,2,51,16,105
969,989.9605123389141,4,44,21,55
416,812.8783658608615,5,81,30,69
665,821.7885543307088,2,46,13,275
638,1018.329491797101,4,70,4,343
407,733.0948926543514,6,64,3,140
906,1011.6091370390373,1,56,49,270
692,1289.832450841062,7,64,45,19
827,1394.8930364326782,4,32,32,17
33,996.6256938155166,5,31,27,24
300,1157.4072104809031,3,86,14,68
641,916.053825177468,3,64,40,15
719,909.096946946536,6,20,19,139
949,988.6034959111255,5,79,12,11
75,345.0637239775639,2,31,36,142
549,1092.7864683428272,3,54,12,339
755,762.6502777920142,1,76,37,207
479,1769.7202021138096,3,25,2,279
705,610.842706619024,4,67,31,229
367,1056.0231204526042,2,38,7,324
473,621.2022344503619,4,30,44,320
99,1001.2783641606152,9,61,6,199
771,1176.9379838638688,5,30,5,216
395,1294.8600301803217,8,52,45,280
865,846.8028273789034,8,53,10,179
520,1187.8467808429473,2,66,1,161
481,968.0206021298084,3,44,26,107
1011,8099.548171928165,16,57,39,214
323,795.4448291916319,9,46,1,84
159,702.1741256993378,7,25,8,144
551,1021.647446868225,4,75,31,358
572,1419.1093280688206,1,34,38,163
854,1297.254132776888,9,74,34,154
423,937.7589628802316,7,47,34,49
1023,9388.889175189091,13,19,19,165
576,447.2161727480287,5,64,49,194
686,1178.249607543097,4,70,20,239
738,1245.6727459863785,4,36,40,347
311,945.579699193195,1,67,33,313
237,493.7143533355982,6,82,46,263
221,1578.664641668377,5,31,1,189
463,825.0686230018537,6,78,16,87
26,1027.7306474274665,9,55,24,207
317,1170.488242823741,1,54,47,115
11,884.1455767968845,2,83,26,227
788,1114.7950198071092,1,85,9,217
754,1263.41044915196,7,56,36,272
656,1014.804608503622,7,68,40,160
460,700.530526852788,3,62,42,301
465,971.9179875772926,5,83,40,340
319,1081.0415881221106,5,52,1,225
40,1049.2153089672809,6,63,20,359
263,189.6831649827319,8,65,9,95
312,1274.6942129967974,3,23,33,93
406,1155.7124830868747,5,30,19,33
525,809.1852108643708,7,67,32,72
178,1363.3835192893293,1,56,40,57
181,1156.4168369412516,1,28,37,25
495,1380.7810193174143,7,48,32,181
511,1067.6142064449598,4,39,6,207
519,1024.8330763573065,7,51,8,335
706,1151.50248783641,3,55,8,219
188,981.7927718357818,5,36,13,145
672,481.6524418979626,8,67,26,172
151,1062.6232125864692,2,65,13,207
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43,971.0879294029398,9,89,20,322
594,1083.4155263217372,4,35,2,14
287,962.0537262411042,5,28,17,86
32,1463.0695461272344,7,63,39,236
314,1203.3774090001596,1,32,3,24
515,855.8071673579168,7,76,20,241
583,1158.1954665265712,7,33,3,242
1030,10330.84456838341,16,80,25,183
681,1180.4180160108087,8,43,29,42
169,938.6529709992824,6,66,28,309
733,1102.063188928618,3,81,20,74
1005,11691.619848248203,18,47,44,6
823,1574.7245309048126,4,36,16,107
579,630.3534385550396,5,30,32,122
897,958.2204799207864,4,69,23,264
547,1144.268031795135,2,73,27,100
154,1058.0634242902509,8,30,10,174
1001,12985.238124865378,19,65,31,41
720,985.7635940697331,5,23,46,50
631,576.8838425712952,7,77,25,246
898,1036.6784216083306,6,42,46,149
924,824.9697962652067,8,82,30,178
740,1253.5925162545327,8,43,23,278
959,1142.441820058051,5,89,40,189
950,1060.8348623306729,5,36,33,344
185,944.1343036685373,1,62,34,197
796,1263.288213333226,2,58,17,252
785,1139.94761198276,5,35,42,187
567,490.46886620553653,2,42,11,126
621,802.381386138672,5,52,18,132
548,949.2386534892519,8,68,20,155
1019,10414.333402162294,13,86,20,25
27,712.2516056444242,3,32,10,136
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780,1155.1680243876694,3,88,23,229
461,1491.1812832290973,3,51,13,244
410,1128.6097085146873,4,39,4,355
1007,8488.34483126196,15,77,45,9
279,1017.4505212475048,6,64,37,192
920,1048.5224982245768,2,66,18,7
45,630.3695024081431,3,52,47,4
211,1142.7226276732918,7,57,21,163
855,1355.1260619974637,1,27,16,245
58,922.6969060371964,2,45,48,339
501,1231.5443868829104,1,89,36,95
751,769.1916884722739,4,25,36,34
438,654.9746354462771,6,18,19,94
129,1024.9128412719103,4,39,32,222
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150,1074.2461683082965,9,52,36,98
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970,642.3062244704879,9,29,21,154
81,945.082028040622,4,25,1,112
915,976.176116903262,6,87,13,293
434,1179.3855639489907,6,82,32,76
10,1135.6400108964913,9,51,18,104
2,965.4339247072038,1,39,10,176
659,1049.5211901919638,9,89,38,162
608,688.8363241721457,9,71,5,42
778,938.1203411121238,6,18,44,137
16,859.4281176897568,7,87,49,251
590,785.9790435227832,5,26,4,316
206,765.5437400212193,3,85,29,362
452,1238.7855801253095,2,51,21,137
162,1196.771150935613,7,56,17,77
108,1043.6444532079597,7,64,28,55
922,743.3711751472349,5,72,43,339
693,729.7341681000257,2,69,47,210
391,775.3963321629105,6,68,36,26
535,837.0409730494603,5,82,29,59
699,1231.0067548017253,6,63,35,153
812,890.0672354314563,6,64,13,259
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586,1062.055146575084,8,71,21,197
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841,987.6340725868918,5,51,39,85
538,903.8611139425436,3,44,13,123
680,1317.8887737485397,8,74,8,341
54,1152.9190722102169,5,68,10,281
61,880.2064405386775,9,80,36,86
492,1369.9860347225065,5,36,1,304
947,618.6187072688153,7,30,32,199
789,982.4585721353383,5,82,22,259
219,878.6591130427241,1,68,4,207
179,933.835791690511,8,30,36,304
321,1024.2489912481794,7,85,34,98
710,1424.1140920725095,3,38,46,327
994,829.5039380005503,4,37,38,75
114,1615.8105281213216,4,36,27,58
955,979.4622054018578,9,40,27,170
569,532.3020197435361,6,29,48,355
979,604.0242941285692,6,79,41,322
931,861.2001183267004,8,52,9,194
891,1205.120545299341,3,26,13,350
110,981.3885210584582,6,71,15,357
207,1128.758816802165,6,50,5,49
471,931.237075712089,9,83,12,224
398,1338.4680935413533,5,62,13,163
294,1212.4005242552562,8,65,36,328
963,1049.1302924253675,6,33,43,283
122,772.6531363013153,5,46,20,107
759,785.4105549546966,4,53,31,78
402,850.1562442615568,2,55,29,355
112,993.3715311376958,1,64,29,196
158,1118.4582302279468,4,79,46,140
543,1132.4510444788207,5,68,11,193
610,1374.0110778722958,2,70,12,244
554,1063.6052108253032,1,77,10,333
425,1161.3439873962868,2,64,48,4
879,995.894275981812,2,33,4,195
413,616.4714573160944,2,71,43,328
126,1547.6139064524946,8,88,49,219
212,1283.8914100451498,3,32,7,140
459,861.5876736632044,4,77,12,15
534,697.2459500593859,2,28,40,38
286,511.97805011937453,6,60,30,75
574,945.224867797784,8,45,14,17
1035,7341.7188028457385,18,53,19,178
828,869.284993211683,2,54,40,89
964,1177.2509393971281,7,68,31,145
831,663.8873722414307,6,41,17,252
83,1369.473511185379,2,58,45,111
852,861.1307202099363,5,71,3,117
384,868.5612445798097,1,79,32,194
536,1011.8496678291035,4,36,7,65
369,1024.419024637208,2,22,29,207
241,801.8698153918249,2,66,39,124
1000,1143.1456953390395,7,79,22,34
65,1203.1314555985496,4,55,1,245
840,946.5027889436228,5,84,4,151
411,1177.9037195222224,1,60,11,21
532,584.61998443276,5,46,25,175
966,1360.0293038623681,9,29,33,318
198,1038.4312764863819,9,42,3,99
691,909.75845854523,5,66,16,136
696,922.6133901715025,4,74,23,267
435,1375.589263024007,2,53,17,297
298,1076.8248802191524,2,21,28,124
844,1095.6024365460125,3,70,13,94
431,704.1853718335562,5,47,48,323
763,1640.0211345671987,1,59,23,105
521,582.6486797196571,5,74,37,159
846,1123.1128160020373,5,86,38,356
474,1341.7185668611312,2,73,11,46
797,990.1112115358362,3,22,15,291
363,972.8099628857856,6,71,16,62
52,903.7294298959209,8,39,15,180
817,1066.7625664673146,6,38,38,131
494,784.6789496679341,3,42,45,315
68,1250.883224473006,4,53,25,157
193,1053.523436032551,6,78,24,43
506,878.218654058826,4,31,15,332
470,856.04543983883,4,77,30,208
902,901.6652969181598,6,68,47,52
296,826.7726011848365,5,82,9,201
775,1155.4524905542992,3,71,46,209
618,1181.441655974673,9,19,11,250
146,1195.4557179443277,5,82,10,334
248,836.667691856572,7,71,45,59
71,1090.3489013771034,3,26,22,115
835,991.3287781533047,8,78,19,89
708,1438.6985454960911,6,32,31,354
826,1363.3461192794252,5,85,25,291
1017,12846.836935684016,11,32,18,151
762,1030.5024536613403,6,60,24,357
798,1170.3751743431562,1,82,42,243
499,781.0954366538107,7,79,13,283
182,785.7106108959293,8,36,15,302
658,993.9687282224992,6,25,30,187
1003,15767.552292384027,18,48,31,190
940,858.9803423158179,3,23,1,225
189,788.3015704828988,5,89,4,272
295,1089.2538714912619,1,73,34,278
171,777.6213925936191,6,34,37,268
229,821.174072685008,3,55,24,245
113,1015.0575524852566,5,32,9,72
694,1153.983901736066,9,71,48,239
18,1078.5618331488186,4,18,7,237
440,986.1130752758453,7,54,29,67
13,1060.4905678915086,8,45,41,187
965,977.5660764281934,5,89,17,314
56,1232.8200297790497,4,59,33,161
472,424.51970881610384,4,83,10,347
874,817.2674900145203,6,44,31,363
882,867.2828067699198,2,38,42,21
599,888.9266849809721,3,38,48,97
802,870.9888179456566,6,85,13,31
1047,5350.162845433485,14,40,18,195
318,922.433310851636,6,59,42,47
582,896.1780215247996,9,19,35,103
700,953.7744658892527,1,43,20,41
228,730.5638055176735,6,19,21,258
929,1391.3810073085851,5,35,19,15
105,959.6785720834977,8,82,35,180
106,1101.0127142036347,8,25,21,310
284,690.5461252932878,7,35,22,2
161,756.3295824431697,2,37,42,336
544,1360.392155164475,4,30,33,284
59,1082.815857850891,1,62,6,139
683,868.8699334300566,5,27,26,199
1049,7679.070825444451,15,68,16,245
997,1449.4216317123805,3,80,38,248
125,1146.7142734500676,1,72,39,204
260,1193.6585133573342,5,58,3,105
958,1114.188304788446,7,74,43,17
216,1189.7423051233168,7,80,20,323
513,940.2629882833976,7,80,20,21
97,1074.030069266144,1,37,32,344
895,1345.5397477593817,4,85,27,178
327,1289.527718375017,7,39,47,63
530,773.9208437238979,5,61,48,351
609,555.3199377739304,9,27,21,352
878,1055.9469879097246,5,56,22,209
251,684.7790114162387,9,62,22,172
422,1438.8352106108011,4,73,31,287
157,1466.4436277861892,6,63,27,89
439,574.1543901612113,3,32,44,134
674,907.1397835010521,6,49,8,140
278,849.9457807103013,4,82,2,21
726,954.0041659118302,4,45,20,268
832,770.3370133789505,2,44,49,256
342,1060.921802872978,8,61,7,363
612,986.1038322723863,3,64,49,354
875,991.7182567815507,2,68,21,94
265,936.8579621517099,2,74,47,201
834,808.0506087239679,4,80,6,86
697,1081.5332555605296,4,64,24,206
426,1342.1578893830872,7,52,25,342
709,479.5176480290695,8,78,30,231
713,863.7702282977625,4,23,12,266
1015,14921.190771066362,17,21,3,336
1041,12960.522864904227,17,19,49,326
617,1027.3486986512232,9,41,14,287
819,1020.570997318856,6,40,7,157
528,1012.0212366653454,5,23,5,200
448,765.0300534181612,2,83,16,180
660,963.9098970190144,6,62,3,196
69,1090.4090062619084,6,57,10,148
325,748.4956546250745,2,64,29,172
593,1119.744956436598,8,53,43,287
390,855.7740860843992,8,70,40,164
862,621.063971250692,7,64,14,116
482,761.1148898498935,2,70,23,18
935,1110.003612513333,2,51,23,332
975,888.4541416963051,9,45,38,219
132,1017.1407437015068,9,83,46,161
130,874.1310864709502,2,62,47,185
503,650.3581065452147,7,69,22,297
442,991.8263129764767,9,25,28,158
848,1613.8250349777236,1,79,28,248
273,1290.79093803874,8,24,23,95
939,1060.9501784427998,7,46,47,101
625,1398.2966566598493,6,29,11,94
128,858.425567599307,6,21,47,102
634,893.279732513145,8,33,22,342
306,1029.3318458271956,4,43,24,236
784,1152.6464363209557,3,34,31,235
485,810.9123136789242,2,34,49,170
291,947.9694374106812,8,86,14,112
22,943.5559248783661,9,21,25,209
757,1123.3294752202223,1,26,22,92
445,673.8826248737867,2,18,6,188
597,932.5312661766571,7,87,25,309
340,859.0688617990063,7,63,49,143
93,824.4867265306619,4,67,9,191
94,918.0844633505579,2,38,45,39
568,747.9784222706489,8,38,30,335
601,1189.247154161338,5,71,23,108
974,836.8440051744022,3,74,22,231
357,995.3717160019025,5,22,25,62
1039,10116.928577102117,10,69,30,213
386,1037.5984466190519,4,32,22,274
310,949.4518368915265,5,68,47,205
347,637.978914625669,9,41,17,187
392,1122.9797928766263,5,64,15,310
14,521.6799388355505,2,64,49,82
550,849.0037033210449,9,34,43,124
921,1268.407937464943,3,64,13,121
765,1287.3183315714189,6,72,2,180
704,647.88467590911,9,50,47,274
927,860.2695381821029,9,49,17,135
477,1144.1392407639416,7,56,17,198
1046,10097.886479532812,15,81,8,311
502,1477.3541601175325,5,71,7,265
82,1089.2781428779367,7,19,49,362
98,1065.2638180449724,9,84,32,179
349,820.388944686891,1,28,25,245
889,1147.6637076730774,6,76,1,250
334,830.7095721219721,1,70,26,271
236,1158.4797555795028,2,52,36,44
79,1022.9401941338756,3,85,45,276
446,1167.4181372075095,2,53,29,338
546,800.7761861323809,4,87,48,11
1048,8486.636491556797,19,46,28,34
781,1044.4252502333134,5,84,17,307
753,756.0316867556668,4,60,32,347
884,1261.040219267268,3,33,20,133
222,533.183701852063,3,68,4,345
256,878.9414817834372,6,84,17,3
928,1094.30296876613,4,85,40,342
701,869.3192448702401,9,22,37,10
352,1368.839054237388,7,41,29,282
488,729.6129990964013,7,30,3,11
984,988.0721609645051,5,64,49,196
866,903.0746100160039,2,58,3,171
361,1129.836628560293,2,81,29,42
973,1210.160887247181,5,22,6,136
559,1098.6130355945743,9,38,31,127
702,1262.2523064592224,2,69,42,293
387,1085.43899394429,3,44,6,105
591,1017.8915593048481,4,25,29,198
776,607.4438200273859,1,44,24,168
247,881.2636722097609,5,38,6,220
804,884.4311778237394,6,28,32,145
562,1518.8501996613597,1,41,13,149
271,1360.3183222665289,7,45,47,138
718,1037.5754403654694,5,30,29,357
663,991.8116824460004,5,84,30,211
892,1126.8185077768244,1,28,17,166
415,1083.078502994898,9,59,27,37
447,1091.649561524212,3,52,17,94
116,1075.3868355834031,1,69,19,1
611,1163.5914140885145,2,61,4,286
372,1124.4995728113624,3,58,2,117
3,1161.922134525173,8,34,17,298
224,596.821032202587,1,45,27,351
209,1128.761921576512,2,36,10,354
383,469.02606892254823,7,50,2,284
165,1240.8440323110804,5,79,43,13
575,1207.3513952958724,3,58,24,293
690,1111.06582787151,9,82,7,76
843,719.3194946094254,5,66,25,100
172,796.0474287586404,2,38,9,240
967,830.9019244851826,3,70,32,91
464,1053.4949776835556,9,33,8,128
1,1124.1785382528083,7,65,29,217
283,1396.504204036338,8,77,30,250
616,1032.3052954938069,9,54,25,189

Download Model Script

Script to Load Trained Model

import mlflow

# Set the MLflow tracking URI
mlflow.set_tracking_uri("http://localhost:3500")

# Define the artifact URI as a string (ensure it's correctly formatted)
artifact_uri = "mlflow-artifacts:/981188833218800573/ca50c2fee6194da689f5c05702d2091e/artifacts/model/model.pkl"

# Download the artifact
local_path = mlflow.artifacts.download_artifacts(artifact_uri=artifact_uri)

print(f"Artifact downloaded to: {local_path}")

Trained Model File

Flask App Script

Web App to Serve Model

from flask import Flask, render_template, request
import pandas as pd
import requests
import base64
import io

app = Flask(__name__)

# Route for the home page
@app.route('/')
def index():
    return render_template('index.html')

# Route to handle the CSV file upload and prediction
@app.route('/predict', methods=['POST'])
def predict():
    file_data = request.form.get('file')
    
    # Decode the Base64 encoded file content
    decoded_file = base64.b64decode(file_data.split(',')[1])

    # Read the decoded content into a DataFrame
    df = pd.read_csv(io.StringIO(decoded_file.decode('utf-8')))

    # Separate the 'claim_id' column if it exists
    if 'claim_id' in df.columns:
        claim_ids = df['claim_id']
        df = df.drop(columns=['claim_id'])
    else:
        claim_ids = None

    # Send the DataFrame to the BentoML service
    response = requests.post(
        'http://127.0.0.1:3000/predict',  # BentoML endpoint
        json=df.to_dict(orient='records')
    )

    # Get predictions from the response
    predictions = response.json()['predictions']

    # Add predictions to the DataFrame
    df['Prediction'] = predictions

    # Reattach the 'claim_id' column to the DataFrame
    if claim_ids is not None:
        df['claim_id'] = claim_ids

    # Reorder columns to have 'claim_id' first
    if 'claim_id' in df.columns:
        df = df[['claim_id'] + [col for col in df.columns if col != 'claim_id']]

    # Render the DataFrame as an HTML table
    return render_template('result.html', tables=[df.to_html(classes='data', header="true")])

if __name__ == '__main__':
    app.run(debug=True, host="0.0.0.0", port=5005)

Templates

Visualize Template

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Visualization</title>
</head>
<body>
    <h1>Prediction Distribution</h1>
    <img src="{{ url_for('static', filename='prediction_pie_chart.png') }}" alt="Prediction Pie Chart">
    <br><br>
    <a href="/">Go Back</a>
</body>
</html>

Result Template

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Prediction Results</title>
    <style>
        table {
            width: 100%;
            border-collapse: collapse;
        }
        table, th, td {
            border: 1px solid black;
        }
        th, td {
            padding: 10px;
            text-align: left;
        }
    </style>
</head>
<body>
    <h1>Prediction Results</h1>
    {% for table in tables %}
        {{ table|safe }}
    {% endfor %}
    <a href="/">Go Back</a>
</body>
</html>

Index Template

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Health Insurance Anomaly Detection</title>
    <style>
        .drag-area {
            border: 2px dashed #ccc;
            border-radius: 10px;
            width: 100%;
            height: 200px;
            text-align: center;
            padding: 20px;
            font-size: 20px;
            color: #333;
        }
        .drag-area.hover {
            border-color: #333;
            background-color: #f0f0f0;
        }
        .drag-area p {
            margin: 30px 0;
        }
        .drag-area button {
            margin-top: 10px;
        }
    </style>
</head>
<body>
    <h1>Upload CSV File</h1>
    <div class="drag-area" id="drag-area">
        <p>Drag & Drop your CSV file here or click to upload</p>
        <input type="file" id="fileInput" name="file" style="display: none;" accept=".csv">
        <button onclick="document.getElementById('fileInput').click();">Browse Files</button>
    </div>
    <form id="uploadForm" action="/predict" method="post" enctype="multipart/form-data">
        <input type="hidden" name="file" id="hiddenFileInput">
    </form>

    <script>
        const dragArea = document.getElementById('drag-area');
        const fileInput = document.getElementById('fileInput');
        const hiddenFileInput = document.getElementById('hiddenFileInput');
        const uploadForm = document.getElementById('uploadForm');

        dragArea.addEventListener('dragover', (e) => {
            e.preventDefault();
            dragArea.classList.add('hover');
        });

        dragArea.addEventListener('dragleave', () => {
            dragArea.classList.remove('hover');
        });

        dragArea.addEventListener('drop', (e) => {
            e.preventDefault();
            dragArea.classList.remove('hover');
            const file = e.dataTransfer.files[0];
            handleFile(file);
        });

        fileInput.addEventListener('change', (e) => {
            const file = e.target.files[0];
            handleFile(file);
        });

        function handleFile(file) {
            if (file && file.type === 'text/csv') {
                const reader = new FileReader();
                reader.readAsDataURL(file);
                reader.onload = () => {
                    hiddenFileInput.value = reader.result;
                    uploadForm.submit();
                };
            } else {
                alert('Please upload a valid CSV file.');
            }
        }
    </script>
</body>
</html>

Supporting Folders

• 3.demo/ – Final project outputs and UI

• 3.demo/labs/ – Project lab environment

• 3.demo/labs/assets/05-project/ – Full working ML app project source

• 3.demo/labs/mlruns/ – MLflow run artifacts

• 3.demo/labs/mlartifacts/ – Model pickle/artifact storage

• 3.demo/labs/bentoml/ – BentoML service-related files and configs