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# Insurance Fraud Prediction Model
This project focuses on building and evaluating a machine learning model to detect fraudulent insurance claims.
The project involves data preprocessing, model training using a RandomForestClassifier, model evaluation with
various metrics and visualizations, and a Streamlit UI for interacting with the model.
Create and activate a virtual environment:
```bash
python -m venv env
source env/bin/activate # On Windows use `env\Scripts\activate`
```
Install the required packages:
```bash
pip install -r requirements.txt
```
### Project Structure
```bash
insurance-fraud-detection/
│
├── dataset/
│ └── insurance_claims.csv
│
├── model/
│ └── only_model.joblib
│
├── train.py
├── prediction.py
├── app.py
├── requirements.txt
└── README.md
```
### Data Preprocessing
#### Data Loading
The data is loaded from a CSV file located at dataset/insurance_claims.csv. During loading, the following steps are
performed:
- Drop the _c39 column.
- Replace '?' with NaN.
#### Data Cleaning
Fill missing values for 'property_damage', 'police_report_available', and 'collision_type' columns with their mode.
Drop duplicate records.
#### Encoding and Feature Selection
Encode categorical variables using Label Encoding.
Drop unnecessary columns that are not relevant for the model.
Select the final set of features for the model.
#### Preprocessed Features
The final set of features used for model training:
incident_severity
insured_hobbies
total_claim_amount
months_as_customer
policy_annual_premium
incident_date
capital-loss
capital-gains
insured_education_level
incident_city
fraud_reported (target variable)
#### Model Training
The model is trained using a RandomForestClassifier with a pipeline that includes preprocessing steps and
hyperparameter tuning using GridSearchCV.
#### Training Steps
Train-test split: The data is split into training and testing sets with a 70-30 split.
Pipeline setup: A pipeline is created to include preprocessing and model training.
Hyperparameter tuning: A grid search is performed to find the best hyperparameters.
Model training: The best model is trained on the training data.
Model saving: The trained model is saved as fraud_insurance_pipeline.joblib.
#### Model Evaluation
The trained model is evaluated using the test set. The evaluation metrics include:
Classification Report: Precision, Recall, F1-score.
AUC Score: Area Under the ROC Curve.
Confusion Matrix: Visual representation of true vs. predicted values.
ROC Curve: Receiver Operating Characteristic curve.
### Usage
#### Training the Model
To train the model, run the following command:
```bash
python train.py
```
#### Evaluating the Model
To evaluate the model, run the following command:
```bash
python predict.py
```
#### Running the Streamlit App
To run the Streamlit app, use the following command:
```bash
streamlit run streamlit_app.py
``` |