CarInsuranceClaimPrediction

This repository is dedicated to developing a Python project using machine learning algorithms to analyze the importance of preprocessing steps in Car Insurance Claim Prediction.

I explore and compare the performance of various machine learning models with and without preprocessing, showcasing how these steps affect the accuracy, precision, recall, and overall predictions.

Key Features

• Data Preprocessing: Demonstrates the importance of feature scaling, oversampling (using SMOTE), and undersampling to address class imbalance.
• Machine Learning Models: Implements multiple classifiers including:
  • Random Forest
  • Gradient Boosting
  • XGBoost
  • Logistic Regression
  • Voting Classifier (Ensemble Model)
• Performance Metrics: Evaluates models using metrics like accuracy, precision, recall, F1-score, and classification reports.

Requirements

• Python 3.x
• Pandas
• NumPy
• Scikit-learn
• XGBoost
• Imbalanced-learn

You can install the required packages using the following command:

pip install -r requirements.txt