Predicting-Claims

This project focuses on performing Exploratory Data Analysis (EDA) and building predictive models on a dataset.

Table of Contents

• Introduction
• Dataset
• Exploratory Data Analysis
  • Univariate Analysis
  • Bivariate Analysis
  • Correlation Analysis
• Predictive Modeling
  • Logistic Regression
  • Random Forest Classifier
• Model Evaluation
  • Classification Report
  • Confusion Matrix
• SHAP Analysis
  • SHAP Summary Plot
  • SHAP Waterfall Plot
• Installation
• Usage
• Contributing
• License

Introduction

This project demonstrates the process of Exploratory Data Analysis and Predictive Modeling using Python. The goal is to gain insights from the dataset and build predictive models to forecast the target variable.

Dataset

The dataset used in this project is DATA.xlsx, which contains information about various features and the target variable.

Exploratory Data Analysis

The EDA section includes the following analyses:

Univariate Analysis

Explores the distribution of individual features using visualizations.

Bivariate Analysis

Investigates the relationship between the target variable and other features.

Correlation Analysis

Examines the correlation between the features to identify potential multicollinearity.

Predictive Modeling

The predictive modeling section includes the implementation of two models:

Logistic Regression

A linear classification model used to predict the target variable.

Random Forest Classifier

An ensemble learning method for classification tasks.

Model Evaluation

The performance of the models is evaluated using the following metrics:

Classification Report

Provides a detailed breakdown of the model's precision, recall, F1-score, and accuracy.

Confusion Matrix

Visualizes the true positive, true negative, false positive, and false negative predictions.

SHAP Analysis

The SHAP (SHapley Additive exPlanations) analysis is used to explain the model's predictions and feature importance.

SHAP Summary Plot

Displays the overall feature importance.

SHAP Waterfall Plot

Explains the prediction for a specific data point.

Installation

1. Clone the repository: git clone https://github.com/your-username/your-repo.git
2. Install the required dependencies: pip install -r requirements.txt

Usage

1. Ensure the dataset file DATA.xlsx is in the same directory as the Python script.
2. Run the Python script

Contributing

If you find any issues or have suggestions for improvements, feel free to open a new issue or submit a pull request.