https://github.com/shubhamsoni98/classification-with-random-forest---2

Fraud detection is a critical task for financial institutions and businesses. This document outlines the end-to-end process of predicting fraudulent activities using a Random Forest model. The process includes data preparation, exploration, model training, and evaluation.
https://github.com/shubhamsoni98/classification-with-random-forest---2

algorithms anaconda data data-science dataflow feature-engineering jupyter-notebook machine-learning model modeltraining prediction python random-forest sql visualization

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Fraud detection is a critical task for financial institutions and businesses. This document outlines the end-to-end process of predicting fraudulent activities using a Random Forest model. The process includes data preparation, exploration, model training, and evaluation.

• Host: GitHub
• URL: https://github.com/shubhamsoni98/classification-with-random-forest---2
• Owner: shubhamsoni98
• Created: 2024-09-17T09:15:33.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-24T13:21:19.000Z (about 2 months ago)
• Last Synced: 2024-10-16T19:50:42.353Z (about 1 month ago)
• Topics: algorithms, anaconda, data, data-science, dataflow, feature-engineering, jupyter-notebook, machine-learning, model, modeltraining, prediction, python, random-forest, sql, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 187 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Fraud Detection Using Random Forest

## Overview

This project aims to predict fraudulent activities using a Random Forest model. The dataset consists of various features related to individuals' financial and personal details. The goal is to classify individuals as either 'Risky' or 'Good' based on their taxable income and other attributes.

## Solution Architecture

### Data Flow

1. **Data Acquisition**: Import dataset from a CSV file.

2. **Data Preprocessing**: Clean and preprocess the data for analysis.

3. **Exploratory Data Analysis (EDA)**: Analyze the dataset to understand its characteristics and visualize important features.

4. **Feature Engineering**: Convert categorical variables into numerical format suitable for the Random Forest model.

5. **Model Training**: Split the data into training and testing sets and train the Random Forest model.

6. **Model Evaluation**: Assess the model's performance using various metrics.

7. **Results Visualization**: Generate visualizations to interpret the model's performance and insights.

### Components

- **Data Handling Libraries**: Pandas, NumPy

- **Visualization Libraries**: Matplotlib, Seaborn

- **Machine Learning Libraries**: Scikit-learn (for preprocessing, model training, and evaluation)

- **Environment**: Python (Jupyter Notebook or any Python IDE)

## Project Setup

### Dependencies

Ensure you have the following Python libraries installed:

- `pandas`

- `numpy`

- `matplotlib`

- `seaborn`

- `scikit-learn`

You can install these dependencies using pip:

```bash

pip install pandas numpy matplotlib seaborn scikit-learn