https://github.com/deliprofesor/income-analytics-interpretable-machine-learning-model

This project predicts whether an individual earns more than 50K using the Adult Income dataset. A Random Forest model is trained and evaluated, with explanations provided through DALEX and LIME for feature importance and model transparency.
https://github.com/deliprofesor/income-analytics-interpretable-machine-learning-model

classification dalex data-preprocessing data-science data-visualization feature-engineering income-prediction lime machine-learning model-explainability predictive-modeling r-programming random-forest

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This project predicts whether an individual earns more than 50K using the Adult Income dataset. A Random Forest model is trained and evaluated, with explanations provided through DALEX and LIME for feature importance and model transparency.

• Host: GitHub
• URL: https://github.com/deliprofesor/income-analytics-interpretable-machine-learning-model
• Owner: deliprofesor
• Created: 2024-12-08T20:50:26.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-14T19:42:39.000Z (2 months ago)
• Last Synced: 2024-12-23T22:15:32.648Z (about 2 months ago)
• Topics: classification, dalex, data-preprocessing, data-science, data-visualization, feature-engineering, income-prediction, lime, machine-learning, model-explainability, predictive-modeling, r-programming, random-forest
• Language: R
• Homepage:
• Size: 626 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Income Prediction and Model Explainability using Random Forest, DALEX, and LIME

![adult](https://github.com/user-attachments/assets/99e1ef19-0b9f-4c98-9ffd-5959742dd933)

## Project Overview

This project aims to predict the income of individuals based on various demographic and employment-related features using machine learning techniques. A **Random Forest** model is used for classification, and two powerful explainability tools, **DALEX** and **LIME**, are employed to interpret and visualize the model's predictions. This helps in understanding which features are the most influential in determining the income class (e.g., ">50K" or "<=50K").

## Dataset

The dataset used in this project is the **Adult Income Dataset** (also known as the **Census Income Dataset**), which contains information about individuals and their income. The dataset includes the following columns:

- **age**: The age of the individual.

- **workclass**: The type of employment.

- **education**: The highest level of education attained.

- **marital.status**: Marital status of the individual.

- **occupation**: The occupation of the individual.

- **relationship**: Relationship status.

- **race**: The race of the individual.

- **sex**: The gender of the individual.

- **native.country**: The country of origin.

- **income**: The target variable, indicating if the individual earns more than 50K a year or not.

The dataset is pre-processed by handling missing values and converting categorical variables to factors.

## Libraries Used

- **tidyverse**: For data manipulation and visualization.

- **caret**: For model training and evaluation.

- **DALEX**: For model explainability and feature importance.

- **lime**: For local interpretable model-agnostic explanations.

## Steps Involved

1. **Data Loading and Preprocessing**:

   - The data is loaded from a CSV file, and the first few rows are displayed.

   - Missing values are checked, and categorical variables are converted into factors.

2. **Data Splitting**:

   - The dataset is split into training (70%) and testing (30%) sets.

3. **Model Training**:

   - A **Random Forest** model is trained using the `caret` package with 10-fold cross-validation to predict income.

4. **Model Evaluation**:

   - After the model is trained, predictions are made on the test set, and performance metrics such as **Confusion Matrix** are calculated.

5. **Model Explainability with DALEX**:

   - The DALEX package is used to explain the model's predictions. Feature importance is visualized to understand which features play a major role in predicting the income.

6. **Model Explainability with LIME**:

   - The LIME package provides local explanations for individual predictions. The explanation for the first five test instances is visualized, and the importance of features such as **gender** is also explored.

## Key Visualizations

- **Variable Importance**: This visualization shows the relative importance of each feature in predicting income.

- **LIME Explanations**: Local explanations for individual predictions show how specific features influence the outcome for individual data points.

## How to Run the Code

To run the code, follow these steps:

1. Install the required packages:

   ```r

   install.packages("tidyverse")

   install.packages("caret")

   install.packages("DALEX")

   install.packages("lime")