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https://github.com/deliprofesor/income-analytics-interpretable-machine-learning-model
https://github.com/deliprofesor/income-analytics-interpretable-machine-learning-model
Last synced: 12 days ago
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- Host: GitHub
- URL: https://github.com/deliprofesor/income-analytics-interpretable-machine-learning-model
- Owner: deliprofesor
- Created: 2024-12-08T20:50:26.000Z (27 days ago)
- Default Branch: main
- Last Pushed: 2024-12-08T22:13:42.000Z (27 days ago)
- Last Synced: 2024-12-08T23:19:58.333Z (27 days ago)
- Size: 619 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Income Prediction and Model Explainability using Random Forest, DALEX, and LIME
## 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")