nhvrm1998/Explainable-AI-Income-Prediction

1import gradio as gr2from model_utils import predict_income3 4# Prediction Function5def predict(6    age,7    fnlwgt,8    education_num,9    capital_gain,10    capital_loss,11    hours_per_week,12    workclass,13    education,14    marital_status,15    occupation,16    relationship,17    race,18    sex,19    native_country20):21 22    user_input = {23        "age": age,24        "fnlwgt": fnlwgt,25        "education_num": education_num,26        "capital_gain": capital_gain,27        "capital_loss": capital_loss,28        "hours_per_week": hours_per_week,29        "workclass": workclass,30        "education": education,31        "marital_status": marital_status,32        "occupation": occupation,33        "relationship": relationship,34        "race": race,35        "sex": sex,36        "native_country": native_country37    }38 39    prediction, confidence = predict_income(user_input)40 41    return (42        f"### Prediction : {prediction}\n\n"43        f"### Confidence : {confidence:.2f}%"44    )45 46 47with gr.Blocks(title="Explainable AI Income Prediction") as demo:48 49    gr.Markdown("# 🤖 Explainable AI Income Prediction")50 51    gr.Markdown("""52Predict whether an individual's annual income is **greater than $50K**.53 54### Model Used55- Random Forest56 57### Explainable AI58- SHAP59- LIME60""")61 62    with gr.Row():63 64        age = gr.Slider(17,90,value=30,label="Age")65 66        fnlwgt = gr.Number(value=150000,label="Final Weight")67 68        education_num = gr.Slider(1,16,value=10,label="Education Number")69 70    with gr.Row():71 72        capital_gain = gr.Number(value=0,label="Capital Gain")73 74        capital_loss = gr.Number(value=0,label="Capital Loss")75 76        hours_per_week = gr.Slider(1,99,value=40,label="Hours Per Week")77 78    with gr.Row():79 80        workclass = gr.Dropdown(81            [82                "Private",83                "Self-emp-not-inc",84                "Self-emp-inc",85                "Federal-gov",86                "Local-gov",87                "State-gov",88                "Without-pay",89                "Never-worked"90            ],91            value="Private",92            label="Workclass"93        )94 95        education = gr.Dropdown(96            [97                "Bachelors",98                "HS-grad",99                "Masters",100                "Some-college",101                "Assoc-voc",102                "Assoc-acdm",103                "Doctorate"104            ],105            value="Bachelors",106            label="Education"107        )108 109    with gr.Row():110 111        marital_status = gr.Textbox(label="Marital Status")112 113        occupation = gr.Textbox(label="Occupation")114 115    with gr.Row():116 117        relationship = gr.Textbox(label="Relationship")118 119        race = gr.Dropdown(120            [121                "White",122                "Black",123                "Asian-Pac-Islander",124                "Other"125            ],126            value="White",127            label="Race"128        )129 130    with gr.Row():131 132        sex = gr.Radio(133            ["Male","Female"],134            value="Male",135            label="Gender"136        )137 138        native_country = gr.Textbox(label="Native Country")139 140    predict_btn = gr.Button("🚀 Predict Income")141 142    output = gr.Markdown()143 144    predict_btn.click(145        fn=predict,146        inputs=[147            age,148            fnlwgt,149            education_num,150            capital_gain,151            capital_loss,152            hours_per_week,153            workclass,154            education,155            marital_status,156            occupation,157            relationship,158            race,159            sex,160            native_country161        ],162        outputs=output163    )164 165    gr.Markdown("---")166 167    168gr.Markdown("""169---170## 📖 About this Project171 172This project demonstrates how Explainable Artificial Intelligence (XAI) improves the transparency of machine learning predictions.173 174### Model175Random Forest Classifier176 177### Explainability178• SHAP179• LIME180 181### Dataset182Adult Census Income Dataset183 184Developed by **Neha Jhakra**185""")186demo.launch()