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import pandas as pd |
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import plotly.express as px |
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from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix |
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import pickle |
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import gradio as gr |
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def predict_credit_worthiness(name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22): |
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path = 'german_credit_risk/model/model.pickle' |
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greet = 'Hey, ' + name + '!' |
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with open(path, 'rb') as file: |
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model = pickle.load(file) |
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inputs = {'Account Balance_1': int(x1), |
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'Account Balance_2': int(x2), |
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'Account Balance_3': int(x3), |
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'Payment Status of Previous Credit_1': int(x4), |
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'Payment Status of Previous Credit_3': int(x5), |
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'Purpose_1': int(x6), |
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'Purpose_4': int(x7), |
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'Value Savings/Stocks_1': int(x8), |
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'Value Savings/Stocks_3': int(x9), |
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'Value Savings/Stocks_5': int(x10), |
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'Length of current employment_1': int(x11), |
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'Length of current employment_4': int(x12), |
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'Instalment per cent_4': int(x13), |
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'Guarantors_1': int(x14), |
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'Duration in Current address_1': int(x15), |
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'Duration in Current address_2': int(x16), |
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'Most valuable available asset_1': int(x17), |
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'Most valuable available asset_4': int(x18), |
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'Concurrent Credits_3': int(x19), |
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'Type of apartment_1': int(x20), |
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'No of Credits at this Bank_1': int(x21), |
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'Occupation_1': int(x22) |
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} |
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prediction = model.predict([list(inputs.values())]) |
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y_test = pd.read_parquet('german_credit_risk/data/processed/y_test.parquet') |
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y_test = y_test.squeeze() |
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yhat = pd.read_parquet('german_credit_risk/data/processed/yhat.parquet') |
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yhat = yhat.squeeze() |
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precision = precision_score(y_test, yhat).round(2) |
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recall = recall_score(y_test, yhat).round(2) |
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f1 = f1_score(y_test, yhat).round(2) |
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features_names = ['No account', 'No balance', 'Some balance', 'No credit problems', |
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'Some credit problems', 'New car', 'Other purpose', 'No savings', |
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'DM betwenn [100, 1000]', 'DM >= 1000', 'Employment: <1 year (or unemployed)', 'Employment: 4<x<7 years', |
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'Installment smaller than 20%', 'No guarantors', 'Less than a year in same address', '1<x<4 years in address', |
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'Not available / no assets', 'Ownership of house or land', 'No further running credits', 'Free ap', |
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'One credit at thins bank','Unemployed or unskilled'] |
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importance = model.feature_importances_ |
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data = pd.DataFrame() |
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data['Feature Importance'] = importance |
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data['Feature'] = features_names |
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p = px.bar(data, y='Feature Importance', x='Feature', width=1200, height=500) |
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cfm = confusion_matrix(y_test, yhat) |
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cfm_plot = px.imshow(cfm, |
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x=['Predicted 0', 'Predicted 1'], |
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y=['Actual 0', 'Actual 1'], |
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color_continuous_scale='Blues', |
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labels=dict(x="Predicted", y="Actual", color="Count"), |
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text_auto=True) |
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if prediction == 1: |
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return (greet + ' According to our model, your client is eligible for the loan.', |
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'Precision: '+ str(precision), |
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'Recall: '+ str(recall), |
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'F1 Score: '+ str(f1), |
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p, |
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cfm_plot) |
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else: |
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return (greet + ' Unfortunately, according to our model, your client is not eligible for the loan for now :(.', |
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'Precision: '+ str(precision), |
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'Recall: '+ str(recall), |
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'F1 Score: '+ str(f1), |
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p, |
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cfm_plot) |
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with gr.Blocks() as demo: |
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gr.Markdown('# Credit Worthiness Prediction') |
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gr.Markdown(""" |
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To predict our clients' creditworthiness, please use this application as follows: |
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1. Enter your name and navigate through the client's information tabs. Select the boxes that best match your client's characteristics. Leave blank if none apply. |
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2. Once completed, click 'Predict' to determine if the client is creditworthy. |
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""") |
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with gr.Accordion('Name'): |
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name = gr.Textbox(lines=1, label='Your name') |
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with gr.Accordion("Enter your client's information"): |
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with gr.Tab('Account Balance'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x1 = gr.Checkbox(1, label='No account') |
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x2 = gr.Checkbox(0, label='No balance') |
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x3 = gr.Checkbox(0, label='Some balance') |
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with gr.Tab('Payment status of previous credit'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x4 = gr.Checkbox(1, label='Some problems') |
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x5 = gr.Checkbox(0, label='No problems in this bank') |
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with gr.Tab('Purpose'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x6 = gr.Checkbox(1, label='New car') |
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x7 = gr.Checkbox(0, label='Other') |
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with gr.Tab('Value savings/stocks'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x8 = gr.Checkbox(1, label='No savings') |
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x9 = gr.Checkbox(0, label='DM betwenn [100, 1000]') |
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x10 = gr.Checkbox(0, label='DM >= 1000') |
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with gr.Tab('Length of current employment'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x11 = gr.Checkbox(1, label='Below 1 year (or unemployed)') |
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x12 = gr.Checkbox(0, label='Between 4 and 7 years') |
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with gr.Tab('Instalment per cent'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x13 = gr.Checkbox(0, label='Smaller than 20%') |
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with gr.Tab('Guarantors'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x14 = gr.Checkbox(0, label='No guarantors') |
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with gr.Tab('Duration in current address'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x15 = gr.Checkbox(1, label='Less than a year') |
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x16 = gr.Checkbox(0, label='Between 1 and 4 years') |
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with gr.Tab('Most valuable available asset'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x17 = gr.Checkbox(1, label='Not available / no assets') |
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x18 = gr.Checkbox(0, label='Ownership of house or land') |
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with gr.Tab('Concurrent credits'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x19 = gr.Checkbox(0, label='No further running credits') |
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with gr.Tab('Type of apartment'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x20 = gr.Checkbox(0, label='Free apartment') |
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with gr.Tab('Number of credits at this Bank'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x21 = gr.Checkbox(0, label='One credit') |
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with gr.Tab('Occupation'): |
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gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.') |
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x22 = gr.Checkbox(0, label='Unemployed or unskilled with no permanent') |
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predict_button = gr.Button('Predict') |
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prediction_output = gr.Label(num_top_classes=2) |
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with gr.Accordion('Metrics and plots'): |
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with gr.Tab('Metrics'): |
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with gr.Row(): |
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precision_output = gr.Label() |
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with gr.Row(): |
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recall_output = gr.Label() |
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with gr.Row(): |
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f1_output = gr.Label() |
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with gr.Tab('Feature Importances'): |
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fimp_output = gr.Plot() |
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with gr.Tab('Confusion Matrix'): |
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cfm_output = gr.Plot() |
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predict_button.click(fn=predict_credit_worthiness, |
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inputs=[name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22], |
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outputs=[prediction_output,precision_output, recall_output, f1_output, fimp_output, cfm_output]) |
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gr.Markdown(''' |
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Want to work in a project together or have interest in my services? Reach me: |
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[Linkedin](https://www.linkedin.com/in/marcilioduarte98/) |
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[Github](https://github.com/marcilioduarte) |
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@marcilioduarte | Economics and Data Science |
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''') |
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demo.launch() |
