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Browse files- .gitattributes +1 -0
- Application_Data.csv +0 -0
- app.py +179 -0
- credit-card.png +0 -0
- h8dsft_P1M2_AlselloDM.ipynb +3 -0
- model +0 -0
- requirements.txt +8 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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h8dsft_P1M2_AlselloDM.ipynb filter=lfs diff=lfs merge=lfs -text
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Application_Data.csv
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The diff for this file is too large to render.
See raw diff
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app.py
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import streamlit as st
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import pandas as pd
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import joblib
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from bs4 import BeautifulSoup
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from imblearn.pipeline import Pipeline as imbpipe
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APP_ICON_URL = "credit-card.png"
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# Setup web page
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st.set_page_config(
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page_title="CREDIT CARD APPROVAL APP",
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page_icon=APP_ICON_URL,
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layout="wide",
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)
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st.markdown("""
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<style type="text/css">
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blockquote {
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margin: 1em 0px 1em -1px;
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padding: 0px 0px 0px 1.2em;
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font-size: 20px;
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border-left: 5px solid rgb(230, 234, 241);
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# background-color: rgb(129, 164, 182);
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}
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blockquote p {
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font-size: 30px;
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color: #FFFFFF;
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}
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[data-testid=stSidebar] {
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background-color: rgb(129, 164, 182);
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color: #FFFFFF;
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}
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[aria-selected="true"] {
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color: #000000;
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}
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</style>
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""", unsafe_allow_html=True)
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with st.container():
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col1,col2,_,_ = st.columns([1,14,1,1],gap="large")
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with col1:
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st.image(APP_ICON_URL, width=80)
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with col2:
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st.header(f"Credit Card Prediction App")
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st.caption(f"App developed by [Alsello](https://github.com/AlselloDM)")
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st.write(f"This application predicts whether your credit card application is **Approved** or **Not Approved**.")
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st.markdown("___")
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@st.cache_data
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def fetch_data():
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df = pd.read_csv('Application_Data.csv')
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return df
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df = fetch_data()
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st.sidebar.title("Approval App 📋")
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st.sidebar.write('Please choose the page')
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#make two pages for EDA and Prediction
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page = st.sidebar.selectbox('Choose a page', ['EDA', 'Approval Form📋'])
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if page == 'EDA':
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st.subheader('Approval Status')
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st.write('Approval Status is the target variable with value of 0 (not approved) and 1 (approved).')
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sns.countplot(x='Status', data=df)
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plt.xticks([0, 1])
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st.pyplot(plt)
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st.write('The data is imbalanced.')
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st.subheader('Correlation between Years Employed and Total Income')
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st.write('Years Employed and Total Income usually has correlation.')
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plt.figure(figsize=(8, 6))
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plt.scatter(df['Years_of_Working'], df['Total_Income'], alpha=0.5)
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plt.xlabel('Years Employed')
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plt.ylabel('Total Income')
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plt.title('Correlation between Years Employed and Total Income')
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st.pyplot(plt)
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st.write('We can see that the longer the Years Employed, it does not increase the Total Income.')
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st.write('')
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st.subheader('Correlation of the Features')
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st.write("One of the many Feature Selection Methods is Pearson's Heatmap Correlation Method.")
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plt.figure(figsize=(15,15))
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sns.heatmap(df.corr(), annot=True)
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plt.show()
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st.pyplot(plt)
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st.write("There are no good enough features to choose so I used another method called 'SelectKBest'.")
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st.write('')
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else:
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st.sidebar.title("Applicant's Profile")
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st.sidebar.write('Please enter the needed profile here')
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def user_input_features():
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# Applicant ID
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id = st.number_input("ID of the applicant (7 Digits of your ID)")
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# Male or Female
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sex = st.selectbox('Gender',('M','F'))
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# Car Ownership
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car_ownership = st.selectbox('Car Ownership (1 : Yes | 0 : No)',(1,0))
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# Property Ownership
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property_ownership = st.selectbox('Property Ownership (1 : Yes | 0 : No)',(1,0))
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# Total Children
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total_children = st.slider('Number of Children?',0,5,0)
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# Income
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income = st.number_input('Yearly Income',27000,1575000)
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# Income Type
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income_type = st.selectbox("Employment Status",('Working','Commercial associate ','State servant','Pensioner','Student'))
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# Education Level
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education = st.selectbox("Education Level",('Lower secondary','Secondary / secondary special','Incomplete higher ','Higher education ','Academic degree'))
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# Marriage Status
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marriage_status = st.selectbox("Marriage Status",('Single / not married','Married','Civil marriage','Separated','Widow'))
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# Residence
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residence = st.selectbox('Residency',('House / apartment','With parents','Municipal apartment','Rented apartment','Office apartment','Co-op apartment'))
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# Mobile Phone Ownership
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mobile_phone = st.selectbox('Mobile Phone Ownership (1 : True | 0 : False)',(1,0))
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# Work Phone Ownership
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work_phone = st.selectbox('Work Phone Ownership (1 : True | 0 : False)',(1,0))
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# Phone Ownership
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phone = st.selectbox('Phone Ownership (1 : True | 0 : False)',(1,0))
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# Email Ownership
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email = st.selectbox('Email Ownership (1 : True | 0 : False)',(1,0))
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# Job Title
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job_title = st.selectbox('Job Title',('Laborers','Core staff','Sales staff','Managers','Drivers','High skill tech staff','Accountants','Medicine staff','Cooking staff','Security staff','Cleaning staff','Private service staff','Low-skill Laborers','Waiters/barmen staff','Secretaries','HR staff','Realty agents','IT staff'))
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# Total Family Member
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total_family = st.slider('Number of Family Member',1,7,1)
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# Applicant Age
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age = st.number_input("Age")
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# Years of Working
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years_of_working = st.number_input("Years of Work")
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# Total Bad Debt
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total_bad_debt = st.slider('Total of Bad Debt',0,49,0)\
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# Total Good Debt
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total_good_debt = st.slider('Total of Good Debt',1,61,1)
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data = {'Applicant_ID': id,'Applicant_Gender':sex,'Owned_Car':car_ownership,
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'Owned_Realty':property_ownership,'Total_Children':total_children,
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'Total_Income':income,'Income_Type':income_type,
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'Education_Type':education,'Family_Status':marriage_status,
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'Housing_Type':residence,'Owned_Mobile_Phone':mobile_phone,'Owned_Work_Phone':work_phone,
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'Owned_Phone':phone,'Owned_Email':email,'Job_Title':job_title,'Total_Family_Members':total_family,
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'Applicant_Age':age,'Years_of_Working':years_of_working,
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'Total_Bad_Debt':total_bad_debt,'Total_Good_Debt':total_good_debt}
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#
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features = pd.DataFrame(data,index=[0])
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return features
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input_df = user_input_features()
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st.subheader('Applicant Data📋')
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st.write(input_df)
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load_model = joblib.load("model")
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if st.button('Predict'):
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try:
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prediction = load_model.predict(input_df)
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prediction_proba = load_model.predict_proba(input_df)
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st.subheader('APPROVAL')
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for i in range(len(prediction)):
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if prediction[i] == 0:
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st.error("This applicant is not **Approved**")
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else:
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st.success("This applicant is **Approved**")
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st.subheader('Probability')
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st.write("0 : No | 1 : Yes")
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st.write(prediction_proba)
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except ValueError:
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st.header("Insufficient Applicant Data")
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credit-card.png
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![]() |
h8dsft_P1M2_AlselloDM.ipynb
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:5a7814b40a00ac7b609b62352d4f87419c9a30ce614a096a622960656ba31228
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size 36101976
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model
ADDED
Binary file (43.5 kB). View file
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requirements.txt
ADDED
@@ -0,0 +1,8 @@
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1 |
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streamlit
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pandas
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matplotlib
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joblib
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seaborn
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imblearn
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scikit-learn==1.1.1
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feature_engine==1.5.2
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