credit_Score_Classification added

credit_Score_Classification/app.py

@@ -0,0 +1,302 @@
+import streamlit as st
+import pandas as pd
+import joblib
+import plotly.graph_objects as go
+import plotly.express as px
+import numpy as np
+import requests
+
+# Load the trained KNN model
+model = joblib.load('knn_model.pkl')
+
+# Define model columns for preprocessing
+model_columns = ['Age', 'Annual_Income', 'Num_Bank_Accounts', 'Num_Credit_Card',
+                 'Interest_Rate', 'Num_of_Loan', 'Num_of_Delayed_Payment',
+                 'Num_Credit_Inquiries', 'Total_Debt', 'Credit_Utilization_Ratio',
+                 'Occupation_Architect', 'Occupation_Developer',
+                 'Occupation_Doctor', 'Occupation_Engineer', 'Occupation_Entrepreneur',
+                 'Occupation_Journalist', 'Occupation_Lawyer', 'Occupation_Manager',
+                 'Occupation_Mechanic', 'Occupation_Media_Manager',
+                 'Occupation_Musician', 'Occupation_Scientist', 'Occupation_Teacher',
+                 'Occupation_Writer', 'Type_of_Credits_Low', 'Type_of_Credits_Medium',
+                 'Previous_Payment_history_Unpaid',
+                 'Payment_Behaviour_High Spent and Medium value payments',
+                 'Payment_Behaviour_High Spent and Small value payments',
+                 'Payment_Behaviour_Low Spent and Large value payments',
+                 'Payment_Behaviour_Low Spent and Medium value payments',
+                 'Payment_Behaviour_Low Spent and Small value payments']
+
+
+# Function to preprocess input data
+def preprocess_input(df):
+    # Encode categorical variables if necessary
+    categorical_cols = ['Occupation', 'Type_of_Credits', 'Previous_Payment_history', 'Payment_Behaviour']
+    df_encoded = pd.get_dummies(df, columns=categorical_cols, drop_first=True)
+    # Ensure all columns are present and in the same order as in training
+    df_encoded = df_encoded.reindex(columns=model_columns, fill_value=0)
+    return df_encoded
+
+
+# Function to predict credit score
+def predict_credit_score(input_data):
+    # Preprocess input data
+    input_df = pd.DataFrame([input_data])
+    input_df_processed = preprocess_input(input_df)
+
+    # Make prediction
+    prediction = model.predict(input_df_processed)
+
+    return prediction[0]  # Return the predicted class index
+
+
+# Function to display semi-circular gauge chart with Plotly
+def display_gauge(predicted_score):
+    plot_bgcolor = "#28282B"  # Dark gray background
+    quadrant_colors = [plot_bgcolor, "#f25829", "#eff229", "#2bad4e"]
+    quadrant_text = ["", "<b>High</b>", "<b>Medium</b>", "<b>Low</b>"]
+    n_quadrants = len(quadrant_colors) - 1
+
+    min_value = 0
+    max_value = 2  # Maximum value for predicted_score
+    hand_length = np.sqrt(2) / 6
+
+    # Calculate hand angle based on predicted score
+    if predicted_score == 0:
+        hand_angle = np.pi * 0.25  # 45 degrees
+    elif predicted_score == 1:
+        hand_angle = np.pi * 0.5  # 90 degrees
+    elif predicted_score == 2:
+        hand_angle = np.pi * 0.75  # 135 degrees
+
+    fig = go.Figure(
+        data=[
+            go.Pie(
+                values=[0.5] + (np.ones(n_quadrants) / 2 / n_quadrants).tolist(),
+                rotation=90,
+                hole=0.5,
+                marker_colors=quadrant_colors,
+                text=quadrant_text,
+                textinfo="text",
+                hoverinfo="skip",
+            ),
+        ],
+        layout=go.Layout(
+            showlegend=False,
+            margin=dict(b=0, t=0, l=10, r=10),
+            width=650,
+            height=450,
+            paper_bgcolor=plot_bgcolor,
+            shapes=[
+                go.layout.Shape(
+                    type="circle",
+                    x0=0.48, x1=0.52,
+                    y0=0.48, y1=0.52,
+                    fillcolor="#ffffff",
+                    line_color="#ffffff",
+                ),
+                go.layout.Shape(
+                    type="line",
+                    x0=0.5, x1=0.5 + hand_length * np.cos(hand_angle),
+                    y0=0.5, y1=0.5 + hand_length * np.sin(hand_angle),
+                    line=dict(color="#ffffff", width=4)
+                )
+            ]
+        )
+    )
+    return fig  # Return the Plotly figure object
+
+
+# Function to fetch credit score information from a mock API
+def fetch_credit_score_info():
+    # Example data, replace this with actual API call
+    credit_info = {
+        "average_credit_score": 690,
+        "highest_credit_score": 850,
+        "lowest_credit_score": 300,
+        "credit_score_distribution": {
+            "Excellent": 20,
+            "Very Good": 25,
+            "Good": 30,
+            "Fair": 15,
+            "Poor": 10
+        }
+    }
+    return credit_info
+
+
+# Function to fetch recent news about credit scores
+# Function to fetch recent news about credit scores and banking
+def fetch_recent_news(api_key):
+    url = f"https://newsapi.org/v2/everything?q=credit+score+banking&apiKey={api_key}&pageSize=10"
+    response = requests.get(url)
+    news_data = response.json()
+    articles = news_data.get('articles', [])
+
+    # Filter articles with missing content or images
+    valid_articles = []
+    for article in articles:
+        if article.get("description") and article.get("urlToImage"):
+            valid_articles.append(article)
+        if len(valid_articles) >= 6:
+            break
+
+    return valid_articles[:6]
+
+
+# Streamlit App
+def main():
+    st.sidebar.title("Navigation")
+    selection = st.sidebar.radio("Go to", ["Credit Score Classification", "Credit Score Information"])
+
+    if selection == "Credit Score Classification":
+        st.title('Credit Score Classification App')
+
+        # Collect user inputs
+        age = st.number_input('Age', min_value=18, max_value=100, value=30)
+        occupation = st.selectbox('Occupation',
+                                  ['Scientist', 'Teacher', 'Engineer', 'Entrepreneur', 'Developer', 'Lawyer',
+                                   'Media_Manager', 'Doctor', 'Journalist', 'Manager', 'Accountant', 'Musician',
+                                   'Mechanic', 'Writer', 'Architect'])
+        annual_income = st.number_input('Annual Income', min_value=0, value=50000)
+        num_bank_accounts = st.number_input('Number of Bank Accounts', min_value=0, value=1)
+        num_credit_cards = st.number_input('Number of Credit Cards', min_value=0, value=1)
+        interest_rate = st.number_input('Interest Rate', min_value=0, value=10)
+        num_of_loan = st.number_input('Number of Loans', min_value=0, value=1)
+        num_of_delayed_payment = st.number_input('Number of Delayed Payments', min_value=0, value=0)
+        num_credit_inquiries = st.number_input('Number of Credit Inquiries', min_value=0, value=1)
+        type_of_credits = st.selectbox('Type of Credits', ['High', 'Medium', 'Low'])
+        total_debt = st.number_input('Total Debt', min_value=0, value=10000)
+        credit_utilization_ratio = st.number_input('Credit Utilization Ratio', min_value=0.0, value=30.0)
+        previous_payment_history = st.selectbox('Previous Payment History', ['Paid', 'Unpaid'])
+        payment_behaviour = st.selectbox('Payment Behaviour', ['High Spent and Small value payments',
+                                                               'Low Spent and Large value payments',
+                                                               'Low Spent and Medium value payments',
+                                                               'Low Spent and Small value payments',
+                                                               'High Spent and Medium value payments',
+                                                               'High Spent and Large value payments'])
+
+        # Create a dictionary of user inputs
+        input_data = {
+            'Age': age,
+            'Occupation': occupation,
+            'Annual_Income': annual_income,
+            'Num_Bank_Accounts': num_bank_accounts,
+            'Num_Credit_Card': num_credit_cards,
+            'Interest_Rate': interest_rate,
+            'Num_of_Loan': num_of_loan,
+            'Num_of_Delayed_Payment': num_of_delayed_payment,
+            'Num_Credit_Inquiries': num_credit_inquiries,
+            'Type_of_Credits': type_of_credits,
+            'Total_Debt': total_debt,
+            'Credit_Utilization_Ratio': credit_utilization_ratio,
+            'Previous_Payment_history': previous_payment_history,
+            'Payment_Behaviour': payment_behaviour
+        }
+
+        # Predict credit score on button click
+        if st.button('Predict', key='prediction_button'):
+            predicted_score = predict_credit_score(input_data)
+
+            # Display result with styled messages
+            st.subheader('Prediction')
+            if predicted_score == 0:
+                st.success(f'**Good Credit Score**')
+            elif predicted_score == 1:
+                st.warning(f'**Standard Credit Score**')
+            elif predicted_score == 2:
+                st.error(f'**Poor Credit Score**')
+
+            # Display gauge chart in the center
+            col1, col2, col3 = st.columns([1, 2, 1])
+            with col2:
+                st.plotly_chart(display_gauge(predicted_score), use_container_width=True)
+
+    elif selection == "Credit Score Information":
+        st.title('Credit Score Information')
+
+        # Fetch credit score information
+        credit_info = fetch_credit_score_info()
+
+        # Display credit score information in a paragraph
+        st.header("About Credit Scores")
+        st.write("""
+            A credit score is a numerical expression based on a level analysis of a person's credit files, 
+            to represent the creditworthiness of an individual. Lenders, such as banks and credit card companies, 
+            use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses 
+            due to bad debt. Credit scores are determined by credit bureaus, who gather information about consumers' 
+            financial history and provide that information in a numerical format.
+        """)
+
+        # Display credit score classification range table
+        st.header("Credit Score Classification")
+        classification_data = {
+            "Credit Score Range": ["300-579", "580-669", "670-739", "740-799", "800-850"],
+            "Classification": ["Poor", "Fair", "Good", "Very Good", "Excellent"],
+            "Significance": [
+                "Considered a poor credit score. Likely to be declined for loans and credit cards.",
+                "Below average. Might qualify for some loans, but with higher interest rates.",
+                "Average. Should qualify for most loans and credit cards with average interest rates.",
+                "Above average. Likely to qualify for loans and credit cards with favorable terms.",
+                "Excellent. Should have no problems getting approved for loans and credit cards with the best terms."
+            ]
+        }
+        classification_df = pd.DataFrame(classification_data)
+        st.table(classification_df)
+
+        # Display credit score statistics
+        st.header("Credit Score Statistics")
+        st.metric("Average Credit Score", credit_info["average_credit_score"])
+        st.metric("Highest Credit Score", credit_info["highest_credit_score"])
+        st.metric("Lowest Credit Score", credit_info["lowest_credit_score"])
+
+        # Display credit score distribution
+        st.header("Credit Score Distribution")
+        distribution = pd.DataFrame(list(credit_info["credit_score_distribution"].items()),
+                                    columns=["Category", "Percentage"])
+        fig = px.bar(distribution, x="Category", y="Percentage", title="Credit Score Distribution",
+                     labels={"Percentage": "Percentage (%)"})
+        st.plotly_chart(fig)
+
+        # Display more organized graphs about credit scores
+        st.header("Additional Credit Score Insights")
+
+        # Example graphs (these should be based on actual data from the API)
+        example_data = pd.DataFrame({
+            "Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
+            "Average Credit Score": [680, 685, 690, 695, 700, 705, 710, 715, 720, 725, 730, 735]
+        })
+        fig1 = px.line(example_data, x="Month", y="Average Credit Score", title="Average Credit Score Over the Year")
+        st.plotly_chart(fig1)
+
+        example_data2 = pd.DataFrame({
+            "Credit Score Range": ["300-579", "580-669", "670-739", "740-799", "800-850"],
+            "Number of People": [50, 100, 200, 150, 75]
+        })
+        fig2 = px.pie(example_data2, values="Number of People", names="Credit Score Range",
+                      title="Credit Score Range Distribution")
+        st.plotly_chart(fig2)
+
+        example_data3 = pd.DataFrame({
+            "Credit Score": ["Poor", "Fair", "Good", "Very Good", "Excellent"],
+            "Interest Rate": [25, 20, 15, 10, 5]
+        })
+        fig3 = px.bar(example_data3, x="Credit Score", y="Interest Rate",
+                      title="Average Interest Rates by Credit Score")
+        st.plotly_chart(fig3)
+
+        st.header("Recent News")
+        api_key = "ENTER_YOUR_NewsAPI_Key"  # Replace with your NewsAPI key obtained from NewsAPI.com
+        news_articles = fetch_recent_news(api_key)
+
+        for i, article in enumerate(news_articles):
+            if i % 2 == 0:
+                col1, col2 = st.columns(2)
+            with col1 if i % 2 == 0 else col2:
+                st.subheader(article["title"])
+                st.image(article["urlToImage"], use_column_width=True)
+                st.write(article["description"])
+                st.write(f"[Read more]({article['url']})")
+
+
+if __name__ == '__main__':
+    main()