initial commit

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Custom Streamlit styling with sticky navbar
+st.markdown(
+    """
+    <style>
+    body {
+        background-color: #1E1E1E;
+        color: #FFFFFF;
+        font-family: 'Arial', sans-serif;
+    }
+    .stButton>button {
+        background-color: #4A90E2;
+        color: #FFFFFF;
+        border-radius: 15px;
+        padding: 12px 24px;
+        font-size: 16px;
+        font-weight: bold;
+    }
+    .title {
+        color: #64FFDA;
+        text-shadow: 1px 1px #FF4C4C;
+    }
+    .stTabs [data-testid="stHorizontalBlock"] {
+        position: sticky;
+        top: 0;
+        background-color: #1E1E1E;
+        z-index: 10;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Load the Telco Customer Churn dataset
+st.title("📲 Telco Customer Churn Prediction")
+st.markdown("<h2 class='title'>Predict whether a customer will churn! 🚀</h2>", unsafe_allow_html=True)
+
+# Load dataset
+file_path = 'WA_Fn-UseC_-Telco-Customer-Churn.csv'
+df = pd.read_csv(file_path)
+
+# Preprocess data and train model (runs once)
+df = df[['tenure', 'MonthlyCharges', 'TotalCharges', 'Churn']]
+df = df.replace(" ", np.nan).dropna()
+df['TotalCharges'] = pd.to_numeric(df['TotalCharges'])
+df['Churn'] = df['Churn'].apply(lambda x: 1 if x == 'Yes' else 0)
+
+# Define features and target
+X = df.drop('Churn', axis=1)
+y = df['Churn']
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Scale data
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Train Support Vector Machine Model
+model = SVC(kernel='linear', probability=True, random_state=42)
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+
+# Top Tabs Navigation
+tab1, tab2, tab3 = st.tabs(["📊 Dataset", "📈 Visualization", "🔮 Prediction"])
+
+# Dataset Section
+with tab1:
+    st.write("### 📊 Dataset Preview")
+    st.dataframe(df.head())
+
+# Visualization Section
+with tab2:
+    # Display model performance
+    accuracy = accuracy_score(y_test, y_pred)
+    st.write("### 🔥 Model Performance")
+    st.write(f"**✅ Model Accuracy:** {accuracy:.2f}")
+
+    # Visualizing performance
+    st.write("### 📊 Performance Breakdown")
+    conf_matrix = confusion_matrix(y_test, y_pred)
+    st.write("Confusion Matrix:")
+    fig, ax = plt.subplots()
+    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='coolwarm', ax=ax)
+    st.pyplot(fig)
+
+# Prediction Section
+with tab3:
+    st.write("### 🔮 Predict Customer Churn")
+    st.markdown("Adjust the stats below to simulate a customer scenario!")
+
+    tenure = st.slider("Customer Tenure (Months)", min_value=0, max_value=72, value=12)
+    monthly_charges = st.slider("Monthly Charges ($)", min_value=0, max_value=200, value=50)
+    total_charges = st.slider("Total Charges ($)", min_value=0, max_value=10000, value=600)
+
+    if st.button("✨ Predict Churn"):
+        input_data = scaler.transform([[tenure, monthly_charges, total_charges]])
+        prediction = model.predict(input_data)[0]
+        prediction_proba = model.predict_proba(input_data)[0]
+
+        st.subheader("🔮 Prediction Result")
+        result_text = "🚨 Customer is likely to CHURN!" if prediction == 1 else "✅ Customer is likely to STAY."
+        st.success(result_text) if prediction == 0 else st.error(result_text)
+        st.write(f"Confidence: {prediction_proba[prediction]:.2f}")
+
+        # Churn/Stay Bar Chart
+        st.write("### 📊 Churn Probability Breakdown")
+        fig, ax = plt.subplots()
+        ax.bar(["Stay", "Churn"], [prediction_proba[0], prediction_proba[1]], color=["#64FFDA", "#FF4C4C"])
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Probability")
+        ax.set_title("Customer Churn Probability")
+        st.pyplot(fig)

requirements.txt

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+streamlit
+pandas
+numpy
+matplotlib
+seaborn
+scikit-learn