Upload 4 files

logistic_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:747725f56f1ade7b3b3ac2cfcc2e8857b146c5af39fdb6ad9e2930cbfce4141f
+size 755

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+scikit-learn
+numpy
+pandas

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a208f078302339ed946721df51be778ae5ab093196f61dfb011ba9b95ec5756e
+size 696

streamlit_app.py

@@ -0,0 +1,34 @@
+import streamlit as st
+import pickle
+import numpy as np
+
+# Load model and scaler
+with open("logistic_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+with open("scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+
+st.title("🚢 Titanic Survival Prediction")
+st.write("Enter passenger details to predict survival.")
+
+# User input fields
+pclass = st.selectbox("Passenger Class", [1, 2, 3])
+sex = st.radio("Sex", ["Male", "Female"])
+age = st.slider("Age", 1, 100, 30)
+fare = st.number_input("Fare", min_value=0.0, step=0.1)
+embarked = st.selectbox("Embarked Port", ["Cherbourg (C)", "Queenstown (Q)", "Southampton (S)"])
+
+# Convert categorical inputs
+sex = 0 if sex == "Male" else 1
+embarked = {"Cherbourg (C)": 0, "Queenstown (Q)": 1, "Southampton (S)": 2}[embarked]
+
+# Normalize input
+input_data = np.array([[pclass, sex, age, fare, embarked]])
+input_data = scaler.transform(input_data)  # Apply same scaling as training
+
+# Prediction
+if st.button("Predict"):
+    prediction = model.predict(input_data)[0]
+    outcome = "Survived 🟢" if prediction == 1 else "Did not survive 🔴"
+    st.write(f"**Prediction:** {outcome}")