Application file

app.py

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+import streamlit as st
+from sklearn.linear_model import LinearRegression
+import pickle
+import numpy as np
+
+# Load the pre-trained model and scaler
+with open('regression_model.pkl', 'rb') as model_file:
+    model = pickle.load(model_file)
+
+with open('scaler.pkl', 'rb') as scaler_file:
+    scaler = pickle.load(scaler_file)
+
+# Streamlit Input Fields
+st.title("Boston Housing Pred App ⌨🏠")
+crim = st.number_input("Enter the crim", value=0.0)
+zn = st.number_input("Enter the zn", value=0.0)
+indus = st.number_input("Enter the indus", value=0.0)
+chas = st.number_input("Enter the chas", value=0.0)
+nox = st.number_input("Enter the nox", value=0.0)
+rm = st.number_input("Enter the rm", value=0.0)
+age = st.number_input("Enter your age", value=0.0)
+dis = st.number_input("Enter the dis", value=0.0)
+rad = st.number_input("Enter the rad", value=0.0)
+ptratio = st.number_input("Enter the ptratio", value=0.0)
+b = st.number_input("Enter B", value=0.0)
+istat = st.number_input("Enter istat", value=0.0)
+tax = st.number_input("Enter tax", value=0.0)
+
+# Predict when button is pressed
+if st.button("Predict"):
+    # Prepare the input data
+    input_data = np.array([[crim,zn, indus, chas, nox, rm, age, dis, rad, ptratio, b, istat, tax]])
+
+    # Scale the input data
+    input_data_scaled = scaler.transform(input_data)
+
+    # Make the prediction
+    result = model.predict(input_data_scaled)
+
+    # Display the prediction
+    st.write(f"The predicted result is: {result[0]:.2f}$")
+