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Superkart_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb4e0820ccebeb7409dfd3701eac8201c539eece7dc98e5b2c1a01a690311bb7
+size 189205

app.py

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+
+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return joblib.load("Superkart_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("Superkart Revenue Prediction App")
+st.write("This tool predicts the Revenue of superkart based on the product details.")
+
+st.subheader("Enter the product details:")
+
+# Collect user input
+Product_Type = st.selectbox("Product Type",["Baking Goods", "Breads", "Breakfast","Canned","Dairy","Frozen Foods","Fruits and Vegetables","Hard Drinks","Health and Hygiene","Household","Meat","Seafood","Snack Foods","Soft Drinks","Starchy Foods","Others"])
+Product_MRP = st.number_input("MRP", min_value=0, max_value=100)
+Product_Weight = st.number_input("Bathrooms", min_value=0, step=1, max_value=100)
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No sugar"])
+Store_Size = st.selectbox("Store_Size", ["Small", "Medium","High"])
+Store_Location_City = st.selectbox("Store_Location_City?", ["Tier1", "Tier2","Tier3"])
+Store_Type = st.selectbox("Store_Type", ["Departmental Store","Food Mart","Supermarket Type 1",'Supermarket Type 2'])
+Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.0, max_value=100.0, step=1.0, value=1.0)
+Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1900, step=1, max_value=2025)
+
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    "Product_Type": Product_Type,
+    "Product_MRP": Product_MRP,
+    "Product_Weight": Product_Weight,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Store_Size": Store_Size,
+    "Store_Location_City": Store_Location_City,
+    "Store_Type": Store_Type,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Store_Establishment_Year": Store_Establishment_Year
+}])
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The Predicted revenue of store is ${np.exp(prediction)[0]:.2f}.")

requirements.txt

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-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2