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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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from sklearn.linear_model import LinearRegression |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import LabelEncoder |
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st.set_page_config(page_title="BigMart Sales Predictor", page_icon="🛒", layout="centered") |
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st.title("🛒 BigMart Sales Prediction using Real World Dataset") |
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st.markdown("Fill in the product details to get a sales prediction.") |
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@st.cache_data |
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def load_data(): |
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data = pd.read_csv("Train.csv") |
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data.fillna(data.mean(numeric_only=True), inplace=True) |
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data.fillna("Unknown", inplace=True) |
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label_enc = LabelEncoder() |
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for col in ['Item_Fat_Content', 'Item_Type', 'Outlet_Identifier', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type']: |
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data[col] = label_enc.fit_transform(data[col]) |
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return data |
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df = load_data() |
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features = ['Item_Weight', 'Item_Visibility', 'Item_MRP'] |
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target = 'Item_Outlet_Sales' |
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X = df[features] |
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y = df[target] |
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model = LinearRegression() |
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model.fit(X, y) |
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product_name = st.text_input("📦 Product Name") |
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item_weight = st.number_input("⚖️ Item Weight (kg)", min_value=0.0, step=0.1) |
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item_visibility = st.slider("👀 Item Visibility", 0.0, 1.0, 0.05) |
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item_mrp = st.number_input("💰 Item MRP", min_value=0.0, step=1.0) |
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if st.button("Predict Sales"): |
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if not product_name: |
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st.warning("Please enter a product name.") |
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else: |
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user_input = np.array([[item_weight, item_visibility, item_mrp]]) |
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predicted_sales = model.predict(user_input)[0] |
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st.success(f"📈 Predicted Sales for '{product_name}': ₹{predicted_sales:,.2f}") |
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result_df = pd.DataFrame({ |
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"Product Name": [product_name], |
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"Item Weight": [item_weight], |
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"Item Visibility": [item_visibility], |
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"Item MRP": [item_mrp], |
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"Predicted Sales": [predicted_sales] |
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}) |
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st.download_button("📥 Download Result as CSV", result_df.to_csv(index=False), file_name="prediction.csv", mime="text/csv") |
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st.sidebar.title("📌 About") |
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st.sidebar.markdown(""" |
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This app uses a **real BigMart dataset** from Kaggle and a **Linear Regression model** to predict sales. |
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You can customize features or switch to advanced ML models later! |
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""") |
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