Upload 11 files

app.py

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+
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load your trained model
+model = joblib.load('models\model2.pkl')
+
+# Function to predict sales
+def predict_sales(input_data):
+    # Make predictions using the loaded model
+    sales_prediction = model.predict(input_data)
+    return sales_prediction
+
+# Streamlit app
+def main():
+    st.title('Sales Prediction App')
+
+    # Input widgets
+    PromoInterval = st.selectbox("Promo Interval", ['No Promotion', 'Jan,Apr,Jul,Oct', 'Feb,May,Aug,Nov', 'Mar,Jun,Sept,Dec'])
+
+    # -----------------------------------------------------------------------------------------------
+    StoreType = st.radio("StoreType", ["Small Shop", "Medium Store", "Large Store", "Hypermarket"])
+    Assortment = st.radio("Assortment", ["basic", "extra", "extended"])
+    
+
+    # Encode StateHoliday as 1 for 'Yes' and 0 for 'No' --------------------------------------
+    StateHoliday = st.radio("State Holiday", ["Yes", "No"])
+    StateHoliday = 1 if StateHoliday == "Yes" else 0
+
+    SchoolHoliday = st.radio("School Holiday", ["Yes", "No"])
+    SchoolHoliday = 1 if SchoolHoliday == "Yes" else 0
+
+    Promo = st.radio("Promotion", ["store is participating", "store is not participating"])
+    Promo = 1 if Promo == "store is participating" else 0
+    # ----------------------------------------------------------------------------------------
+    
+
+    Store = st.slider("Store", 1, 1115)
+    Customers = st.slider("Customers", 0, 7388)
+    CompetitionDistance = st.slider("Competition Distance", 20, 75860)
+    CompetitionOpenSinceMonth = st.slider("Competition Open Since Month", 1, 12)
+    CompetitionOpenSinceYear = st.slider("Competition Open Since Year", 1998, 2015)
+    # ----------------------------------------------------------------------------------------
+
+    # Store user inputs
+    input_data = pd.DataFrame({
+        'PromoInterval': [PromoInterval],
+        'StoreType': [StoreType],
+        'Assortment': [Assortment],
+        'StateHoliday': [StateHoliday],
+        'Store': [Store],
+        'Customers': [Customers],
+        'Promo': [Promo],
+        'SchoolHoliday': [SchoolHoliday],
+        'CompetitionDistance': [CompetitionDistance],
+        'CompetitionOpenSinceMonth': [CompetitionOpenSinceMonth],
+        'CompetitionOpenSinceYear': [CompetitionOpenSinceYear]
+    })
+
+    # Display input data
+    st.subheader('Input Data:')
+    st.write(input_data)
+
+    # Predict sales
+    # if st.button('Predict Sales'):
+    #     prediction = predict_sales(input_data)
+    #     st.write('Predicted Sales:', prediction)
+
+    if st.button('Predict Sales'):
+        prediction = predict_sales(input_data)[0]
+        formatted_prediction = "{:.2f}".format(prediction)  # Format prediction to display two decimal points
+        st.write('Predicted Sales:', formatted_prediction)
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+# Record at index 795018:
+# PromoInterval                Jan,Apr,Jul,Oct
+# StoreType                         Small Shop
+# Assortment                             basic
+# StateHoliday                               0
+# Store                                    650
+# Customers                                636
+# Promo                                      1
+# SchoolHoliday                              0
+# CompetitionDistance                     1420
+# CompetitionOpenSinceMonth                 10
+# CompetitionOpenSinceYear                2012
+# Sales                                   6322
+# Name: 795018, dtype: object

models/Rossmann_Model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a4264097df040b74694426b0b55cd70008790bec56cdeefa4eb7de0144096d1
+size 22628002

models/model1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f44f412a489f3fe693806f91c0b379f6fa5ba685d29f905dacaf223449ac6279
+size 22632250

models/model2.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60fb4f99c82338b7cb8917fe1b0734c96c98df89ae01f9fb9617cd94ab967005
+size 24157012

pages/Data Overview.py

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+import streamlit as st
+import pandas as pd
+
+# st.title("hi")
+
+# Function for data overview
+def show_data_overview():
+    # Load data from CSV file
+    data = pd.read_csv("Dataset\Rossmann_Cleaned_data.csv")
+    
+    # Display data overview
+    st.subheader("Data Overview")
+    st.write(data)
+
+
+show_data_overview()  # Call the function to show data overview