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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hero.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1 @@
----
-title: Big Mart Sales
-emoji: 🐨
-colorFrom: gray
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# sales_prediction_deployment

big_mart.py

@@ -0,0 +1,114 @@
+"""
+@author: Abdulmalik Adeyemo
+"""
+import pandas as pd
+from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder
+
+
+import pickle
+import streamlit as st
+from streamlit_option_menu import option_menu
+
+
+# loading the saved models
+model = pickle.load(open('big_mart_model.pkl', 'rb'))
+
+
+# sidebar for navigation
+with st.sidebar:
+    
+    selected = option_menu('Sales Prediction System', #Title of the OptionMenu
+                          
+                          ['Big Mart Sales Prediction','Financial Inclusion'], #You can add more options to the sidebar
+                          icons=['shop', 'cash'], #BootStrap Icons - Add more depending on the number of sidebar options you have.
+                          default_index=0) #Default side bar selection
+    
+    
+# Sales Prediction Page
+if (selected == 'Big Mart Sales Prediction'):
+    
+    # page title
+    st.title('Sales Prediction using ML')
+    
+    #Image
+    st.image('hero.jpg')
+    
+    # getting the input data from the user
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        Item_Visibility = st.number_input('Item Visibility', min_value=0.00, max_value=0.40, step=0.01)
+
+    with col1:
+        Item_MRP = st.number_input('Item MRP', min_value=30.00, max_value=270.00, step=1.00)
+
+    with col1:
+        Outlet_Size = st.selectbox('Outlet Size', ['Small', 'Medium', 'High'])
+
+    with col2:
+        Item_Fat_Content = st.selectbox('Item Fat Content', ['Low Fat', 'Regular'])
+
+    with col2:
+        Outlet_Location_Type = st.selectbox('Outlet Location Type', ['Tier 1', 'Tier 2', 'Tier 3'])
+    
+    #Data Preprocessing
+        
+    data = {
+            'Item_Visibility': Item_Visibility,
+            'Item_MRP' : Item_MRP,
+            'Outlet_Size' : Outlet_Size,
+            'Item_Fat_Content_Regular': Item_Fat_Content,
+            'Outlet_Location_Type' : Outlet_Location_Type
+             }
+    
+    oe = OrdinalEncoder(categories = [['Small','Medium','High']])
+    scaler = StandardScaler()
+    
+    def make_prediction(data):
+        df = pd.DataFrame(data, index=[0])
+        
+        if df['Item_Fat_Content_Regular'].values == 'Low Fat':
+            df['Item_Fat_Content_Regular'] = 0.0
+  
+        if df['Item_Fat_Content_Regular'].values == 'Regular':
+          df['Item_Fat_Content_Regular'] = 1.0
+
+        if df['Outlet_Location_Type'].values == 'Tier 1':
+          df[['Outlet_Location_Type_Tier 1','Outlet_Location_Type_Tier 2', 'Outlet_Location_Type_Tier 3']] = [1.0, 0.0, 0.0]
+
+        if df['Outlet_Location_Type'].values == 'Tier 2':
+          df[['Outlet_Location_Type_Tier 1','Outlet_Location_Type_Tier 2', 'Outlet_Location_Type_Tier 3']] = [0.0, 1.0, 0.0]
+
+        if df['Outlet_Location_Type'].values == 'Tier 3':
+          df[['Outlet_Location_Type_Tier 1','Outlet_Location_Type_Tier 2', 'Outlet_Location_Type_Tier 3']] = [0.0, 0.0, 1.0]
+        
+        df['Outlet_Size'] = oe.fit_transform(df[['Outlet_Size']])
+        df = df.drop(columns = ['Outlet_Location_Type'], axis = 1 )
+        df[['Item_Visibility', 'Item_MRP']] = StandardScaler().fit_transform(df[['Item_Visibility', 'Item_MRP']])
+        
+        prediction = model.predict(df)
+        
+        return round(float(prediction),2)
+        
+
+    
+    # code for Prediction
+    # sales_prediction_output = ""
+    
+    # creating a button for Prediction
+    
+    if st.button('Predict Sales'):
+        sales_prediction = make_prediction(data)
+        sales_prediction_output = f"The sales is predicted to be {sales_prediction}"
+
+        
+    st.success(sales_prediction_output)
+
+
+
+
+
+    
+
+
+

big_mart_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e1ae7dd1c756f785476360921a24f11d8a4a4d702ae72d9a2af5cf3fded7db3
+size 148898

requirements.txt

@@ -0,0 +1,4 @@
+scikit-learn
+pandas
+streamlit
+streamlit-option-menu