Upload 7 files

.gitattributes

@@ -33,3 +33,4 @@ 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
+train.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,110 @@
+import streamlit as st
+import pandas as pd
+import datetime
+import pickle
+
+# Step 1
+def extract_week(df):
+    try:
+        df['Date'] = pd.to_datetime(df['Date'])
+        df['Week'] = df['Date'].dt.isocalendar().week
+        df['Year'] = pd.DatetimeIndex(df['Date']).year
+        df['Month'] = pd.DatetimeIndex(df['Date']).month
+    except Exception as e:
+        st.error(f"Error during date extraction: {str(e)}")
+    return df
+
+# Step 2
+def maping_type(df):
+    try:
+        df['Type'] = df['Type'].map({'A': 1, 'B': 2, 'C': 3})  # Convert to integer directly
+    except Exception as e:
+        st.error(f"Error during type mapping: {str(e)}")
+    return df
+
+# Step 3
+def convert_to_int(df):
+    try:
+        df['Type'] = df['Type'].astype(int)
+        df['IsHoliday'] = df['IsHoliday'].astype(int)
+    except Exception as e:
+        st.error(f"Error during data type conversion: {str(e)}")
+    return df
+
+# Step 4
+def input_col_sel(df):
+    input_col = ['Store', 'Dept', 'IsHoliday', 'Type', 'Size', 'Temperature',
+                 'Fuel_Price', 'CPI', 'Unemployment', 'Week', 'Year', 'Month',
+                 'Fuel_Price_Cat']
+    return df[input_col]
+
+def load_model():
+    try:
+        # Load the pre-trained model from the pickle file
+        model = pickle.load(open('model.pkl', 'rb'))
+        return model
+    except Exception as e:
+        st.error(f"Error loading the model: {str(e)}")
+        return None
+
+def predict_sales(model, input_data):
+    try:
+        # Make predictions using the loaded model
+        # Replace this line with the appropriate prediction logic based on your model
+        prediction = model.predict(input_data)
+        return prediction
+    except Exception as e:
+        st.error(f"Error during prediction: {str(e)}")
+        return None
+
+def main():
+    st.title("Walmart Sales Forecasting App")
+
+    # Create input fields for manual input
+    st.sidebar.header("Inputs")
+    store = st.sidebar.number_input("Store", min_value=1, max_value=45, step=1, value=1)
+    dept = st.sidebar.number_input("Dept", min_value=1, max_value=99, step=1, value=1)
+    is_holiday = st.sidebar.checkbox("Is Holiday")
+    type_input = st.sidebar.selectbox("Type", ['A', 'B', 'C'])
+    size = st.sidebar.number_input("Size", min_value=34875, max_value=219622, step=1, value=34875)
+    temperature = st.sidebar.number_input("Temperature", min_value=-2, max_value=100)
+    fuel_price = st.sidebar.number_input("Fuel Price", min_value=2.5, max_value=4.4)
+    cpi = st.sidebar.number_input("CPI", min_value=126, max_value=227)
+    unemployment = st.sidebar.number_input("Unemployment", min_value=3.879, max_value=14.00, value=3.879)
+    min_date = datetime.date(2010, 3, 1)  # Minimum allowed date
+    input_date = st.sidebar.date_input("Date", min_value=min_date, value=datetime.date.today())
+    fuel_price_cat = st.sidebar.number_input("Fuel Price Category", min_value=2.00, max_value=4.25)
+
+    # Create a dataframe from manual input
+    input_data = pd.DataFrame({
+        'Store': [store],
+        'Dept': [dept],
+        'IsHoliday': [int(is_holiday)],
+        'Type': [type_input],
+        'Size': [size],
+        'Temperature': [temperature],
+        'Fuel_Price': [fuel_price],
+        'CPI': [cpi],
+        'Unemployment': [unemployment],
+        'Date': [input_date],  # Use the manually input date
+        'Fuel_Price_Cat': [fuel_price_cat]
+    })
+
+    # Load the model
+    model = load_model()
+
+    if model is not None and st.button("Predict Sales Amount"):
+        # Apply the preprocessing steps to the manual input
+        input_data = extract_week(input_data)
+        input_data = maping_type(input_data)
+        input_data = convert_to_int(input_data)
+        input_data = input_col_sel(input_data)
+
+        # Make predictions
+        prediction = predict_sales(model, input_data)
+
+        if prediction is not None:
+            st.success(f"Prediction: {prediction}")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,3 @@
+streamlit==0.90.0
+pandas==1.3.3
+scikit-learn==0.24.2

stores.csv

@@ -0,0 +1 @@
+Store,Type,Size

train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65834f65a2ddccc85d8f4bf6544f73625be553f8ca5f8fdee976b9d0c900e95d
+size 12842546