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preprocessor.joblib

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requirements.txt

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streamlit_app.py

@@ -0,0 +1,217 @@
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+# Machine Learning Modeling
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import RandomizedSearchCV
+import xgboost as xgb
+from sklearn.metrics import mean_squared_error
+import joblib
+
+# Set the page layout to full width
+st.set_page_config(layout="wide")
+# Initialize df as None
+df = None
+
+st.sidebar.title("Favorita Stores")
+selected_option = st.sidebar.radio("Select to Proceed", ["Data Statistics", "Visuals", "Time Series Analysis", "Forecasting"])
+
+# Custom CSS styling for the title
+st.markdown(
+    """
+    <style>
+    .title-text {
+        font-size: 28px;
+        text-align: center;
+        background-color: #3498db;
+        color: white;
+        padding: 10px 0;
+        width: 100%;
+        position: sticky;
+        top: 0;
+        z-index: 1;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Streamlit App Title
+st.markdown('<p class="title-text">Machine Learning App for Sales Prediction</p>', unsafe_allow_html=True)
+
+# Function to load and process the data
+def load_and_process_data():
+    global df
+    # Allow the user to upload an Excel file
+    uploaded_file = st.file_uploader("Choose an Excel file", type=["xlsx"])
+    if uploaded_file is not None:
+        # Check if the file is an Excel file
+        if uploaded_file.type == 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet':
+            # Read the Excel file into a DataFrame
+            df = pd.read_excel(uploaded_file)
+            # Remove null values
+            df.dropna(inplace=True)
+            df = df.drop(columns='Unnamed: 0')
+        else:
+            st.write("Please upload a valid Excel file.")
+
+# Load and process the data
+load_and_process_data()
+
+if selected_option == "Data Statistics":
+    # Rest of the code for "Data Statistics" option using df
+    if df is not None:
+        number_sample = st.number_input("Enter sample size to display data", min_value=5, max_value=10, step=1, value=5)
+        displayed_data = df.head(number_sample)
+        st.write("Sample data", displayed_data)
+        st.write("Summary Statistics of float/Integer columns", df.describe())
+        object_columns = df.select_dtypes(include='object').columns.tolist()
+        selected_column = st.selectbox("Select column of Data Type Object to View Unique values", object_columns)
+        if selected_column:
+            unique_values = df[selected_column].unique()
+            st.write("Unique values are", unique_values)
+
+elif selected_option == "Visuals":
+    # Rest of the code for "Visuals" option using df
+    if df is not None:
+        object_columns = df.select_dtypes(include='object').columns.tolist()
+        selected_column = st.selectbox("Select column of Data Type Object for Visualization", object_columns)
+        if selected_column:
+            df['date'] = pd.to_datetime(df['date'])  # Convert to datetime if applicable
+            df_grouped = df.groupby(selected_column)['sales'].sum().head(10)
+            df_grouped = df_grouped.sort_values(ascending=False)
+            fig, ax = plt.subplots(figsize=(15, 6))
+            ax.bar(df_grouped.index, df_grouped.values)
+            ax.set_xlabel(selected_column)
+            ax.set_ylabel('Sales Count')
+            ax.set_title(f'Top 10 Sales Count for {selected_column}')
+            st.pyplot(fig)  # Pass the figure to st.pyplot()
+elif selected_option == "Time Series Analysis":
+    if df is not None:
+        # Choose date and sales columns
+        timeseriesdata = df[['sales', 'date']]
+        timeseriesdata.index = timeseriesdata['date']
+        # Make date the index
+        timeseriesdata = timeseriesdata.resample('D').sum()  # Resample to daily sales
+
+        # Resample the data based on user's choice
+        resample_method = st.selectbox("Select a resampling method", ['M', 'Q', 'Y'])
+        if resample_method:
+            resampled_data = timeseriesdata.resample(resample_method).sum()
+
+            # Plot the time series using Seaborn lineplot
+            plt.figure(figsize=(15, 6))
+            sns.lineplot(data=resampled_data)
+            plt.ylabel('Sales')
+            plt.title(f'Sales Time Series (Resampled by {resample_method})')
+            st.pyplot(plt.gcf())
+else:
+    st.write("Please enter these inputs to predict sales. Thank you!")
+    # Load the pre-trained model and preprocessor
+    model = joblib.load('./xgb_model.joblib')
+    preprocessor = joblib.load('./preprocessor.joblib') 
+
+    
+
+    # Create a layout with 2 columns for even distribution
+    col1, col2 = st.columns(2)  
+
+    # User Inputs - Number
+    with col1:
+        # Create a date input using st.date_input
+        date = st.date_input("Enter Date")      
+
+        # Convert the selected date to a string in the desired format (e.g., YYYY-MM-DD)
+        formatted_date = date.strftime('%Y-%m-%d')      
+
+    # User Inputs - Year
+    with col2:
+        family = st.selectbox("Select product family", ['CELEBRATION', 'CLEANING', 'DAIRY', 'DELI', 'EGGS', 'FROZEN FOODS',
+           'GROCERY I', 'GROCERY II', 'HARDWARE', 'HOME AND KITCHEN I',
+           'HOME AND KITCHEN II', 'HOME APPLIANCES', 'HOME CARE',
+           'LADIESWEAR', 'LAWN AND GARDEN', 'LINGERIE', 'LIQUOR,WINE,BEER',
+           'MAGAZINES', 'MEATS', 'PERSONAL CARE', 'PET SUPPLIES',
+           'PLAYERS AND ELECTRONICS', 'POULTRY', 'PREPARED FOODS', 'PRODUCE',
+           'SCHOOL AND OFFICE SUPPLIES', 'SEAFOOD', 'AUTOMOTIVE', 'BABY CARE',
+           'BEAUTY', 'BEVERAGES', 'BOOKS', 'BREAD/BAKERY']) 
+
+    # User Inputs - On Promotion
+    with col1:
+        onpromotion = st.number_input("Enter Number for onpromotion", min_value=0, step=1)  
+
+
+    # User Inputs - Day of the Week
+    with col2:
+        city = st.selectbox("Select city", ['Quito', 'Cayambe', 'Latacunga', 'Riobamba', 'Ibarra',
+           'Santo Domingo', 'Guaranda', 'Puyo', 'Ambato', 'Guayaquil',
+           'Salinas', 'Daule', 'Babahoyo', 'Quevedo', 'Playas', 'Libertad',
+           'Cuenca', 'Loja', 'Machala', 'Esmeraldas', 'Manta', 'El Carmen'])    
+
+    # User Inputs - Product Category
+    with col1:
+        oil_prices = st.number_input("Enter oil price", min_value=1, step=1)    
+ 
+
+    # User Inputs - Day of the Week
+    with col2:
+        holiday_type = st.selectbox("Select holiday type", ['Holiday', 'Additional', 'Transfer', 'Event', 'Bridge'])    
+
+    # User Inputs - Product Category
+    with col1:
+        sales_lag_1 = st.number_input("Enter Number for sales lag", min_value=0, step=1)    
+
+
+    # User Inputs - Day of the Week
+    with col2:
+        moving_average = st.number_input("Enter Number for moving average", min_value=0, step=1)    
+
+    # Placeholder for Predicted Value   
+
+    # Add custom spacing between columns
+    st.markdown("<hr>", unsafe_allow_html=True) 
+
+
+
+    # Predict Button
+    if st.button("Predict"):
+        # Prepare input data for prediction
+        # Prepare input data for prediction
+        # Create a DataFrame with all required columns except "sales"
+        prediction_placeholder = st.empty()
+        input_df = pd.DataFrame({
+            "family": [family],
+            "onpromotion": [onpromotion],
+            "city": [city],
+            "oil_prices": [oil_prices],
+            "holiday_type": [holiday_type],
+            "sales_lag_1": [sales_lag_1],
+            "moving_average": [moving_average]
+        })
+
+        # Transform the input DataFrame using the preprocessor
+        preprocessed_data = preprocessor.transform(input_df)
+
+
+
+        # Make a prediction
+        prediction = model.predict(preprocessed_data)   
+
+         
+        # Display the prediction
+        prediction_placeholder.text(f"Predicted Value for sales: {prediction[0]: ,.2f}")  
+
+        if prediction >= 0:
+            prediction_placeholder.markdown(
+            f'Predicted Value for sales: <span style="background-color: green; padding: 2px 5px; border-radius: 5px;">${prediction[0]:,.2f}</span>',
+            unsafe_allow_html=True
+        )
+        else:
+            prediction_placeholder.markdown(
+            f'Predicted Value for sales: <span style="background-color: red; padding: 2px 5px; border-radius: 5px;">${prediction[0]:,.2f}</span>',
+            unsafe_allow_html=True
+        )

xgb_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62c4a5b6126d3a372dcbcab316b5c27dbce01b9c57a155ef0064bd8626fd8f96
+size 419184