Machine Learning App for Sales Prediction
', 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'] timeseriesdata = timeseriesdata[['sales']] # Keep only the 'sales' column # 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("