# import streamlit as st # import pandas as pd # import numpy as np # from sklearn.preprocessing import LabelEncoder # import joblib # # Load your trained model # model = joblib.load('models\model1.pkl') # # Function to predict sales # def predict_sales(input_data): # 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", ["a", "b", "c", "d"]) # Assortment = st.radio("Assortment", ["a", "b", "c"]) # StateHoliday = st.radio("State Holiday", ["1", "0"]) # SchoolHoliday = st.radio("School Holiday", ["1", "0"]) # Promo = st.radio("Promo", ["1", "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) # # PromoInterval StoreType Assortment StateHoliday Store Customers Promo SchoolHoliday CompetitionDistance CompetitionOpenSinceMonth CompetitionOpenSinceYear # # 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 __name__ == '__main__': # main() # import streamlit as st # import pandas as pd # import numpy as np # from sklearn.preprocessing import LabelEncoder # import joblib # # Load your trained model # model = joblib.load('models\model1.pkl') # # Function to predict sales # def predict_sales(input_data): # sales_prediction = model.predict(input_data) # return sales_prediction # # Function for data analysis # def perform_analysis(data): # # Add your analysis code here # st.subheader("Analysis") # st.write("Performing analysis...") # # Function for data overview # def show_data_overview(): # # Load data from CSV file # data = pd.read_csv('Dataset/rossmann.csv') # # Display data overview # st.subheader("Data Overview") # st.write(data) # # Streamlit app # def main(): # st.title('Sales Prediction App') # # Sidebar options # sidebar_option = st.sidebar.radio("Navigation", ["Main", "Analysis", "Data Overview"]) # if sidebar_option == "Main": # # 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", ["a", "b", "c", "d"]) # Assortment = st.radio("Assortment", ["a", "b", "c"]) # # StateHoliday = st.radio("State Holiday", ["1", "0"]) # # ------------------------------------------------------------------------------- # # Define the options and their corresponding labels # options = ["1", "0"] # labels = ["Yes", "No"] # # Create the radio button with labels # StateHoliday = st.radio("State Holiday", labels) # # Convert the selected label back to its corresponding option value # # selected_option = options[labels.index(state_holiday)] # # ------------------------------------------------------------------------------- # SchoolHoliday = st.radio("School Holiday", ["1", "0"]) # Promo = st.radio("promotion", ["1", "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) # elif sidebar_option == "Analysis": # perform_analysis(None) # Pass data for analysis if needed # elif sidebar_option == "Data Overview": # show_data_overview() # Pass data for overview if needed # if __name__ == '__main__': # main() 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