Added my Project Files

app.py

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+# Loading key libraries
+import streamlit as st
+import os
+import pickle
+import numpy as np
+import pandas as pd
+import re
+from pathlib import Path
+from PIL import Image
+from category_encoders.binary import BinaryEncoder
+from sklearn.preprocessing import StandardScaler
+
+# Setting the page configurations
+st.set_page_config(page_title= "Sales Prediction Forecasting", page_icon= ":heavy_dollar_sign:", layout= "wide", initial_sidebar_state= "auto")
+
+# Setting the page title
+st.title("Grocery Store Sales Time Series Model Prediction")
+
+# Function to load the dataset
+@st.cache_resource
+def load_data(relative_path):
+   data= pd.read_csv(relative_path, index_col= 0)
+   #merged["date"] = pd.to_datetime(merged["date"])
+   return data
+
+    
+
+
+# Loading the base dataframe
+rpath = r"merged_train_data.csv"
+data = load_data(rpath)
+
+
+
+# Load the model and encoder ans scaler
+model = pickle.load(open("model.pkl", "rb"))
+encoder = pickle.load(open("encoder.pkl", "rb"))
+scaler = pickle.load(open("scaler.pkl", "rb"))
+
+# main sections of the app
+header = st.container()
+dataset = st.container()
+features_and_output = st.container()
+
+
+
+
+# Designing the sidebar
+st.sidebar.header("Brief overview of the Columns")
+st.sidebar.markdown(""" 
+                    - **store_nbr** identifies the store at which the products are sold.
+                    - **family** identifies the type of product sold.
+                    - **sales** is the total sales for a product family at a particular store at a given date. Fractional values are possible since products can be sold in fractional units(1.5 kg of cheese, for instance, as opposed to 1 bag of chips).
+                    - **onpromotion** gives the total number of items in a product family that were being promoted at a store at a given date.
+                    - **date** is the date on which a transaction / sale was made
+                    - **city** is the city in which the store is located
+                    - **state** is the state in which the store is located
+                    - **store_type** is the type of store, based on Corporation Favorita's own type system
+                    - **cluster** is a grouping of similar stores.
+                    - **oil_price** is the daily oil price
+                    """)
+
+# Structuring the dataset section
+with dataset:
+    if dataset.checkbox("Preview the dataset"):
+        dataset.write(data.head())
+        dataset.write("Further information will preview when take a look at the  sidebar")
+    dataset.write("---")
+
+
+
+
+# Icon for the page
+image = Image.open(r"beautiful image.png")
+
+# inputs from the user
+form = st.form(key="information", clear_on_submit=True)
+
+# Structuring the header section
+with header:
+    header.write("This an application to build a model that more accurately predicts the unit sales for thousands of items sold at different Favorita stores")
+
+    header.image(image)
+   
+    header.write("---")
+
+
+ 
+
+# Structuring the features and output section
+with features_and_output:
+    features_and_output.subheader("Inputs")
+    features_and_output.write("This section captures your input to be used in predictions")
+
+    left_col, mid_col, right_col = features_and_output.columns(3)
+
+    # Designing the input section of the app
+    with form:
+        left_col.markdown("***Combined data on Product and Transaction***")
+        date = left_col.date_input("Select a date:")
+        family = left_col.selectbox("Product family:", options= sorted(list(data["family"].unique())))
+        onpromotion = left_col.number_input("Number of products on promotion:", min_value= data["onpromotion"].min(), value= data["onpromotion"].min())
+        city = left_col.selectbox("City:", options= sorted(set(data["city"])))
+    
+        mid_col.markdown("***Data on Location and type***")
+        store_nbr = mid_col.selectbox("Store number:", options= sorted(set(data["store_nbr"])))
+        type_x = mid_col.radio("type_x:", options= sorted(set(data["type_x"])), horizontal= True)
+        type_y = mid_col.radio("type_y:", options= sorted(set(data["type_y"])), horizontal= True)
+        cluster = mid_col.select_slider("Store cluster:", options= sorted(set(data["cluster"])))
+        state = mid_col.selectbox("State:", options= sorted(set(data["state"])))       
+    
+        right_col.markdown("***Data on Economical Factors***")
+        oil_price = right_col.number_input("Oil price:", min_value= data["oil_price"].min(), value= data["oil_price"].min())
+        
+         # Submission point
+        submitted = form.form_submit_button(label= "Submit button")
+
+        if submitted:
+                 with features_and_output:
+                    input_features = {
+                       "date":[date],
+                       "store_nbr": [store_nbr],
+                       "family": [family],
+                       "onpromotion": [onpromotion],
+                       "city": [city],
+                       "state": [state],
+                       "type_x": [type_x],
+                       "cluster":[cluster],
+                       "oil_price": [oil_price],
+                       "type_y": [type_y],
+                    }
+                                  
+
+
+# Define the function to make predictions
+def predict_sales(input_data, input_df):
+    # defining categories and numeric columns
+    categoric_columns = ['family', 'city', 'state', 'type_y', 'type_x']
+    columns = list(input_df.columns) 
+    numeric_columns = [i for i in columns if i not in categoric_columns]
+    scaled_num = scaler.fit_transform(input_df[numeric_columns])
+    encoded_cat = encoder.transform(input_df[categoric_columns])
+    input_data = pd.concat([scaled_num, encoded_cat], axis=1)
+    # convert input_data to a numpy array before flattening to convert it back to a 2D array
+    input_data = input_data.to_numpy()
+    prediction = model.predict(input_data.flatten().reshape(1, -1))
+    return prediction
+
+ #Convert input parameters to a pandas DataFrame
+input_dict = {
+   'store_nbr': store_nbr,
+   'cluster': cluster,
+   'city': city,
+   'state': state,
+   'family': family,
+   'type_x': type_x,
+   'type_y': type_y,
+   'onpromotion': onpromotion,
+   'oil_price': oil_price,
+   'date' : date
+  }
+input_df = pd.DataFrame([input_dict])
+
+
+@st.cache_resource
+def getDateFeatures(df):
+    df['date'] = pd.to_datetime(df['date'], errors='coerce')
+    df['month'] = df['date'].dt.month
+    df['day_of_month'] = df['date'].dt.day
+    df['day_of_year'] = df['date'].dt.dayofyear
+    df['week_of_year'] = df['date'].dt.isocalendar().week
+    df['week_of_year'] = df['week_of_year'].astype(float)
+    df['day_of_week'] = df['date'].dt.dayofweek
+    df['year'] = df['date'].dt.year
+    df["is_weekend"] = np.where(df['day_of_week'] > 4, 1, 0)
+    df['is_month_start'] = df['date'].dt.is_month_start.astype(int)
+    df['quarter'] = df['date'].dt.quarter
+    df['is_month_end'] = df['date'].dt.is_month_end.astype(int)
+    df['is_quarter_start'] = df['date'].dt.is_quarter_start.astype(int)
+    df['is_quarter_end'] = df['date'].dt.is_quarter_end.astype(int)
+    df['is_year_start'] = df['date'].dt.is_year_start.astype(int)
+    df['is_year_end'] = df['date'].dt.is_year_end.astype(int)
+
+    df["season"] = np.where(df.month.isin([12,1,2]), 0, 1)
+    df["season"] = np.where(df.month.isin([6,7,8]), 2, df["season"])
+    df["season"] = pd.Series(np.where(df.month.isin([9, 10, 11]), 3, df["season"])).astype("int8")
+    df['pay_day'] = np.where((df['day_of_month']==15) | (df['is_month_end']==1), 1, 0)
+    df['earthquake_impact'] = np.where(df['date'].isin(
+        pd.date_range(start='2016-04-16', end='2016-12-31', freq='D')), 1, 0)
+
+    return df
+input_df = getDateFeatures(input_df)
+input_df = input_df.drop(columns= ['date'], axis=1)
+
+# Make prediction and show results
+if st.button('Predict'):
+  prediction = predict_sales(input_df.values, input_df)
+  st.success('The predicted sales amount is $' + str(round(prediction[0],2)))
+  
+  
+  # ----- Defining and structuring the footer
+footer = st.expander("**Subsequent Information**")
+with footer:
+    if footer.button("Special Thanks"):
+        footer.markdown("*We want to express our appreciation and gratitude to Emmanuel,Racheal, Mavies and Richard for their great insights and contributions!*")

encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83eee99c982dfe47d4a410eaae318efae7b927a6b1bcbbefffaae7693c854556
+size 10242

requirements.txt

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+pandas==1.5.3
+numpy==1.24.2
+scikit-learn==1.2.2
+pytest
+category_encoders

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b21ec3396db4610a47906f21c3c5c8e567f633fbd96c2725cdaf3606c5c79716
+size 1370