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# app.py
import streamlit as st
import pandas as pd
import numpy as np
import joblib
from eda import (average_sales_by_region, average_sales_and_profit_over_time,
segment_vs_region_distribution, sales_vs_profit_across_segments,
category_composition_for_profit_and_sales)
from prediction import make_prediction
# Load the dataset for EDA
@st.cache
def load_data():
return pd.read_csv('superstore_clean.csv')
df = load_data()
# Load the pipeline and model for predictions
pipeline = joblib.load('full_pipeline_with_unit_price.pkl')
model = joblib.load('best_model.pkl')
# Sidebar for navigation
st.sidebar.title("Navigation")
selection = st.sidebar.radio("Go to", ["Home", "EDA", "Make a Prediction"])
if selection == "Home":
st.title("Welcome to the Superstore Sales Dashboard")
elif selection == "EDA":
st.title("Exploratory Data Analysis (EDA)")
# Average Sales by Region
st.header("Average Sales by Region")
fig1 = average_sales_by_region(df)
st.pyplot(fig1)
# Average Sales and Profit Over Time
st.header("Average Sales and Profit Over Time")
fig2 = average_sales_and_profit_over_time(df)
st.pyplot(fig2)
# Segment vs. Region Distribution
st.header("Segment vs. Region Distribution")
fig3 = segment_vs_region_distribution(df)
st.pyplot(fig3)
# Sales vs. Profit Across Different Customer Segments
st.header("Sales vs. Profit Across Different Customer Segments")
fig4 = sales_vs_profit_across_segments(df)
st.pyplot(fig4)
# Category Composition for Profit and Sales
st.header("Category Composition for Profit and Sales")
fig5 = category_composition_for_profit_and_sales(df)
st.pyplot(fig5)
elif selection == "Make a Prediction":
st.title("Make a Sales Prediction")
# Input form
with st.form("input_form"):
row_id = st.number_input('Row ID', min_value=1, value=1, step=1)
order_id = st.text_input('Order ID')
order_date = st.date_input('Order Date')
ship_date = st.date_input('Ship Date')
ship_mode = st.selectbox('Ship Mode', ['First Class', 'Second Class', 'Standard Class', 'Same Day'])
customer_id = st.text_input('Customer ID')
customer_name = st.text_input('Customer Name')
segment = st.selectbox('Segment', ['Consumer', 'Corporate', 'Home Office'])
country = st.text_input('Country', value='United States')
city = st.text_input('City')
state = st.text_input('State')
postal_code = st.text_input('Postal Code')
region = st.selectbox('Region', ['South', 'West', 'Central', 'East'])
product_id = st.text_input('Product ID')
category = st.selectbox('Category', ['Furniture', 'Office Supplies', 'Technology'])
sub_category = st.selectbox('Sub-Category', ['Bookcases', 'Chairs', 'Labels', 'Tables', 'Storage', 'Furnishings', 'Art', 'Phones', 'Binders', 'Appliances', 'Paper', 'Accessories', 'Envelopes', 'Fasteners', 'Supplies', 'Machines', 'Copiers'])
product_name = st.text_input('Product Name')
sales = st.number_input('Sales', value=0.0, format="%.2f")
quantity = st.number_input('Quantity', value=1, format="%d")
discount = st.number_input('Discount', value=0.0, format="%.2f")
profit = st.number_input('Profit', value=0.0, format="%.2f")
submit_button = st.form_submit_button("Predict")
if submit_button:
# Construct the input DataFrame. Modify as necessary to fit the model's expected input
input_data = pd.DataFrame([[sales, quantity, discount, sub_category]],
columns=['sales', 'quantity', 'discount', 'sub_category'])
# Call prediction function
predicted_profit = make_prediction(input_data)
st.write(f'Predicted Profit: {predicted_profit:.2f}') |