Purchase Recommendation Dashboard Code
Overview
This repository contains the code to create a Streamlit dashboard for analyzing the Purchase Recommendation Dataset. The dashboard provides insights into customer purchase behavior, including top products, spending patterns, and revenue trends.
Features
Top 10 Purchased Products : Displays the most popular products purchased.Top Spending Customers : Highlights the customers who spent the most over the year.Monthly Revenue Trends : Shows revenue trends by month.Purchase Distribution : Visualizes the distribution of purchase amounts.Customer Engagement : Analyzes the number of purchases per customer.
Requirements
Python 3.7+
Libraries:
streamlitpandasmatplotlibseaborndatasets
Install the required libraries:
pip install streamlit pandas matplotlib seaborn datasetsHow to Use
Step 1: Clone the Repository
Clone this repository to your local machine or use it directly in your Hugging Face Space.
Step 2: Run the Dashboard
Run the Streamlit application using the following command:
Step 3: Access the Dashboard
Once the application is running, open the provided URL in your web browser to view the dashboard.
Code Explanation
Loading the Dataset
The dataset is directly loaded from Hugging Face using the datasets library:
1 from datasets import load_dataset
2
3 # Load dataset from Hugging Face
4 dataset = load_dataset ( "your-dataset-name" )
5 df = dataset [ 'train' ] . to_pandas ( )
6 df [ 'PurchaseDate' ] = pd . to_datetime ( df [ 'PurchaseDate' ] )
7 df [ 'Month' ] = df [ 'PurchaseDate' ] . dt . to_period ( 'M' )
Generating Insights
Top Products :
top_products = df['ProductName'].value_counts().head(10)Top Customers :
top_customers = df.groupby('CustomerName')['AmountSpent'].sum().sort_values(ascending=False).head(10)Monthly Revenue :
monthly_revenue = df.groupby('Month')['AmountSpent'].sum()
Creating the Dashboard
The Streamlit application is structured with the following sections:
1 import streamlit as st
2
3 st . title ( "Purchase Recommendation Dashboard" )
4
5 st . header ( "Top 10 Purchased Products" )
6 st . bar_chart ( top_products )
7
8 st . header ( "Top Spending Customers" )
9 st . bar_chart ( top_customers )
10
11 st . header ( "Monthly Revenue" )
12 st . line_chart ( monthly_revenue )
13
14 st . header ( "Distribution of Purchase Amounts" )
15 st . histogram_chart ( df [ 'AmountSpent' ] )
16
17 st . header ( "Customer Engagement" )
18 st . histogram_chart ( df [ 'CustomerName' ] . value_counts ( ) )
Deployment
On Hugging Face Spaces
Create a Space : Navigate to Hugging Face Spaces and create a new space.Choose Streamlit SDK : Select Streamlit as the SDK.Upload Files : Upload the app.py file containing the dashboard code. No need to upload the dataset, as it is loaded from Hugging Face.Deploy : The space will automatically install dependencies and deploy the application.
Example Output
Top Products : Bar chart showing the most purchased products.Top Customers : Bar chart highlighting customers with the highest spending.Monthly Revenue : Line chart showing revenue trends.Purchase Distribution : Histogram of purchase amounts.Customer Engagement : Histogram of customer purchase frequencies.
License
This code is provided under the MIT License . Feel free to use, modify, and distribute it.
Contact
For questions or issues, please contact [Your Name/Organization] at [Your Email].
