Initial Plan for the E-Commerce MVP

Objective

Proposed Architecture

Key Features

Sitemap and Structure

project_root/
├── frontend/
│   ├── index.html
│   ├── styles.css
│   ├── category.html (future)
│   ├── product.html (future)
│   └── assets/
│       └── images/
├── backend/
│   ├── app.py
│   ├── requirements.txt
│   └── api/
│       └── (API endpoints)
├── data/
│   ├── products.csv
│   └── products_with_categories.csv
└── infra/
    ├── templates/
    ├── scripts/
    │   └── category_extraction.py
    └── README.md

Current Product Schema

Field Description Example
uniq_id Unique identifier e0c74430be28f32f8c2a29060
product_url Link to product detail page http://example.com/products/e0c74430be28
product_name Name of the product Ultra HD Television
product_category_tree Hierarchical product categorisation Electronics > Televisions > LED TVs
pid Internal product ID PID-982310
retail_price Recommended retail price 799.99
image URL/path to product image http://example.com/images/tv.png
description Product features and attributes 55-inch 4K LED TV with HDR support
product_rating Platform-assigned rating 4.2
overall_rating Aggregated rating from multiple sources 4.0
brand Brand or manufacturer Samsung

Project Update: Progress, Key Learnings & Next Steps

Progress to Date

Key Learnings

Technical Steps

Script to extract top-level category:

import pandas as pd

df = pd.read_csv("products.csv")

def extract_top_category(category_tree):
    if pd.notnull(category_tree):
        return category_tree.split(' > ')[0]
    return None

df['category'] = df['product_category_tree'].apply(extract_top_category)
df.to_csv("products_with_categories.csv", index=False)

print("Top-level categories extracted!")

Immediate Next Steps

Ongoing Vision