Turma 8

📈 Stores Sales Analysis

🎯 Main goal: data‑driven recommendation on which of four stores should be shold

💻 Project Overview

This repository contains the complete workflow used to analyse the performance of four retail stores (Loja 1 – Loja 4) and to recommend which outlet the owner (Sr. João) should sell. The analysis integrates Python, Pandas, Matplotlib, Prettytable and Google Colab otebooks to evaluate sales, revenue, customer ratings and product mix.

📋 Objectives

• Consolidate raw CSV sales data for all stores
• Calculate key metrics: revenue, net margin, average freight, customer rating
• Identify best/worst‑selling products and categories per store
• Visualise insights with at least three chart types (bar, pie, scatter/geo‑map)
• Produce a written report recommending the store to be sold (Loja 4)

📊 Visualisations

✏️ Conclusion

1. Revenue analysis

• Store 1 leads in both gross and net revenue.
• Store 4 shows the worst performance in both respects.

2. Sales analysis by category and product

• Category with the highest number of sales: furniture, with Store 3 standing out.
• Category with the worst sales performance: housewares in Stores 1 and 2 and musical instruments in Stores 3 and 4—Store 4 showing the poorest performance overall.

3. Customer experience analysis

• Store 3 ranks highest in customer satisfaction.
• Store 1 has the lowest rating; however, it yields the greatest profit, indicating that improvements in logistics and after-sales service could raise its evaluation.

4. Shipping cost analysis

• Store 4 has the lowest shipping cost, but this is not reflected in higher profit or greater customer satisfaction.
• Store 1 has the highest shipping cost, yet is the store that generates the most profit.

SUMMARY

• Store 1 – strengths: highest revenue. weaknesses: most expensive shipping and lowest customer-satisfaction score.
• Store 2 – strengths: good rating and solid revenue. weaknesses: slow inventory turnover of the “board game” item.
• Store 3 – strengths: best rating and good revenue. weaknesses: revenue slightly below Stores 1 and 2.
• Store 4 – strengths: cheapest shipping. weaknesses: lowest revenue, slow sales of musical instruments, and average rating.

Sales recommendation

Store 4

Store 4 displays inferior financial performance compared with the others, slow inventory turnover in musical instruments, and—despite its lower shipping cost—this does not translate into greater customer satisfaction.

🤖 Technologies used:

• Python 3 v3.10
• Pandas
• Matiplotlib
• Prettytable

Project versions:

🔗 Colab:

Colab

📂 Paycharm:

# Crate a project folder:
mkdir store_project

#open the folder:
cd store_project

Make Conda enviroment:

conda create --name store
conda activate store

Clone the repo:

git clone https://github.com/daniel-neves-dev/alura_store.git
cd alura_store
cd paycharm

# Install packages: 
pip install -r requirements.txt

# Start the program:
python3 main.py