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Sales Dashboard Possibilities

Visão geral do Projeto Inovador

Project Overview

 

A comprehensive sales dashboard providing insights into sales performance across various dimensions, with potential for AI-driven data interpretation.

 

Key Features

 

Revenue Overview

  • Total revenue
  • Total sales

 

Revenue Visualizations

  • By state (bar chart)
  • Top 7 product categories
  • Geographical distribution (interactive map) with pydeck

 

Sales Data Analysis

  • Monthly sales table and trend chart (2020-2023)
  • Detailed dataframe with product, seller, and transaction information

 

 

Filtering and Export

  • Filters for product category, price range, and date
  • CSV export option

 

User Interface

  • Dark theme and White, responsive design
  • Navigation tabs: Dataset, Total Revenue, Monthly Revenue, Daily Revenue, Sellers

 

 

AI Interpretation Potential

The rich dataset and various visualizations provide an excellent foundation for AI-driven insights:

  1. Predictive Analytics: Forecast future sales trends based on historical data.
  2. Anomaly Detection: Identify unusual sales patterns or outliers.
  3. Customer Segmentation: Analyze buying behaviors to create targeted marketing strategies.
  4. Product Recommendation: Suggest related products based on purchase history.
  5. Performance Optimization: Recommend inventory management and pricing strategies.

 

MercadoPago Potential

  • Real-time Payment Processing: Integrate actual transaction data from MercadoPago
  • Payment Analytics: Analyze payment methods, installment preferences, and transaction success rates
  • Fraud Detection: Implement AI-driven fraud detection systems using transaction data

 

Data Insights

The dashboard offers both high-level overviews and detailed analysis capabilities, enabling users to identify top-performing products, categories, and regions, as well as track seasonal trends and sales team performance.

This tool serves as a powerful asset for business intelligence, with the potential for AI enhancement to provide even deeper, data-driven insights and recommendations.

Python Git OpenAI Streamlit
Start date: --
Completion date: --
Client: --
Category: Data Science

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