OlistDSProject

This project focuses on leveraging data to make informed decisions for a company. It utilizes techniques from both Data Science and Software Quality disciplines to analyze data, generate insights, and create actionable metrics. The project involves working with real-world data from Olist, a Brazilian e-commerce platform.

Key Components:

Data Acquisition and Preparation:

• Extracted data from Olist's dataset provided in CSV format.
• Cleaned and preprocessed the data to ensure accuracy and consistency.
• Utilized database diagram for understanding data relationships and structure.

Exploratory Data Analysis (EDA):

• Conducted EDA to gain insights into the dataset's characteristics and trends.
• Identified patterns, outliers, and correlations within the data.
• Utilized visualizations such as graphs and tables to present findings.

Decision Making:

• Identified five key decisions that a company could make based on the data.
• Each decision was supported by relevant analysis, graphs, and tables.
• Metrics were defined for tracking and evaluating the outcomes of each decision.

Software Quality Assessment:

• Ensured the quality of the information presented in the project.
• Evaluated the accuracy, completeness, and reliability of data.
• Implemented best practices in data analysis and visualization.

Documentation:

• Documented the entire process including data preprocessing, analysis, and decision-making steps.
• Provided clear explanations for each decision and associated metrics.
• Included instructions for replicating the project on other datasets.

Project Files:

• Code: Avaiable in .py format and .ipynb (if possible consider vizualizing in .ipynb for better project structure).
• olist_data.zip: Compressed file containing the dataset in CSV format.
• Database Diagram: Visual representation of the dataset's structure and relationships.

Outcome:

• The project successfully demonstrates the application of data science techniques in making data-driven decisions for a company. By analyzing Olist's dataset, valuable insights were obtained, leading to actionable decisions. The inclusion of metrics and quality assessment ensures the reliability and effectiveness of the decision-making process. This project serves as a valuable resource for understanding the importance of data-driven decision-making and its implementation in real-world scenarios