We are required to analyze olist e-commerce dataset as we're trying to grasp trend analysis on product selling. The process involves accessing the database to retrieve the required data, cleaning and analyzing the data, and visualizing the results. Jupyter notebook, Google Colab, SQLite Studio, and Tableau are used for finishing the task.
There are 9 table in the database, which are (1) Olist Customer, (2) Olist Geolocation, (3) Olist Order Items, (4) Olist Order Payments, (5) Olist Reviews, (6) Olist Orders, (7) Olist Product, (8) Olist Seller, and (9) Olist Category Name Translation. To accomplish the task given, we retrieve only the required data from database. We fetch the following dataset from each table: (1) customer_city and customer_state from Olist Customer, (2) order_id and price from Olist Order Items, (3) payment_type from Olist Order Payments, (4) review_score from Olist Reviews, (5) customer_id, order_status , order_purchase_timestamp from Olist Orders, (6) product_id from Olist Product, and (6) product_category_name_translation from Olist Category Name Translation.
- Accesing the database to retrieve the required data
Using SQLite Studio, we fetch the necessary data across tables. The final dataset is including 11 columns: order_id , customer_id, category, price, payment_type, city, state, time_purchased, status , and review_score, with 115.723 entries.
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Cleaning and analyzing the data The task is accomplished using Jupyter Notebook and Google Colab, in Python language programming. You can access the attached file here (olist-final-project.ipynb).
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Visualizing the result You can click link here to go to the tableau dashboard.
Read the full report here: https://medium.com/@adindazr/what-olist-e-commerce-data-tells-us-1f4fa28e466d
Project : https://youtu.be/KsPmYFbqgQ0