Olist

I turned 100K+ rows of raw E-commerce data into actionable business insights. ?

, I worked on a complete Data Analysis project using the famous Olist E-commerce Dataset.

Instead of just creating charts, I focused on answering real business questions.

Here are some insights I discovered ?

? Revenue Insights

• A small number of product categories generate the majority of revenue (Pareto principle).

• Certain cities dominate sales activity.

? Product Insights

• Some categories have high sales volume but low revenue.

• Others have low volume but very high prices.

? Payment Behavior

• Credit cards dominate payment methods.

• Installment payments are strongly associated with high order values.

⭐ Customer Satisfaction

• Delivery delays significantly impact customer reviews.

• Some product categories consistently receive lower ratings.

? Customer Geography

• Customers are heavily concentrated in specific Brazilian states.

• Geolocation data helped visualize customer density across regions.

? Tools I Used

• Python (Data Analysis)

• Pandas

• Seaborn

• SQL

• Power BI

? Project Workflow:

Data Cleaning → Data Integration → Exploratory Data Analysis → Visualization → Business Insights

This project strengthened my skills in:

✔ Data Cleaning

✔ Exploratory Data Analysis

✔ Business Insight Extraction

✔ Data Visualization

I'm excited to continue building data-driven solutions and turning data into real insights.