Just completed an Ecommerce Customer & Sales Analysis project as part of my machine learning journey.
This project helped me practice:
Pandas
NumPy
Matplotlib
Seaborn
Exploratory Data Analysis (EDA)
Next step in the journey:
Machine Learning Fundamentals 🚀
Starting with:
Supervised Learning
Regression
Classification
Model evaluation concepts
Still learning. Still building.
I’d also genuinely appreciate feedback from people more experienced in data analysis or machine learning.
If there are areas where:
my analysis could improve
my visualizations could be better
I missed important insights
my workflow can be optimized
feel free to point them out.
So I said I was continuing my machine learning journey with libraries… and I began with pandas 📊
To practice, I picked a dataset of fuel prices in Nigeria (1924–2024) and honestly… I didn’t expect it to be this interesting
Here are a few things that stood out to me:
This was my first time really using pandas like this — cleaning data, plotting trends, checking relationships.
Still learning, but this made everything feel more real.
Next stop: NumPy