Forem: Shukurat Bello

Machine Learning Zoomcamp Week 5

Shukurat Bello — Tue, 04 Nov 2025 11:06:02 +0000

🎉 Week 5 of ML Zoomcamp was all about Deployment and getting our ML models into production.

Learned about

Saving and loading models

Flask web services

Churn model deployment with Flask

Virtual environments with Pipenv

Docker containerization

AWS Elastic Beanstalk deployment

From local development to cloud deployment, models can now be used by end users

Machine Learning Zoomcamp Week 4

Shukurat Bello — Wed, 22 Oct 2025 14:11:34 +0000

Week 4 of #mlzoomcamp was all about ML Evaluation

The lessons covered Evaluation Metrics on classification models.

After training a model, it’s performance needs to be evaluated on a test set. This helps to understand how well the model will generalize on a new data.
There are a number of different evaluation metrics that we can use for binary classification problems.

Some of the most common evaluation metrics and concepts include:
☑ Accuracy
☑ Confusion Matrix
☑ Precision
☑ Recall
☑ Class Imbalance and it's importance
☑ F1 Score
☑ Receiver Operating Characteristic Area Under the Curve (ROC AUC).
☑ ROC Curve
☑ K-Fold Cross Validation

The goal of the homework was to apply the evaluation metrics on the classification problem (Bank Marketing dataset - desired target for classification task will be the 'converted' variable - has the client signed up to the platform or not?) from Week 3

ML Zoomcamp Week 3

Shukurat Bello — Tue, 14 Oct 2025 01:12:21 +0000

This is week 3 of #mlzoomcamp and it was all about ML for Classification

I learned how to predict the likelihood of a customer churning using a telco dataset from kaggle. I have worked on this problem before so it was easy to understand.
The assignment was to to use the lead scoring dataset Bank Marketing dataset to classify if the client signed up to the platform or not; using the converted variable (column).

Machine Learning Zoomcamp Week 2

Shukurat Bello — Mon, 06 Oct 2025 11:42:40 +0000

This is week 2 of #mlzoomcamp and it was all about Linear Regression.

You can view this weeks homework

The goal of this homework was to create a regression model for predicting the car fuel efficiency (column 'fuel_efficiency_mpg') and I did that in the following steps: 1️⃣ EDA and preparing the dataset by splitting it into training, validation and testing set. 2️⃣ Shuffling and normalizing the data by applying log transformation. 3️⃣ Building a linear regression from scratch using Numpy. 4️⃣ Evaluating a model on a validation set by using the Root Mean Squared Error (RMSE). 5️⃣ Making predictions with the model.

The focus of the lessons for this week was learning how to perform Linear Regression from scratch, without using any machine learning library, and it was great to know how the 🚗 Case study: Car price prediction 🤖 Data Cleaning & Exploration 🧮 Linear Regression 🔄 Root Mean Squared Error 🐍 Feature Engineering 📚 Regularization 📊 Model training and using the model and others.