I began learning machine learning with the Python library scikit-learn. I am learning the DataCamp machine learning scientist with Python course now, and through interactive code and guided exercises, I understood the basic idea of supervised learning specifically focusing on classification problems.
My first implementation was a K-Nearest Neighbors(KNN) model, where I predicted customer churn based on features like account length and customer service calls. I gradually gained a complete understanding of scikit-learn’s utilities and real machine learning pipelines.
At this stage of my training, I learned fundamentals in classification with scikit-learn. I learned about binary classification, where we want to predict one of two options (e.g., churn or not churn). I learned to train and predict using the k-Nearest Neighbors (k-NN) model and how to divide data into test and training sets properly using train_test_split. I checked model performance using accuracy metrics and understood how model complexity influences them and the risks of overfitting and underfitting. The way accuracy changes with various values of k was visualized and assisted me in choosing the best model. I also used key performance measurement metrics like accuracy_score, .score(), and classification_report. All these skills were practiced in an interactive churn prediction task, where I utilized the whole supervised learning process.
Project: Customer Churn Classification
Using a real-world Telco Churn dataset, I applied KNN and logistic regression to
– Clean and preprocess data (handle missing values, feature encode)
– Select relevant features
– Train, tune, and evaluate classification models
– Model performance comparison and overfitting mitigation
Tools & Libraries Used
scikit-learn: Machine learning models and utilities
– pandas: Data manipulation
– NumPy: Numerical operations
– matplotlib/seaborn: Visualization
– Python: Programming logic and structure
Challenges Faced
- Method use confusion in the start (fit() vs. predict() vs. score())
– Interpreting error messages and debugging model shape issues
– Choosing the correct evaluation metric depending on the task
– Processing categorical data in preprocessing
– Interpreting model complexity plot results
Learning Outcomes
– Developed hands-on experience with real-world datasets.
– Developed the skill to balance model complexity through visualization techniques and cross-validation.
– Improved understanding of data science pipelines through the implementation of scikit-learn.
– Developed confidence to explore more advanced algorithms (e.g., Decision Trees, Logistic Regression).
To support my learning, I referred to:
“Machine Learning with PyTorch and Scikit-Learn”
Sebastian Raschka, Yuxi Liu, Vahid Mirjalili — O’Reilly Media
This book provides modern, hands-on guidance for applying machine learning using both traditional scikit-learn and deep learning tools like PyTorch.