I recently completed a Supervised Learning project where I worked with the Breast Cancer Wisconsin dataset to develop predictive models. The key steps involved:
✅ Data Cleaning & Preprocessing – Handling missing values, feature selection, and scaling. ✅ Exploratory Data Analysis (EDA) – Visualizing data with Seaborn & Matplotlib to understand patterns. ✅ Model Building & Evaluation – Implementing multiple ML models, including: 🔹 Logistic Regression 🔹 Decision Trees 🔹 K-Nearest Neighbors (KNN) 🔹 Naïve Bayes 🔹 Support Vector Machines (SVM) ✅ Hyperparameter Tuning – Using GridSearchCV to optimize models for better performance. ✅ Model Performance – Evaluated using accuracy, confusion matrix, classification reports, and ROC-AUC scores.
After testing multiple models, I finally chose Logistic Regression, achieving an accuracy of 98.25%! 📈🎯 This model provided the best balance of performance and interpretability for the dataset.
This project helped me deepen my understanding of supervised learning techniques and model evaluation. Excited to take on more ML challenges! 🚀
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