breast-cancer-prediction
✨ Predicting whether breast cancer tumors are malignant or benign
MIT License
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Jupyter Notebook
Breast Cancer Prediction
✨ Notebook: Predicting whether breast cancer tumors are malignant or benign.
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Import & Data: importing libraries, loading data, and transforming it into a dataframe -
Data Analysis & Exploration: exploring features, data types, missing values, removing unnecessary 'features', and analyzing data distribution and variable correlation -
Outlier Analysis: visually potential outliers, using z-score to spot those outliers, and building a dataframe without them -
Data Preprocessing: separate the target from the predictors, label encode the target, and scale the data (both to the original dataframe and the one w/o outliers) -
Helper Functions: build functions to help with performing the predictions (test-training split and w/ PCA) and to plot graphs about the models' metrics -
Prediction Models: perform predictions with different models (Naive Bayes, Logistic Regression, SVM, KNN, Decision Tree, Random Forest, XGBoost) -
Model Analysis & Comparison: compare all models' metrics