Predict whether a mammogram mass is benign or malignant.

The data used for this project comes from the "mammographic masses" public dataset from the UCI repository (source: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass).

This data contains 961 instances of masses detected in mammograms, and contains the following attributes:

1. BI-RADS assessment (ordinal) - 1 to 5
2. Age (integer) - Patient's age in years 
3. Mass shape (nominal)- Mass shape: round=1 oval=2 lobular=3 irregular=4 
4. Mass margin (nominal) - circumscribed=1; micro-lobulated=2; obscured=3; ill-defined=4; spiculated=5
5. Mass density (ordinal) - high=1; iso=2; low=3; fat-containing=4 
6. Severity (binomial) - benign=0 or malignant=1

Data pre-processing:

• Data exploration
• Handling missing data
• Feature selection
• Normalization

Apply several different classification supervised machine learning techniques and see which one yields the highest accuracy.

Models tested:

• Logistic Regression
• KNN
• Naive Bayes
• Decision Tree
• Random Forest
• SVM
• Neural network

Models performances are measured using K-fold cross validation (K=10).