|Provided by||Jayde Thompson (Senior in Applied Math and Statistics)|
|Team Members||Hunter Hodge, Jayde Thompson|
|Start Date||May 9, 2019 @ 5:30pm (Logistics Meeting)|
Everyone interested is welcome
|Meeting Location||Starbucks in Brown|
|Contact if email@example.com|
We plan to use various classification methods on breast cancer data obtained from the University of California website. Main goal is to create and find the best predictive model using all given features in predicting if future breast melanomas will recur or not, after treatment.
We could use classification methods to predict (R, N)
Types of classification methods to use:
- Logistic Regression
- Linear Discriminant Analysis
- Quadratic Discriminant Analysis
- K-Nearest Neighbors.
- We can also try Linear Programming and
- Decision Trees.
The information below was obtained from UCI.
Data Set Information
Each record represents follow-up data for one breast cancer case. These are consecutive patients seen by Dr. Wolberg since 1984 and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis.
The first 30 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe the characteristics of the cell nuclei present in the image.
1) ID number
2) Outcome (R = recur, N = nonrecur)
3) Time (recurrence time if field 2 = R, disease-free time if field 2 = N)
4-33) Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area – 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
j) fractal dimension (“coastline approximation” – 1)from UCI Machine Learning Repository