Breast Cancer Prediction
Machine Learning Project
Logistic Regression Model
Sklearn library & LogisticRegression methods were used to import this classification algoritham.
Step 1: Load the data
Step 2: Logistic Regression measures the relationship between the dependent. Variable and the independent variables, by estimating probabilities using its underlying logistic function.
Step 3: These probabilities must then be transformed into binary values in order to actually make a prediction
Step 4: The Function takes any real-valued number and map it into a value between the range of 0 and 1.
Logistic Regression Model with Full 30 Features
Logistic Regression Model with 7 BestFeature Selected Features
Logistic Regression Model with 7 Correlation Selected Features
Observations:
1. Model accuracy only declined slightly after reducing feature number from 30 to 7.
2. Although accuracy for both 7 feature models were the same, the model with 7 BestFeature selected features was better at predicting Benign while the model with 7 correlation selected features was better at predicting Malignant.