[Solved] CS3120 Hw2 Logistic Regression for binary classification

(NumPy, Pandas and data visualization packages are allowed.)

(SKLearn regression models are allowed!)

Reference code: 2_Logostic_ExSKLearn_Demo.py in blackboard

1. Select a dataset with binary target values using https://machinelearningmastery.com/standard-machine-learning-datasets/

e.g. banknote or diabetes dataset

2. Use pandas to read CSV file as dataframe. (1pt)

e.g. The following code helps import pima diabetes dataset

col_names = [pregnant, glucose, bp, skin, insulin, bmi, pedigree, age, label]

# load dataset

pima = pd.read_csv(pima-indians-diabetes-database.csv, header=None, names=col_names)

3. Select 5 (if not possible then select 4) features from the chosen dataset. (1pt)

List all features you selected in your report.

For example, the following code will select two features

feature_cols = [pregnant, age]

X = pima[feature_cols]

4. Use train _test_split from sklearn.cross_validationtrain to split test and training data by 40% testing + 60% training. (1pt)

5. Fit your model with training data and test your model after fitting.

6. Calculate and plot out

the confusion matrix (1pt)

precision score, recall score, F score (3pts)

Copy your console output (these scores) to your report.

7. Plot out the ROC curve and print out the ROC_AUC score (sklearn.metrics.roc_curve() and sklearn.metrics.roc_auc_score() can be used.) (3pts)

Submit your report and your code in two different files.

Please include the required figure/plot in your report.

e.g.

File1: Assignment2_FirstnameLastname.doc/.pdf (this is the report)

+

File2: Assignment2_ FirstnameLastname.py (this is the code. only .py files accepted.

OR

Assignment2_ FirstnameLastname.zip if you have multiple .py files.)