CS3120 Hw2 Logistic Regression for binary classification Solved

Description

(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)

 

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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.)