ortsed / dsc-logistic-regression-in-scikit-learn-lab-dc-ds-career-042219

Logistic Regression in SciKit Learn - Lab

Introduction

In this lab, you are going to fit a logistic regression model to a dataset concerning heart disease. Whether or not a patient has heart disease is indicated in the final column labeled 'target'. 1 is for positive for heart disease while 0 indicates no heart disease.

Objectives

You will be able to:

• Implement logistic regression in sci-kit learn
• Form conclusions about the performance of a model

Let's get started!

#Starter Code
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd

#Starter Code
df = pd.read_csv('heart.csv')
df.head()

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	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1

Define appropriate X and y

Recall the dataset is whether or not a patient has heart disease and is indicated in the final column labelled 'target'. With that, define appropriate X and y in order to model whether or not a patient has heart disease.

#Your code here 
X = 
y = 

Normalize the Data

Normalize the data prior to fitting the model.

#Your code here

Train Test Split

Split the data into train and test sets.

#Your code here

Fit a model

Fit an initial model to the training set. In sci-kit learn you do this by first creating an instance of the regression class. From there, then use the fit method from your class instance to fit a model to the training data.

logreg = LogisticRegression(fit_intercept = False, C = 1e12) #Starter code
#Your code here

Predict

Generate predictions for the train and test sets. Use the predict method from the logreg object.

#Your code here

Initial Evaluation

How many times was the classifier correct for the training set?

#Your code here

How many times was the classifier correct for the test set?

#Your code here

Analysis

Describe how well you think this initial model is performing based on the train and test performance. Within your description, make note of how you evaluated performance as compared to your previous work with regression.

#Your answer here

Summary

In this lab, you practiced a standard data science pipeline: importing data, splitting into train and test sets, and fitting a logistic regression model. In the upcoming labs and lessons, you'll continue to investigate how to analyze and tune these models for various scenarios.