In the last lesson you were given a broad overview of logistic regression. This included an introduction to two separate packages for creating logistic regression models. In this lab, you'll be investigating fitting logistic regressions with statsmodels. For your first foray into logistic regression, you are going to attempt to build a model that classifies whether an individual survived the Titanic shipwreck or not (yes, it's a bit morbid).

In this lab you will:

• Implement logistic regression with statsmodels
• Interpret the statistical results associated with model parameters

Import the data stored in the file 'titanic.csv' and print the first five rows of the DataFrame to check its contents.

# Import the data


df = None

Your target variable is in the column 'Survived'. A 0 indicates that the passenger didn't survive the shipwreck. Print the total number of people who didn't survive the shipwreck. How many people survived?

# Total number of people who survived/didn't survive

Only consider the columns specified in relevant_columns when building your model. The next step is to create dummy variables from categorical variables. Remember to drop the first level for each categorical column and make sure all the values are of type float:

# Create dummy variables
relevant_columns = ['Pclass', 'Age', 'SibSp', 'Fare', 'Sex', 'Embarked', 'Survived']
dummy_dataframe = None

dummy_dataframe.shape

Did you notice above that the DataFrame contains missing values? To keep things simple, simply delete all rows with missing values.

NOTE: You can use the .dropna() method to do this.

# Drop missing rows
dummy_dataframe = None
dummy_dataframe.shape

Finally, assign the independent variables to X and the target variable to y:

# Split the data into X and y
y = None
X = None

Now with everything in place, you can build a logistic regression model using statsmodels (make sure you create an intercept term as we showed in the previous lesson).

Warning: Did you receive an error of the form "LinAlgError: Singular matrix"? This means that statsmodels was unable to fit the model due to certain linear algebra computational problems. Specifically, the matrix was not invertible due to not being full rank. In other words, there was a lot of redundant, superfluous data. Try removing some features from the model and running it again.

# Build a logistic regression model using statsmodels

Generate the summary table for your model. Then, comment on the p-values associated with the various features you chose.

# Summary table

# Your comments here

Create a new model, this time only using those features you determined were influential based on your analysis of the results above. How does this model perform?

# Your code here

# Your comments here

Well done! In this lab, you practiced using statsmodels to build a logistic regression model. You then interpreted the results, building upon your previous stats knowledge, similar to linear regression. Continue on to take a look at building logistic regression models in Scikit-learn!