Logistic regression involves modeling probabilities of a specific outcome given input variables. The outcome of a logistic regression model is a binary outcome such as a yes/no, or a true/false value. Multinomial logistic regression can model more than two possible outcomes.
This week, you will use the flight data (a description of the data is also found in the second tab of the dataset) to build a logistic regression model. (Note: File size is big, so sent drive link to access data set.)
Import the dataset into your Jupyter Notebook and use Python. Complete the following steps:
- Perform descriptive analysis.
- Conduct airline analysis using visual representation.
- Conduct day-of-the-week analysis using visual representation.
- Perform correlational analysis.
- Split the data into training and testing sets.
- Perform logistic regression on the training set.
- Perform predictions on the testing set.
- Perform cross-validation and model comparison.
Briefly describe each step. Submit your assignment supported using a screenshot of your Python codes and results.
