COMP 237 - Online lab assignment “Logistic Regression” - IT Assignment Help

Assignment Task:

Task:

Pre-requisite to carrying out the assignment:
1. Download from the course shell the following comma separated file: titanic.csv. This file contains the details of each passenger on the Titanic and also whether they survived or not.

Field Descriptions
survival Survival(0 = No, 1 = Yes)
pclass Passenger class(1 = 1st, 2 = 2nd, 3 = 3rd)
name Name of the passenger
sex Gender of the passenger
age Age of the passenger
sibsp Number of siblings/spouses aboard
parch Number of parents/children aboard
ticket Ticket number
fare Passenger fare
cabin Cabin
embarked Port of embarkation

(C = Cherbourg, Q = Queenstown, S = Southampton)

A brief description of the column names of the dataset is, as follows:

2. Go through and watch all “Linear & Logistic” lecture and lab tutorials related to modules # 5 & 6 to understand the concepts and the presented code.
Submission: for each exercise that requires code, please create a project folder and include all project python scripts/modules and screenshot of output, as needed. Name the folder “Exercise#X_firstname”, where X is the exercise number and firstname is your first name. (In total 1 folders for this assignment).
For all questions that require written or graphic response create one word document and indicate the exercise number and then state your response. Name the document “Written_responses_firstname”, where firstname is your firstname. (In total one word or pdf document).
Create one zipped folder containing all of the above, name it Logistic_firstname where firstname is your firstname.
Note: In all coding amendments, make sure to comment your code with explanations.
Due date: End of week # 7

Assignment - exercises:
1. Exercise # 1: titanic analysis and logistic regression (100 marks)
Requirements:
a. Get the data :
1. Load the “titanic.csv” data into a data frame, name the dataframe titaninc_firstname , where firstname is your firstname.

b. Initial Exploration:
1. Display (print) the first 3 records.
2. Display (print) the shape of the dataframe.
3. Display (print) the names, types and counts (showing missing values per column).
Use pandas built in method info. For more info checkout:
https://pandas.pydata.org/pandas-
docs/stable/reference/api/pandas.DataFrame.info.html
4. From the info identify four columns that are not going to be useful for the model.
Note them in your written response and explain why you chose them. (hint columns with unique values, columns with a lot of missing values)
5. Display (print the unique values for the following columns : (“Sex”, “Pclass”)
c. Data visualization
1. Use pandas crosstab and matplotlib to generate the following diagrams plots:
a. A bar chart showing the # of survived versus the passenger class. Give an appropriate name for the x and y axis in addition to an appropriate title that includes your name.
b. A bar chart showing the # of survived versus the gender. Give an appropriate name for the x and y axis in addition to an appropriate title that includes your name.
c. Analyze both plots and write a conclusion from each plot in your written response.

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