Feature-Engineering

The functions we created are included in a script. The necessary parts for pre-processing were taken. Analysis complete.

Business Problem

/Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared.

/When the dataset is passed through this script, the modeling starts. Expected to be ready.

Dataset Story

*The data set is the data set of the people who were in the Titanic shipwreck. *It consists of 768 observations and 12 variables.

The target variable is specified as "Survived";

1: one's survival, 0: indicates the person's inability to survive.

Variables

• Survived

• 0 Died, 1 Survived

• Pclass – Ticket Class

• 1 = Grade 1, 2 = Grade 2, 3 = Grade 3

• Age – Age

• Sibsp – Number of siblings / spouses on the Titanic

• Sex – Gender

• Parch – Number of parents/children on Titanic

• Embarked: – Passenger embarkation port

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

• Fare – Ticket fare

• Cabin: Cabin number

Project Tasks

1- Open a directory called helpers in the working directory and enter it. Add a script named data_prep.py. In the Feature Engineering section, all of our own collect functions into this script. Functions that should be here: ▪ outlier_thresholds ▪ replace_with_thresholds ▪ check_outlier ▪ grab_outliers ▪ remove_outlier ▪ missing_values_table ▪ missing_vs_target ▪ label_encoder ▪ one_hot_encoder ▪ rare_analyser ▪ rare_encoder

2- Write a function called titanic_data_prep. Data preprocessing or EDA functions required for this function, Get it from the eda.py and data_prep.py files in the helpers.

3- Save the data set you preprocessed to the disk with pickle.