Titanic_Survival_Prediction_Algorithm

Steps

1) Import the relevant libraries.

2) Preprocess the Raw Data:-

• i. Variables of Interest
• ii. Checking for Missing Values
• iii. Dealing with Missing Values
• iv. Descriptive Stats [all variables]
• v. Survival Count/s V/S Desired Variables
• vi. Dummy Categorical [relaxing 1st variable]
• vii. Standardization

3) Logistic Regression Model

• i. Training the Dataset:-
• ii. Declare Targets and Inputs
• iii. Optimization Algorithm (Stratified K-Folds Cross-Validator)
• iv. Logistic Regression Model
• v. Modeling with the best parameter
• vi. Accuracy of the training and testing dataset
• vii. Precision, Recall, F-Score, Support
• viii. ROC AUC Score

4) Testing the Data:-

• i. Variables of Interest
• ii. Checking for Missing Values
• iii. Dealing with Missing Values
• iv. Descriptive Stats [all variables]
• v. Survival Count/s V/S Desired Variables
• vi. Dummy Categorical [relaxing 1st variable]
• vii. Prediction’s Dataset Performance