hanyakov/Titanic-Disaster-Survival-Prediction-Using-Logistic-Regression

This project aims to predict the survival of passengers on the Titanic using logistic regression. The dataset used for this analysis is the famous Titanic dataset, which provides information on the passengers. The goal is to build a machine learning model that can predict whether a passenger survived the disaster based on these features.

Titanic-Disaster-Survival-Prediction-Using-Logistic-Regression

This project aims to predict the survival of passengers on the Titanic using logistic regression. The dataset used for this analysis is the famous Titanic dataset, which provides information on the passengers. The goal is to build a machine learning model that can predict whether a passenger survived the disaster based on these features. Table of Contents Project Description Data Description Data Preprocessing Model Training and Evaluation Installation Usage Results Contributing License Project Description This project involves the following steps:

Data Loading: Load the Titanic dataset from a CSV file. Data Cleaning: Handle missing values and irrelevant columns. Feature Engineering: Create new features from existing data. Data Preprocessing: Convert categorical data to numerical values and scale the features. Model Training: Train a logistic regression model. Model Evaluation: Evaluate the model using confusion matrix, classification report, and accuracy score. Data Description The dataset contains the following columns:

Survived: Survival (0 = No, 1 = Yes) Pclass: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd) Sex: Sex Age: Age in years SibSp: Number of siblings/spouses aboard the Titanic Parch: Number of parents/children aboard the Titanic Ticket: Ticket number Fare: Passenger fare Cabin: Cabin number Embarked: Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton) Data Preprocessing Handling Missing Values:

Fill missing age values with the median age. Create a new column hasCabin to indicate the presence of cabin information (1 if present, 0 if not). Feature Engineering:

Convert the Sex column to numerical values using one-hot encoding. Drop irrelevant columns such as Ticket and Embarked. Scaling:

Scale the features using StandardScaler. Model Training and Evaluation Data Splitting:

Split the data into training, validation, and test sets. Training:

Train a logistic regression model on the training set. Evaluation:

Evaluate the model on the validation and test sets using confusion matrix, classification report, and accuracy score. Installation To run this project, you need to have Python installed. You can install the required libraries using pip: