A machine learning project using the Titanic dataset to predict passenger survival based on features such as age, sex, class, and more. This project demonstrates data preprocessing, exploratory data analysis (EDA), feature engineering, model building, and evaluation. đ Dataset The dataset used is the Titanic dataset provided by Kaggle, which includes information about the passengers aboard the Titanic, such as: â˘PassengerId â˘Pclass â˘Name â˘Sex â˘Age â˘SibSp â˘Parch â˘Ticket â˘Fare â˘Cabin â˘Embarked â˘Survived (target variable)
đ Project Highlights â˘Data Cleaning: Handled missing values, encoded categorical features, dropped irrelevant columns. â˘Exploratory Data Analysis (EDA): Visualized data distributions and feature relationships using Seaborn and Matplotlib. â˘Feature Engineering: Created new features and selected relevant ones for modeling. â˘Model Training: Trained models including Logistic Regression, Decision Trees, and Random Forests. â˘Model Evaluation: Assessed performance using accuracy score and confusion matrix.
đ Results Achieved strong prediction accuracy using the Random Forest Classifier.