This repository consists of three machine learning projects developed as part of the CodSoft task. Each project focuses on different aspects of predictive modeling and includes detailed documentation in separate Readme File (.md).
Predicting the survival status of passengers on the Titanic based on various features. The dataset underwent preprocessing steps, including duplicate removal, missing value treatment and model building using Logistic Regression, Random Forest, Gradient Boosting, XGBoost and AdaBoost.
- TITANIC SURVIVAL PREDICTION.ipynb: Jupyter Notebook containing the code.
Predicting the species of Iris flowers based on features like sepal length, sepal width, petal length and petal width. The project involves duplicate removal, outlier treatment, label encoding and model building using Random Forest.
- Iris Flower.ipynb: Jupyter Notebook containing the code.
Predicting credit card fraud using transaction data. The project includes duplicate removal, missing value check, outlier treatment, data scaling, data balancing data and model building using Logistic Regression.
- Credit Card Fraud Detection.ipynb: Jupyter Notebook containing the code.
- Folder containing the datasets of Titanic Survival prediction and Iris Flower used in the projects and Credit Card Fraud Detection Dataset link "https://www.google.com/url?q=https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud&sa=D&source=apps-viewer-frontend&ust=1707936380974013&usg=AOvVaw1vmOmFpYt8ZNywSId1bDaZ&hl=en-GB".
These projects collectively showcase a range of machine learning techniques for predictive modeling in different domains.