In this project, we will be using machine learning algorithms to predict credit card approvals for individuals. The project will involve the following phases:
Requirements gathering and analysis: In this phase, we will gather and analyze the requirements for the project, including the features to be used for predicting credit card approvals, the performance metrics to be used to evaluate the model, and any other business requirements.
Data collection and processing: In this phase, we will collect a dataset of credit card applications and process it by handling missing values, converting categorical features to numerical features, and scaling numerical features.
Machine learning model development: In this phase, we will develop and train several machine learning model such as logistic regression.
Integration with credit card issuers' systems: In this phase, we will integrate the credit card approval prediction system with the systems used by credit card issuers to streamline their approval process and improve their decision-making capabilities.
Testing and deployment: In this phase, we will test the system thoroughly to ensure that it is functioning properly and deploy it to a production environment.
Overall, this project will provide valuable insights into the credit card approval process and help individuals better understand their chances of getting approved for a credit card. Additionally, the predictive model developed in this project can be used by credit card companies to improve their approval process and reduce the risk of fraud. The user interface will provide an easy-to-use and intuitive way for users to determine their credit card approval status, and the integration with credit card issuers' systems will allow for seamless communication and decision-making.
Hardware: machine for data processing and machine learning model training. development server for development and testing.
Software: Python 3.x Python Libraries: NumPy, Pandas, Scikit-Learn, and TensorFlow for machine learning development. Git for version control.
Task 1: Data Inspection
Task 2: Handling Missing Values
Task 3: Data Preprocessing
Task 4: Splitting the Dataset
Task 5: Fitting a Logistic Regression Model
Task 6: Grid Searching and Model Performance Improvement
Task 7: Finding the Best Performing Model