Credit_Card_Approval_prediction

In this project, we are diving into the world of credit card approvals. The Credit Card Approval dataset from the UCI Machine Learning Repository is used.

• Start by familiarizing yourself with the dataset, understanding the features, and getting a sense of the overall structure.

Inspect the data:

• Dive deeper into the dataset, examining the characteristics of credit card applications. This step is crucial for gaining insights and understanding the data we are working with.

Handle Missing Values:

• Missing data is a common challenge. Address this issue in multiple steps to ensure a comprehensive approach to handling missing values, making the dataset more robust.

Preprocess the Data:

• Prepare the data for machine learning. This involves transforming and cleaning the dataset to ensure it's in a format that can be fed into a model.

Split the Dataset into Train and Test Sets:

• Divide your dataset into two parts: one for training your model and the other for testing its performance. This helps assess how well your model generalizes to new, unseen data.

Fit a Logistic Regression Model to the Train Set:

• Time to choose a model! Fit a logistic regression model to the training set. Logistic regression is a common choice for binary classification problems like credit card approval.

Make Predictions and Evaluate Performance:

• Once your model is trained, use it to make predictions on the test set. Evaluate its performance using appropriate metrics to ensure it's making accurate predictions.

Grid Search to make the Model Perform Better:

• Fine-tune your model by conducting a grid search. This involves systematically testing different hyperparameter combinations to find the optimal settings for your logistic regression model.

Find the Best Performing Model:

After the grid search, identify the model with the best performance. This is the culmination of your efforts, resulting in an automatic credit card approval predictor ready for deployment.