bank loan classification
18/08/2024
Project Description:
The Bank Loan Classification project focuses on building a machine learning model that predicts whether a customer is eligible for a loan based on specific features such as income, credit history, employment status, loan amount, and other relevant factors.
Key Steps
Data Collection & Preprocessing: Gather and clean data from banking records. Handle missing values, normalize the data, and perform feature engineering to create useful predictors. Exploratory Data Analysis (EDA): Analyze the dataset to understand relationships between variables, detect patterns, and visualize insights using tools like Pandas, Seaborn, and Matplotlib. Model Building: Choose appropriate machine learning algorithms (e.g., Logistic Regression, Decision Trees, Random Forest, etc.). Split the data into training and testing sets. Train the model on the training data and validate it on the test data. Model Evaluation: Evaluate the model using metrics such as accuracy, precision, recall, and F1-score. Perform hyperparameter tuning to optimize the model's performance. Deployment: Deploy the model into a web application or system to automate loan eligibility predictions for new customers.
Technologies Used
Programming Languages: Python Libraries: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn Machine Learning Models: Logistic Regression, Decision Trees, Random Forest, etc. Tools: Jupyter Notebook, Flask/Django (for deployment)
