Loan Approval Prediction with Machine Learning

1. Analyzed and preprocessed dataset:
• Removed irrelevant columns like 'Loan_ID'.
• Handled missing values appropriately.
2. Explored data through EDA:
• Visualized feature distributions.
• Investigated relationships with 'Loan_Status'.
3. Prepared data for modeling:
• Converted categorical columns using one-hot encoding.
• Split dataset into training and testing sets.
• Scaled numerical features using StandardScaler.
4. Trained and evaluated ML model:
• Selected Support Vector Machine (SVM) classifier.
• Trained model on training data.
• Evaluated performance using accuracy and other metrics.