This project uses the Random Forest machine learning algorithm to predict whether a loan application should be approved or rejected. By analyzing applicant information such as credit history, income, and employment, the model identifies patterns to make a confident decision. The use of a Random Forest, an ensemble learning method, ensures a robust and highly accurate model by combining the predictive power of multiple individual decision trees.
-
Data Preprocessing: Cleans and prepares the dataset, handling missing values and converting categorical features to a numerical format suitable for the model.
-
Feature Importance: Visualizes and quantifies which features (e.g., credit history, income) are most influential in the model's decision-making process.Model Training: Trains a RandomForestClassifier on the processed data.
-
Hyperparameter Tuning: Explores key hyperparameters such as the number of trees (n_estimators) and the maximum depth of each tree (max_depth) to optimize model performance.
-
Evaluation: Provides a detailed Classification Report and Confusion Matrix to assess the model's performance, with a special focus on the F1-Score, a crucial metric for this type of problem where misclassifying a "bad" loan is more costly than a "good" one.
-
Python: The core programming language.
-
pandas: For efficient data manipulation and analysis.
-
scikit-learn: For implementing the Random Forest algorithm, preprocessing steps, and evaluation metrics.
-
matplotlib & seaborn: For creating informative plots and visualizations, particularly for feature importance.
The project uses the Loan Prediction dataset. The initial steps involve:Loading the dataset from a public URL.Handling missing values by filling them with the mode for categorical features and the mean for numerical features.Encoding categorical variables (e.g., Gender, Married, Education) into a numerical format using one-hot encoding.Splitting the data into a training set and a testing set to evaluate the model on unseen data.
The primary model is the Random Forest Classifier. This algorithm works by creating a "forest" of multiple decision trees. Each tree is trained on a random subset of the data and a random subset of features. The final prediction is determined by a majority vote from all the trees, which significantly reduces the risk of overfitting and improves accuracy.
- Clone the repository:
git clone <https://github.com/sjain2580/Loan-Approval-Prediction-with-Random-Forest>
cd <repository_name>- Create and activate a virtual environment (optional but recommended):python -m venv venv
- On Windows:
.\venv\Scripts\activate- On macOS/Linux:
source venv/bin/activate- Install the required libraries:
pip install -r requirements.txt- Run the Script:
python prediction.py- Run the App locally:
streamlit run app.pyThe project includes a visualization of Feature Importance to help understand which factors most influence the model's decisions. This helps in gaining business insights from the model.
Check the live app here: https://loan-predictorr.streamlit.app/
https://github.com/sjain2580 Feel free to fork this repository, submit issues, or pull requests to improve the project. Suggestions for model enhancement or additional visualizations are welcome!
Feel free to reach out if you have any questions or just want to connect!
