Loan Prediction Model using SVM

This repository contains a simple loan prediction model implemented using Support Vector Machines (SVM). The model utilizes the following libraries:

• NumPy: For efficient numerical computations and linear algebra operations.
• Pandas: For data processing, including reading and manipulating CSV files.
• Seaborn: For advanced data visualization and statistical plotting.
• Matplotlib: For creating visualizations and charts.
• Scikit-learn: For machine learning tasks, including the SVM algorithm, train-test split, and performance evaluation metrics.

Dataset

The loan prediction model is trained on a dataset that contains relevant features for predicting loan approval status. The dataset can be found in the repository. The data is pre-processed and analyzed using the Pandas library to prepare it for training the SVM model.

Installation

To run the loan prediction model, follow the steps below:

1. Install the necessary libraries by executing the following command:

pip install numpy pandas seaborn matplotlib scikit-learn

2. Clone or download the repository to your local machine.

3. Open the Jupyter Notebook or Python script containing the model implementation.

4. Run the code to train the SVM model, evaluate its performance, and make loan predictions.

Usage

To use the loan prediction model, follow these steps:

1. Ensure that you have installed the required libraries and downloaded the dataset.

2. Load the dataset using the Pandas library and perform any necessary data preprocessing steps, such as handling missing values or encoding categorical variables.

3. Split the dataset into training and testing sets using the train_test_split function from scikit-learn.

4. Instantiate an SVM classifier using the svm.SVC class.

5. Train the SVM model on the training data using the fit method.

6. Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1-score.

7. Make loan predictions on new, unseen data using the trained model.

Contributing

Contributions to this loan prediction model are welcome. If you would like to contribute, please follow the standard GitHub workflow of creating a fork, making changes in a branch, and submitting a pull request. Be sure to include a detailed description of the changes and any relevant documentation updates.

License

This project is licensed under the MIT License. You are free to modify, distribute, and use the code and resources in this repository according to the terms of the license.

Contact

For any questions or inquiries related to this loan prediction model, please contact the project owner:

• Name: Fatai Azeez