🏦 Loan Approval Prediction 📊

This project aims to develop a model that can predict loan approval for individuals. The goal is to automate and simplify the loan approval process, reducing risk and improving decision-making efficiency.

📋 Problem Statement

Develop a model to predict loan eligibility for individuals. Use various data mining techniques to build a loan approval decision predicting model, which can make decisions based on the information provided by the individuals.

🛠️ Steps Involved

1. 🔎 Data Exploration and Preprocessing: Analyze the dataset, handle missing values, convert categorical variables into numeric form, and normalize or standardize numerical variables if required.

2. ✂️ Splitting the Data: Divide the dataset into features (X) and the target variable (y). Further split it into training and test sets to assess model performance.

3. ⚙️ Model Training: Build and train a KNN classifier and a Decision Tree classifier on the training data.

4. 📈 Model Evaluation: Make predictions using the trained models on the test set and evaluate their performance using metrics such as accuracy, precision, recall, and F1-score.

5. ⚖️ Model Comparison: Compare the performance of the KNN and Decision Tree models to determine which one performs better for loan approval prediction.

📦 Instructions for Running the Project

1. Python: You need to have Python installed on your machine. You can download Python from here.

2. Clone this repo:

git clone https://github.com/Minakoaino/Loan-approval-prediction.git

3. Navigate to the Project Directory: Change your current directory to the directory of the project where the requirements.txt file is located:

cd Loan-approval-prediction

4. Virtual Environment (Optional): Creating a virtual environment is a good practice to keep the project's dependencies isolated from other projects:

• Create a new virtual environment:

  python3 -m venv env
  

• Activate the virtual environment:

  • For Unix or MacOS:

    source env/bin/activate
    

  • For Windows:

    .\env\Scripts\activate
    

5. Install the Project Dependencies: The project dependencies are listed in the requirements.txt file. You can install these using the following command: