This repository contains a solution to the loan prediction problem sourced from the Analytics Vidhya competition Practice Problem - Loan Prediction III. The problem at hand is a binary classification task where the goal is to predict whether a loan will be approved or not based on various features.

The objective of this project is to build a predictive model that can accurately classify whether a loan application will be approved or rejected. The dataset provided includes several features such as applicant income, loan amount, credit history, etc., which are used to make these predictions.

The dataset for this project is available in the data directory. The dataset includes both training and test sets in CSV format (train.csv and test.csv).

The notebook loan_prediction_eda.ipynb contains the exploratory data analysis performed on the dataset. It includes visualizations, summary statistics, and insights gained from the data exploration process.

Handling missing values and outliers is crucial for building a robust predictive model. The notebook missing_values_outliers.ipynb details the methods used to address missing values and outliers in the dataset.

The process of model building and evaluation is documented in the notebook model_building.ipynb. Various machine learning models such as logistic regression, decision trees, random forests, and gradient boosting were explored and evaluated using cross-validation techniques.

To reproduce the analysis and run the code, ensure you have the following dependencies installed:

Python 3.x

Jupyter Notebook

pandas

numpy

matplotlib

seaborn

scikit-learn

Clone this repository: git clone https://github.com/joyceoluwasegun/loan-prediction.git

Navigate to the repository: cd loan-prediction

Install the required dependencies: pip install -r requirements.txt

Launch Jupyter Notebook: jupyter notebook

Open and run the notebooks in the following order:

Feel free to explore the notebooks, data, and code to understand the analysis and replicate the results.

If you find any issues, have suggestions, or want to contribute to this project, please feel free to open an issue or create a pull request.