Loan_Status_Prediction
The aim of this project is to predict whether the loan of a person will be approved or not using a dataset from Kaggle using machine learning.
Libraries which we have used: -
- Numpy
Importing Numpy Library
About Numpy
Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
- Pandas
Importing Pandas Library
About Pandas
Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
- Sklearn
Importing Sklearn
About Sklearn
Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistent interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.
Different functions imported from Sklearn:-
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train_test_split- Using train_test_split() from the data science library scikit-learn, you can split your dataset into subsets that minimize the potential for bias in your evaluation and validation process.
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svm- Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers’ detection. It is used in applications like handwriting recognition, intrusion detection, face detection, email classification, gene classification, and in web pages.
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accuracy_score- This function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.
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Seaborn
Importing Seaborn
About Seaborn
Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures. It helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.
Data Analysis
- Based on Education
- Based on Marital Status
Model Analysis
- With an accuracy score of 79.86% on training data and an accuracy of 83.33% on test data, our model will predict whether the loan will be approved or rejected.
It’s easy to get a loan, unless you need it!-----
