Python, Machine Learning, SVM, KNN, Random forest, and Naive Bayes
Overview
The Loan Prediction System is designed to predict loan approval outcomes based on various factors. Utilizing machine learning algorithms such as SVM, KNN, and RandomForestClassifier, this project aims to provide accurate predictions to assist in decision-making processes. The system is developed in Python, leveraging libraries like scikit-learn, matplotlib, seaborn, and more for data processing and visualization.
Features
- Data Analysis: Explore and visualize the dataset to understand the distribution and correlation between different variables.
- Model Training: Implements machine learning models including SVM (Support Vector Machine), KNN (K-Nearest Neighbors), RandomForest, and Naive Bayes to predict loan approval.
- Evaluation: Assess the accuracy of different models to identify the most effective predictor.
Getting Started
To run this project, ensure you have Python installed on your machine along with the necessary libraries. Here's a quick guide:
Prerequisites
- Python 3.x
- Jupyter Notebook or any Python IDE
Installation
Install the required Python libraries using pip:
pip install numpy pandas scikit-learn matplotlib seaborn
Running the Notebook
- Clone the repository or download the Jupyter notebook file.
- Open the notebook in Jupyter Notebook or JupyterLab.
- Run each cell sequentially to analyze the dataset, train models, and evaluate their performance.
Libraries Used
- Scikit-learn (for machine learning models)
- Matplotlib and seaborn (for data visualization)
- NumPy and pandas (for data manipulation)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import svm