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‎.github/FUNDING.yml

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# These are supported funding model platforms
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github: # [ekrishnachaitanya2004,eksandy]
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patreon: # patreon.com/ekrishnachaitanya2004
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open_collective: # ekrishnachaitanya2004
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ko_fi: # ko-fi.com/krishnachaitanya2004
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tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
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community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
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liberapay: # Replace with a single Liberapay username
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issuehunt: # Replace with a single IssueHunt username
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lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
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polar: # Replace with a single Polar username
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buy_me_a_coffee: # Replace with a single Buy Me a Coffee username
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thanks_dev: # Replace with a single thanks.dev username
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custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

‎.github/ISSUE_TEMPLATE/feature_request.md

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---
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name: Feature request
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about: Suggest an idea for this project
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title: ''
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labels: ''
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assignees: ''
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---
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**Is your feature request related to a problem? Please describe.**
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A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
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**Describe the solution you'd like**
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A clear and concise description of what you want to happen.
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**Describe alternatives you've considered**
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A clear and concise description of any alternative solutions or features you've considered.
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**Additional context**
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Add any other context or screenshots about the feature request here.

‎README.md

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# **Dev-Blogs**
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# [**Dev-Blogs**](https://developersblogs.netlify.app)
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[![License](https://img.shields.io/github/license/ekrishnachaitanya2004/dev-blogs?style=flat-square)](LICENSE)
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[![Issues](https://img.shields.io/github/issues/ekrishnachaitanya2004/dev-blogs?style=flat-square)](https://github.com/ekrishnachaitanya2004/dev-blogs/issues)
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## **Contributors**
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We appreciate all contributions to **Dev-Blogs**! You can see the full list of contributors [here](https://github.com/eksandy/dev-blogs/graphs/contributors).
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We appreciate all contributions to **Dev-Blogs**! You can see the full list of contributors [here](https://github.com/ekrishnachaitanya2004/dev-blogs/graphs/contributors).
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## **License**
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If you have any questions or want to get involved, feel free to reach out to us through:
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- GitHub Issues: [Submit a question or issue](https://github.com/ekrishnachaitanya2004/dev-blogs/issues)
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- Email: [[email protected]]
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- Email: [[email protected]]

‎SECURITY.md

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# **Security Policy**
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## **Supported Versions**
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The following table shows which versions of **Dev-Blogs** are currently supported with security updates:
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| Version | Supported |
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| ------- | ------------------ |
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| 1.0.0 | Blogs |
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Only versions 1.0 and above are actively maintained and receive security updates. Please ensure that you are using a supported version to benefit from the latest security patches.
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---
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## **Reporting a Vulnerability**
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If you discover a security vulnerability in **Dev-Blogs**, we encourage you to help improve the security of the project by reporting it responsibly. Here’s how you can report a vulnerability:
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1. **Contact**: Send an email to **[[email protected]]** with details of the vulnerability.
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2. **Response Time**: You can expect a response within **48 hours** after the report is received.
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3. **Resolution**: Once the vulnerability is validated, we will work to resolve it as soon as possible. You will receive updates on the status of the issue periodically.
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4. **Disclosure**: We follow a policy of responsible disclosure. Once the vulnerability is patched, we will credit you (if desired) in the release notes unless the reporter prefers anonymity.
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Your report will be handled with the highest priority, and we appreciate your contribution to keeping **Dev-Blogs** secure.

‎src/content/blog/english/post-3.md

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---
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title: "How to build an Application with modern Technology"
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title: "Loan Eligibility Predective Analyser"
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meta_title: ""
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description: "this is meta description"
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date: 2022-04-04T05:00:00Z
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description: "This repository presents a comprehensive predictive analytics solution for determining loan eligibility, leveraging advanced machine learning techniques and data preprocessing methodologies. The project is implemented in Google Colab, utilizing a dataset from Kaggle's Loan Data repository, which encompasses a diverse range of attributes pertinent to creditworthiness assessment."
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date: 2024-09-15T05:00:00Z
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image: "/images/image-placeholder.png"
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categories: ["Software"]
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author: "John Doe"
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tags: ["software", "tailwind"]
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author: "Sanjeevan S"
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tags: ["Machine Learning", "Python3", "Kaggle" , "Pandas" , "Numpy"]
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draft: false
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---
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## Creative Design
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---
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## Loan Eligibility Predictive Analytics: A Comprehensive Solution
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**This project offers an advanced machine learning solution for determining loan eligibility, leveraging powerful data preprocessing techniques and predictive algorithms.** Implemented in **Google Colab**, it utilizes the **Loan Data repository** from Kaggle, comprising diverse features critical for creditworthiness assessment.
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### 🔧 **Technical Overview**
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- **Programming Language**: Python 3.x
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- **Libraries and Frameworks**:
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- **Pandas**: Data manipulation and analysis
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- **NumPy**: Numerical computations
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- **Scikit-learn**: Machine learning model development and evaluation
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- **Matplotlib & Seaborn**: Data visualization tools
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---
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### 📊 **Dataset Overview**
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The dataset consists of **615 observations** and **13 features**, including:
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- **Loan ID**
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- **Gender**
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- **Married**
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- **Dependents**
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- **Education**
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- **Self-Employed**
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- **Applicant Income**
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- **Coapplicant Income**
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- **Loan Amount**
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- **Loan Amount Term**
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- **Credit History**
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- **Property Area**
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- **Loan Status**
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---
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### ⚙️ **Methodology**
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#### **Data Preprocessing**:
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- **Imputation**: Handling missing values with advanced techniques
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- **Encoding**: One-hot encoding for categorical variables
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- **Scaling/Normalization**: Applying transformations to numerical features
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#### **Feature Engineering**:
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- **Feature Extraction**: Identifying key attributes impacting loan eligibility
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- **Interaction Terms**: Creating relationships between variables for better predictions
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---
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### 🚀 **Model Development**
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We evaluated multiple machine learning algorithms:
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- **Logistic Regression**
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- **Decision Trees**
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- **Random Forest**
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- **Gradient Boosting**
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Each model was trained and fine-tuned to optimize performance.
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---
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### 📈 **Model Evaluation**
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Models were assessed using key metrics:
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- **Accuracy, Precision, Recall, F1 Score, ROC-AUC**
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- **Confusion Matrix**: To understand classification results
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- **ROC Curves**: For visualizing true positive and false positive rates
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---
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### 📂 **Repository Structure**
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- **Loan_Eligibility_Predictive_Analyser.ipynb**: The main notebook containing the code and documentation.
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- **data**: Folder containing the Loan Data dataset.
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- **utils**: Module for helper functions used in preprocessing and visualization.
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---
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### 🛠 **Resources**
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- GitHub Profile: [**I-SANJEEVAN**](https://github.com/I-SANJEEVAN)
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- Repository Link: [**Loan Eligibility Predictor**](https://github.com/I-SANJEEVAN/Loan_eligibility_predective_analyser/tree/main)
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- Try it on Colab: [**Google Colab Notebook**](https://colab.research.google.com/drive/1CHJh7jBemyhLNENAd2qEMgctKT6h_4mV?usp=sharing)
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---
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This project offers a **powerful tool** for financial institutions to automate loan eligibility assessments, providing insightful predictions backed by machine learning!
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This version improves readability and flow, making it more engaging for a blog audience while preserving the technical depth.

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