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Financial-Risk-Modelling

Overview

This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.

Dataset

  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset

Steps Involved

1. Data Preprocessing

  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data

2. Exploratory Data Analysis (EDA)

  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)

3. Machine Learning Model

  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:
    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report

Dependencies

Ensure you have the following Python libraries installed:

pip install numpy pandas matplotlib scikit-learn statsmodels

How to Run

  1. Clone the repository: 
    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory: 
    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook: 
    jupyter notebook Credit_Modelling_Project_BOB.ipynb

Results

  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization

Future Enhancements

  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application

Contributors

License

This project is licensed under the MIT License.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

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Packages

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
Financial-Risk-Modelling
Overview
This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.
Dataset
  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset
Steps Involved
1. Data Preprocessing
  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data
2. Exploratory Data Analysis (EDA)
  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)
3. Machine Learning Model
  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:

    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report
Dependencies
Ensure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn statsmodels
How to Run
  1. Clone the repository:

    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory:

    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook:

    jupyter notebook Credit_Modelling_Project_BOB.ipynb
Results
  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization
Future Enhancements
  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application
Contributors
License
This project is licensed under the MIT License.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
README.md
Repository files navigation
Financial-Risk-Modelling
Overview
This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.
Dataset
  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset
Steps Involved
1. Data Preprocessing
  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data
2. Exploratory Data Analysis (EDA)
  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)
3. Machine Learning Model
  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:

    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report
Dependencies
Ensure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn statsmodels
How to Run
  1. Clone the repository:

    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory:

    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook:

    jupyter notebook Credit_Modelling_Project_BOB.ipynb
Results
  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization
Future Enhancements
  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application
Contributors
License
This project is licensed under the MIT License.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
README.md
Repository files navigation
Financial-Risk-Modelling
Overview
This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.
Dataset
  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset
Steps Involved
1. Data Preprocessing
  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data
2. Exploratory Data Analysis (EDA)
  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)
3. Machine Learning Model
  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:

    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report
Dependencies
Ensure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn statsmodels
How to Run
  1. Clone the repository:

    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory:

    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook:

    jupyter notebook Credit_Modelling_Project_BOB.ipynb
Results
  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization
Future Enhancements
  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application
Contributors
License
This project is licensed under the MIT License.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Credit_Modelling_Project_BOB.ipynb
README.md
README.md
Repository files navigation
Financial-Risk-Modelling
Overview
This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.
Dataset
  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset
Steps Involved
1. Data Preprocessing
  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data
2. Exploratory Data Analysis (EDA)
  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)
3. Machine Learning Model
  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:

    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report
Dependencies
Ensure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn statsmodels
How to Run
  1. Clone the repository:

    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory:

    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook:

    jupyter notebook Credit_Modelling_Project_BOB.ipynb
Results
  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization
Future Enhancements
  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application
Contributors
License
This project is licensed under the MIT License.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Credit_Modelling_Project_BOB.ipynb
README.md
README.md
Repository files navigation
Financial-Risk-Modelling
Overview
This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.
Dataset
  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset
Steps Involved
1. Data Preprocessing
  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data
2. Exploratory Data Analysis (EDA)
  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)
3. Machine Learning Model
  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:

    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report
Dependencies
Ensure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn statsmodels
How to Run
  1. Clone the repository:

    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory:

    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook:

    jupyter notebook Credit_Modelling_Project_BOB.ipynb
Results
  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization
Future Enhancements
  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application
Contributors
License
This project is licensed under the MIT License.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Credit_Modelling_Project_BOB.ipynb
README.md
README.md
Repository files navigation
Financial-Risk-Modelling
Overview
This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.
Dataset
  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset
Steps Involved
1. Data Preprocessing
  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data
2. Exploratory Data Analysis (EDA)
  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)
3. Machine Learning Model
  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:

    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report
Dependencies
Ensure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn statsmodels
How to Run
  1. Clone the repository:

    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory:

    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook:

    jupyter notebook Credit_Modelling_Project_BOB.ipynb
Results
  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization
Future Enhancements
  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application
Contributors
License
This project is licensed under the MIT License.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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Financial-Risk-Modelling
Overview
This project focuses on financial risk modeling using credit data from the Bank of Baroda. The primary objective is to analyze internal product files and CIBIL reports, clean and preprocess the data, and apply machine learning techniques to assess credit risk.
Dataset
  • Case Study 1: Internal product file (Bank of Baroda)
  • Case Study 2: CIBIL report for the same dataset
Steps Involved
1. Data Preprocessing
  • Uploading and loading the datasets
  • Cleaning missing values (e.g., removing rows with excessive null values)
  • Merging datasets based on common parameters
  • Handling imbalanced data
2. Exploratory Data Analysis (EDA)
  • Statistical summary of features
  • Visualizations to identify trends and correlations
  • Checking for multicollinearity using Variance Inflation Factor (VIF)
3. Machine Learning Model
  • Splitting the dataset into training and testing sets
  • Implementing a Random Forest Classifier
  • Evaluating performance using:

    • Accuracy Score
    • Precision, Recall, and F1-score
    • Classification Report
Dependencies
Ensure you have the following Python libraries installed:
pip install numpy pandas matplotlib scikit-learn statsmodels
How to Run
  1. Clone the repository:

    git clone https://github.com/your-repo/Financial-Risk-Modelling.git
  2. Navigate to the project directory:

    cd Financial-Risk-Modelling
  3. Run the Jupyter Notebook:

    jupyter notebook Credit_Modelling_Project_BOB.ipynb
Results
  • The model's predictions on credit risk classification
  • Insights derived from data analysis
  • Potential areas for further optimization
Future Enhancements
  • Implementing additional ML models (Logistic Regression, XGBoost, etc.)
  • Enhancing feature engineering
  • Deploying the model as a web application
Contributors
License
This project is licensed under the MIT License.
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