Update README.md

README.md

@@ -1,152 +1,170 @@
-# Expense AI Intelligence System
-
-**Author:** Krishnamohan Yagneswaran
-
----
-
-## Overview
-
-This project is a machine learning system that analyzes expense data and provides useful financial insights. It combines multiple models to predict spending, detect risk, classify transactions, and understand user behavior.
-
-The system is designed to work with real-world expense data and help users make better financial decisions.
-
----
-
-## What This Model Does
-
-This system provides the following outputs:
-
-* Predicts how much money you may spend
-* Identifies if a transaction is high risk or low risk
-* Classifies the type of expense (food, travel, bills, etc.)
-* Groups spending behavior into clusters
-* Provides a simple financial decision suggestion
-
----
-
-## About the `.pkl` Files
-
-All the `.pkl` files in this repository are **trained machine learning models and components**. These files are saved using `joblib` and allow the system to be reused without retraining.
-
-### Files Explanation:
-
-* `spending_model.pkl`
-  → Predicts future spending amount
-
-* `risk_model.pkl`
-  → Classifies whether spending is HIGH or LOW risk
-
-* `nlp_model.pkl`
-  → Predicts category from text input
-
-* `tfidf.pkl`
-  → Converts text into numerical format for NLP
-
-* `kmeans.pkl`
-  → Groups spending behavior into clusters
-
-* `feature_columns.pkl`
-  → Stores the correct input structure used during training
-
----
-
-## How to Use
-
-### 1. Install Required Libraries
-
-```bash
-pip install pandas numpy scikit-learn joblib
-```
-
-### 2. Load Models
-
-```python
-import joblib
-
-spending_model = joblib.load("spending_model.pkl")
-risk_model = joblib.load("risk_model.pkl")
-nlp_model = joblib.load("nlp_model.pkl")
-vectorizer = joblib.load("tfidf.pkl")
-kmeans = joblib.load("kmeans.pkl")
-```
-
-### 3. Example Input
-
-```python
-input_data = {
-    "weekday": 2,
-    "rolling_avg": 500
-}
-```
-
-### 4. Predict
-
-```python
-prediction = spending_model.predict([list(input_data.values())])
-print(prediction)
-```
-
----
-
-## System Features
-
-* Uses multiple machine learning models together
-* Works with structured CSV expense data
-* Includes NLP for text-based classification
-* Uses clustering for behavior analysis
-* Provides decision-making suggestions
-
----
-
-## Use Cases
-
-* Personal finance tracking
-* Budget planning
-* Expense monitoring applications
-* Financial analytics systems
-* Academic and learning projects
-
----
-
-## License
-
-This project is licensed under the **MIT License**.
-
-You are free to:
-
-* Use
-* Modify
-* Distribute
-* Use commercially
-
-As long as the original license and author credit are included.
-
----
-
-## Credits
-
-If you use this project in your work, research, application, or product, it would be appreciated if you provide credit:
-
-**Krishnamohan Yagneswaran**
-
-This helps support further development and recognition of the project.
-
----
-
-## Future Improvements
-
-* Web application interface
-* Mobile application integration
-* API deployment
-* Real-time analytics
-
----
-
-## Conclusion
-
-This project demonstrates how multiple machine learning techniques can be combined into a single intelligent system. It is simple, practical, and scalable for real-world applications.
-
----
-
-**Created by:**
-Krishnamohan Yagneswaran
+---
+license: mit
+language:
+- en
+metrics:
+- mae
+- recall
+base_model:
+- risingodegua/scikit-learn-models
+tags:
+- machine-learning
+- finance
+- expense-prediction
+- tabular-data
+- nlp
+- clustering
+- ai-system
+---
+# Expense AI Intelligence System
+
+**Author:** Krishnamohan Yagneswaran
+
+---
+
+## Overview
+
+This project is a machine learning system that analyzes expense data and provides useful financial insights. It combines multiple models to predict spending, detect risk, classify transactions, and understand user behavior.
+
+The system is designed to work with real-world expense data and help users make better financial decisions.
+
+---
+
+## What This Model Does
+
+This system provides the following outputs:
+
+* Predicts how much money you may spend
+* Identifies if a transaction is high risk or low risk
+* Classifies the type of expense (food, travel, bills, etc.)
+* Groups spending behavior into clusters
+* Provides a simple financial decision suggestion
+
+---
+
+## About the `.pkl` Files
+
+All the `.pkl` files in this repository are **trained machine learning models and components**. These files are saved using `joblib` and allow the system to be reused without retraining.
+
+### Files Explanation:
+
+* `spending_model.pkl`
+  → Predicts future spending amount
+
+* `risk_model.pkl`
+  → Classifies whether spending is HIGH or LOW risk
+
+* `nlp_model.pkl`
+  → Predicts category from text input
+
+* `tfidf.pkl`
+  → Converts text into numerical format for NLP
+
+* `kmeans.pkl`
+  → Groups spending behavior into clusters
+
+* `feature_columns.pkl`
+  → Stores the correct input structure used during training
+
+---
+
+## How to Use
+
+### 1. Install Required Libraries
+
+```bash
+pip install pandas numpy scikit-learn joblib
+```
+
+### 2. Load Models
+
+```python
+import joblib
+
+spending_model = joblib.load("spending_model.pkl")
+risk_model = joblib.load("risk_model.pkl")
+nlp_model = joblib.load("nlp_model.pkl")
+vectorizer = joblib.load("tfidf.pkl")
+kmeans = joblib.load("kmeans.pkl")
+```
+
+### 3. Example Input
+
+```python
+input_data = {
+    "weekday": 2,
+    "rolling_avg": 500
+}
+```
+
+### 4. Predict
+
+```python
+prediction = spending_model.predict([list(input_data.values())])
+print(prediction)
+```
+
+---
+
+## System Features
+
+* Uses multiple machine learning models together
+* Works with structured CSV expense data
+* Includes NLP for text-based classification
+* Uses clustering for behavior analysis
+* Provides decision-making suggestions
+
+---
+
+## Use Cases
+
+* Personal finance tracking
+* Budget planning
+* Expense monitoring applications
+* Financial analytics systems
+* Academic and learning projects
+
+---
+
+## License
+
+This project is licensed under the **MIT License**.
+
+You are free to:
+
+* Use
+* Modify
+* Distribute
+* Use commercially
+
+As long as the original license and author credit are included.
+
+---
+
+## Credits
+
+If you use this project in your work, research, application, or product, it would be appreciated if you provide credit:
+
+**Krishnamohan Yagneswaran**
+
+This helps support further development and recognition of the project.
+
+---
+
+## Future Improvements
+
+* Web application interface
+* Mobile application integration
+* API deployment
+* Real-time analytics
+
+---
+
+## Conclusion
+
+This project demonstrates how multiple machine learning techniques can be combined into a single intelligent system. It is simple, practical, and scalable for real-world applications.
+
+---
+
+**Created by:**
+Krishnamohan Yagneswaran