Update README.md

README.md

@@ -15,7 +15,7 @@ library_name: sklearn
 | Use Case                         | Description |
 |----------------------------------|-------------|
 | 🔐 Password strength scoring     | Quantitative scoring (0–10) for any given password |
-| 🧠 Risk classification           | Categorizes passwords as `Safe`, `Weak`, `Common`, `Leaked`, or `Guessable` |
+| 🧠 Risk classification           | Categorizes passwords as `Weak`, `Fairly Strong`, `Strong`  |
 | 🕵️ Threat emulation             | Emulates password cracking heuristics to spot vulnerable patterns |
 | 🧰 DevSecOps integration         | Plug into CI/CD pipelines for password policy enforcement |
 | 👨‍💻 User awareness tools         | Build frontend UX tools to give users feedback on password creation |
@@ -33,7 +33,7 @@ based on real-world patterns and security datasets.
 ## 🚀 Core Capabilities
 
 ### ✅ Password Strength Classification
-Trace.AI scores passwords as **Weak**, **Moderate**, or **Strong** using a combination of rule-based feature extraction and machine learning.
+Trace.AI scores passwords as **Weak**, **Fairly Strong**, or **Strong** using a combination of rule-based feature extraction and machine learning.
 
 ### 🎯 Pattern Recognition
 Detects predictable and insecure patterns such as:
@@ -54,11 +54,9 @@ Checks if passwords meet enterprise-grade security policies, including:
 
 Trace.AI was trained using curated, high-quality password datasets:
 
-| Dataset | Description | Purpose |
-|--------|-------------|---------|
-| **RockYou (Filtered)** | Real-world leaked passwords, filtered for quality | Weak/Breached training data |
-| **Have I Been Pwned (HIBP)** | 613M+ SHA-1 leaked passwords | Breach detection, negative samples |
-| **zxcvbn Dataset** | Rule-based scoring framework by Dropbox | Strength benchmarking and feature design |
+| Dataset | Description |
+|--------|-------------|
+| **cleanpasswordlist(modified)** | Real-world passwords list, modified and feature engineered for better prediction and scoring |
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@@ -70,7 +68,7 @@ Trace.AI supports and evaluates multiple ML models for robustness:
 |-------|-----------|-----|
 | **RandomForest** | Non-linear classification, interpretable, fast | Production baseline |
 | **XGBoost** | Handles imbalance, high accuracy, fast inference | Advanced detection |
-| **Logistic Regression** | Lightweight, interpretable | Edge device / fallback model |
+| **Decision Trees** | Lightweight, interpretable | Edge device / fallback model |
 
 All models are trained using engineered features like:
 - Length, character diversity
@@ -89,7 +87,6 @@ Trace.AI is engineered to support the following goals:
 | 🔐 Password Strength Estimator | Predict if password is Weak, Moderate, or Strong |
 | 🧠 Pattern Analyzer | Identify insecure sequences, leetspeak, keyboard walks |
 | 📜 Policy Validator | Check adherence to defined password policies |
-| 🧾 Breach Cross-check | Compare against breached datasets (HIBP) |
 | 📤 Exportable Reports | Download prediction logs for security audits |
 | 📈 Visual Dashboard | UI-based analysis of strength and structure (via Gradio) |
 ---