docs: fix README formatting and submission details

Author: Muawiya-contact

ai_academic_fixed/REAMDME.md

@@ -1,60 +1,50 @@
-# 🎓 AcadAI — AI Academic Performance Prediction & Career Guidance System
+# AcadAI — AI Academic Performance Prediction & Career Guidance System
 
-<div align="center">
+**A professional AI-powered desktop application that predicts student GPA, detects weak subjects, recommends careers, and generates personalized study plans — built as a 4th Semester BSAI Project.**
 
-![Python](https://img.shields.io/badge/Python-3.10+-3776AB?style=for-the-badge&logo=python&logoColor=white)
-![PyQt5](https://img.shields.io/badge/PyQt5-GUI-41CD52?style=for-the-badge&logo=qt&logoColor=white)
-![scikit-learn](https://img.shields.io/badge/scikit--learn-ML-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)
-![SQLite](https://img.shields.io/badge/SQLite-Database-003B57?style=for-the-badge&logo=sqlite&logoColor=white)
-![License](https://img.shields.io/badge/License-MIT-green?style=for-the-badge)
+---
 
-**A professional AI-powered desktop application that predicts student GPA, detects weak subjects, recommends careers, and generates personalized study plans — built as a 4th Semester BSAI Project.**
+## Project Info
 
-[Features](#-features) · [Screenshots](#-screenshots) · [Installation](#-installation) · [Usage](#-usage) · [Tech Stack](#-tech-stack) · [Project Structure](#-project-structure)
+**Subject:** Artificial Intelligence  
+**Semester:** 4th  
+**Submitted To:** [Dr. Muhammad Siddique](mailto:msiddique@nfciet.edu.pk)
 
-</div>
+**Members:**
+- [Faizan Ishfaq](https://github.com/faizanrajpoot774-debug)
+- [Muawiya Amir](https://github.com/Muawiya-contact)
 
 ---
 
-## 📌 Overview
+## Overview
 
-**AcadAI** is an intelligent academic analytics platform built for university students. It uses **Machine Learning** to predict future GPA, detect at-risk students, recommend career paths based on academic profile, and generate AI-powered study plans — all inside a clean, professional desktop GUI.
+**AcadAI** is an intelligent academic analytics platform built for university students. It uses Machine Learning to predict future GPA, detect at-risk students, recommend career paths based on academic profile, and generate AI-powered study plans — all inside a clean, professional desktop GUI.
 
-> Built with Python · PyQt5 · scikit-learn · SQLite · Matplotlib
+Built with Python · PyQt5 · scikit-learn · SQLite · Matplotlib
 
 ---
 
-## ✨ Features
+## Features
 
 | Feature | Description |
 |---|---|
-| 📊 **GPA Prediction** | Random Forest + Ridge Regression model predicts next semester GPA with ~91% confidence |
-| ⚠️ **Weak Subject Detection** | ML model identifies subjects needing attention and suggests improvement strategies |
-| 🎯 **Career Recommendation** | AI matches student profile (GPA + skills + interests) to best-fit career paths |
-| 🧠 **Skill Roadmap** | Personalized step-by-step learning roadmap for chosen career goal |
-| 📅 **Study Planner** | AI generates weekly study schedules prioritizing weak subjects |
-| 🤖 **AI Chatbot** | NLP-based academic assistant that reads live database and answers student queries |
-| 📈 **Analytics Dashboard** | Interactive charts: GPA trends, subject radar, risk distribution, attendance analysis |
-| 👥 **Student Management** | Add, edit, import students via CSV — full CRUD with SQLite backend |
-| 📤 **CSV Import/Export** | Bulk import 60+ students from CSV; export reports |
+| GPA Prediction | Random Forest + Ridge Regression model predicts next semester GPA with ~91% accuracy |
+| Weak Subject Detection | ML model identifies subjects needing attention and suggests improvement strategies |
+| Career Recommendation | Matches student profile (GPA + skills + interests) to best-fit career paths |
+| Skill Roadmap | Personalized step-by-step learning roadmap for chosen career goal |
+| Study Planner | Generates weekly study schedules prioritizing weak subjects |
+| AI Chatbot 🤖 | NLP-based academic assistant that reads live database and answers student queries |
+| Analytics Dashboard | Interactive charts: GPA trends, subject radar, risk distribution, attendance analysis |
+| Student Management | Add, edit, import students via CSV — full CRUD with SQLite backend |
+| CSV Import/Export | Bulk import 60+ students from CSV; export reports |
 
 ---
 
-## 🖼️ Screenshots
-
-> _Add screenshots of your running app here after launch_
-
-```
-Dashboard → Analytics → Career Page → Chatbot → Study Planner
-```
-
----
-
-## 🛠️ Installation
+## Installation
 
 ### Prerequisites
 
-Make sure you have **Python 3.10+** installed. Check with:
+Make sure you have **Python 3.10+** installed:
 
 ```bash
 python --version
@@ -79,7 +69,7 @@ pip install -r requirements.txt
 python generate_dataset.py
 ```
 
-This creates `datasets/student_data.csv` with **60 realistic student records**.
+This creates `datasets/student_data.csv` with 60 realistic student records.
 
 ### Step 4 — Run the Application
 
@@ -89,16 +79,14 @@ python main.py
 
 ---
 
-## 📦 Requirements
-
-```
+## Requirements
+```batch
 PyQt5>=5.15.0
 matplotlib>=3.7.0
 scikit-learn>=1.3.0
 pandas>=2.0.0
 numpy>=1.24.0
 ```
-
 Install all at once:
 
 ```bash
@@ -107,9 +95,8 @@ pip install PyQt5 matplotlib scikit-learn pandas numpy
 
 ---
 
-## 🗂️ Project Structure
-
-```
+## Project Structure
+```txt
 acadai-academic-system/
 │
 ├── main.py                      # App entry point
@@ -119,29 +106,29 @@ acadai-academic-system/
 ├── README.md
 │
 ├── database/
-│   ├── __init__.py
+│   ├── init.py
 │   └── db_manager.py            # SQLite CRUD + stats queries
 │
 ├── models/
-│   ├── __init__.py
+│   ├── init.py
 │   ├── prediction_model.py      # GPAPredictor — Random Forest ensemble
 │   └── training.py              # Model training from CSV data
 │
 ├── chatbot/
-│   ├── __init__.py
+│   ├── init.py
 │   └── chatbot_engine.py        # NLP chatbot — reads live DB data
 │
 ├── analytics/
-│   ├── __init__.py
+│   ├── init.py
 │   └── analytics_engine.py      # Stats engine for dashboard
 │
 ├── recommender/
-│   ├── __init__.py
+│   ├── init.py
 │   ├── career_recommender.py    # Career match scoring
 │   └── study_planner.py         # Weekly study plan generator
 │
 ├── ui/
-│   ├── __init__.py
+│   ├── init.py
 │   ├── main_window.py           # Main app window + navigation
 │   ├── dashboard_page.py        # Overview + metric cards
 │   ├── analytics_page.py        # Charts + AI predictor
@@ -151,7 +138,7 @@ acadai-academic-system/
 │   ├── planner_page.py          # Study planner
 │   ├── chatbot_page.py          # AI chatbot interface
 │   └── components/
-│       ├── __init__.py
+│       ├── init.py
 │       ├── cards.py             # Reusable card widgets
 │       ├── charts.py            # Matplotlib chart components
 │       └── sidebar.py           # Navigation sidebar
@@ -161,28 +148,27 @@ acadai-academic-system/
 │       └── theme.py             # Global QSS stylesheet
 │
 └── datasets/
-    └── student_data.csv         # Auto-generated by generate_dataset.py
+└── student_data.csv         # Auto-generated by generate_dataset.py
 ```
-
 ---
 
-## 🤖 AI & ML Concepts Used
+## AI & ML Concepts Used
 
 | Concept | Implementation |
 |---|---|
-| **Random Forest** | GPA prediction model (`models/prediction_model.py`) |
-| **Ridge Regression** | Ensemble GPA prediction alongside Random Forest |
-| **Classification** | Student risk level detection (High / Medium / Low) |
-| **Recommendation System** | Career matching based on GPA + skills + interests |
-| **NLP (Keyword Matching)** | Chatbot intent detection and response generation |
-| **Data Analytics** | Performance trend analysis across semesters |
-| **Predictive Modeling** | Future GPA projection for next 2 semesters |
+| Random Forest | GPA prediction model (`models/prediction_model.py`) |
+| Ridge Regression | Ensemble GPA prediction alongside Random Forest |
+| Classification | Student risk level detection (High / Medium / Low) |
+| Recommendation System | Career matching based on GPA + skills + interests |
+| NLP (Keyword Matching) | Chatbot intent detection and response generation |
+| Data Analytics | Performance trend analysis across semesters |
+| Predictive Modeling | Future GPA projection for next 2 semesters |
 
 ---
 
-## 📊 Dataset
+## Dataset
 
-The included dataset generator (`generate_dataset.py`) creates realistic student records with:
+The dataset generator (`generate_dataset.py`) creates realistic student records with:
 
 | Column | Description |
 |---|---|
@@ -194,68 +180,58 @@ The included dataset generator (`generate_dataset.py`) creates realistic student
 | `assignment` | Assignment score (38–96/100) |
 | `midterm` | Midterm exam score (32–95/100) |
 | `study_hours` | Daily study hours (0.5–7.0) |
-| `gpa` | Calculated GPA (0.0–4.0) |
+| `gpa` | Predicted GPA (0.0–4.0) |
 | `interest` | Career interest area |
 | `skills` | Current technical skills |
 
-**GPA Formula:**
-```
-GPA = (Attendance×0.20 + Quiz×0.20 + Assignment×0.20 + Midterm×0.30 + StudyHours×0.10) × 4.0
-```
-
 ---
 
-## 💬 Chatbot — How It Works
+## Chatbot — How It Works
 
-The AI chatbot reads **live data from the SQLite database** and generates dynamic responses. It is not hardcoded.
+The AI chatbot reads live data from the SQLite database and generates dynamic responses.
 
-**Example questions you can ask:**
-```
-"How can I improve my GPA?"
+Example questions:
+>"How can I improve my GPA?"
 "Who are the top students?"
 "Which career suits me?"
 "What skills should I learn for AI?"
 "How many students are at risk?"
 "Generate a study plan"
 "What is the class average GPA?"
-```
 
 ---
 
-## 🎯 Career Recommendation System
+## Career Recommendation System
 
 Career matches are scored using a weighted algorithm:
 
 ```python
 match_score = (
-    gpa_score      × 0.40 +   # Academic performance
-    skills_match   × 0.35 +   # Matching skills
-    interest_match × 0.25     # Career interest alignment
+    gpa_score      * 0.40 +   # Academic performance
+    skills_match   * 0.35 +   # Matching skills
+    interest_match * 0.25     # Career interest alignment
 )
 ```
 
 ---
 
-## 👨‍💻 Author
+## Authors
+
+**Faizan Ishfaq** (2k24_bsai_50) — BS Artificial Intelligence, Semester 4, NFC IET, Multan 
 
-**Ali Khan**
-BS Artificial Intelligence — Semester 4
-COMSATS University Islamabad
+**Muawiya Amir** (2k24_bsai_72) — BS Artificial Intelligence, Semester 4, NFC IET, Multan
 
 ---
 
-## 📄 License
+## License
+
+This project is licensed under the MIT License — free to use, modify, and distribute.
 
-This project is licensed under the **MIT License** — feel free to use, modify, and distribute.
 ---
-## 🙏 Acknowledgements
+
+## Acknowledgements
 
 - [scikit-learn](https://scikit-learn.org/) — Machine Learning library
 - [PyQt5](https://riverbankcomputing.com/software/pyqt/) — Desktop GUI framework
 - [Matplotlib](https://matplotlib.org/) — Data visualization
-- [Kaggle](https://kaggle.com/) — Dataset inspiration
-
----
-<div align="center">
-  <b>⭐ Star this repo if you found it helpful!</b>
-</div>
+- [Kaggle](https://kaggle.com/) — Dataset inspiration