Final push: AcadAI v1.0 — 4th semester BSAI project complete

Co-authored-by: Faizan Ishfaq <faizanrajpoot774-debug@users.noreply.github.com>
Co-authored-by: Muawiya Amir <Muawiya-contact@users.noreply.github.com>

Author: Muawiya-contact

ai_academic_fixed/README.md

@@ -0,0 +1,105 @@
+# AcadAI — AI Academic Performance Prediction & Career Guidance System
+
+**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
+
+**Subject:** Artificial Intelligence  
+**Semester:** 4th  
+**Submitted To:** [Dr. Muhammad Siddique](mailto:msiddique@nfciet.edu.pk)
+
+**Members:**
+
+- [Faizan Ishfaq](https://github.com/faizanrajpoot774-debug)
+- [Muawiya Amir](https://github.com/Muawiya-contact)
+
+---
+
+## 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.
+
+Built with Python · PyQt5 · scikit-learn · SQLite · Matplotlib
+
+---
+
+## Features
+
+| Feature                | Description                                                                           |
+| ---------------------- | ------------------------------------------------------------------------------------- |
+| 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                                     |
+
+---
+
+## Installation
+
+### Prerequisites
+
+Make sure you have **Python 3.10+** installed:
+
+```bash
+python --version
+```
+
+### Step 1 — Clone the Repository
+
+```bash
+git clone https://github.com/your-username/acadai.git
+cd acadAI
+```
+
+### Step 2 — Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### Step 3 — Generate Sample Dataset (First Time Only)
+
+```bash
+python generate_dataset.py
+```
+
+This creates `datasets/student_data.csv` with 60 realistic student records.
+
+### Step 4 — Run the Application
+
+```bash
+python main.py
+```
+
+---
+
+## 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
+pip install PyQt5 matplotlib scikit-learn pandas numpy
+```
+
+---
+
+## Project Structure
+
+```txt
+AcadAI/
+```

ai_academic_fixed/REAMDME.md

@@ -1,6 +1,7 @@
 # analytics/analytics_engine.py
 import statistics
 from database.db_manager import DatabaseManager
+from config import MAX_GPA_CHART_STUDENTS
 
 class AnalyticsEngine:
     def __init__(self, db: DatabaseManager):
@@ -11,6 +12,9 @@ def get_dashboard_stats(self):
 
     def get_gpa_chart_data(self):
         students = self.db.get_all_students()
+        # Limit number of students shown in the GPA chart to avoid visual saturation
+        max_n = MAX_GPA_CHART_STUDENTS if MAX_GPA_CHART_STUDENTS and MAX_GPA_CHART_STUDENTS > 0 else len(students)
+        students = students[:max_n]
         names = [s['name'].split()[0] for s in students]
         gpas = [s['gpa'] for s in students]
         return names, gpas