Add project AIC.

ai_academic_fixed/README.md

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+# 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/analytics/__init__.py

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+# pkg

ai_academic_fixed/analytics/analytics_engine.py

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+# 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):
+        self.db = db
+
+    def get_dashboard_stats(self):
+        return self.db.get_stats()
+
+    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
+
+    def get_subject_chart_data(self):
+        avgs = self.db.get_subject_averages()
+        labels = ['Attendance', 'Quiz', 'Assignment', 'Midterm']
+        values = [avgs['attendance'], avgs['quiz'], avgs['assignment'], avgs['midterm']]
+        return labels, values
+
+    def get_risk_breakdown(self):
+        students = self.db.get_all_students()
+        low = sum(1 for s in students if s['risk_level'] == 'Low')
+        med = sum(1 for s in students if s['risk_level'] == 'Medium')
+        high = sum(1 for s in students if s['risk_level'] == 'High')
+        return {'Low': low, 'Medium': med, 'High': high}
+
+    def get_gpa_bins(self):
+        gpas = self.db.get_gpa_distribution()
+        bins = {'<2.0': 0, '2.0–2.5': 0, '2.5–3.0': 0, '3.0–3.5': 0, '3.5–4.0': 0}
+        for g in gpas:
+            if g < 2.0:   bins['<2.0'] += 1
+            elif g < 2.5: bins['2.0–2.5'] += 1
+            elif g < 3.0: bins['2.5–3.0'] += 1
+            elif g < 3.5: bins['3.0–3.5'] += 1
+            else:         bins['3.5–4.0'] += 1
+        return bins
+
+    def get_performance_insights(self):
+        stats = self.get_dashboard_stats()
+        insights = []
+        avg_gpa = stats.get('avg_gpa', 0) or 0
+        avg_att = stats.get('avg_att', 0) or 0
+        at_risk = stats.get('at_risk', 0) or 0
+        if avg_gpa >= 3.3:
+            insights.append({'icon': '🌟', 'text': f'Average GPA {avg_gpa:.2f} — class is performing well', 'color': '#10B981'})
+        elif avg_gpa < 2.8:
+            insights.append({'icon': '⚠️', 'text': f'Average GPA {avg_gpa:.2f} — intervention recommended', 'color': '#EF4444'})
+        if avg_att >= 85:
+            insights.append({'icon': '✅', 'text': f'Attendance at {avg_att:.1f}% — excellent engagement', 'color': '#10B981'})
+        elif avg_att < 75:
+            insights.append({'icon': '📅', 'text': f'Attendance at {avg_att:.1f}% — needs improvement', 'color': '#F59E0B'})
+        if at_risk > 0:
+            insights.append({'icon': '🚨', 'text': f'{at_risk} student(s) at high risk — require attention', 'color': '#EF4444'})
+        if not insights:
+            insights.append({'icon': '📊', 'text': 'All metrics within normal range', 'color': '#4F6EF7'})
+        return insights

ai_academic_fixed/assets/__init__.py

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+# pkg

ai_academic_fixed/assets/styles/__init__.py

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+# pkg