_VTUStudyMarkAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0d58260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your name: Farha Kousar\n",
      "Enter your USN: 1N21AI010\n",
      "Enter semester: 5TH SEM\n",
      "Enter branch: AIML\n",
      "Enter the number of subjects: 9\n",
      "Enter subject 1 name: ATC\n",
      "Enter credits for ATC: 3\n",
      "Enter grade marks for ATC: 100\n",
      "Enter subject 2 name: CN\n",
      "Enter credits for CN: 4\n",
      "Enter grade marks for CN: 100\n",
      "Enter subject 3 name: DMS\n",
      "Enter credits for DMS: 3\n",
      "Enter grade marks for DMS: 100\n",
      "Enter subject 4 name: PAI\n",
      "Enter credits for PAI: 3\n",
      "Enter grade marks for PAI: 100\n",
      "Enter subject 5 name: DMS LAB\n",
      "Enter credits for DMS LAB: 1\n",
      "Enter grade marks for DMS LAB: 100\n",
      "Enter subject 6 name: RMS\n",
      "Enter credits for RMS: 2\n",
      "Enter grade marks for RMS: 100\n",
      "Enter subject 7 name: ES\n",
      "Enter credits for ES: 1\n",
      "Enter grade marks for ES: 100\n",
      "Enter subject 8 name: AE\n",
      "Enter credits for AE: 1\n",
      "Enter grade marks for AE: 100\n",
      "Enter subject 9 name: YOGPH\n",
      "Enter credits for YOGPH: 0\n",
      "Enter grade marks for YOGPH: 100\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----- Marksheet -----\n",
      "\n",
      "Name: Farha Kousar\tUSN: 1N21AI010\tSemester: 5TH SEM\tBranch: AIML\n",
      "\n",
      "Subject\t\tCredits\tGrade Marks\n",
      "------------------------------------\n",
      "ATC\t\t3\t100\n",
      "CN\t\t4\t100\n",
      "DMS\t\t3\t100\n",
      "PAI\t\t3\t100\n",
      "DMS LAB\t\t1\t100\n",
      "RMS\t\t2\t100\n",
      "ES\t\t1\t100\n",
      "AE\t\t1\t100\n",
      "YOGPH\t\t0\t100\n",
      "------------------------------------\n",
      "Total Marks: 900\n",
      "Percentage: 100.00%\n",
      "Grade Letter: A+\n",
      "Grade Points: 10\n",
      "------------------------------------\n",
      "Weak Scores:\n",
      "Strong Scores:\n",
      "ATC: 100\n",
      "CN: 100\n",
      "DMS: 100\n",
      "PAI: 100\n",
      "DMS LAB: 100\n",
      "RMS: 100\n",
      "ES: 100\n",
      "AE: 100\n",
      "YOGPH: 100\n",
      "------------------------------------\n",
      "\n",
      "Motivation Quote:\n",
      "\n",
      "Farha Kousar, your dedication and creativity shine through! Keep up the fantastic work on your project!\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class VTUStudyMarkAnalysis:\n",
    "    def __init__(self):\n",
    "        self.name = \"\"\n",
    "        self.usn = \"\"\n",
    "        self.semester = \"\"\n",
    "        self.branch = \"\"\n",
    "        self.num_subjects = 0\n",
    "        self.subjects = []\n",
    "\n",
    "    def input_student_details(self):\n",
    "        self.name = input(\"Enter your name: \")\n",
    "        self.usn = input(\"Enter your USN: \")\n",
    "        self.semester = input(\"Enter semester: \")\n",
    "        self.branch = input(\"Enter branch: \")\n",
    "        self.num_subjects = int(input(\"Enter the number of subjects: \"))\n",
    "\n",
    "    def input_subject_details(self):\n",
    "        for i in range(self.num_subjects):\n",
    "            subject_name = input(f\"Enter subject {i + 1} name: \")\n",
    "            credits = int(input(f\"Enter credits for {subject_name}: \"))\n",
    "            grade_marks = int(input(f\"Enter grade marks for {subject_name}: \"))\n",
    "            self.subjects.append({\n",
    "                'name': subject_name,\n",
    "                'credits': credits,\n",
    "                'grade_marks': grade_marks\n",
    "            })\n",
    "\n",
    "    def calculate_total_marks(self):\n",
    "        return sum(subject['grade_marks'] for subject in self.subjects)\n",
    "\n",
    "    def calculate_percentage(self, total_marks):\n",
    "        return (total_marks / (self.num_subjects * 100)) * 100\n",
    "\n",
    "    def calculate_grade_letter(self, percentage):\n",
    "        if percentage >= 90:\n",
    "            return 'A+'\n",
    "        elif 80 <= percentage < 90:\n",
    "            return 'A'\n",
    "        elif 70 <= percentage < 80:\n",
    "            return 'B+'\n",
    "        elif 60 <= percentage < 70:\n",
    "            return 'B'\n",
    "        elif 55 <= percentage < 60:\n",
    "            return 'C'\n",
    "        elif 50 <= percentage < 55:\n",
    "            return 'P'\n",
    "        else:\n",
    "            return 'F'\n",
    "\n",
    "    def calculate_grade_points(self, percentage):\n",
    "        if percentage >= 90:\n",
    "            return 10\n",
    "        elif 80 <= percentage < 90:\n",
    "            return 9\n",
    "        elif 70 <= percentage < 80:\n",
    "            return 8\n",
    "        elif 60 <= percentage < 70:\n",
    "            return 7\n",
    "        elif 55 <= percentage < 60:\n",
    "            return 6\n",
    "        elif 50 <= percentage < 55:\n",
    "            return 4\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "    def analyze_weak_strong_scores(self):\n",
    "        weak_scores = [subject for subject in self.subjects if subject['grade_marks'] < 60]\n",
    "        strong_scores = [subject for subject in self.subjects if subject['grade_marks'] >= 60]\n",
    "        return weak_scores, strong_scores\n",
    "\n",
    "    def visualize_weak_strong_scores(self, weak_scores, strong_scores):\n",
    "        labels = ['Weak Scores', 'Strong Scores']\n",
    "        sizes = [len(weak_scores), len(strong_scores)]\n",
    "        plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "        plt.title('Analysis of Weak and Strong Scores')\n",
    "        plt.show()\n",
    "\n",
    "    def generate_marksheet(self, total_marks, percentage, grade_letter, grade_points, weak_scores, strong_scores):\n",
    "        print(\"\\n----- Marksheet -----\\n\")\n",
    "        print(f\"Name: {self.name}\\tUSN: {self.usn}\\tSemester: {self.semester}\\tBranch: {self.branch}\\n\")\n",
    "        print(\"Subject\\t\\tCredits\\tGrade Marks\")\n",
    "        print(\"------------------------------------\")\n",
    "        for subject in self.subjects:\n",
    "            print(f\"{subject['name']}\\t\\t{subject['credits']}\\t{subject['grade_marks']}\")\n",
    "        print(\"------------------------------------\")\n",
    "        print(f\"Total Marks: {total_marks}\")\n",
    "        print(f\"Percentage: {percentage:.2f}%\")\n",
    "        print(f\"Grade Letter: {grade_letter}\")\n",
    "        print(f\"Grade Points: {grade_points}\")\n",
    "        print(\"------------------------------------\")\n",
    "        print(\"Weak Scores:\")\n",
    "        for subject in weak_scores:\n",
    "            print(f\"{subject['name']}: {subject['grade_marks']}\")\n",
    "        print(\"Strong Scores:\")\n",
    "        for subject in strong_scores:\n",
    "            print(f\"{subject['name']}: {subject['grade_marks']}\")\n",
    "        print(\"------------------------------------\")\n",
    "\n",
    "    def display_motivation(self):\n",
    "        motivation_quotes = {\n",
    "            'Farha Kousar': \"Farha Kousar, your dedication and creativity shine through! Keep up the fantastic work on your project!\",\n",
    "            'John Doe': \"Success is not final, failure is not fatal: It is the courage to continue that counts.\",\n",
    "            'Jane Smith': \"Believe you can and you're halfway there.\",\n",
    "            # Add more quotes as needed\n",
    "        }\n",
    "        print(\"\\nMotivation Quote:\\n\")\n",
    "        print(motivation_quotes.get(self.name, \"Stay motivated and keep working towards your goals!\"))\n",
    "\n",
    "    def run_analysis(self):\n",
    "        self.input_student_details()\n",
    "        self.input_subject_details()\n",
    "\n",
    "        total_marks = self.calculate_total_marks()\n",
    "        percentage = self.calculate_percentage(total_marks)\n",
    "        grade_letter = self.calculate_grade_letter(percentage)\n",
    "        grade_points = self.calculate_grade_points(percentage)\n",
    "\n",
    "        weak_scores, strong_scores = self.analyze_weak_strong_scores()\n",
    "        self.visualize_weak_strong_scores(weak_scores, strong_scores)\n",
    "\n",
    "        self.generate_marksheet(total_marks, percentage, grade_letter, grade_points, weak_scores, strong_scores)\n",
    "\n",
    "        self.display_motivation()\n",
    "\n",
    "# Create an instance of the VTUStudyMarkAnalysis class and run the analysis\n",
    "vtu_analysis = VTUStudyMarkAnalysis()\n",
    "vtu_analysis.run_analysis()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "841f0609",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a90afe9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "017c15de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a290357",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ddcbcae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}