student decision.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=['Drawing','Dancing','Singing','Sports','Video Game','Acting','Travelling',\n",
    "    'Gardening','Animals','Photography','Teaching',\n",
    "    'Exercise','Coding','Electricity Components','Mechanic Parts','Computer Parts','Researching','Architecture',\n",
    "    'Historic Collection','Botany','Zoology','Physics','Accounting','Economics','Sociology','Geography',\n",
    "    'Psycology','History','Science','Bussiness Education','Chemistry','Mathematics','Biology','Makeup','Designing',\n",
    "    'Content writing','Crafting','Literature','Reading','Cartooning','Debating','Asrtology',\n",
    "    'Hindi','French','English','Other Language',\n",
    "    'Solving Puzzles','Gymnastics','Yoga','Engeeniering','Doctor','Pharmisist','Cycling','Knitting',\n",
    "    'Director','Journalism','Bussiness','Listening Music'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Course=['BBA- Bachelor of Business Administration',\n",
    "'BEM- Bachelor of Event Management',\n",
    "'Integrated Law Course- BA + LL.B',\n",
    "'BJMC- Bachelor of Journalism and Mass Communication',\n",
    "'BFD- Bachelor of Fashion Designing',\n",
    "'BBS- Bachelor of Business Studies',\n",
    "'BTTM- Bachelor of Travel and Tourism Management',\n",
    "'BVA- Bachelor of Visual Arts',\n",
    "'BA in History',\n",
    "'B.Arch- Bachelor of Architecture',\n",
    "'BCA- Bachelor of Computer Applications',\n",
    "'B.Sc.- Information Technology',\n",
    "'B.Sc- Nursing',\n",
    "'BPharma- Bachelor of Pharmacy',\n",
    "'BDS- Bachelor of Dental Surgery',\n",
    "'Animation, Graphics and Multimedia',\n",
    "'B.Sc- Applied Geology',\n",
    "'B.Sc.- Physics',\n",
    "'B.Sc. Chemistry',\n",
    "'B.Sc. Mathematics',\n",
    "'B.Tech.-Civil Engineering',\n",
    "'B.Tech.-Computer Science and Engineering',\n",
    "'B.Tech.-Electrical and Electronics Engineering',\n",
    "'B.Tech.-Electronics and Communication Engineering',\n",
    "'B.Tech.-Mechanical Engineering',\n",
    "'B.Com- Bachelor of Commerce',\n",
    "'BA in Economics',\n",
    "'CA- Chartered Accountancy',\n",
    "'CS- Company Secretary',\n",
    "'Diploma in Dramatic Arts',\n",
    "'MBBS',\n",
    "'Civil Services',\n",
    "'BA in English',\n",
    "'BA in Hindi',\n",
    "'B.Ed.'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2=[]\n",
    "for x in range(0,len(l1)):\n",
    "    l2.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"stud_training.csv\")   ##Path of your training csv file\n",
    "\n",
    "df.replace({'Courses':{'BBA- Bachelor of Business Administration':0,\n",
    "'BEM- Bachelor of Event Management':1,\n",
    "'Integrated Law Course- BA + LL.B':2,\n",
    "'BJMC- Bachelor of Journalism and Mass Communication':3,\n",
    "'BFD- Bachelor of Fashion Designing':4,\n",
    "'BBS- Bachelor of Business Studies':5,\n",
    "'BTTM- Bachelor of Travel and Tourism Management':6,\n",
    "'BVA- Bachelor of Visual Arts':7,\n",
    "'BA in History':8,\n",
    "'B.Arch- Bachelor of Architecture':9,\n",
    "'BCA- Bachelor of Computer Applications':10,\n",
    "'B.Sc.- Information Technology':11,\n",
    "'B.Sc- Nursing':12,\n",
    "'BPharma- Bachelor of Pharmacy':13,\n",
    "'BDS- Bachelor of Dental Surgery':14,\n",
    "'Animation, Graphics and Multimedia':15,\n",
    "'B.Sc- Applied Geology':16,\n",
    "'B.Sc.- Physics':17,\n",
    "'B.Sc. Chemistry':18,\n",
    "'B.Sc. Mathematics':19,\n",
    "'B.Tech.-Civil Engineering':20,\n",
    "'B.Tech.-Computer Science and Engineering':21,\n",
    "'B.Tech.-Electrical and Electronics Engineering':22,\n",
    "'B.Tech.-Electronics and Communication Engineering':23,\n",
    "'B.Tech.-Mechanical Engineering':24,\n",
    "'B.Com- Bachelor of Commerce':25,\n",
    "'BA in Economics':26,\n",
    "'CA- Chartered Accountancy':27,\n",
    "'CS- Company Secretary':28,\n",
    "'Diploma in Dramatic Arts':29,\n",
    "'MBBS':30,\n",
    "'Civil Services':31,\n",
    "'BA in English':32,\n",
    "'BA in Hindi':33,\n",
    "'B.Ed.':34\n",
    "}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  0, ..., 34, 34, 34], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= df[l1]\n",
    "\n",
    "y = df[[\"Courses\"]]\n",
    "np.ravel(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 23, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr=pd.read_csv(\"stud_testing.csv\",error_bad_lines=False) ##Path of your testing csv file\n",
    "\n",
    "tr.replace({'Courses':{'BBA- Bachelor of Business Administration':0,\n",
    "'BEM- Bachelor of Event Management':1,\n",
    "'Integrated Law Course- BA + LL.B':2,\n",
    "'BJMC- Bachelor of Journalism and Mass Communication':3,\n",
    "'BFD- Bachelor of Fashion Designing':4,\n",
    "'BBS- Bachelor of Business Studies':5,\n",
    "'BTTM- Bachelor of Travel and Tourism Management':6,\n",
    "'BVA- Bachelor of Visual Arts':7,\n",
    "'BA in History':8,\n",
    "'B.Arch- Bachelor of Architecture':9,\n",
    "'BCA- Bachelor of Computer Applications':10,\n",
    "'B.Sc.- Information Technology':11,\n",
    "'B.Sc- Nursing':12,\n",
    "'BPharma- Bachelor of Pharmacy':13,\n",
    "'BDS- Bachelor of Dental Surgery':14,\n",
    "'Animation, Graphics and Multimedia':15,\n",
    "'B.Sc- Applied Geology':16,\n",
    "'B.Sc.- Physics':17,\n",
    "'B.Sc. Chemistry':18,\n",
    "'B.Sc. Mathematics':19,\n",
    "'B.Tech.-Civil Engineering':20,\n",
    "'B.Tech.-Computer Science and Engineering':21,\n",
    "'B.Tech.-Electrical and Electronics Engineering':22,\n",
    "'B.Tech.-Electronics and Communication Engineering':23,\n",
    "'B.Tech.-Mechanical Engineering':24,\n",
    "'B.Com- Bachelor of Commerce':25,\n",
    "'BA in Economics':26,\n",
    "'CA- Chartered Accountancy':27,\n",
    "'CS- Company Secretary':28,\n",
    "'Diploma in Dramatic Arts':29,\n",
    "'MBBS':30,\n",
    "'Civil Services':31,\n",
    "'BA in English':32,\n",
    "'BA in Hindi':33,\n",
    "'B.Ed.':34\n",
    "}},inplace=True)\n",
    "\n",
    "X_test= tr[l1]\n",
    "y_test = tr[[\"Courses\"]]\n",
    "np.ravel(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DecisionTree():\n",
    "\n",
    "    from sklearn import tree\n",
    "\n",
    "    clf3 = tree.DecisionTreeClassifier()   # empty model of the decision tree\n",
    "    clf3 = clf3.fit(X,y)\n",
    "\n",
    "    # calculating accuracy-------------------------------------------------------------------\n",
    "#     from sklearn.metrics import accuracy_score\n",
    "#     y_pred=clf3.predict(X_test)\n",
    "#     print(accuracy_score(y_test, y_pred))\n",
    "#     print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "    # -----------------------------------------------------\n",
    "\n",
    "    inter = [Interest1.get(),Interest2.get(),Interest3.get(),Interest4.get(),Interest5.get()]\n",
    "\n",
    "    for k in range(0,len(l1)):\n",
    "        # print (k,)\n",
    "        for z in inter:\n",
    "            if(z==l1[k]):\n",
    "                l2[k]=1\n",
    "\n",
    "    inputtest = [l2]\n",
    "    predict = clf3.predict(inputtest)\n",
    "    predicted=predict[0]\n",
    "\n",
    "    h='no'\n",
    "    for a in range(0,len(Course)):\n",
    "        if(predicted == a):\n",
    "            h='yes'\n",
    "            break\n",
    "\n",
    "\n",
    "    if (h=='yes'):\n",
    "        t1.delete(\"1.0\", END)\n",
    "        t1.insert(END, Course[a])\n",
    "    else:\n",
    "        t1.delete(\"1.0\", END)\n",
    "        t1.insert(END, \"Not Found\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomforest():\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    clf4 = RandomForestClassifier()\n",
    "    clf4 = clf4.fit(X,np.ravel(y))\n",
    "\n",
    "    # calculating accuracy-------------------------------------------------------------------\n",
    "#     from sklearn.metrics import accuracy_score\n",
    "#     y_pred=clf4.predict(X_test)\n",
    "#     print(accuracy_score(y_test, y_pred))\n",
    "#     print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "    # -----------------------------------------------------\n",
    "\n",
    "    inter = [Interest1.get(),Interest2.get(),Interest3.get(),Interest4.get(),Interest5.get()]\n",
    "\n",
    "    for k in range(0,len(l1)):\n",
    "        for z in inter:\n",
    "            if(z==l1[k]):\n",
    "                l2[k]=1\n",
    "\n",
    "    inputtest = [l2]\n",
    "    predict = clf4.predict(inputtest)\n",
    "    predicted=predict[0]\n",
    "\n",
    "    h='no'\n",
    "    for a in range(0,len(Course)):\n",
    "        if(predicted == a):\n",
    "            h='yes'\n",
    "            break\n",
    "\n",
    "    if (h=='yes'):\n",
    "        t2.delete(\"1.0\", END)\n",
    "        t2.insert(END, Course[a])\n",
    "    else:\n",
    "        t2.delete(\"1.0\", END)\n",
    "        t2.insert(END, \"Not Found\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NaiveBayes():\n",
    "    from sklearn.naive_bayes import GaussianNB\n",
    "    gnb = GaussianNB()\n",
    "    gnb=gnb.fit(X,np.ravel(y))\n",
    "\n",
    "    # calculating accuracy-------------------------------------------------------------------\n",
    "#     from sklearn.metrics import accuracy_score\n",
    "#     y_pred=gnb.predict(X_test)\n",
    "#     print(accuracy_score(y_test, y_pred))\n",
    "#     print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "    # -----------------------------------------------------\n",
    "\n",
    "    inter = [Interest1.get(),Interest2.get(),Interest3.get(),Interest4.get(),Interest5.get()]\n",
    "    for k in range(0,len(l1)):\n",
    "        for z in inter:\n",
    "            if(z==l1[k]):\n",
    "                l2[k]=1\n",
    "\n",
    "    inputtest = [l2]\n",
    "    predict = gnb.predict(inputtest)\n",
    "    predicted=predict[0]\n",
    "\n",
    "    h='no'\n",
    "    for a in range(0,len(Course)):\n",
    "        if(predicted == a):\n",
    "            h='yes'\n",
    "            break\n",
    "\n",
    "    if (h=='yes'):\n",
    "        t3.delete(\"1.0\", END)\n",
    "        t3.insert(END, Course[a])\n",
    "    else:\n",
    "        t3.delete(\"1.0\", END)\n",
    "        t3.insert(END, \"Not Found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = Tk()\n",
    "root.configure(background='blue')\n",
    "\n",
    "# entry variables\n",
    "Interest1 = StringVar()\n",
    "Interest1.set(None)\n",
    "Interest2 = StringVar()\n",
    "Interest2.set(None)\n",
    "Interest3 = StringVar()\n",
    "Interest3.set(None)\n",
    "Interest4 = StringVar()\n",
    "Interest4.set(None)\n",
    "Interest5 = StringVar()\n",
    "Interest5.set(None)\n",
    "Name = StringVar()\n",
    "\n",
    "# Heading\n",
    "w2 = Label(root, justify=LEFT, text=\"Course Predictor using Machine Learning\", fg=\"white\", bg=\"blue\")\n",
    "w2.config(font=(\"Elephant\", 30))\n",
    "w2.grid(row=1, column=0, columnspan=2, padx=100)\n",
    "w2.config(font=(\"Aharoni\", 30))\n",
    "w2.grid(row=2, column=0, columnspan=2, padx=100)\n",
    "\n",
    "# labels\n",
    "NameLb = Label(root, text=\"Name of the Student\", fg=\"yellow\", bg=\"black\")\n",
    "NameLb.grid(row=6, column=0, pady=15, sticky=W)\n",
    "\n",
    "\n",
    "S1Lb = Label(root, text=\"Interest 1\", fg=\"yellow\", bg=\"black\")\n",
    "S1Lb.grid(row=7, column=0, pady=10, sticky=W)\n",
    "\n",
    "S2Lb = Label(root, text=\"Interest 2\", fg=\"yellow\", bg=\"black\")\n",
    "S2Lb.grid(row=8, column=0, pady=10, sticky=W)\n",
    "\n",
    "S3Lb = Label(root, text=\"Interest 3\", fg=\"yellow\", bg=\"black\")\n",
    "S3Lb.grid(row=9, column=0, pady=10, sticky=W)\n",
    "\n",
    "S4Lb = Label(root, text=\"Interest 4\", fg=\"yellow\", bg=\"black\")\n",
    "S4Lb.grid(row=10, column=0, pady=10, sticky=W)\n",
    "\n",
    "S5Lb = Label(root, text=\"Interest 5\", fg=\"yellow\", bg=\"black\")\n",
    "S5Lb.grid(row=11, column=0, pady=10, sticky=W)\n",
    "\n",
    "\n",
    "lrLb = Label(root, text=\"DecisionTree\", fg=\"white\", bg=\"red\")\n",
    "lrLb.grid(row=15, column=0, pady=10,sticky=W)\n",
    "\n",
    "destreeLb = Label(root, text=\"RandomForest\", fg=\"white\", bg=\"red\")\n",
    "destreeLb.grid(row=17, column=0, pady=10, sticky=W)\n",
    "\n",
    "ranfLb = Label(root, text=\"NaiveBayes\", fg=\"white\", bg=\"red\")\n",
    "ranfLb.grid(row=19, column=0, pady=10, sticky=W)\n",
    "\n",
    "# entries\n",
    "OPTIONS = sorted(l1)\n",
    "\n",
    "NameEn = Entry(root, textvariable=Name)\n",
    "NameEn.grid(row=6, column=1)\n",
    "\n",
    "S1En = OptionMenu(root, Interest1,*OPTIONS)\n",
    "S1En.grid(row=7, column=1)\n",
    "\n",
    "S2En = OptionMenu(root, Interest2,*OPTIONS)\n",
    "S2En.grid(row=8, column=1)\n",
    "\n",
    "S3En = OptionMenu(root, Interest3,*OPTIONS)\n",
    "S3En.grid(row=9, column=1)\n",
    "\n",
    "S4En = OptionMenu(root, Interest4,*OPTIONS)\n",
    "S4En.grid(row=10, column=1)\n",
    "\n",
    "S5En = OptionMenu(root, Interest5,*OPTIONS)\n",
    "S5En.grid(row=11, column=1)\n",
    "\n",
    "\n",
    "dst = Button(root, text=\"DecisionTree\", command=DecisionTree,bg=\"green\",fg=\"yellow\")\n",
    "dst.grid(row=8, column=3,padx=10)\n",
    "\n",
    "rnf = Button(root, text=\"Randomforest\", command=randomforest,bg=\"green\",fg=\"yellow\")\n",
    "rnf.grid(row=9, column=3,padx=10)\n",
    "\n",
    "lr = Button(root, text=\"NaiveBayes\", command=NaiveBayes,bg=\"green\",fg=\"yellow\")\n",
    "lr.grid(row=10, column=3,padx=10)\n",
    "\n",
    "#textfileds\n",
    "t1 = Text(root, height=1, width=40,bg=\"orange\",fg=\"black\")\n",
    "t1.grid(row=15, column=1, padx=10)\n",
    "\n",
    "t2 = Text(root, height=1, width=40,bg=\"orange\",fg=\"black\")\n",
    "t2.grid(row=17, column=1 , padx=10)\n",
    "\n",
    "t3 = Text(root, height=1, width=40,bg=\"orange\",fg=\"black\")\n",
    "t3.grid(row=19, column=1 , padx=10)\n",
    "\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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