STUDENT-PERFORMANCE-PREDICTION

import pandas as pd import seaborn as sb import matplotlib.pyplot as plt import time as t import sklearn.utils as u import sklearn.preprocessing as pp import sklearn.tree as tr import sklearn.ensemble as es import sklearn.metrics as m import sklearn.linear_model as lm import sklearn.neural_network as nn import numpy as np import warnings as w

data = pd.read_csv('/content/xAPI-Edu-Data.csv')

w.filterwarnings('ignore')

ch = 0 while(ch != 10): print("1.Marks Class Count Graph\t2.Marks Class Semester-wise Graph\n3.Marks Class Gender-wise Graph\t4.Marks Class Nationalitywise Graph\n5.Marks Class Grade-wise Graph\t6.Marks Class Sectionwise Graph\n7.Marks Class Topic-wise Graph\t8.Marks Class Stage-wise Graph\n9.Marks Class Absent Days-wise\t10.No Graph\n") ch = int(input("Enter Choice: ")) if (ch == 1): print("Loading Graph....\n") t.sleep(1) print("\tMarks Class Count Graph") axes = sb.countplot(x='Class', data=data, order=['L', 'M', 'H']) plt.show() elif (ch == 2): print("Loading Graph....\n") t.sleep(1) print("\tMarks Class Semester-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='Semester', hue='Class', data=data, hue_order=['L', 'M', 'H'], axes=axesarr) plt.show() elif (ch == 3): print("Loading Graph..\n") t.sleep(1) print("\tMarks Class Gender-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='gender', hue='Class', data=data, order=['M', 'F'], hue_order=['L', 'M', 'H'], axes=axesarr) plt.show() elif (ch == 4): print("Loading Graph..\n") t.sleep(1) print("\tMarks Class Nationality-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='NationalITy', hue='Class', data=data, hue_order=['L', 'M', 'H'], axes=axesarr) plt.show() elif (ch == 5): print("Loading Graph: \n") t.sleep(1) print("\tMarks Class Grade-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='GradeID', hue='Class', data=data, order=['G-02', 'G-04', 'G-05', 'G-06', 'G-07', 'G-08', 'G-09', 'G-10', 'G-11', 'G-12'], hue_order = ['L', 'M', 'H'], axes=axesarr) plt.show() elif (ch ==6): print("Loading Graph..\n") t.sleep(1) print("\tMarks Class Section-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='SectionID', hue='Class', data=data, hue_order = ['L', 'M', 'H'], axes=axesarr) plt.show() elif (ch == 7): print("Loading Graph..\n") t.sleep(1) print("\tMarks Class Topic-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='Topic', hue='Class', data=data, hue_order = ['L', 'M', 'H'], axes=axesarr) plt.show() elif (ch == 8): print("Loading Graph..\n") t.sleep(1) print("\tMarks Class Stage-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='StageID', hue='Class', data=data, hue_order = ['L', 'M', 'H'], axes=axesarr) plt.show() elif (ch == 9): print("Loading Graph..\n") t.sleep(1) print("\tMarks Class Absent Days-wise Graph") fig, axesarr = plt.subplots(1, figsize=(10, 6)) sb.countplot(x='StudentAbsenceDays', hue='Class', data=data, hue_order = ['L', 'M', 'H'], axes=axesarr) plt.show() if(ch == 10): print("Exiting..\n") t.sleep(1)

#cor = data.corr() #print(cor) data = data.drop("gender", axis=1) data = data.drop("StageID", axis=1) data = data.drop("GradeID", axis=1) data = data.drop("NationalITy", axis=1) data = data.drop("PlaceofBirth", axis=1) data = data.drop("SectionID", axis=1) data = data.drop("Topic", axis=1) data = data.drop("Semester", axis=1) data = data.drop("Relation", axis=1) data = data.drop("ParentschoolSatisfaction", axis=1) data = data.drop("ParentAnsweringSurvey", axis=1) #data = data.drop("VisITedResources", axis=1) data = data.drop("AnnouncementsView", axis=1) u.shuffle(data) countD = 0 countP = 0 countL = 0 countR = 0 countN = 0

gradeID_dict = {"G-01": 1, "G-02": 2, "G-03": 3, "G-04": 4, "G-05": 5, "G-06": 6, "G-07": 7, "G-08": 8, "G-09": 9, "G-10": 10, "G-11": 11, "G-12": 12} data = data.replace({"GradeID": gradeID_dict})

ind = int(len(data) * 0.70) feats = data.values[:, 0:4] lbls = data.values[:, 4]

feats_Train = feats[0:ind] feats_Test = feats[(ind+1):len(feats)] lbls_Train = lbls[0:ind] lbls_Test = lbls[(ind+1):len(lbls)]

modelD = tr.DecisionTreeClassifier() modelD.fit(feats_Train, lbls_Train) lbls_predD = modelD.predict(feats_Test)

for a, b in zip(lbls_Test, lbls_predD): if(a == b): countD += 1

accD = (countD / len(lbls_Test)) print("\nAccuracy measures using Decision Tree:") print(m.classification_report(lbls_Test, lbls_predD), "\n") print("\nAccuracy using Decision Tree: ", str(round(accD, 3))) t.sleep(1)

modelR = es.RandomForestClassifier() modelR.fit(feats_Train, lbls_Train) lbls_predR = modelR.predict(feats_Test)

for a, b in zip(lbls_Test, lbls_predR): if(a == b): countR += 1

print("\nAccuracy Measures for Random Forest Classifier: \n")

print("\nConfusion Matrix: \n", m.confusion_matrix(lbls_Test, lbls_predR))

print("\n", m.classification_report(lbls_Test, lbls_predR))

accR = countR / len(lbls_Test) print("\nAccuracy using Random Forest: ", str(round(accR, 3))) t.sleep(1)

modelP = lm.Perceptron() modelP.fit(feats_Train, lbls_Train) lbls_predP = modelP.predict(feats_Test)

for a, b in zip(lbls_Test, lbls_predP): if a == b: countP += 1

accP = countP / len(lbls_Test) print("\nAccuracy measures using Linear Model Perceptron:") print(m.classification_report(lbls_Test, lbls_predP), "\n") print("\nAccuracy using Linear Model Perceptron: ", str(round(accP, 3)), "\n") t.sleep(1)

modelL = lm.LogisticRegression() modelL.fit(feats_Train, lbls_Train) lbls_predL = modelL.predict(feats_Test)

for a, b in zip(lbls_Test, lbls_predL): if a == b: countL += 1

accL = countL / len(lbls_Test) print("\nAccuracy measures using Linear Model Logistic Regression:") print(m.classification_report(lbls_Test, lbls_predL), "\n") print("\nAccuracy using Linear Model Logistic Regression: ", str(round(accP, 3)), "\n") t.sleep(1)

modelN = nn.MLPClassifier(activation="logistic") modelN.fit(feats_Train, lbls_Train) lbls_predN = modelN.predict(feats_Test)

for a, b in zip(lbls_Test, lbls_predN): if a == b: countN += 1

print("\nAverage value of Sigmoid Function: ", str(round(np.average(sig), 3)))

print("\nAccuracy measures using MLP Classifier:") print(m.classification_report(lbls_Test, lbls_predN), "\n")

accN = countN / len(lbls_Test) print("\nAccuracy using Neural Network MLP Classifier: ", str(round(accN, 3)), "\n")

choice = input("Do you want to test specific input (y or n): ")

if(choice.lower() == "y"): gen = input("Enter Gender (M or F): ") if (gen.upper() == "M"): gen = 1 elif (gen.upper() == "F"): gen = 0

nat = input("Enter Nationality: ")
pob = input("Place of Birth: ")
sta = input("Enter Stage ID(Lower level, Middle school, High school): ")

if (sta == "Lower level"):
    sta = 2
elif (sta == "Middle school"):
    sta = 1
elif (sta == "High school"):
    sta = 0

gra = input("Grade ID as (G-<grade>): ")
if (gra == "G-02"):
    gra = 2
elif (gra == "G-04"):
    gra = 4
elif (gra == "G-05"):
    gra = 5
elif (gra == "G-06"):
    gra = 6
elif (gra == "G-07"):
    gra = 7
elif (gra == "G-08"):
    gra = 8
elif (gra == "G-09"):
    gra = 9
elif (gra == "G-10"):
    gra = 10
elif (gra == "G-11"):
    gra = 11
elif (gra == "G-12"):
    gra = 12

sec = input("Enter Section: ")
top = input("Enter Topic: ")
sem = input("Enter Semester (F or S): ")
if (sem.upper() == "F"):
    sem = 0
elif (sem.upper() == "S"):
    sem = 1

rel = input("Enter Relation (Father or Mum): ")
if (rel == "Father"):
    rel = 0
elif (rel == "Mum"):
    rel = 1

rai = int(input("Enter raised hands: "))
res = int(input("Enter Visited Resources: "))
ann = int(input("Enter announcements viewed: "))
dis = int(input("Enter no. of Discussions: "))
sur = input("Enter Parent Answered Survey (Y or N): ")
if (sur.upper() == "Y"):
    sur = 1
elif (sur.upper() == "N"):
    sur = 0

sat = input("Enter Parent School Satisfaction (Good or Bad): ")
if (sat == "Good"):
    sat = 1
elif (sat == "Bad"):
    sat = 0
absc = input("Enter No. of Abscenes(Under-7 or Above-7): ")
if (absc == "Under-7"):
  absc = 1
elif (absc == "Above-7"):
  absc = 0
arr = np.array([rai, res, dis, absc])

arr = np.array([gen, rnd.randint(0, 30), rnd.randint(0, 30), sta, gra, rnd.randint(0, 30), rnd.randint(0, 30), sem, rel, rai, res, ann, dis, sur, sat, absc])

predD = modelD.predict(arr.reshape(1, -1)) predR = modelR.predict(arr.reshape(1, -1)) predP = modelP.predict(arr.reshape(1, -1)) predL = modelL.predict(arr.reshape(1, -1)) predN = modelN.predict(arr.reshape(1, -1))

if (predD == 0): predD = "H" elif (predD == 1): predD = "M" elif (predD == 2): predD = "L"

if (predR == 0): predR = "H" elif (predR == 1): predR = "M" elif (predR == 2): predR = "L"

if (predP == 0): predP = "H" elif (predP == 1): predP = "M" elif (predP == 2): predP = "L"

if (predL == 0): predL = "H" elif (predL == 1): predL = "M" elif (predL == 2): predL = "L"

if (predN == 0): predN = "H" elif (predN == 1): predN = "M" elif (predN == 2): predN = "L"

t.sleep(1) print("\nUsing Decision Tree Classifier: ", predD)