Project.py

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 random as rnd
import warnings as w
w.filterwarnings('ignore')
data = pd.read_csv("/AI-Data.csv")
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 Nationality-wise Graph\n5.Marks Class Grade-wise Graph\t6.Marks Class Section-wise 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})
#sig = []
for column in data.columns:
    if data[column].dtype == type(object):
        le = pp.LabelEncoder()
        data[column] = le.fit_transform(data[column])
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):
    #sig.append(1/(1+ np.exp(-b)))
    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: ")
    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)
    t.sleep(1)
    print("Using Random Forest Classifier: ", predR)
    t.sleep(1)
    print("Using Linear Model Perceptron: ", predP)
    t.sleep(1)
    print("Using Linear Model Logisitic Regression: ", predL)
    t.sleep(1)
    print("Using Neural Network MLP Classifier: ", predN)
    print("\nExiting...")
    t.sleep(1)
else:
    print("Exiting..")
    t.sleep(1)