Update IT17163682/Experience.txt, IT17163682/Final_Points.csv,...

Update IT17163682/Experience.txt, IT17163682/Final_Points.csv, IT17163682/Final_Prediction.py, IT17163682/Final_Report.xlsx, IT17163682/Final_Score.txt files

IT17163682/Final_Points.csv

+Name,Alma Mater,Skill Points,Extraversion Points,Personality Type,Final Grade,CV Status
+Annah,Ness Wadia College,62.02,58.5,INTJ,57.86,Satisfied
+Bastein,Information Technology Xaviers College,48.04,41.92,INFP,38.35,Low
+Mahela,SLIIT University,27.74,57.46,INFJ,32.95,Poor
+Sangakara,Computer Science Mercy College,62.02,63.55,INFP,60.79,Satisfied
+Virat,Computer Science Saviour College,55.47,54.87,INFJ,51.12,Satisfied

IT17163682/Final_Prediction.py

+import pandas as pd
+import json
+import math
+import numpy as np
+import sklearn
+#import matplotlib.pyplot as plt
+from sklearn import linear_model
+from sklearn.utils import shuffle
+
+
+df = pd.read_csv("dummy data.csv")
+reg = linear_model.LinearRegression()
+reg.fit(df[['Skills', 'Extraversion']],df.Points)
+
+res = open("Final_Score.txt", 'r', encoding='utf-8').read()
+wrds = res.split()
+skills = wrds[3]
+extraversion = wrds[5]
+
+Grade = reg.predict([[float(skills), float(extraversion)]])
+fin_grd = Grade/2
+score = round(float(fin_grd), 2)
+
+if(score >= 100):
+    score = 99
+
+cv_stat = ""
+
+if(score >= 85):
+    cv_stat = "Excellent"
+elif(score >= 70):
+    cv_stat = "Good"
+elif (score >= 50):
+    cv_stat = "Satisfied"
+elif (score >= 35):
+    cv_stat = "Low"
+else :
+    cv_stat = "Poor"
+
+with open('Final_Score.txt', 'a') as the_file:
+    the_file.write('Grade ' + str(score) + '\n')
+    the_file.write('CV_Stat ' + cv_stat + '\n')

IT17163682/Final_Score.txt

+Name Virat
+Skills 55.47
+Extraversion 54.87
+Personality INFJ
+Grade 51.12
+CV_Stat Satisfied