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final_new.py
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# -*- coding: utf-8 -*-
#from math import sqrt
#import matplotlib.pyplot as plt; plt.rcdefaults()
#import numpy as np
#import matplotlib.pyplot as plt
from sample_dt import Dataset_A
#from datasett import dataset
print(Dataset_A)
Dataset=Dataset_A
def worth():
Dataset=Dataset_A
wor=nwor=0
i=0
while i<len(Dataset):
if(Dataset[i][0]==1):
wor+=1
else:
nwor+=1
i+=1
print("Worth course \nout of ",len(Dataset))
print("Worth",wor,"\nNot worth",nwor)
worth()
"""def coll_filter():
def pearson_correlation(person1,person2):
both_rated = {}
for item in dataset[person1]:
if item in dataset[person2]:
both_rated[item] = 1
number_of_ratings = len(both_rated)
if number_of_ratings == 0:
return 0
# Add up all the preferences of each user
person1_preferences_sum = sum([dataset[person1][item] for item in both_rated])
person2_preferences_sum = sum([dataset[person2][item] for item in both_rated])
person1_square_preferences_sum = sum([pow(dataset[person1][item],2) for item in both_rated])
person2_square_preferences_sum = sum([pow(dataset[person2][item],2) for item in both_rated])
product_sum_of_both_users = sum([dataset[person1][item] * dataset[person2][item] for item in both_rated])
numerator_value = product_sum_of_both_users - (person1_preferences_sum*person2_preferences_sum/number_of_ratings)
denominator_value = sqrt((person1_square_preferences_sum - pow(person1_preferences_sum,2)/number_of_ratings) * (person2_square_preferences_sum -pow(person2_preferences_sum,2)/number_of_ratings))
if denominator_value == 0:
return 0
else:
r = numerator_value/denominator_value
return r
def most_similar_users(person,number_of_users):
scores = [(pearson_correlation(person,other_person),other_person) for other_person in dataset if other_person != person ]
scores.sort()
scores.reverse()
return scores[0:number_of_users]
def user_recommendations(person):
totals = {}
simSums = {}
rankings_list =[]
for other in dataset:
if other == person:
continue
sim = pearson_correlation(person,other)
if sim <=0:
continue
for item in dataset[other]:
if item not in dataset[person] or dataset[person][item] == 0:
totals.setdefault(item,0)
totals[item] += dataset[other][item]* sim
simSums.setdefault(item,0)
simSums[item]+= sim
rankings = [(total/simSums[item],item) for item,total in totals.items()]
rankings.sort()
rankings.reverse()
recommendataions_list = [(recommend_item,score) for score,recommend_item in rankings]
return recommendataions_list
new=user_recommendations('Alumni5')
dataset['Alumni5'].update(new)
dataset['Alumni5']['ds']=round(dataset['Alumni5']['ds'],1)
dataset['Alumni5']['python']=round(dataset['Alumni5']['python'],1)
new=user_recommendations('Alumni4')
dataset["Alumni4"].update(new)
dataset['Alumni4']['stat']=round(dataset['Alumni4']['stat'],1)
csa=ppl=mfcs=ds=stat=python=0
for key in dataset:
ppl+=dataset[key]['ppl']
csa+=dataset[key]['csa']
stat+=dataset[key]['stat']
mfcs+=dataset[key]['mfcs']
ds+=dataset[key]['ds']
python+=dataset[key]['python']
print("ppl:",ppl,"csa:",csa,"stat:",stat,"mfcs:",mfcs,"ds:",ds,"python:",python)
mean=[]
print("length of dataset",len(Dataset))
mean.append(ppl/len(dataset))
mean.append(csa/len(dataset))
mean.append(round(stat/len(dataset),1))
mean.append(mfcs/len(dataset))
mean.append(round(ds/len(dataset),1))
mean.append(round(python/len(dataset)))
print(mean)
mean.sort()
mean.reverse()
print(mean)
objects = ('python','stat','mfcs','ds','csa','ppl')
y_pos = np.arange(len(objects))
plt.bar(y_pos, mean, align='center', alpha=0.75)
plt.xticks(y_pos, objects)
plt.ylabel('rating')
plt.xlabel('courses')
plt.title('overall rating for sem1')
plt.show()
coll_filter()"""
def lab():
i=0
laby=labn=0
while i<len(Dataset):
if(Dataset[i][2]==1):
laby+=1
else:
labn+=1
i+=1
print("\n\nLab for this course\nOut of ",len(Dataset))
print("lab needed",laby,"\nNot needed",labn)
lab()
def content():
i=0
cy=cn=0
while i<len(Dataset):
if(Dataset[i][3]==1):
cy+=1
else:
cn+=1
i+=1
print("\n\nContent of this course\nout of",len(Dataset))
print("Content ok:",cy,"\nNot ok:",cn)
content()
def precourse():
course_list=[]
py=pn=0
i=0
while i<len(Dataset):
s=str(Dataset[i][4])
s=s.lower()
if(s=='no' or s=='-'):
pn+=1
else:
py+=1
course_list.append(Dataset[i][4])
if(Dataset[i][6]==1):
course_list.append('1')
else:
course_list.append('0')
i+=1
print("\n pre courses list\n")
print(course_list)
print("\n\pre requisistes for this course\nOut of ",len(Dataset))
print("pre course needed",py,"\nNot needed",pn)
precourse()
def suggest():
course_list=[]
py=pn=sy=sn=0
i=0
while i<len(Dataset):
li=[]
s=str(Dataset[i][7])
s=s.lower()
if(s=='no' or s=='-'):
sn+=1
else:
sy+=1
li.append(Dataset[i][7])
if(Dataset[i][9]==1):
li.append('1')
else:
li.append('0')
s1=str(Dataset[i][10])
s1=s1.lower()
if(s1=='no' or s1=='-'):
pn+=1
li.append(Dataset[i][10])
else:
py+=1
li.append(Dataset[i][10])
if(s!='no' or s!='-'):
course_list.append(li)
i+=1
print("suggest list\n")
print(course_list,"\n suggested :",sy,"\n No suggestion",sn)
suggest()