Course-Recommendation-System/CourseRecommendationSystem.py at main · Ganesh2409/Course-Recommendation-System · GitHub

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import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
print('Dependencies Imported')

data = pd.read_csv("Data/Coursera.csv")
data.head(5)

"""# Basic Data Analysis"""

data.shape #3522 courses and 7 columns with different attributes

data.info()

data.isnull().sum() #no value is missing

data['Difficulty Level'].value_counts()

data['Course Rating'].value_counts()

data['University'].value_counts()

data['Course Name']


data = data[['Course Name','Difficulty Level','Course Description','Skills']]

data.head(5)


# Removing spaces between the words (Lambda funtions can be used as well)

data['Course Name'] = data['Course Name'].str.replace(' ',',')
data['Course Name'] = data['Course Name'].str.replace(',,',',')
data['Course Name'] = data['Course Name'].str.replace(':','')
data['Course Description'] = data['Course Description'].str.replace(' ',',')
data['Course Description'] = data['Course Description'].str.replace(',,',',')
data['Course Description'] = data['Course Description'].str.replace('_','')
data['Course Description'] = data['Course Description'].str.replace(':','')
data['Course Description'] = data['Course Description'].str.replace('(','')
data['Course Description'] = data['Course Description'].str.replace(')','')

#removing paranthesis from skills columns
data['Skills'] = data['Skills'].str.replace('(','')
data['Skills'] = data['Skills'].str.replace(')','')

data.head(5)


data['tags'] = data['Course Name'] + data['Difficulty Level'] + data['Course Description'] + data['Skills']

data.head(5)

data['tags'].iloc[1]


new_df = data[['Course Name','tags']]

new_df.head(5)

new_df['tags'] = data['tags'].str.replace(',',' ')

new_df['Course Name'] = data['Course Name'].str.replace(',',' ')

new_df.rename(columns = {'Course Name':'course_name'}, inplace = True)

new_df['tags'] = new_df['tags'].apply(lambda x:x.lower()) #lower casing the tags column

new_df.head(5)

new_df.shape #3522 courses with tags and 2 columns (course_name and tags)


from sklearn.feature_extraction.text import CountVectorizer

cv = CountVectorizer(max_features=5000,stop_words='english')

vectors = cv.fit_transform(new_df['tags']).toarray()

print(vectors[0])


#!pip install nltk

import nltk #for stemming process

from nltk.stem.porter import PorterStemmer
ps = PorterStemmer()

#defining the stemming function
def stem(text):
    y=[]

    for i in text.split():
        y.append(ps.stem(i))

    return " ".join(y)

new_df['tags'] = new_df['tags'].apply(stem) #applying stemming on the tags column

"""# Similarity Measure"""

from sklearn.metrics.pairwise import cosine_similarity

similarity = cosine_similarity(vectors)

"""# Recommendation Function"""

def recommend(course):
    course_index = new_df[new_df['course_name'] == course].index[0]
    distances = similarity[course_index]
    course_list = sorted(list(enumerate(distances)),reverse=True, key=lambda x:x[1])[1:7]

    for i in course_list:
        print(new_df.iloc[i[0]].course_name)

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# Exporting the Model


import pickle

pickle.dump(similarity,open('models/similarity.pkl','wb'))
pickle.dump(new_df.to_dict(),open('models/course_list.pkl','wb')) #contains the dataframe in dict
pickle.dump(new_df,open('models/courses.pkl','wb'))