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import streamlit as st |
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import difflib |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import plotly.express as px |
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import warnings |
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warnings.filterwarnings("ignore") |
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%matplotlib inline |
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import re |
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from nltk.corpus import stopwords |
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from nltk.stem.porter import PorterStemmer |
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from wordcloud import WordCloud |
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from sklearn.feature_extraction.text import TfidfVectorizer |
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from sklearn.metrics.pairwise import cosine_similarity |
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lpi_df = pd.read_csv('Learning_Pathway_Index.csv') |
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lpi_df['combined_features'] = lpi_df['Course_Learning_Material']+' '+lpi_df['Source']+' '+lpi_df['Course_Level']+' '+lpi_df['Type']+' '+lpi_df['Module']+' '+lpi_df['Difficulty_Level']+' '+lpi_df['Keywords_Tags_Skills_Interests_Categories'] |
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combined_features = lpi_df['combined_features'] |
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porter_stemmer = PorterStemmer() |
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def stemming(content): |
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stemmed_content = re.sub('[^a-zA-Z]',' ',content) |
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stemmed_content = stemmed_content.lower() |
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stemmed_content = stemmed_content.split() |
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stemmed_content = [porter_stemmer.stem(word) for word in stemmed_content if not word in stopwords.words('english')] |
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stemmed_content = ' '.join(stemmed_content) |
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return stemmed_content |
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combined_features = combined_features.apply(stemming) |
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vectorizer = TfidfVectorizer() |
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vectorizer.fit(combined_features) |
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combined_features = vectorizer.transform(combined_features) |
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similarity = cosine_similarity(combined_features) |
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st.title('Course Recommendation App') |
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user_input = st.text_input('Enter What You Want to Learn : ') |
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if user_input: |
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list_of_all_titles = lpi_df['Module'].tolist() |
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find_close_match = difflib.get_close_matches(user_input, list_of_all_titles) |
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if find_close_match: |
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close_match = find_close_match[0] |
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index_of_the_course = lpi_df[lpi_df.Module == close_match].index.values[0] |
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similarity_score = list(enumerate(similarity[index_of_the_course])) |
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sorted_similar_course = sorted(similarity_score, key=lambda x: x[1], reverse=True) |
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st.write('Courses suggested for you :') |
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i = 1 |
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for course in sorted_similar_course: |
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index = course[0] |
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title_from_index = lpi_df[lpi_df.index == index]['Module'].values[0] |
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if i < 30: |
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st.write(f"{i}. {title_from_index}") |
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i += 1 |
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if i == 1: |
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st.write('No close matches found.') |
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else: |
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st.write('No close matches found.') |
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