Teamwork project with v-nafise

IMDb-storyline-TextProccessing

A Python program web crawling and text proccessing of storylines for 250 top IMDb movies

At first the app needs to extract storyline text of movies via web crawlling using :

• BeautifulSoap

• RegEx

Then analysing and extracting the keywords of the prepared text through TextRank algorithm + text proccessing like tokenizing, deleting stopwords and lemmatizing are implemented on story-line text of each movie

After that trying to find the common keywords employed on each movie's storyline text And to show the result by a weighted graph in which the weight refers to the number of common keywords and the nodes are movies, using :

• Netwokrx

Finally the weighted graph is stored in a csv file.

output example of 250 movie's common words computed graph:

Digikala-Offers Scraping

A Python program scraping the special offers of products and showing the results in a web using django framework

technologies used in this app are:

• craping with BeautifulSoup library

• using regex for extracting exact details

• saving files into json and csv format file

• using django fixtures for populating database with the data derived from previous steps