In this article I will cover how to build a basic movie recommendation system with an integrated graphical user interface.
The dataset consists out of six .csv files and a readme file explaining the dataset. Feel free to have a look at it if you wish. We will only be using these 3:
movies.csv ; ratings.csv ; tags.csv
A couple of python libraries will also be required and installed if you do not have them yet:
-progress (pip install progress)
-fuzzywuzzy (pip install fuzzywuzzy & pip install python-Levenshtein)
I believe they can all be pip installed, the exact commands will be OS dependent. Once we have the data and the libraries installed we are good to go. Any python IDE should work, I use Geany which is a lightweight IDE for Raspbian.
#gui #data-science #python-programming #pandas-dataframe #python
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
Do you wonder how Netflix suggests movies that align your interests so much? Or maybe you want to build a system that can make such suggestions to its users too?
If your answer was yes, then you’ve come to the right place as this article will teach you how to build a movie recommendation system by using Python.
However, before we start discussing the ‘How’ we must be familiar with the ‘What.’
#data science #movie recommendation system #movie recommendation system using python #python
Our working final product can be tested here.
Have you ever wondered what a chatbot is and how to build one?
In this three-part series, we will teach you everything you need to build and deploy your Chatbot. By “we” here, I mean my team members (Ahmed, Dennis, Pedro, and Steven), four data science students at the Minerva Schools at KGI. The series will cover the following topics:
We use a Jupyter Python 3 notebook as a collaborative coding environment for this project, and other bits of code for the web app development and deployment. All the code for this series is available in this GitHub repository.
Businesses integrate chatbots into many processes and applications. You might need to interact with one while buying an item from Sephora, booking a flight from British Airways, or even customizing your cup of coffee from Starbucks. Developers build chatbots to understand customers’ needs and assist them without needing human help, making chatbots very useful for many customer-facing businesses. So how does a chatbot work?
Generally, there are three types of chatbots:
The chatbot we settled on creating is retrieval-based. Our bot can take a diverse set of responses, which are only slightly structured and output tailored recommendations. We had two main challenges to making this work: first, to build a flexible recommendation system in Python capable of taking in written requests by users and outputting decent recommendations. Second, implementing that algorithm in a web-app that is user-friendly and easy to use.
#movie-recommendation #towards-data-science #recommendation-system #chatbots #how to build a flexible movie recommender chatbot in python #chatbot in python
Recommender systems predict a user’s future choices/preferences and recommend products/items they might be interested in.
The two most common types are:
This kind of system gives recommendations based on the knowledge of a user’s attitude towards a product. It works on the logic that if users have agreed upon something in the past, then they will do so in the future as well.
#python #data #programming #movie similarity recommendations using python #movie similarity recommendations #similarity recommendations