Oleta  Becker

Oleta Becker


Movie Recommendation System in Python

In this article, I will cover how to build a basic movie recommendation system with an integrated graphical user interface.

First and foremost we will need data. In order to get a good idea of how well the recommendation system actually performs we will need a sizable dataset.

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.

A quick peek at the dataset:

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Above we have the movies.csv file which has 3 columns namely: movieId, title and genres. All very handy and straight forward. We will be using all 3.

Below we have the tags.csv file. Here we will only be using the ‘movieId’ and ‘tag’ columns which will link the tag to the ‘movieId’ columns also found in movies.csv & ratings.csv

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Movie Recommendation System in Python
Ray  Patel

Ray Patel


top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

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

Create Your Own Movie Recommendation System Using Python

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.’

Recommendation System: What is It?

Types of Recommendation Systems

Building a Basic Movie Recommendation System

**Learn More About a Movie Recommendation System **

Final Thoughts

#data science #movie recommendation system #movie recommendation system using python #python

How to Build A Flexible Movie Recommender Chatbot In Python

Follow our steps to discover what it takes to create a web-app that recommends movies based on open-ended user inputs!

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:

  1. Rule-based Chatbots: these bots can answer customers’ requests based on pre-defined rules that we created. These bots are suitable to handle simple, repetitive, and predicted tasks but might fail to address complex ones.
  2. **Retrieval-based Chatbots: **these bots utilize advanced algorithms to select the most suitable response from a pool of diverse answers to accommodate customer’s needs. The retrieval approach is more intelligent than the rule-based fixed algorithm as it considers the message and the context of the conversation in answering customer’s requests.
  3. **Generative Chatbots: **these bots use Machine Learning algorithms to simulate how humans understand and respond to customers’ requests. Like humans, they can generate new and different responses based on the context and wording of customer’s questions. Although Generative bots are the most intelligent type of chatbots, it is challenging to build and train them.

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

August  Larson

August Larson


Movie Similarity Recommendations Using Python

A guide to learning about and implementing recommender systems in Python

What Is a Recommender System?

Recommender systems predict a user’s future choices/preferences and recommend products/items they might be interested in.

What Are the Types of Recommender Systems?

The two most common types are:

  1. Content-based recommender systems
  2. Collaborative filtering

What Is a Content-Based Recommender System?

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