Simple Movie Recommendation System using Python

Recommendation systems are quite popular now. They can be used in either movie recommendation or shopping or e-commerce websites or on social media websites. It’s used intensively. There are three types of recommendation systems — Content-based, Popularity based and Collaborative based. Popularity based is simple and recommends based on the popularity of the product of video or movie or product. Content-based systems are based on the content of the movie or show and recommend similar shows. The collaborative is based on user patterns. If two users are similar and one watches a movie, the other is recommended to watch it too!

Now, we will see how to build a simple content-based movie recommendation system. This can be used for songs or video recommendations as per the requirements with the change of data set and little tweaks in code.

So, we need to follow some simple steps to build our recommendation system. Let’s begin:-

1. First, we need to import the required packages which we will be needing to build our recommendation system.

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import difflib

Importing the packages

2. Next, we need to read our data set. We will use pandas read_csv for doing it. Then we can see its headers and look up the data to determine which features are useful in recommendations and which are not. We then filter out headers which we are going to use in our classifier. We will fill out all empty rows in those columns with an empty string. Then we need to merge data from all those columns into one. So, we define a function to combine those features into one and name that column as combined_features. We will be using this column for building our model.

#programming #coding #machine-learning #python

What is GEEK

Buddha Community

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

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

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

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

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:

• Part 1: Building a content-based recommender using a cosine similarity algorithm.
• Part 2: Deploying the model on AWS serverless computing platform and creating an interactive chatbot web application.
• Part 3: Model analysis and future recommendations.

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.

Introduction

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