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# Simple Movie Recommender System with Correlation Coefficient with Python

## Introduction

Recommender systems are the systems that are designed to recommend things to the user based on many different factors

Pearson’s Correlation Coefficient is a very simple yet effective way to find how 1 variable linearly changes with respect to another. we can use this to our advantage and build a recommender system with this concept

## Working of correlation coefficient

If correlation coefficient is closer to 1 for two variables, these variables are directly proportional to each other.

If it is closer to -1 , these variables are inversely proportional to each other.

If the magnitude of the correlation coefficient is lower or closer to 0, the variables are probably don’t have a strong dedpendency with respect to each other.

## Python Code :

1. Importing the necessary libraries.
``````#importing the libraries
import numpy as np
import pandas as pd
``````

2. Dataset (MovieLens Dataset)

for the purpose of implementing recommender systems, I have used the movielens dataset which contains the ratings for 100k movies

``````#data import

"website","rat1","rat2","rat3","rat4","rat5","rat6","rat7","rat8","rat9","rat10","rat11","rat12","rat13",
"rat14","rat15","rat16","rat17","rat18","rat19","rat20"])
``````

#deep-learning

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## 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

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## 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.’

### Final Thoughts

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

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## 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.

## 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

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## Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

## 5 Reasons to Utilize Python for Programming Web Apps

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

Utilized by the best

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

Massive community support

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

Progressive applications

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

### Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

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## 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