Have you ever questioned how Netflix makes recommendations for movies based on the ones you’ve already seen? Or how can choices like “Frequently Bought Together” appear on an e-commerce website? Although they may appear to be straightforward choices, a sophisticated statistical method is used to forecast these suggestions. Recommendation engines, recommendation systems, and recommender systems are all terms used to describe these systems. A recommender system is one of the most well-known uses of data science and machine learning. But how do you build a movie recommendation system?
The answer is in this course using the kandi 1-click solution kit. You will have a working model at the end of the session!
Learning Objectives
After completing this course you will:
- Have a good working knowledge of the Fundamentals of Data Analysis.
- Learn various concepts involved in building the Recommender such as use of a collaborative filtering mechanism using implementation pandas and NumPy library.
- Have a fully functional Movie Recommender prototype that you can customize, showcase, and share, using the existing source code.
Complete this course in 3 easy steps to earn your certificate!
STEP 1 : Watch the below self-guided tutorial.
STEP 2 : Practice as you watch the video by installing and working with the kandi 1-click solution kit.
STEP 3 : Complete the assessment to receive your certificate.
STEP 1 : TUTORIAL
Watch this self-guided tutorial on Movie Recommendation System with Pandas. This includes an understanding of Python language; an IDE like jupyter or PyCharm to write Python code and essential libraries like pandas, numpy to build your own Movie Recommendation System with Pandas.
STEP 2 : PRACTICAL EXERCISE
Click the below button to access the movie recommendation system kandi kit. This kit has all the required dependencies and resources you need to build your application.
kandi 1click kits include Python, Jupyter notebook and helps you learn to implement a Movie Recommendation System using Pandas, work with the different types of dataset to analyze, merge, sort a dataframe and use the data analysis libraries like NumPy and Pandas to work through a simple data analysis solution using the concept of collaborative filtering.
Click on the 1-Click Installer button on the kandi kit page to install the movie recommendation system kit. On installing and running this kit, you will have a working model that you can customize and use in your project.
Code Snippet Exercises
Below are three coding exercises that will help you advance in your journey in Movie Recommendation System Using Python. To get started, use the relevant keywords to search for simple code snippets in the search bar on kandi.
Exercise 1 - pandas dataframe join: This exercise helps join/merge columns with other DataFrame either on index or on a column.
Exercise 2 - sort pandas dataframe: In order to sort the data frame in pandas, function [sort_values() is used. Pandas sort_values() can sort the data frame in ascending or descending order.
Exercise 3 - Removing duplicate rows in a dataframe using pandas: Pandas drop_duplicates() method helps in removing duplicates from the Pandas Dataframe in python.
After completing this step, proceed to STEP 3.
STEP 3 : ASSESSMENT
Complete a short assessment and earn your certificate now. 
Your assessment will be reviewed and you will receive a verified certificate via email within a week.
SUPPORT
Reach out to us by replying below for any help you may need with this course.
We hope you enjoyed using kandi! Continue your learning journey with kandi
EXPLORE MORE TOPICS:
