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Movie Recommendation System with Pandas

by kandikits Updated: Oct 20, 2022


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.


Based on the similarity between the items or the similarity between the users who previously evaluated those entities, a recommender system uses a statistical algorithm to forecast users' ratings for a specific entity. The assumption is that users of like categories will rate a group of items similarly.


kandi kit provides you with a fully deployable Movie Recommendation System with Pandas. The source code is included so that you can customize it for your requirement.

Deployment Information

This is a simple content-based recommendation system with pandas. You can use it for your pandas and statistical exposure.


  1. Download, extract and double-click kit installer file to install the kit. Ensure you extract the zip file before running it.
  2. After successful installation of the kit, locate the zip file 'MovieRecommender-main'
  3. Extract the zip file and navigate to the directory 'MovieRecommender-main'
  4. Open the command prompt in the extracted directory 'MovieRecommender-main' and run the command 'jupyter notebook'
  5. Locate and open the 'MovieRecommender-main' notebook from the Jupyter Notebook browser window.
  6. Execute cells in the notebook


Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your App.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

pandasby pandas-dev

Python star image 36647 Version:1.5.2

License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python star image 36647 Version:1.5.2 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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numpyby numpy

Python star image 22526 Version:1.24.1

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 22526 Version:1.24.1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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Kit Solution Source

MovieRecommenderby kandi1clickkits

Jupyter Notebook star image 0 Version:v1.0.0

License: Permissive (MIT)

Movie Recommender AI System

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MovieRecommenderby kandi1clickkits

Jupyter Notebook star image 0 Version:v1.0.0 License: Permissive (MIT)

Movie Recommender AI System
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Quality
Security
License
Reuse

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.