Updated: Aug 28, 2023
1-Click Kit 
A recommender system uses a statistical algorithm to forecast users’ ratings for a specific entity. The assumption is users will rate a group of items.
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.
For Windows OS,
- Download, extract and double-click the kit installer file to install the kit. Do ensure to extract the zip file before running it.
- The installation may take from 2 to 10 minutes based on bandwidth.
- When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from Menu bar.
- To run the app manually, press N when you're prompted and follow the below instructions.
- Navigate to the C:/kandikits/MovieRecommender folder.
- Open cmd prompt in this folder and run the following commands in the cmd prompt to start a Jupyter Notebook:
- movie-recommender-env\Scripts\activate.bat
- cd MovieRecommender
- jupyter notebook
For other Operating System,
- Install Python
- Download the repository
- Extract the zip file and navigate to the directory MovieRecommender
- Open a terminal in the extracted directory MovieRecommender
- Install dependencies by executing the command pip install -r requirements.txt
- Run the command jupyter 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
38689 Version:v2.0.2Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandasby pandas-dev
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)
numpyby numpy
23755 Version:v1.25.0rc1The fundamental package for scientific computing with Python.
numpyby numpy
Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)
Kit Solution Source
MovieRecommenderby kandi1clickkits
0 Version:v1.0.0Movie Recommender AI System
MovieRecommenderby kandi1clickkits
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (MIT)
Support
If you need help using this kit, you may reach us at the OpenWeaver Community.