https://github.com/tadiusfrank2001/reccomendation_system_datamining_project

Implementing a movie recommendation system that utilizes both wide and deep learning using Python AI & ML Libraries
https://github.com/tadiusfrank2001/reccomendation_system_datamining_project

data-science machine-learning numpy-library skit-learn tensorflow

Last synced: 1 day ago
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Implementing a movie recommendation system that utilizes both wide and deep learning using Python AI & ML Libraries

• Host: GitHub
• URL: https://github.com/tadiusfrank2001/reccomendation_system_datamining_project
• Owner: tadiusfrank2001
• License: mit
• Created: 2024-01-24T11:23:36.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-02-10T10:10:14.000Z (over 1 year ago)
• Last Synced: 2025-08-24T18:31:34.355Z (10 months ago)
• Topics: data-science, machine-learning, numpy-library, skit-learn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 45.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Wide and Deep Reccomendation System

## Overview

This wide-and-deep model combines the memorization capabilities of a linear model with the generalization capabilities of deep learning that can allow us to create a recommendation system that can predict a wider variety of choices for users. In this case, a 20 million entry movie rating and table data set from Kaggle was utlized to build a sample movie reccomendation system as a capstone project for my Data Mining course.

## Skills

+ Statistical Analysis 

+ Data Science

+ Data Analysis (Pre-processing and Cleaning, Analysis, Visualization)

+ Advanced Mathematical Logic (Linear Algebra, Vector Calculus)

+ Machine Learning (Linear Models and Deep Learning Models)

+ Python ML & DL Libraries: TensorFlow, MLflow, NumPy, Pandas, Scikit-learn, Keras

## Technologies Used

+`Python:`A high-level, versatile programming language that is widely used in data science, machine learning, and artificial intelligence due to its readability, simplicity, and large community support.

+`TensorFlow`: Framework for deep learning.

+`MLflow`: Manages machine learning workflows.

+`NumPy`: Supports numerical calculations.

+`Pandas`: Handles data manipulation and analysis.

+`Scikit-learn`: Machine learning algorithms and tools.

+`Keras`: User-friendly interface for building neural networks.

+`Jupyter`: an interactive computing environment, allowing users to create notebooks that integrate code, visualizations, and narrative text. I documented the process in various notebooks for ease of understanding and learning! :)

## Project Organization

`data_preparation.ipynb`: The purpose of this notebook is to prepare the dataset we will use to build the wide-and-deep recommendation model.

`feature_engineering.ipynb`: In this notebook, we will engineer the features we will use to build the wide-and-deep collaborative filter recommender.

`model_preparation.ipynb`: In this notebook, we train and evaluate the wide-and-deep collaborative filtering recommender using features engineered in the prior notebook.