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This Django-based E-commerce recommendation system uses machine learning models to provide product recommendations based on user input and similarity scores. It scrapes data from Amazon, preprocesses it, and displays product recommendations in a user-friendly interface.

License: MIT License

Jupyter Notebook 93.98% Python 4.19% CSS 0.39% HTML 1.44%

e-commerce_recommendation_system's Introduction

Hello, I am Shawon 👋✌️

🔭 I’m currently working on problem solving
🌱 I’m currently learning JAVASCRIPT and Machine learning
👯 I’m looking to collaborate on competitive programming and Django project
🤝 I’m looking for help with Python for Back-End
📫 How to reach me:
Gmail: [email protected] or [email protected]
LinkedIn: https://www.linkedin.com/in/shawon-barman-688968176/
Facebook: https://www.facebook.com/shawon.barmon.2
👨‍💻 All of my projects are available at https://shawonbarman.github.io/myPortfolio/
💬 Ask me about ,,,,,,,,,, or any Technology related Stuff
⚡ Fun fact I almost spend 3 hours Watching Movies or Korean Drama or Web series and 2 hours playing PUBG MOBILE game Everyday

Languages and Tools:

Available On-

{...} My Stack

{
   "Proficient": ["Python", "PHP", "Java", "C", "C++", "SQL"],
   "Familiar with": ["JavaScript", "HTML", "CSS", "Bootstrap"],
   "Backend": ["Django", "Laravel"],
   "Database": ["MySql", "MS SQL Server"],
   "AI framework":["Keras", "Scikit-learn", "Tensorflow"],
   "Experimenting": ["Django"],
   "Modules & Library":["Pandas", "Numpy", "Matplotlib & seaborn", "Selenium", "tkinter", "random", "datetime", "etc"],
                          
   "Machine Learning Algorithms": ["LinearRegression", "LogisticRegression", "KNeighborsRegressor", "SVR", "DecisionTree", 
                                    "RandomForest", "SVM", "KNN", "Naive Bayes", "K-means"],
   
   "Deep Learning": ["CNN", "Transfer Learning", "Model optimization and quantization"]
}