Project Name: Fasion Recommendation System for Ecommerce

A Deep learning based streamlit web app which can recommened you various types of fasion products with respect to your choices.

Recommender systems are the systems that are designed to recommend things to the user based on many different factors. These systems predict the most likely product that the users are most likely to purchase and are of interest to. Companies like Netflix, Amazon, etc. use recommender systems to help their users to identify the correct product or movies for them.

The recommender system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.

Both the users and the services provided have benefited from these kinds of systems. The quality and decision-making process has also improved through these kinds of systems.

Original repo:

• Project Repo link

Demo Video:

• Demo video link

This is a methods of identifying similar products check various aspects on pictures, including: shape, colors, edges, features (including the lighting of the photo) and euclidean distance of vectors in a 'n' dim features space.

Dataset has been used:

• High Resolution Dataset link
• Low Resolution Dataset link

Some Real Time Demo:

Web app look

Lets check some of images

This really performing good you can consider by seeing this result 😀

STEPS to run this project:

You can also use others images

STEP 01:

Clone the repository

git clone https://github.com/entbappy/Deep-Learning-Based-Fasion-Recommendation-System.git

STEP 02:

Create an environment

conda create -n fasion python=3.7 -y

STEP 03:

Install the requirements

pip install -r requirements.txt

STEP 04:

Download the data from the link and keep it in your project directory. Make sure all the images should be in just one folder called data, like that

STEP 05:

Just execute this command & wait, it may take some time

python run.py

STEP 06:

Now to start the webapp run the following command

streamlit run app.py

yes!! Now you can start predicting 🙂

Authors:

Author: Bappy Ahmed
Data Scientist
Email: [email protected]