Building recommondation system for products electronics and clothing
Online E-commerce websites like Amazon,Flipkart uses different recommondation models to provide different suggestions to different users. ecommerce sites currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real time. This type of filtering matches each of the user's purchased and rated items to similar items, then combines those similar items into a recommendation list for the user. In this project we are going to build recommendation model with the similar kind of approach.
- Predict the rating that a user would give to a product that he has not yet rated.
- Minimize the difference between predicted and actual rating (RMSE)
You need to have installed following softwares and libraries in your machine before running this project.
- Python 3
- Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, scipy.
- Python 3: https://www.python.org/downloads/
- Anaconda: https://www.anaconda.com/download/
Built With
- ipython-notebook - Python Text Editor
- sklearn - Machine learning library
- seaborn, matplotlib.pyplot, - Visualization libraries
- numpy, scipy- number python library
- pandas - data handling library
1.Read and explore the given dataset.
2.Take a subset of the dataset to make it less sparse/ denser.
3.Split the data randomly into train and test dataset.
4.Build Popularity Recommender model.
5.Build Collaborative Filtering model.
6.Evaluate both the models.
7.Get top - N ( N = 5) recommendations. Since our goal is to recommend new products foreach user based on his/her habits, we will recommend 5 new products.
8.Summarise your insights.
I have referred many blogs while making of this project