/MeetingRecommender

Meeting Recommendation System and Analysis of matches made

Primary LanguageJupyter Notebook

Meeting Recommender Application

There are two programs in this project. 1. test.py 2. recommender.ipynb

Problem Statement

  • Profile Complete.csv has a list of profiles, who are customers of a company. Company has the business model of arranging meetings between customers who are raising money via offering some services and customers who are investing money or those who are looking for specific services.
  • profiles.xlsx contains a short list curated from Profile Complete.csv.
  • matches.xlsx contains list of profiles who are matched for meeting.

Solution description

  • test.py : this solution is based on two datasets, profiles and matches and tries to analyse parameters which played a role in match making. It involves data cleaning and processing via techniques like encoding.

  • recommender.ipynb : this is a recommender system which suggests profiles to an input profile for meeting recommendation. It is based on Item based collaborative filtering.

Important Note

I built this project on the dummy data provided by a company. This helped me learn importance of data pre processing techniques and data cleaning. This project is not upto the mark of code standards, however, I have applied my learnings in this project from time to time. This project might not present best use case to learn recommendation system or data pre processing, however, I developed this project over a period of time, as my learnings progresse via Kaggle exercises.

Improvements are welcome.