🔰Insurance Recommendation System

Introduction

This project aims to develop a recommendation platform for a leading insurance provider. The platform includes three types of recommender systems: popularity-based, content-based, and collaborative filtering. The system is designed to help users find the best insurance products tailored to their needs and preferences.

Objectives

1. Popularity-based Recommender System:

   • Recommend top N products within a specific insurance type (g).
   • Consider products with a minimum rating threshold (t).
   • Order products by ratings in descending order, ensuring each product has at least (t) reviews.

2. Content-based Recommender System:

   • Recommend top N products based on similar product types.

3. Collaborative-based Recommender System:

   • Recommend top N products based on "K" similar users for a target user "u".

Methodology

Data Preparation

1. Import Libraries and Load Dataset:
   • Import necessary Python libraries for data manipulation, visualization, and modeling.
   • Load the dataset containing insurance product information and user reviews.

Exploratory Data Analysis (EDA)

1. Understanding Feature Distribution:
   • Analyze the distribution of various features in the dataset.
2. Unique Users and Products:
   • Identify the number of unique users and products in the dataset.
3. Average Rating and Total Products by Insurance Type:
   • Calculate the average rating and total number of products for each insurance type.
4. Unique Insurances:
   • Determine the unique insurance types considered in the dataset.

Recommendation Modules

1. Popularity-based Recommender System:

   • Filter products based on the insurance type and minimum rating threshold.
   • Sort products by ratings in descending order.
   • Recommend the top N products.

2. Content-based Recommender System:

   • Use product features to find similar products.
   • Recommend top N products based on similarity to a given product type.

3. Collaborative-based Recommender System:

   • Identify similar users based on their ratings and preferences.
   • Recommend top N products for a target user based on the preferences of similar users.

GUI Interface

• Create a user-friendly interface using Streamlit to interact with the recommendation modules.
• Allow users to input parameters such as insurance type, minimum rating threshold, number of recommendations, product type, and target user.

Folder Structure

• data/: Contains the dataset used for the project.
• notebooks/: Jupyter notebooks for data analysis, modeling, and testing.
• src/: Python scripts for the recommendation modules and Streamlit app.
• api/: Api code (if any).
• models/: Trained models and saved results.
• docs/: Output files including recommendation results and evaluation metrics.

Installation and Usage

1. Clone the Repository:

   git clone https://github.com/MariahFerns/Product-Recommender---Insurance.git
   cd Product-Recommender---Insurance
   

2. Install the required libraries:

   pip install -r requirements.txt
   

3. Run Jupyter Notebooks: Navigate to the notebooks/ folder and open the notebooks to explore data analysis and model development.

4. Run Streamlit Application:

   streamlit run src/app.py