update readme

README.md

@@ -24,7 +24,7 @@ pip install requests
 # Project Structure
 The project is organized into the following key components:
 
-## Configuration
+# Configuration
 1. ***configs.py***
  - Defines an `EvalConfig` class for storing configurations for evaluating multiple recommendation models. 
     
@@ -122,13 +122,10 @@ The system supports the following regression models for predicting user ratings:
 - `lightgbm`
 
 
-
-
-
-# Datasets
+# Data folder
 
 1. ***small***
- - Contains traditional MovieLens data and is used for evaluating models and building our recommendation system.
+ - Contains traditional MovieLens data and is used for evaluating models and building our recommendation system based on ratings and movie data.
 
 2. ***test***
  - A smaller dataset (6 users and 10 items) used for understanding algorithm workings during model development.and how algorithms work during model development.
@@ -138,15 +135,28 @@ The system supports the following regression models for predicting user ratings:
 
 
 
-
-### Web Page - Overview
+# Web Page - Overview
 
 Our web page serves as the user interface for the Recommender System. It allows users to interact with the recommendation models & view recommended items. It was built in backend powered by Flask (Python) to handle requests and serve recommendations. 
 
-1. ***Features***
-- Home
-- Discovery
-- Search
+1. ***Home***
+The Home page provides a set of recommendations for the user based on user-based, content-based, and latent factor model algorithms.
+
+###### pages folder
+
+2. ***Discovery***
+The Discover page allows the user to find recommendations based on characteristics inherent to the movies themselves.
+
+3. ***Search***
+The Search page allows users to search the general database and apply filters.
+
+
+
+
+# Recommender system
+***recommender.py***
+
+
 
 
 ## References