From f83dbcd31b27a48b8b32ebb6afe4ebc39c7d6f8d Mon Sep 17 00:00:00 2001
From: Adrien <adrien.payen@student.uclouvain.be>
Date: Fri, 24 May 2024 22:27:22 +0200
Subject: [PATCH] update readme

---
 README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 5a08275e..e63840ef 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@ This project aims to develop data analysis and recommendation tools.
 
 This README outlines the structure and summarizes our project.
 
-## Python Libraries :
+# Python Libraries :
 
 To run the various scripts and notebooks, ensure that you have installed the following Python libraries:
 
@@ -21,10 +21,10 @@ pip install streamlit
 pip install requests
 ```
 
-## Project Structure
+# 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. 
     
@@ -56,16 +56,16 @@ The project is organized into the following key components:
 - Recommendation algorithms are defined as classes inheriting from Surprise's `AlgoBase` class, each implementing an `estimate`  method to predict user ratings for items.
 
 
-### Backend folder
+# Backend folder
 This folder contains all the data, jupyter notebook, scripts python used to improve user experience.
 
-#### Analytics
+## Analytics
 ***analytics_small.ipynb***
 - Performs data analysis to understand the datasets and their properties.
 
 - Analyzes the number of ratings, unique users, unique items, and distribution of ratings.
 
-#### Evaluation 
+## Evaluation 
 ***evaluator.ipynb***
 
  - Evaluates different recommendation models using various cross-validation techniques.
@@ -81,7 +81,7 @@ This folder contains all the data, jupyter notebook, scripts python used to impr
  - Exports the evaluation report to a CSV file.
 
 
-#### Content Based
+## Content Based
 ***hackathon_make_predictions.ipynb***
 
 Define a function make_hackathon_prediction that takes feature_method and regressor_method as input.
@@ -124,7 +124,7 @@ The system supports the following regression models for predicting user ratings:
 
 
 
-### Datasets
+# Datasets
 
 1. ***small***
  - Contains traditional MovieLens data and is used for evaluating models and building our recommendation system.
-- 
GitLab