From c6abc9abc056121e52c00f82f99a9649fbc3873b Mon Sep 17 00:00:00 2001
From: Adrien <adrien.payen@student.uclouvain.be>
Date: Fri, 24 May 2024 21:29:14 +0200
Subject: [PATCH] update readme

---
 README.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/README.md b/README.md
index 76390b13..b77ffff2 100644
--- a/README.md
+++ b/README.md
@@ -63,6 +63,8 @@ The project is organized into the following key components:
 
 ### Features and Models
 
+***models.py*** and  ***content_based.ipynb*** 
+
 1. ***Feature Extraction Methods***
 The system supports the following feature extraction methods:
 - `genre`: Extracts genres of the movies using TF-IDF vectorization.
@@ -106,6 +108,25 @@ Definition of a dictionary containing available metrics for different evaluation
 
 Loading data, generating evaluation reports for different models, and saving experimental outcomes.
 
+###  Hackathon_make_predictions.ipynb
+
+It defines a function make_hackathon_prediction that takes feature_method and regressor_method as input.
+
+Inside this function:
+
+    - It loads the training data and converts it into the format suitable for Surprise.
+
+    - Trains a Content-Based model (ContentBased) on the training set using the specified feature and regressor methods.
+
+    - Makes predictions on the test set by loading the test data from a CSV file and converting it into records.
+
+Converts the predictions into a DataFrame and saves them as a CSV file.
+
+It then calls this function with specific parameters and prints the generated predictions.
+
+
+
+
 ### Dataset
 Organized under the data/test/ directory, which contains three subdirectories:
 - content: Contains movies.csv & tags.csv files.
-- 
GitLab