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Author: amajji

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+# Multiclass classification.
+Data scientist | [Anass MAJJI](https://www.linkedin.com/in/anass-majji-729773157/)
+***
+
+## :monocle_face: Description
+- This project aims to implement a multi-class classification model. we have four classes with a minority class (less than 1%), 
+so we are in the case of unbalanced classes. We used regularization methods in order to penalize the errors made on this minority class.
+The model is trained to predict around 4 different class. </br>
+
+ 
+
+## :rocket: Repository Structure
+The repository contains the following files & directories:
+- **Dataset directory:** It contains a data pre-processing notebook where the train.csv file is used for training 
+the model. Il contains also the predictions of test.csv dataframe.
+- **model_weights:** It contains all the weights of the models : one-hot-encoder, target encoder, random forest model.
+
+- **App directory:** Code for the web application that was developed for the model deployment. It contains Flask API code for the Back-End,
+and HTML/CSS/Javascript code for the Front-End.
+
+
+![](last_gif.gif)
+
+## :chart_with_upwards_trend: Performance & results
+
+- The test dataset contains **25 000 samples**. Each sample contains many features, and its corresponding label.
+
+- The model used for this multi-class classification task is a **Random Forest** model.
+
+- The metric used to measure the model's performance is **F1-score**. After testing the model, I obtained a test F1-score of **72 %**
+
+
+
+
+---
+## :mailbox_closed: Contact
+For any information, feedback or questions, please [contact me][anass-email]
+
+
+
+
+
+[anass-email]: mailto:[email protected]