M2_SISE-EasyDate

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This project is part of the Python Programming evaluation modalities of the Master 2 SISE at the Université Lumière Lyon 2 for the year 2022 - 2023.

Description of the git repository:

• initial_datasets, folder containing the original datasets for the project
• scripts_colab, folder containing the different works on the design of the predictive model
  • cleaning_process, folder containing train and submissions data processing scripts and the finalized datasets
  • data_exploration, folder containing the exploratory scripts on the data, relationship between variables, dimension reduction and visualizations
  • models, folder containing the classification model scripts used with decisiontree and boosting
    • significance_var, folder containing the feature selection script and the finalized dataset
• dash, folder containing the python scripts for creating and building a dash application

Link of the kaggle competition related to this project: https://www.kaggle.com/competitions/easydate-ai-match

Table of contents

1. Introduction
2. About the Data
3. Project Overview
4. Installation

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1. Introduction

In today’s busy world, finding and dating a romantic partner seems more time consuming than ever. As a result, many people have turned to speed dating as a solution that allows one to meet and interact with a large number of potential partners in a short amount of time. In this report, we want to explore what people are looking for in their speed dating matches, what it takes to become successful in getting approvals from a potential partner, if there exist any gender differences, and if any other factors influence peoples’ decisions.

2. About the Data

The dataset contains records from 6804 participants of Speed Dating Experiment. Data can be found in Kaggle dataset. They consists mainly of general info about each participant, attributes they look for in speed dating partner and their matching decisions.

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3. Project Overview

The aim of this project is to get a hands-on practice on typical workflows usually seen in any machine learning project. Below mentioned is the basic workflow for this project:

• Exploratory Data Analysis
• Pre-processing
• Modelling
• Test Analysis
• Dash App

Note: For more details, please refer to the notebook for each part.

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4. Installation

• google colab
• Python 3.10
• Python libraries(Pandas,Numpy,Matplotlib,Scikit-learn,plotly.express...)
• Visual Studio Code