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Complete refactoring for regression / classification (#66)
* remove xavierdupre * rst * fix links * requirements * fix link
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,220 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Evaluation Python / Machine Learning année 2017 - énoncé\n", | ||
| "\n", | ||
| "Le répertoire [_data/2017](https://github.com/sdpython/teachpyx/tree/main/_data/2017) contient deux fichiers csv simulés aléatoirement dont il faudra se servir pour répondre aux 10 questions qui suivent. Chaque question vaut deux points. Le travail est à rendre pour le lundi 20 février sous la forme d'un notebook envoyé en pièce jointe d'un mail." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "source": [ | ||
| "## 1\n", | ||
| "\n", | ||
| "Deux fichiers sont extraits de la base de données d'un médecin.\n", | ||
| "Un fichier contient des informations sur des personnes, un autre\n", | ||
| "sur les rendez-vous pris par ces personnes. Quels sont-ils ?" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 2\n", | ||
| "\n", | ||
| "On souhaite étudier la relation entre le prix moyen payé par une personne,\n", | ||
| "son âge et son genre. Calculer le prix moyen payé par une personne ?" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 3\n", | ||
| "\n", | ||
| "Faire la jointure entre les deux tables." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 4\n", | ||
| "\n", | ||
| "Tracer deux nuages de points (age, prix moyen) et (genre, prix moyen) ?" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 5\n", | ||
| "\n", | ||
| "Calculer les coefficients de la régression $prix\\_moyen \\sim age + genre$." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 6\n", | ||
| "\n", | ||
| "On souhaite étudier le prix d'une consultation en fonction du jour de la semaine.\n", | ||
| "Ajouter une colonne dans la table de votre choix avec le jour de la semaine." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 7\n", | ||
| "\n", | ||
| "Créer un graphe moustache qui permet de vérifier cette hypothèse." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 8\n", | ||
| "\n", | ||
| "Ajouter une colonne dans la table de votre choix qui contient 365 si c'est le premier rendez-vous, le nombre de jour écoulés depuis le précédent rendez-vous. On appelle cette colonne $delay$. On ajoute également la colonne $1/delay$." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 9 \n", | ||
| "\n", | ||
| "Calculer les coefficients de la régression $prix \\sim age + genre + delay + 1/delay + jour\\_semaine$.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## 10\n", | ||
| "\n", | ||
| "Comment comparer ce modèle avec le précédent ? Implémentez le calcul qui vous permet de répondre à cette question." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 10, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.5.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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