{ "cells": [ { "cell_type": "markdown", "id": "88dc2c37", "metadata": {}, "source": [ "## LabML03 ##\n", "\n", "Consider the following files:\n", "\n", " https://github.com/masterfloss/data/raw/main/ERP.xlsx\n", " https://github.com/masterfloss/data/raw/main/ScienceTechnGraduate.xlsx \n", " https://github.com/masterfloss/data/raw/main/egov.xlsx \n", " https://github.com/masterfloss/data/raw/main/ecommerce01.xlsx \n", " \n", "* Create a dataset with collecting a variable from each file. Merge all the files considering data and country.\n", "\n", "* Use PCA to reduce the number of variables to 2\n", "\n", "* Create homoegeneous groups. Analyse what is the best number of clusters. \n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "237a2828", "metadata": {}, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": null, "id": "83398e99", "metadata": {}, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": null, "id": "14368738", "metadata": {}, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": null, "id": "3a7e1a04", "metadata": {}, "outputs": [], "source": [ "#" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }