{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# run this cell to install pycaret in Google Colab\n", "# !pip install pycaret" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# If you are using Jupyter notebook, you can pip install pycaret using jupyter notebook or command line\n", "# pip install pycaret" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0.0\n" ] } ], "source": [ "from pycaret.utils import version\n", "version()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# only run this cell if you are using google colab\n", "# from pycaret.utils import enable_colab\n", "# enable_colab()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Importing Dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrychild_mortexportshealthimportsincomeinflationlife_expectotal_fergdpp
0Afghanistan90.210.07.5844.916109.4456.25.82553
1Albania16.628.06.5548.699304.4976.31.654090
2Algeria27.338.44.1731.41290016.1076.52.894460
3Angola119.062.32.8542.9590022.4060.16.163530
4Antigua and Barbuda10.345.56.0358.9191001.4476.82.1312200
\n", "
" ], "text/plain": [ " country child_mort exports health imports income \\\n", "0 Afghanistan 90.2 10.0 7.58 44.9 1610 \n", "1 Albania 16.6 28.0 6.55 48.6 9930 \n", "2 Algeria 27.3 38.4 4.17 31.4 12900 \n", "3 Angola 119.0 62.3 2.85 42.9 5900 \n", "4 Antigua and Barbuda 10.3 45.5 6.03 58.9 19100 \n", "\n", " inflation life_expec total_fer gdpp \n", "0 9.44 56.2 5.82 553 \n", "1 4.49 76.3 1.65 4090 \n", "2 16.10 76.5 2.89 4460 \n", "3 22.40 60.1 6.16 3530 \n", "4 1.44 76.8 2.13 12200 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.datasets import get_data\n", "data = get_data('country-data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Setting up Environment" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Setup Succesfully Completed!\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Description Value
0session_id 786
1Original Data (167, 10)
2Missing Values False
3Numeric Features 9
4Categorical Features 1
5Ordinal Features False
6High Cardinality Features False
7Transformed Data (167, 9)
8Numeric Imputer mean
9Categorical Imputer constant
10Normalize True
11Normalize Method zscore
12Transformation False
13Transformation Method None
14PCA False
15PCA Method None
16PCA components None
17Ignore Low Variance False
18Combine Rare Levels False
19Rare Level Threshold None
20Numeric Binning False
21Remove Multicollinearity False
22Multicollinearity Threshold None
23Group Features False
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.clustering import *\n", "clu1 = setup(data, normalize=True, session_id=786,\n", " ignore_features = ['country'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Train Clustering Model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "kmeans = create_model('kmeans')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n", " n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n", " random_state=786, tol=0.0001, verbose=0)\n" ] } ], "source": [ "print(kmeans)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "hclust = create_model('hclust')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n", " connectivity=None, distance_threshold=None,\n", " linkage='ward', memory=None, n_clusters=4)\n" ] } ], "source": [ "print(hclust)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Analyze Trained Model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverlabel": { "namelength": 0 }, "hovertemplate": "Cluster=Cluster 0
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\n", 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\n", 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countrychild_mortexportshealthimportsincomeinflationlife_expectotal_fergdppCluster
0Afghanistan90.210.07.5844.916109.4456.25.82553Cluster 2
1Albania16.628.06.5548.699304.4976.31.654090Cluster 0
2Algeria27.338.44.1731.41290016.1076.52.894460Cluster 0
3Angola119.062.32.8542.9590022.4060.16.163530Cluster 2
4Antigua and Barbuda10.345.56.0358.9191001.4476.82.1312200Cluster 0
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" ], "text/plain": [ " country child_mort exports health imports income \\\n", "0 Afghanistan 90.2 10.0 7.58 44.9 1610 \n", "1 Albania 16.6 28.0 6.55 48.6 9930 \n", "2 Algeria 27.3 38.4 4.17 31.4 12900 \n", "3 Angola 119.0 62.3 2.85 42.9 5900 \n", "4 Antigua and Barbuda 10.3 45.5 6.03 58.9 19100 \n", "\n", " inflation life_expec total_fer gdpp Cluster \n", "0 9.44 56.2 5.82 553 Cluster 2 \n", "1 4.49 76.3 1.65 4090 Cluster 0 \n", "2 16.10 76.5 2.89 4460 Cluster 0 \n", "3 22.40 60.1 6.16 3530 Cluster 2 \n", "4 1.44 76.8 2.13 12200 Cluster 0 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = assign_model(kmeans)\n", "results.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "predictions = predict_model(kmeans, data=data)\n", "predictions.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning Resources:\n", "\n", "- PyCaret Clustering Module : https://www.pycaret.org/clustering\n", "- Clustering Tutorial (Level Beginner) : https://pycaret.org/clu101/\n", "- Clustering in PyCaret (Video Tutorial) : https://www.youtube.com/watch?v=2oxLDir7foQ" ] } ], "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }