{
"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": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" child_mort | \n",
" exports | \n",
" health | \n",
" imports | \n",
" income | \n",
" inflation | \n",
" life_expec | \n",
" total_fer | \n",
" gdpp | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Afghanistan | \n",
" 90.2 | \n",
" 10.0 | \n",
" 7.58 | \n",
" 44.9 | \n",
" 1610 | \n",
" 9.44 | \n",
" 56.2 | \n",
" 5.82 | \n",
" 553 | \n",
"
\n",
" \n",
" 1 | \n",
" Albania | \n",
" 16.6 | \n",
" 28.0 | \n",
" 6.55 | \n",
" 48.6 | \n",
" 9930 | \n",
" 4.49 | \n",
" 76.3 | \n",
" 1.65 | \n",
" 4090 | \n",
"
\n",
" \n",
" 2 | \n",
" Algeria | \n",
" 27.3 | \n",
" 38.4 | \n",
" 4.17 | \n",
" 31.4 | \n",
" 12900 | \n",
" 16.10 | \n",
" 76.5 | \n",
" 2.89 | \n",
" 4460 | \n",
"
\n",
" \n",
" 3 | \n",
" Angola | \n",
" 119.0 | \n",
" 62.3 | \n",
" 2.85 | \n",
" 42.9 | \n",
" 5900 | \n",
" 22.40 | \n",
" 60.1 | \n",
" 6.16 | \n",
" 3530 | \n",
"
\n",
" \n",
" 4 | \n",
" Antigua and Barbuda | \n",
" 10.3 | \n",
" 45.5 | \n",
" 6.03 | \n",
" 58.9 | \n",
" 19100 | \n",
" 1.44 | \n",
" 76.8 | \n",
" 2.13 | \n",
" 12200 | \n",
"
\n",
" \n",
"
\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": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Setup Succesfully Completed!\n"
]
},
{
"data": {
"text/html": [
" | Description | Value |
\n",
" \n",
" 0 | \n",
" session_id | \n",
" 786 | \n",
"
\n",
" \n",
" 1 | \n",
" Original Data | \n",
" (167, 10) | \n",
"
\n",
" \n",
" 2 | \n",
" Missing Values | \n",
" False | \n",
"
\n",
" \n",
" 3 | \n",
" Numeric Features | \n",
" 9 | \n",
"
\n",
" \n",
" 4 | \n",
" Categorical Features | \n",
" 1 | \n",
"
\n",
" \n",
" 5 | \n",
" Ordinal Features | \n",
" False | \n",
"
\n",
" \n",
" 6 | \n",
" High Cardinality Features | \n",
" False | \n",
"
\n",
" \n",
" 7 | \n",
" Transformed Data | \n",
" (167, 9) | \n",
"
\n",
" \n",
" 8 | \n",
" Numeric Imputer | \n",
" mean | \n",
"
\n",
" \n",
" 9 | \n",
" Categorical Imputer | \n",
" constant | \n",
"
\n",
" \n",
" 10 | \n",
" Normalize | \n",
" True | \n",
"
\n",
" \n",
" 11 | \n",
" Normalize Method | \n",
" zscore | \n",
"
\n",
" \n",
" 12 | \n",
" Transformation | \n",
" False | \n",
"
\n",
" \n",
" 13 | \n",
" Transformation Method | \n",
" None | \n",
"
\n",
" \n",
" 14 | \n",
" PCA | \n",
" False | \n",
"
\n",
" \n",
" 15 | \n",
" PCA Method | \n",
" None | \n",
"
\n",
" \n",
" 16 | \n",
" PCA components | \n",
" None | \n",
"
\n",
" \n",
" 17 | \n",
" Ignore Low Variance | \n",
" False | \n",
"
\n",
" \n",
" 18 | \n",
" Combine Rare Levels | \n",
" False | \n",
"
\n",
" \n",
" 19 | \n",
" Rare Level Threshold | \n",
" None | \n",
"
\n",
" \n",
" 20 | \n",
" Numeric Binning | \n",
" False | \n",
"
\n",
" \n",
" 21 | \n",
" Remove Multicollinearity | \n",
" False | \n",
"
\n",
" \n",
" 22 | \n",
" Multicollinearity Threshold | \n",
" None | \n",
"
\n",
" \n",
" 23 | \n",
" Group Features | \n",
" False | \n",
"
\n",
"
"
],
"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": 4,
"metadata": {},
"outputs": [],
"source": [
"kmeans = create_model('kmeans')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": 6,
"metadata": {},
"outputs": [],
"source": [
"hclust = create_model('hclust')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": 8,
"metadata": {},
"outputs": [
{
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_model(kmeans, plot = 'elbow')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_model(kmeans, plot = 'silhouette')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"alignmentgroup": "True",
"bingroup": "x",
"hoverlabel": {
"namelength": 0
},
"hovertemplate": "Cluster=Cluster 0
income=%{x}
count=%{y}",
"legendgroup": "Cluster=Cluster 0",
"marker": {
"color": "#636efa",
"opacity": 0.7
},
"name": "Cluster=Cluster 0",
"offsetgroup": "Cluster=Cluster 0",
"orientation": "v",
"showlegend": true,
"type": "histogram",
"x": [
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"text/html": [
"\n",
" \n",
" \n",
"
\n",
" \n",
"
"
]
},
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],
"source": [
"plot_model(kmeans, plot = 'distribution', feature = 'income')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5. Assign Labels / Predict on new data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" child_mort | \n",
" exports | \n",
" health | \n",
" imports | \n",
" income | \n",
" inflation | \n",
" life_expec | \n",
" total_fer | \n",
" gdpp | \n",
" Cluster | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Afghanistan | \n",
" 90.2 | \n",
" 10.0 | \n",
" 7.58 | \n",
" 44.9 | \n",
" 1610 | \n",
" 9.44 | \n",
" 56.2 | \n",
" 5.82 | \n",
" 553 | \n",
" Cluster 2 | \n",
"
\n",
" \n",
" 1 | \n",
" Albania | \n",
" 16.6 | \n",
" 28.0 | \n",
" 6.55 | \n",
" 48.6 | \n",
" 9930 | \n",
" 4.49 | \n",
" 76.3 | \n",
" 1.65 | \n",
" 4090 | \n",
" Cluster 0 | \n",
"
\n",
" \n",
" 2 | \n",
" Algeria | \n",
" 27.3 | \n",
" 38.4 | \n",
" 4.17 | \n",
" 31.4 | \n",
" 12900 | \n",
" 16.10 | \n",
" 76.5 | \n",
" 2.89 | \n",
" 4460 | \n",
" Cluster 0 | \n",
"
\n",
" \n",
" 3 | \n",
" Angola | \n",
" 119.0 | \n",
" 62.3 | \n",
" 2.85 | \n",
" 42.9 | \n",
" 5900 | \n",
" 22.40 | \n",
" 60.1 | \n",
" 6.16 | \n",
" 3530 | \n",
" Cluster 2 | \n",
"
\n",
" \n",
" 4 | \n",
" Antigua and Barbuda | \n",
" 10.3 | \n",
" 45.5 | \n",
" 6.03 | \n",
" 58.9 | \n",
" 19100 | \n",
" 1.44 | \n",
" 76.8 | \n",
" 2.13 | \n",
" 12200 | \n",
" Cluster 0 | \n",
"
\n",
" \n",
"
\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 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": 14,
"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"
]
}
],
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