{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clusters (example 01) ##\n",
"\n",
"\n",
"Purpose: Identify clusters in a random generated blobs sample\n",
"\n",
"**1** import libraries needed:numpy, sklearn, matplotlib and pandas\n",
"\n",
"**2** generate a sample of blobs and convert it into a dataframe called df1\n",
"\n",
"**3** Verify datatype\n",
"\n",
"**4** Plot the blobs\n",
"\n",
"**5** calculete WCSS\n",
"\n",
"**6** plot the new chart with centroids\n",
"\n",
"**7** identify to what group does each item belongs\n",
"\n",
"**8** add new column\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"from sklearn.cluster import KMeans"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets.samples_generator import make_blobs\n",
"XY,y= make_blobs(n_samples=400, centers=5, cluster_std=0.60, random_state=0)\n",
"df1=pd.DataFrame(XY)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 float64\n",
"1 float64\n",
"dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(400, 2)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
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