{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "47bd0cfa",
"metadata": {},
"outputs": [],
"source": [
"# Q4A: K-Means clustering on Iris.csv; choose k with the elbow method.\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import StandardScaler\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "82b6cace",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset shape: (150, 6)\n",
"Features: ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Id | \n",
" SepalLengthCm | \n",
" SepalWidthCm | \n",
" PetalLengthCm | \n",
" PetalWidthCm | \n",
" Species | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 1 | \n",
" 5.1 | \n",
" 3.5 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 1 | \n",
" 2 | \n",
" 4.9 | \n",
" 3.0 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 2 | \n",
" 3 | \n",
" 4.7 | \n",
" 3.2 | \n",
" 1.3 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 3 | \n",
" 4 | \n",
" 4.6 | \n",
" 3.1 | \n",
" 1.5 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 4 | \n",
" 5 | \n",
" 5.0 | \n",
" 3.6 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
"
\n",
"