kMeansClustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a23ba648",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Import Dataset\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea40cec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign X\n",
    "X = df[['total_bill', 'size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7806f412",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Python\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16.48378378  2.35135135]\n",
      " [29.03870968  3.35483871]\n",
      " [37.175       3.78571429]\n",
      " [22.08313725  2.68627451]\n",
      " [10.93641791  1.97014925]\n",
      " [46.90571429  3.85714286]]\n"
     ]
    }
   ],
   "source": [
    "# Assign Algorithm\n",
    "model = KMeans(n_clusters=6)\n",
    "\n",
    "# Fit Algorithm\n",
    "model.fit(X)\n",
    "\n",
    "# Run Algorithm\n",
    "model_predict = model.predict(X)\n",
    "centroids = model.cluster_centers_\n",
    "print(model.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6fccf05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25e6ee49d10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Centroids\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.scatter(X['total_bill'], X['size'], c=model_predict, cmap='rainbow', s=50)\n",
    "plt.scatter(centroids[:,0], centroids[:,1], c='black', s=200, alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea34cf88",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}