From 060e3386ec50754277ce9ee99827301fe2a91a67 Mon Sep 17 00:00:00 2001
From: Jake Walker <jacob.walker@qinetiq.com>
Date: Tue, 20 Feb 2024 10:17:19 +0000
Subject: [PATCH] add k-means clustering notebook

---
 K-Means Clustering.ipynb |  887 +++++++++++++
 poetry.lock              | 2624 ++++++++++++++++++++++++++++++++++++++
 pyproject.toml           |   20 +
 3 files changed, 3531 insertions(+)
 create mode 100644 K-Means Clustering.ipynb
 create mode 100644 poetry.lock
 create mode 100644 pyproject.toml

diff --git a/K-Means Clustering.ipynb b/K-Means Clustering.ipynb
new file mode 100644
index 0000000..5a12062
--- /dev/null
+++ b/K-Means Clustering.ipynb	
@@ -0,0 +1,887 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# K-Means Clustering\n",
+    "\n",
+    "From: <https://realpython.com/k-means-clustering-python/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from kneed import KneeLocator\n",
+    "from sklearn.datasets import make_blobs\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.metrics import silhouette_score\n",
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate synthetic data & labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features, true_labels = make_blobs(\n",
+    "    n_samples=200,\n",
+    "    centers=3,\n",
+    "    cluster_std=2.75,\n",
+    "    random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  9.77075874,   3.27621022],\n",
+       "       [ -9.71349666,  11.27451802],\n",
+       "       [ -6.91330582,  -9.34755911],\n",
+       "       [-10.86185913, -10.75063497],\n",
+       "       [ -8.50038027,  -4.54370383]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_labels[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature scaling\n",
+    "\n",
+    "The standard scaler shifts the values for each numerical feature so they have a mean of 0 and standard deviation of 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.13082109,  0.25604351],\n",
+       "       [-1.52698523,  1.41036744],\n",
+       "       [-1.00130152, -1.56583175],\n",
+       "       [-1.74256891, -1.76832509],\n",
+       "       [-1.29924521, -0.87253446]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaled_features = scaler.fit_transform(features)\n",
+    "\n",
+    "scaled_features[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## K-Means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(\n",
+    "    init=\"random\",\n",
+    "    n_clusters=3,\n",
+    "    n_init=10,\n",
+    "    max_iter=300,\n",
+    "    random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(init=&#x27;random&#x27;, n_clusters=3, n_init=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(init=&#x27;random&#x27;, n_clusters=3, n_init=10, random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(init='random', n_clusters=3, n_init=10, random_state=42)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.fit(scaled_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "74.57960106819853"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.inertia_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.19539276,  0.13158148],\n",
+       "       [-0.25813925,  1.05589975],\n",
+       "       [-0.91941183, -1.18551732]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.cluster_centers_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.n_iter_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.labels_[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Choosing the appropriate number of clusters\n",
+    "\n",
+    "**SSE** (Sum of Squared Errors) is a measure of the total variation within each cluster. A lower SSE value indicates that the data points are closer to the cluster's center. The SSE is calculated as the sum of the squared differences between each observation and its group's mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans_kwargs = {\n",
+    "    \"init\": \"random\",\n",
+    "    \"n_init\": 10,\n",
+    "    \"max_iter\": 300,\n",
+    "    \"random_state\": 42\n",
+    "}\n",
+    "\n",
+    "see = []\n",
+    "for k in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters=k, **kmeans_kwargs)\n",
+    "    kmeans.fit(scaled_features)\n",
+    "    see.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use(\"fivethirtyeight\")\n",
+    "plt.plot(range(1, 11), see)\n",
+    "plt.xticks(range(1, 11))\n",
+    "plt.xlabel(\"Number of Clusters\")\n",
+    "plt.ylabel(\"SEE\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kl = KneeLocator(range(1, 11), see, curve=\"convex\", direction=\"decreasing\")\n",
+    "kl.elbow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **silhouette coefficient** is a measure of cluster cohesion and separation, based on:\n",
+    " - How close the data point is to other points in the cluster\n",
+    " - How far away the data point is from points in other clusters\n",
+    "\n",
+    "Values from -1 to 1, higher values are better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "silhouette_coefficients = []\n",
+    "\n",
+    "for k in range(2, 11):\n",
+    "    kmeans = KMeans(n_clusters=k, **kmeans_kwargs)\n",
+    "    kmeans.fit(scaled_features)\n",
+    "    score = silhouette_score(scaled_features, kmeans.labels_)\n",
+    "    silhouette_coefficients.append(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use(\"fivethirtyeight\")\n",
+    "plt.plot(range(2, 11), silhouette_coefficients)\n",
+    "plt.xticks(range(2, 11))\n",
+    "plt.xlabel(\"Number of clusters\")\n",
+    "plt.ylabel(\"Silhouette coefficient\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating clustering performance with advanced techniques"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "from sklearn.datasets import make_moons\n",
+    "from sklearn.metrics import adjusted_rand_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features, true_labels = make_moons(n_samples=250, noise=0.05, random_state=42)\n",
+    "scaled_features = scaler.fit_transform(features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kmeans_silhouette=0.50, dbscan_silhouette=0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(n_clusters=2)\n",
+    "dbscan = DBSCAN(eps=0.3)\n",
+    "\n",
+    "kmeans.fit(scaled_features)\n",
+    "dbscan.fit(scaled_features)\n",
+    "\n",
+    "kmeans_silhouette = silhouette_score(scaled_features, kmeans.labels_)\n",
+    "dbscan_silhouette = silhouette_score(scaled_features, dbscan.labels_)\n",
+    "\n",
+    "print(f\"{kmeans_silhouette=:.2f}, {dbscan_silhouette=:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adjusted rand index (ARI) uses true cluster assignments to measure the similarity between true and predicted labels. This gives a value between -1 and 1. A score close to 0 indicates random assignments, and a score close to 1 indicates perfectly labeled clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ari_kmeans=0.47, ari_dbscan=1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "ari_kmeans = adjusted_rand_score(true_labels, kmeans.labels_)\n",
+    "ari_dbscan = adjusted_rand_score(true_labels, dbscan.labels_)\n",
+    "\n",
+    "print(f\"{ari_kmeans=:.2f}, {ari_dbscan=:.2f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "data-science-research-W2__OPFf-py3.11",
+   "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": 2
+}
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..b59d50d
--- /dev/null
+++ b/poetry.lock
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+[[package]]
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+description = "RFC 6570 URI Template Processor"
+optional = false
+python-versions = ">=3.7"
+files = [
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+[package.extras]
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+
+[[package]]
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+version = "2.0.7"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.7"
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+
+[package.extras]
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+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.9"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[[package]]
+name = "webcolors"
+version = "1.13"
+description = "A library for working with the color formats defined by HTML and CSS."
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.extras]
+docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
+tests = ["pytest", "pytest-cov"]
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[[package]]
+name = "websocket-client"
+version = "1.6.4"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
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+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.11"
+content-hash = "92e1c0f4a71374b3a636832fb4b8b4b9c7ebef9cdedc6173cfd44ae2a3df8cf5"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..36e749c
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "data-science-research"
+version = "0.1.0"
+description = ""
+authors = ["Jake Walker <hi@jakew.me>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+jupyterlab = "^4.0.8"
+matplotlib = "^3.8.3"
+seaborn = "^0.13.2"
+numpy = "^1.26.4"
+kneed = "^0.8.5"
+scikit-learn = "^1.4.1.post1"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"