K-Means_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f91de7b9-ab89-4a75-8caa-badceed6fa9b",
   "metadata": {},
   "source": [
    "# K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ca87790-56ab-4a1e-b209-ded129db81da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "#from sklearn.datasets.samples_generator import make_blobs\n",
    "#matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f0b9d5f-2b8f-46e8-b21f-297a1f024e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X, y = make_blobs (n_samples = 5000, centers = [[4,1], [-2,-1],[2,-3], [1,1]], cluster_std=0.9)\n",
    "\n",
    "# Generate a synthetic dataset with 5000 samples, 4 centers, and a standard deviation of 0.9 for each cluster\n",
    "X, y = make_blobs(\n",
    "    n_samples=5000, # The total number of data points to generate\n",
    "    centers=[[4, 1], [-2, -1], [2, -3], [1, 1]],  # The coordinates of the centers of the clusters\n",
    "    cluster_std=0.9 # The standard deviation of the clusters\n",
    ")\n",
    "\n",
    "# X contains the generated sample data points\n",
    "# y contains the cluster labels for each data point, though it is not used further in this context"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a9faef-4a57-4cdb-8916-583d41aaff43",
   "metadata": {},
   "source": [
    "**make_blobs Parameters**:\n",
    "  - `n_samples`: Number of data points to generate.\n",
    "  - `centers`: Coordinates of cluster centers.\n",
    "  - `cluster_std`: Standard deviation of each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25bfdd3c-d0a4-4f32-8f2c-4162cb6c7293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of the generated data points\n",
    "plt.scatter(\n",
    "    X[:, 0],  # Select all rows in the first column (feature 1) of the generated data points\n",
    "    X[:, 1],  # Select all rows in the second column (feature 2) of the generated data points\n",
    "    marker='.'  # Use a dot marker for each data point\n",
    ")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f3002c-041c-436c-a21a-f1bbb39ef3e2",
   "metadata": {},
   "source": [
    "\n",
    "## Retrieve Cluster Labels and Centroids\n",
    "\n",
    "```python\n",
    "# Get cluster labels and centroids\n",
    "k_means_labels = kmeans.labels_            # Cluster labels assigned to each data point\n",
    "k_means_cluster_centers = kmeans.cluster_centers_  # Coordinates of cluster centers\n",
    "```\n",
    "\n",
    "- **kmeans.labels_**: Array containing the cluster label for each data point.\n",
    "- **kmeans.cluster_centers_**: Coordinates of the centroids (cluster centers) identified by K-means.\n",
    "\n",
    "## Plotting the Clusters and Centroids\n",
    "\n",
    "```python\n",
    "# Plotting the clusters and centroids\n",
    "fig = plt.figure(figsize=(8, 6))           # Create a new figure with specified size\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))  # Generate colors for clusters\n",
    "ax = fig.add_subplot(1, 1, 1)              # Add a subplot to the figure\n",
    "\n",
    "# Iterate over each cluster to plot its data points and centroid\n",
    "for k, col in zip(range(len(k_means_cluster_centers)), colors):\n",
    "    my_members = (k_means_labels == k)     # Boolean mask for data points in the current cluster\n",
    "    cluster_center = k_means_cluster_centers[k]  # Coordinates of the centroid of current cluster\n",
    "    \n",
    "    # Plot data points of current cluster with white markers\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plot centroid of current cluster with a circle marker\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=10)\n",
    "\n",
    "# Set title and labels for the plot\n",
    "ax.set_title('KMeans Clustering')\n",
    "ax.set_xlabel('Feature 1')\n",
    "ax.set_ylabel('Feature 2')\n",
    "\n",
    "# Remove ticks from both axes\n",
    "ax.set_xticks(())\n",
    "ax.set_yticks(())\n",
    "plt.show()  # Display the plot\n",
    "```\n",
    "\n",
    "- **Figure and Subplot Creation**:\n",
    "  - `fig = plt.figure(figsize=(8, 6))`: Creates a new figure with a size of 8x6 inches.\n",
    "  - `ax = fig.add_subplot(1, 1, 1)`: Adds a subplot to the figure.\n",
    "\n",
    "- **Color Generation**:\n",
    "  - `colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))`: Generates a range of colors for visualizing clusters.\n",
    "\n",
    "- **Plotting Data Points and Centroids**:\n",
    "  - Iterates over each cluster (`k`) to plot its data points and centroid.\n",
    "  - `ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')`: Plots data points of the current cluster with white markers.\n",
    "  - `ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=10)`: Plots centroid of the current cluster with a circle marker.\n",
    "\n",
    "- **Setting Title and Labels**:\n",
    "  - `ax.set_title('KMeans Clustering')`: Sets the title of the plot.\n",
    "  - `ax.set_xlabel('Feature 1')` and `ax.set_ylabel('Feature 2')`: Label the x-axis and y-axis, respectively.\n",
    "\n",
    "- **Final Adjustments**:\n",
    "  - `ax.set_xticks(())` and `ax.set_yticks(())`: Remove ticks from both axes for a cleaner plot.\n",
    "  - `plt.show()`: Displays the finalized plot.\n",
    "\n",
    "These markdown notes provide a detailed explanation of each part of the Python code, helping to understand the fundamentals of K-means clustering and visualization using `sklearn` and `matplotlib`. Adjustments can be made based on specific dataset characteristics and visualization requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c83032e-fc2d-45e4-b206-1439ea5a3d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize K-means clustering with 4 clusters (since we generated 4 centers)\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters=4, n_init = 'auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be70cd8-8504-4857-8b25-9699b9a634d5",
   "metadata": {},
   "source": [
    "**K-means Initialization (`init=\"k-means++\"`)**:\n",
    "   - `init=\"k-means++\"`: This parameter specifies the method for initialization of centroids before clustering begins. `\"k-means++\"` is a smart initialization technique that selects initial centroids in a way that speeds up convergence and leads to better results compared to random initialization. It ensures that centroids are spread out and not too close together initially.\n",
    "   - `k-means++` is preferred over random initialization (`init=\"random\"`) because it typically results in faster convergence and better clustering outcomes, especially with complex or large datasets.\n",
    "     \n",
    "**Number of Clusters (`n_clusters=4`)**:\n",
    "   - `n_clusters=4`: Specifies the number of clusters to form as part of the K-means clustering process. In this case, `4` indicates that the algorithm will aim to identify and assign each data point to one of four clusters based on similarity.\n",
    "\n",
    "**Number of Initializations (`n_init='auto'`)**:\n",
    "   - `n_init='auto'`: This parameter determines the number of times the K-means algorithm will run with different initial centroid seeds. `'auto'` means that the algorithm will automatically choose a suitable number of initializations based on the size of the dataset (`n_samples`).\n",
    "   - Each initialization produces a different clustering result, and the final model selects the one with the lowest inertia (sum of squared distances from data points to their assigned centroids), indicating the most optimal clustering.\n",
    " - The higher the `n_init`, the more computationally expensive the clustering process becomes, as the algorithm runs multiple times. However, it increases the likelihood of finding the globally optimal clustering solution.\n",
    "\n",
    "These parameters collectively define how K-means clustering will be performed on the given dataset, aiming to identify and partition data points into four clusters based on their features. Adjustments to these parameters can influence the clustering results and performance based on the characteristics of the data being analyzed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e953f51-a67e-4d5e-9880-48ba6af25da6",
   "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(n_clusters=4)</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/1.5/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(n_clusters=4)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit K-means clustering to the data\n",
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8c0737f-2fc4-471a-bb24-8f582a911d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 3, 0, 3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Cluster labels assigned to each data point\n",
    "k_means_labels = k_means.labels_\n",
    "k_means_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40085de7-f2c8-4661-93ba-3e2c97e7a5f0",
   "metadata": {},
   "source": [
    "**`k_means.labels_`**:\n",
    "   - `k_means.labels_` is an attribute of the K-means clustering model (`k_means`). After fitting the model to the data, this attribute contains the cluster label assigned to each data point.\n",
    "\n",
    "**Assigning Labels**:\n",
    "   - Each data point in the dataset (`X`) is assigned a cluster label (an integer) based on its proximity to the centroid of each cluster. The labels are determined during the fitting process of the K-means algorithm.\n",
    "\n",
    "**Usage**:\n",
    "   - `k_means_labels` is a NumPy array where each element corresponds to a data point in `X` and indicates which cluster that data point belongs to according to the fitted K-means model.\n",
    "\n",
    "The line `k_means_labels = k_means.labels_` retrieves the cluster labels assigned by the K-means clustering algorithm to each data point in the dataset `X`. These labels are crucial for understanding how data points are grouped into clusters based on their feature similarities according to the K-means clustering model's output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d20b2fe-5865-47ca-9ac4-50a9f9e1c79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the cluster labels for the data points\n",
    "y_pred = k_means.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf12109b-54f6-4c61-a3b8-91451117cf88",
   "metadata": {},
   "source": [
    "**`k_means.predict(X)`**:\n",
    "   - `k_means` is an instance of the KMeans class (already fitted to the data).\n",
    "   - `.predict(X)` is a method provided by the KMeans class in scikit-learn. It predicts the cluster labels for the data points in `X` based on the centroids obtained during the fitting of the K-means model.\n",
    "\n",
    "**Usage**:\n",
    "   - After fitting the K-means model (`k_means.fit(X)`), you can use `.predict(X)` to assign cluster labels to new data points or the same data points used for training (`X`).\n",
    "\n",
    "**Return Value (`y_pred`)**:\n",
    "   - `y_pred` is a NumPy array where each element corresponds to a data point in `X` and indicates the predicted cluster label assigned by the K-means model.\n",
    "\n",
    "\n",
    "The line `y_pred = k_means.predict(X)` predicts the cluster labels for the data points in `X` using the fitted K-means model (`k_means`). This allows for further analysis and evaluation of the clustering results based on the predicted labels assigned to each data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bc45c47-20ed-43b4-ba40-a568c83ba48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.97226962,  1.00429298],\n",
       "       [-2.04625731, -1.00361082],\n",
       "       [ 4.01838939,  1.01719784],\n",
       "       [ 1.95554736, -2.97274339]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the cluster centers\n",
    "k_means_cluster_centers = k_means.cluster_centers_\n",
    "k_means_cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2cf5b64-74d1-4afe-9e50-86051254fda2",
   "metadata": {},
   "source": [
    "**`k_means.cluster_centers_`**:\n",
    "   - `k_means` is an instance of the KMeans class (already fitted to the data).\n",
    "   - `.cluster_centers_` is an attribute of the KMeans class in scikit-learn. It returns the coordinates of the cluster centers (centroids) determined by the K-means algorithm during the fitting process.\n",
    "\n",
    "**Usage**:\n",
    "   - After fitting the K-means model (`k_means.fit(X)`), you can access `.cluster_centers_` to retrieve the centroids of each cluster identified by the model.\n",
    "\n",
    "**Return Value (`k_means_cluster_centers`)**:\n",
    "   - `k_means_cluster_centers` is a NumPy array where each row represents the coordinates of a cluster center in the feature space. The number of rows corresponds to the number of clusters specified (`n_clusters`) when initializing the KMeans model.\n",
    "\n",
    "\n",
    "The line `k_means_cluster_centers = k_means.cluster_centers_` retrieves the coordinates of the cluster centers (centroids) identified by the K-means clustering algorithm using the fitted KMeans model (`k_means`). These centroids represent the central points around which data points in each cluster are grouped based on similarity in feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23486db1-e35e-4ba1-9e6d-671e61436ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new figure with a size of 8x6 inches\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Generate colors for each cluster label using the Spectral colormap\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Add a subplot to the figure\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Iterate over each cluster to plot its data points and centroid\n",
    "for k, col in zip(range(len([[4, 4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
    "    # Create a boolean mask for data points in the current cluster\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Get the coordinates of the centroid of the current cluster\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plot data points of the current cluster with white markers\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plot centroid of the current cluster with a circle marker\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Set title and labels for the plot\n",
    "ax.set_title('KMeans Clustering')\n",
    "ax.set_xlabel('Feature 1')\n",
    "ax.set_ylabel('Feature 2')\n",
    "\n",
    "# Remove ticks from both axes\n",
    "ax.set_xticks(())\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d0f1ae-3ef6-4077-8da7-e8c677b2c2dd",
   "metadata": {},
   "source": [
    "**Figure and Axes Setup**:\n",
    "   - Initializes a figure (`fig`) with specified size (8x6 inches).\n",
    "   - Adds a subplot (`ax`) to the figure for plotting.\n",
    "\n",
    "**Color Generation**:\n",
    "   - Utilizes the Spectral colormap to generate a range of colors for visualizing clusters.\n",
    "\n",
    "**Plotting Clusters**:\n",
    "   - Iterates over each cluster, plotting data points with white markers and cluster centroids with larger circles.\n",
    "\n",
    "**Formatting**:\n",
    "   - Sets title, axis labels (`Feature 1`, `Feature 2`), and removes numerical ticks for clarity.\n",
    "\n",
    "**Displaying the Plot**:\n",
    "   - Shows the finalized plot using `plt.show()`.\n",
    "\n",
    "This visualization provides insights into how K-means clustering assigns data points to clusters and identifies centroids based on their feature similarities."
   ]
  }
 ],
 "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": "3.12.4"
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