weighted cluster for mall dataset part 3 (1).ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "240618ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "66980e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Annual Income (k$)  Spending Score (1-100)\n",
      "0                    15                      39\n",
      "1                    15                      81\n",
      "2                    16                       6\n",
      "3                    16                      77\n",
      "4                    17                      40\n",
      "..                  ...                     ...\n",
      "195                 120                      79\n",
      "196                 126                      28\n",
      "197                 126                      74\n",
      "198                 137                      18\n",
      "199                 137                      83\n",
      "\n",
      "[200 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# load the dataset\n",
    "data = pd.read_csv(\"mall_customers.csv\")\n",
    "X = data[[\"Annual Income (k$)\", \"Spending Score (1-100)\"]]\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a717eec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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    }
   ],
   "source": [
    "# Scale the data\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "print(X)\n",
    "# number of clusters\n",
    "k = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68b4cfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize centroids randomly\n",
    "centroids = np.random.rand(k, X.shape[1])\n",
    "\n",
    "# initialize weights as an empty array\n",
    "weights = np.zeros((X.shape[0], k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3999c754",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(20):\n",
    "    # calculate the distance between each data point and the centroids\n",
    "    distances = np.linalg.norm(X[:, np.newaxis] - centroids, axis=2)\n",
    "\n",
    "    # assign each data point to the closest centroid\n",
    "    clusters = np.argmin(distances, axis=1)\n",
    "\n",
    "    # calculate the reciprocal of the distance between each data point and the centroid of the cluster\n",
    "    for j in range(k):\n",
    "        weights[clusters == j, j] = 1 / distances[clusters == j, j]\n",
    "\n",
    "    # calculate the weighted average of the data points in each cluster to update the centroids\n",
    "    for j in range(k):\n",
    "        if np.sum(weights[clusters == j, j]) != 0:\n",
    "            centroids[j] = np.average(X[clusters == j], axis=0, weights=weights[clusters == j, j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5bb48a67",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "plt.scatter(X[:,0], X[:,1], c=clusters)\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], c='red', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aaf3e38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score: 0.4487438839180797\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "score = silhouette_score(X, clusters)\n",
    "print(\"Silhouette Score:\", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b890fc06",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'weights' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mweights\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'weights' is not defined"
     ]
    }
   ],
   "source": [
    "print(weights)"
   ]
  }
 ],
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