Agglomarative (Hirarichry) Algoritham.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04343554-78aa-4023-aa9b-ff0f2428bfe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8dcbb8fb-e228-46f9-bdf3-101c018f4dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"agglomarative.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf57d8eb-29d2-4541-ad5e-8698696134fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Genre\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93540cb8-e55b-406d-89be-1efaf3be2e6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0             1   19                  15                      39\n",
       "1             2   21                  15                      81\n",
       "2             3   20                  16                       6\n",
       "3             4   23                  16                      77\n",
       "4             5   31                  17                      40\n",
       "..          ...  ...                 ...                     ...\n",
       "195         196   35                 120                      79\n",
       "196         197   45                 126                      28\n",
       "197         198   32                 126                      74\n",
       "198         199   32                 137                      18\n",
       "199         200   30                 137                      83\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed4f6f5e-2ba1-4fbc-8c34-cd9bee7d5226",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.iloc[:,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4505b9a0-0cc5-4609-8c96-b819f9510c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Annual Income (k$)  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]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5c95a0f-0d66-46d2-94e9-31a9bdeeb775",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster._hierarchy as shc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dfbda54f-4434-4ae5-9b2f-3a8b07e8e777",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg=AgglomerativeClustering(n_clusters=5,metric=\"euclidean\",linkage=\"ward\")\n",
    "labels_=agg.fit_predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0977a6a-ef70-4703-a529-b8ca9108e4e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x203038706b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(df[\"Annual Income (k$)\"],df[\"Spending Score (1-100)\"],c=agg.labels_ ,cmap=\"rainbow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1b15fe9b-bbd0-4835-ac17-009186b2efa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Annual Income (k$)', 'Spending Score (1-100)'], dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "212bb6f5-2ba9-445e-acc1-2177f6dd1b7b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac6cef4-1123-44ce-9512-6425cc4aa73b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f36b44e8-f1a9-41bf-9574-b82c11cd5ad0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a919c270-159c-457c-9bd9-eee72e09a3cd",
   "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": "3.12.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}