KMeans cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bdb2b5af-c42b-4eeb-8a8c-d97834610bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca93fe17-4fe8-4b42-a988-e5651588f413",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"student_clustering.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1055a38d-505b-493d-aaec-90d0339fab62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "82766bbc-c5bf-4a2b-b236-0de91e4616f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27092e2ecf0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"cgpa\"],df[\"iq\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d10097a-c920-4519-b300-ba9cd320148e",
   "metadata": {},
   "source": [
    "# how to know how much cluster should i take (Elbow_Method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "816a9f8d-679b-442b-8be4-0e3fefee1e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss=[]\n",
    "for i in range(1,11):\n",
    "    km=KMeans(n_clusters=i)\n",
    "    km.fit_predict(df)\n",
    "    wcss.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1ce4872e-d8ca-4c0c-9a0b-eb71c62d7707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27091faaf00>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,11),wcss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e8a41eb-b002-45d7-a6f2-123bcc4dab19",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster=KMeans(n_clusters=4)\n",
    "y_pred=cluster.fit_predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cf274464-154b-486c-83f9-d97de880afde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27091f265a0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[y_pred==0,0], df[y_pred== 0,1], color=\"red\")\n",
    "plt.scatter(df[y_pred==1,0], df[y_pred==1,1], color=\"green\")\n",
    "plt.scatter(df[y_pred==2,0], df[y_pred==2,1], color=\"yellow\")\n",
    "plt.scatter(df[y_pred==3,0], df[y_pred==3,1], color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e137b7ce-0a18-446d-862d-83dd1075d68c",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f46f15fe-8f42-46ac-a198-388ec5233a94",
   "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
}