K-Means-Clustering/Unsupervised_Learning_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/ayush-09/K-Means-Clustering/blob/master/Unsupervised_Learning_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WdGfBpUVoyJ_"
   },
   "source": [
    "# Unsupervised Learning Algorithm\n",
    "- Salaray Dataset from kaggle name salary.csv : https://www.kaggle.com/rsadiq/salary\n",
    "- K-means++ Clustering algoritm(k=3), I choose K-means++ instead instead of K-means because it doesnot chosse any random for centroid.\n",
    "k=3 is fit after check the wscc and silhouette score for each k value.\n",
    "- I plot both the graphs **Elbow Method** and **Silhoutte** for the best cluster value.\n",
    "- After made the model I will check the sum of square error of the first 10 iteration of the model by plot the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f2jVcjGfGpuA"
   },
   "source": [
    "## Connect with drive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iqQ8Pt17GpH7",
    "outputId": "ddb95c5b-fae4-427c-9167-4abc76fc538d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "\n",
    "drive.mount(\"/content/gdrive\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ALFYwu6oHUVU"
   },
   "source": [
    "## Import the libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "4jK_6EZqEnCv"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score,silhouette_samples\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9rM8vR3kXzzy"
   },
   "source": [
    "## Visualize the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "uedCNPgSEnCw",
    "outputId": "dd3e2dac-e7e4-4975-fb6a-36180ee68f51"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience  Salary\n",
       "0              1.1   39343\n",
       "1              1.3   46205\n",
       "2              1.5   37731\n",
       "3              2.0   43525\n",
       "4              2.2   39891"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"/content/gdrive/MyDrive/archive/Salary.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "BRp_Z0-6EnCx",
    "outputId": "aaaf49c0-a056-422b-a7aa-4348a7a17d3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9eb3baab10>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['YearsExperience'],df['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "k8kzph36Rj71"
   },
   "outputs": [],
   "source": [
    "X=df[['YearsExperience','Salary']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q4Xgg5R_rcKM"
   },
   "source": [
    "## Calculate the WCSS and Silhouette Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8Y6xQrl8RbXU",
    "outputId": "ada348b3-660f-4534-d784-bc39080779ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster = 2, wcss=6062232833.744474, Silhouette= 0.7028572890853004\n",
      "Cluster = 3, wcss=2903864662.5549126, Silhouette= 0.6331618019952167\n",
      "Cluster = 4, wcss=1663816733.7989414, Silhouette= 0.6368549820565115\n",
      "Cluster = 5, wcss=894398887.721595, Silhouette= 0.6173381925348792\n",
      "Cluster = 6, wcss=652477856.6519287, Silhouette= 0.627089162952214\n",
      "Cluster = 7, wcss=441789859.79249996, Silhouette= 0.6421588941076305\n"
     ]
    }
   ],
   "source": [
    "wcss =[]\n",
    "silhouette=[]\n",
    "for i in range(2,8):\n",
    "    k_means = KMeans(n_clusters=i,init = 'k-means++', random_state=20)\n",
    "    k_means.fit(X)\n",
    "    wcss.append(k_means.inertia_)\n",
    "    pred = k_means.predict(X)\n",
    "    silhouette.append(silhouette_score(X,pred))\n",
    "    print(\"Cluster = {0}, wcss={1}, Silhouette= {2}\".format(i,k_means.inertia_,silhouette_score(X,pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NGdx-zSDsJXU"
   },
   "source": [
    "## Ploting the Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "_-31zovzLY2x",
    "outputId": "3c47cd93-c177-42f4-8178-c501177cf402"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(2,8),wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('No. of clusters')\n",
    "plt.ylabel('wcss score')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "bgAtKjOAR1aF",
    "outputId": "9e23b406-b881-4076-f94f-95ad5db99442"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(2,8),silhouette)\n",
    "plt.title('Silhouette')\n",
    "plt.xlabel('No. of clusters')\n",
    "plt.ylabel('Silhouette score')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rIeAHgsJsP-n"
   },
   "source": [
    "## Train the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zbzrurv3EnCx",
    "outputId": "213d54db-2fdd-43ea-99be-59445f77f141"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=100,\n",
       "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=True)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km=KMeans(n_clusters=5, init='k-means++',max_iter=100,verbose=True)\n",
    "km"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rtf4lXtDsUtB"
   },
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a_rjkpRYEnCy",
    "outputId": "6296e2f8-7255-46cd-f7e1-40d0e8d56389"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 944865214.6304046\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 944865214.6304046\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 1504371017.23425\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 1504371017.23425\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 1198190608.6271667\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 1198190608.6271667\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 1251859328.2879996\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 1158509896.5262141\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 1158509896.5262141\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 919084793.4247856\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 919084793.4247856\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 917097459.1652617\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 917097459.1652617\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 1570578612.1835585\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 1392384670.572329\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 1248208083.0671272\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 3, inertia 944865214.6304046\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 4, inertia 944865214.6304046\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 1053324728.9333895\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 944865214.6304046\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 944865214.6304046\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 944865214.6304046\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 944865214.6304046\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 1198190608.6271667\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 1198190608.6271667\n",
      "center shift 0.000000e+00 within tolerance 5.024411e+04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2,\n",
       "       2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted=km.fit_predict(df[['YearsExperience','Salary']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "rS6hTWw2EnCy",
    "outputId": "9067a9a4-d0ee-43d9-9d7d-e5d927a07606"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11.2</td>\n",
       "      <td>127345</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11.5</td>\n",
       "      <td>126756</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>12.3</td>\n",
       "      <td>128765</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12.9</td>\n",
       "      <td>135675</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>13.5</td>\n",
       "      <td>139465</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience  Salary  cluster\n",
       "0               1.1   39343        4\n",
       "1               1.3   46205        4\n",
       "2               1.5   37731        4\n",
       "3               2.0   43525        4\n",
       "4               2.2   39891        4\n",
       "5               2.9   56642        0\n",
       "6               3.0   60150        0\n",
       "7               3.2   54445        0\n",
       "8               3.2   64445        0\n",
       "9               3.7   57189        0\n",
       "10              3.9   63218        0\n",
       "11              4.0   55794        0\n",
       "12              4.0   56957        0\n",
       "13              4.1   57081        0\n",
       "14              4.5   61111        0\n",
       "15              4.9   67938        0\n",
       "16              5.1   66029        0\n",
       "17              5.3   83088        2\n",
       "18              5.9   81363        2\n",
       "19              6.0   93940        2\n",
       "20              6.8   91738        2\n",
       "21              7.1   98273        2\n",
       "22              7.9  101302        2\n",
       "23              8.2  113812        1\n",
       "24              8.7  109431        1\n",
       "25              9.0  105582        1\n",
       "26              9.5  116969        1\n",
       "27              9.6  112635        1\n",
       "28             10.3  122391        3\n",
       "29             10.5  121872        3\n",
       "30             11.2  127345        3\n",
       "31             11.5  126756        3\n",
       "32             12.3  128765        3\n",
       "33             12.9  135675        3\n",
       "34             13.5  139465        3"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster']=y_predicted\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OauQMQkrNiBO",
    "outputId": "e25a0b6e-b30a-49d6-9af4-984ea626cc49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0, 1, 2, 3, 4], dtype=int32), array([12,  5,  6,  7,  5]))\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(km.labels_,return_counts= True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "wJQphFQNEnCy",
    "outputId": "731e78f4-24e4-4dd2-b94b-b196ca5dc08d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1=df[df.cluster==0]\n",
    "df2=df[df.cluster==1]\n",
    "df3=df[df.cluster==2]\n",
    "df4=df[df.cluster==3]\n",
    "df5=df[df.cluster==4]\n",
    "plt.scatter(df1.YearsExperience, df1['Salary'],color='green')\n",
    "plt.scatter(df2.YearsExperience, df2['Salary'],color='red')\n",
    "plt.scatter(df3.YearsExperience, df3['Salary'],color='black')\n",
    "plt.scatter(df4.YearsExperience, df4['Salary'],color='blue')\n",
    "plt.scatter(df5.YearsExperience, df5['Salary'],color='purple')\n",
    "plt.xlabel('YearsExperience')\n",
    "plt.ylabel('Salary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f81w__fxsbdp"
   },
   "source": [
    "## Normalization and then again check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "D6H77wGlEnCz",
    "outputId": "8d876e6a-3533-4ae3-effa-684345613a62"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015845</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.083296</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.072581</td>\n",
       "      <td>0.056952</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.088710</td>\n",
       "      <td>0.021232</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.145161</td>\n",
       "      <td>0.185887</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.153226</td>\n",
       "      <td>0.220369</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.169355</td>\n",
       "      <td>0.164291</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.169355</td>\n",
       "      <td>0.262587</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.209677</td>\n",
       "      <td>0.191263</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.250526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.233871</td>\n",
       "      <td>0.177551</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.233871</td>\n",
       "      <td>0.188983</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.241935</td>\n",
       "      <td>0.190202</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.274194</td>\n",
       "      <td>0.229815</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.306452</td>\n",
       "      <td>0.296921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.278157</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.338710</td>\n",
       "      <td>0.445839</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.387097</td>\n",
       "      <td>0.428883</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.395161</td>\n",
       "      <td>0.552509</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.459677</td>\n",
       "      <td>0.530865</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.595101</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.624875</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.572581</td>\n",
       "      <td>0.747842</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.704779</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.637097</td>\n",
       "      <td>0.666945</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.778874</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.685484</td>\n",
       "      <td>0.736273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.832170</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.758065</td>\n",
       "      <td>0.827069</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.814516</td>\n",
       "      <td>0.880866</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.875076</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.894824</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.951613</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary  cluster\n",
       "0          0.000000  0.015845        4\n",
       "1          0.016129  0.083296        4\n",
       "2          0.032258  0.000000        4\n",
       "3          0.072581  0.056952        4\n",
       "4          0.088710  0.021232        4\n",
       "5          0.145161  0.185887        0\n",
       "6          0.153226  0.220369        0\n",
       "7          0.169355  0.164291        0\n",
       "8          0.169355  0.262587        0\n",
       "9          0.209677  0.191263        0\n",
       "10         0.225806  0.250526        0\n",
       "11         0.233871  0.177551        0\n",
       "12         0.233871  0.188983        0\n",
       "13         0.241935  0.190202        0\n",
       "14         0.274194  0.229815        0\n",
       "15         0.306452  0.296921        0\n",
       "16         0.322581  0.278157        0\n",
       "17         0.338710  0.445839        2\n",
       "18         0.387097  0.428883        2\n",
       "19         0.395161  0.552509        2\n",
       "20         0.459677  0.530865        2\n",
       "21         0.483871  0.595101        2\n",
       "22         0.548387  0.624875        2\n",
       "23         0.572581  0.747842        1\n",
       "24         0.612903  0.704779        1\n",
       "25         0.637097  0.666945        1\n",
       "26         0.677419  0.778874        1\n",
       "27         0.685484  0.736273        1\n",
       "28         0.741935  0.832170        3\n",
       "29         0.758065  0.827069        3\n",
       "30         0.814516  0.880866        3\n",
       "31         0.838710  0.875076        3\n",
       "32         0.903226  0.894824        3\n",
       "33         0.951613  0.962746        3\n",
       "34         1.000000  1.000000        3"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=MinMaxScaler()\n",
    "scaler.fit(df[['Salary']])\n",
    "df['Salary']=scaler.transform(df[['Salary']])\n",
    "scaler.fit(df[['YearsExperience']])\n",
    "df['YearsExperience']=scaler.transform(df[['YearsExperience']])\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0s7kGdAGEnCz",
    "outputId": "07278e7f-cfa7-4121-962f-71f7c9daaac7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.24599877930393588\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.21469464378831662\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 0.21469464378831662\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.24976734481600707\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.2340310975743367\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 0.21368181551259902\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 3, inertia 0.21368181551259902\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.276907403808879\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.24463440390283883\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 0.2154232730708973\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 3, inertia 0.2154232730708973\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.21469464378831662\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.21469464378831662\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.24463440390283883\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.2154232730708973\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 0.2154232730708973\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.2154232730708973\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.2154232730708973\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.2154232730708973\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.2154232730708973\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.4307209532375872\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.4233023757955806\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 0.4233023757955806\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.2608751054662088\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.21469464378831662\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 0.21469464378831662\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n",
      "Initialization complete\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 0, inertia 0.2451278190383588\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 1, inertia 0.21368181551259902\n",
      "start iteration\n",
      "done sorting\n",
      "end inner loop\n",
      "Iteration 2, inertia 0.21368181551259902\n",
      "center shift 0.000000e+00 within tolerance 8.990979e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2,\n",
       "       2, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4], dtype=int32)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km=KMeans(n_clusters=5, init='k-means++',max_iter=100,verbose=True)\n",
    "y_predicted=km.fit_predict(df[['YearsExperience','Salary']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "NSLvhEcuEnCz",
    "outputId": "63aae2f7-f85e-48de-9625-d5dcd1c0ff24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1=df[df.cluster==0]\n",
    "df2=df[df.cluster==1]\n",
    "df3=df[df.cluster==2]\n",
    "df4=df[df.cluster==3]\n",
    "df5=df[df.cluster==4]\n",
    "plt.scatter(df1.YearsExperience, df1['Salary'],color='green')\n",
    "plt.scatter(df2.YearsExperience, df2['Salary'],color='red')\n",
    "plt.scatter(df3.YearsExperience, df3['Salary'],color='black')\n",
    "plt.scatter(df4.YearsExperience, df4['Salary'],color='blue')\n",
    "plt.scatter(df5.YearsExperience, df5['Salary'],color='purple')\n",
    "plt.xlabel('YearsExperience')\n",
    "plt.ylabel('Salary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0XcrjJDtsnhX"
   },
   "source": [
    "## Cluster Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bJ3J8xpSEnC0",
    "outputId": "e7c176fb-93f3-47a5-a4ab-599c1b9849d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22379032, 0.21971268],\n",
       "       [0.66935484, 0.75627898],\n",
       "       [0.43548387, 0.5296787 ],\n",
       "       [0.04193548, 0.03546504],\n",
       "       [0.9016129 , 0.92270234]])"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "qXxAiISrEnC0",
    "outputId": "61732b04-0857-4436-9c72-e8b5616ceb08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9eb3b23650>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1=df[df.cluster==0]\n",
    "df2=df[df.cluster==1]\n",
    "df3=df[df.cluster==2]\n",
    "df4=df[df.cluster==3]\n",
    "df5=df[df.cluster==4]\n",
    "plt.scatter(df1.YearsExperience, df1['Salary'],color='green')\n",
    "plt.scatter(df2.YearsExperience, df2['Salary'],color='red')\n",
    "plt.scatter(df3.YearsExperience, df3['Salary'],color='black')\n",
    "plt.scatter(df4.YearsExperience, df4['Salary'],color='blue')\n",
    "plt.scatter(df5.YearsExperience, df5['Salary'],color='purple')\n",
    "plt.xlabel('YearsExperience')\n",
    "plt.ylabel('Salary')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='yellow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EvZ0OYekstVb"
   },
   "source": [
    "## Sum of Square Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "EDenEChVEnC0"
   },
   "outputs": [],
   "source": [
    "k_rng=range(1,10)\n",
    "sse=[]\n",
    "for k in k_rng:\n",
    "    km=KMeans(n_clusters=k,init='k-means++')\n",
    "    km.fit(df[['YearsExperience','Salary']])\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JXPVlnE-EnC1",
    "outputId": "2bd21e27-d926-4b71-c6a5-bef652c4d04e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.293685484541874,\n",
       " 1.292690919141886,\n",
       " 0.6527227565513628,\n",
       " 0.4101141952961812,\n",
       " 0.21368181551259902,\n",
       " 0.15226682764323596,\n",
       " 0.12182725552641988,\n",
       " 0.08849338117507208,\n",
       " 0.0759704313741079]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N0ED_rACs4b8"
   },
   "source": [
    "## Plot the SSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "jxbEV7RoEnC1",
    "outputId": "5a4bb2c7-260e-4d38-9437-75213cf0abb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ea9a34e10>]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('k')\n",
    "plt.ylabel('sum of square error')\n",
    "plt.plot(k_rng,sse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8na1sMNvEnC3",
    "outputId": "2a9b215e-5140-40dd-b8a2-9fec0cc7cf97"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['K-means++_model.pkl']"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(km,'K-means++_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fv1I8H1mq53L"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Unsupervised Learning .ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}