jupyter notebook/CLASSIFICATION_THROUGH_SKILLS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification through skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "            ID  Category         0         1         2         3         4  \\\n",
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       "1            2        HR -0.696925 -0.054160  0.214620  0.141425 -0.232469   \n",
       "2            3        HR -0.664720  0.177237  0.218054 -0.170673 -0.074760   \n",
       "3            4        HR -0.718947  0.115111  0.168066 -0.063626  0.069027   \n",
       "4            5        HR -0.689963  0.155193  0.132712 -0.184576 -0.053019   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
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       "\n",
       "             5         6         7  ...       290       291       292  \\\n",
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       "...        ...       ...       ...  ...       ...       ...       ...   \n",
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       "3531  0.076339  0.032394  0.018534  ... -0.433505  0.271037 -0.030667   \n",
       "\n",
       "           293       294       295       296       297       298       299  \n",
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       "1     0.544004 -0.318300 -0.092770  0.115245  0.156241  0.258390 -0.508145  \n",
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       "4     0.284246 -0.196378 -0.172525  0.105732  0.009684  0.253444 -0.098125  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "3527  0.194559 -0.257270 -0.221437  0.007480 -0.007909  0.245887 -0.128840  \n",
       "3528  0.169673 -0.119322 -0.054518  0.060938  0.142400  0.180208 -0.158444  \n",
       "3529  0.178224 -0.303597  0.068539  0.245716 -0.039387 -0.039181 -0.018406  \n",
       "3530  0.267443 -0.080555  0.024349  0.122349  0.011404  0.034929 -0.027530  \n",
       "3531  0.007548 -0.133162 -0.204937  0.171209 -0.141996  0.065774  0.047269  \n",
       "\n",
       "[3532 rows x 302 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resume_skills = pd.read_csv('Skills vector.csv')\n",
    "resume_skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = resume_skills.drop(columns = ['ID','Category']).to_numpy()\n",
    "y = resume_skills['Category'] # Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30,)\n"
     ]
    }
   ],
   "source": [
    "nums_of_category = y.drop_duplicates().shape # numbers of different category, k = 30\n",
    "print(nums_of_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>0.685049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.267382</td>\n",
       "      <td>0.959621</td>\n",
       "      <td>0.777337</td>\n",
       "      <td>0.715784</td>\n",
       "      <td>0.185091</td>\n",
       "      <td>0.581530</td>\n",
       "      <td>0.578921</td>\n",
       "      <td>0.718273</td>\n",
       "      <td>0.704854</td>\n",
       "      <td>0.192795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.861479</td>\n",
       "      <td>0.541162</td>\n",
       "      <td>0.721367</td>\n",
       "      <td>0.467990</td>\n",
       "      <td>0.407732</td>\n",
       "      <td>0.354331</td>\n",
       "      <td>0.420778</td>\n",
       "      <td>0.584636</td>\n",
       "      <td>0.459023</td>\n",
       "      <td>0.721114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368847</td>\n",
       "      <td>0.404932</td>\n",
       "      <td>0.617955</td>\n",
       "      <td>0.605731</td>\n",
       "      <td>0.362737</td>\n",
       "      <td>0.651051</td>\n",
       "      <td>0.520688</td>\n",
       "      <td>0.668759</td>\n",
       "      <td>0.637850</td>\n",
       "      <td>0.318007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.741506</td>\n",
       "      <td>0.509410</td>\n",
       "      <td>0.681711</td>\n",
       "      <td>0.537723</td>\n",
       "      <td>0.499636</td>\n",
       "      <td>0.458076</td>\n",
       "      <td>0.639391</td>\n",
       "      <td>0.473331</td>\n",
       "      <td>0.595121</td>\n",
       "      <td>0.432096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438717</td>\n",
       "      <td>0.381878</td>\n",
       "      <td>0.555565</td>\n",
       "      <td>0.603146</td>\n",
       "      <td>0.428407</td>\n",
       "      <td>0.579159</td>\n",
       "      <td>0.626047</td>\n",
       "      <td>0.667511</td>\n",
       "      <td>0.542401</td>\n",
       "      <td>0.591484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.805630</td>\n",
       "      <td>0.529895</td>\n",
       "      <td>0.653664</td>\n",
       "      <td>0.458934</td>\n",
       "      <td>0.421628</td>\n",
       "      <td>0.299958</td>\n",
       "      <td>0.263701</td>\n",
       "      <td>0.560791</td>\n",
       "      <td>0.495963</td>\n",
       "      <td>0.767454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277251</td>\n",
       "      <td>0.466229</td>\n",
       "      <td>0.680784</td>\n",
       "      <td>0.599940</td>\n",
       "      <td>0.279362</td>\n",
       "      <td>0.542106</td>\n",
       "      <td>0.572362</td>\n",
       "      <td>0.624773</td>\n",
       "      <td>0.701708</td>\n",
       "      <td>0.466224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.760120  0.412646  0.600954  0.505179  0.376098  0.397528  0.576088   \n",
       "1  0.790228  0.422897  0.718643  0.671297  0.306929  0.484956  0.407226   \n",
       "2  0.861479  0.541162  0.721367  0.467990  0.407732  0.354331  0.420778   \n",
       "3  0.741506  0.509410  0.681711  0.537723  0.499636  0.458076  0.639391   \n",
       "4  0.805630  0.529895  0.653664  0.458934  0.421628  0.299958  0.263701   \n",
       "\n",
       "          7         8         9  ...       290       291       292       293  \\\n",
       "0  0.463267  0.500353  0.704016  ...  0.412833  0.591439  0.664545  0.524688   \n",
       "1  0.501970  0.612268  0.685049  ...  0.267382  0.959621  0.777337  0.715784   \n",
       "2  0.584636  0.459023  0.721114  ...  0.368847  0.404932  0.617955  0.605731   \n",
       "3  0.473331  0.595121  0.432096  ...  0.438717  0.381878  0.555565  0.603146   \n",
       "4  0.560791  0.495963  0.767454  ...  0.277251  0.466229  0.680784  0.599940   \n",
       "\n",
       "        294       295       296       297       298       299  \n",
       "0  0.586499  0.596106  0.615107  0.620191  0.564076  0.497881  \n",
       "1  0.185091  0.581530  0.578921  0.718273  0.704854  0.192795  \n",
       "2  0.362737  0.651051  0.520688  0.668759  0.637850  0.318007  \n",
       "3  0.428407  0.579159  0.626047  0.667511  0.542401  0.591484  \n",
       "4  0.279362  0.542106  0.572362  0.624773  0.701708  0.466224  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn import preprocessing\n",
    "import sklearn.cluster as cluster\n",
    "import sklearn.metrics as metrics\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "scale = scaler.fit_transform(resume_skills[resume_skills.columns[2:302]])\n",
    "\n",
    "df_scale = pd.DataFrame(scale, columns = [str(x) for x in range(300)]);\n",
    "df_scale.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.059474</td>\n",
       "      <td>0.108919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.273253</td>\n",
       "      <td>-0.316807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.421690</td>\n",
       "      <td>-0.498985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.826598</td>\n",
       "      <td>-0.282538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.100464</td>\n",
       "      <td>-0.278690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2\n",
       "0               0.059474               0.108919\n",
       "1               1.273253              -0.316807\n",
       "2              -0.421690              -0.498985\n",
       "3              -0.826598              -0.282538\n",
       "4              -0.100464              -0.278690"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(df_scale)\n",
    "pca_df = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Elbow Method with Within-Cluster-Sum of Squared Error (WCSS)](https://en.wikipedia.org/wiki/Elbow_method_(clustering))\n",
    "> In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use. The same method can be used to choose the number of parameters in other data-driven models, such as the number of principal components to describe a data set.\n",
    "\n",
    "> In the [Elbow method](https://www.analyticsvidhya.com/blog/2021/01/in-depth-intuition-of-k-means-clustering-algorithm-in-machine-learning/#:~:text=Elbow%20Method,-In%20the%20Elbow&text=WCSS%20is%20the%20sum%20of,is%20largest%20when%20K%20%3D%201.), we are actually varying the number of clusters ( K ) from 1 – 10. For each value of K, we are calculating WCSS ( Within-Cluster Sum of Square ). WCSS is the sum of squared distance between each point and the centroid in a cluster. When we plot the WCSS with the K value, the plot looks like an Elbow. As the number of clusters increases, the WCSS value will start to decrease. WCSS value is largest when K = 1. When we analyze the graph we can see that the graph will rapidly change at a point and thus creating an elbow shape. From this point, the graph starts to move almost parallel to the X-axis. The K value corresponding to this point is the optimal K value or an optimal number of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18e4291dd30>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "K=range(25,37)\n",
    "wss = []\n",
    "for k in K:\n",
    "    kmeans=cluster.KMeans(n_clusters=k)\n",
    "    kmeans=kmeans.fit(pca_df)\n",
    "    wss_iter = kmeans.inertia_\n",
    "    wss.append(wss_iter)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Within-Cluster-Sum of Squared Errors (WSS)')\n",
    "plt.plot(K,wss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Silhouette (clustering)](https://en.wikipedia.org/wiki/Silhouette_(clustering)) \n",
    "\n",
    "> Silhouette refers to a method of interpretation and validation of consistency within clusters of data. The technique provides a succinct graphical representation of how well each object has been classified.[1] It was proposed by Belgian statistician Peter Rousseeuw in 1987. <br />\n",
    "The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters.<br />\n",
    "The silhouette can be calculated with any distance metric, such as the Euclidean distance or the Manhattan distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score for k(clusters) = 25 is 0.7753639949230402\n",
      "Silhouette score for k(clusters) = 26 is 0.7909724333629772\n",
      "Silhouette score for k(clusters) = 27 is 0.7968354375612499\n",
      "Silhouette score for k(clusters) = 28 is 0.8198890366619458\n",
      "Silhouette score for k(clusters) = 29 is 0.8291560667169837\n",
      "Silhouette score for k(clusters) = 30 is 0.8317569929647527\n",
      "Silhouette score for k(clusters) = 31 is 0.8000806656445087\n",
      "Silhouette score for k(clusters) = 32 is 0.8101449914065367\n",
      "Silhouette score for k(clusters) = 33 is 0.8040511688207421\n",
      "Silhouette score for k(clusters) = 34 is 0.7990580719483387\n",
      "Silhouette score for k(clusters) = 35 is 0.7764429969867577\n",
      "Silhouette score for k(clusters) = 36 is 0.7591615559365332\n"
     ]
    }
   ],
   "source": [
    "import sklearn.cluster as cluster\n",
    "import sklearn.metrics as metrics\n",
    "for i in range(25,37):\n",
    "    labels=cluster.KMeans(n_clusters=i,random_state=200).fit(pca_df).labels_\n",
    "    print (\"Silhouette score for k(clusters) = \"+str(i)+\" is \"\n",
    "           +str(metrics.silhouette_score(pca_df,labels,metric=\"euclidean\",sample_size=1000,random_state=200)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=30)\n",
    "kmeans = kmeans.fit(pca_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='principal component 1', ylabel='principal component 2'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_df['Clusters'] = kmeans.labels_\n",
    "\n",
    "sns.scatterplot(x=\"principal component 1\", y=\"principal component 2\",hue = 'Clusters',  data=pca_df,palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>558</td>\n",
       "      <td>AGRICULTURE</td>\n",
       "      <td>-0.655780</td>\n",
       "      <td>0.143012</td>\n",
       "      <td>0.184913</td>\n",
       "      <td>-0.275173</td>\n",
       "      <td>-0.137315</td>\n",
       "      <td>-0.063996</td>\n",
       "      <td>-0.108617</td>\n",
       "      <td>-0.016094</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.500930</td>\n",
       "      <td>0.073217</td>\n",
       "      <td>0.058121</td>\n",
       "      <td>0.345650</td>\n",
       "      <td>-0.003107</td>\n",
       "      <td>0.044188</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.300713</td>\n",
       "      <td>0.205721</td>\n",
       "      <td>-0.435437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>559</td>\n",
       "      <td>AGRICULTURE</td>\n",
       "      <td>-0.667090</td>\n",
       "      <td>0.091262</td>\n",
       "      <td>0.310103</td>\n",
       "      <td>-0.324840</td>\n",
       "      <td>0.029549</td>\n",
       "      <td>-0.089255</td>\n",
       "      <td>-0.194463</td>\n",
       "      <td>0.053524</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.849813</td>\n",
       "      <td>0.199300</td>\n",
       "      <td>0.052646</td>\n",
       "      <td>0.331516</td>\n",
       "      <td>-0.178100</td>\n",
       "      <td>-0.069893</td>\n",
       "      <td>0.073340</td>\n",
       "      <td>0.272487</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>-0.436403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>560</td>\n",
       "      <td>AGRICULTURE</td>\n",
       "      <td>-0.659420</td>\n",
       "      <td>0.048198</td>\n",
       "      <td>0.345900</td>\n",
       "      <td>-0.570230</td>\n",
       "      <td>0.090037</td>\n",
       "      <td>-0.051197</td>\n",
       "      <td>-0.230580</td>\n",
       "      <td>0.003736</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.858450</td>\n",
       "      <td>-0.211400</td>\n",
       "      <td>0.067577</td>\n",
       "      <td>0.543920</td>\n",
       "      <td>-0.217990</td>\n",
       "      <td>0.112600</td>\n",
       "      <td>0.157140</td>\n",
       "      <td>0.440050</td>\n",
       "      <td>0.342490</td>\n",
       "      <td>-0.453590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>561</td>\n",
       "      <td>AGRICULTURE</td>\n",
       "      <td>-0.750810</td>\n",
       "      <td>-0.022794</td>\n",
       "      <td>-0.178900</td>\n",
       "      <td>-0.006871</td>\n",
       "      <td>-0.518410</td>\n",
       "      <td>-0.101670</td>\n",
       "      <td>0.118010</td>\n",
       "      <td>0.658580</td>\n",
       "      <td>...</td>\n",
       "      <td>1.088600</td>\n",
       "      <td>0.262360</td>\n",
       "      <td>0.226230</td>\n",
       "      <td>-0.161500</td>\n",
       "      <td>-0.281000</td>\n",
       "      <td>-0.232460</td>\n",
       "      <td>0.093438</td>\n",
       "      <td>0.405720</td>\n",
       "      <td>-0.203180</td>\n",
       "      <td>-0.456840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>564</td>\n",
       "      <td>AGRICULTURE</td>\n",
       "      <td>-0.648500</td>\n",
       "      <td>0.332640</td>\n",
       "      <td>-0.137060</td>\n",
       "      <td>0.314940</td>\n",
       "      <td>-0.592020</td>\n",
       "      <td>-0.089594</td>\n",
       "      <td>0.135310</td>\n",
       "      <td>-0.055752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214110</td>\n",
       "      <td>0.642450</td>\n",
       "      <td>0.039209</td>\n",
       "      <td>-0.050890</td>\n",
       "      <td>0.426660</td>\n",
       "      <td>-0.092637</td>\n",
       "      <td>-0.220700</td>\n",
       "      <td>0.022039</td>\n",
       "      <td>-0.067818</td>\n",
       "      <td>-0.399130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>12085736</td>\n",
       "      <td>DIGITAL-MEDIA</td>\n",
       "      <td>-0.712673</td>\n",
       "      <td>0.238975</td>\n",
       "      <td>0.046997</td>\n",
       "      <td>-0.103429</td>\n",
       "      <td>-0.205531</td>\n",
       "      <td>0.055937</td>\n",
       "      <td>0.104965</td>\n",
       "      <td>-0.088966</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.332382</td>\n",
       "      <td>0.125692</td>\n",
       "      <td>0.168534</td>\n",
       "      <td>0.225448</td>\n",
       "      <td>0.119206</td>\n",
       "      <td>-0.147494</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>0.059746</td>\n",
       "      <td>0.058457</td>\n",
       "      <td>0.145000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>20210676</td>\n",
       "      <td>DIGITAL-MEDIA</td>\n",
       "      <td>-0.716653</td>\n",
       "      <td>0.282512</td>\n",
       "      <td>0.074865</td>\n",
       "      <td>-0.004772</td>\n",
       "      <td>-0.225644</td>\n",
       "      <td>0.123147</td>\n",
       "      <td>0.090973</td>\n",
       "      <td>-0.180081</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.439313</td>\n",
       "      <td>0.074165</td>\n",
       "      <td>0.031870</td>\n",
       "      <td>0.194257</td>\n",
       "      <td>0.054352</td>\n",
       "      <td>-0.149167</td>\n",
       "      <td>-0.064347</td>\n",
       "      <td>0.007942</td>\n",
       "      <td>0.090825</td>\n",
       "      <td>-0.085955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>22706174</td>\n",
       "      <td>DIGITAL-MEDIA</td>\n",
       "      <td>-0.729209</td>\n",
       "      <td>0.462759</td>\n",
       "      <td>-0.068892</td>\n",
       "      <td>-0.068329</td>\n",
       "      <td>-0.240570</td>\n",
       "      <td>0.047197</td>\n",
       "      <td>0.139965</td>\n",
       "      <td>-0.149206</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.371191</td>\n",
       "      <td>0.088664</td>\n",
       "      <td>0.128106</td>\n",
       "      <td>0.248867</td>\n",
       "      <td>0.068333</td>\n",
       "      <td>-0.287904</td>\n",
       "      <td>-0.075675</td>\n",
       "      <td>-0.152608</td>\n",
       "      <td>0.099766</td>\n",
       "      <td>0.121491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>18354623</td>\n",
       "      <td>DIGITAL-MEDIA</td>\n",
       "      <td>-0.727891</td>\n",
       "      <td>0.188900</td>\n",
       "      <td>0.023002</td>\n",
       "      <td>-0.005946</td>\n",
       "      <td>-0.305524</td>\n",
       "      <td>0.122977</td>\n",
       "      <td>0.152952</td>\n",
       "      <td>-0.059270</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.281506</td>\n",
       "      <td>0.058397</td>\n",
       "      <td>0.100979</td>\n",
       "      <td>0.230364</td>\n",
       "      <td>0.074011</td>\n",
       "      <td>-0.280572</td>\n",
       "      <td>-0.051087</td>\n",
       "      <td>-0.087045</td>\n",
       "      <td>-0.012822</td>\n",
       "      <td>0.262501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>17432318</td>\n",
       "      <td>DIGITAL-MEDIA</td>\n",
       "      <td>-0.714240</td>\n",
       "      <td>0.326376</td>\n",
       "      <td>0.132435</td>\n",
       "      <td>0.037071</td>\n",
       "      <td>-0.214953</td>\n",
       "      <td>0.124529</td>\n",
       "      <td>0.091589</td>\n",
       "      <td>0.013675</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.552083</td>\n",
       "      <td>0.408242</td>\n",
       "      <td>0.064199</td>\n",
       "      <td>0.233853</td>\n",
       "      <td>0.040334</td>\n",
       "      <td>-0.339564</td>\n",
       "      <td>-0.160596</td>\n",
       "      <td>-0.085599</td>\n",
       "      <td>0.077552</td>\n",
       "      <td>0.078935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 302 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID       Category         0         1         2         3  \\\n",
       "0         558    AGRICULTURE -0.655780  0.143012  0.184913 -0.275173   \n",
       "1         559    AGRICULTURE -0.667090  0.091262  0.310103 -0.324840   \n",
       "2         560    AGRICULTURE -0.659420  0.048198  0.345900 -0.570230   \n",
       "3         561    AGRICULTURE -0.750810 -0.022794 -0.178900 -0.006871   \n",
       "4         564    AGRICULTURE -0.648500  0.332640 -0.137060  0.314940   \n",
       "..        ...            ...       ...       ...       ...       ...   \n",
       "214  12085736  DIGITAL-MEDIA -0.712673  0.238975  0.046997 -0.103429   \n",
       "215  20210676  DIGITAL-MEDIA -0.716653  0.282512  0.074865 -0.004772   \n",
       "216  22706174  DIGITAL-MEDIA -0.729209  0.462759 -0.068892 -0.068329   \n",
       "217  18354623  DIGITAL-MEDIA -0.727891  0.188900  0.023002 -0.005946   \n",
       "218  17432318  DIGITAL-MEDIA -0.714240  0.326376  0.132435  0.037071   \n",
       "\n",
       "            4         5         6         7  ...       290       291  \\\n",
       "0   -0.137315 -0.063996 -0.108617 -0.016094  ... -0.500930  0.073217   \n",
       "1    0.029549 -0.089255 -0.194463  0.053524  ... -0.849813  0.199300   \n",
       "2    0.090037 -0.051197 -0.230580  0.003736  ... -0.858450 -0.211400   \n",
       "3   -0.518410 -0.101670  0.118010  0.658580  ...  1.088600  0.262360   \n",
       "4   -0.592020 -0.089594  0.135310 -0.055752  ...  0.214110  0.642450   \n",
       "..        ...       ...       ...       ...  ...       ...       ...   \n",
       "214 -0.205531  0.055937  0.104965 -0.088966  ... -0.332382  0.125692   \n",
       "215 -0.225644  0.123147  0.090973 -0.180081  ... -0.439313  0.074165   \n",
       "216 -0.240570  0.047197  0.139965 -0.149206  ... -0.371191  0.088664   \n",
       "217 -0.305524  0.122977  0.152952 -0.059270  ... -0.281506  0.058397   \n",
       "218 -0.214953  0.124529  0.091589  0.013675  ... -0.552083  0.408242   \n",
       "\n",
       "          292       293       294       295       296       297       298  \\\n",
       "0    0.058121  0.345650 -0.003107  0.044188  0.031193  0.300713  0.205721   \n",
       "1    0.052646  0.331516 -0.178100 -0.069893  0.073340  0.272487  0.279820   \n",
       "2    0.067577  0.543920 -0.217990  0.112600  0.157140  0.440050  0.342490   \n",
       "3    0.226230 -0.161500 -0.281000 -0.232460  0.093438  0.405720 -0.203180   \n",
       "4    0.039209 -0.050890  0.426660 -0.092637 -0.220700  0.022039 -0.067818   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "214  0.168534  0.225448  0.119206 -0.147494  0.032891  0.059746  0.058457   \n",
       "215  0.031870  0.194257  0.054352 -0.149167 -0.064347  0.007942  0.090825   \n",
       "216  0.128106  0.248867  0.068333 -0.287904 -0.075675 -0.152608  0.099766   \n",
       "217  0.100979  0.230364  0.074011 -0.280572 -0.051087 -0.087045 -0.012822   \n",
       "218  0.064199  0.233853  0.040334 -0.339564 -0.160596 -0.085599  0.077552   \n",
       "\n",
       "          299  \n",
       "0   -0.435437  \n",
       "1   -0.436403  \n",
       "2   -0.453590  \n",
       "3   -0.456840  \n",
       "4   -0.399130  \n",
       "..        ...  \n",
       "214  0.145000  \n",
       "215 -0.085955  \n",
       "216  0.121491  \n",
       "217  0.262501  \n",
       "218  0.078935  \n",
       "\n",
       "[219 rows x 302 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agriculture = resume_skills[resume_skills[\"Category\"] == 'AGRICULTURE']\n",
    "digital_media = resume_skills[resume_skills[\"Category\"] == 'DIGITAL-MEDIA']\n",
    "combine = pd.concat([agriculture, digital_media], ignore_index=True, sort=False)\n",
    "combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = combine.drop(columns = ['ID','Category']).to_numpy()\n",
    "yc = combine['Category'] # Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tkm = KMeans(n_clusters=2,random_state = 0)\n",
    "ty_km = tkm.fit_predict(Xc)\n",
    "ty_km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the centroids\n",
    "plt.scatter(\n",
    "    tkm.cluster_centers_[:, 0], tkm.cluster_centers_[:, 1],\n",
    "    s=250, marker='*',\n",
    "    c='red', edgecolor='black',\n",
    "    label='centroids'\n",
    ")\n",
    "plt.legend(scatterpoints=1)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "557     ['monitoring', 'testing', 'software']\n",
      "558    ['monitoring', 'software', 'business']\n",
      "559                ['monitoring', 'software']\n",
      "560                             ['Zookeeper']\n",
      "563                               ['testing']\n",
      "564    ['monitoring', 'software', 'arborist']\n",
      "566                               ['testing']\n",
      "567                ['monitoring', 'software']\n",
      "568                              ['software']\n",
      "560                             ['Zookeeper']\n",
      "Name: skills, dtype: object\n",
      "2501    ['collaboration', 'iOS', 'Collaboration', 'sup...\n",
      "2502    ['workflow', 'support', 'Customer relationship...\n",
      "2503    ['support', 'Spring', 'Software', 'computer ha...\n",
      "2504    ['Twitter', 'design', 'Business', 'Communicati...\n",
      "2505    ['MARKETING', 'Marketing', 'business', 'Softwa...\n",
      "2506    ['Search Engine', 'support', 'MARKETING', 'mar...\n",
      "2507    ['support', 'communications', 'Mobile', 'CRM',...\n",
      "Name: skills, dtype: object\n"
     ]
    }
   ],
   "source": [
    "skills = pd.read_csv('Extract skills.csv')\n",
    "print(skills['skills'].loc[[557,558,559,560,563,564,566,567,568,560]])\n",
    "print(skills['skills'].loc[[2501,2502,2503,2504,2505,2506,2507]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.935164</td>\n",
       "      <td>0.505701</td>\n",
       "      <td>0.642731</td>\n",
       "      <td>0.350104</td>\n",
       "      <td>0.573685</td>\n",
       "      <td>0.386565</td>\n",
       "      <td>0.212578</td>\n",
       "      <td>0.521148</td>\n",
       "      <td>0.578238</td>\n",
       "      <td>0.675931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204220</td>\n",
       "      <td>0.384271</td>\n",
       "      <td>0.432873</td>\n",
       "      <td>0.593672</td>\n",
       "      <td>0.500515</td>\n",
       "      <td>0.667924</td>\n",
       "      <td>0.584490</td>\n",
       "      <td>0.834357</td>\n",
       "      <td>0.675762</td>\n",
       "      <td>0.127942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.892066</td>\n",
       "      <td>0.478255</td>\n",
       "      <td>0.840084</td>\n",
       "      <td>0.304199</td>\n",
       "      <td>0.740065</td>\n",
       "      <td>0.355403</td>\n",
       "      <td>0.089631</td>\n",
       "      <td>0.570559</td>\n",
       "      <td>0.536311</td>\n",
       "      <td>0.746034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029556</td>\n",
       "      <td>0.466207</td>\n",
       "      <td>0.423845</td>\n",
       "      <td>0.577126</td>\n",
       "      <td>0.306908</td>\n",
       "      <td>0.558274</td>\n",
       "      <td>0.638556</td>\n",
       "      <td>0.800803</td>\n",
       "      <td>0.748328</td>\n",
       "      <td>0.127010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.921293</td>\n",
       "      <td>0.455415</td>\n",
       "      <td>0.896515</td>\n",
       "      <td>0.077394</td>\n",
       "      <td>0.800378</td>\n",
       "      <td>0.402355</td>\n",
       "      <td>0.037905</td>\n",
       "      <td>0.535222</td>\n",
       "      <td>0.367357</td>\n",
       "      <td>0.619051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025232</td>\n",
       "      <td>0.199311</td>\n",
       "      <td>0.448464</td>\n",
       "      <td>0.825767</td>\n",
       "      <td>0.262774</td>\n",
       "      <td>0.733678</td>\n",
       "      <td>0.746055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.809701</td>\n",
       "      <td>0.110440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.573048</td>\n",
       "      <td>0.417763</td>\n",
       "      <td>0.069205</td>\n",
       "      <td>0.598085</td>\n",
       "      <td>0.193696</td>\n",
       "      <td>0.340086</td>\n",
       "      <td>0.537148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.507187</td>\n",
       "      <td>0.710048</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.193062</td>\n",
       "      <td>0.402022</td>\n",
       "      <td>0.664338</td>\n",
       "      <td>0.959189</td>\n",
       "      <td>0.275322</td>\n",
       "      <td>0.107307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.962904</td>\n",
       "      <td>0.606274</td>\n",
       "      <td>0.135163</td>\n",
       "      <td>0.895522</td>\n",
       "      <td>0.120300</td>\n",
       "      <td>0.354984</td>\n",
       "      <td>0.561925</td>\n",
       "      <td>0.493000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.789690</td>\n",
       "      <td>...</td>\n",
       "      <td>0.562197</td>\n",
       "      <td>0.754192</td>\n",
       "      <td>0.401691</td>\n",
       "      <td>0.129480</td>\n",
       "      <td>0.975997</td>\n",
       "      <td>0.536414</td>\n",
       "      <td>0.261359</td>\n",
       "      <td>0.503072</td>\n",
       "      <td>0.407883</td>\n",
       "      <td>0.162945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.935164  0.505701  0.642731  0.350104  0.573685  0.386565  0.212578   \n",
       "1  0.892066  0.478255  0.840084  0.304199  0.740065  0.355403  0.089631   \n",
       "2  0.921293  0.455415  0.896515  0.077394  0.800378  0.402355  0.037905   \n",
       "3  0.573048  0.417763  0.069205  0.598085  0.193696  0.340086  0.537148   \n",
       "4  0.962904  0.606274  0.135163  0.895522  0.120300  0.354984  0.561925   \n",
       "\n",
       "          7         8         9  ...       290       291       292       293  \\\n",
       "0  0.521148  0.578238  0.675931  ...  0.204220  0.384271  0.432873  0.593672   \n",
       "1  0.570559  0.536311  0.746034  ...  0.029556  0.466207  0.423845  0.577126   \n",
       "2  0.535222  0.367357  0.619051  ...  0.025232  0.199311  0.448464  0.825767   \n",
       "3  1.000000  0.029176  0.000000  ...  1.000000  0.507187  0.710048  0.000000   \n",
       "4  0.493000  1.000000  0.789690  ...  0.562197  0.754192  0.401691  0.129480   \n",
       "\n",
       "        294       295       296       297       298       299  \n",
       "0  0.500515  0.667924  0.584490  0.834357  0.675762  0.127942  \n",
       "1  0.306908  0.558274  0.638556  0.800803  0.748328  0.127010  \n",
       "2  0.262774  0.733678  0.746055  1.000000  0.809701  0.110440  \n",
       "3  0.193062  0.402022  0.664338  0.959189  0.275322  0.107307  \n",
       "4  0.975997  0.536414  0.261359  0.503072  0.407883  0.162945  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "scale = scaler.fit_transform(combine[combine.columns[2:302]])\n",
    "\n",
    "df_scale = pd.DataFrame(scale, columns = [str(x) for x in range(300)]);\n",
    "df_scale.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.563994</td>\n",
       "      <td>-0.851295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.018780</td>\n",
       "      <td>-1.599028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.322601</td>\n",
       "      <td>-1.910626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.178681</td>\n",
       "      <td>2.447146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.046781</td>\n",
       "      <td>1.267368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2\n",
       "0               2.563994              -0.851295\n",
       "1               2.018780              -1.599028\n",
       "2               3.322601              -1.910626\n",
       "3               0.178681               2.447146\n",
       "4               1.046781               1.267368"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(df_scale)\n",
    "pca_df = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "K=range(1,5)\n",
    "wss = []\n",
    "for k in K:\n",
    "    kmeans=cluster.KMeans(n_clusters=k)\n",
    "    kmeans=kmeans.fit(pca_df)\n",
    "    wss_iter = kmeans.inertia_\n",
    "    wss.append(wss_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18e40937940>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('K')\n",
    "plt.ylabel('Within-Cluster-Sum of Squared Errors (WSS)')\n",
    "plt.plot(K,wss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score for k(clusters) = 2 is 0.330803825924208\n",
      "Silhouette score for k(clusters) = 3 is 0.346317065516937\n",
      "Silhouette score for k(clusters) = 4 is 0.33979360743998965\n",
      "Silhouette score for k(clusters) = 5 is 0.35453075181807064\n",
      "Silhouette score for k(clusters) = 6 is 0.34461967544375205\n"
     ]
    }
   ],
   "source": [
    "import sklearn.cluster as cluster\n",
    "import sklearn.metrics as metrics\n",
    "for i in range(2,7):\n",
    "    labels=cluster.KMeans(n_clusters=i,random_state=200).fit(pca_df).labels_\n",
    "    print (\"Silhouette score for k(clusters) = \"+str(i)+\" is \"\n",
    "           +str(metrics.silhouette_score(pca_df,labels,metric=\"euclidean\",sample_size=1000,random_state=200)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=5)\n",
    "kmeans = kmeans.fit(pca_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='principal component 1', ylabel='principal component 2'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_df['Clusters'] = kmeans.labels_\n",
    "\n",
    "sns.scatterplot(x=\"principal component 1\", y=\"principal component 2\",hue = 'Clusters',  data=pca_df,palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # Set Train and Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors = 5)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rates = [] # find the optimal k\n",
    "for i in np.arange(1, 40):\n",
    "\n",
    "    new_model = KNeighborsClassifier(n_neighbors = i)\n",
    "\n",
    "    new_model.fit(X_train, y_train)\n",
    "\n",
    "    new_predictions = new_model.predict(X_test)\n",
    "\n",
    "    error_rates.append(np.mean(new_predictions != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(error_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc_K_NN = accuracy_score(predictions, y_test)\n",
    "acc_K_NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=30)\n",
    "knn.fit(X_train, y_train)\n",
    "knn_prediction = knn.predict(X_test)\n",
    "knn_score = knn.score(X_test, y_test)\n",
    "print(\"KNN Classification Test Accuracy: {}%\".format(round(knn.score(X_test,y_test)*100,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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