hw7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework 7: Studying Employee Attrition With K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name: Quan Khuc\n",
    "\n",
    "Student ID: 488453\n",
    "\n",
    "Collaborators:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instructions\n",
    "\n",
    "In this homework, we will be exploring a more realistic application of clustering. It might be helpful to review **Lab 7 (K-Means Clustering)** first. Most of the things we ask you to do in this homework are explained in the lab. In general, you should feel free to import any package that we have previously used in class. Ensure that all plots have the necessary components that a plot should have (e.g. axes labels, a title, a legend).\n",
    "\n",
    "Furthermore, in addition to recording your collaborators on this homework, please also remember to cite/indicate all external sources used when finishing this assignment. This includes peers, TAs, and links to online sources. Note that these citations will not free you from your obligation to submit your _own_ code and write-ups, however, they will be taken into account during the grading and regrading process.\n",
    "\n",
    "### Submission instructions\n",
    "* Submit this python notebook including your answers in the code cells as homework submission.\n",
    "* **Feel free to add as many cells as you need to** — just make sure you don't change what we gave you. \n",
    "* **Does it spark joy?** Note that you will be partially graded on the presentation (_cleanliness, clarity, comments_) of your notebook so make sure you [Marie Kondo](https://lifehacker.com/marie-kondo-is-not-a-verb-1833373654) your notebook before submitting it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. The Initial Centroids Matter!\n",
    "\n",
    "The $k$-means algorithm is sensitive to the initial starting points. In this section, let's explore this. For a toy dataset with some more clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utility.util_hw import load_toy, configure_plots\n",
    "from utility.util_hw import sample_centroids, fit, plot_kmeans\n",
    "\n",
    "# run this cell twice to have pretty plots\n",
    "configure_plots()\n",
    "\n",
    "X, _ = load_toy(500, 5, width=0.07, random_state=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1.1\n",
    "\n",
    "Let's take a quick peek at what the data looks like.\n",
    "\n",
    "**Try this!** Plot the toy data $X$. Make sure that your plot has the necessary components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b0c8e5bfc8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here \n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(X[:,0], X[:,1], label = \"Data Point\")\n",
    "plt.title(\"Toy Data\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1.2\n",
    "\n",
    "**Try this!** Using the functions `sample_centroids`, `fit`, and `plot_kmeans`, experiment with different `random_state`s to see if you can observe different final centroids depending on the initial starting points. Use the data sampled above and produce two plots in the two code cells provided below; one of them should show a reasonabley nice $k$-means solution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here \n",
    "init_centroids = sample_centroids(X, 4, random_state = 3)\n",
    "centroids, assignments = fit(X, 4, initial = init_centroids)\n",
    "plot_kmeans(X, centroids, assignments = assignments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here \n",
    "init_centroids = sample_centroids(X, 5, random_state = 10)\n",
    "centroids, assignments = fit(X, 5, initial = init_centroids)\n",
    "plot_kmeans(X, centroids, assignments = assignments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1.3\n",
    "\n",
    "**Write-up!** What might be causing this to happen? How might we better choose our initial centroids? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    " - When I increased the random_state from 4 to 10, the centroids seem to be more inaccurately spreaded than the centroids generated by a lower random_state. Therefore, it is probably better to pick a low random_state that is closed to the number of clusters. When I decreased the value of random_state that is significantly less than number of clusters (I tried with 5 clusters and random_state was 2), the centroids seem to be inaccurately spreaded. Therefore, I think there is a correlation between number of clusters and random_state so that their values have to be close to one another to avoid inaccuarately spreaded centroids. I think the best spreaded clusters are when the random_state is one less than the number of centroids\n",
    " - In this randomly generated toy dataset and from the plot, I think 5 clusters is the right number of clusters. However, one can argue that the right number of centroids can be different than 5. When I ploted 4 centroids with random_state = 3, the clusters seem accurately spreaded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Using `sklearn` for $k$-means \n",
    "\n",
    "In this section, we will explore the [$k$-means model from `sklearn`](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) and discuss some of the additional features supplied by their implementation. Before we begin, it is suggested that you work through Lab 7 if you haven't already as we will assume familiarity with the terms used there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking Into the Model\n",
    "\n",
    "Now let's create a new $k$-means model and learn about it's interface. In general, you will find that the $k$-means model from `sklearn` shares a lot of the same methods as the other models that we have looked at. However, there are some differences that are notable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "model = KMeans()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2.1\n",
    "\n",
    "**Write-up!** Use the IPython `?` operator to answer the following question: how do you specify the number of clusters you would like to fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use this cell to explore\n",
    "?KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    "I can specify the number of clusters to form as well as the number of centroids to generate by specifying n_clusters to be the number of clusters that I would like to fit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2.2\n",
    "\n",
    "**Write-up!** Use the IPython `?` operator to answer the following questions: How does the model initialize centroids by default? How does it work and why is it better than randomly choosing random starting centroids?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use this cell to explore\n",
    "?KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    "Source: https://www.geeksforgeeks.org/ml-k-means-algorithm/\n",
    " - The function initializes the cluster centers in a smart wat to speed up convergence\n",
    " - The function runs the k-means++ algorithm 10 times to avoid choosing the inaccurately spreaded centers, which is the drawback of the k-means algorithm chooses number of clusters randomly. In other words, the k-mean algorithm may randomly choose centroids that are way too fat from most of the data points, which ultimately results in a centroid that may not have any points associated to it. The function gives us a choice of deciding number of times that the k-means algorithm repeated, if we don't want the default parameter, which is the 10 times.\n",
    " - The k-mean++ algorithm randomly selected a first random centroid from the data points. Then, for each data point, it calculates the distance between the chosen centroid to the data point. Finaly, the algorithm selects the next centroid so that the probabilty of choosing a centroid is directly proportional to its distance to the nearest previously chosen centroid. By selecting centroids in this step-wise method, there will exist centroids that cover \"most extreme\" data points as well as centroids that cover \"more common\" data points.\n",
    " - It is better than choosing random starting centroids because this k-means++ algorithm can mitigate the drawbacks of k-means algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2.3\n",
    "\n",
    "**Write-up!** Use the IPython `?` operator to answer the following questions: How does the model decide that the centroids have converged? Why might we need to adjust this based on our input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use this cell to explore\n",
    "?KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    " - The function has max_iter and tol to have a stop to iterations of k-means algorithm. If the algorithm runs for a selected maximum number of iterations and has its calculated tolerance with regards to Frobenius norm to be less than the selected tolerance, the algorithm stops. \n",
    " - We might need to adjust these parameters when the data contains a lot of data points that are far off from others. If the algorithm stops because of those chosen parameters, the center of clusters may not the means of the points in each cluster. Moreover, labels and cluster centers may not be consistent. This could lead to wrong predictions by using this clustering model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering the Toy Data with `sklearn`\n",
    "\n",
    "Let's try using `sklearn` to cluster our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3.1\n",
    "\n",
    "**Try this!** Create and fit a _new_ `KMeans` model of our data with the default arguments except for `random_state` which should be set to 11. _Be sure to store the fit centroids and assignments in variables for use later._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]),\n",
       " array([ 51,  63, 100,  84,  52,  53,  46,  51], dtype=int64))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here \n",
    "import numpy as np\n",
    "kmeans = KMeans(random_state = 11).fit(X)\n",
    "centroids = kmeans.cluster_centers_\n",
    "assignments = kmeans.labels_\n",
    "np.unique(assignments, return_counts= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write-up!** How many centroids were fit by the model? How many points were assigned to each cluster?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    "There are 8 centroids that were fit by the model. Each cluster has 56, 63, 100, 84, 52, 53, 46, 51 counts respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3.2\n",
    "\n",
    "**Try this!** Create a plot showing the centroids that were produced by the model and the data points colored by their cluster assignment. Be sure to include any necessary plot components and remember that presentation matters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here\n",
    "ax = plt.subplot()\n",
    "ax.scatter(X[:,0], X[:,1], c=assignments)\n",
    "ax.scatter(centroids[:,0], centroids[:,1], s=90, color = 'red')\n",
    "plt.title('Toy DataSet')\n",
    "plt.xlabel('X_0')\n",
    "plt.ylabel('X_1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write-up!** Given this plot, do you think this is a reasonable clustering of the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    "I think a reasonable number of cluster in this plot is 4. Having 8 clusters in this plot is not very reasonable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3.3\n",
    "\n",
    "Consider the Sum of Squared Distances $SSD_j$ as the sum of all points in the $j$th cluster to its corresponsing $$SSD_j = \\sum_{i=1}^{n} \\gamma_{ij} \\;d(x_i,c_j)^2,$$\n",
    "where $\\gamma_{ij}$ is 1 if $x_i$ belongs to cluster $j$ and 0 otherwise. \n",
    "\n",
    "Then, the objective function that $k$-means optimizes is the sum of the $SSD_j$ over all clusters. This means that, we want to find clusters of points that are close to one another. We can estimate how close the cluster points are to one another by measuring how far each point assigned to the cluster is from its center.\n",
    "\n",
    "\n",
    "**Try this!** Use the elbow method described in both lecture and Lab 7 to find a good clustering for our data. Produce a plot that shows the model performance $SSD^{(k)}$ as a function of $k$, where $SSD^{(k)} = \\sum_{j=1}^k SSD_j$.  Make sure to create new models when appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74.80700351426964 0\n",
      "3.5849142395467424 0\n",
      "40.738108023161985 1\n",
      "7.532106338822387 0\n",
      "11.117020578369129 1\n",
      "11.915989632624903 2\n",
      "0.9538151782322116 0\n",
      "4.538729417778954 1\n",
      "5.337698472034727 2\n",
      "6.097007556763145 3\n",
      "0.7593090847284183 0\n",
      "1.622467281787574 1\n",
      "2.421436336043348 2\n",
      "3.37525151427556 3\n",
      "4.151763180359529 4\n",
      "0.7593090847284183 0\n",
      "1.117986811453486 1\n",
      "1.9169558657092602 2\n",
      "2.8707710439414718 3\n",
      "3.511629557481937 4\n",
      "3.7618453306353223 5\n",
      "0.6902534169465372 0\n",
      "1.1830925509498935 1\n",
      "1.9820616052056677 2\n",
      "2.452618275912748 3\n",
      "2.683990351234219 4\n",
      "3.1958304438684864 5\n",
      "3.3815126211601965 6\n",
      "0.5560612280681003 0\n",
      "0.8988535956331407 1\n",
      "1.608144673271185 2\n",
      "1.9242857536564089 3\n",
      "2.248151261065373 4\n",
      "2.594558112390575 5\n",
      "2.8993537285405364 6\n",
      "3.0933960963161673 7\n",
      "0.18875572480605418 0\n",
      "0.7005958174403214 1\n",
      "1.01075145549335 2\n",
      "1.398825400543287 3\n",
      "1.8916645345466434 4\n",
      "2.153214113953759 5\n",
      "2.39241814859036 6\n",
      "2.5934960988616216 7\n",
      "2.8248681741830928 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utility.util_hw import squared_distance\n",
    "\n",
    "# Source: https://www.scivision.dev/matplotlib-force-integer-labeling-of-axis/. I used this source to force the x_axis to only show integers\n",
    "#         https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html. I used this source to set x_axis label\n",
    "#         https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html. I used this source to set y_axis label\n",
    "# your code here \n",
    "def elbow_method(data, k_max):\n",
    "    import matplotlib.pyplot as plt\n",
    "    from matplotlib.ticker import MaxNLocator\n",
    "    distances = []\n",
    "    for k in range(1,k_max):\n",
    "        kmeans = KMeans(n_clusters=k).fit(data) #rebuild the model for each value of k and consequentially recalculate assignments and centroids according to k\n",
    "        assignments = kmeans.labels_\n",
    "        centroids = kmeans.cluster_centers_\n",
    "        distance = 0\n",
    "        for i in np.unique(assignments): #I can't use enumerate method here because the assignment array increases its size by one and its value increments by 1\n",
    "            distance_to_point = squared_distance(data, centroids[i,:])\n",
    "            distance += np.sum(np.where(assignments == i, distance_to_point, 0)) #if the squared distance is multiplied with 0, it is 0\n",
    "            print(distance, i)\n",
    "        distances.append([k,distance])\n",
    "    distances = np.array(distances)\n",
    "    ax = plt.figure().gca()\n",
    "    ax.plot(distances[:,0], distances[:,1])\n",
    "    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    ax.set_title('The Elbow plot of optimal k')\n",
    "    ax.set_xlabel('Number of clusters')\n",
    "    ax.set_ylabel('SSD')\n",
    "\n",
    "elbow_method(X,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write-up!** State which $k$ you would choose and explain why. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    "From the plot, I would choose K to be 4 because it is the smallest number of clusters so that its corresponding SSD is lower than 10. For me, my tolerance for SSD is within a range of 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tackling Employee Attrition\n",
    "\n",
    "\n",
    "A real problem that Human Resources (HR) departments in companies across the work would like to address is employee attrition, or turnover. They would like to reduce the number of employees who leave the company as hiring new employees is expensive. In this section, we would like to see if we can make use of $k$-means to identify patterns in employee attrition so that we might suggest which areas an HR department should intervene in.\n",
    "\n",
    "To show off there shiny Watson platform, IBM released a (fictional) [sample dataset](https://www.ibm.com/communities/analytics/watson-analytics-blog/hr-employee-attrition/) in 2015 containing employee statistics and whether or not they left the company. We'll use this dataset in our own exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('./utility/data/HR-Employee-Attrition.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4.1\n",
    "\n",
    "With our problem in mind, the next thing to do is to acquire and process our data.\n",
    "\n",
    "**Try this!** Describe the data in `HR-Employee-Attrition.csv` (`data`), answering questions including, but not limited to, these: How many examples and features does the dataset have? What kinds of features are in the dataset? What values can these features take?\n",
    "> `Hint` Consider the steps of EDA; what would you like to know about this dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          int64\n",
       "Attrition                   object\n",
       "BusinessTravel              object\n",
       "DailyRate                    int64\n",
       "Department                  object\n",
       "DistanceFromHome             int64\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "EmployeeCount                int64\n",
       "EmployeeNumber               int64\n",
       "EnvironmentSatisfaction      int64\n",
       "Gender                      object\n",
       "HourlyRate                   int64\n",
       "JobInvolvement               int64\n",
       "JobLevel                     int64\n",
       "JobRole                     object\n",
       "JobSatisfaction              int64\n",
       "MaritalStatus               object\n",
       "MonthlyIncome                int64\n",
       "MonthlyRate                  int64\n",
       "NumCompaniesWorked           int64\n",
       "Over18                      object\n",
       "OverTime                    object\n",
       "PercentSalaryHike            int64\n",
       "PerformanceRating            int64\n",
       "RelationshipSatisfaction     int64\n",
       "StandardHours                int64\n",
       "StockOptionLevel             int64\n",
       "TotalWorkingYears            int64\n",
       "TrainingTimesLastYear        int64\n",
       "WorkLifeBalance              int64\n",
       "YearsAtCompany               int64\n",
       "YearsInCurrentRole           int64\n",
       "YearsSinceLastPromotion      int64\n",
       "YearsWithCurrManager         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    " - There are 1470 examples in this dataset and 35 features\n",
    " - Features take in either categorical or numerical variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4.2\n",
    "\n",
    "Now that we have a feel for what data we have, let's do some data wrangling.\n",
    "\n",
    "**Try this!** In the cells below (feel free to add more as you need them), explain and perform the steps that you need to prepare this data for further analysis. Make sure that your analysis and work is presented well and effectively communicates your work.\n",
    "> In this process, consider whether each feature is informative (eg. EmployeeNumber might not be) and remove those that are not from your dataset. `Hint` You can use the `pandas.DataFrame.drop` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>5993</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>5130</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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",
       "      <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>1465</th>\n",
       "      <td>36</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>884</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>2571</td>\n",
       "      <td>12290</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>613</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>9991</td>\n",
       "      <td>21457</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>155</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>6142</td>\n",
       "      <td>5174</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1023</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>5390</td>\n",
       "      <td>13243</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>4404</td>\n",
       "      <td>10228</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0      41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1      49        No  Travel_Frequently        279  Research & Development   \n",
       "2      37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3      33        No  Travel_Frequently       1392  Research & Development   \n",
       "4      27        No      Travel_Rarely        591  Research & Development   \n",
       "...   ...       ...                ...        ...                     ...   \n",
       "1465   36        No  Travel_Frequently        884  Research & Development   \n",
       "1466   39        No      Travel_Rarely        613  Research & Development   \n",
       "1467   27        No      Travel_Rarely        155  Research & Development   \n",
       "1468   49        No  Travel_Frequently       1023                   Sales   \n",
       "1469   34        No      Travel_Rarely        628  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EnvironmentSatisfaction  \\\n",
       "0                    1          2  Life Sciences                        2   \n",
       "1                    8          1  Life Sciences                        3   \n",
       "2                    2          2          Other                        4   \n",
       "3                    3          4  Life Sciences                        4   \n",
       "4                    2          1        Medical                        1   \n",
       "...                ...        ...            ...                      ...   \n",
       "1465                23          2        Medical                        3   \n",
       "1466                 6          1        Medical                        4   \n",
       "1467                 4          3  Life Sciences                        2   \n",
       "1468                 2          3        Medical                        4   \n",
       "1469                 8          3        Medical                        2   \n",
       "\n",
       "      Gender  HourlyRate  JobInvolvement  JobLevel                    JobRole  \\\n",
       "0     Female          94               3         2            Sales Executive   \n",
       "1       Male          61               2         2         Research Scientist   \n",
       "2       Male          92               2         1      Laboratory Technician   \n",
       "3     Female          56               3         1         Research Scientist   \n",
       "4       Male          40               3         1      Laboratory Technician   \n",
       "...      ...         ...             ...       ...                        ...   \n",
       "1465    Male          41               4         2      Laboratory Technician   \n",
       "1466    Male          42               2         3  Healthcare Representative   \n",
       "1467    Male          87               4         2     Manufacturing Director   \n",
       "1468    Male          63               2         2            Sales Executive   \n",
       "1469    Male          82               4         2      Laboratory Technician   \n",
       "\n",
       "      JobSatisfaction MaritalStatus  MonthlyIncome  MonthlyRate  \\\n",
       "0                   4        Single           5993        19479   \n",
       "1                   2       Married           5130        24907   \n",
       "2                   3        Single           2090         2396   \n",
       "3                   3       Married           2909        23159   \n",
       "4                   2       Married           3468        16632   \n",
       "...               ...           ...            ...          ...   \n",
       "1465                4       Married           2571        12290   \n",
       "1466                1       Married           9991        21457   \n",
       "1467                2       Married           6142         5174   \n",
       "1468                2       Married           5390        13243   \n",
       "1469                3       Married           4404        10228   \n",
       "\n",
       "      NumCompaniesWorked OverTime  PercentSalaryHike  PerformanceRating  \\\n",
       "0                      8      Yes                 11                  3   \n",
       "1                      1       No                 23                  4   \n",
       "2                      6      Yes                 15                  3   \n",
       "3                      1      Yes                 11                  3   \n",
       "4                      9       No                 12                  3   \n",
       "...                  ...      ...                ...                ...   \n",
       "1465                   4       No                 17                  3   \n",
       "1466                   4       No                 15                  3   \n",
       "1467                   1      Yes                 20                  4   \n",
       "1468                   2       No                 14                  3   \n",
       "1469                   2       No                 12                  3   \n",
       "\n",
       "      RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
       "0                            1             80                 0   \n",
       "1                            4             80                 1   \n",
       "2                            2             80                 0   \n",
       "3                            3             80                 0   \n",
       "4                            4             80                 1   \n",
       "...                        ...            ...               ...   \n",
       "1465                         3             80                 1   \n",
       "1466                         1             80                 1   \n",
       "1467                         2             80                 1   \n",
       "1468                         4             80                 0   \n",
       "1469                         1             80                 0   \n",
       "\n",
       "      TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "0                     8                      0                1   \n",
       "1                    10                      3                3   \n",
       "2                     7                      3                3   \n",
       "3                     8                      3                3   \n",
       "4                     6                      3                3   \n",
       "...                 ...                    ...              ...   \n",
       "1465                 17                      3                3   \n",
       "1466                  9                      5                3   \n",
       "1467                  6                      0                3   \n",
       "1468                 17                      3                2   \n",
       "1469                  6                      3                4   \n",
       "\n",
       "      YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0                  6                   4                        0   \n",
       "1                 10                   7                        1   \n",
       "2                  0                   0                        0   \n",
       "3                  8                   7                        3   \n",
       "4                  2                   2                        2   \n",
       "...              ...                 ...                      ...   \n",
       "1465               5                   2                        0   \n",
       "1466               7                   7                        1   \n",
       "1467               6                   2                        0   \n",
       "1468               9                   6                        0   \n",
       "1469               4                   3                        1   \n",
       "\n",
       "      YearsWithCurrManager  \n",
       "0                        5  \n",
       "1                        7  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        2  \n",
       "...                    ...  \n",
       "1465                     3  \n",
       "1466                     7  \n",
       "1467                     3  \n",
       "1468                     8  \n",
       "1469                     2  \n",
       "\n",
       "[1470 rows x 32 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here \n",
    "data = data.dropna()\n",
    "# I don't think that EmployeeCount, Over18,EmployeeNumber are important to build a model to intervine employee from leaving the company\n",
    "not_interested = ['EmployeeCount', 'Over18', 'EmployeeNumber']\n",
    "data.drop(not_interested, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4.3\n",
    "\n",
    "Another step to do in our data processing phase is to replace categorical variables that are represented as strings with an enumeration. For example, `'Attrition'` has `'Yes'` and `'No'` values that we would like to encode as `1` and `0` respectively.\n",
    "\n",
    "**Try this!** In the following cell, [`replace`](http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) the string values in categorical variables with enumerations. Make uses of the `encoded` `DataFrame` which is a copy of `data`. `Hint` You can use the `unique` and `enumerate` functions to help you do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded = data.copy()\n",
    "\n",
    "# Source: https://medium.com/analytics-vidhya/categorical-variable-encoding-techniques-17e607fe42f9. I used this source to create the two helper functions below\n",
    "# your code here \n",
    "# return a dictionary that has key as category and values as number\n",
    "def find_category_mappings(data, variable):\n",
    "    return {k: i for i, k in enumerate(data[variable].unique())}\n",
    "\n",
    "# Returns the column after mapping with dictionary\n",
    "def integer_encode(data,variable, ordinal_mapping):\n",
    "    data[variable] = data[variable].map(ordinal_mapping)\n",
    "\n",
    "categorical_data = encoded.select_dtypes(include=['object'])\n",
    "categorical_names = categorical_data.columns\n",
    "for categorical_name in categorical_names:\n",
    "    mappings = find_category_mappings(encoded,categorical_name)\n",
    "    integer_encode(encoded,categorical_name, mappings)\n",
    "    \n",
    "assert encoded.shape == data.shape, 'The encoded version of `data` should have the same shape'\n",
    "assert np.all(encoded.dtypes == 'int64'), 'All values in `encoded` should be integers'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4.4\n",
    "\n",
    "Now that we have a processed dataset, let's move on to forming clusters with $k$-means. Normally, we would do some EDA here, but in the interest of time, we will forgo that part of the data science workflow. If you want to, we still encourage you to do so.\n",
    "\n",
    "That said, we will need to prepare an $X$ matrix of our dataset. At this point, we will drop the `'Attrition'` column from our dataset. We will also [scale our data](https://scikit-learn.org/stable/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "\n",
    "X = scale(np.float64(encoded.copy().drop('Attrition', axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Try this!** In the cell below, build an elbow plot for $k \\in [1, 21]$ as you did in [Problem 3.3](#Problem-3.3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here \n",
    "elbow_method(X, 21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write-up!** State which 𝑘 you would choose and explain why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    "From the plot above, I picked k to be 6 because I noticed a significant drop from SSD = 44000 to SSD = 36000. Even though k increases up to 20, the drop is only from SSD = 36000 to SSD = 32000. Adding more clusters and having no significant drop after k = 6 may cause inaccurate predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4.5\n",
    "\n",
    "**Try this!** Using the $k$ you selected in [Problem 4.4](#Problem-4.4), create and fit a new model. Remember to save the cluster assignments and centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here \n",
    "kmeans = KMeans(n_clusters=6).fit(X)\n",
    "assignments = kmeans.labels_\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4.6\n",
    "\n",
    "Now that we have cluster assignments from $k$-means, we need to analyze the significance of each cluster. To do that, let's return to our original DataFrame, `data`.\n",
    "\n",
    "In the following cell, we add our cluster assignments to `data`. We also compute a pivot table which provides a summary of each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "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>Age</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.272727</td>\n",
       "      <td>872.583333</td>\n",
       "      <td>8.810606</td>\n",
       "      <td>3.174242</td>\n",
       "      <td>1</td>\n",
       "      <td>939.871212</td>\n",
       "      <td>2.727273</td>\n",
       "      <td>67.590909</td>\n",
       "      <td>2.825758</td>\n",
       "      <td>3.765152</td>\n",
       "      <td>2.742424</td>\n",
       "      <td>14214.439394</td>\n",
       "      <td>15671.484848</td>\n",
       "      <td>4.537879</td>\n",
       "      <td>15.053030</td>\n",
       "      <td>3.098485</td>\n",
       "      <td>2.742424</td>\n",
       "      <td>80</td>\n",
       "      <td>0.734848</td>\n",
       "      <td>22.659091</td>\n",
       "      <td>2.598485</td>\n",
       "      <td>2.742424</td>\n",
       "      <td>5.098485</td>\n",
       "      <td>3.159091</td>\n",
       "      <td>1.303030</td>\n",
       "      <td>2.825758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.620209</td>\n",
       "      <td>774.296167</td>\n",
       "      <td>9.519164</td>\n",
       "      <td>3.041812</td>\n",
       "      <td>1</td>\n",
       "      <td>1036.139373</td>\n",
       "      <td>2.804878</td>\n",
       "      <td>62.425087</td>\n",
       "      <td>2.783972</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>2.790941</td>\n",
       "      <td>6263.463415</td>\n",
       "      <td>13981.585366</td>\n",
       "      <td>1.867596</td>\n",
       "      <td>13.853659</td>\n",
       "      <td>3.003484</td>\n",
       "      <td>2.547038</td>\n",
       "      <td>80</td>\n",
       "      <td>0.815331</td>\n",
       "      <td>12.306620</td>\n",
       "      <td>2.895470</td>\n",
       "      <td>2.783972</td>\n",
       "      <td>10.641115</td>\n",
       "      <td>7.655052</td>\n",
       "      <td>4.006969</td>\n",
       "      <td>7.592334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.493261</td>\n",
       "      <td>873.229111</td>\n",
       "      <td>9.582210</td>\n",
       "      <td>3.048518</td>\n",
       "      <td>1</td>\n",
       "      <td>1095.164420</td>\n",
       "      <td>2.838275</td>\n",
       "      <td>65.514825</td>\n",
       "      <td>2.730458</td>\n",
       "      <td>1.566038</td>\n",
       "      <td>2.803235</td>\n",
       "      <td>4401.746631</td>\n",
       "      <td>13775.663073</td>\n",
       "      <td>3.024259</td>\n",
       "      <td>14.129380</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.754717</td>\n",
       "      <td>80</td>\n",
       "      <td>1.452830</td>\n",
       "      <td>7.792453</td>\n",
       "      <td>2.811321</td>\n",
       "      <td>2.725067</td>\n",
       "      <td>4.032345</td>\n",
       "      <td>2.296496</td>\n",
       "      <td>0.784367</td>\n",
       "      <td>2.237197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.243094</td>\n",
       "      <td>739.552486</td>\n",
       "      <td>8.522099</td>\n",
       "      <td>2.610497</td>\n",
       "      <td>1</td>\n",
       "      <td>1012.124309</td>\n",
       "      <td>2.618785</td>\n",
       "      <td>68.563536</td>\n",
       "      <td>2.690608</td>\n",
       "      <td>1.403315</td>\n",
       "      <td>2.640884</td>\n",
       "      <td>3851.132597</td>\n",
       "      <td>14988.859116</td>\n",
       "      <td>2.433702</td>\n",
       "      <td>14.027624</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.787293</td>\n",
       "      <td>80</td>\n",
       "      <td>0.124309</td>\n",
       "      <td>6.256906</td>\n",
       "      <td>2.809392</td>\n",
       "      <td>2.781768</td>\n",
       "      <td>3.447514</td>\n",
       "      <td>2.116022</td>\n",
       "      <td>0.806630</td>\n",
       "      <td>2.044199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.978836</td>\n",
       "      <td>802.455026</td>\n",
       "      <td>9.724868</td>\n",
       "      <td>2.772487</td>\n",
       "      <td>1</td>\n",
       "      <td>963.052910</td>\n",
       "      <td>2.640212</td>\n",
       "      <td>65.714286</td>\n",
       "      <td>2.719577</td>\n",
       "      <td>1.656085</td>\n",
       "      <td>2.708995</td>\n",
       "      <td>4695.592593</td>\n",
       "      <td>13937.862434</td>\n",
       "      <td>2.465608</td>\n",
       "      <td>21.719577</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.597884</td>\n",
       "      <td>80</td>\n",
       "      <td>0.798942</td>\n",
       "      <td>8.735450</td>\n",
       "      <td>2.788360</td>\n",
       "      <td>2.761905</td>\n",
       "      <td>5.756614</td>\n",
       "      <td>3.899471</td>\n",
       "      <td>1.687831</td>\n",
       "      <td>3.798942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46.085271</td>\n",
       "      <td>766.666667</td>\n",
       "      <td>8.837209</td>\n",
       "      <td>3.023256</td>\n",
       "      <td>1</td>\n",
       "      <td>1010.891473</td>\n",
       "      <td>2.604651</td>\n",
       "      <td>65.705426</td>\n",
       "      <td>2.635659</td>\n",
       "      <td>4.031008</td>\n",
       "      <td>2.635659</td>\n",
       "      <td>15277.240310</td>\n",
       "      <td>13859.821705</td>\n",
       "      <td>2.751938</td>\n",
       "      <td>15.271318</td>\n",
       "      <td>3.178295</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>80</td>\n",
       "      <td>0.782946</td>\n",
       "      <td>25.201550</td>\n",
       "      <td>2.744186</td>\n",
       "      <td>2.775194</td>\n",
       "      <td>21.263566</td>\n",
       "      <td>9.674419</td>\n",
       "      <td>7.689922</td>\n",
       "      <td>9.465116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age   DailyRate  DistanceFromHome  Education  EmployeeCount  \\\n",
       "Cluster                                                                      \n",
       "0        48.272727  872.583333          8.810606   3.174242              1   \n",
       "1        36.620209  774.296167          9.519164   3.041812              1   \n",
       "2        35.493261  873.229111          9.582210   3.048518              1   \n",
       "3        32.243094  739.552486          8.522099   2.610497              1   \n",
       "4        34.978836  802.455026          9.724868   2.772487              1   \n",
       "5        46.085271  766.666667          8.837209   3.023256              1   \n",
       "\n",
       "         EmployeeNumber  EnvironmentSatisfaction  HourlyRate  JobInvolvement  \\\n",
       "Cluster                                                                        \n",
       "0            939.871212                 2.727273   67.590909        2.825758   \n",
       "1           1036.139373                 2.804878   62.425087        2.783972   \n",
       "2           1095.164420                 2.838275   65.514825        2.730458   \n",
       "3           1012.124309                 2.618785   68.563536        2.690608   \n",
       "4            963.052910                 2.640212   65.714286        2.719577   \n",
       "5           1010.891473                 2.604651   65.705426        2.635659   \n",
       "\n",
       "         JobLevel  JobSatisfaction  MonthlyIncome   MonthlyRate  \\\n",
       "Cluster                                                           \n",
       "0        3.765152         2.742424   14214.439394  15671.484848   \n",
       "1        2.142857         2.790941    6263.463415  13981.585366   \n",
       "2        1.566038         2.803235    4401.746631  13775.663073   \n",
       "3        1.403315         2.640884    3851.132597  14988.859116   \n",
       "4        1.656085         2.708995    4695.592593  13937.862434   \n",
       "5        4.031008         2.635659   15277.240310  13859.821705   \n",
       "\n",
       "         NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
       "Cluster                                                             \n",
       "0                  4.537879          15.053030           3.098485   \n",
       "1                  1.867596          13.853659           3.003484   \n",
       "2                  3.024259          14.129380           3.000000   \n",
       "3                  2.433702          14.027624           3.000000   \n",
       "4                  2.465608          21.719577           4.000000   \n",
       "5                  2.751938          15.271318           3.178295   \n",
       "\n",
       "         RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
       "Cluster                                                              \n",
       "0                        2.742424             80          0.734848   \n",
       "1                        2.547038             80          0.815331   \n",
       "2                        2.754717             80          1.452830   \n",
       "3                        2.787293             80          0.124309   \n",
       "4                        2.597884             80          0.798942   \n",
       "5                        2.883721             80          0.782946   \n",
       "\n",
       "         TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "Cluster                                                              \n",
       "0                22.659091               2.598485         2.742424   \n",
       "1                12.306620               2.895470         2.783972   \n",
       "2                 7.792453               2.811321         2.725067   \n",
       "3                 6.256906               2.809392         2.781768   \n",
       "4                 8.735450               2.788360         2.761905   \n",
       "5                25.201550               2.744186         2.775194   \n",
       "\n",
       "         YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "Cluster                                                                \n",
       "0              5.098485            3.159091                 1.303030   \n",
       "1             10.641115            7.655052                 4.006969   \n",
       "2              4.032345            2.296496                 0.784367   \n",
       "3              3.447514            2.116022                 0.806630   \n",
       "4              5.756614            3.899471                 1.687831   \n",
       "5             21.263566            9.674419                 7.689922   \n",
       "\n",
       "         YearsWithCurrManager  \n",
       "Cluster                        \n",
       "0                    2.825758  \n",
       "1                    7.592334  \n",
       "2                    2.237197  \n",
       "3                    2.044199  \n",
       "4                    3.798942  \n",
       "5                    9.465116  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cluster'] = assignments\n",
    "pivot = data.pivot_table(index='Cluster', aggfunc=np.mean)\n",
    "pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Try this!** Compute the percentage of total attrition accounted for by each cluster and store the result in `pivot['% of Attrition']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here \n",
    "_, each_assignment_count = np.unique(assignments[data['Attrition'] == 'Yes'], return_counts=True)\n",
    "pivot['% of Attrition'] = each_assignment_count/np.sum(each_assignment_count) *100\n",
    "\n",
    "assert pivot['% of Attrition'].sum() == 100, 'Sum of percentage attrition should be 1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4.7\n",
    "\n",
    "Let's take a look at the results and identify potential areas for intervention to suggets to the HR department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Cluster</th>\n",
       "      <th>0</th>\n",
       "      <th>5</th>\n",
       "      <th>1</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>48.272727</td>\n",
       "      <td>46.085271</td>\n",
       "      <td>36.620209</td>\n",
       "      <td>34.978836</td>\n",
       "      <td>35.493261</td>\n",
       "      <td>32.243094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DailyRate</th>\n",
       "      <td>872.583333</td>\n",
       "      <td>766.666667</td>\n",
       "      <td>774.296167</td>\n",
       "      <td>802.455026</td>\n",
       "      <td>873.229111</td>\n",
       "      <td>739.552486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>8.810606</td>\n",
       "      <td>8.837209</td>\n",
       "      <td>9.519164</td>\n",
       "      <td>9.724868</td>\n",
       "      <td>9.582210</td>\n",
       "      <td>8.522099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>3.174242</td>\n",
       "      <td>3.023256</td>\n",
       "      <td>3.041812</td>\n",
       "      <td>2.772487</td>\n",
       "      <td>3.048518</td>\n",
       "      <td>2.610497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeCount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <td>939.871212</td>\n",
       "      <td>1010.891473</td>\n",
       "      <td>1036.139373</td>\n",
       "      <td>963.052910</td>\n",
       "      <td>1095.164420</td>\n",
       "      <td>1012.124309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <td>2.727273</td>\n",
       "      <td>2.604651</td>\n",
       "      <td>2.804878</td>\n",
       "      <td>2.640212</td>\n",
       "      <td>2.838275</td>\n",
       "      <td>2.618785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HourlyRate</th>\n",
       "      <td>67.590909</td>\n",
       "      <td>65.705426</td>\n",
       "      <td>62.425087</td>\n",
       "      <td>65.714286</td>\n",
       "      <td>65.514825</td>\n",
       "      <td>68.563536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobInvolvement</th>\n",
       "      <td>2.825758</td>\n",
       "      <td>2.635659</td>\n",
       "      <td>2.783972</td>\n",
       "      <td>2.719577</td>\n",
       "      <td>2.730458</td>\n",
       "      <td>2.690608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>3.765152</td>\n",
       "      <td>4.031008</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>1.656085</td>\n",
       "      <td>1.566038</td>\n",
       "      <td>1.403315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <td>2.742424</td>\n",
       "      <td>2.635659</td>\n",
       "      <td>2.790941</td>\n",
       "      <td>2.708995</td>\n",
       "      <td>2.803235</td>\n",
       "      <td>2.640884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>14214.439394</td>\n",
       "      <td>15277.240310</td>\n",
       "      <td>6263.463415</td>\n",
       "      <td>4695.592593</td>\n",
       "      <td>4401.746631</td>\n",
       "      <td>3851.132597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyRate</th>\n",
       "      <td>15671.484848</td>\n",
       "      <td>13859.821705</td>\n",
       "      <td>13981.585366</td>\n",
       "      <td>13937.862434</td>\n",
       "      <td>13775.663073</td>\n",
       "      <td>14988.859116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>4.537879</td>\n",
       "      <td>2.751938</td>\n",
       "      <td>1.867596</td>\n",
       "      <td>2.465608</td>\n",
       "      <td>3.024259</td>\n",
       "      <td>2.433702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>15.053030</td>\n",
       "      <td>15.271318</td>\n",
       "      <td>13.853659</td>\n",
       "      <td>21.719577</td>\n",
       "      <td>14.129380</td>\n",
       "      <td>14.027624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PerformanceRating</th>\n",
       "      <td>3.098485</td>\n",
       "      <td>3.178295</td>\n",
       "      <td>3.003484</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <td>2.742424</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>2.547038</td>\n",
       "      <td>2.597884</td>\n",
       "      <td>2.754717</td>\n",
       "      <td>2.787293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StandardHours</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>0.734848</td>\n",
       "      <td>0.782946</td>\n",
       "      <td>0.815331</td>\n",
       "      <td>0.798942</td>\n",
       "      <td>1.452830</td>\n",
       "      <td>0.124309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>22.659091</td>\n",
       "      <td>25.201550</td>\n",
       "      <td>12.306620</td>\n",
       "      <td>8.735450</td>\n",
       "      <td>7.792453</td>\n",
       "      <td>6.256906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>2.598485</td>\n",
       "      <td>2.744186</td>\n",
       "      <td>2.895470</td>\n",
       "      <td>2.788360</td>\n",
       "      <td>2.811321</td>\n",
       "      <td>2.809392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <td>2.742424</td>\n",
       "      <td>2.775194</td>\n",
       "      <td>2.783972</td>\n",
       "      <td>2.761905</td>\n",
       "      <td>2.725067</td>\n",
       "      <td>2.781768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>5.098485</td>\n",
       "      <td>21.263566</td>\n",
       "      <td>10.641115</td>\n",
       "      <td>5.756614</td>\n",
       "      <td>4.032345</td>\n",
       "      <td>3.447514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <td>3.159091</td>\n",
       "      <td>9.674419</td>\n",
       "      <td>7.655052</td>\n",
       "      <td>3.899471</td>\n",
       "      <td>2.296496</td>\n",
       "      <td>2.116022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>1.303030</td>\n",
       "      <td>7.689922</td>\n",
       "      <td>4.006969</td>\n",
       "      <td>1.687831</td>\n",
       "      <td>0.784367</td>\n",
       "      <td>0.806630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <td>2.825758</td>\n",
       "      <td>9.465116</td>\n",
       "      <td>7.592334</td>\n",
       "      <td>3.798942</td>\n",
       "      <td>2.237197</td>\n",
       "      <td>2.044199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% of Attrition</th>\n",
       "      <td>3.375527</td>\n",
       "      <td>4.641350</td>\n",
       "      <td>14.345992</td>\n",
       "      <td>15.611814</td>\n",
       "      <td>19.831224</td>\n",
       "      <td>42.194093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cluster                              0             5             1  \\\n",
       "Age                          48.272727     46.085271     36.620209   \n",
       "DailyRate                   872.583333    766.666667    774.296167   \n",
       "DistanceFromHome              8.810606      8.837209      9.519164   \n",
       "Education                     3.174242      3.023256      3.041812   \n",
       "EmployeeCount                 1.000000      1.000000      1.000000   \n",
       "EmployeeNumber              939.871212   1010.891473   1036.139373   \n",
       "EnvironmentSatisfaction       2.727273      2.604651      2.804878   \n",
       "HourlyRate                   67.590909     65.705426     62.425087   \n",
       "JobInvolvement                2.825758      2.635659      2.783972   \n",
       "JobLevel                      3.765152      4.031008      2.142857   \n",
       "JobSatisfaction               2.742424      2.635659      2.790941   \n",
       "MonthlyIncome             14214.439394  15277.240310   6263.463415   \n",
       "MonthlyRate               15671.484848  13859.821705  13981.585366   \n",
       "NumCompaniesWorked            4.537879      2.751938      1.867596   \n",
       "PercentSalaryHike            15.053030     15.271318     13.853659   \n",
       "PerformanceRating             3.098485      3.178295      3.003484   \n",
       "RelationshipSatisfaction      2.742424      2.883721      2.547038   \n",
       "StandardHours                80.000000     80.000000     80.000000   \n",
       "StockOptionLevel              0.734848      0.782946      0.815331   \n",
       "TotalWorkingYears            22.659091     25.201550     12.306620   \n",
       "TrainingTimesLastYear         2.598485      2.744186      2.895470   \n",
       "WorkLifeBalance               2.742424      2.775194      2.783972   \n",
       "YearsAtCompany                5.098485     21.263566     10.641115   \n",
       "YearsInCurrentRole            3.159091      9.674419      7.655052   \n",
       "YearsSinceLastPromotion       1.303030      7.689922      4.006969   \n",
       "YearsWithCurrManager          2.825758      9.465116      7.592334   \n",
       "% of Attrition                3.375527      4.641350     14.345992   \n",
       "\n",
       "Cluster                              4             2             3  \n",
       "Age                          34.978836     35.493261     32.243094  \n",
       "DailyRate                   802.455026    873.229111    739.552486  \n",
       "DistanceFromHome              9.724868      9.582210      8.522099  \n",
       "Education                     2.772487      3.048518      2.610497  \n",
       "EmployeeCount                 1.000000      1.000000      1.000000  \n",
       "EmployeeNumber              963.052910   1095.164420   1012.124309  \n",
       "EnvironmentSatisfaction       2.640212      2.838275      2.618785  \n",
       "HourlyRate                   65.714286     65.514825     68.563536  \n",
       "JobInvolvement                2.719577      2.730458      2.690608  \n",
       "JobLevel                      1.656085      1.566038      1.403315  \n",
       "JobSatisfaction               2.708995      2.803235      2.640884  \n",
       "MonthlyIncome              4695.592593   4401.746631   3851.132597  \n",
       "MonthlyRate               13937.862434  13775.663073  14988.859116  \n",
       "NumCompaniesWorked            2.465608      3.024259      2.433702  \n",
       "PercentSalaryHike            21.719577     14.129380     14.027624  \n",
       "PerformanceRating             4.000000      3.000000      3.000000  \n",
       "RelationshipSatisfaction      2.597884      2.754717      2.787293  \n",
       "StandardHours                80.000000     80.000000     80.000000  \n",
       "StockOptionLevel              0.798942      1.452830      0.124309  \n",
       "TotalWorkingYears             8.735450      7.792453      6.256906  \n",
       "TrainingTimesLastYear         2.788360      2.811321      2.809392  \n",
       "WorkLifeBalance               2.761905      2.725067      2.781768  \n",
       "YearsAtCompany                5.756614      4.032345      3.447514  \n",
       "YearsInCurrentRole            3.899471      2.296496      2.116022  \n",
       "YearsSinceLastPromotion       1.687831      0.784367      0.806630  \n",
       "YearsWithCurrManager          3.798942      2.237197      2.044199  \n",
       "% of Attrition               15.611814     19.831224     42.194093  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot.sort_values(by='% of Attrition', axis=0).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write-up!** Describe the clusters produced and interpret their meaning. What makes each one a separate cluster? Is there anything that stands out with respect to attrition rate? What might you suggest HR look into to improve employee retention?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here:** \n",
    " - The highest attrition rate calculated is from the 3rd cluster, which has about 42.2%. Cluster 1, 4, and 2 have very similar percentages of attrition, which are about 16%.\n",
    " - Monthly Income, Age and JobLevel make each separate cluster from others. From the data, it seems that a group of employees who are 46+ have a significant better income than a group of employees who are below 36. However, when I examine the average performance rating of each cluster, its value is very similar. So I thought that there exist a group of young employees who work at the company for couple years want better pay roles, even though they know that they are doing their job as well as others who are older and may have experience. Concisely, I believe the issue is that a lot of younger employees wanted to leave the company for the fact that there is a group of older employees who are about 45+ and have the same about the same number of years working at the company, but have a significantly better pay role. Therefore, this group of younger employees decided to leave the company to look for better opprtunity!.\n",
    " - Another interesting thing is that this group who wants to get a better opportunity may not be dissatified with their job or their working enviroment. I think that they are nonchalant about their job or their working environment but just want to have a better pay roles!\n",
    " - I suggested HR to pay more attention at a group of employees who are about 35 and conduct a survey to find further reasons that they want to leave the company"
   ]
  }
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