{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8OQfbkF2qf5W"
   },
   "source": [
    " #  <p style=\"text-align: center;\">Technical Support Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h1CyfJ0yqf5Z"
   },
   "source": [
    "Technical support data can often be a rich source of information on opportunities for improving customer experience. The lesser the trouble customers have with the product the better. Even better when the customers are able to overcome\n",
    "technical challenge quickly with minimal effort. Let us analyze the tech support data and do some basic analysis on problem types, time to resolve the problem and channel of suppor that is most suitable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ozpdWvkQqf5b"
   },
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-S9w2oBpqf5d",
    "outputId": "1c586e99-a114-4744-f627-0886fe5f76d9",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PROBLEM_TYPE              object\n",
       "no_of_cases                int64\n",
       "Avg_pending_calls        float64\n",
       "Avg_resol_time             int64\n",
       "recurrence_freq          float64\n",
       "Replace_percent          float64\n",
       "In_warranty_percent        int64\n",
       "Post_warranty_percent      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from scipy.stats import zscore\n",
    "\n",
    "#import sklearn.metrics\n",
    "\n",
    "tech_supp_df = pd.read_csv(\"technical_support_data.csv\")\n",
    "tech_supp_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eW88bjuvqf5o"
   },
   "source": [
    "The dataset contains one record for each unique problem type. It has metrics for each type like count, average calls to resolve, average resolution time etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 8)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tech_supp_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DFBxs9Syqf5q",
    "outputId": "bd72e031-6f39-4639-d5d0-152b50c38ffe"
   },
   "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>PROBLEM_TYPE</th>\n",
       "      <th>no_of_cases</th>\n",
       "      <th>Avg_pending_calls</th>\n",
       "      <th>Avg_resol_time</th>\n",
       "      <th>recurrence_freq</th>\n",
       "      <th>Replace_percent</th>\n",
       "      <th>In_warranty_percent</th>\n",
       "      <th>Post_warranty_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Temperature control not working</td>\n",
       "      <td>170</td>\n",
       "      <td>1.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power chord does not tightly fit</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fan swing not working</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.2</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Main switch does not on</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Forgot mobile app password</td>\n",
       "      <td>45</td>\n",
       "      <td>2.3</td>\n",
       "      <td>54</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PROBLEM_TYPE  no_of_cases  Avg_pending_calls  \\\n",
       "0   Temperature control not working          170                1.3   \n",
       "1  power chord does not tightly fit           12                2.0   \n",
       "2             Fan swing not working            5                1.0   \n",
       "3           Main switch does not on            3                2.0   \n",
       "4        Forgot mobile app password           45                2.3   \n",
       "\n",
       "   Avg_resol_time  recurrence_freq  Replace_percent  In_warranty_percent  \\\n",
       "0              32             0.04              0.0                   75   \n",
       "1             150             0.01              0.5                    5   \n",
       "2              35             0.02              0.2                   90   \n",
       "3               8             0.01              0.7                    5   \n",
       "4              54             0.15              0.0                   99   \n",
       "\n",
       "   Post_warranty_percent  \n",
       "0                     25  \n",
       "1                     95  \n",
       "2                     10  \n",
       "3                     95  \n",
       "4                      1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tech_supp_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a1e7e1198>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "techSuppAttr=tech_supp_df.iloc[:,1:]\n",
    "techSuppScaled=techSuppAttr.apply(zscore)\n",
    "sns.pairplot(techSuppScaled,diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dbn46iRuqf5v"
   },
   "source": [
    "## Group Data into similar clusters\n",
    "\n",
    "Now, we will use K-Means clustering to group data based on their attribute. First, we need to determine the optimal number of groups. For that we conduct the knee test to see where the knee happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GJ_z7Buwqf5x",
    "outputId": "2fe63549-869d-419f-cd19-ff1242268b8b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Selecting k with the Elbow Method')"
      ]
     },
     "execution_count": 22,
     "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": [
    "#Finding optimal no. of clusters\n",
    "from scipy.spatial.distance import cdist\n",
    "clusters=range(1,10)\n",
    "meanDistortions=[]\n",
    "\n",
    "for k in clusters:\n",
    "    model=KMeans(n_clusters=k)\n",
    "    model.fit(techSuppScaled)\n",
    "    prediction=model.predict(techSuppScaled)\n",
    "    meanDistortions.append(sum(np.min(cdist(techSuppScaled, model.cluster_centers_, 'euclidean'), axis=1)) / techSuppScaled.shape[0])\n",
    "\n",
    "\n",
    "plt.plot(clusters, meanDistortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Average distortion')\n",
    "plt.title('Selecting k with the Elbow Method')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LeWqzIRNqf54"
   },
   "source": [
    "Though the bend is not coming out clearly as there are many bends, let us look at 3 clusters and 5 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EsP8_LBKqf56",
    "outputId": "1a8bd8e4-b729-46c8-dc27-3755c010ad56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Groups Assigned : \n",
      "\n"
     ]
    },
    {
     "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>PROBLEM_TYPE</th>\n",
       "      <th>no_of_cases</th>\n",
       "      <th>Avg_pending_calls</th>\n",
       "      <th>Avg_resol_time</th>\n",
       "      <th>recurrence_freq</th>\n",
       "      <th>Replace_percent</th>\n",
       "      <th>In_warranty_percent</th>\n",
       "      <th>Post_warranty_percent</th>\n",
       "      <th>GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Temperature control not working</td>\n",
       "      <td>170</td>\n",
       "      <td>1.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power chord does not tightly fit</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fan swing not working</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.2</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Main switch does not on</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Forgot mobile app password</td>\n",
       "      <td>45</td>\n",
       "      <td>2.3</td>\n",
       "      <td>54</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PROBLEM_TYPE  no_of_cases  Avg_pending_calls  \\\n",
       "0   Temperature control not working          170                1.3   \n",
       "1  power chord does not tightly fit           12                2.0   \n",
       "2             Fan swing not working            5                1.0   \n",
       "3           Main switch does not on            3                2.0   \n",
       "4        Forgot mobile app password           45                2.3   \n",
       "\n",
       "   Avg_resol_time  recurrence_freq  Replace_percent  In_warranty_percent  \\\n",
       "0              32             0.04              0.0                   75   \n",
       "1             150             0.01              0.5                    5   \n",
       "2              35             0.02              0.2                   90   \n",
       "3               8             0.01              0.7                    5   \n",
       "4              54             0.15              0.0                   99   \n",
       "\n",
       "   Post_warranty_percent  GROUP  \n",
       "0                     25      0  \n",
       "1                     95      2  \n",
       "2                     10      0  \n",
       "3                     95      2  \n",
       "4                      1      1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us first start with K = 3\n",
    "final_model=KMeans(3)\n",
    "final_model.fit(techSuppScaled)\n",
    "prediction=final_model.predict(techSuppScaled)\n",
    "\n",
    "#Append the prediction \n",
    "tech_supp_df[\"GROUP\"] = prediction\n",
    "techSuppScaled[\"GROUP\"] = prediction\n",
    "print(\"Groups Assigned : \\n\")\n",
    "tech_supp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ck4XGXSjqf6C"
   },
   "source": [
    "### Analyze the distribution of the data among the two groups (K = 3). One of the most informative visual tool is boxplot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>no_of_cases</th>\n",
       "      <th>Avg_pending_calls</th>\n",
       "      <th>Avg_resol_time</th>\n",
       "      <th>recurrence_freq</th>\n",
       "      <th>Replace_percent</th>\n",
       "      <th>In_warranty_percent</th>\n",
       "      <th>Post_warranty_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUP</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>250.444444</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>0.125556</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>85.555556</td>\n",
       "      <td>14.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>3.157143</td>\n",
       "      <td>132.571429</td>\n",
       "      <td>0.125714</td>\n",
       "      <td>0.021429</td>\n",
       "      <td>99.142857</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>112.142857</td>\n",
       "      <td>1.828571</td>\n",
       "      <td>47.857143</td>\n",
       "      <td>0.165714</td>\n",
       "      <td>0.272857</td>\n",
       "      <td>20.714286</td>\n",
       "      <td>79.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       no_of_cases  Avg_pending_calls  Avg_resol_time  recurrence_freq  \\\n",
       "GROUP                                                                    \n",
       "0       250.444444           1.166667       20.666667         0.125556   \n",
       "1        35.000000           3.157143      132.571429         0.125714   \n",
       "2       112.142857           1.828571       47.857143         0.165714   \n",
       "\n",
       "       Replace_percent  In_warranty_percent  Post_warranty_percent  \n",
       "GROUP                                                               \n",
       "0             0.026667            85.555556              14.444444  \n",
       "1             0.021429            99.142857               0.857143  \n",
       "2             0.272857            20.714286              79.285714  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techSuppClust = tech_supp_df.groupby(['GROUP'])\n",
    "techSuppClust.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QzlZMEEaqf6E",
    "outputId": "920d7ccb-5c96-4c35-e601-94e64e55e1da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a23c7bf60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2431fa90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2428ea58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a242b9a20>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a243c6a20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a243f1cf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24417fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24447320>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "techSuppScaled.boxplot(by='GROUP', layout = (2,4),figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CqpJwcybqf6M"
   },
   "outputs": [],
   "source": [
    "#  Let us next try with K = 5, the next elbow point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HsmQzYLzqf6T",
    "outputId": "d8faa25b-43a2-4783-db03-159c30f90cbd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Groups Assigned : \n",
      "\n"
     ]
    },
    {
     "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>PROBLEM_TYPE</th>\n",
       "      <th>no_of_cases</th>\n",
       "      <th>Avg_pending_calls</th>\n",
       "      <th>Avg_resol_time</th>\n",
       "      <th>recurrence_freq</th>\n",
       "      <th>Replace_percent</th>\n",
       "      <th>In_warranty_percent</th>\n",
       "      <th>Post_warranty_percent</th>\n",
       "      <th>GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Temperature control not working</td>\n",
       "      <td>170</td>\n",
       "      <td>1.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power chord does not tightly fit</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fan swing not working</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.2</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Main switch does not on</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Forgot mobile app password</td>\n",
       "      <td>45</td>\n",
       "      <td>2.3</td>\n",
       "      <td>54</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PROBLEM_TYPE  no_of_cases  Avg_pending_calls  \\\n",
       "0   Temperature control not working          170                1.3   \n",
       "1  power chord does not tightly fit           12                2.0   \n",
       "2             Fan swing not working            5                1.0   \n",
       "3           Main switch does not on            3                2.0   \n",
       "4        Forgot mobile app password           45                2.3   \n",
       "\n",
       "   Avg_resol_time  recurrence_freq  Replace_percent  In_warranty_percent  \\\n",
       "0              32             0.04              0.0                   75   \n",
       "1             150             0.01              0.5                    5   \n",
       "2              35             0.02              0.2                   90   \n",
       "3               8             0.01              0.7                    5   \n",
       "4              54             0.15              0.0                   99   \n",
       "\n",
       "   Post_warranty_percent  GROUP  \n",
       "0                     25      4  \n",
       "1                     95      0  \n",
       "2                     10      4  \n",
       "3                     95      0  \n",
       "4                      1      1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us first start with K = 5\n",
    "final_model=KMeans(5)\n",
    "final_model.fit(techSuppScaled)\n",
    "prediction=final_model.predict(techSuppScaled)\n",
    "\n",
    "#Append the prediction \n",
    "tech_supp_df[\"GROUP\"] = prediction\n",
    "techSuppScaled[\"GROUP\"] = prediction\n",
    "print(\"Groups Assigned : \\n\")\n",
    "tech_supp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>no_of_cases</th>\n",
       "      <th>Avg_pending_calls</th>\n",
       "      <th>Avg_resol_time</th>\n",
       "      <th>recurrence_freq</th>\n",
       "      <th>Replace_percent</th>\n",
       "      <th>In_warranty_percent</th>\n",
       "      <th>Post_warranty_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUP</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>7.50</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.00</td>\n",
       "      <td>3.157143</td>\n",
       "      <td>132.571429</td>\n",
       "      <td>0.125714</td>\n",
       "      <td>0.021429</td>\n",
       "      <td>99.142857</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>395.25</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>154.00</td>\n",
       "      <td>1.760000</td>\n",
       "      <td>35.400000</td>\n",
       "      <td>0.228000</td>\n",
       "      <td>0.142000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134.60</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.034000</td>\n",
       "      <td>0.048000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       no_of_cases  Avg_pending_calls  Avg_resol_time  recurrence_freq  \\\n",
       "GROUP                                                                    \n",
       "0             7.50           2.000000       79.000000         0.010000   \n",
       "1            35.00           3.157143      132.571429         0.125714   \n",
       "2           395.25           1.025000        7.750000         0.240000   \n",
       "3           154.00           1.760000       35.400000         0.228000   \n",
       "4           134.60           1.280000       31.000000         0.034000   \n",
       "\n",
       "       Replace_percent  In_warranty_percent  Post_warranty_percent  \n",
       "GROUP                                                               \n",
       "0             0.600000             5.000000              95.000000  \n",
       "1             0.021429            99.142857               0.857143  \n",
       "2             0.000000            87.500000              12.500000  \n",
       "3             0.142000            27.000000              73.000000  \n",
       "4             0.048000            84.000000              16.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techSuppClust = tech_supp_df.groupby(['GROUP'])\n",
    "techSuppClust.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "86Ypg_PHqf6a",
    "outputId": "2753099c-25b9-4e1d-be5f-fe9685ca6b7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a24579b70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2481c7f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24a6aa90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24a91d68>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a24ac2080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24ae9358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24b12630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24b39940>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "techSuppScaled.boxplot(by='GROUP', layout = (2,4),figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "tech_supp_analysis.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}