1. Exploratory Data Analysis.ipynb

{
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   "outputs": [],
   "source": [
    "# Loading the Important Libaries \n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# For Visualization\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# To Ignore Warnings \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Data \n",
    "\n",
    "train_data = pd.read_csv(\"train_data.csv\")\n",
    "\n",
    "test_data = pd.read_csv(\"test_data.csv\")\n",
    "\n",
    "assembly_line_info = pd.read_csv(\"assembly_line_info.csv\")\n",
    "\n",
    "issue_info = pd.read_csv(\"issue_info.csv\")\n",
    "\n",
    "log_report_type_data = pd.read_csv(\"log_report_type_data.csv\")\n",
    "\n",
    "car_variant_data = pd.read_csv(\"car_variant_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>factory_number</th>\n",
       "      <th>downtime_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13366</td>\n",
       "      <td>factory_number_415</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6783</td>\n",
       "      <td>factory_number_474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9519</td>\n",
       "      <td>factory_number_931</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10202</td>\n",
       "      <td>factory_number_700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4555</td>\n",
       "      <td>factory_number_600</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id      factory_number  downtime_duration\n",
       "0  13366  factory_number_415                  1\n",
       "1   6783  factory_number_474                  0\n",
       "2   9519  factory_number_931                  1\n",
       "3  10202  factory_number_700                  1\n",
       "4   4555  factory_number_600                  2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>factory_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3340</td>\n",
       "      <td>factory_number_344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14067</td>\n",
       "      <td>factory_number_933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1134</td>\n",
       "      <td>factory_number_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>factory_number_793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9793</td>\n",
       "      <td>factory_number_344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      id      factory_number\n",
       "0   3340  factory_number_344\n",
       "1  14067  factory_number_933\n",
       "2   1134   factory_number_16\n",
       "3     27  factory_number_793\n",
       "4   9793  factory_number_344"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>assembly_line_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6597</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8011</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2597</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5022</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6852</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id    assembly_line_type\n",
       "0  6597  assembly_line_type_8\n",
       "1  8011  assembly_line_type_8\n",
       "2  2597  assembly_line_type_8\n",
       "3  5022  assembly_line_type_8\n",
       "4  6852  assembly_line_type_8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assembly_line_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>issue_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6597</td>\n",
       "      <td>issue_type_2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8011</td>\n",
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       "      <th>2</th>\n",
       "      <td>2597</td>\n",
       "      <td>issue_type_2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5022</td>\n",
       "      <td>issue_type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6852</td>\n",
       "      <td>issue_type_1</td>\n",
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      "text/plain": [
       "     id    issue_type\n",
       "0  6597  issue_type_2\n",
       "1  8011  issue_type_2\n",
       "2  2597  issue_type_2\n",
       "3  5022  issue_type_1\n",
       "4  6852  issue_type_1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issue_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>log_report_type</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6597</td>\n",
       "      <td>log_report_type_68</td>\n",
       "      <td>6</td>\n",
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       "      <th>1</th>\n",
       "      <td>8011</td>\n",
       "      <td>log_report_type_68</td>\n",
       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>2597</td>\n",
       "      <td>log_report_type_68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5022</td>\n",
       "      <td>log_report_type_172</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>log_report_type_56</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "     id      log_report_type  volume\n",
       "0  6597   log_report_type_68       6\n",
       "1  8011   log_report_type_68       7\n",
       "2  2597   log_report_type_68       1\n",
       "3  5022  log_report_type_172       2\n",
       "4  5022   log_report_type_56       1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "log_report_type_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>car_variant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6597</td>\n",
       "      <td>car_variant_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8011</td>\n",
       "      <td>car_variant_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2597</td>\n",
       "      <td>car_variant_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5022</td>\n",
       "      <td>car_variant_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>car_variant_11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     id     car_variant\n",
       "0  6597  car_variant_11\n",
       "1  8011  car_variant_15\n",
       "2  2597  car_variant_15\n",
       "3  5022  car_variant_15\n",
       "4  5022  car_variant_11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_variant_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_data dimention:\n",
      "\n",
      "(5904, 3)\n",
      "\n",
      "\n",
      "test_data dimention:\n",
      "\n",
      "(1477, 2)\n",
      "\n",
      "\n",
      "assembly_line_info dimention:\n",
      "\n",
      "(21076, 2)\n",
      "\n",
      "\n",
      "issue info dimention:\n",
      "\n",
      "(18552, 2)\n",
      "\n",
      "\n",
      "log_report_type_data dimention:\n",
      "\n",
      "(58671, 3)\n",
      "\n",
      "\n",
      "car variant data dimention:\n",
      "(31170, 2)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking the dimentions of the data \n",
    "print(\"train_data dimention:\\n\")\n",
    "print(train_data.shape)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"test_data dimention:\\n\")\n",
    "print(test_data.shape)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"assembly_line_info dimention:\\n\")\n",
    "print(assembly_line_info.shape)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"issue info dimention:\\n\")\n",
    "print(issue_info.shape)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"log_report_type_data dimention:\\n\")\n",
    "print(log_report_type_data.shape)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"car variant data dimention:\")\n",
    "print(car_variant_data.shape)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_data levels:\n",
      "\n",
      "id                   5904\n",
      "factory_number        876\n",
      "downtime_duration       3\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "test_data levels:\n",
      "\n",
      "id                1477\n",
      "factory_number     531\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "assembly_line_info levels:\n",
      "\n",
      "id                    18552\n",
      "assembly_line_type       10\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "issue info levels:\n",
      "\n",
      "id            18552\n",
      "issue_type        5\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "log_report_type_data levels:\n",
      "\n",
      "id                 18552\n",
      "log_report_type      386\n",
      "volume               341\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "car variant data levels:\n",
      "id             18552\n",
      "car_variant       53\n",
      "dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking the unique levels in each data \n",
    "print(\"train_data levels:\\n\")\n",
    "print(train_data.nunique())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"test_data levels:\\n\")\n",
    "print(test_data.nunique())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"assembly_line_info levels:\\n\")\n",
    "print(assembly_line_info.nunique())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"issue info levels:\\n\")\n",
    "print(issue_info.nunique())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"log_report_type_data levels:\\n\")\n",
    "print(log_report_type_data.nunique())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"car variant data levels:\")\n",
    "print(car_variant_data.nunique())\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysing Unique Levels in each dataset.\n",
    "\n",
    "* There are **876** unique factories in `train_data`. \n",
    "* There are **531** unique factories in `test_data`. \n",
    "* There are **10** different types of `assembly lines`. \n",
    "* **5** unique issue types are there in the `issues` data. \n",
    "* There are **386** different log_report_types and 341 volumes in `log report data`.\n",
    "* Currently there are **53** types of car variant in the `car variant data`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining Data For visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data= train_data.drop_duplicates(subset=['id'])\n",
    "assembly_line_info= assembly_line_info.drop_duplicates(subset=['id'])\n",
    "\n",
    "# Merging train data and assembly data\n",
    "df1 = pd.merge(train_data,assembly_line_info, how = 'left',on = 'id') \n",
    "issue_info= issue_info.drop_duplicates(subset=['id'])\n",
    "\n",
    "# Merging issue info\n",
    "df2 = pd.merge(df1,issue_info, how='left', on = 'id')\n",
    "log_report_type_data= log_report_type_data.drop_duplicates(subset=['id'])\n",
    "\n",
    "# Merging log report type\n",
    "df3 = pd.merge(df2,log_report_type_data, how='left',on = 'id')\n",
    "car_variant_data= car_variant_data.drop_duplicates(subset=['id'])\n",
    "\n",
    "# Merging car variant \n",
    "data = pd.merge(df3, car_variant_data, how='left',on = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>factory_number</th>\n",
       "      <th>downtime_duration</th>\n",
       "      <th>assembly_line_type</th>\n",
       "      <th>issue_type</th>\n",
       "      <th>log_report_type</th>\n",
       "      <th>volume</th>\n",
       "      <th>car_variant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13366</td>\n",
       "      <td>factory_number_415</td>\n",
       "      <td>1</td>\n",
       "      <td>assembly_line_type_2</td>\n",
       "      <td>issue_type_4</td>\n",
       "      <td>log_report_type_312</td>\n",
       "      <td>1</td>\n",
       "      <td>car_variant_35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6783</td>\n",
       "      <td>factory_number_474</td>\n",
       "      <td>0</td>\n",
       "      <td>assembly_line_type_2</td>\n",
       "      <td>issue_type_2</td>\n",
       "      <td>log_report_type_312</td>\n",
       "      <td>2</td>\n",
       "      <td>car_variant_35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9519</td>\n",
       "      <td>factory_number_931</td>\n",
       "      <td>1</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "      <td>issue_type_2</td>\n",
       "      <td>log_report_type_203</td>\n",
       "      <td>3</td>\n",
       "      <td>car_variant_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10202</td>\n",
       "      <td>factory_number_700</td>\n",
       "      <td>1</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "      <td>issue_type_1</td>\n",
       "      <td>log_report_type_54</td>\n",
       "      <td>1</td>\n",
       "      <td>car_variant_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4555</td>\n",
       "      <td>factory_number_600</td>\n",
       "      <td>2</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "      <td>issue_type_2</td>\n",
       "      <td>log_report_type_82</td>\n",
       "      <td>30</td>\n",
       "      <td>car_variant_15</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5899</th>\n",
       "      <td>1910</td>\n",
       "      <td>factory_number_403</td>\n",
       "      <td>1</td>\n",
       "      <td>assembly_line_type_2</td>\n",
       "      <td>issue_type_2</td>\n",
       "      <td>log_report_type_232</td>\n",
       "      <td>1</td>\n",
       "      <td>car_variant_34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5900</th>\n",
       "      <td>10475</td>\n",
       "      <td>factory_number_821</td>\n",
       "      <td>0</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "      <td>issue_type_1</td>\n",
       "      <td>log_report_type_74</td>\n",
       "      <td>4</td>\n",
       "      <td>car_variant_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5901</th>\n",
       "      <td>10675</td>\n",
       "      <td>factory_number_798</td>\n",
       "      <td>2</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "      <td>issue_type_2</td>\n",
       "      <td>log_report_type_203</td>\n",
       "      <td>3</td>\n",
       "      <td>car_variant_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5902</th>\n",
       "      <td>14714</td>\n",
       "      <td>factory_number_210</td>\n",
       "      <td>0</td>\n",
       "      <td>assembly_line_type_2</td>\n",
       "      <td>issue_type_2</td>\n",
       "      <td>log_report_type_232</td>\n",
       "      <td>2</td>\n",
       "      <td>car_variant_35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5903</th>\n",
       "      <td>6253</td>\n",
       "      <td>factory_number_1007</td>\n",
       "      <td>0</td>\n",
       "      <td>assembly_line_type_8</td>\n",
       "      <td>issue_type_2</td>\n",
       "      <td>log_report_type_201</td>\n",
       "      <td>6</td>\n",
       "      <td>car_variant_15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5904 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id       factory_number  downtime_duration    assembly_line_type  \\\n",
       "0     13366   factory_number_415                  1  assembly_line_type_2   \n",
       "1      6783   factory_number_474                  0  assembly_line_type_2   \n",
       "2      9519   factory_number_931                  1  assembly_line_type_8   \n",
       "3     10202   factory_number_700                  1  assembly_line_type_8   \n",
       "4      4555   factory_number_600                  2  assembly_line_type_8   \n",
       "...     ...                  ...                ...                   ...   \n",
       "5899   1910   factory_number_403                  1  assembly_line_type_2   \n",
       "5900  10475   factory_number_821                  0  assembly_line_type_8   \n",
       "5901  10675   factory_number_798                  2  assembly_line_type_8   \n",
       "5902  14714   factory_number_210                  0  assembly_line_type_2   \n",
       "5903   6253  factory_number_1007                  0  assembly_line_type_8   \n",
       "\n",
       "        issue_type      log_report_type  volume     car_variant  \n",
       "0     issue_type_4  log_report_type_312       1  car_variant_35  \n",
       "1     issue_type_2  log_report_type_312       2  car_variant_35  \n",
       "2     issue_type_2  log_report_type_203       3  car_variant_15  \n",
       "3     issue_type_1   log_report_type_54       1  car_variant_11  \n",
       "4     issue_type_2   log_report_type_82      30  car_variant_15  \n",
       "...            ...                  ...     ...             ...  \n",
       "5899  issue_type_2  log_report_type_232       1  car_variant_34  \n",
       "5900  issue_type_1   log_report_type_74       4  car_variant_15  \n",
       "5901  issue_type_2  log_report_type_203       3  car_variant_15  \n",
       "5902  issue_type_2  log_report_type_232       2  car_variant_35  \n",
       "5903  issue_type_2  log_report_type_201       6  car_variant_15  \n",
       "\n",
       "[5904 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Variable Destribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = data.downtime_duration.value_counts()\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.style.use('ggplot')\n",
    "Y.plot.pie(autopct=\"%.1f%%\",explode=[0.05]*3)\n",
    "plt.title('Percentage of Different Downtime Durations', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* According to the data, **64.8%** have low downtime (15 minutes to 1 hour), followed by **25.3%** downtime which lasts anywhere between 1 hour and 24 hours and **9.8%** for long downtimes that can last from 24 hours to sometimes even several days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 factories most prone to downtime. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.DataFrame(data.groupby(['factory_number'])['downtime_duration'].sum())\n",
    "X = X.sort_values('downtime_duration', ascending=False)\n",
    "X = X[0:10].reset_index()\n",
    "X.sort_values('downtime_duration',inplace = True, ascending = False)\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.style.use('ggplot')\n",
    "sns.barplot('factory_number','downtime_duration',data = X,palette='crest')\n",
    "plt.ylabel('Total Downtime', fontsize=20)\n",
    "plt.xlabel('Factory Number', fontsize=20)\n",
    "plt.title('Top 10 Factories thar are most prone to downtime', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top factories that are most prone to downtime are - \n",
    "\n",
    " * Factory Number - **1107**\n",
    " * Factory Number - **821**\n",
    " * Factory Number - **1100**\n",
    " * Factory Number - **600**\n",
    " * Factory Number - **734**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assembly line type that are suspected of long downtime duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.DataFrame(data.groupby(['assembly_line_type'])['downtime_duration'].sum())\n",
    "X = X.sort_values('downtime_duration', ascending=False)\n",
    "X = X[0:3].reset_index()\n",
    "X.sort_values('downtime_duration',inplace = True,ascending = False)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.style.use('ggplot')\n",
    "sns.barplot('assembly_line_type','downtime_duration',data = X,palette='flare')\n",
    "plt.ylabel('Total Downtime', fontsize=15)\n",
    "plt.xlabel('Assembly Line Type', fontsize=15)\n",
    "plt.title('Top 3 Assembly Line Type that are suspected of long downtime duration.', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Assembly Line Type `8` &  Assembly Line Type `2` are responsible for majority of the downtime. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Car Varients that are effecting the downtime duration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.DataFrame(data.groupby(['car_variant'])['downtime_duration'].sum())\n",
    "X = X.sort_values('downtime_duration', ascending=False)\n",
    "X = X[0:3].reset_index()\n",
    "X.sort_values('downtime_duration',inplace = True,ascending = False)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.style.use('ggplot')\n",
    "sns.barplot('car_variant','downtime_duration',data = X,palette='crest')\n",
    "plt.ylabel('Total Downtime', fontsize=15)\n",
    "plt.xlabel('Car Variant', fontsize=15)\n",
    "plt.title('Top 3 Car Variants that are suspected of long downtime duration.', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Car Variant `11` and Car variant `15` are also responsible for majority of the downtime. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_END_"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}