Predictive_Maintaince_Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hargurjeet/MachineLearning/blob/master/Predictive_Maintaince_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmznRkMefrzz"
      },
      "source": [
        "# **Predictive Maintaince Classification**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qZx2AMuJ0Xw"
      },
      "source": [
        "Introduction of predictive approach and monitoring has to this day, prevented plenty of accidents and saved millions of dollars in plant downtime and equipment failure. In today’s competitive market, no business can afford unpredicted downtime or accidental machine breakdown. By implementing IoT-powered predictive maintenance (PdM) solutions, oil refining and petrochemical companies can maximize asset performance, increase operational performance, improve product quality, minimize downtime, and reduce costs.\n",
        "\n",
        "In this case study I buuild a machine learning model that can predict the failure of the machinaery based of the set of parameter/features collected by IoT devices."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gOccwMd4Fxtj"
      },
      "source": [
        "# **Table Of Contents**<a name=\"top\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "  1. [About the Dataset](#1)\n",
        "  2. [Loading the dataset Preprocessing](#2)\n",
        "  3. [Explainatory Data Analysis](#3)\n",
        "  4. [Featuring Engineering](#4)\n",
        "  5. [Building ML Pipelines](#5)\n",
        "  6. [Training ML models](#6)\n",
        "  7. [Hyperparmeterization](#7)\n",
        "  8. [Model Evaluation](#8)\n",
        "  9. [Summary](#9)\n",
        "  10. [Future Work](#10)\n",
        "  11. [Reference](#11)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJ2LZMxTHDRm"
      },
      "source": [
        "# 1: AboutDataset <a name=\"1\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvxdOYoIKh6l"
      },
      "source": [
        "The dataset consists of 10 000 data points stored as rows with 14 features in columns\n",
        "\n",
        "- UID: unique identifier ranging from 1 to 10000\n",
        "- productID: consisting of a letter L, M, or H for low (50% of all products), medium (30%), and high (20%) as product quality variants and a variant-specific serial number\n",
        "- air temperature [K]: generated using a random walk process later normalized to a standard deviation of 2 K around 300 K\n",
        "- process temperature [K]: generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K.\n",
        "- rotational speed [rpm]: calculated from powepower of 2860 W, overlaid with a normally distributed noise\n",
        "- torque [Nm]: torque values are normally distributed around 40 Nm with an Ïƒ = 10 Nm and no negative values.\n",
        "- tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process. and a\n",
        "'machine failure' label that indicates, whether the machine has failed in this particular data point for any of the following failure modes are true.\n",
        "- Target : Failure or Not\n",
        "- Failure Type : Type of Failure"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YogTWzRcHG08"
      },
      "source": [
        "# 2: Loading the dataset Preprocessing <a name=\"2\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BK3fJdlaNZ2w"
      },
      "source": [
        "We load the dataset from [kaggle](https://www.kaggle.com/datasets) using a custom made library opendatasets.\n",
        "\n",
        "I also import other standard imports to perfrom data analysis, model building...etc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGjgVxYbbgiN",
        "outputId": "9667bb8f-d387-4696-841e-8be4b007e974"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting opendatasets\n",
            "  Downloading opendatasets-0.1.20-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (from opendatasets) (1.5.12)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from opendatasets) (7.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from opendatasets) (4.62.3)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (2.23.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (1.15.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (2021.10.8)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (5.0.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle->opendatasets) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle->opendatasets) (3.0.4)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.20\n"
          ]
        }
      ],
      "source": [
        "!pip install opendatasets "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Vn0wAb1Nfuyl"
      },
      "outputs": [],
      "source": [
        "# To Imporing dataset from kaggle\n",
        "import opendatasets as od\n",
        "\n",
        "# For data preprocessing\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from collections import Counter\n",
        "from imblearn.over_sampling import SMOTENC\n",
        "\n",
        "# To build visualizations\n",
        "from matplotlib.pyplot import figure\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import scipy.stats\n",
        "import pylab\n",
        "\n",
        "# Librareis for building pipelines\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "# To build ML models\n",
        "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "import lightgbm as lgb\n",
        "from sklearn.metrics import accuracy_score,classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.naive_bayes import GaussianNB"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BO2te5GoNvWW"
      },
      "source": [
        "To download the dataset from Kaggle you need to generate the API key from kaggle website. You can go through this [article](https://jovian.ai/forum/t/what-is-kaggle-api-key-how-to-get-my-kaggle-key/17721) to understand the process"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "EpCOFwJBf0-u",
        "outputId": "2d6b2b7a-a297-4c81-905d-5d28dc39b276"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: hargurjeet\n",
            "Your Kaggle Key: ··········\n",
            "Downloading machine-predictive-maintenance-classification.zip to ./machine-predictive-maintenance-classification\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 137k/137k [00:00<00:00, 20.5MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e9338011-ffca-4312-ab52-883a48b30d0d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>UDI</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature [K]</th>\n",
              "      <th>Rotational speed [rpm]</th>\n",
              "      <th>Torque [Nm]</th>\n",
              "      <th>Tool wear [min]</th>\n",
              "      <th>Target</th>\n",
              "      <th>Failure Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M14860</td>\n",
              "      <td>M</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1551</td>\n",
              "      <td>42.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>L47181</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408</td>\n",
              "      <td>46.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>L47182</td>\n",
              "      <td>L</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498</td>\n",
              "      <td>49.4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>L47183</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>L47184</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9338011-ffca-4312-ab52-883a48b30d0d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e9338011-ffca-4312-ab52-883a48b30d0d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e9338011-ffca-4312-ab52-883a48b30d0d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   UDI Product ID Type  ...  Tool wear [min]  Target  Failure Type\n",
              "0    1     M14860    M  ...                0       0    No Failure\n",
              "1    2     L47181    L  ...                3       0    No Failure\n",
              "2    3     L47182    L  ...                5       0    No Failure\n",
              "3    4     L47183    L  ...                7       0    No Failure\n",
              "4    5     L47184    L  ...                9       0    No Failure\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# using pandas library to import the dataset\n",
        "# Setting the data columns to index and for the convience to perfrom data analysis\n",
        "od.download('https://www.kaggle.com/shivamb/machine-predictive-maintenance-classification')\n",
        "df = pd.read_csv(\"/content/machine-predictive-maintenance-classification/predictive_maintenance.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZV09qmON3_L"
      },
      "source": [
        "Before we process any analysis it is important for us to check if any junk data is avaliable in our dataset, If yes then its needs to be cleaned up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXQtPGwehBpa",
        "outputId": "4c82fb05-45fa-47b1-fe8b-867218d58ce7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 10 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   UDI                      10000 non-null  int64  \n",
            " 1   Product ID               10000 non-null  object \n",
            " 2   Type                     10000 non-null  object \n",
            " 3   Air temperature [K]      10000 non-null  float64\n",
            " 4   Process temperature [K]  10000 non-null  float64\n",
            " 5   Rotational speed [rpm]   10000 non-null  int64  \n",
            " 6   Torque [Nm]              10000 non-null  float64\n",
            " 7   Tool wear [min]          10000 non-null  int64  \n",
            " 8   Target                   10000 non-null  int64  \n",
            " 9   Failure Type             10000 non-null  object \n",
            "dtypes: float64(3), int64(4), object(3)\n",
            "memory usage: 781.4+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43lQMYFJN-UE"
      },
      "source": [
        "It is observed that dataset doesnot contain any null values. This is great news !!!\n",
        "\n",
        "Now the dataset has two target columns i.e. Target and Failure type. For the analysis of this case study I will choose the Target columns and proceed with my analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mu9bGZG9gCoo",
        "outputId": "2c2d8e0a-3b80-45fb-a4ea-f67eca6a82fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    9661\n",
            "1     339\n",
            "Name: Target, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "## Accessing the Target feature\n",
        "print(df.Target.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVP8vZYJO80b"
      },
      "source": [
        "Checking the measure of central tendecy and disperssion of data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "kYmKN1-piMFo",
        "outputId": "9d329844-f53e-48e2-c34a-ea732e8d33f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ac28b434-df71-4efb-b026-320c0e2734c2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>UDI</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>5000.50000</td>\n",
              "      <td>2886.895680</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2500.75</td>\n",
              "      <td>5000.5</td>\n",
              "      <td>7500.25</td>\n",
              "      <td>10000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>300.00493</td>\n",
              "      <td>2.000259</td>\n",
              "      <td>295.3</td>\n",
              "      <td>298.30</td>\n",
              "      <td>300.1</td>\n",
              "      <td>301.50</td>\n",
              "      <td>304.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Process temperature [K]</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>310.00556</td>\n",
              "      <td>1.483734</td>\n",
              "      <td>305.7</td>\n",
              "      <td>308.80</td>\n",
              "      <td>310.1</td>\n",
              "      <td>311.10</td>\n",
              "      <td>313.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rotational speed [rpm]</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>1538.77610</td>\n",
              "      <td>179.284096</td>\n",
              "      <td>1168.0</td>\n",
              "      <td>1423.00</td>\n",
              "      <td>1503.0</td>\n",
              "      <td>1612.00</td>\n",
              "      <td>2886.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Torque [Nm]</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>39.98691</td>\n",
              "      <td>9.968934</td>\n",
              "      <td>3.8</td>\n",
              "      <td>33.20</td>\n",
              "      <td>40.1</td>\n",
              "      <td>46.80</td>\n",
              "      <td>76.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tool wear [min]</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>107.95100</td>\n",
              "      <td>63.654147</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.00</td>\n",
              "      <td>108.0</td>\n",
              "      <td>162.00</td>\n",
              "      <td>253.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Target</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.03390</td>\n",
              "      <td>0.180981</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac28b434-df71-4efb-b026-320c0e2734c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ac28b434-df71-4efb-b026-320c0e2734c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ac28b434-df71-4efb-b026-320c0e2734c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                           count        mean  ...      75%      max\n",
              "UDI                      10000.0  5000.50000  ...  7500.25  10000.0\n",
              "Air temperature [K]      10000.0   300.00493  ...   301.50    304.5\n",
              "Process temperature [K]  10000.0   310.00556  ...   311.10    313.8\n",
              "Rotational speed [rpm]   10000.0  1538.77610  ...  1612.00   2886.0\n",
              "Torque [Nm]              10000.0    39.98691  ...    46.80     76.6\n",
              "Tool wear [min]          10000.0   107.95100  ...   162.00    253.0\n",
              "Target                   10000.0     0.03390  ...     0.00      1.0\n",
              "\n",
              "[7 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "xAEFd5Fjicpo"
      },
      "outputs": [],
      "source": [
        "# Columns not relvant for our analysis\n",
        "df_relevant_features = df.drop('UDI', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLY7eaHcHHRR"
      },
      "source": [
        "# 3: Explainatory Data Analysis <a name=\"3\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrgTz-gvhVex"
      },
      "source": [
        "## 3.1 Visualizing all relevant featureas and Target columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "id": "9vUHfAiGoypA",
        "outputId": "833fd6d7-5925-446a-9d9c-f2cbc912c1a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Failure Type')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1584x864 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(2, 4, figsize=(22,12))\n",
        "fig.suptitle('Visualization of maintiance features')\n",
        "\n",
        "#Type\n",
        "sns.countplot(ax=axes[0,0], x=df_relevant_features.Type, data=df_relevant_features)\n",
        "axes[0, 0].set_title('Type')\n",
        "\n",
        "#Air Temperature\n",
        "sns.histplot(ax=axes[0,1], x=\"Air temperature [K]\", data=df_relevant_features, kde=True)\n",
        "axes[0, 1].set_title(\"Air Temperature\")\n",
        "\n",
        "#Process temperature [K]\t\n",
        "sns.kdeplot(ax=axes[0,2], x=\"Process temperature [K]\", data=df_relevant_features, color='green')\n",
        "axes[0, 2].set_title(\"Process temperature\")\n",
        "\n",
        "# Rotational speed [rpm]\n",
        "sns.kdeplot(ax=axes[0,3], x=\"Rotational speed [rpm]\", data=df_relevant_features)\n",
        "axes[0, 3].set_title(\"Rotational speed\")\n",
        "\n",
        "# Torque [Nm]\n",
        "sns.kdeplot(ax=axes[1,0], x=\"Torque [Nm]\", data=df_relevant_features, fill=True)\n",
        "axes[1, 0].set_title(\"Process temperature\")\n",
        "\n",
        "#Tool wear [min]\n",
        "sns.boxplot(ax=axes[1,1], x=\"Tool wear [min]\", data=df_relevant_features, color='grey')\n",
        "axes[1, 1].set_title(\"Tool wear [min]\")\n",
        "\n",
        "#Target\n",
        "sns.countplot(ax=axes[1,2], x=df_relevant_features.Target, data=df_relevant_features)\n",
        "axes[1, 2].set_title('Target')\n",
        "\n",
        "#Failure Type\n",
        "g = sns.countplot(ax=axes[1,3], x=df_relevant_features['Failure Type'], data=df_relevant_features)\n",
        "g.set_xticklabels(labels = df_relevant_features['Failure Type'].value_counts().index, rotation=90)\n",
        "axes[1, 3].set_title('Failure Type')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-fdj1yklLCw"
      },
      "source": [
        "Insights - \n",
        "- As L, M and H represend the product quality. Low quality products are 6 times more than the high quality products.\n",
        "- Data points at torque seems to be normally distributed. \n",
        "- The feature 'Target' are highly imbalanced, This will cause overfitting issue to one column, Hence an importance step would be to overcome the class imbalance.\n",
        "- The distribution of Rational speed is left skewed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKJSM37kpSRb"
      },
      "source": [
        "## 3.2 Features relations\n",
        "\n",
        "Finding and understanding the co relations between basic feature linkages\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "opB5FbP3kM-e",
        "outputId": "ac30e0a1-e000-43d5-c6c0-200184c63154"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Co Relation plot')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Compute the correlation matrix\n",
        "corr = df_relevant_features.corr()\n",
        "\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5}).set_title('Co Relation plot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icYr5ccJlRt8",
        "outputId": "ccd08167-4681-413e-e455-2b78c050bda9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Air temperature [K]        0.082556\n",
              "Process temperature [K]    0.035946\n",
              "Rotational speed [rpm]    -0.044188\n",
              "Torque [Nm]                0.191321\n",
              "Tool wear [min]            0.105448\n",
              "Target                     1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df_relevant_features.corrwith(df_relevant_features['Target'], axis = 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PT2buDE4ofvA"
      },
      "source": [
        "Insights - \n",
        "- Strong -ve co realtion between torque and rational speed.\n",
        "- Tool wear in hihgly co realted to Target.\n",
        "- Air Temperature and Process Temperature seems to be +ve co related."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjEiJOQYo01X",
        "outputId": "eb2ad0b9-09ed-4aae-c34b-6b8bf573d111"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Product ID', 'Type', 'Air temperature [K]', 'Process temperature [K]',\n",
              "       'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Target',\n",
              "       'Failure Type'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df_relevant_features.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z-6h5_6ZqGIj",
        "outputId": "5b344499-607a-42ba-e547-6d0b59511580"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2091ae0b50>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1296x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(18,4))\n",
        "fig.suptitle('Impact of Torque and Rotaitonal speed on Failure')\n",
        "\n",
        "#Target\n",
        "# sns.countplot(ax=axes[0,0], x=df_relevant_features.Type, data=df_relevant_features)\n",
        "sns.scatterplot(ax=axes[0], x=\"Rotational speed [rpm]\", y= 'Torque [Nm]', data=df_relevant_features, hue='Target')\n",
        "sns.scatterplot(ax=axes[1], x=\"Rotational speed [rpm]\", y= 'Torque [Nm]', data=df_relevant_features, hue='Failure Type')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcWRkBsbpeJD"
      },
      "source": [
        "insights - \n",
        "- The chances of failure increase if the torque is too high or when the Roataional speed is too high.\n",
        "- On high torque following failure are pre dominant\n",
        "  - Power Failure\n",
        "  - Obverstrain Failure\n",
        "  - tool wear Fialure\n",
        "  - Heat dessipation Failure\n",
        "\n",
        "- On high rotational speeds, Power failure seems to be the only reason for cause of failure"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "WlP2_AhwkMzv",
        "outputId": "d4d6fcbf-65a5-4982-fdd8-8d5282682846"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f209194a5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1296x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(2, 2, figsize=(18,8))\n",
        "fig.suptitle('Impact of Torque and Rotaitonal speed on Failure')\n",
        "\n",
        "sns.kdeplot(ax=axes[0,0], data=df_relevant_features, x='Process temperature [K]', hue='Target', multiple=\"stack\")\n",
        "sns.scatterplot(ax=axes[0,1], data=df_relevant_features[['Air temperature [K]', 'Process temperature [K]']])\n",
        "sns.kdeplot(ax=axes[1,0], data=df_relevant_features, x='Air temperature [K]', hue='Target', multiple=\"stack\")\n",
        "sns.scatterplot(ax=axes[1,1], data=df_relevant_features, x='Air temperature [K]', y='Process temperature [K]', hue='Failure Type')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0TpBPeLrV2a"
      },
      "source": [
        "Insights\n",
        "- Air Temeprature and Process temperture are highly co related\n",
        "- There is no clear realtionship btw failures and both the temperature variables\n",
        "- Failure like Tool wear failure and overstrain failure is less likely to happen at lower levels of Air Temperature and Process Temperature"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RULXs_tlHK18"
      },
      "source": [
        "# 4: Featuring Engineering <a name=\"4\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rs1IqILsmjwO"
      },
      "source": [
        "As we observed during the initial analysis, there is high imbalance between targets we now implement SMOTE technique to overcome imbalance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYAQnmfLTCWU",
        "outputId": "8487c611-00ca-4c02-a331-b4e2d118fd21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    9661\n",
              "1     339\n",
              "Name: Target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df_relevant_features.Target.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ZVarFq4IkMuf"
      },
      "outputs": [],
      "source": [
        "X = df_relevant_features[['Type', 'Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']]\n",
        "y = df_relevant_features.Target\n",
        "\n",
        "# split data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "\n",
        "# create artificial data with SMOTE\n",
        "oversample = SMOTENC(categorical_features=[0], random_state=42)\n",
        "X_train_smote, y_train_smote = oversample.fit_resample(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-X63oOUp-y8"
      },
      "source": [
        "After implementing SMOTE class, Checking the targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAkf8xFm0iXv",
        "outputId": "078c004d-0bc0-4fb2-85d6-b20d3b6e339c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({0: 7233, 1: 7233})\n"
          ]
        }
      ],
      "source": [
        "# count number of records in each class\n",
        "count = Counter(y_train_smote)\n",
        "print(count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCzj9bV51b1z"
      },
      "source": [
        "As the imbalance is taken care off now let us build pipeline to handle missing values, Standarizaiton, label encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18kPItwoHL5n"
      },
      "source": [
        "# 5: Building ML Pipelines <a name=\"5\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUjH9GiRoc5a"
      },
      "source": [
        "Let us first segregate numberical and categorical columns. As we are building sklearn pipelines both of the columns types are needed to be handled seperately."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5bUSAbDYeVl",
        "outputId": "b21cd09c-b2bf-4224-bec8-e88f43bd6dc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']\n",
            "\n",
            "['Type']\n"
          ]
        }
      ],
      "source": [
        "numeric_cols = X_train.select_dtypes(include=np.number).columns.tolist()\n",
        "categorical_cols = X_train.select_dtypes('object').columns.tolist()\n",
        "print(numeric_cols)\n",
        "print()\n",
        "print(categorical_cols)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qY9OCgmpowJw"
      },
      "source": [
        "Below cells display the implementation of sklearn pipeline. On a high level following steps are requeired to be performed\n",
        "\n",
        "- Numberical transformer - We pass all the nubmerical columns for inputation and scaling.\n",
        "\n",
        "- Categorical transformer - here we pass all the categrocial columns for imputation and one hot encoding\n",
        "- Preprocessor - The preprocessor object bundles up above two steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "3JTvqYcG0iUu"
      },
      "outputs": [],
      "source": [
        "# Preprocessing for numerical data\n",
        "numerical_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(missing_values = np.nan, strategy='mean')),\n",
        "    ('scaler', MinMaxScaler())\n",
        "])\n",
        "\n",
        "\n",
        "# Preprocessing for categorical data\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(missing_values = np.nan , strategy='most_frequent')),\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
        "])\n",
        "\n",
        "# Bundle preprocessing for numerical and categorical data\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numerical_transformer, numeric_cols),\n",
        "        ('cat', categorical_transformer, categorical_cols)\n",
        "    ])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEE4AcOPHNJ4"
      },
      "source": [
        "# 6: Training ML models <a name=\"6\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eGpL0FZ2u-H"
      },
      "source": [
        "As we are currently working on classification problem, I take the following ML model classifer and test out their perpormance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "9MBVnssk0iM-"
      },
      "outputs": [],
      "source": [
        "# spot check some algorithms\n",
        "classifier = []\n",
        "classifier.append(('LR', LogisticRegression()))\n",
        "classifier.append(('LDA', LinearDiscriminantAnalysis()))\n",
        "classifier.append(('KNN', KNeighborsClassifier()))\n",
        "classifier.append(('NB', GaussianNB()))\n",
        "classifier.append(('SVM', SVC()))\n",
        "classifier.append(('CTREE', DecisionTreeClassifier()))\n",
        "classifier.append(('RFT', RandomForestClassifier()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-ASB2pR0iPh",
        "outputId": "57d939b9-9a5e-4180-9d12-5362b4711f00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:LR 0.8296\n",
            "Accuracy:LDA 0.83\n",
            "Accuracy:KNN 0.9156\n",
            "Accuracy:NB 0.5628\n",
            "Accuracy:SVM 0.8972\n",
            "Accuracy:CTREE 0.9488\n",
            "Accuracy:RFT 0.9552\n"
          ]
        }
      ],
      "source": [
        "# Bundle preprocessing and modeling code in a pipeline\n",
        "for model, clf in classifier:\n",
        "  my_pipeline = Pipeline(steps=[('preprocessor', preprocessor)\n",
        "                                ,('model', clf)\n",
        "                              ])\n",
        "  # # Preprocessing of training data, fit model \n",
        "  my_pipeline.fit(X_train_smote, y_train_smote)\n",
        "\n",
        "  # Preprocessing of validation data, get predictions\n",
        "  preds = my_pipeline.predict(X_test)\n",
        "\n",
        "  # Evaluate the model\n",
        "  score = accuracy_score(y_test, preds)\n",
        "  \n",
        "  print('Accuracy:' + model, score)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yb0C7thpqWld"
      },
      "source": [
        "One of the best way to check your model performance is by training it through the cross validations. My pipelines really make it easy to setup the cross validations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1HvQWA40iKU",
        "outputId": "81e980ca-3682-4b8d-c51c-bb4fd1d80902"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: 0.834024 (0.004687)\n",
            "LDA: 0.831466 (0.005104)\n",
            "KNN: 0.953339 (0.007348)\n",
            "NB: 0.724250 (0.007073)\n",
            "SVM: 0.916771 (0.007359)\n",
            "CTREE: 0.961082 (0.005260)\n",
            "RFT: 0.975529 (0.003982)\n"
          ]
        }
      ],
      "source": [
        "# test options\n",
        "num_folds = 10\n",
        "seed = 7\n",
        "scoring = 'accuracy'\n",
        "\n",
        "results = []\n",
        "names = []\n",
        "for name, clf in classifier:\n",
        "    my_pipeline = Pipeline(steps=[('preprocessor', preprocessor)\n",
        "                                ,('model', clf)\n",
        "                              ])\n",
        "    kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
        "    cv_results = cross_val_score(my_pipeline, X_train_smote, y_train_smote, cv=kfold, scoring=scoring)\n",
        "    results.append(cv_results)\n",
        "    names.append(name)\n",
        "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        "    print(msg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuM97OTmHPOl"
      },
      "source": [
        "# 7: Hyperparmeterization <a name=\"7\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5aW_BbdWBBGP"
      },
      "source": [
        "Random Forest seems to be having the best accuracy. Hence I choose random forest to further perform the hyperparameterizaiton to further enhance the model performace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "CFROjdqMVNdH"
      },
      "outputs": [],
      "source": [
        "# KNN algorithm tuning\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid_param = [\n",
        "                 {\"classifier\": [RandomForestClassifier()],\n",
        "                 \"classifier__n_estimators\": [10, 100, 1000],\n",
        "                 \"classifier__max_depth\":[5,8,15,25,30,None],\n",
        "                 \"classifier__min_samples_leaf\":[1,2,5,10,15,100],\n",
        "                 \"classifier__max_leaf_nodes\": [2, 5,10]}]\n",
        "# create a gridsearch of the pipeline, the fit the best model\n",
        "\n",
        "my_pipeline = Pipeline(steps=[('preprocessor', preprocessor)\n",
        "                                ,(\"classifier\", RandomForestClassifier())\n",
        "                              ])\n",
        "gridsearch = GridSearchCV(my_pipeline, grid_param, cv=5, verbose=0,n_jobs=-1) # Fit grid search\n",
        "best_model = gridsearch.fit(X_train_smote,y_train_smote)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSKr3zZJEHWq",
        "outputId": "bf229cf0-be12-4764-83f4-36b0eeba89ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pipeline(steps=[('preprocessor',\n",
            "                 ColumnTransformer(transformers=[('num',\n",
            "                                                  Pipeline(steps=[('imputer',\n",
            "                                                                   SimpleImputer()),\n",
            "                                                                  ('scaler',\n",
            "                                                                   MinMaxScaler())]),\n",
            "                                                  ['Air temperature [K]',\n",
            "                                                   'Process temperature [K]',\n",
            "                                                   'Rotational speed [rpm]',\n",
            "                                                   'Torque [Nm]',\n",
            "                                                   'Tool wear [min]']),\n",
            "                                                 ('cat',\n",
            "                                                  Pipeline(steps=[('imputer',\n",
            "                                                                   SimpleImputer(strategy='most_frequent')),\n",
            "                                                                  ('onehot',\n",
            "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
            "                                                  ['Type'])])),\n",
            "                ('classifier',\n",
            "                 RandomForestClassifier(max_leaf_nodes=10, min_samples_leaf=10,\n",
            "                                        n_estimators=1000))])\n",
            "The mean accuracy of the model is: 0.9215401631411586\n"
          ]
        }
      ],
      "source": [
        "print(best_model.best_estimator_)\n",
        "print(\"The mean accuracy of the model is:\",best_model.score(X_train_smote,y_train_smote))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEZDLsBTHQXn"
      },
      "source": [
        "# 8: Model Evaluation <a name=\"8\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ZQ_Sn_nHsfJG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06cfed36-1eaa-4952-96d9-138eff85d61d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.88      0.94      2428\n",
            "           1       0.18      0.83      0.29        72\n",
            "\n",
            "    accuracy                           0.88      2500\n",
            "   macro avg       0.59      0.86      0.61      2500\n",
            "weighted avg       0.97      0.88      0.92      2500\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred=gridsearch.predict(X_test)\n",
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iozmUE1hHQ6_"
      },
      "source": [
        "# 9: Summary <a name=\"9\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4ZCw1w2rzwa"
      },
      "source": [
        "Following is the summary of the steps we performed while doing the analysis.\n",
        "\n",
        "- Downloaded the dataset from Kaggle.\n",
        "- Performed pre processing like checking for null values, removing redundant columns, importing all the required libraries.\n",
        "- Performed explainatory data analysis\n",
        "  - few key insights\n",
        "    - Few features are normally distributed and few are highly skewed. \n",
        "    - Target feature is highly imbalanced.\n",
        "    - Strong co realation between torque and rotational speed.\n",
        "    - Air Temperature and Process Temperature seems to be +ve co related.\n",
        "- Implemented SMOTE technique to overcome the class imbalance.\n",
        "- Build sklearn pipelines and trained muliple ML models on top of those configured pipelines.\n",
        "- Indentified the best model and performed hyperparametrization to further enchance the performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MPqjKSLoHRqg"
      },
      "source": [
        "# 10: Future Work <a name=\"10\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lH151Pzr0n5"
      },
      "source": [
        "- I have not tried boosted trees. Models like XGBoost and LightGBM can also be tried to check the performance.\n",
        "- You can try building neural network and comapare the performace againt the developed ML model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECp-MYe6HSTC"
      },
      "source": [
        "# 11: Reference <a name=\"11\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCUkiHLrr1aN"
      },
      "source": [
        "- https://www.kaggle.com/shivamb/machine-predictive-maintenance-classification"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Predictive Maintaince Classification.ipynb",
      "toc_visible": true,
      "provenance": [],
      "authorship_tag": "ABX9TyN2b7vKOseOykJmNAgPWrqk",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}