Analysis_of_Temperature_Rise_in_PMSM.ipynb

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      "name": "Analysis_of_Temperature_Rise_in_PMSM.ipynb",
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    },
    "kernelspec": {
      "name": "python3",
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      "name": "python"
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  "cells": [
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      "source": [
        "**Date Created**: July 10 , 2021\n",
        "\n",
        "**Topic**: EDA on PMSM Temperature Analysis\n",
        "\n",
        "**Author**:\n",
        " Shivani Jitendra Khare\n",
        "\n",
        " **Reach Out** : [Email](shivanikhare2000@gmail.com) | [LinkedIn](www.linkedin.com/in/shivani-khare) | [GitHub](https://github.com/Shivani-Khare/MLnow_2.0)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtaOZ6KCCGA8"
      },
      "source": [
        "##**Table of Contents**\n",
        "\n",
        "  1. [Introduction](#section1)<br>\n",
        "  2. [Problem Statement](#section2)\n",
        "  3. [Data Introduction](#section3)\n",
        "  4. [Importing Dependencies](#section4)\n",
        "  5. [Data Loading and Description](#section5)\n",
        "  6. [Data Profiling](#section6)\n",
        "        * 6.1. [Understanding the Dataset](#section6.1)\n",
        "        * 6.2. [Pre-Profiling](#section6.2)\n",
        "        * 6.3. [Pre-Processing](#section6.3)\n",
        "        * 6.4. [Post-Profiling](#section6.4)\n",
        "  7. [Exploratory Data Analysis](#section7)\n",
        "        * 7.1. [Which parameters are highly correlated and negatively correlated with each other?](#section7.1.)\n",
        "        * 7.2. [Which profile id varies maximum and which one varies minimum for temperature rise for various parameters?](#section7.2.)\n",
        "        * 7.3. [Which profile id has highiest pm temperature?](#section7.3.)\n",
        "        * 7.4. [Which component of stator (from stator_yoke, stator winding, stator tooth) observes highiest variations for profile_id 20 (highiest varying profile_id)?](#section7.4.)\n",
        "  8. [Conclusion](#section8)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKLM1DRoE5U4"
      },
      "source": [
        "<a id=section1></a>\n",
        "## 1. Introduction\n",
        "\n",
        "<center><img src=\"https://d1c4d7gnm6as1q.cloudfront.net/Pictures/480xany/4/7/1/10471_tn_csr-tq600-innotrans2014.jpg\" width=\"400\" height=\"300\" /></center>\n",
        "<br> \n",
        "\n",
        "* Permanent Magnet Synchronous Motors (PMSM) are **AC synchronous motors** whose feild excitation is provided by **Permanent Magnets** (PM).\n",
        "\n",
        "* For High-speed PMSM, **efficiency** is adversely affected by demagnetization of PM which is caused by **increase in temperature** of the motor. \n",
        "\n",
        "* Hence, it is important to study the **parameters** that contribute towards rise in temperature of the machine causing reduction in efficiency of PMSM. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wuq_-UJJE0o"
      },
      "source": [
        "<a id=section2></a>\n",
        "\n",
        "## 2. Problem Statement\n",
        " <center><img src = \"https://icdn.tradew.com/file/201606/1569468/gif/7325096.gif\" width = \"400\" ; height = \"300\" /></center> <br>\n",
        "\n",
        " * The target features of PMSM are the **rotor** which is made of permanent magnet and **torque**.\n",
        " * By performng the analysis of how different parameters affect the temperature rise in the motor parts enables automotive industries to manufacture motors with parameters that are capable of **maximizing** the **efficiency** of the PMSM motor for **Electric Vehicle** (EV) application.\n",
        " * **Irreversible demagnetization** caused due to the effect of **overheating** is capable of causing severe damages to the machine parts as well as the output. \n",
        " * There is a severe need to **analyse the overheating effect** to avoid the demagnetization thereby having maximum efficiency. \n",
        "\n",
        " \n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njJJNyw7NN9b"
      },
      "source": [
        "<a id = section3></a>\n",
        "## 3. Data Introduction\n",
        "\n",
        "The data set comprises several sensor data collected from a permanent magnet synchronous motor (PMSM) deployed on a test bench sampled at a frequency of 2 Hz. Test bench measurements were collected by the LEA department at Paderborn University. This data set is mildly anonymized. \n",
        "\n",
        "The data set `measures.csv` contains data of 41 unique profile id's and 13 columns. \n",
        "  * Each row represents the collected data for the particular `profile id`.\n",
        "  * Each column represents observations for the following parameters:\n",
        "      * u_q : Voltage q-component measurement in dq-coordinates (in V).\n",
        "      * coolant : Coolant temperature (in °C).\n",
        "      * stator_windings : Stator winding temperature (in °C) measured with thermocouples.\n",
        "      * u_d : Voltage d-component measurement in dq-coordinates.\n",
        "      * stator_tooth : Stator tooth temperature (in °C) measured with thermocouples.\n",
        "      * motor_speed : Motor speed (in rpm).\n",
        "      * i_d : Current d-component measurement in dq-coordinates.\n",
        "      * i_q : Current q-component measurement in dq-coordinates.\n",
        "      * pm : Permanent magnet temperature (in °C) measured with thermocouples and transmitted wirelessly via a thermography unit.\n",
        "      * stator_yoke : Stator yoke temperature (in °C) measured with thermocouples.\n",
        "      * ambient : Ambient Temperature (in °C).\n",
        "      * torque : Torque generated (in rpm).\n",
        "      * profile_id : distinguishing feature of multiple measurement sessions.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9MOg0dwY0N9"
      },
      "source": [
        "<a id=section4></a>\n",
        "## 4. Importing Dependencies\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-ufsNtb_l45"
      },
      "source": [
        "import numpy as np                                                 # Implements multi-dimensional array and matrices\n",
        "import pandas as pd                                                # For data manipulation and analysis\n",
        "import pandas_profiling                                            # For simple Exploratory data analysis\n",
        "import matplotlib.pyplot as plt                                    # Plotting library for Python programming language and it's numerical mathematics extension NumPy\n",
        "import seaborn as sns                                              # Provides a high level interface for drawing attractive and informative statistical graphics\n",
        "%matplotlib inline\n",
        "sns.set()\n",
        "\n",
        "from subprocess import check_output"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNKM3dyHakJs"
      },
      "source": [
        "<a id=section5></a>\n",
        "##5. Data Loading and Description\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vfpDXKrbXYF",
        "outputId": "0138d13c-e268-4174-a585-94b11e7efcda"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNW7wyhmbk2V",
        "outputId": "4af40fb9-7a9e-401a-941d-4ac6eaa48f29"
      },
      "source": [
        "cd '/content/drive/MyDrive/PMSM/measures_v2.csv'"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/PMSM/measures_v2.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWdKTPKTdFes",
        "outputId": "bdcb1dbc-5733-44c2-9c95-2a760dedf39d"
      },
      "source": [
        "ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "measures_v2.csv  pmsm_after_preprocessing.html  pmsm_before_preprocessing.html\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0SybmftfdI5Z"
      },
      "source": [
        "pmsm_data = pd.read_csv(\"measures_v2.csv\", sep = ',')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "qlvFTMmHeFxy",
        "outputId": "afc9e567-ca03-411e-fc66-521437a29cd2"
      },
      "source": [
        "pmsm_data.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>u_q</th>\n",
              "      <th>coolant</th>\n",
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              "      <th>pm</th>\n",
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              "      <th>torque</th>\n",
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            ],
            "text/plain": [
              "        u_q    coolant  stator_winding  ...    ambient    torque  profile_id\n",
              "0 -0.450682  18.805172       19.086670  ...  19.850691  0.187101          17\n",
              "1 -0.325737  18.818571       19.092390  ...  19.850672  0.245417          17\n",
              "2 -0.440864  18.828770       19.089380  ...  19.850657  0.176615          17\n",
              "3 -0.327026  18.835567       19.083031  ...  19.850647  0.238303          17\n",
              "4 -0.471150  18.857033       19.082525  ...  19.850639  0.208197          17\n",
              "\n",
              "[5 rows x 13 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "DhPt7y-QeKjD",
        "outputId": "ad0ac7cd-345f-4164-e970-1ff24a31d379"
      },
      "source": [
        "pmsm_data.tail()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>u_q</th>\n",
              "      <th>coolant</th>\n",
              "      <th>stator_winding</th>\n",
              "      <th>u_d</th>\n",
              "      <th>stator_tooth</th>\n",
              "      <th>motor_speed</th>\n",
              "      <th>i_d</th>\n",
              "      <th>i_q</th>\n",
              "      <th>pm</th>\n",
              "      <th>stator_yoke</th>\n",
              "      <th>ambient</th>\n",
              "      <th>torque</th>\n",
              "      <th>profile_id</th>\n",
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              "      <td>71</td>\n",
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              "      <td>-2.100158</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "              u_q    coolant  ...        torque  profile_id\n",
              "1330811 -2.152128  30.721298  ... -8.116730e-60          71\n",
              "1330812 -2.258684  30.721306  ... -5.815891e-60          71\n",
              "1330813 -2.130312  30.721312  ... -4.167268e-60          71\n",
              "1330814 -2.268498  30.721316  ... -2.985978e-60          71\n",
              "1330815 -2.100158  30.721319  ... -2.139547e-60          71\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E19hxKDaeW-i"
      },
      "source": [
        "<a id=section6></a>\n",
        "## 6. Data Profiling\n",
        "\n",
        "To gain insights from data we must look into each aspect of it very carefully. We will start with observing few rows and columns of data both from the starting and from the end."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghVm2Gj8wm3a"
      },
      "source": [
        "<a id=section6.1.></a>\n",
        "###6.1. Understanding the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_FzKhBUftiy",
        "outputId": "7ae38e5c-693d-467e-bd6d-58715f720a5e"
      },
      "source": [
        "pmsm_data.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1330816, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoFerpjMf6t2"
      },
      "source": [
        "**Observations**: \n",
        "\n",
        "The dataset has 1330816 rows and 13 columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJ8QJQTjgI3t",
        "outputId": "3a61388d-1b4d-4322-9b49-c93b084d7571"
      },
      "source": [
        "pmsm_data.columns"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['u_q', 'coolant', 'stator_winding', 'u_d', 'stator_tooth',\n",
              "       'motor_speed', 'i_d', 'i_q', 'pm', 'stator_yoke', 'ambient', 'torque',\n",
              "       'profile_id'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-46jmjlgNPq",
        "outputId": "b22f9d1b-bda3-47fb-b6fd-2eac9406aea4"
      },
      "source": [
        "pmsm_data.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1330816 entries, 0 to 1330815\n",
            "Data columns (total 13 columns):\n",
            "u_q               1330816 non-null float64\n",
            "coolant           1330816 non-null float64\n",
            "stator_winding    1330816 non-null float64\n",
            "u_d               1330816 non-null float64\n",
            "stator_tooth      1330816 non-null float64\n",
            "motor_speed       1330816 non-null float64\n",
            "i_d               1330816 non-null float64\n",
            "i_q               1330816 non-null float64\n",
            "pm                1330816 non-null float64\n",
            "stator_yoke       1330816 non-null float64\n",
            "ambient           1330816 non-null float64\n",
            "torque            1330816 non-null float64\n",
            "profile_id        1330816 non-null int64\n",
            "dtypes: float64(12), int64(1)\n",
            "memory usage: 132.0 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KPrE7GR8gamW"
      },
      "source": [
        "**Observations**:\n",
        "* We can see that the data contains equal number of records (i.e., 1330816) and hence, it **doesnot** contain missing data.\n",
        "* All variables are present in appropiate datatype."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "5FlvuV-og27M",
        "outputId": "3b0f0ded-b59a-43f5-f0d0-5080dea1d4dd"
      },
      "source": [
        "pmsm_data.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>u_q</th>\n",
              "      <th>coolant</th>\n",
              "      <th>stator_winding</th>\n",
              "      <th>u_d</th>\n",
              "      <th>stator_tooth</th>\n",
              "      <th>motor_speed</th>\n",
              "      <th>i_d</th>\n",
              "      <th>i_q</th>\n",
              "      <th>pm</th>\n",
              "      <th>stator_yoke</th>\n",
              "      <th>ambient</th>\n",
              "      <th>torque</th>\n",
              "      <th>profile_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "      <td>1.330816e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.427900e+01</td>\n",
              "      <td>3.622999e+01</td>\n",
              "      <td>6.634275e+01</td>\n",
              "      <td>-2.513381e+01</td>\n",
              "      <td>5.687858e+01</td>\n",
              "      <td>2.202081e+03</td>\n",
              "      <td>-6.871681e+01</td>\n",
              "      <td>3.741278e+01</td>\n",
              "      <td>5.850678e+01</td>\n",
              "      <td>4.818796e+01</td>\n",
              "      <td>2.456526e+01</td>\n",
              "      <td>3.110603e+01</td>\n",
              "      <td>4.079306e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.417323e+01</td>\n",
              "      <td>2.178615e+01</td>\n",
              "      <td>2.867206e+01</td>\n",
              "      <td>6.309197e+01</td>\n",
              "      <td>2.295223e+01</td>\n",
              "      <td>1.859663e+03</td>\n",
              "      <td>6.493323e+01</td>\n",
              "      <td>9.218188e+01</td>\n",
              "      <td>1.900150e+01</td>\n",
              "      <td>1.999100e+01</td>\n",
              "      <td>1.929522e+00</td>\n",
              "      <td>7.713575e+01</td>\n",
              "      <td>2.504549e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.529093e+01</td>\n",
              "      <td>1.062375e+01</td>\n",
              "      <td>1.858582e+01</td>\n",
              "      <td>-1.315304e+02</td>\n",
              "      <td>1.813398e+01</td>\n",
              "      <td>-2.755491e+02</td>\n",
              "      <td>-2.780036e+02</td>\n",
              "      <td>-2.934268e+02</td>\n",
              "      <td>2.085696e+01</td>\n",
              "      <td>1.807669e+01</td>\n",
              "      <td>8.783478e+00</td>\n",
              "      <td>-2.464667e+02</td>\n",
              "      <td>2.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.206992e+01</td>\n",
              "      <td>1.869814e+01</td>\n",
              "      <td>4.278796e+01</td>\n",
              "      <td>-7.869090e+01</td>\n",
              "      <td>3.841601e+01</td>\n",
              "      <td>3.171107e+02</td>\n",
              "      <td>-1.154061e+02</td>\n",
              "      <td>1.095863e+00</td>\n",
              "      <td>4.315158e+01</td>\n",
              "      <td>3.199033e+01</td>\n",
              "      <td>2.318480e+01</td>\n",
              "      <td>-1.374265e-01</td>\n",
              "      <td>1.700000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.893818e+01</td>\n",
              "      <td>2.690014e+01</td>\n",
              "      <td>6.511013e+01</td>\n",
              "      <td>-7.429755e+00</td>\n",
              "      <td>5.603635e+01</td>\n",
              "      <td>1.999977e+03</td>\n",
              "      <td>-5.109376e+01</td>\n",
              "      <td>1.577401e+01</td>\n",
              "      <td>6.026629e+01</td>\n",
              "      <td>4.562551e+01</td>\n",
              "      <td>2.479733e+01</td>\n",
              "      <td>1.086035e+01</td>\n",
              "      <td>4.300000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.003439e+01</td>\n",
              "      <td>4.985749e+01</td>\n",
              "      <td>8.814114e+01</td>\n",
              "      <td>1.470271e+00</td>\n",
              "      <td>7.558668e+01</td>\n",
              "      <td>3.760639e+03</td>\n",
              "      <td>-2.979688e+00</td>\n",
              "      <td>1.006121e+02</td>\n",
              "      <td>7.200837e+01</td>\n",
              "      <td>6.146083e+01</td>\n",
              "      <td>2.621702e+01</td>\n",
              "      <td>9.159718e+01</td>\n",
              "      <td>6.500000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.330370e+02</td>\n",
              "      <td>1.015985e+02</td>\n",
              "      <td>1.413629e+02</td>\n",
              "      <td>1.314698e+02</td>\n",
              "      <td>1.119464e+02</td>\n",
              "      <td>6.000015e+03</td>\n",
              "      <td>5.189670e-02</td>\n",
              "      <td>3.017079e+02</td>\n",
              "      <td>1.136066e+02</td>\n",
              "      <td>1.011480e+02</td>\n",
              "      <td>3.071420e+01</td>\n",
              "      <td>2.610057e+02</td>\n",
              "      <td>8.100000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                u_q       coolant  ...        torque    profile_id\n",
              "count  1.330816e+06  1.330816e+06  ...  1.330816e+06  1.330816e+06\n",
              "mean   5.427900e+01  3.622999e+01  ...  3.110603e+01  4.079306e+01\n",
              "std    4.417323e+01  2.178615e+01  ...  7.713575e+01  2.504549e+01\n",
              "min   -2.529093e+01  1.062375e+01  ... -2.464667e+02  2.000000e+00\n",
              "25%    1.206992e+01  1.869814e+01  ... -1.374265e-01  1.700000e+01\n",
              "50%    4.893818e+01  2.690014e+01  ...  1.086035e+01  4.300000e+01\n",
              "75%    9.003439e+01  4.985749e+01  ...  9.159718e+01  6.500000e+01\n",
              "max    1.330370e+02  1.015985e+02  ...  2.610057e+02  8.100000e+01\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "h5ihOeJFg_My",
        "outputId": "de561b48-cb3a-40b4-c5bd-c32fb4a61665"
      },
      "source": [
        "pmsm_data.hist(bins=20, figsize=(18,16))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
            "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
            "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
            "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
            "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
            "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
            "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
            "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
            "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae94fff90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae9457710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae940bd50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae93ca410>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae93ffa10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae93c30d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae93767d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae932bd90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae932bdd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae9420310>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae92f8dd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae920b890>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae92379d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae9185710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae91bcf50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3ae91835d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1152 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFqiU6-8wXqe"
      },
      "source": [
        "<a id=section6.2.></a>\n",
        "### 6.2. Pre - Profiling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGxqwdMBwxEn"
      },
      "source": [
        "- By pandas profiling, an __interactive HTML report__ gets generated which contins all the information about the columns of the dataset, like the __counts and type__ of each _column_. Detailed information about each column, __coorelation between different columns__ and a sample of dataset.<br/>\n",
        "- It gives us __visual interpretation__ of each column in the data.\n",
        "- _Spread of the data_ can be better understood by the distribution plot. \n",
        "- _Grannular level_ analysis of each column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjH2_AUgTh6S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0d27b7e-54a0-412e-8e1a-f5e08c19bc21"
      },
      "source": [
        "!pip install pandas==0.25                 # You need to install pandas version 0.25 for pandas profiling or it might give some error while profiling"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandas==0.25 in /usr/local/lib/python3.7/dist-packages (0.25.0)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from pandas==0.25) (2.8.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from pandas==0.25) (1.19.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas==0.25) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.6.1->pandas==0.25) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcgLYmBs1dzY",
        "outputId": "337d9980-cd0a-4ee9-ac7c-92fc111ccbe7"
      },
      "source": [
        "profile = pandas_profiling.ProfileReport(pmsm_data)\n",
        "profile.to_file(outputfile=\"pmsm_before_preprocessing.html\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas_profiling/describe.py:392: FutureWarning: The join_axes-keyword is deprecated. Use .reindex or .reindex_like on the result to achieve the same functionality.\n",
            "  variable_stats = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8q0_cpr8DpaW"
      },
      "source": [
        "Here, we have done Pandas Profiling before preprocessing our dataset, so we have named the html file as titanic_before_preprocessing.html. Take a look at the file and see what useful insight you can develop from it.\n",
        "Now we will process our data to better understand it.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Fb8uwDvD06a"
      },
      "source": [
        "**Observations from Pandas Profiling before Data Processing**\n",
        "__Dataset info__:\n",
        "- Number of variables\t13\n",
        "- Number of observations\t1330816\n",
        "- Total Missing (%)\t0.0%\n",
        "- Total size in memory\t132.0 MiB\n",
        "- Average record size in memory\t104.0 B\n",
        "\n",
        "**Variables types**:\n",
        "- Numeric 10\n",
        "- Categorical\t0\n",
        "- Boolean\t0\n",
        "- Date\t0\n",
        "- Text (Unique)\t0\n",
        "- Rejected\t3\n",
        "- Unsupported\t0\n",
        "\n",
        "\n",
        "* stator_tooth is highly correlated with stator_winding (ρ = 0.97013).\n",
        "* stator_yoke is highly correlated with stator_tooth (ρ = 0.95311).\n",
        "* torque is highly correlated with i_q (ρ = 0.99646).\n",
        "\n",
        "**Key Notes**\n",
        "\n",
        "* Minimum value of voltage component along q axis (u_q) is -25.291 V and maximum value is 133.04.\n",
        "* Minimum value of voltage component along d axis (u_d) is -131.53 V and maximum value is 131.47.\n",
        "* Maximum motor speed attained is 6000 rpm with a mean of 2202.2 rpm.\n",
        "* Minimum value of current component along q axis (u_q) is -293.43 A and maximum value is 301.71 A.\n",
        "* Minimum value of current component along d axis (u_d) is -278 A and maximum value is 0.051897 A.\n",
        "* Minimum temperature of the permanent magnet is 20.857 deg C and maximum is 113.61 deg C.\n",
        "* Minimum ambient temperature is 8.7835 deg C and maximum is 30.714 deg C.\n",
        "* Minimum coolant temperature is 10.624 deg C and maximum is 101.6 deg C.\n",
        "\n",
        "\n",
        "Hence, we can consider `pm`, `stator_yoke`, `stator_tooth`, `stator_winding` for analysis with respect to various `profile_id`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpfhDRn_IaBR"
      },
      "source": [
        "<a id=section6.3.></a>\n",
        "### 6.3. Pre - Processing\n",
        "\n",
        "- Dealing with missing values<br/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nj7Rk2vaI42M",
        "outputId": "39b834f4-7dc9-422b-eaee-ebd4663610d2"
      },
      "source": [
        "pmsm_data.isnull().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "u_q               0\n",
              "coolant           0\n",
              "stator_winding    0\n",
              "u_d               0\n",
              "stator_tooth      0\n",
              "motor_speed       0\n",
              "i_d               0\n",
              "i_q               0\n",
              "pm                0\n",
              "stator_yoke       0\n",
              "ambient           0\n",
              "torque            0\n",
              "profile_id        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AcKdb33eI_AY"
      },
      "source": [
        "**Observation**: There are `No Null values` on the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBZc9HcxJskz"
      },
      "source": [
        "## 6.4. Post - Pandas Profiling\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yBTJDn8KeFA"
      },
      "source": [
        "Since the data doesnot have null values, post profiling is not necessary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_izIO2_vV4ou"
      },
      "source": [
        "##Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnMDWw2zWAjH"
      },
      "source": [
        "###7.1. Which parameters are highly correlated and negatively correlated with each other?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-i5FbCGKW0xw",
        "outputId": "3c171afc-9249-4685-dde3-b4f2634a4dfb"
      },
      "source": [
        "corr = pmsm_data.corr()    \n",
        "mask = np.zeros_like(corr, dtype=np.bool)  # Generate a mask for the upper triangle\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "cmap = \"PuBuGn\"  # Generate a custom diverging colormap\n",
        "\n",
        "plt.figure(figsize=(14,14))\n",
        "_ = sns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
        "\n",
        "plt.title(\"Heat Map\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Heat Map')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b6dOnFRcXp+nTpxucMu/l9h6rcOHC8vHxUZMmTUy9ay3gSCxWakvkc9WqVctWekjK8QK1f/9+e8cyxKBBg3T8+HHNmDFDoaGh+uCDD+Tl5aVVq1Zp5cqVmjt3brbFpGEOPEeue/XVV7V582ZNmTJFjRo1sh3/8ssvNXjwYDVt2lTjx483MKF9rV27VpMnT9aJEydsx3x8fDR06FBFREQYmCzvtWnTRlOnTtWDDz6oNm3a/O25FotFq1atslMy++OxyF3NmjU1Y8YMNW3aNMfYxo0b9eKLL2rv3r0GJLO/d999V7GxsUpKSpLVapXFYpGnp6eioqL09NNPGx3Pbjp37qxixYopNjZWFotFNWrUUHx8vPz8/LRu3Tq9/vrr2rx5s9ExkY9lZmbKz8/P9vcGyM+YEYR8b+XKlTmOpaSk6IsvvtD69es1ZswYA1IZIyEhQSNGjMi29kfZsmX1wgsvyGq1auzYsZo/f76BCWEEniPXbdq0SYMHD85WAklSo0aN9NJLL2nKlCmmKoIiIiIUERGho0eP6vz583J3d1flypWNjmUXNWrUULFixSRJfn5+ppvZ8Gc8FrmrUKGCLly4kOtYamqqadZBWbZsmcaNG6fWrVurVatW8vLy0rlz5/Tpp59q3LhxcnFxUadOnYyOaRf79u3TzJkz5eLikmORfQ8PDyUmJhqUDAD+fRRByPeqVauW6/GgoCAVLVpUy5YtU4MGDeycyhipqany8PCQk5OTSpQoke1NyY1LXxzVCy+8oKFDh6pixYp64YUX/vZci8Wi2bNn2ymZ8XiOXHflyhV5eXnlOubt7W3aRZIrVaqkSpUqGR3DriZOnGj7+vXXXzcwifF4LHI3ePBgjR07Vvfee6/q169vO/71118rJiZGI0eONDCd/cTFxalLly4aPnx4tuPNmjWTh4eHFixYYJoiqFixYrp48WKuYydOnDDNJVHh4eG3XRhbLBZt2LAhjxMByAsUQSjQateurQULFhgdw27KlSunM2fOSJIeeOABffzxx7Zp7Rs2bHDoNymXLl2yfUJ36dIlg9MUHGZ6jlSvXl1LlixR48aN5ezsbDuelZWld955Rw899JCB6ezv0KFDio2NtS16umzZMvn5+WnatGmqXbu2QkNDjY4IGCY6OlqpqamKjIyUq6urSpUqpfPnzys1NVVubm6aMmWKpkyZIsmxL5n7/fffc708TpLCwsK0dOlSOycyTuPGjTV79mw1bNjQtp6cxWLR1atXtXjxYtP8m9msWTOtX79eFy9eVHBwsG29ve3bt8vV1VUtWrQwOiKAfwFFEAo0Ry8//iosLExffvmlWrVqpd69e6tPnz627dPPnTunl19+2eiIeeadd97J9Wv8PTM9RwYNGqRu3brp4YcfVrNmzeTl5aXExERt2LBB586d08KFC42OaDdffvmlevXqJT8/P7Vp0ybbDLlChQrp/fffN80PNUBu/Pz82D1P12dLfvvttwoODs4x9t1332W7FN3RDR48WE899ZRatmypoKAgWSwWTZ8+XT///LMsFosGDhxodES7KFOmjO69917Nnz8/14XUvby81LNnTwMT5l9OTk5q166dSpUqZXQU4JZYLBr5Xm6XAWVkZOjo0aM6efKkBg8erG7duhmQzHj79u3Thg0bdPXqVQUHB/ODnUnxHPnD999/rzlz5mjXrl26cOGC3N3dVadOHb3wwgumWrixffv2euCBBzR58mRdu3Yt26KnGzZs0NixY7Vt2zajYwIwWGxsrGJjY9W9e3dFRETI09NTSUlJWrNmjRYsWKA+ffqod+/eRse0mwsXLiguLk7bt2+3ra3WsGFDPffcc6b5UCU0NFSvvfaawsPDc4xt3LhRY8aMMeXrx7Zt22wzbHv37q2yZcsqISFB9913n8qUKWN0POCOMSMI+V5ulwEVKVJEwcHBatmypUJCQgxIlT/4+/vL39/f6Bh2ERMTc0fn9+3bN4+S5D88R/5Qo0aNO/674ogOHTqkQYMGScq5g5ybm5vOnz9vRCwA+Uzv3r114cIFLViwINs6g87OzurSpYupSiDp+r+P/fv3V//+/Y2OYpiUlBSlpqbmOpaamnrTRdYdVVJSkqKiorRnzx7de++9OnnypJ588kmVLVtW8fHxKlasmF577TWjYwJ3jCII+Z7ZLwNKTk6+o/Md9ROrRYsWZbudkZGhq1evSrpeeqSlpUmSihYtqsKFC5uqCDL7c+SvrFarjh49qpSUFJUsWVIVK1Y03U5J7u7utvXE/uqXX34x1eUeAG7OYrFo6NCh6tWrl/bu3auUlBS5u7urZs2aXN5iUg0aNNCUKVNyXUh96tSppth84s/Gjx+v8+fPa/Xq1apQoUK2S0obNmxoqs1J4FgogoB8rkGDBnf0Q+z+/fvzMI1xEhISbF/v27dPAwcOVFRUlFq2bKkSJUro4sWLWrt2rWbPnq1p06YZmBRGevfddxUbG6ukpCRZrVZZLBZ5enoqKipKTz/9tNHx7KZ58+aaOXOmAgICVKFCBUnXf+A7e/asFixYoJYtWxqcEEB+cPjwYVWuXFmlSpUy5eXlbdq00dSpU/Xggw+qTZs2f3uuIy8a/mdjx45V7969c11IvXr16hozZozREe1q69atGjdunCpXrmzbtOSGe++9V6dPnzYoGfDPUAQB+dyECRNMN5vhVsaNG6fu3burQ4cOtmMlSpRQx44dlZaWprFjx2r58uUGJoQRli1bpnHjxql169Zq1aqVvLy8dO7cOX366acaN26cXFxcTLMN8qBBg7Rv3z61bdtWDz74oCTp1Vdf1W+//aZKlSqZasYcgJtr3bq1AgMD1bFjR7Vq1UrFixc3OpJd1ahRQ8WKFZN0fQFx3m9JpUuXVnx8vLZt26a9e/fq7Nmz8vb2Vs2aNdWkSROj49ldZmbmTZ8XFy5ckIuLi50TAf8OFosGUODUrFlTMTExub4h2bZtm/r166c9e/YYkAxGeuSRR9S4cWMNHz48x9j48eP1+eefa+3atQYkM0ZGRoZWrVqVbdHT4OBgPfbYYypcuLDR8QDkA1u3btVHH32kTZs2ycXFRREREerYsaNq165tdDQYIC0tTQMGDFD37t1Vr149o+PkC5GRkXJ3d9ebb76pzMxM+fn52TZfePHFF3XlyhXNmTPH6JjAHWNGEFAApaSk5LiW393d3ehYduPr66ulS5cqJCQk26d3VqtV7733nsqWLWtgOhjl999/V9OmTXMdCwsL09KlS+2cyFguLi7q0KFDtplzAPBnoaGhCg0NVXJyslatWqUVK1bo6aefVsWKFdWxY0f95z//kZeXl9Ex7c5qter8+fMqVaqUqWYJFSlSRAkJCXr22WeNjpJvDBw4UF27dtUzzzyjli1bymKxaMOGDXrrrbe0detWvffee0ZHBO4KM4KAAsRqtSo6OlpLlixRenq67XjhwoXVpUsXDR482MB09rNhwwYNGDBAZcuWVdOmTeXp6anExERt3rxZJ06c0IwZM9S8eXOjY8LOwsPD1aFDB/Xp0yfH2KxZsxQfH69NmzYZkMw4R48etU3tL126tGrUqKH777/f6FgA8rEff/xREydO1M6dO+Xs7KyQkBBFRkaaYpHgL774QjExMfrhhx907do1FSpUSH5+furTp49pduDs37+/KlSoYNt5EtK3336rqVOn6ttvv1VmZqYsFosCAwM1ZMgQ1apVy+h4wF2hCAIKkNmzZysmJkY9evTQI488YlsDZc2aNZo/f7769++vXr16GR3TLvbv36+5c+fmuH69Z8+eql69utHxYIDY2FjFxsaqe/fuioiIkKenp5KSkrRmzRotWLBAffr0Mc1WyJcuXdKoUaO0Zs0aZWVl2XbWc3JyUkREhMaNG6d77rnH6JgA8pELFy5o1apV+uijj/Tjjz/K399fDz/8sLZt26Zdu3apT58+Dr2+WHx8vEaMGKG6deuqRYsWtg+Z1q1bp127dmncuHHq2LGj0THz3NatWzVq1Cg1a9ZMoaGh8vT0zDErys/Pz6B0xrp69apSUlLk5uZmW1sKKKgogoACJDw8XO3bt8/1jVhMTIztOn/AjKxWqyZNmqQlS5Zk29nD2dlZXbp00ZAhQ0wzxX/YsGFat26dhg8fnmNnvQkTJqhly5aaOHGi0TEB5ANffvml4uPjtXHjRhUpUkRt2rRRp06dVK1aNds5cXFxio2N1TfffGNg0rwVHh6uBg0aaMKECTnGhg0bpq+//toU77H+/P9dUo5L8C0Wi8PuUAuYCWsEAQXI2bNnb7qAY61atfTWW2/ZORGQf1gsFg0dOlS9evXKsYZWqVKljI5nV+vWrdPLL79805313njjDYogAGratKlOnTql2rVra8yYMXrkkUdUpEiRHOfVrVtXFy5cMCCh/SQlJal169a5jrVu3Vpr1qyxcyJjLF682OgIhvvvf/97R+ePGDEij5IAeYciCChAfH19tWXLFgUHB+cY27p1q3x9fQ1IZX9ZWVn68MMPtW7dOp06dUppaWnZxm8s5AdzWblypUJDQ1WqVCmFhoZmG0tOTtaWLVv0n//8x6B09lWkSBGVK1cu17Hy5curUCFe/gHItktY5cqV//a8GjVq6MCBA3ZKZYyAgAD98MMPatSoUY6xG5fKmUH9+vWNjmC4O5n5ZbFYKIJQIPFOEChAnn32WY0ePVpJSUm2NVASExO1du1a/e9//9Po0aONjmgX0dHRevvtt1WvXj0FBQXJxcXF6EjIB4YNG6Zly5blOvvn999/17Bhw0xTBLVv317vv//+TXfWa9++vYHpAOQXr7zyym2fm5WVpYcfflhz5sxRlSpV8jCV/SQnJ9u+fumll/TSSy8pPT1dzZs3qdQ+BgAAHfBJREFUl4eHh5KSkvTZZ59p5cqVeuONNwxMan+HDh3Srl27bLNr69Sp4zD/32/FDJcAAhRBQAHy5JNPKiMjQ7GxsVq9erUsFousVqs8PDw0fPhwPfHEE0ZHtItPPvlE/fr1y3V3KJjX3y15d+HCBVMtjuzu7q4ff/xRLVq0yLGzXnp6uurUqaO3335b0vVPM9kqGMCtWK1WHT9+PNuupQVdgwYNcpTlMTExmjVrVrZj0vX3YGZYGyc9PV2DBw/W+vXrZbVaVbhwYaWnp8tisahly5aaPHmyChcubHRMAP8QRRBQwHTp0kXPPPOMjhw5opSUFJUsWVKVKlWSk5OT0dHsJj09/aZrJcFctm7dqs8//9x2e+HChfLy8sp2Tlpamr766itT7Sb350+uc1vvYerUqbavKYIAmNWECRNMs4nA7XrjjTe0detWjRkzRq1atbJtNvDpp59q4sSJmjZt2h3NJCuI1q9frwYNGsjNzU3r16+/5fktWrSwQyrg38WuYQAKnHHjxsnJyUnDhw83OgoMtmjRIi1atEiSdPLkSXl6eub4pNLFxUWVK1fWSy+9pAceeMCImABQ4GVmZsrPz0/x8fGm3T7cDEJCQvT888+ra9euOcYWLVqk+fPnZ/sAxhFVq1ZNH3zwgWrWrJljF7W/Yhc1FFTMCAIKmB9//FFz5szR7t27lZycrJIlS6pOnTp64YUXTDPjISAgQNOnT1diYqKCg4Pl5uaW4xw+nTGHyMhIRUZGSrq+9W9sbOwt37QBAIDcpaSk6P7778917P7771dKSoqdE9nfxo0bVbp0advXgCOiCAIKkJ07d+q5556Tt7e3WrdubVv347PPPtMTTzyhhQsXqm7dukbHzHNDhgyRJJ04cUKffvppjnE+nTGnO1nc0REXPf2z77//XqmpqWrYsKGk62/so6OjdfjwYQUHB6tPnz6mupwUAG4lIyNDb7/9ttauXauTJ0/m2JFUknbv3m1AMvu6//779fHHH6tx48Y5xlatWnXTksiR9O3bV5MnT1aVKlW0YsUKderUSWXKlDE6FvCvoggCCpApU6aofv36euutt7Jt/zxkyBD17NlTU6dO1fvvv29gQvvg0xn8U4646OmfTZw4UQ0bNrQVQRMmTNCGDRvUqFEjLVy4UE5OTiy2DuCOOfJ6OmPGjNHKlSsVHh6ukJAQ0+5IGhUVpQEDBuj48eNq0aKFvLy8lJiYqHXr1um7777TjBkzjI6Y5w4dOqRLly5JkmbNmqUmTZpQBMHhUAQBBcj+/fv15ptvZiuBJMnZ2Vldu3ZV//79DUpmX76+vkZHAPK1n3/+WT179pQkXb16VevWrdPIkSPVoUMHvfvuu1q8eDFFEGByVqtVKSkpKl68+G3vAuXIS4t+9tlnGjZsmJ555hmjoxiqRYsWtp3TJk2aJKvVKovFourVqysmJkbh4eFGR8xzvr6++vDDD5WWliar1aoff/wx1xliN9SrV8+O6YB/B0UQUIAUK1ZMiYmJuY6dO3dOxYoVs3Mi+0lOTpabm5ucnJyUnJx8y/NLlixph1RA/nT16lXbvwe7d+9Wenq6mjVrJkmqWrWqTp06ZWQ8APlARkaGgoODFRsbq7CwsFue7+zsrAMHDuR9MIMUL15c5cuXNzqG4YYNG6aoqCh99NFHunz5slJTU+Xq6qrixYvr+PHjGjZsmCZOnGh0zDzVt29fDR8+XB999JEsFotGjx5903NZjgAFFUUQUIA0bdpUU6ZMkY+Pj4KDg23Ht2/frjfeeMOhP6Vp2LChli1bppo1a6pBgwa3nJ7OizLMrHz58tq2bZvq16+vTz75RH5+frZyNDExUSVKlDA4IQCjFS5cWD4+PsrMzDQ6Sr7w3HPP6b333lOjRo3k7OxsdBzDrFixQk899ZTKly+v4sWLq3jx4rax8+fPa+XKlQ5fBLVp00bNmjXT77//rrZt22rSpEmqWrWq0bGAfxVFEFCADB06VD///LO6d++uEiVKyMPDQ0lJSbp48aL8/f31yiuvGB0xz0yYMMH2Sd2ECRMcep0C4J969tlnNWLECC1fvlwpKSmaPHmybeybb77hDS0ASdLTTz+tuLg4NW7cWEWKFDE6jqG6du2qM2fO6OGHH1bdunVz3ZF0xIgRBiTLP44dO2aaGdfFixfXgw8+qL59+6pBgwasEQSHY7E68sW+gAPKysrS5s2btWvXLl24cEHu7u6qU6eOwsLC2AUIuE2ZmZmqUaOGli9fLj8/P6Pj5ImEhATt27dPDz30kBo0aGA7PnPmTPn7+9/WpSAAHNu4ceO0bt06ZWZmqn79+vLy8srxQYtZyo/Vq1dryJAhslgs8vDwyLFYtMVicdjNKt577z3bZiM///yzypcvn6MYTE9P1/Hjx9WyZUtNnTrViJiGslqtOnr0qFJSUuTu7q5KlSrxoSQKNIogoADZsWOHTpw4oQ4dOuQY++ijj1S2bNlsP/A5qoULF6pevXry8/Oj/IKkO1/0NDMzU35+foqPj3fYIuh2ZWVl6dlnn9XYsWNVsWJFo+MAsKNbXVLuyOXHXzVt2lSBgYEaN26c6S6f3bBhg+3/84oVKxQaGioPD49s57i4uOj+++9Xx44dTff4vPvuu4qNjVVSUpLtmKenp6KiovT0008bmAy4e1waBhQg06dPty34+ldJSUn64IMPtHTpUjunsr+FCxcqOjpaxYoVU61atVS3bl3VrVtXAQEBt73zCRwLi57ePavVqm+++ca2VS4A89i0aZPREfKN5ORkPf7446YrOSSpefPmat68ue12VFQUC2f/v2XLlmncuHFq3bq1WrVqJS8vL507d06ffvqpxo0bJxcXF3Xq1MnomMAdowgCCpBDhw5pwIABuY75+flpzpw5dk5kjC+++EK//PKLEhIStHPnTn344YeaMWOGChcuLH9/f9WrV08DBw40OibsiEVPAQD/RGhoqL799ls1bNjQ6CiGcvSFoO9UXFycunTpouHDh2c73qxZM3l4eGjBggUUQSiQKIKAAsRisSg1NTXXsZSUFFP9EFyxYkVVrFjR9uL71VdfadasWUpISNDu3bspgkyIRU8B4M6dPn1acXFx2r17t5KTk1WyZEnVqVNHkZGRplogt2PHjhozZoyuXr2qhg0b5rpYtNkvJTaj33//XU2bNs11LCwszBQz8eGYKIKAAiQgIEDvvvuuWrRokW2BOqvVqvfee08BAQEGprOvI0eOKCEhQd9884127typs2fPqnLlynrqqadUr149o+PBACdPntTRo0cVFhZm+kVPAeB2/PTTT+rcubMyMjLUqFEjVatWTYmJiVq6dKni4+O1ZMkSValSxeiYdtGjRw9J0ty5czV37twc77MsFov2799vVDwYxNvbW99++62Cg4NzjH333Xfy9vY2IBXwz1EEAQVIv3791LVrV7Vt21bt2rWTt7e3zpw5o5UrV+qXX37RO++8Y3REu2jUqJGSk5NVtWpV1a9fXyNHjlSdOnVUqlQpo6PBQJs3b7atEbVv374c4xaLhSIIAP5k0qRJKl++vBYuXCh3d3fb8ZSUFHXr1k2TJk3S/PnzDUxoP4sXLzY6AvKhjh07KjY2Vunp6YqIiJCnp6eSkpK0Zs0aLViwQH369DE6InBXKIKAAqRWrVqKi4tTdHS0pkyZoqysLDk5OSkwMFBxcXEKDAw0OqJdpKWlyWKxqGjRoipSpIjtvzA3Fj0FgDuze/duRUdHZyuBJMnd3V29e/fW4MGDDUpmf/Xr1zc6AvKh3r1768KFC1qwYIHmzp1rO+7s7KwuXbqod+/eBqYD7h5FEFDA1KlTR0uXLtXVq1eVkpIiNzc3FStWzOhYdpWQkKD9+/dnWyz6woULql69uurWrat69erdcktcAADMztnZWenp6bmOpaeny9nZ2c6JjLVy5UotW7ZMv/zyi9LS0nKM796924BUMIrValVKSopefPFF9erVS3v37lVKSorc3d1Vs2ZNZqKjQLNYrVar0SEA4J/asWOHYmNjlZCQwHX8Jsaip9dnzAUHBys6Ovq2C9Hjx4+rdOnScnFxyeN0APKT/v3768CBA3rrrbdUqVIl2/FffvlFvXr1UtWqVfXmm28amNB+Pv74Y40YMULt2rXTBx98oA4dOigrK0ubNm2Sm5ubHnvsMfXt29fomLCj9PR0BQYGKjY2VmFhYUbHAf5VzAgCUCAdP37cNiMoISFBv/76q5ydnW3bx8N8WPT0uiJFiqhYsWJ39Em+r69vHiYCkF8NHTpUnTt3VuvWrVWlShV5eXkpMTFRP/30k+69914NGzbM6Ih28/bbbysqKko9e/bUBx98oKefflp+fn66ePGiunfvrnvuucfoiLCzwoULy8fHx1S78sI8mBEEoMBp2rSpTp06JRcXF1vxU69ePdWqVUvFixc3Oh4M0r17dyUnJ9900dNSpUqZZtHTKVOm6NixY5o5c6bRUQDkc5cuXVJ8fLx27dqlCxcuyN3dXXXq1FH79u1NVX7UqlVLc+bMUVBQkPz8/LRw4UIFBQVJkjZs2KAJEyawFp0JzZ8/X1u3btX8+fNZjxIOhRlBAAqcTp06qV69egoICLDtEgWw6Okf3Nzc9N1336lNmzYKCQmRl5dXtq2QLRaLnn32WeMCAsg37rnnHnXt2lVdu3Y1OoqhSpQoYVsvqUyZMvr5559tRVBmZqbOnz9vZDwY5OTJkzp69KjCwsJUv379HK+nktiRFAUSRRCAAicqKuq2z83KytLDDz+sOXPmmOKyIDNj0dM/vPHGG5Kks2fP6tChQznGKYIASFL16tW1bNky1axZM8fY999/r06dOplmzb0aNWro4MGDCgkJUXh4uGbNmiWr1apChQpp7ty5ptmZFdlt3rzZ9qHjvn37coxbLBaKIBRIFEEAHJrVatXx48dvWhDAcQQHB2v69OmqXr16jkVPZ8yYoeDgYAPT2deBAweMjgCgAPi7FSIyMzNNVaD36tVLJ06ckHR9Ee3jx49rwoQJysrKkr+/v8aOHWtwQhiBywHhqCiCAAAOgUVPAeDWzp49qzNnzthuHzlyJEfhk5aWpvj4eJUtW9be8QwTGBhom/Xj5uam2bNnKz09Xenp6SpRooTB6WCkpKQkLVq0SHv27NHZs2fl7e2tgIAARUZGysPDw+h4wF1hsWgADi0zM1N+fn6Kj4+Xn5+f0XGQx1j09A+XL1/WihUrtGvXLqWkpNgei3bt2rGoOmBiMTExiomJybHOyZ9ZrVY5Ozvrtdde0+OPP27HdED+smfPHvXo0UNZWVkKDg6Wp6enEhMTtX37dknSwoULFRAQYHBK4M5RBAFwaBRBMKOTJ0+qS5f/a+/uY6ou/z+Ov/AIbMZ9CAJxjwsFGZhDs3kzY6ltzvAmu/GeUHGtRmlEoVltkDVNN8CGoOWYJhslltkfRvlHsZqhM51DnYnbAYUQuWnKMTi/P/x1/BLYvjfBdY7n+fjvfK6P2+sf7l5e1/taJqvVqsTERMcvrg0NDYqIiNC+ffsUFhZmOiYAA6xWq6xWq+x2u1asWKHNmzcrISGh3zuenp6KiYlRYGCgoZSAc1iwYIG8vb21e/fufjvDurq6lJ2drdu3b6u6utpgQuC/w9EwAMB9gaGndxUVFUmSjhw5ori4OMfzS5cuad26dXrvvfe0c+dOU/EAGBQREaGIiAhJ0r59+zR+/HiOPgH3cPHiRe3cuXPA14ivr6+ys7OVm5trKBnwvxlhOgAADLW/2/6O+wdDT+/64Ycf9Morr/QrgSQpLi5OL7/8sr7//ntDyQA4k/T0dEog4G9ER0ers7Nz0LWuri5FRkYOcyLgn8GOIAAuxW63q6OjQ6NGjXJc5/nv/Bvcnxh6Orje3l55e3sPuubt7a3e3t5hTgTAWR06dEgHDx7U5cuX1dPTM2C9vr7eQCrAOWzcuFHvvPOOwsLClJ6e7nj+448/qri4WJs2bTKYDvjvMSMIgEux2WxKTU1VaWmpZs6caToODGPo6eCysrLU0dGhvXv3ytfX1/G8q6tLq1atkr+/vyoqKgwmBOAMampqVFBQoMzMTFVVVWnhwoXq6+tTbW2t/Pz8NH/+fL344oumYwLGzJs3Ty0tLers7JSvr68CAwPV3t6urq4u+fn5KSQkxPGuh4eHDh8+bDAt8O9jRxAAl+Ll5aUxY8awowGSpMzMTKWnpzP09C/y8vK0dOlSzZgxQ1OmTFFwcLDa2tpUV1cnT09PFRYWmo4IwAns3btX69ev15o1a1RVVaXnnntOSUlJ6u7uVlZWltvdtgj8VVJSkpKTk03HAP5x7AgC4HLKy8t1/PhxlZeX3/P4C9zPTz/9xNDTf3H16lXt3btXP//8szo7Ox3Xx69cuVJjxowxHQ+AE0hLS9NHH32kyZMnKykpSXv27NHkyZMlSceOHVNhYaFqa2sNpwQA/NPYEQTA5TQ3N+vXX3/VzJkzlZ6eruDg4AFHgwoKCgylgyn/enYf0pgxY5Sfn286BgAn5uPjI5vNJkkKDQ3VxYsXHUVQb2+v2tvbTcYDAAwRiiAALufbb791DIr+5ZdfBqx7eHhQBLkphp7e8fjjj6ukpESJiYkD1s6fP6+cnBx98803BpIBcCbJyclqaGjQtGnTNGvWLJWUlMhut2vkyJEqKytTamqq6YgAgCFAEQTA5bBNHYOpqanRpk2blJmZqZMnTw469NRdWK1Wx//y/9WtW7d09erVYU4EwBmtXbtWTU1NkqSXXnpJVqtVhYWF6uvr04QJE/T2228bTggAGAoUQQCA+4K7Dz3t6enRzZs39efov+7ubt24cWPAO8eOHet3ywkA95WamurY9ePn56ddu3bJZrPJZrMxbw0A7mMUQQBc0rVr1/Txxx+rvr5eN27cUEBAgB555BGtWLFCoaGhpuPBgMbGRk2cOFEWi0UWi0Xd3d2S7szAyM7OVmFhoVatWmU45dDZvXu3SkpKJN05HpmVlXXPd7kOGoAk5efna/369YqMjHQ88/LykpeXl6xWq4qLi1VUVGQwIQBgKFAEAXA558+f19KlS3X79m099thjSkxMVFtbmz799FNVV1ersrJSY8eONR0Tw8zdh55mZGQoIiJCdrtdb7zxhnJychQVFdXvHU9PT8XHx2vcuHGGUgJwJp9//rmeffbZfkXQn9rb23Xo0CGKIAC4D1EEAXA5W7duVWRkpPbs2SN/f3/H846ODq1evVpbt25VeXm5wYQwwd2HniYmJjqGQ3t4eGjGjBkKCgoynAqAq2psbFRAQIDpGACAIUARBMDl1NfX64MPPuhXAkmSv7+/cnJytHHjRkPJYBJDT+/KzMyUdKccPX36tDo6OuTv76+UlJQBXzcA3Mv+/ft14MABSXdK4w0bNsjb27vfOzabTVarVbNnzzYREQAwxCiCALgci8VyzxuRbDabLBbLMCeCM2DoaX/vv/++Kisr+32teHl5admyZZSlgBsLCQlRcnKyJOnChQuKjY0dsHvQ09NTcXFxWrRokYmIAIAhRhEEwOVMnTpVO3bs0Lhx4xQbG+t4fvnyZe3cuVNTp041mA6mMPT0rl27dumTTz7RCy+8oLlz5yo4OFi//fabjh49qvLycvn5+Wnt2rWmYwIwICMjQxkZGY7Pf/2+efPmTbW0tCgqKkoeHh4mIgIAhpiH/c97ZgHARTQ1NWnp0qW6evWqxo4dq+DgYLW1ten8+fMKCwtTZWWlwsLCTMfEMEtMTFRVVZVSUlIGrJ05c0aLFy/WuXPnDCQbfrNmzdKCBQsGvR2suLhYn332mWpraw0kA+BMysvLdevWLcf3ihMnTignJ0fd3d166KGHVFFRMWDoPADA9Y0wHQAA/lPh4eH64osv9PrrrysmJkZ9fX2KiYlRfn6+Dh8+TAmEAdxt6Glra6smTpw46FpaWppaW1uHOREAZ1RdXa3Q0FDH56KiIiUkJKi0tFSBgYHavn27wXQAgKHC0TAALumBBx7Q8uXLtXz5ctNRYBBDTwcXERGh7777btBjksePH1dERISBVACcTXNzs6KjoyVJ165d09mzZ1VZWalJkyapt7dXW7ZsMRsQADAkKIIAuJxx48bp4MGDHAECQ0/vYeXKldqyZYuuX7+uOXPm6MEHH1RbW5u+/vprHTlyhD/uAEiSvL291d3dLUmqq6vTqFGjlJaWJkny9fVVV1eXyXgAgCFCEQTA5fzdaLPe3l5uDXMjDD0d3DPPPKPbt2+rtLRUX375peN5UFCQ3nzzTS1ZssRgOgDOIiUlRWVlZRoxYoQqKio0ffp0x8/QK1eu9Ds2BgC4f1AEAXAJra2tamlpcXy+dOnSgMKnp6dH1dXVCg8PH+54cALx8fGqqalh6On/W7ZsmZ5//nldunRJHR0dCggIUGxsrEaMYDwggDvy8vK0du1arVu3TuHh4crNzXWsHT161LE7CABwf+HWMAAuobi4WMXFxX+7q8Nut8tiseitt97S008/PYzp4Azmzp2r1atXa/HixZKkhQsXysvLS2vWrNGuXbsUHh6uHTt2GE45fJqbm3Xs2DE1NzfLZrMNWC8oKDCQCoAzam9vV2BgYL9nDQ0NGj169IDjtgAA18eOIAAuITMzU+np6bLb7VqxYoU2b96shISEfu94enoqJiZmwC+zcA8MPb3rq6++0muvvSa73a6goCB5enr2W/fw8KAIAuAw2M/Nhx9+2EASAMBwoAgC4BIiIiIcNx3t27dP48ePl4+Pj+FUcCYMPb3rww8/VEZGht599135+vqajgMAAAAnQhEEwOWkp6ebjgAnxNDTu65fv64lS5ZQAgEAAGAAiiAALunQoUM6ePCgLl++rJ6engHr9fX1BlLBJIae3jVt2jSdOnVKjz76qOkoAAAAcDIMiwbgcmpqalRQUKDMzExVVVVp4cKF6uvrU21trfz8/DR//nzHzVFwPww9lTo6OpSbm6sJEyZoypQp8vPzG/BOUlKSgWQAAAAwjSIIgMt56qmnNHv2bK1Zs0ZJSUmqrq5WUlKSuru7lZWVpTlz5mjVqlWmYwLGNDU16dVXX9XJkycH3LRnt9vl4eGhc+fOGUoHAAAAkzgaBsDlNDY2auLEibJYLLJYLI4BwT4+PsrOzlZhYSFFENxaXl6empubtWnTJsXExAy4NQwAAADuiyIIgMvx8fGRzWaTJIWGhurixYuaPHmyJKm3t1ft7e0m4wHGnT59Wtu2bVNGRobpKAAAAHAyFEEAXE5ycrIaGho0bdo0zZo1SyUlJbLb7Ro5cqTKysqUmppqOiJgVHR0tP744w/TMQAAAOCEmBEEwOWcOnVKTU1NevLJJ9XZ2am8vDwdP35cfX19mjBhgrZt26aoqCjTMQFj6urqtHXrVm3btk3x8fGm4wAAAMCJUAQBuC/YbDbZbDb5+PiYjgIYN2/ePLW2tqqzs1MhISHy9fXtt+7h4aHDhw8bSgcAAACTOBoGwOXk5+dr/fr1ioyMdDzz8vKSl5eXrFariouLVVRUZDAhYFZSUtKA28IAAAAAiR1BAFxQYmKiqqqqlJKSMmDtzJkzWrx4MVdjAwAAAMAgRpgOAAD/pMbGRgUEBJiOAQAAAABOiaNhAFzC/v37deDAAUl35pts2LBB3t7e/d6x2WyyWq2aPXu2iYgAAAAA4PQoggC4hJCQECUnJ0uSLly4oNjYWAUFBfV7x9PTU3FxcVq0aJGJiAAAAADg9JgRBMDlDDYs+ubNm2ppaVFUVBRDcgEAAADgHpgRBMDlxMfHq6amxvH5xIkTmj59uubMmaMnnnhCV65cMZgOAAAAAJwXRRAAl1NdXa3Q0FDH56KiIiUkJKi0tFSBgYHavn27wXQAAAAA4LyYEQTA5TQ3Nys6OlqSdO3aNZ09e1aVlZWaNGmSent7tWXLFrMBAQAAAMBJsSMIgMvx9vZWd3e3JKmurk6jRo1SWlqaJMnX11ddXV0m4wEAAACA02JHEACXk5KSorKyMo0YMUIVFRWaPn26LBaLJOnKlSv9jo0BAAAAAO5iRxAAl5OXl6fW1latW7dOv//+u3Jzcx1rR48edewOAgAAAAD0x/XxAFxWe3u7AgMD+z1raGjQ6NGjFRQUZCgVAAAAADgviiAAAAAAAAA3wdEwAAAAAAAAN0ERBAAAAAAA4CYoggAAAAAAANwERRAAAAAAAICboAgCAAAAAABwExRBAAAAAAAAboIiCAAAAAAAwE38H4DQjHJZ6/C+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1400x1400 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T62MWpzYdFnd"
      },
      "source": [
        "**Observations**:\n",
        "\n",
        "From the Heat Map plotted above:\n",
        "* High linear correlation between `i_q` and `torque` is observed.\n",
        "* High Correlation between `stator tooth` and `stator winding` is observed.\n",
        "* High Correlation between `stator yoke` and `stator tooth` is observed from the profile report. \n",
        "\n",
        "* Negative Linear correlation is observed between `u_d` and `i_q`.\n",
        "* Negative Linear correlation is observed between `u_d` and `torque`.\n",
        "* Negative Linear correlation is observed between `i_d` and `stator winding`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvTi481xoE9s"
      },
      "source": [
        "###7.2. Which `profile id` varies maximum and which one varies minimum for temperature rise for various parameters?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "a9eql_ByqVvW",
        "outputId": "382fe0af-da27-4c85-8c40-3127aa4a4b3a"
      },
      "source": [
        "plt.figure(figsize=(20,8));\n",
        "sns.countplot(pmsm_data['profile_id'], color = 'blue')\n",
        "plt.title(\"profile_id vs count\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'profile_id vs count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KwSgIAEwHG2"
      },
      "source": [
        "**Observations**:\n",
        "* `profile_id` 20 observes maximum variation as seen from the graph above.\n",
        "* `profile_id` 46 and 47 observe minimum variation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWA_FXsyMn2N"
      },
      "source": [
        "###7.3. Which `profile id` has highiest `pm` temperature?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "KgmMUMUmNIHt",
        "outputId": "fa5656d1-fc78-4b8d-83f7-aaa2865b69a0"
      },
      "source": [
        "plt.figure(figsize=(,8));\n",
        "\n",
        "sns.barplot(x=pmsm_data.groupby('profile_id').agg('count').sort_values(by='pm').index,\n",
        "            y=pmsm_data.groupby('profile_id').agg('count').sort_values(by='pm')['pm'],\n",
        "            order=pmsm_data.groupby('profile_id').agg('count').sort_values(by='pm').index,\n",
        "           orient='v',color='blue');\n",
        "\n",
        "plt.title('Profile_id vs data points');"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtbuGnAiOwjg"
      },
      "source": [
        "**Observations**:\n",
        "* The maximum `pm` is found for the `profile_id` 20.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTYfAUGYw1S_"
      },
      "source": [
        "### 7.4. Which component of stator (from `stator_yoke`, `stator winding`, `stator tooth`) observes highiest variations for `profile_id` 20 (highiest varying `profile_id`)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "FyHCoMaLxKfv",
        "outputId": "a1344096-0626-4230-ddd1-26df8fc0725f"
      },
      "source": [
        "plt.figure(figsize=(20,5))\n",
        "pmsm_data[pmsm_data['profile_id'] == 20]['stator_yoke'].plot(label = 'stator yoke')\n",
        "pmsm_data[pmsm_data['profile_id'] == 20]['stator_winding'].plot(label = 'stator winding')\n",
        "pmsm_data[pmsm_data['profile_id'] == 20]['stator_tooth'].plot(label = 'stator tooth')\n",
        "plt.legend()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3ab41cc890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1Dq5jh2T_s6"
      },
      "source": [
        "**Observations**:\n",
        "It can be observed that the graph obtained for `stator_winding`,`stator_tooth`,`stator_yoke` are similar with `stator_winding` observing maximum changes in magnitude for the highiest `profile_id` 20."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urVqd3V7ZBeA"
      },
      "source": [
        "##**8. Conclusion**\n",
        "- With the help of this notebook we learnt how exploratory data analysis can be carried out using Pandas plotting.\n",
        "- Also we have seen making use of packages like __matplotlib and seaborn__ to develop better insights about the data.<br/>\n",
        "- We have also seen how __pre-proceesing__ helps in dealing with _missing_ values and irregualities present in the data. However, in this dataset, there were no missing values. \n",
        "- We also make use of __pandas profiling__ feature to generate an html report containing all the information of the various features present in the dataset.\n",
        "- We have seen the impact of columns like _stator_winding, stator_yoke, stator_tooth, pm,and profile_id_ on the temperature.\n",
        "- The most important inference drawn from all this analysis is, we get to know what are the __features on which temperature is highly positively and negatively correlated with.__ \n",
        "- This analysis will help us to choose which __machine learning model__ we can apply to predict survival of test dataset. "
      ]
    }
  ]
}