Poly_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries"
      ],
      "metadata": {
        "id": "KDno7Hsy5jLL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "EvCDxR965pnT"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imporing Datasets"
      ],
      "metadata": {
        "id": "pmFQ3FAG55Uy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "print(X)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kr25rNi259bv",
        "outputId": "16fd5b98-d974-46ef-fcac-63f37f9d3ba3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1]\n",
            " [ 2]\n",
            " [ 3]\n",
            " [ 4]\n",
            " [ 5]\n",
            " [ 6]\n",
            " [ 7]\n",
            " [ 8]\n",
            " [ 9]\n",
            " [10]]\n",
            "[  45000   50000   60000   80000  110000  150000  200000  300000  500000\n",
            " 1000000]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the Linear Regression on the whole Dataset"
      ],
      "metadata": {
        "id": "3uScC0tL8t9c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "yVe7UxK8828p",
        "outputId": "2bee412f-f357-4aed-86c5-c7c24bfe1d5c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the Polynomial Regression on the whole Dataset"
      ],
      "metadata": {
        "id": "9yK9plqu-QWH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 4)\n",
        "poly_reg.fit_transform(X)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "lin_reg_2 = LinearRegression()\n",
        "lin_reg_2.fit(X_poly, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "zqU3K62p-Z7i",
        "outputId": "9683ec1a-6765-4699-9cd1-424a92ee31b6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing Linear Regression Results"
      ],
      "metadata": {
        "id": "jIHTnQYNCqTD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X, y, color = 'yellow')\n",
        "plt.plot(X, lin_reg.predict(X), color = 'blue')\n",
        "plt.title('Linear Regression')\n",
        "plt.xlabel('Position')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "_xosUlPwCvKh",
        "outputId": "d20adc89-85e8-4700-c677-2ff9eac082f8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X, y, color = 'Brown')\n",
        "plt.plot(X, lin_reg_2.predict(X_poly), color = 'blue')\n",
        "plt.title('Polynomial Regression')\n",
        "plt.xlabel('Position')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "w-zL9aWbE6i1",
        "outputId": "540246de-c7f7-4cfd-c99e-ea8955593d67"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visulaising the polynomial regression results for higher resolution and smoother curve"
      ],
      "metadata": {
        "id": "jOicKotFGKS4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_grid = np.arange(min(X), max(X), 0.1)\n",
        "X_grid = X_grid.reshape((len(X_grid), 1))\n",
        "plt.scatter(X, y, color = 'red')\n",
        "plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')\n",
        "plt.title('Polynomial Regression')\n",
        "plt.xlabel('Position')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "Dkx1ze2SGWNF",
        "outputId": "50aa4cae-969d-4b20-8f59-d6fedd68f907"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-87ad9ef40a5d>:1: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  X_grid = np.arange(min(X), max(X), 0.1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## predicting the salary on linear regression result for the 6.5 level salary"
      ],
      "metadata": {
        "id": "AXtbp66FHSI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg.predict([[6.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26aLTb-rHWem",
        "outputId": "af622236-0c33-446f-9f06-b34abcac9eb4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([330378.78787879])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting the salary on polynomial regression result for 6.5 level salary"
      ],
      "metadata": {
        "id": "rQ_bO71zIPD5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg_2.predict(poly_reg.fit_transform([[6.5]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "as3mb7kpIbrO",
        "outputId": "10fe2891-40dd-41fb-9406-dff7a6c49c68"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([158862.45265155])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
  ]
}