diff --git a/LR(placement_ananlysis).ipynb b/LR(placement_ananlysis).ipynb
new file mode 100644
index 00000000..0777bc40
--- /dev/null
+++ b/LR(placement_ananlysis).ipynb
@@ -0,0 +1,2063 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOiYcn/jlZQbitGFtUTFLqo",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/levi178u/AlgoWiki/blob/gh-pages/LR(placement_ananlysis).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "auM6-TKX8V_j"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df =pd.read_csv(\"/content/placement.csv\")"
+      ],
+      "metadata": {
+        "id": "LhQNwzpX8285"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "APIrE--g89Df",
+        "outputId": "45cec2e3-40c5-4941-dd48-b1531d71e926"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   cgpa  package\n",
+              "0  6.89     3.26\n",
+              "1  5.12     1.98\n",
+              "2  7.82     3.25\n",
+              "3  7.42     3.67\n",
+              "4  6.94     3.57"
+            ],
+            "text/html": [
+              "\n",
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+              "\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>cgpa</th>\n",
+              "      <th>package</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>6.89</td>\n",
+              "      <td>3.26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5.12</td>\n",
+              "      <td>1.98</td>\n",
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+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.82</td>\n",
+              "      <td>3.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7.42</td>\n",
+              "      <td>3.67</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>6.94</td>\n",
+              "      <td>3.57</td>\n",
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+              "  </svg>\n",
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+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
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+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        document.querySelector('#df-8b580ab3-cf7f-4a01-8f83-b9d1f76e8157 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8b580ab3-cf7f-4a01-8f83-b9d1f76e8157');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
+              "<div id=\"df-0097aa45-3ed2-40cb-a496-4dc96f83ef30\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0097aa45-3ed2-40cb-a496-4dc96f83ef30')\"\n",
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+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "    padding: 0;\n",
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+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "        document.querySelector('#' + key + ' button');\n",
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+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "\n",
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+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0694086798895523,\n        \"min\": 4.26,\n        \"max\": 9.58,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          8.71,\n          6.37,\n          7.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"package\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6916442411250219,\n        \"min\": 1.37,\n        \"max\": 4.62,\n        \"num_unique_values\": 138,\n        \"samples\": [\n          3.76,\n          3.13,\n          2.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "0_JqOyni8-Lw",
+        "outputId": "4ec68292-0698-4bac-b40c-958cbb8c90b5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             cgpa     package\n",
+              "count  200.000000  200.000000\n",
+              "mean     6.990500    2.996050\n",
+              "std      1.069409    0.691644\n",
+              "min      4.260000    1.370000\n",
+              "25%      6.190000    2.487500\n",
+              "50%      6.965000    2.995000\n",
+              "75%      7.737500    3.492500\n",
+              "max      9.580000    4.620000"
+            ],
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+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>cgpa</th>\n",
+              "      <th>package</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>200.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>6.990500</td>\n",
+              "      <td>2.996050</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.069409</td>\n",
+              "      <td>0.691644</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>4.260000</td>\n",
+              "      <td>1.370000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>6.190000</td>\n",
+              "      <td>2.487500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>6.965000</td>\n",
+              "      <td>2.995000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>7.737500</td>\n",
+              "      <td>3.492500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>9.580000</td>\n",
+              "      <td>4.620000</td>\n",
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+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-464e1b4a-b5e8-4f04-aa86-2ebe6d1254fe');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-caea06bb-a6e9-4b18-891f-56b0e3ac048a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-caea06bb-a6e9-4b18-891f-56b0e3ac048a')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-caea06bb-a6e9-4b18-891f-56b0e3ac048a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68.59624286082793,\n        \"min\": 1.0694086798895523,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.990500000000001,\n          6.965,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"package\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.77914766041287,\n        \"min\": 0.6916442411250219,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.9960500000000003,\n          2.995,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Let,s draw a scatterplot so that i can know linear regression properly\n",
+        "\n",
+        "plt.scatter(df['cgpa'], df['package'])\n",
+        "plt.xlabel(\"CGPA\")\n",
+        "plt.ylabel(\"Package (In LPA)\")\n",
+        "plt.title(\"Placement Analysis\")\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "OhRpjTaL9AiH",
+        "outputId": "e3cd87ae-0fea-4052-90ae-ae8a29fa9da8"
+      },
+      "execution_count": null,
+      "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": [
+        "x= df.iloc[:,0:1].values\n",
+        "y= df.iloc[:,-1].values\n"
+      ],
+      "metadata": {
+        "id": "KMEQDbE5-KhA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zfYSGO4V-wbN",
+        "outputId": "13d5f7ea-4f55-4881-cb6a-5aeb071a72c5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[6.89],\n",
+              "       [5.12],\n",
+              "       [7.82],\n",
+              "       [7.42],\n",
+              "       [6.94],\n",
+              "       [7.89],\n",
+              "       [6.73],\n",
+              "       [6.75],\n",
+              "       [6.09],\n",
+              "       [8.31],\n",
+              "       [5.32],\n",
+              "       [6.61],\n",
+              "       [8.94],\n",
+              "       [6.93],\n",
+              "       [7.73],\n",
+              "       [7.25],\n",
+              "       [6.84],\n",
+              "       [5.38],\n",
+              "       [6.94],\n",
+              "       [7.48],\n",
+              "       [7.28],\n",
+              "       [6.85],\n",
+              "       [6.14],\n",
+              "       [6.19],\n",
+              "       [6.53],\n",
+              "       [7.28],\n",
+              "       [8.31],\n",
+              "       [5.42],\n",
+              "       [5.94],\n",
+              "       [7.15],\n",
+              "       [7.36],\n",
+              "       [8.1 ],\n",
+              "       [6.96],\n",
+              "       [6.35],\n",
+              "       [7.34],\n",
+              "       [6.87],\n",
+              "       [5.99],\n",
+              "       [5.9 ],\n",
+              "       [8.62],\n",
+              "       [7.43],\n",
+              "       [9.38],\n",
+              "       [6.89],\n",
+              "       [5.95],\n",
+              "       [7.66],\n",
+              "       [5.09],\n",
+              "       [7.87],\n",
+              "       [6.07],\n",
+              "       [5.84],\n",
+              "       [8.63],\n",
+              "       [8.87],\n",
+              "       [9.58],\n",
+              "       [9.26],\n",
+              "       [8.37],\n",
+              "       [6.47],\n",
+              "       [6.86],\n",
+              "       [8.2 ],\n",
+              "       [5.84],\n",
+              "       [6.6 ],\n",
+              "       [6.92],\n",
+              "       [7.56],\n",
+              "       [5.61],\n",
+              "       [5.48],\n",
+              "       [6.34],\n",
+              "       [9.16],\n",
+              "       [7.36],\n",
+              "       [7.6 ],\n",
+              "       [5.11],\n",
+              "       [6.51],\n",
+              "       [7.56],\n",
+              "       [7.3 ],\n",
+              "       [5.79],\n",
+              "       [7.47],\n",
+              "       [7.78],\n",
+              "       [8.44],\n",
+              "       [6.85],\n",
+              "       [6.97],\n",
+              "       [6.94],\n",
+              "       [8.99],\n",
+              "       [6.59],\n",
+              "       [7.18],\n",
+              "       [7.63],\n",
+              "       [6.1 ],\n",
+              "       [5.58],\n",
+              "       [8.44],\n",
+              "       [4.26],\n",
+              "       [4.79],\n",
+              "       [7.61],\n",
+              "       [8.09],\n",
+              "       [4.73],\n",
+              "       [6.42],\n",
+              "       [7.11],\n",
+              "       [6.22],\n",
+              "       [7.9 ],\n",
+              "       [6.79],\n",
+              "       [5.83],\n",
+              "       [6.63],\n",
+              "       [7.11],\n",
+              "       [5.98],\n",
+              "       [7.69],\n",
+              "       [6.61],\n",
+              "       [7.95],\n",
+              "       [6.71],\n",
+              "       [5.13],\n",
+              "       [7.05],\n",
+              "       [7.62],\n",
+              "       [6.66],\n",
+              "       [6.13],\n",
+              "       [6.33],\n",
+              "       [7.76],\n",
+              "       [7.77],\n",
+              "       [8.18],\n",
+              "       [5.42],\n",
+              "       [8.58],\n",
+              "       [6.94],\n",
+              "       [5.84],\n",
+              "       [8.35],\n",
+              "       [9.04],\n",
+              "       [7.12],\n",
+              "       [7.4 ],\n",
+              "       [7.39],\n",
+              "       [5.23],\n",
+              "       [6.5 ],\n",
+              "       [5.12],\n",
+              "       [5.1 ],\n",
+              "       [6.06],\n",
+              "       [7.33],\n",
+              "       [5.91],\n",
+              "       [6.78],\n",
+              "       [7.93],\n",
+              "       [7.29],\n",
+              "       [6.68],\n",
+              "       [6.37],\n",
+              "       [5.84],\n",
+              "       [6.05],\n",
+              "       [7.2 ],\n",
+              "       [6.1 ],\n",
+              "       [5.64],\n",
+              "       [7.14],\n",
+              "       [7.91],\n",
+              "       [7.19],\n",
+              "       [7.91],\n",
+              "       [6.76],\n",
+              "       [6.93],\n",
+              "       [4.85],\n",
+              "       [6.17],\n",
+              "       [5.84],\n",
+              "       [6.07],\n",
+              "       [5.66],\n",
+              "       [7.57],\n",
+              "       [8.28],\n",
+              "       [6.3 ],\n",
+              "       [6.12],\n",
+              "       [7.37],\n",
+              "       [7.94],\n",
+              "       [7.08],\n",
+              "       [6.98],\n",
+              "       [7.38],\n",
+              "       [6.47],\n",
+              "       [5.95],\n",
+              "       [8.71],\n",
+              "       [7.13],\n",
+              "       [7.3 ],\n",
+              "       [5.53],\n",
+              "       [8.93],\n",
+              "       [9.06],\n",
+              "       [8.21],\n",
+              "       [8.6 ],\n",
+              "       [8.13],\n",
+              "       [8.65],\n",
+              "       [9.31],\n",
+              "       [6.22],\n",
+              "       [8.01],\n",
+              "       [6.93],\n",
+              "       [6.75],\n",
+              "       [7.32],\n",
+              "       [7.04],\n",
+              "       [6.29],\n",
+              "       [7.09],\n",
+              "       [8.15],\n",
+              "       [7.14],\n",
+              "       [6.19],\n",
+              "       [8.22],\n",
+              "       [5.88],\n",
+              "       [7.28],\n",
+              "       [7.88],\n",
+              "       [6.31],\n",
+              "       [7.84],\n",
+              "       [6.26],\n",
+              "       [7.35],\n",
+              "       [8.11],\n",
+              "       [6.19],\n",
+              "       [7.28],\n",
+              "       [8.25],\n",
+              "       [4.57],\n",
+              "       [7.89],\n",
+              "       [6.93],\n",
+              "       [5.89],\n",
+              "       [7.21],\n",
+              "       [7.63],\n",
+              "       [6.22]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YTSR_NP6-yQB",
+        "outputId": "8ecc7b4c-d999-41b3-db86-b402073ed211"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([3.26, 1.98, 3.25, 3.67, 3.57, 2.99, 2.6 , 2.48, 2.31, 3.51, 1.86,\n",
+              "       2.6 , 3.65, 2.89, 3.42, 3.23, 2.35, 2.09, 2.98, 2.83, 3.16, 2.93,\n",
+              "       2.3 , 2.48, 2.71, 3.65, 3.42, 2.16, 2.24, 3.49, 3.26, 3.89, 3.08,\n",
+              "       2.73, 3.42, 2.87, 2.84, 2.43, 4.36, 3.33, 4.02, 2.7 , 2.54, 2.76,\n",
+              "       1.86, 3.58, 2.26, 3.26, 4.09, 4.62, 4.43, 3.79, 4.11, 2.61, 3.09,\n",
+              "       3.39, 2.74, 1.94, 3.09, 3.31, 2.19, 1.61, 2.09, 4.25, 2.92, 3.81,\n",
+              "       1.63, 2.89, 2.99, 2.94, 2.35, 3.34, 3.62, 4.03, 3.44, 3.28, 3.15,\n",
+              "       4.6 , 2.21, 3.  , 3.44, 2.2 , 2.17, 3.49, 1.53, 1.48, 2.77, 3.55,\n",
+              "       1.48, 2.72, 2.66, 2.14, 4.  , 3.08, 2.42, 2.79, 2.61, 2.84, 3.83,\n",
+              "       3.24, 4.14, 3.52, 1.37, 3.  , 3.74, 2.82, 2.19, 2.59, 3.54, 4.06,\n",
+              "       3.76, 2.25, 4.1 , 2.37, 1.87, 4.21, 3.33, 2.99, 2.88, 2.65, 1.73,\n",
+              "       3.02, 2.01, 2.3 , 2.31, 3.16, 2.6 , 3.11, 3.34, 3.12, 2.49, 2.01,\n",
+              "       2.48, 2.58, 2.83, 2.6 , 2.1 , 3.13, 3.89, 2.4 , 3.15, 3.18, 3.04,\n",
+              "       1.54, 2.42, 2.18, 2.46, 2.21, 3.4 , 3.67, 2.73, 2.76, 3.08, 3.99,\n",
+              "       2.85, 3.09, 3.13, 2.7 , 3.04, 4.08, 2.93, 3.33, 2.55, 3.91, 3.82,\n",
+              "       4.08, 3.98, 3.6 , 3.52, 4.37, 2.87, 3.76, 2.51, 2.56, 2.99, 3.5 ,\n",
+              "       3.23, 3.64, 3.63, 3.03, 2.72, 3.89, 2.08, 2.72, 3.14, 3.18, 3.47,\n",
+              "       2.44, 3.08, 4.06, 2.69, 3.48, 3.75, 1.94, 3.67, 2.46, 2.57, 3.24,\n",
+              "       3.96, 2.33])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=2)"
+      ],
+      "metadata": {
+        "id": "q4rR1DcD-2D4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n"
+      ],
+      "metadata": {
+        "id": "9pxoQ4aQ_S57"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr=LinearRegression()"
+      ],
+      "metadata": {
+        "id": "CsdLDMOF_eBo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "yjFgDQxk_0zu",
+        "outputId": "ccebf44b-4624-4e4e-a3b6-ed18110205b4"
+      },
+      "execution_count": null,
+      "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": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_test,\n",
+        "print(\" \")\n",
+        "y_test\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LO61uSdw_3S2",
+        "outputId": "45810a7b-23ba-4801-b0af-623c4f993b36"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([4.1 , 3.49, 2.08, 2.33, 1.94, 1.48, 1.86, 3.09, 4.21, 2.87, 3.65,\n",
+              "       4.  , 2.89, 2.6 , 2.99, 3.25, 1.86, 3.67, 2.37, 3.42, 2.48, 3.65,\n",
+              "       2.6 , 2.83, 4.08, 2.56, 3.58, 3.81, 4.09, 2.01, 3.63, 2.92, 3.51,\n",
+              "       1.94, 2.21, 3.34, 3.34, 3.23, 2.01, 2.61])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "H2_WnFnYApj4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr.predict(x_test[0].reshape(1,1))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eEKKsp10_91C",
+        "outputId": "ff4a6bd6-e9ab-4649-8542-fb1fac7b18fb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([3.89111601])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(df['cgpa'],df['package'])\n",
+        "plt.plot(x_test,lr.predict(x_test),color='red')\n",
+        "plt.xlabel('CGPA')\n",
+        "plt.ylabel('Package(In LPA)')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "TgH4O5daAOLz",
+        "outputId": "09218a84-a754-42fc-ba87-74d8bda76103"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Package(In LPA)')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        },
+        {
+          "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(df['cgpa'],df['package'])\n",
+        "plt.plot(x_train,lr.predict(x_train),color='red')\n",
+        "plt.xlabel('CGPA')\n",
+        "plt.ylabel('Package(In LPA)')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "MMAwuHifA9pl",
+        "outputId": "ebd6ba77-53c7-46c7-bbff-4dc900eb74bf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Package(In LPA)')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        },
+        {
+          "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": [
+        "m= lr.coef_\n",
+        "c= lr.intercept_\n",
+        "print(\"slope =\", m, \"Y-intercept =\",c)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-ms-hsvNBGK1",
+        "outputId": "709b310a-241d-4f01-ed2d-9ff794d0211e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "slope = [0.55795197] Y-intercept = -0.8961119222429144\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#y= mx +c\n",
+        "\n",
+        "y = m*8.26 + c"
+      ],
+      "metadata": {
+        "id": "BTWE0b5NBMIx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CPvFFiiYBiw5",
+        "outputId": "ed637675-2ead-41a6-f2d3-39f91f6b6d80"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([3.71257138])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = m*9.26 + c"
+      ],
+      "metadata": {
+        "id": "nJgd0YdUBjau"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IHDenAYuBoI_",
+        "outputId": "51f040f9-7bcf-4714-d7ff-3e7e543a3d90"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([4.27052335])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = m*10.26 + c"
+      ],
+      "metadata": {
+        "id": "wdO-xjgWBpZm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BLrf6fbsBqv0",
+        "outputId": "51337d56-bcfe-4118-9d3f-cb0f31387c9b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([4.82847533])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WDo9ytp4_RYC",
+        "outputId": "07c863e2-b014-4940-bac4-dabf89138312"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(160, 1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for i in range (x.shape[0]):\n",
+        "\n",
+        "  if(x[i]>10):\n",
+        "    print(\"Invalid input\")\n",
+        "    break;\n",
+        "\n",
+        "  else:\n",
+        "    print(\"Valid input\")\n",
+        "    y_pred=m*x[i]+c\n",
+        "    print(y_pred)\n",
+        "    break;\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sZlCdIHlBsBB",
+        "outputId": "ed1f19c7-7efe-4350-9156-5add88c18a7a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Valid input\n",
+            "[2.94817717]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Learn:\n",
+        "\n",
+        "  def __init__(self):\n",
+        "    self.m =None\n",
+        "    self.c =None\n",
+        "\n",
+        "\n",
+        "  def fit(self, x_train, y_train):\n",
+        "    num=0\n",
+        "    den=0\n",
+        "\n",
+        "    for i in range (x_train.shape[0]):\n",
+        "      num= num+((x_train[i]-x_train.mean())*(y_train[i]-y_train.mean()))\n",
+        "      den += x_train[i]-x_train.mean()**2\n",
+        "\n",
+        "    self.m=num/den\n",
+        "    self.c=y_train.mean()-(self.m*x_train.mean())\n",
+        "    print(self.m)\n",
+        "    print(self.c)\n",
+        "\n",
+        "\n",
+        "\n",
+        "  def predict(self,x_test):\n",
+        "    print(x_test)\n",
+        "    print(\"slope\",self.m)\n",
+        "    print(\"Y-Intercept\",self.c)\n",
+        "\n",
+        "    return self.m*x_test+self.c\n",
+        "\n",
+        "  def score(self,y_pred,y_test):\n",
+        "    u=((y_test-y_pred)**2).sum()\n",
+        "    v=((y_test-y_test.mean())**2).sum()\n",
+        "    return 1-(u/v)\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "YaoYsC2gB8e7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "mlTEflrg_Pk9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_test.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NTuVT2LK-2CX",
+        "outputId": "8ad5a443-57fe-4cf9-b85c-56bbf651a23b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(40, 1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 116
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "llr = Learn()"
+      ],
+      "metadata": {
+        "id": "QKB6sQaY_Egh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "llr.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "42s8kk3v_wJE",
+        "outputId": "efb24a3e-75f5-46ee-9f38-3ea309487d25"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[-0.01510711]\n",
+            "[3.10953526]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IoP-cuiR_3DA",
+        "outputId": "4621f3d6-9a37-4647-87b0-92b9ef99d141"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([7.14])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 119
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train.mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-11wa5Gp_8wS",
+        "outputId": "0a32be7e-91d9-413b-9c24-4825351cc337"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6.989937500000001"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 120
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_test[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9123eoKh_-Ke",
+        "outputId": "8e93d8a6-3510-4e2b-e6b6-a2e0408d6b77"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([8.58])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 121
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(llr.predict(x_test[0]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sIt-ctLr__sX",
+        "outputId": "787b1523-6cb1-4659-d961-35859a7eafc0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[8.58]\n",
+            "slope [-0.01510711]\n",
+            "Y-Intercept [3.10953526]\n",
+            "[2.97991625]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(llr.score(llr.predict(x_test[0]),y_test))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "enH7lbUlAEI4",
+        "outputId": "c9dd5a69-5a4d-412c-e0e1-9e365efce7f8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[8.58]\n",
+            "slope [-0.01510711]\n",
+            "Y-Intercept [3.10953526]\n",
+            "-0.0004296382569233259\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "kGea3FcAA-NN"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/LR_learning(Diabetes_dataset).ipynb b/LR_learning(Diabetes_dataset).ipynb
new file mode 100644
index 00000000..0c33dfec
--- /dev/null
+++ b/LR_learning(Diabetes_dataset).ipynb
@@ -0,0 +1,2159 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyP6SZeucNcwbQLbfGFt8wWS",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/levi178u/AlgoWiki/blob/gh-pages/LR_learning(Diabetes_dataset).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "id": "bc1fHYcac96S"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn.datasets import load_diabetes\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x,y=  load_diabetes(return_X_y=True,as_frame=True)"
+      ],
+      "metadata": {
+        "id": "kxChBx3XP8wK"
+      },
+      "execution_count": 76,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "ClW-HlKlQCtS",
+        "outputId": "6ec9ab4b-e40e-47bc-c4d3-73a02e5f4b9f"
+      },
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          age       sex       bmi        bp        s1        s2        s3  \\\n",
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+              "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+              "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+              "..        ...       ...       ...       ...       ...       ...       ...   \n",
+              "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
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+              "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
+              "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
+              "\n",
+              "           s4        s5        s6  \n",
+              "0   -0.002592  0.019907 -0.017646  \n",
+              "1   -0.039493 -0.068332 -0.092204  \n",
+              "2   -0.002592  0.002861 -0.025930  \n",
+              "3    0.034309  0.022688 -0.009362  \n",
+              "4   -0.002592 -0.031988 -0.046641  \n",
+              "..        ...       ...       ...  \n",
+              "437 -0.002592  0.031193  0.007207  \n",
+              "438  0.034309 -0.018114  0.044485  \n",
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+              "440  0.026560  0.044529 -0.025930  \n",
+              "441 -0.039493 -0.004222  0.003064  \n",
+              "\n",
+              "[442 rows x 10 columns]"
+            ],
+            "text/html": [
+              "\n",
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
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+              "      <th>s1</th>\n",
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+              "      <th>s5</th>\n",
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+              "      <td>-0.009362</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.005383</td>\n",
+              "      <td>-0.044642</td>\n",
+              "      <td>-0.036385</td>\n",
+              "      <td>0.021872</td>\n",
+              "      <td>0.003935</td>\n",
+              "      <td>0.015596</td>\n",
+              "      <td>0.008142</td>\n",
+              "      <td>-0.002592</td>\n",
+              "      <td>-0.031988</td>\n",
+              "      <td>-0.046641</td>\n",
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+              "    <tr>\n",
+              "      <th>...</th>\n",
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+              "    .colab-df-container {\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        document.querySelector('#df-0ec68eda-c4e6-4ee9-828d-6405cae0c243 button.colab-df-convert');\n",
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+              "        const dataTable =\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-7997271d-aacc-42d2-bd79-89cbf72ad8ea button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_627c1739-2fa6-40db-b08f-34fb88b3b77c\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_627c1739-2fa6-40db-b08f-34fb88b3b77c button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('x');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "x",
+              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 442,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0476190476190476,\n        \"min\": -0.1072256316073538,\n        \"max\": 0.11072667545381144,\n        \"num_unique_values\": 58,\n        \"samples\": [\n          0.038075906433423026,\n          -0.09269547780327612,\n          0.01991321417832592\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0476190476190478,\n        \"min\": -0.044641636506989144,\n        \"max\": 0.05068011873981862,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -0.044641636506989144,\n          0.05068011873981862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904761,\n        \"min\": -0.09027529589850945,\n        \"max\": 0.17055522598064407,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          0.008883414898524095,\n          -0.05470749746044306\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904762,\n        \"min\": -0.11239880254408448,\n        \"max\": 0.13204361674121307,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.03333707926361473,\n          0.02531523648988596\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047619047619047554,\n        \"min\": -0.12678066991651324,\n        \"max\": 0.15391371315651542,\n        \"num_unique_values\": 141,\n        \"samples\": [\n          0.041085578784023497,\n          0.08374011738825825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047619047619047644,\n        \"min\": -0.11561306597939897,\n        \"max\": 0.19878798965729408,\n        \"num_unique_values\": 302,\n        \"samples\": [\n          0.07415490186505921,\n          0.07008397186179521\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904758,\n        \"min\": -0.10230705051741597,\n        \"max\": 0.18117906039727852,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          0.09281975309919192,\n          0.15908923357275687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047619047619047554,\n        \"min\": -0.0763945037500033,\n        \"max\": 0.18523444326019867,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          0.08670845052151895,\n          -0.024732934523729287\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904763,\n        \"min\": -0.12609712083330468,\n        \"max\": 0.13359728192191356,\n        \"num_unique_values\": 184,\n        \"samples\": [\n          -0.07213275338232743,\n          -0.021395309255276825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904766,\n        \"min\": -0.13776722569000302,\n        \"max\": 0.13561183068907107,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          -0.01764612515980379,\n          -0.09634615654165846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 77
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LgB3wpUxQGIo",
+        "outputId": "d81a13db-bb8e-4d6f-870a-17e8e926a014"
+      },
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(442, 10)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 390
+        },
+        "id": "CUeqRQ73QJfg",
+        "outputId": "c9939ec9-8014-4076-f978-f4c406bfc587"
+      },
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                age           sex           bmi            bp            s1  \\\n",
+              "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02   \n",
+              "mean  -2.511817e-19  1.230790e-17 -2.245564e-16 -4.797570e-17 -1.381499e-17   \n",
+              "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02   \n",
+              "min   -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123988e-01 -1.267807e-01   \n",
+              "25%   -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665608e-02 -3.424784e-02   \n",
+              "50%    5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670422e-03 -4.320866e-03   \n",
+              "75%    3.807591e-02  5.068012e-02  3.124802e-02  3.564379e-02  2.835801e-02   \n",
+              "max    1.107267e-01  5.068012e-02  1.705552e-01  1.320436e-01  1.539137e-01   \n",
+              "\n",
+              "                 s2            s3            s4            s5            s6  \n",
+              "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  \n",
+              "mean   3.918434e-17 -5.777179e-18 -9.042540e-18  9.293722e-17  1.130318e-17  \n",
+              "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  \n",
+              "min   -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260971e-01 -1.377672e-01  \n",
+              "25%   -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324559e-02 -3.317903e-02  \n",
+              "50%   -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947171e-03 -1.077698e-03  \n",
+              "75%    2.984439e-02  2.931150e-02  3.430886e-02  3.243232e-02  2.791705e-02  \n",
+              "max    1.987880e-01  1.811791e-01  1.852344e-01  1.335973e-01  1.356118e-01  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-31c95ab6-fd98-4d4a-8ebe-7a419303c886\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>bmi</th>\n",
+              "      <th>bp</th>\n",
+              "      <th>s1</th>\n",
+              "      <th>s2</th>\n",
+              "      <th>s3</th>\n",
+              "      <th>s4</th>\n",
+              "      <th>s5</th>\n",
+              "      <th>s6</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "      <td>4.420000e+02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-2.511817e-19</td>\n",
+              "      <td>1.230790e-17</td>\n",
+              "      <td>-2.245564e-16</td>\n",
+              "      <td>-4.797570e-17</td>\n",
+              "      <td>-1.381499e-17</td>\n",
+              "      <td>3.918434e-17</td>\n",
+              "      <td>-5.777179e-18</td>\n",
+              "      <td>-9.042540e-18</td>\n",
+              "      <td>9.293722e-17</td>\n",
+              "      <td>1.130318e-17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "      <td>4.761905e-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-1.072256e-01</td>\n",
+              "      <td>-4.464164e-02</td>\n",
+              "      <td>-9.027530e-02</td>\n",
+              "      <td>-1.123988e-01</td>\n",
+              "      <td>-1.267807e-01</td>\n",
+              "      <td>-1.156131e-01</td>\n",
+              "      <td>-1.023071e-01</td>\n",
+              "      <td>-7.639450e-02</td>\n",
+              "      <td>-1.260971e-01</td>\n",
+              "      <td>-1.377672e-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-3.729927e-02</td>\n",
+              "      <td>-4.464164e-02</td>\n",
+              "      <td>-3.422907e-02</td>\n",
+              "      <td>-3.665608e-02</td>\n",
+              "      <td>-3.424784e-02</td>\n",
+              "      <td>-3.035840e-02</td>\n",
+              "      <td>-3.511716e-02</td>\n",
+              "      <td>-3.949338e-02</td>\n",
+              "      <td>-3.324559e-02</td>\n",
+              "      <td>-3.317903e-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>5.383060e-03</td>\n",
+              "      <td>-4.464164e-02</td>\n",
+              "      <td>-7.283766e-03</td>\n",
+              "      <td>-5.670422e-03</td>\n",
+              "      <td>-4.320866e-03</td>\n",
+              "      <td>-3.819065e-03</td>\n",
+              "      <td>-6.584468e-03</td>\n",
+              "      <td>-2.592262e-03</td>\n",
+              "      <td>-1.947171e-03</td>\n",
+              "      <td>-1.077698e-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>3.807591e-02</td>\n",
+              "      <td>5.068012e-02</td>\n",
+              "      <td>3.124802e-02</td>\n",
+              "      <td>3.564379e-02</td>\n",
+              "      <td>2.835801e-02</td>\n",
+              "      <td>2.984439e-02</td>\n",
+              "      <td>2.931150e-02</td>\n",
+              "      <td>3.430886e-02</td>\n",
+              "      <td>3.243232e-02</td>\n",
+              "      <td>2.791705e-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.107267e-01</td>\n",
+              "      <td>5.068012e-02</td>\n",
+              "      <td>1.705552e-01</td>\n",
+              "      <td>1.320436e-01</td>\n",
+              "      <td>1.539137e-01</td>\n",
+              "      <td>1.987880e-01</td>\n",
+              "      <td>1.811791e-01</td>\n",
+              "      <td>1.852344e-01</td>\n",
+              "      <td>1.335973e-01</td>\n",
+              "      <td>1.356118e-01</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31c95ab6-fd98-4d4a-8ebe-7a419303c886')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-31c95ab6-fd98-4d4a-8ebe-7a419303c886 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-31c95ab6-fd98-4d4a-8ebe-7a419303c886');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-95531e94-0572-42ae-bb9c-25839582b693\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95531e94-0572-42ae-bb9c-25839582b693')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-95531e94-0572-42ae-bb9c-25839582b693 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26771856938913,\n        \"min\": -0.1072256316073538,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -2.511816797794472e-19,\n          0.005383060374248237,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26984438387296,\n        \"min\": -0.044641636506989144,\n        \"max\": 442.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.2307902309192911e-17,\n          0.05068011873981862,\n          0.0476190476190478\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26467538082832,\n        \"min\": -0.09027529589850945,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -2.2455642172282577e-16,\n          -0.007283766209687899,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26755458292146,\n        \"min\": -0.11239880254408448,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -4.7975700837874414e-17,\n          -0.00567042229275739,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26735866123312,\n        \"min\": -0.12678066991651324,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -1.3814992387869595e-17,\n          -0.004320865536613489,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26423640225082,\n        \"min\": -0.11561306597939897,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.918434204559376e-17,\n          -0.0038190651205350003,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26485662659837,\n        \"min\": -0.10230705051741597,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -5.7771786349272854e-18,\n          -0.006584467611155497,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26310825587572,\n        \"min\": -0.0763945037500033,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -9.042540472060099e-18,\n          -0.002592261998183278,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.26797139645322,\n        \"min\": -0.12609712083330468,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.293722151839546e-17,\n          -0.0019471710869220743,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.2686414608801,\n        \"min\": -0.13776722569000302,\n        \"max\": 442.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1303175590075123e-17,\n          -0.0010776975004659671,\n          442.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 79
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "EGn4-8AGQKWf",
+        "outputId": "2207e6e9-0296-424f-ea51-3774692d23b9"
+      },
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0      151.0\n",
+              "1       75.0\n",
+              "2      141.0\n",
+              "3      206.0\n",
+              "4      135.0\n",
+              "       ...  \n",
+              "437    178.0\n",
+              "438    104.0\n",
+              "439    132.0\n",
+              "440    220.0\n",
+              "441     57.0\n",
+              "Name: target, Length: 442, dtype: float64"
+            ],
+            "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>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>151.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>75.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>141.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>206.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>135.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>437</th>\n",
+              "      <td>178.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>438</th>\n",
+              "      <td>104.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>439</th>\n",
+              "      <td>132.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>440</th>\n",
+              "      <td>220.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>441</th>\n",
+              "      <td>57.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>442 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 80
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mDmXiPLoQVBG",
+        "outputId": "cb536104-016a-45ff-a582-8f6cfa81772a"
+      },
+      "execution_count": 81,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(442,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 81
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 335
+        },
+        "id": "tdO6tevTQWHN",
+        "outputId": "2f4ed6c2-0fe2-4492-c74c-0bc434275963"
+      },
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    442.000000\n",
+              "mean     152.133484\n",
+              "std       77.093005\n",
+              "min       25.000000\n",
+              "25%       87.000000\n",
+              "50%      140.500000\n",
+              "75%      211.500000\n",
+              "max      346.000000\n",
+              "Name: target, dtype: float64"
+            ],
+            "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>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>442.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>152.133484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>77.093005</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>25.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>87.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>140.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>211.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>346.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 82
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now doing Linear Regression on this dataset\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=2)"
+      ],
+      "metadata": {
+        "id": "szXNVMfnQXQu"
+      },
+      "execution_count": 83,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(x_train)\n",
+        "print(\"-----------------------------------\")\n",
+        "print(y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aKE9Vi1eQiBX",
+        "outputId": "993cd70a-3f10-44a0-e171-403d16a8f0dd"
+      },
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "          age       sex       bmi        bp        s1        s2        s3  \\\n",
+            "70  -0.001882 -0.044642 -0.069797 -0.012556 -0.000193 -0.009143  0.070730   \n",
+            "37  -0.009147 -0.044642  0.011039 -0.057313 -0.024960 -0.042963  0.030232   \n",
+            "170  0.023546  0.050680 -0.020218 -0.036656 -0.013953 -0.015092  0.059685   \n",
+            "400 -0.023677 -0.044642  0.045529  0.090729 -0.018080 -0.035447  0.070730   \n",
+            "286 -0.038207 -0.044642 -0.054707 -0.077970 -0.033216 -0.086490  0.140681   \n",
+            "..        ...       ...       ...       ...       ...       ...       ...   \n",
+            "299  0.038076  0.050680 -0.013751 -0.015999 -0.035968 -0.021982 -0.013948   \n",
+            "22  -0.085430 -0.044642 -0.004050 -0.009113 -0.002945  0.007767  0.022869   \n",
+            "72   0.063504  0.050680 -0.004050 -0.012556  0.103003  0.048790  0.056003   \n",
+            "15  -0.052738  0.050680 -0.018062  0.080401  0.089244  0.107662 -0.039719   \n",
+            "168  0.001751  0.050680  0.059541 -0.002228  0.061725  0.063195 -0.058127   \n",
+            "\n",
+            "           s4        s5        s6  \n",
+            "70  -0.039493 -0.062917  0.040343  \n",
+            "37  -0.039493  0.017036 -0.005220  \n",
+            "170 -0.039493 -0.096435 -0.017646  \n",
+            "400 -0.039493 -0.034522 -0.009362  \n",
+            "286 -0.076395 -0.019198 -0.005220  \n",
+            "..        ...       ...       ...  \n",
+            "299 -0.002592 -0.025953 -0.001078  \n",
+            "22  -0.039493 -0.061176 -0.013504  \n",
+            "72  -0.002592  0.084492 -0.017646  \n",
+            "15   0.108111  0.036060 -0.042499  \n",
+            "168  0.108111  0.068986  0.127328  \n",
+            "\n",
+            "[353 rows x 10 columns]\n",
+            "-----------------------------------\n",
+            "70      48.0\n",
+            "37     276.0\n",
+            "170     47.0\n",
+            "400    175.0\n",
+            "286     60.0\n",
+            "       ...  \n",
+            "299     83.0\n",
+            "22      68.0\n",
+            "72     202.0\n",
+            "15     171.0\n",
+            "168    268.0\n",
+            "Name: target, Length: 353, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression"
+      ],
+      "metadata": {
+        "id": "VYF1BilUQqmf"
+      },
+      "execution_count": 85,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "ckhiB_PjQ4eh",
+        "outputId": "3ba6f798-891a-44cd-f856-e851c4c02ebf"
+      },
+      "execution_count": 86,
+      "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": 86
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred= lr.predict(x_test)"
+      ],
+      "metadata": {
+        "id": "_9fx87T1Q7fy"
+      },
+      "execution_count": 87,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import r2_score\n",
+        "r2_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RBohs0xdQ_3P",
+        "outputId": "50b46d0d-fe01-4726-f753-95833c49bb38"
+      },
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.439933866156897"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 88
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "m =lr.coef_\n",
+        "m"
+      ],
+      "metadata": {
+        "id": "p65TLRciRG0q",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "08e6f625-b5f9-4f7f-b8ce-93d1042f2ca0"
+      },
+      "execution_count": 89,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([  -9.15865318, -205.45432163,  516.69374454,  340.61999905,\n",
+              "       -895.5520019 ,  561.22067904,  153.89310954,  126.73139688,\n",
+              "        861.12700152,   52.42112238])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 89
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "c =lr.intercept_\n",
+        "c"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KTs8Gc5AAGgB",
+        "outputId": "f3ecf74b-452e-460e-c89a-3253b5cff34b"
+      },
+      "execution_count": 90,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "151.88331005254167"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 90
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "### Making a Regression based class"
+      ],
+      "metadata": {
+        "id": "VtrUQhQqAmuz"
+      },
+      "execution_count": 91,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class algo:\n",
+        "  def __init__(self):\n",
+        "    self.m=None\n",
+        "    self.c=None\n",
+        "\n",
+        "  def fit(self,x_train,y_train):\n",
+        "    x_train = np.insert(x_train,0,1,axis =1 )\n",
+        "\n",
+        "    b = np.linalg.inv(np.dot(x_train.T,x_train)).dot(x_train.T).dot(y_train)\n",
+        "\n",
+        "    self.m = b[1:]\n",
+        "    self.c = b[0]\n",
+        "\n",
+        "    print(self.m)\n",
+        "    print(self.c)\n",
+        "\n",
+        "\n",
+        "\n",
+        "  def predict(self,x_test):\n",
+        "    y_pred = np.dot(x_test,self.m) + self.c\n",
+        "    return y_pred\n",
+        "\n",
+        "  def score(self,y_test,y_pred):\n",
+        "    u = ((y_test - y_pred)**2).sum()\n",
+        "    v = ((y_test - y_test.mean())**2).sum()\n",
+        "    return 1 - u/v\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "21tmFLWEAqiQ"
+      },
+      "execution_count": 92,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "al = algo()\n",
+        "al.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Kb876IrJA-nP",
+        "outputId": "a3f93dcc-633a-4e65-e803-e9848049fab5"
+      },
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[  -9.15865318 -205.45432163  516.69374454  340.61999905 -895.5520019\n",
+            "  561.22067904  153.89310954  126.73139688  861.12700152   52.42112238]\n",
+            "151.88331005254165\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train.shape\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iya2qAq0BAi3",
+        "outputId": "6a3c1301-d497-484a-fc15-293f3f66a140"
+      },
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(353, 10)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 94
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.insert(x_train,0,1,axis =1 ).shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2DpvFfRJBB5m",
+        "outputId": "b13bc80c-4850-4d32-bdff-4a637b0c1ff3"
+      },
+      "execution_count": 95,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(353, 11)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 95
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = al.predict(x_test)"
+      ],
+      "metadata": {
+        "id": "ZWc8dIGaGW3f"
+      },
+      "execution_count": 96,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "r2_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "atLg3GHwGbSS",
+        "outputId": "1ee42711-dff9-41c6-c4d8-a9095e1de64e"
+      },
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.43993386615689634"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 97
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "al.m\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5FiOXS1lGged",
+        "outputId": "b6a4c77b-f57a-4e9f-e07c-fcc44a785674"
+      },
+      "execution_count": 98,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([  -9.15865318, -205.45432163,  516.69374454,  340.61999905,\n",
+              "       -895.5520019 ,  561.22067904,  153.89310954,  126.73139688,\n",
+              "        861.12700152,   52.42112238])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 98
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "al.c"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vK2YtIf8GmRd",
+        "outputId": "f95b43c2-4787-4120-9829-809729c488e5"
+      },
+      "execution_count": 99,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "151.88331005254165"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 99
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install plotly\n",
+        "import plotly.express as px"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "22O74l7PGmwl",
+        "outputId": "994c575a-cbac-4e30-ff0d-af814c7a6125"
+      },
+      "execution_count": 100,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.24.1)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (9.0.0)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (24.1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "utEBLywnGyfD"
+      },
+      "execution_count": 100,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig = px.scatter_3d(\n",
+        "    x=x_train.iloc[:, 0],\n",
+        "    y=x_train.iloc[:, 1],\n",
+        "    z=y_train,\n",
+        "    color=x_train.iloc[:, 0]\n",
+        ")\n",
+        "\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "RhxvaWgEt9qp",
+        "outputId": "32069063-88e1-4384-c793-827207abb802"
+      },
+      "execution_count": 102,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+}
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