train_test__split.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
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      "source": [
        "df=pd.read_csv('carprices.csv')\n",
        "df"
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              "    Mileage  Age(yrs)  Sell Price($)\n",
              "0     69000         6          18000\n",
              "1     35000         3          34000\n",
              "2     57000         5          26100\n",
              "3     22500         2          40000\n",
              "4     46000         4          31500\n",
              "5     59000         5          26750\n",
              "6     52000         5          32000\n",
              "7     72000         6          19300\n",
              "8     91000         8          12000\n",
              "9     67000         6          22000\n",
              "10    83000         7          18700\n",
              "11    79000         7          19500\n",
              "12    59000         5          26000\n",
              "13    58780         4          27500\n",
              "14    82450         7          19400\n",
              "15    25400         3          35000\n",
              "16    28000         2          35500\n",
              "17    69000         5          19700\n",
              "18    87600         8          12800\n",
              "19    52000         5          28200"
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20595,\n        \"min\": 22500,\n        \"max\": 91000,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          69000,\n          35000,\n          59000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age(yrs)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          3,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sell Price($)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7834,\n        \"min\": 12000,\n        \"max\": 40000,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          18000,\n          19700,\n          35000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 6
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['Mileage'],df['Sell Price($)'])\n",
        "plt.xlabel('Mileage')\n",
        "plt.ylabel('Selling Price')"
      ],
      "metadata": {
        "colab": {
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          "height": 467
        },
        "id": "L1PP3qGuBZKy",
        "outputId": "faa06670-98d6-4a8f-a0e3-82c14593b0e0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Selling Price')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['Age(yrs)'],df['Sell Price($)'])\n",
        "plt.xlabel('Age(yrs)')\n",
        "plt.ylabel('Selling Price')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "KZwsHJqCCVdP",
        "outputId": "5d1c5031-e711-4a4b-a129-08af5aa3ea96"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Selling Price')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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[['Mileage','Age(yrs)']]#independent variables- double brackets!\n",
        "y=df['Sell Price($)']\n"
      ],
      "metadata": {
        "id": "3LyMQZj3DM-y"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "7ZAU1cQ0DoQG"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test=train_test_split(X,y, test_size=0.2,random_state=10)##no. 10 indicates this type of random state"
      ],
      "metadata": {
        "id": "I82kgobwD5sr"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model=LinearRegression()"
      ],
      "metadata": {
        "id": "QkzG36w_Ev1h"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "1riXKkynGvHk",
        "outputId": "7bc30b0e-1471-419b-d2f4-cbab21e36032"
      },
      "execution_count": 27,
      "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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-vx5iKnGxy4",
        "outputId": "c4e33f9b-2970-493c-99bb-4508b4216071"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([20474.0745775 , 16352.07892168, 25174.14834912, 27197.42175439])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "eW9O25-iG1GV",
        "outputId": "ea4d8d5c-a1a0-4d77-9bf3-d64e636da384"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7     19300\n",
              "10    18700\n",
              "5     26750\n",
              "6     32000\n",
              "Name: Sell Price($), dtype: int64"
            ],
            "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>Sell Price($)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>19300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>18700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>26750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>32000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7Gs1_g1HCtV",
        "outputId": "b9a40dfd-1fe8-4d5b-dae6-eeb896407c7d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7332339593090147"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t_Gy-kx9HHst"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}