slr.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Linear Regression\n"
      ],
      "metadata": {
        "id": "ofI_rN2eweAz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IcHXMiZ_wbGz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reading CSV file"
      ],
      "metadata": {
        "id": "2nYZ48_Ow2Hg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Salary_Data.csv')\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QanCL9Ewz_h",
        "outputId": "7fd60259-85b6-406c-c701-95b6049c7911"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   YearsExperience   Salary\n",
            "0              1.1  39343.0\n",
            "1              1.3  46205.0\n",
            "2              1.5  37731.0\n",
            "3              2.0  43525.0\n",
            "4              2.2  39891.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values\n"
      ],
      "metadata": {
        "id": "Wr7y_Y6ExQem"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "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=0)"
      ],
      "metadata": {
        "id": "d54Q3OP3B_dG"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "\n",
        "regressor.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "dUhUW9iuCuBg",
        "outputId": "8c8ad26a-a121-4fd8-e019-dd651539e949"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regressor.predict(x_test)"
      ],
      "metadata": {
        "id": "ay5x-UO8FVDR"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x_train, y_train, color='red')\n",
        "plt.plot(x_train, regressor.predict(x_train), color = 'blue')\n",
        "plt.title('Salary Vs Experience (Training set)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3kdofhQhFd9g",
        "outputId": "523ce246-6578-4faa-a013-e6da3f9eaed0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x_test, y_test, color='red')\n",
        "plt.plot(x_train, regressor.predict(x_train), color = 'blue')\n",
        "plt.title('Salary Vs Experience (Test set)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "NlYY5CdxHTGt",
        "outputId": "18c4d281-b241-4047-a82d-facdb7ff1f1f"
      },
      "execution_count": 19,
      "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": [],
      "metadata": {
        "id": "yAna2EeHHtVa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}