Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "smlP2py6DlpX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('Salary_data.csv')\n",
        "print(df.head())\n",
        " print(df.shape)\n",
        "print(df.isnull().sum())\n"
      ],
      "metadata": {
        "id": "EPxMM6vCIuT2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "458f413f-8472-4422-cc09-17a6858be338"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Salaries Reported  Experience   Salary\n",
            "0                  3           6   400000\n",
            "1                  3           7   400000\n",
            "2                  3          11  1000000\n",
            "3                  3           9   300000\n",
            "4                  3          12   600000\n",
            "(29, 3)\n",
            "Salaries Reported    0\n",
            "Experience           0\n",
            "Salary               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[ : , : -1]\n",
        "y=df.iloc[ : , -1 ]\n",
        "print(x)\n",
        "print(y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmvc6aiNpPMP",
        "outputId": "95fafbab-d1de-4911-8745-7b5fb2eadda2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Salaries Reported  Experience\n",
            "0                   3           6\n",
            "1                   3           7\n",
            "2                   3          11\n",
            "3                   3           9\n",
            "4                   3          12\n",
            "5                   3          12\n",
            "6                   3           3\n",
            "7                   3           5\n",
            "8                   3           8\n",
            "9                   3           8\n",
            "10                  3           9\n",
            "11                  2          10\n",
            "12                  2          11\n",
            "13                  2           2\n",
            "14                  2           6\n",
            "15                  2           4\n",
            "16                  2           7\n",
            "17                  2           8\n",
            "18                  2           8\n",
            "19                  2           7\n",
            "20                  2           9\n",
            "21                  2           3\n",
            "22                  2          12\n",
            "23                  2           3\n",
            "24                  2           6\n",
            "25                  2           8\n",
            "26                  2           9\n",
            "27                  2           3\n",
            "28                  2           2\n",
            "0      400000\n",
            "1      400000\n",
            "2     1000000\n",
            "3      300000\n",
            "4      600000\n",
            "5      100000\n",
            "6      192000\n",
            "7      400000\n",
            "8      300000\n",
            "9      600000\n",
            "10     300000\n",
            "11     240000\n",
            "12     900000\n",
            "13      24000\n",
            "14     600000\n",
            "15     228000\n",
            "16     600000\n",
            "17     360000\n",
            "18    1000000\n",
            "19     500000\n",
            "20     408000\n",
            "21     300000\n",
            "22     800000\n",
            "23     228000\n",
            "24     100000\n",
            "25     500000\n",
            "26     200000\n",
            "27     500000\n",
            "28     228000\n",
            "Name: Salary, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "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=0.10,random_state = 7)\n",
        " \n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)"
      ],
      "metadata": {
        "id": "FDLi1CB_JVUh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06f4b63e-0372-4461-d091-ef76e80fdbef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(26, 2)\n",
            "(3, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regr = LinearRegression()\n",
        "regr.fit(x_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdP-UAlxuZnK",
        "outputId": "5890566b-f408-49ff-a118-d3c12f339c72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regr.predict(x_test)\n",
        "print('Predictions for test data:', y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXm378H-3pRP",
        "outputId": "c1cf98dc-239f-4451-f932-5e1f9880437e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions for test data: [464062.08499336 488476.09561753 318201.52722444]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score,mean_squared_error\n",
        "print(\"r2 score of our model is:\", r2_score(y_test,y_pred))\n",
        "print(\"mean absolute error of our model is:\", mean_squared_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rG2EAObn35AZ",
        "outputId": "79ce4601-ff89-4dac-dd1f-a14ff4f39cdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2 score of our model is: -0.20757214484648867\n",
            "mean absolute error of our model is: 103475515345.06801\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,y_pred)\n",
        "plt.xlabel('Experience')\n",
        "plt.ylabel('salary')\n",
        "plt.title('experience vs salary')"
      ],
      "metadata": {
        "id": "9QqX9p_tJdML",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "a534cb2f-0694-40a7-8ea3-b83be61b8c5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'experience vs salary')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}