salary_prediction_based_on_years_of_experience.ipynb

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        "<a href=\"https://colab.research.google.com/github/DLSireesha/Linear-Regression/blob/main/salary_prediction_based_on_years_of_experience.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfossNGm6zJQ"
      },
      "source": [
        "**SIMPLE LINEAR REGRESSION**\n",
        "\n",
        "*SALARY PREDICTION BASED ON YEARS OF EXPERIENCE*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCf_k9L97OlI"
      },
      "source": [
        "import libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "fLgcZc6V5NZp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhZWa2HI7U7f"
      },
      "source": [
        "import dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "4hNdZ6Nq5mXo"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv(\"/content/drive/MyDrive/Datasets/Salary.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "visualize the dataset"
      ],
      "metadata": {
        "id": "MOtccJIIMlCe"
      }
    },
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        },
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        "outputId": "0c7e7fa0-caf0-4d11-e4b7-57e0bab48236"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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"
            ],
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              "      <td>37731.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>43525.0</td>\n",
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        "data.head(5)"
      ]
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        {
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            "text/plain": [
              "Index(['YearsExperience', 'Salary'], dtype='object')"
            ]
          },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsExperience    float64\n",
              "Salary             float64\n",
              "dtype: object"
            ]
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          "execution_count": 9
        }
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            "text/plain": [
              "YearsExperience    0\n",
              "Salary             0\n",
              "dtype: int64"
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          "execution_count": 10
        }
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      "source": [
        "x = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, 1].values"
      ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(x,y,color='b')\n",
        "plt.xlabel('Years of Expreience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spliting the dataset into training and testing set"
      ],
      "metadata": {
        "id": "6yruQcSpNE7m"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "05VWYxJv_Uxf"
      },
      "outputs": [],
      "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=1/3,random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fitting Simple Linear Regression to the Training set"
      ],
      "metadata": {
        "id": "lAOxgUsxNWle"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sST9PuKeARuf",
        "outputId": "e7c8d77d-c4ec-4863-e667-f211215a7d3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(x_train, y_train)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "predicting the results"
      ],
      "metadata": {
        "id": "stZpRSvRNZEu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred=regressor.predict(x_test)"
      ],
      "metadata": {
        "id": "mkfw1Wc8IT9u"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "visualize the training data"
      ],
      "metadata": {
        "id": "NMkskjZcNmeF"
      }
    },
    {
      "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(\"experience vs salary\")\n",
        "plt.xlabel(\"experience\")\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z3Gyer6xImi_",
        "outputId": "6b9496d4-815c-48f5-d8a0-060790f0306c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "visualize the testing data"
      ],
      "metadata": {
        "id": "DtbKz6F4NqOu"
      }
    },
    {
      "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(\"experience vs salary\")\n",
        "plt.xlabel('experience')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "xre-W2mLJOTe",
        "outputId": "7b32f7fa-cf03-4615-f5be-92cd95b41783"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
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        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1wLcjxuCGHY6NxyaR_6YdH6rnDhPPFfga",
      "authorship_tag": "ABX9TyPsD2A0Tflf8He9RYxSB2oZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}