HR.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **HR Salary Calculation Using Polinomial Linear Regression**"
      ],
      "metadata": {
        "id": "2AH7ZzaRsTjR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Polynomial Linear Regression general formula :\n",
        "y = c0 + c1*x + c2 *x² + ⋯ + cn *xn (power)"
      ],
      "metadata": {
        "id": "15cWMYRxtbj2"
      }
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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      "source": [
        "cd '/content/drive/MyDrive/Colab_Notebooks/Datasets'"
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        "id": "GcaDIalZvdCN",
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      "execution_count": 13,
      "outputs": [
        {
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          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/Datasets\n"
          ]
        }
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              "   Level  Salary\n",
              "0      1    4500\n",
              "1      2    5000\n",
              "2      3    6000\n",
              "3      4    8000\n",
              "4      5   11000\n",
              "5      6   15000\n",
              "6      7   20000\n",
              "7      8   30000\n",
              "8      9   50000\n",
              "9     10  100000"
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      "source": [
        "df=pd.read_excel(\"salarydata.xlsx\")\n",
        "df"
      ]
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        "y=df[['Salary']]\n",
        "x=df[['Level']]\n",
        "x"
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              "   Salary\n",
              "0    4500\n",
              "1    5000\n",
              "2    6000\n",
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              "4   11000\n",
              "5   15000\n",
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              "   Salary\n",
              "0    4500\n",
              "1    5000\n",
              "2    6000\n",
              "3    8000\n",
              "4   11000\n",
              "5   15000\n",
              "6   20000\n",
              "7   30000\n",
              "8   50000\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-3d494bd0-1410-434c-a011-ac000dcac868 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-3d494bd0-1410-434c-a011-ac000dcac868');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, I visualize my dataset to reveal some pattern:"
      ],
      "metadata": {
        "id": "oNm9O-MUzQ5f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x,y)\n",
        "plt.xlabel(\"Experience level\")\n",
        "plt.ylabel(\"salary\")\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "O60Z9g0kyExI",
        "outputId": "82f62dde-135c-4a73-da6e-10a6239144ba"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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": "code",
      "source": [
        "polynomial_regression = PolynomialFeatures(degree = 4)\n",
        "x_polynomial = polynomial_regression.fit_transform(x)"
      ],
      "metadata": {
        "id": "cU2roTPQ0tXB"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fitting my model\n",
        "reg = LinearRegression()\n",
        "reg.fit(x_polynomial,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZW_7AYU0tUD",
        "outputId": "a8398426-5b46-4e56-f3da-e9298b0cd536"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = reg.predict(x_polynomial)\n",
        "plt.plot(x,y_pred,color=\"red\",label= \"polynomial regression\")\n",
        "plt.legend()\n",
        "\n",
        "plt.scatter(x,y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "1uHYXp4g0tQ6",
        "outputId": "797768c4-b449-456e-fee7-6cec2fde135e"
      },
      "execution_count": 29,
      "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": [
        "Everytime I will change the polynomial degree and observe the results above. the degree that yield a better fit is the one I will adopt."
      ],
      "metadata": {
        "id": "8cVLvHGO3c16"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now I move on to calculating new employees' salaries with different experience levels using the model I built:"
      ],
      "metadata": {
        "id": "lL4IR-Y030VW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_polynomial_new = polynomial_regression.fit_transform([[4.5]])\n",
        "reg.predict(x_polynomial_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayJystwK0tL4",
        "outputId": "4bd55bf4-b4c2-4517-838e-fa1b7bf858ea"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10958.21496211]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X3cfeDIV0tFf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}