polynomialRegressionDegree4.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/DivyaWadehra/AI-LAB/blob/master/polynomialRegressionDegree4.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": "code",
      "metadata": {
        "id": "hi3CmvmpmHB7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ag6HzlLemdPo",
        "colab_type": "code",
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      },
      "source": [
        "dataset=pd.read_csv('Position_Salaries.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "11c38186-1ebf-4b1c-976c-b485ce0e2734"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Position</th>\n",
              "      <th>Level</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Business Analyst</td>\n",
              "      <td>1</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Junior Consultant</td>\n",
              "      <td>2</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Senior Consultant</td>\n",
              "      <td>3</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Manager</td>\n",
              "      <td>4</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Country Manager</td>\n",
              "      <td>5</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Region Manager</td>\n",
              "      <td>6</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Partner</td>\n",
              "      <td>7</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Senior Partner</td>\n",
              "      <td>8</td>\n",
              "      <td>300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>C-level</td>\n",
              "      <td>9</td>\n",
              "      <td>500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>CEO</td>\n",
              "      <td>10</td>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Position  Level   Salary\n",
              "0   Business Analyst      1    45000\n",
              "1  Junior Consultant      2    50000\n",
              "2  Senior Consultant      3    60000\n",
              "3            Manager      4    80000\n",
              "4    Country Manager      5   110000\n",
              "5     Region Manager      6   150000\n",
              "6            Partner      7   200000\n",
              "7     Senior Partner      8   300000\n",
              "8            C-level      9   500000\n",
              "9                CEO     10  1000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DepXHvHnmhyq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=dataset.iloc[:,1:-1].values\n",
        "y=dataset.iloc[:,-1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHMUTIMsm0DN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "02826b1d-93bf-47ae-a2ee-0f8ac20cfedc"
      },
      "source": [
        "X"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1],\n",
              "       [ 2],\n",
              "       [ 3],\n",
              "       [ 4],\n",
              "       [ 5],\n",
              "       [ 6],\n",
              "       [ 7],\n",
              "       [ 8],\n",
              "       [ 9],\n",
              "       [10]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81Eggxa5m0r-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e190ccb1-52f6-4c29-95f0-3502f7f27567"
      },
      "source": [
        "y"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  45000,   50000,   60000,   80000,  110000,  150000,  200000,\n",
              "        300000,  500000, 1000000])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRYN86Pcm2ET",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inxr14aEnAkP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lin_reg=LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQ_R-hbMniU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1e3502b4-ff86-4a1b-adb1-29e4de3e3f31"
      },
      "source": [
        "lin_reg.fit(X,y)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PMTBNNGnoup",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTplHvbCnvkX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "poly_reg=PolynomialFeatures(degree=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHUsRErqny44",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_poly=poly_reg.fit_transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EB3nWpO_n6f8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "04e01c86-e1bc-412a-fd26-d1e53d4e7703"
      },
      "source": [
        "X_poly"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00],\n",
              "       [1.000e+00, 2.000e+00, 4.000e+00, 8.000e+00, 1.600e+01],\n",
              "       [1.000e+00, 3.000e+00, 9.000e+00, 2.700e+01, 8.100e+01],\n",
              "       [1.000e+00, 4.000e+00, 1.600e+01, 6.400e+01, 2.560e+02],\n",
              "       [1.000e+00, 5.000e+00, 2.500e+01, 1.250e+02, 6.250e+02],\n",
              "       [1.000e+00, 6.000e+00, 3.600e+01, 2.160e+02, 1.296e+03],\n",
              "       [1.000e+00, 7.000e+00, 4.900e+01, 3.430e+02, 2.401e+03],\n",
              "       [1.000e+00, 8.000e+00, 6.400e+01, 5.120e+02, 4.096e+03],\n",
              "       [1.000e+00, 9.000e+00, 8.100e+01, 7.290e+02, 6.561e+03],\n",
              "       [1.000e+00, 1.000e+01, 1.000e+02, 1.000e+03, 1.000e+04]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNvav5jGn9lz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lin_reg2=LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxddqzT7oEw3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c692a077-b684-4883-e6ae-34fffb69b4ac"
      },
      "source": [
        "lin_reg2.fit(X_poly,y)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jXj-w0voJlR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "3759a0a0-ca80-4c6d-b9bb-fb293061c21b"
      },
      "source": [
        "plt.scatter(X,y,color='red')\n",
        "plt.plot(X,lin_reg.predict(X),color='blue')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9feb3596a0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mrSoq4IknwuL53/99qKM+YEATddQl77R6zcTdr3f3Hu5eSlhAf87dy4HngW/EzUYCj8fn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\nJiIikpjCREREEvt/nyVCfBvrn2oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7eFdlU1oj9M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "4db2bc76-642b-4b1c-d4a6-5bae8702750f"
      },
      "source": [
        "plt.scatter(X,y,color='red')\n",
        "plt.plot(X,lin_reg2.predict(poly_reg.fit_transform(X)),color='blue')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9feb2bde10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NKZ0Z1NdTVxf+KG/p1qurVjVd5gPCL+z0cEl/PWRIK2c17WHPZMcOWLnyo6Gx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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vY4p5Khfo9pG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}