simple_linear_regression_model.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model"
      ]
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      "source": [
        "data = pd.read_csv(\"Company_data.csv\")\n",
        "data.head()"
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              "      TV  Radio  Newspaper  Sales\n",
              "0  230.1   37.8       69.2   22.1\n",
              "1   44.5   39.3       45.1   10.4\n",
              "2   17.2   45.9       69.3   12.0\n",
              "3  151.5   41.3       58.5   16.5\n",
              "4  180.8   10.8       58.4   17.9"
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              "      <td>230.1</td>\n",
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        "X = data.drop('Sales', axis = 1).values\n",
        "y = data['Sales'].values"
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            "text/plain": [
              "                 TV     Radio  Newspaper     Sales\n",
              "TV         1.000000  0.054809   0.056648  0.901208\n",
              "Radio      0.054809  1.000000   0.354104  0.349631\n",
              "Newspaper  0.056648  0.354104   1.000000  0.157960\n",
              "Sales      0.901208  0.349631   0.157960  1.000000"
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        "x_tv = X[:, 0]"
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        "plt.scatter(x_tv, y)\n",
        "plt.xlabel('Number of TV Advertisements')\n",
        "plt.ylabel('Values of sales')\n",
        "plt.show()"
      ],
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_tv = x_tv.reshape(-1, 1)\n",
        "y = y.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "5MM0_J5kBoYF"
      },
      "execution_count": 10,
      "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_tv, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "6aTPSQrtCERg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQ9fEaeCCcMY",
        "outputId": "8219cba9-b02f-4b96-997a-fed29e370ce4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((160, 1), (40, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()"
      ],
      "metadata": {
        "id": "sSGO_12mClwh"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcGzdVcECr2S",
        "outputId": "cdabf325-7e69-465c-ef66-b24eef9d229a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "y_pred1 = reg.predict(X_train)"
      ],
      "metadata": {
        "id": "i2sgWEWBCxNz"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Performance of the model on training data :\\n')\n",
        "print(f'MAE = {mean_absolute_error(Y_train, y_pred1)}')\n",
        "print(f'MSE = {mean_squared_error(Y_train, y_pred1)}')\n",
        "print(f'RMSE = {np.sqrt(mean_squared_error(Y_train, y_pred1))}')\n",
        "print(f'R_2 = {r2_score(Y_train, y_pred1)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgQ_bWniDeA9",
        "outputId": "ed122ff4-f2d2-40e0-894e-0a74d24ff0e0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performance of the model on training data :\n",
            "\n",
            "MAE = 1.8005092256620792\n",
            "MSE = 4.998442356450173\n",
            "RMSE = 2.235719650683013\n",
            "R_2 = 0.8134866044709264\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nXwqT7Q9Die9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}