Day_52_personal_LR.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_52_personal_LR.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",
      "execution_count": 1,
      "metadata": {
        "id": "-Stpuj8LXXVS"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.datasets import load_diabetes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X,y = load_diabetes(return_X_y = True)"
      ],
      "metadata": {
        "id": "COTdVbHUXlPd"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Quh0kSEiXvaO",
        "outputId": "7a62aa72-8591-40a1-ef20-06db4374d8b8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((442, 10), (442,))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "vGgNT87fX4pT"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_val,y_train, y_val = train_test_split(X,y,test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "j-KLnDROX9cf"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "kcjG0x6DYNw0"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "fB3XAOYnYWBS"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "GM6H7QAmYbzk",
        "outputId": "56ec2939-aaf6-4670-caca-1e23eb1f1d54"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_val)"
      ],
      "metadata": {
        "id": "MiFmolnvYeHH"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "W4x4_KcXYhRl"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.sqrt(mean_squared_error(y_pred,y_val)))\n",
        "print(r2_score(y_val,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U524j8vcYoXI",
        "outputId": "138f5ecc-2440-4d67-de48-98f1450a5f46"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "53.85344583676593\n",
            "0.4526027629719195\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "08d0ed98-cd88-4ba3-f660-433c1185dcfc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  37.90402135, -241.96436231,  542.42875852,  347.70384391,\n",
              "       -931.48884588,  518.06227698,  163.41998299,  275.31790158,\n",
              "        736.1988589 ,   48.67065743])"
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          "metadata": {},
          "execution_count": 14
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SI8rJp-2Y7ge",
        "outputId": "3595689f-4240-41d4-e38f-839775fad73c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "151.34560453985995"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "My own Regression class"
      ],
      "metadata": {
        "id": "3YctCQNDZCaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyLR:\n",
        "\n",
        "  def __init__(self):\n",
        "    self.coef_ = None\n",
        "    self.intercept_ = None\n",
        "\n",
        "  def fit(self, X_train, y_train):\n",
        "    X_train = np.insert(X_train, 0, 1, axis = 1)\n",
        "\n",
        "    betas = np.linalg.inv(np.dot(X_train.T, X_train)).dot(X_train.T).dot(y_train)\n",
        "    self.intercept_ = betas[0]\n",
        "    self.coef_ = betas[1:]\n",
        "\n",
        "  def predict(self, x_val):\n",
        "    # print(self.intercept_, self.coef_)\n",
        "    y_pred = np.dot(X_val, self.coef_) +self.intercept_\n",
        "    return y_pred"
      ],
      "metadata": {
        "id": "p6SuatUBZBfl"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = MyLR()"
      ],
      "metadata": {
        "id": "8ULrtveiZbfm"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "YpA0g_vfZbcD"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted = lr.predict(X_train)"
      ],
      "metadata": {
        "id": "uQ5-9dz2ZbYh"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.sqrt(mean_squared_error(y_predicted,y_val)))\n",
        "print(r2_score(y_val,y_predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJyUPGAobb8u",
        "outputId": "b8397250-876e-43f0-91db-fc10507512b0"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "53.85344583676591\n",
            "0.4526027629719199\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hkMvwnUvcTz-"
      },
      "execution_count": null,
      "outputs": []
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}