Day_48_SimpleLR.ipynb

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        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_48_SimpleLR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "baeaNQZtasC0"
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      "source": [
        "class myLR:\n",
        "  def __init__(self):\n",
        "    self.b = None\n",
        "    self.m = None\n",
        "  \n",
        "  def fit(self, X_train, y_train):\n",
        "\n",
        "    num = 0\n",
        "    den = 0\n",
        "    for i in range(X_train.shape[0]):\n",
        "      num += (X_train[i] - X_train.mean())*(y_train[i] - y_train.mean())\n",
        "      den += (X_train[i] - X_train.mean())**2\n",
        "\n",
        "    self.m = num/den\n",
        "    self.b = y_train.mean() - (self.m * X_train.mean())\n",
        "    print(self.m, self.b)\n",
        "\n",
        "\n",
        "\n",
        "  def predict(self, X_test):\n",
        "    return self.m*X_test + self.b"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "BkyE7b67blKP"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day48-simple-linear-regression/placement.csv')"
      ],
      "metadata": {
        "id": "SSwBRJrkboOB"
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      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "df.head()"
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      "execution_count": 4,
      "outputs": [
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          "data": {
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              "   cgpa  package\n",
              "0  6.89     3.26\n",
              "1  5.12     1.98\n",
              "2  7.82     3.25\n",
              "3  7.42     3.67\n",
              "4  6.94     3.57"
            ],
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              "      <td>6.89</td>\n",
              "      <td>3.26</td>\n",
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              "      <th>1</th>\n",
              "      <td>5.12</td>\n",
              "      <td>1.98</td>\n",
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              "      <th>2</th>\n",
              "      <td>7.82</td>\n",
              "      <td>3.25</td>\n",
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              "      <th>3</th>\n",
              "      <td>7.42</td>\n",
              "      <td>3.67</td>\n",
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              "      <th>4</th>\n",
              "      <td>6.94</td>\n",
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        "X = df.iloc[:,0].values\n",
        "y = df.iloc[:,1].values"
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          "data": {
            "text/plain": [
              "array([6.89, 5.12, 7.82, 7.42, 6.94, 7.89, 6.73, 6.75, 6.09, 8.31, 5.32,\n",
              "       6.61, 8.94, 6.93, 7.73, 7.25, 6.84, 5.38, 6.94, 7.48, 7.28, 6.85,\n",
              "       6.14, 6.19, 6.53, 7.28, 8.31, 5.42, 5.94, 7.15, 7.36, 8.1 , 6.96,\n",
              "       6.35, 7.34, 6.87, 5.99, 5.9 , 8.62, 7.43, 9.38, 6.89, 5.95, 7.66,\n",
              "       5.09, 7.87, 6.07, 5.84, 8.63, 8.87, 9.58, 9.26, 8.37, 6.47, 6.86,\n",
              "       8.2 , 5.84, 6.6 , 6.92, 7.56, 5.61, 5.48, 6.34, 9.16, 7.36, 7.6 ,\n",
              "       5.11, 6.51, 7.56, 7.3 , 5.79, 7.47, 7.78, 8.44, 6.85, 6.97, 6.94,\n",
              "       8.99, 6.59, 7.18, 7.63, 6.1 , 5.58, 8.44, 4.26, 4.79, 7.61, 8.09,\n",
              "       4.73, 6.42, 7.11, 6.22, 7.9 , 6.79, 5.83, 6.63, 7.11, 5.98, 7.69,\n",
              "       6.61, 7.95, 6.71, 5.13, 7.05, 7.62, 6.66, 6.13, 6.33, 7.76, 7.77,\n",
              "       8.18, 5.42, 8.58, 6.94, 5.84, 8.35, 9.04, 7.12, 7.4 , 7.39, 5.23,\n",
              "       6.5 , 5.12, 5.1 , 6.06, 7.33, 5.91, 6.78, 7.93, 7.29, 6.68, 6.37,\n",
              "       5.84, 6.05, 7.2 , 6.1 , 5.64, 7.14, 7.91, 7.19, 7.91, 6.76, 6.93,\n",
              "       4.85, 6.17, 5.84, 6.07, 5.66, 7.57, 8.28, 6.3 , 6.12, 7.37, 7.94,\n",
              "       7.08, 6.98, 7.38, 6.47, 5.95, 8.71, 7.13, 7.3 , 5.53, 8.93, 9.06,\n",
              "       8.21, 8.6 , 8.13, 8.65, 9.31, 6.22, 8.01, 6.93, 6.75, 7.32, 7.04,\n",
              "       6.29, 7.09, 8.15, 7.14, 6.19, 8.22, 5.88, 7.28, 7.88, 6.31, 7.84,\n",
              "       6.26, 7.35, 8.11, 6.19, 7.28, 8.25, 4.57, 7.89, 6.93, 5.89, 7.21,\n",
              "       7.63, 6.22])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "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,y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "rkFHyqSIcCNB"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = myLR()"
      ],
      "metadata": {
        "id": "FzcCdLrqcQOj"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CbnsVwdWclLf",
        "outputId": "ee57aafc-a5f9-43e9-c85d-6aa67e6b197a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5742564727019197 -1.0270069374542108\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TnexUsqwe0Zw",
        "outputId": "5d6e6189-515a-4c99-f295-afa66f71f5d7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.63"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1x9uZ7AcnUj",
        "outputId": "b879dce3-2c97-411c-994c-52dedef8a33e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.7803134765595168"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nh28ZxFZes03"
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      "execution_count": null,
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