ML4.ipynb

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        "id": "NEmPLnNecWuu"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data=pd.read_csv('Salary_data.csv')\n",
        "data\n"
      ],
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          "height": 990
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              "    YearsExperience    Salary\n",
              "0               1.1   39343.0\n",
              "1               1.3   46205.0\n",
              "2               1.5   37731.0\n",
              "3               2.0   43525.0\n",
              "4               2.2   39891.0\n",
              "5               2.9   56642.0\n",
              "6               3.0   60150.0\n",
              "7               3.2   54445.0\n",
              "8               3.2   64445.0\n",
              "9               3.7   57189.0\n",
              "10              3.9   63218.0\n",
              "11              4.0   55794.0\n",
              "12              4.0   56957.0\n",
              "13              4.1   57081.0\n",
              "14              4.5   61111.0\n",
              "15              4.9   67938.0\n",
              "16              5.1   66029.0\n",
              "17              5.3   83088.0\n",
              "18              5.9   81363.0\n",
              "19              6.0   93940.0\n",
              "20              6.8   91738.0\n",
              "21              7.1   98273.0\n",
              "22              7.9  101302.0\n",
              "23              8.2  113812.0\n",
              "24              8.7  109431.0\n",
              "25              9.0  105582.0\n",
              "26              9.5  116969.0\n",
              "27              9.6  112635.0\n",
              "28             10.3  122391.0\n",
              "29             10.5  121872.0"
            ],
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              "      <th></th>\n",
              "      <th>YearsExperience</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.1</td>\n",
              "      <td>39343.0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>46205.0</td>\n",
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              "      <td>1.5</td>\n",
              "      <td>37731.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>43525.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>2.2</td>\n",
              "      <td>39891.0</td>\n",
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              "      <th>5</th>\n",
              "      <td>2.9</td>\n",
              "      <td>56642.0</td>\n",
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              "      <th>6</th>\n",
              "      <td>3.0</td>\n",
              "      <td>60150.0</td>\n",
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              "      <th>7</th>\n",
              "      <td>3.2</td>\n",
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              "      <th>9</th>\n",
              "      <td>3.7</td>\n",
              "      <td>57189.0</td>\n",
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              "      <th>10</th>\n",
              "      <td>3.9</td>\n",
              "      <td>63218.0</td>\n",
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              "      <th>11</th>\n",
              "      <td>4.0</td>\n",
              "      <td>55794.0</td>\n",
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              "      <th>12</th>\n",
              "      <td>4.0</td>\n",
              "      <td>56957.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4.1</td>\n",
              "      <td>57081.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>4.5</td>\n",
              "      <td>61111.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>4.9</td>\n",
              "      <td>67938.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.1</td>\n",
              "      <td>66029.0</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.3</td>\n",
              "      <td>83088.0</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.9</td>\n",
              "      <td>81363.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>6.0</td>\n",
              "      <td>93940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>6.8</td>\n",
              "      <td>91738.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>7.1</td>\n",
              "      <td>98273.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>7.9</td>\n",
              "      <td>101302.0</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>8.2</td>\n",
              "      <td>113812.0</td>\n",
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              "      <th>24</th>\n",
              "      <td>8.7</td>\n",
              "      <td>109431.0</td>\n",
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              "      <th>25</th>\n",
              "      <td>9.0</td>\n",
              "      <td>105582.0</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>9.5</td>\n",
              "      <td>116969.0</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>9.6</td>\n",
              "      <td>112635.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>10.3</td>\n",
              "      <td>122391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10.5</td>\n",
              "      <td>121872.0</td>\n",
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "\n",
        "X=data['YearsExperience']\n",
        "y=data['Salary']\n",
        "\n",
        "plt.scatter(X,y)\n",
        "plt.xlabel('YearsExperience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()\n",
        "\n"
      ],
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        {
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class OLS:\n",
        "    def __init__(self):\n",
        "        self.coeff=[0,0]\n",
        "    \n",
        "    def train(self, X, y):\n",
        "        diff_x=X-np.mean(X)\n",
        "        diff_y=y-np.mean(y)\n",
        "        self.coeff[1] = sum(diff_x*diff_y)/sum(diff_x*diff_x)\n",
        "        self.coeff[0] = np.mean(y)-self.coeff[1]*np.mean(X)\n",
        "        \n",
        "    def predict(self,X):\n",
        "        y=self.coeff[0]+self.coeff[1]*X\n",
        "        return y\n",
        "    \n",
        "    def RSS(self,y,y_pred):\n",
        "        error=y-y_pred\n",
        "        rss=sum(error*error)\n",
        "        return rss\n",
        "    \n",
        "    def TSS(self,y):\n",
        "        error=y-y.mean()\n",
        "        tss=sum(error*error)\n",
        "        return tss\n",
        "    \n",
        "    def R2(self,rss,tss):\n",
        "        r2= 1-(rss/tss)\n",
        "        return r2\n",
        "    \n",
        "\n"
      ],
      "metadata": {
        "id": "qCsOWJanc0KT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)\n",
        "\n",
        "model=OLS()\n",
        "model.train(X_train,y_train)\n",
        "\n",
        "print('Value of coefficient b:', model.coeff[0])\n",
        "\n",
        "print('Value of coefficient m: ',model.coeff[1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlaII5Igc-qh",
        "outputId": "15542ae2-e7e6-4b6c-c4b5-2ff61e113f25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value of coefficient b: 26777.39134119761\n",
            "Value of coefficient m:  9360.261286193658\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, model.coeff[0] + (model.coeff[1] * X), 'r-')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()\n",
        "\n",
        "y_pred=model.predict(X_test)\n",
        "y_pred\n",
        "\n",
        "#calculating R2 Score for testing data\n",
        "\n",
        "rss=model.RSS(y_test,y_pred)\n",
        "tss=model.TSS(y_test)\n",
        "r2=model.R2(rss,tss)\n",
        "print('R2 Score: ', r2)\n",
        "\n",
        "#calculating MSE for testing data\n",
        "\n",
        "mse=rss/len(y_test)\n",
        "print('MSE: ',mse)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "dKlqQ6bjdByy",
        "outputId": "5fe3180d-d4f2-47e5-c738-67b099739f4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score:  0.9740993407213511\n",
            "MSE:  23370078.800832942\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ilxksLGwdDm7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}