ML7011_Linear_Regression_Implementation_in_Python_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML7011 - Linear Regression Implementation in Python .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOsKbmVrcyr1DCogBvpbmzE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DeveloperAkram/Colab-Notebooks/blob/master/ML7011_Linear_Regression_Implementation_in_Python_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQZZXesxG1C9"
      },
      "source": [
        "# Importing Dependencies\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSYH5078HnnM",
        "outputId": "127c5911-c4dd-4806-e092-8b0b0aa28a42"
      },
      "source": [
        "# Load Dataset\n",
        "\n",
        "from google.colab import drive\n",
        "drive._mount('/content/akramchowdhury')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/akramchowdhury; to attempt to forcibly remount, call drive.mount(\"/content/akramchowdhury\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irW5N95XIw4R"
      },
      "source": [
        "dataset = pd.read_csv('/content/akramchowdhury/MyDrive/Colab Notebooks/ML7001 - Machine Learning Algorithms/data1.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "8UO7KuTyJQ_7",
        "outputId": "7b3e171a-91ad-4d9b-de2f-3e5327d5dd51"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   X  Y\n",
              "0  1  2\n",
              "1  2  4\n",
              "2  3  5\n",
              "3  4  4\n",
              "4  5  5"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "tbv2QlkbLJUb",
        "outputId": "5e3c0db5-e8ab-4684-c9aa-1363877fc46d"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('y')\n",
        "plt.scatter(dataset.X, dataset.Y, color = 'red', marker='+')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f0209e1cd90>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6AN45eHNDva"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HknPMPdlNQPQ"
      },
      "source": [
        "lireg = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8P5iLDLkNWYj",
        "outputId": "9fc9be15-aece-4144-abc4-d91846e0aeb1"
      },
      "source": [
        "lireg.fit(dataset[['X']], dataset.Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6fs3u8wN2Q-",
        "outputId": "0336cbbc-46df-4075-a088-89a7a72667ea"
      },
      "source": [
        "lireg.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.6])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXoVLTIcN6eG",
        "outputId": "2d729637-5741-424d-c776-49ef3be06610"
      },
      "source": [
        "lireg.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNssz080NfhW"
      },
      "source": [
        "y_pred = np.array([3]).reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBkRa23cNp56",
        "outputId": "df36922c-16ec-4e64-fe11-eee73a4c80b1"
      },
      "source": [
        "lireg.predict(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4gb3eG8NuJa",
        "outputId": "ce7c4aa9-5d8b-4f4a-c724-08b36e671cd4"
      },
      "source": [
        "# y = m*x + b\n",
        "\n",
        "0.6*3+2.19999999"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.9999999899999996"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "p-JMhpX5Onbw",
        "outputId": "814435a8-481b-411e-c9ba-63b68b1a21ef"
      },
      "source": [
        "%matplotlib inline\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('y')\n",
        "plt.scatter(dataset.X, dataset.Y, color = 'red', marker='+')\n",
        "plt.plot(dataset.X, lireg.predict(dataset[['X']]), color= 'blue')\n",
        "plt.grid()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zyu2DWvPMbE"
      },
      "source": [
        "data2 = pd.read_csv('/content/akramchowdhury/MyDrive/Colab Notebooks/ML7001 - Machine Learning Algorithms/data2.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "-HNgMXASQGpv",
        "outputId": "5c205d51-9b8c-40c0-c9a9-7d4722065ea4"
      },
      "source": [
        "data2"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>X</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    X\n",
              "0  11\n",
              "1  12\n",
              "2  13\n",
              "3  14\n",
              "4  15\n",
              "5  17\n",
              "6  20\n",
              "7  25"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "907ukEmQQIRp",
        "outputId": "5d6d3163-c650-43f1-e25b-dafc6f164a1f"
      },
      "source": [
        "lireg.predict(data2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8.8,  9.4, 10. , 10.6, 11.2, 12.4, 14.2, 17.2])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URmzAmm9QMhp"
      },
      "source": [
        "pred = lireg.predict(data2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kE_2FzC6QRta",
        "outputId": "133ec161-ca38-48cc-deb8-f7d5a4a7d581"
      },
      "source": [
        "pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8.8,  9.4, 10. , 10.6, 11.2, 12.4, 14.2, 17.2])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVXsd87SQTuK"
      },
      "source": [
        "data2['y_pred'] = pred"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "h6UfDYrsQkJB",
        "outputId": "0294cc65-c275-47e9-af22-9251c6132303"
      },
      "source": [
        "data2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <th>y_pred</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>10.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>14</td>\n",
              "      <td>10.6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15</td>\n",
              "      <td>11.2</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>17</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>20</td>\n",
              "      <td>14.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>25</td>\n",
              "      <td>17.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    X  y_pred\n",
              "0  11     8.8\n",
              "1  12     9.4\n",
              "2  13    10.0\n",
              "3  14    10.6\n",
              "4  15    11.2\n",
              "5  17    12.4\n",
              "6  20    14.2\n",
              "7  25    17.2"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "roCtsxN6Qxyd",
        "outputId": "578d8e31-2beb-44f1-bb35-1348e61426b0"
      },
      "source": [
        "pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qU3O5hrQllk"
      },
      "source": [
        "data2.to_csv('data_pred.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbkRaT6JQ43P"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}