Linear_Regression_1.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#**Linear Regression 1**"
      ],
      "metadata": {
        "id": "0oTuDHg_ylJb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Creating a Dataset"
      ],
      "metadata": {
        "id": "FH5BALds1yot"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "x = np.arange(1,25).reshape(12,2)\n",
        "y = np.array([0,1,1,0,1,0,0,1,1,0,1,0])"
      ],
      "metadata": {
        "id": "lpgjbtiS1xaN"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3KEisij_72D",
        "outputId": "6468b392-1055-41c0-fe9c-047e948a673c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2],\n",
              "       [ 3,  4],\n",
              "       [ 5,  6],\n",
              "       [ 7,  8],\n",
              "       [ 9, 10],\n",
              "       [11, 12],\n",
              "       [13, 14],\n",
              "       [15, 16],\n",
              "       [17, 18],\n",
              "       [19, 20],\n",
              "       [21, 22],\n",
              "       [23, 24]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRnyXoYFAXn4",
        "outputId": "f8b86da1-9386-4d93-884d-4bca36c04f9c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To get your data, you use arange(), which is very convenient for generating arrays based on numerical ranges. You also use .reshape() to modify the shape of the array returned by arange() and get a two-dimensional data structure."
      ],
      "metadata": {
        "id": "JPaf4iAW49-z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Practical Concepts For Model Building"
      ],
      "metadata": {
        "id": "A-ZIhj8lysHy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Training Data Vs Validation Data Vs Testing Data**\n",
        "\n",
        "***Training Data:***\n",
        "\n",
        "Data that our algorithm learns from to produce results(model)\n",
        "\n",
        "<br>\n",
        "\n",
        "***Validation Data:***\n",
        "\n",
        "Data that we use to test our results(model) in the process of building our model.\n",
        "\n",
        "<br>\n",
        "\n",
        "***Testing Data:***\n",
        "\n",
        "Data that we use to test our final results(model) that our algorithm had produced. Our algorithm MUST not see any part of this data during the process. It should represent the real worl data that our model will interract with."
      ],
      "metadata": {
        "id": "YrqR25CZy6-z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "###**Splitting Our Dataset**"
      ],
      "metadata": {
        "id": "9hZXDp_XzxSv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can split your data by hand based on your intuition, domain knowledge or problem statement. We split our data using the ***train_test_split()*** present in sklearn's model_selection.\n",
        "\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "4tkMMEYw0GLu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "You can split both input and output datasets with a single function call:"
      ],
      "metadata": {
        "id": "Tojb6yYd4z9g"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LdxUQTg8yW2O"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2SKRiqQD0_a",
        "outputId": "afa4dbde-3a47-43c9-b2f5-97bb015cb9a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2],\n",
              "       [ 5,  6],\n",
              "       [ 9, 10],\n",
              "       [23, 24],\n",
              "       [17, 18],\n",
              "       [15, 16],\n",
              "       [ 3,  4],\n",
              "       [11, 12],\n",
              "       [19, 20]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hu0Fk6mzDJPE",
        "outputId": "a968890b-63b3-4a4d-ef4e-8fb1237a3374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[19, 20],\n",
              "       [15, 16],\n",
              "       [ 5,  6],\n",
              "       [ 1,  2],\n",
              "       [21, 22],\n",
              "       [23, 24],\n",
              "       [11, 12],\n",
              "       [17, 18],\n",
              "       [ 3,  4]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5U8rHMftD6Hb",
        "outputId": "942330b1-58b8-4a5c-8a3e-75100f32e337"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 9, 10],\n",
              "       [19, 20],\n",
              "       [23, 24]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kUC1DP8DRWu",
        "outputId": "a852e1bb-4389-45db-bb55-3139ab4b04ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7,  8],\n",
              "       [ 9, 10],\n",
              "       [13, 14]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYRIzxrJEARg",
        "outputId": "f2902d70-c80a-456a-fae5-db9d33ba1793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 1, 0, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YzJejg1DYOQ",
        "outputId": "8a5849ef-6b21-4d8c-b98c-17ab4b898407"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7ew7Gn4DfSg",
        "outputId": "07709d85-e39b-40f9-b2d7-cc99035ae0c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "cYJfodv51GVy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1000)"
      ],
      "metadata": {
        "id": "f2Lu_mGq5Bdd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dEDcHHzEkrx",
        "outputId": "a9ba99c5-7411-49b6-8aa4-a424ba26744e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11, 12],\n",
              "       [ 3,  4],\n",
              "       [ 9, 10],\n",
              "       [13, 14],\n",
              "       [23, 24],\n",
              "       [ 1,  2],\n",
              "       [21, 22],\n",
              "       [15, 16],\n",
              "       [ 7,  8]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRwVJTXb5FBa",
        "outputId": "b61d1493-19d5-41fe-cc51-5ce11ecea9b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11, 12],\n",
              "       [ 3,  4],\n",
              "       [ 9, 10],\n",
              "       [13, 14],\n",
              "       [23, 24],\n",
              "       [ 1,  2],\n",
              "       [21, 22],\n",
              "       [15, 16],\n",
              "       [ 7,  8]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VWxXIIY5PwZ",
        "outputId": "a8dceeeb-175b-4d58-f8bd-a1f1f46cbf52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5,  6],\n",
              "       [19, 20],\n",
              "       [17, 18]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30, random_state=1000)"
      ],
      "metadata": {
        "id": "ZpA2SrIg5R1S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zh29jwRpFfiV",
        "outputId": "96044887-f59b-481f-ab18-808ef5fc3c07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3,  4],\n",
              "       [ 9, 10],\n",
              "       [13, 14],\n",
              "       [23, 24],\n",
              "       [ 1,  2],\n",
              "       [21, 22],\n",
              "       [15, 16],\n",
              "       [ 7,  8]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7RASZQXFqbo",
        "outputId": "c8b1d0ff-e4e7-4c3f-e63c-8d4d480c58e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5,  6],\n",
              "       [19, 20],\n",
              "       [17, 18],\n",
              "       [11, 12]])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAluct_oG5bf",
        "outputId": "761e3c87-91c3-469a-d14e-7252aae62b0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6ZINf9AG-Gw",
        "outputId": "cf7673f1-3a6a-4396-848e-9d33dfc3a9f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You probably got different results from what you see here. This is because dataset splitting is random by default. The result differs each time you run the function. However, this often isn’t what you want.\n",
        "\n",
        "Sometimes, to make your tests reproducible, you need a random split with the same output for each function call. You can do that with the parameter random_state. The value of random_state isn’t important—it can be any non-negative integer. You could use an instance of numpy.random.RandomState instead, but that is a more complex approach.\n",
        "\n",
        "In the previous example, you used a dataset with twelve observations (rows) and got a training sample with nine rows and a test sample with three rows. That’s because you didn’t specify the desired size of the training and test sets. By default, 25 percent of samples are assigned to the test set. This ratio is generally fine for many applications, but it’s not always what you need.\n",
        "\n",
        "Typically, you’ll want to define the size of the test (or training) set explicitly, and sometimes you’ll even want to experiment with different values. You can do that with the parameters train_size or test_size.\n",
        "\n",
        "Modify the code so you can choose the size of the test set and get a reproducible result:"
      ],
      "metadata": {
        "id": "joxzZ8EA3-qp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30, random_state=1000, stratify=y)"
      ],
      "metadata": {
        "id": "nh2jALe84ABa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WGz183lOABBm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TXNfXvngABjS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XNTjtFvrADGl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fUjFjHoDAE1t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OGcKbQcLAHsd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "With this change, you get a different result from before. Earlier, you had a training set with nine items and test set with three items. Now, thanks to the argument test_size=4, the training set has eight items and the test set has four items. You’d get the same result with test_size=0.33 because 33 percent of twelve is approximately four.\n",
        "\n",
        "There’s one more very important difference between the last two examples: You now get the same result each time you run the function. This is because you’ve fixed the random number generator with random_state=4.\n",
        "\n",
        "The figure below shows what’s going on when you call train_test_split():"
      ],
      "metadata": {
        "id": "GQsBAal-As2g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "zqLpnYbjAx-K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The samples of the dataset are shuffled randomly and then split into the training and test sets according to the size you defined.\n",
        "\n",
        "You can see that y has six zeros and six ones. However, the test set has three zeros out of four items. If you want to (approximately) keep the proportion of y values through the training and test sets, then pass stratify=y. This will enable stratified splitting:"
      ],
      "metadata": {
        "id": "qvCvpLZgA4NZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pyCCfgjZAtPx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7-7CKLRCBDXk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tHy5nRNWBFuf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dvQAe_EkBHFu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5UFJhk65BIcc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Supervised Machine Learning With train_test_split()**: Linear Regression\n",
        "Now it’s time to see train_test_split() in action when solving supervised learning problems. You’ll start with a small regression problem that can be solved with linear regression before looking at a bigger problem."
      ],
      "metadata": {
        "id": "Hmt3e3msBmDd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "AUg24L_wBKjh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(20).reshape(-1, 1)\n",
        "y = np.array([5, 12, 11, 19, 30, 29, 23, 40, 51, 54, 74,62, 68, 73, 89, 84, 89, 101, 99, 106])"
      ],
      "metadata": {
        "id": "Zw2HZXUlCNfx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "sp2pbEqhCgMg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8377de24-0d93-4c90-8c52-db7c5dfecff0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0],\n",
              "       [ 1],\n",
              "       [ 2],\n",
              "       [ 3],\n",
              "       [ 4],\n",
              "       [ 5],\n",
              "       [ 6],\n",
              "       [ 7],\n",
              "       [ 8],\n",
              "       [ 9],\n",
              "       [10],\n",
              "       [11],\n",
              "       [12],\n",
              "       [13],\n",
              "       [14],\n",
              "       [15],\n",
              "       [16],\n",
              "       [17],\n",
              "       [18],\n",
              "       [19]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzm6efENO8PO",
        "outputId": "a42927f2-c9d3-4589-877b-d699a0797b2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  5,  12,  11,  19,  30,  29,  23,  40,  51,  54,  74,  62,  68,\n",
              "        73,  89,  84,  89, 101,  99, 106])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=8, random_state=0)"
      ],
      "metadata": {
        "id": "7jkthnK4PJAE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jchScsoEPhoI",
        "outputId": "7c738405-2914-4489-c07a-31f14be43380"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4],\n",
              "       [ 2],\n",
              "       [ 5],\n",
              "       [14],\n",
              "       [ 9],\n",
              "       [ 7],\n",
              "       [16],\n",
              "       [11],\n",
              "       [ 3],\n",
              "       [ 0],\n",
              "       [15],\n",
              "       [12]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqEAoq6pPnxc",
        "outputId": "556c74d8-3bae-45e3-95ef-9ef9a2f11a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18],\n",
              "       [ 1],\n",
              "       [19],\n",
              "       [ 8],\n",
              "       [10],\n",
              "       [17],\n",
              "       [ 6],\n",
              "       [13]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Your dataset has twenty observations, or x-y pairs. You specify the argument test_size=8, so the dataset is divided into a training set with twelve observations and a test set with eight observations.\n",
        "\n",
        "Now you can use the training set to fit the model:"
      ],
      "metadata": {
        "id": "w51QxhtSCpyJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=LinearRegression()\n",
        "model.fit(x_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlyiBqw0G4tA",
        "outputId": "11218f58-e964-4276-8789-e2787478bcd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyLEl1t6Ckem",
        "outputId": "c1eec3fc-5b02-48d5-a5cc-a67082429f71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.1617195496417523"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxZsymMDDEo8",
        "outputId": "24f7352d-b294-4235-a764-09deb9ddae61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.53121801])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = mx + c\n",
        "\n",
        "y = 5.53x + 3.16"
      ],
      "metadata": {
        "id": "YHMv0tqVCzTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o86m8qP8U6c9",
        "outputId": "69da39c3-ff82-49e6-fd80-1ef9311193e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18],\n",
              "       [ 1],\n",
              "       [19],\n",
              "       [ 8],\n",
              "       [10],\n",
              "       [17],\n",
              "       [ 6],\n",
              "       [13]])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKZUJL7IRSO3",
        "outputId": "b3a38f5d-c939-43b0-acec-9918a606f28f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([102.72364381,   8.69293756, 108.25486182,  47.41146366,\n",
              "        58.47389969,  97.19242579,  36.34902764,  75.06755374])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LinearRegression creates the object that represents the model, while .fit() trains, or fits, the model and returns it. With linear regression, fitting the model means determining the best intercept (model.intercept_) and slope (model.coef_) values of the regression line.\n",
        "\n",
        "Although you can use x_train and y_train to check the goodness of fit, this isn’t a best practice. An unbiased estimation of the predictive performance of your model is based on test data:"
      ],
      "metadata": {
        "id": "cFVMwQGDDPkg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_train,y_train)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xfb5C_PqDHS5",
        "outputId": "10e6c35f-cbd5-4251-c79e-1f839b353f9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98.68175024574795"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_test,y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-X9sV4_iR5rG",
        "outputId": "353451b6-813d-4d2e-9ba9-e8d2bf9f11e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94.65896927715023"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***.score()*** returns the coefficient of determination, or R², for the data passed. Its maximum is 1. The higher the R² value, the better the fit. In this case, the training data yields a slightly higher coefficient. However, the R² calculated with test data is an unbiased measure of your model’s prediction performance."
      ],
      "metadata": {
        "id": "lmWrwMQGD0kP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_prediction=model.predict(x_test)"
      ],
      "metadata": {
        "id": "jBiXjVNYEzKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsLpKUZsRn24",
        "outputId": "c5e23e42-713d-46bf-d333-e3bc6a54126e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([102.72364381,   8.69293756, 108.25486182,  47.41146366,\n",
              "        58.47389969,  97.19242579,  36.34902764,  75.06755374])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "x=pd.DataFrame(x, columns=['X'])\n",
        "y=pd.DataFrame(y, columns=['Y'])"
      ],
      "metadata": {
        "id": "NFIWmmcTVPpy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames=[x,y]\n",
        "\n",
        "df=pd.concat(frames,axis=1)"
      ],
      "metadata": {
        "id": "hmeJeBTMVfo7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "iB5I3ByKUYcV",
        "outputId": "febce186-63da-4e6d-c7f9-c21259292e1e"
      },
      "execution_count": null,
      "outputs": [
        {
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              "     X    Y\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c697559d-1785-41d7-86ac-3efc59618b0a')\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c697559d-1785-41d7-86ac-3efc59618b0a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c697559d-1785-41d7-86ac-3efc59618b0a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_prediction=model.predict(x)\n",
        "y_predictions=pd.DataFrame(model_prediction,columns=['y_pred'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JS3qTVfsS-y2",
        "outputId": "1ed616e1-1517-42b0-b76f-acd0d9394ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
            "  f\"X has feature names, but {self.__class__.__name__} was fitted without\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_frames=[df,y_predictions]\n",
        "\n",
        "df=pd.concat(y_frames,axis=1)"
      ],
      "metadata": {
        "id": "ObKnn2qaTTiA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df=df.drop('y_pred',axis=1)"
      ],
      "metadata": {
        "id": "eX4SK-YaSskJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "fSJtHNeeFitY",
        "outputId": "5eada13e-7dce-4a67-fe31-e0952fcd3ad9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     X    Y      y_pred\n",
              "0    0    5    3.161720\n",
              "1    1   12    8.692938\n",
              "2    2   11   14.224156\n",
              "3    3   19   19.755374\n",
              "4    4   30   25.286592\n",
              "5    5   29   30.817810\n",
              "6    6   23   36.349028\n",
              "7    7   40   41.880246\n",
              "8    8   51   47.411464\n",
              "9    9   54   52.942682\n",
              "10  10   74   58.473900\n",
              "11  11   62   64.005118\n",
              "12  12   68   69.536336\n",
              "13  13   73   75.067554\n",
              "14  14   89   80.598772\n",
              "15  15   84   86.129990\n",
              "16  16   89   91.661208\n",
              "17  17  101   97.192426\n",
              "18  18   99  102.723644\n",
              "19  19  106  108.254862"
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              "      <td>101</td>\n",
              "      <td>97.192426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>18</td>\n",
              "      <td>99</td>\n",
              "      <td>102.723644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>106</td>\n",
              "      <td>108.254862</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c671806b-6709-4881-9c33-184357731509')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c671806b-6709-4881-9c33-184357731509 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c671806b-6709-4881-9c33-184357731509');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "MAE=metrics.mean_absolute_error(df.Y, df.y_pred)\n",
        "print(MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roXVLaJyU2s9",
        "outputId": "e6e4b139-7586-43b0-ed69-ed008c0ef0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.9822415557830126\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RMSE=np.sqrt(MAE)\n",
        "print(RMSE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-_g63sFW_yE",
        "outputId": "f8510570-75dc-4092-ed1d-69a9318c0859"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.9955554504405566\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = mx + c\n",
        "\n",
        "y = 5.53x + 3.16"
      ],
      "metadata": {
        "id": "ygACrAF5XUQB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "5.53*1 + 3.16"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqNkb5zWX6lf",
        "outputId": "fb61dccd-5d25-425d-8acc-87b3f669db0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.690000000000001"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.regplot(x='y_pred',y='Y',data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "EsW54VmmYAta",
        "outputId": "d86b312a-58f1-4f42-e35c-02c85ee7891c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cM8c1IPAYzbR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}