Sol1_Linear_Regression_(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HariniMS/-Arithmetic-Operations-python/blob/master/Sol1_Linear_Regression_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzfdMfk10NE6"
      },
      "source": [
        "## **Linear Regression with Python Scikit Learn**\n",
        "In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.\n",
        "\n",
        "### **Simple Linear Regression**\n",
        "In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9QN2ZxC38pB"
      },
      "source": [
        "# Importing all libraries required in this notebook\n",
        "import pandas as pd\n",
        "import numpy as np  \n",
        "import matplotlib.pyplot as plt  \n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtU4YMEhqm9m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "b65fd597-0c4f-418c-bbfd-e575c4a6dbd5"
      },
      "source": [
        "# Reading data from remote link\n",
        "url = \"http://bit.ly/w-data\"\n",
        "data = pd.read_csv(url)\n",
        "print(\"Data imported successfully\")\n",
        "\n",
        "data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data imported successfully\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.5</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9.2</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.5</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.3</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5      21\n",
              "1    5.1      47\n",
              "2    3.2      27\n",
              "3    8.5      75\n",
              "4    3.5      30\n",
              "5    1.5      20\n",
              "6    9.2      88\n",
              "7    5.5      60\n",
              "8    8.3      81\n",
              "9    2.7      25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHsPneuM4NgB"
      },
      "source": [
        "Let's plot our data points on 2-D graph to eyeball our dataset and see if we can manually find any relationship between the data. We can create the plot with the following script:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxYBZkhAqpn9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "783503c4-82d6-4b21-fbf3-d360262f5b95"
      },
      "source": [
        "# Plotting the distribution of scores\n",
        "data.plot(x='Hours', y='Scores', style='o')  \n",
        "plt.title('Hours vs Percentage')  \n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Score')  \n",
        "plt.show()"
      ],
      "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": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiQaULio4Rzr"
      },
      "source": [
        "**From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWtEr64M4jdz"
      },
      "source": [
        "### **Preparing the data**\n",
        "\n",
        "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiJ5210e4tNX"
      },
      "source": [
        "X = data.iloc[:, :-1].values  \n",
        "y = data.iloc[:, 1].values  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Riz-ZiZ34fO4"
      },
      "source": [
        "Now that we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udFYso1M4BNw"
      },
      "source": [
        "from sklearn.model_selection import train_test_split  \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                            test_size=0.2, random_state=0) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6WXptFU5CkC"
      },
      "source": [
        "### **Training the Algorithm**\n",
        "We have split our data into training and testing sets, and now is finally the time to train our algorithm. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qddCuaS84fpK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "31849501-bd7e-4dab-c5fa-2be0a6f40ba2"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression  \n",
        "regressor = LinearRegression()  \n",
        "regressor.fit(X_train, y_train) \n",
        "\n",
        "print(\"Training complete.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training complete.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J61NX2_2-px7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b0afbb3e-25af-4d34-fb43-0e4f3ec9d542"
      },
      "source": [
        "# Plotting the regression line\n",
        "line = regressor.coef_*X+regressor.intercept_\n",
        "\n",
        "# Plotting for the test data\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, line);\n",
        "plt.show()"
      ],
      "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": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCQn-g4m5OK2"
      },
      "source": [
        "### **Making Predictions**\n",
        "Now that we have trained our algorithm, it's time to make some predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt-Fmzu55EGM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "924d88d5-2602-4f9a-fef0-baf8e934cd97"
      },
      "source": [
        "print(X_test) # Testing data - In Hours\n",
        "y_pred = regressor.predict(X_test) # Predicting the scores"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bmZUMZh5QLb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c69c6c64-c3bd-43bf-dfa1-adff441eab96"
      },
      "source": [
        "# Comparing Actual vs Predicted\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
        "df "
      ],
      "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",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>16.884145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.732261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>75.357018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>26.794801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>60.491033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0      20  16.884145\n",
              "1      27  33.732261\n",
              "2      69  75.357018\n",
              "3      30  26.794801\n",
              "4      62  60.491033"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAFO8zbx-AH1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "02604a2e-05c7-4aab-8938-29946e7333f6"
      },
      "source": [
        "# You can also test with your own data\n",
        "hours = 9.25\n",
        "own_pred = regressor.predict(hours)\n",
        "print(\"No of Hours = {}\".format(hours))\n",
        "print(\"Predicted Score = {}\".format(own_pred[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-f699e5800750>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# You can also test with your own data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mhours\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m9.25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mown_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhours\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No of Hours = {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhours\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Predicted Score = {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mown_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \"\"\"\n\u001b[0;32m--> 225\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[1;32m    209\u001b[0m                                dense_output=True) + self.intercept_\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    547\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m                     \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m    550\u001b[0m             \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got scalar array instead:\narray=9.25.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AAsPVA_6KmK"
      },
      "source": [
        "### **Evaluating the model**\n",
        "\n",
        "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5UOrRH-5VCQ"
      },
      "source": [
        "from sklearn import metrics  \n",
        "print('Mean Absolute Error:', \n",
        "      metrics.mean_absolute_error(y_test, y_pred)) "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}