Task_1_Linear Regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZT9cfz98EGRj"
      },
      "source": [
        "# G.vinay venkata sai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fm6l4EiLEGRp"
      },
      "source": [
        "### Data Science and Business Analytics Intern at The Sparks Foundation "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOA-xAe3EGRr"
      },
      "source": [
        "## Prediction using Supervised Machine Learning "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4cwm-h-EGRs"
      },
      "source": [
        "### Question : Need to find out the score of a student based on the study hours "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V9QN2ZxC38pB"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "LtU4YMEhqm9m",
        "outputId": "5b4b36af-1545-497e-a6dc-7658bab71dbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data imported successfully\n"
          ]
        },
        {
          "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"
            ]
          },
          "execution_count": 2,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Reading data from remote link\n",
        "url = \"http://bit.ly/w-data\"\n",
        "s_data = pd.read_csv(url)\n",
        "print(\"Data imported successfully\")\n",
        "\n",
        "s_data.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHsPneuM4NgB"
      },
      "source": [
        "using scatter plot we can easily find out the theme of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "qxYBZkhAqpn9",
        "outputId": "37264af1-786d-4e0c-a668-383264d1ddd1"
      },
      "outputs": [
        {
          "data": {
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1B7YAHgEmEfT6J4XPpR0dFSsrVTEzFVETKU9xJv0bgOlm9jTQEzgFeBG43cxO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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f463cea7f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting the distribution of scores\n",
        "s_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()"
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "id": "LiJ5210e4tNX"
      },
      "outputs": [],
      "source": [
        "X = s_data.iloc[:, :-1].values  \n",
        "y = s_data.iloc[:, 1].values  "
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "id": "udFYso1M4BNw"
      },
      "outputs": [],
      "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) "
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "qddCuaS84fpK",
        "outputId": "befbd977-772c-4bd1-bb48-ee5dd6bae73c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training complete.\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression  \n",
        "regressor = LinearRegression()  \n",
        "regressor.fit(X_train, y_train) \n",
        "\n",
        "print(\"Training complete.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "J61NX2_2-px7",
        "outputId": "d20ec1fd-3e2d-4eae-84a2-a0df57d31009"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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9f3lzA0+/sjZs7PA+Pbl5+oiM1ymSaQpvCbREu/11pu1/EnQKbwm8WN3+OnMK7RmThnLG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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4637579d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "Tt-Fmzu55EGM",
        "outputId": "46f1acf8-91ac-4984-cfbe-e614aa9ea849"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ]
        }
      ],
      "source": [
        "print(X_test) # Testing data - In Hours\n",
        "y_pred = regressor.predict(X_test) # Predicting the scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "6bmZUMZh5QLb",
        "outputId": "8ea11a9e-c1b7-4fab-ab62-4dcbd2c8607b"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 9,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Comparing Actual vs Predicted\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
        "df "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "KAFO8zbx-AH1",
        "outputId": "fcb3830f-3cda-4dcb-f122-84b71f101fae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No of Hours = 9.25\n",
            "Predicted Score = 93.69173248737539\n"
          ]
        }
      ],
      "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]))"
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "r5UOrRH-5VCQ",
        "outputId": "7b9ddcf1-2848-408f-d81f-7a60652c381e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 4.183859899002982\n"
          ]
        }
      ],
      "source": [
        "from sklearn import metrics  \n",
        "print('Mean Absolute Error:', \n",
        "      metrics.mean_absolute_error(y_test, y_pred)) "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Linear Regression.ipynb",
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