task_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "task 1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO4NtPunaA1L2/o1ZK4Bo67",
      "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/nishabalamurugan/nishabalamurugan/blob/main/task_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***NISHA B***      "
      ],
      "metadata": {
        "id": "qTW2DWRVCZnl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PREDICTION USING SUPERVISED MACHINE LEARNING \n"
      ],
      "metadata": {
        "id": "qH7KGIxzJSuV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import necessary libraries \n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt "
      ],
      "metadata": {
        "id": "lRhfXmcUJmrL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "READ DATASET"
      ],
      "metadata": {
        "id": "V9eAYqrWK0tS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv(\"http://bit.ly/w-data\")\n",
        "print(dataset.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgR8l6mGKTvL",
        "outputId": "3a09006f-f16b-4a48-ad02-8ada31762f8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "VISUALISING DATASET"
      ],
      "metadata": {
        "id": "FfQZ4CQfLJ8z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds=dataset.head(10)\n",
        "x=ds.Hours \n",
        "y=ds.Scores \n",
        "plt.grid(True)\n",
        "plt.scatter(x,y,color='green')\n",
        "plt.title(\"Hours studied vs Score\")\n",
        "plt.xlabel('No.of hours studied')\n",
        "plt.ylabel('Score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "-szTmLCOLNGv",
        "outputId": "0c87d71d-38f8-48b9-fad8-358410609855"
      },
      "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": "markdown",
      "source": [
        "EXTRACT DEPENDENT AND INDEPENDENT FEATURES"
      ],
      "metadata": {
        "id": "bDFsnLJzOJN8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=dataset.iloc[:, :-1].values  \n",
        "y=dataset.iloc[:, 1].values   \n",
        "print('dependent and independent features are extracted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uaua1TCHOOD3",
        "outputId": "d67422a7-09e5-43d6-e249-7b29941e5cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dependent and independent features are extracted\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SPLITTING INTO TEST AND TRAIN DATASET"
      ],
      "metadata": {
        "id": "blu6lzFiPDx1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split \n",
        "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.25)"
      ],
      "metadata": {
        "id": "6s7uj6zZPEna"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "LINEAR REGRESSION"
      ],
      "metadata": {
        "id": "625FeAp-PhK3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression   \n",
        "x=LinearRegression()\n",
        "x.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ra7Rz8jzPjKd",
        "outputId": "647c8590-be2f-4bcf-8c3b-96f7ca2a9e88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TESTING THE MODEL"
      ],
      "metadata": {
        "id": "AVmbDo4SVoEN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ypred=x.predict(xtest)\n",
        "print(\"Predicted values are:\")\n",
        "print(ypred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDNBPyXwP4dC",
        "outputId": "1e384f57-b333-4a3d-b6cb-7e25f081a342"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted values are:\n",
            "[91.13451078 88.24930563 26.6982624  17.08091189 26.6982624  69.01460462\n",
            " 55.55031391]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hrs=np.array([9.25]).reshape(1,-1) \n",
        "y=x.predict(hrs)\n",
        "print(y[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBNT0uwW9vWy",
        "outputId": "ac24434d-6755-499c-e77e-a127d4540048"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "91.6153783039681\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.DataFrame({'ACTUAL': ytest, 'PREDICTED': ypred})   \n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXVhSriNP9HO",
        "outputId": "44fa4c29-9f90-479f-a27f-e73b1fbf8ea4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ACTUAL  PREDICTED\n",
            "0      88  91.134511\n",
            "1      95  88.249306\n",
            "2      30  26.698262\n",
            "3      20  17.080912\n",
            "4      21  26.698262\n",
            "5      76  69.014605\n",
            "6      60  55.550314\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(xtest,ytest,color='red')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "akSmHqY1SJ9G",
        "outputId": "8c6b7563-ccd4-4e2a-921f-ba316d58dc8c"
      },
      "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": "markdown",
      "source": [
        "MODEL EVALUATION"
      ],
      "metadata": {
        "id": "79DVZmk6S_nV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics \n",
        "print(metrics.mean_absolute_error(ytest,ypred)) \n",
        "print('Model evaluated')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BELu4Ll5TBwe",
        "outputId": "afd105f9-8376-4767-e99e-02c019da6db4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.748482104329113\n",
            "Model evaluated\n"
          ]
        }
      ]
    }
  ]
}