Spark_foundation_Task1Datasciene.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Spark foundation1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN4V4AneQp+NoDaZMxGNlpR",
      "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/Abiganesh/The-Spark-Foundation/blob/main/Spark_foundation_Task1Datasciene.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQ4rgofhFiiK"
      },
      "source": [
        "**GANESH J** <BR>\n",
        "THE SPARK FOUNDATIONS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NowLhA95XN8y"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "pPKkHdKqXZ7R",
        "outputId": "55ec6e41-d7dc-4c77-9d1d-13927fb74da0"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a8b92317-9134-4004-a286-53afc8672383\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a8b92317-9134-4004-a286-53afc8672383\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving studebt.xlsx to studebt (1).xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "bK1Fgon7Zw_Z",
        "outputId": "6373a43d-a52e-4944-c57f-c5d62ba88fa8"
      },
      "source": [
        "df=pd.read_excel(\"studebt.xlsx\")\n",
        "df.head()"
      ],
      "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>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",
              "  </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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "7v8BVW0hZ70o",
        "outputId": "18762e99-2684-4893-9257-29afa9ca3e2f"
      },
      "source": [
        "df.describe()"
      ],
      "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>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>24.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.895833</td>\n",
              "      <td>50.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.510236</td>\n",
              "      <td>24.764068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.700000</td>\n",
              "      <td>29.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.650000</td>\n",
              "      <td>44.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.025000</td>\n",
              "      <td>70.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>95.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours     Scores\n",
              "count  24.000000  24.000000\n",
              "mean    4.895833  50.041667\n",
              "std     2.510236  24.764068\n",
              "min     1.100000  17.000000\n",
              "25%     2.700000  29.250000\n",
              "50%     4.650000  44.500000\n",
              "75%     7.025000  70.500000\n",
              "max     9.200000  95.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KV5XpIW-aSYO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "887d1e30-8d4c-41de-9ec9-15e95d356bb9"
      },
      "source": [
        "df.corr()"
      ],
      "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>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Hours</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.976178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scores</th>\n",
              "      <td>0.976178</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours    Scores\n",
              "Hours   1.000000  0.976178\n",
              "Scores  0.976178  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICRqx288abs7"
      },
      "source": [
        "train_x = np.asanyarray(df[['Hours']])\n",
        "train_y = np.asanyarray(df[['Scores']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCpqtb_jBUYl"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_NOdS89BoR8"
      },
      "source": [
        "model=LinearRegression()\n",
        "models=model.fit(train_x,train_y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TDDRZ5lB5eZ",
        "outputId": "5b86ace2-cf29-458a-8b50-de2bd3e8139c"
      },
      "source": [
        "print ('Coefficients: ', models.coef_)\n",
        "print ('Intercept: ',models.intercept_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients:  [[9.63022272]]\n",
            "Intercept:  [2.89370125]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "deRpBaZICstI",
        "outputId": "f1a5a7e2-00d2-42f0-eb33-3f6848869ccb"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(df.Hours, df.Scores,color='blue',label=\"Actual\")\n",
        "plt.plot(train_x, models.coef_[0][0]*train_x + models.intercept_[0], '-r',label=\"Predicted\")\n",
        "plt.title(\"Percentage plot\")\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel(\"Hours\")\n",
        "plt.ylabel(\"Scores\")\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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEM7SuKmDCtr",
        "outputId": "54b0f9b4-b2fa-48f7-9718-ffd175f03356"
      },
      "source": [
        "y_predicted = models.predict([[9.25]])\n",
        "print(y_predicted)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[91.97326144]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IF3Fb7LDpQr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}