AdmissionPrediction-Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "exam_results_linear_reg.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCoc7B5C6Ayn"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcKDXMHc6FLy",
        "outputId": "ba660adc-f601-4e77-fbcd-a48ea1566ec6",
        "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
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5099fcc7-f349-4203-b7e2-7da989a1b9ec\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5099fcc7-f349-4203-b7e2-7da989a1b9ec\">\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 Admission_Predict.csv to Admission_Predict.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gx-1ny_-6Kfj"
      },
      "source": [
        "import io\n",
        "df=pd.read_csv(io.BytesIO(uploaded['Admission_Predict.csv']))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gKoV08f8Ngk",
        "outputId": "c4c35fd7-b205-4b5e-d3e6-6ed4300cec42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df.columns\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
              "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_AM4lT_8RfJ",
        "outputId": "e3847eaa-de27-439d-d455-40ccef3fddf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "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>Serial No.</th>\n",
              "      <th>GRE Score</th>\n",
              "      <th>TOEFL Score</th>\n",
              "      <th>University Rating</th>\n",
              "      <th>SOP</th>\n",
              "      <th>LOR</th>\n",
              "      <th>CGPA</th>\n",
              "      <th>Research</th>\n",
              "      <th>Chance of Admit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>337</td>\n",
              "      <td>118</td>\n",
              "      <td>4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>9.65</td>\n",
              "      <td>1</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>324</td>\n",
              "      <td>107</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>8.87</td>\n",
              "      <td>1</td>\n",
              "      <td>0.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>316</td>\n",
              "      <td>104</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>8.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>322</td>\n",
              "      <td>110</td>\n",
              "      <td>3</td>\n",
              "      <td>3.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>8.67</td>\n",
              "      <td>1</td>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>314</td>\n",
              "      <td>103</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.21</td>\n",
              "      <td>0</td>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Serial No.  GRE Score  TOEFL Score  ...  CGPA  Research  Chance of Admit \n",
              "0           1        337          118  ...  9.65         1              0.92\n",
              "1           2        324          107  ...  8.87         1              0.76\n",
              "2           3        316          104  ...  8.00         1              0.72\n",
              "3           4        322          110  ...  8.67         1              0.80\n",
              "4           5        314          103  ...  8.21         0              0.65\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qY2p1sxm8UDE",
        "outputId": "18ae4da3-89a4-4584-e922-ca27fdbe9dd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
              "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyC8fxzC9E_U"
      },
      "source": [
        "#features=['Serial No.','GRE Score','TOEFL Score','University Rating','SOP','LOR','CGPA','Research']\n",
        "#X=df[['Serial No.','GRE Score','TOEFL Score','University Rating','SOP','LOR','CGPA','Research']]\n",
        "features=['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
        "       'LOR ', 'CGPA', 'Research']\n",
        "X=df[features]\n",
        "y=df['Chance of Admit ']\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4vAGICm9rUD"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0)\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irT-81GuLm6g"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model=LinearRegression()\n",
        "model.fit(X_train,y_train)\n",
        "predictions=model.predict(X_test)\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZ3WWOmSMbky",
        "outputId": "e72d6774-6f19-4961-feab-3b3b8df20df7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "train=model.score(X_train,y_train)\n",
        "print(\"training score  : \",train)\n",
        "test=model.score(X_test,y_test)\n",
        "print(\"testing score : \",test)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training score  :  0.8372150293755063\n",
            "testing score :  0.7466998120242241\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9bmqKVjONDA",
        "outputId": "6771b1bf-51b1-4b63-cca4-8da98ef0a98e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "print(\"mean abs error : \",mean_absolute_error(y_test,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean abs error :  0.047691403393013164\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjIUUIceQoHl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}