admission_predict.ipynb

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      "source": [
        "# step 1\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#step 2\n",
        "admsn = pd.read_csv(\"https://github.com/ybifoundation/Dataset/raw/main/Admission%20Chance.csv\")"
      ],
      "metadata": {
        "id": "wf6XDMZdYHqb"
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      "outputs": []
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      "source": [
        "admsn.head()"
      ],
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              "   Serial No  GRE Score  TOEFL Score  University Rating   SOP  LOR   CGPA  \\\n",
              "0          1        337          118                  4   4.5   4.5  9.65   \n",
              "1          2        324          107                  4   4.0   4.5  8.87   \n",
              "2          3        316          104                  3   3.0   3.5  8.00   \n",
              "3          4        322          110                  3   3.5   2.5  8.67   \n",
              "4          5        314          103                  2   2.0   3.0  8.21   \n",
              "\n",
              "   Research  Chance of Admit   \n",
              "0         1              0.92  \n",
              "1         1              0.76  \n",
              "2         1              0.72  \n",
              "3         1              0.80  \n",
              "4         0              0.65  "
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Serial No          400 non-null    int64  \n",
            " 1   GRE Score          400 non-null    int64  \n",
            " 2   TOEFL Score        400 non-null    int64  \n",
            " 3   University Rating  400 non-null    int64  \n",
            " 4    SOP               400 non-null    float64\n",
            " 5   LOR                400 non-null    float64\n",
            " 6   CGPA               400 non-null    float64\n",
            " 7   Research           400 non-null    int64  \n",
            " 8   Chance of Admit    400 non-null    float64\n",
            "dtypes: float64(4), int64(5)\n",
            "memory usage: 28.2 KB\n"
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              "        Serial No   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
              "count  400.000000  400.000000   400.000000         400.000000  400.000000   \n",
              "mean   200.500000  316.807500   107.410000           3.087500    3.400000   \n",
              "std    115.614301   11.473646     6.069514           1.143728    1.006869   \n",
              "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
              "25%    100.750000  308.000000   103.000000           2.000000    2.500000   \n",
              "50%    200.500000  317.000000   107.000000           3.000000    3.500000   \n",
              "75%    300.250000  325.000000   112.000000           4.000000    4.000000   \n",
              "max    400.000000  340.000000   120.000000           5.000000    5.000000   \n",
              "\n",
              "             LOR         CGPA    Research  Chance of Admit   \n",
              "count  400.000000  400.000000  400.000000        400.000000  \n",
              "mean     3.452500    8.598925    0.547500          0.724350  \n",
              "std      0.898478    0.596317    0.498362          0.142609  \n",
              "min      1.000000    6.800000    0.000000          0.340000  \n",
              "25%      3.000000    8.170000    0.000000          0.640000  \n",
              "50%      3.500000    8.610000    1.000000          0.730000  \n",
              "75%      4.000000    9.062500    1.000000          0.830000  \n",
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              "      <td>400.000000</td>\n",
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              "      <td>400.000000</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>3.087500</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>3.452500</td>\n",
              "      <td>8.598925</td>\n",
              "      <td>0.547500</td>\n",
              "      <td>0.724350</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.340000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>308.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>8.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.640000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>200.500000</td>\n",
              "      <td>317.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>8.610000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>325.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.920000</td>\n",
              "      <td>1.000000</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "admsn.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pXy6XerZKz3",
        "outputId": "274e2151-0b24-45dd-8d6a-8e20ef9a4399"
      },
      "execution_count": null,
      "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": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "admsn.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgVy9NPiZLW0",
        "outputId": "8fcd8d7c-37bb-4b8a-a707-74671ab2a617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "admsn.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "sPal9i9FOmpx",
        "outputId": "cc7d4872-926f-47fb-fd6e-13add41bdf3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Serial No  GRE Score  TOEFL Score  University Rating  \\\n",
              "Serial No           1.000000  -0.097526    -0.147932          -0.169948   \n",
              "GRE Score          -0.097526   1.000000     0.835977           0.668976   \n",
              "TOEFL Score        -0.147932   0.835977     1.000000           0.695590   \n",
              "University Rating  -0.169948   0.668976     0.695590           1.000000   \n",
              " SOP               -0.166932   0.612831     0.657981           0.734523   \n",
              "LOR                -0.088221   0.557555     0.567721           0.660123   \n",
              "CGPA               -0.045608   0.833060     0.828417           0.746479   \n",
              "Research           -0.063138   0.580391     0.489858           0.447783   \n",
              "Chance of Admit     0.042336   0.802610     0.791594           0.711250   \n",
              "\n",
              "                        SOP      LOR       CGPA  Research  Chance of Admit   \n",
              "Serial No         -0.166932 -0.088221 -0.045608 -0.063138          0.042336  \n",
              "GRE Score          0.612831  0.557555  0.833060  0.580391          0.802610  \n",
              "TOEFL Score        0.657981  0.567721  0.828417  0.489858          0.791594  \n",
              "University Rating  0.734523  0.660123  0.746479  0.447783          0.711250  \n",
              " SOP               1.000000  0.729593  0.718144  0.444029          0.675732  \n",
              "LOR                0.729593  1.000000  0.670211  0.396859          0.669889  \n",
              "CGPA               0.718144  0.670211  1.000000  0.521654          0.873289  \n",
              "Research           0.444029  0.396859  0.521654  1.000000          0.553202  \n",
              "Chance of Admit    0.675732  0.669889  0.873289  0.553202          1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0868123a-7141-4932-9c15-1e178d3805f3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <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",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Serial No</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.097526</td>\n",
              "      <td>-0.147932</td>\n",
              "      <td>-0.169948</td>\n",
              "      <td>-0.166932</td>\n",
              "      <td>-0.088221</td>\n",
              "      <td>-0.045608</td>\n",
              "      <td>-0.063138</td>\n",
              "      <td>0.042336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GRE Score</th>\n",
              "      <td>-0.097526</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.835977</td>\n",
              "      <td>0.668976</td>\n",
              "      <td>0.612831</td>\n",
              "      <td>0.557555</td>\n",
              "      <td>0.833060</td>\n",
              "      <td>0.580391</td>\n",
              "      <td>0.802610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOEFL Score</th>\n",
              "      <td>-0.147932</td>\n",
              "      <td>0.835977</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.695590</td>\n",
              "      <td>0.657981</td>\n",
              "      <td>0.567721</td>\n",
              "      <td>0.828417</td>\n",
              "      <td>0.489858</td>\n",
              "      <td>0.791594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>University Rating</th>\n",
              "      <td>-0.169948</td>\n",
              "      <td>0.668976</td>\n",
              "      <td>0.695590</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.734523</td>\n",
              "      <td>0.660123</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.447783</td>\n",
              "      <td>0.711250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOP</th>\n",
              "      <td>-0.166932</td>\n",
              "      <td>0.612831</td>\n",
              "      <td>0.657981</td>\n",
              "      <td>0.734523</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.729593</td>\n",
              "      <td>0.718144</td>\n",
              "      <td>0.444029</td>\n",
              "      <td>0.675732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LOR</th>\n",
              "      <td>-0.088221</td>\n",
              "      <td>0.557555</td>\n",
              "      <td>0.567721</td>\n",
              "      <td>0.660123</td>\n",
              "      <td>0.729593</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.670211</td>\n",
              "      <td>0.396859</td>\n",
              "      <td>0.669889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CGPA</th>\n",
              "      <td>-0.045608</td>\n",
              "      <td>0.833060</td>\n",
              "      <td>0.828417</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.718144</td>\n",
              "      <td>0.670211</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.521654</td>\n",
              "      <td>0.873289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Research</th>\n",
              "      <td>-0.063138</td>\n",
              "      <td>0.580391</td>\n",
              "      <td>0.489858</td>\n",
              "      <td>0.447783</td>\n",
              "      <td>0.444029</td>\n",
              "      <td>0.396859</td>\n",
              "      <td>0.521654</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.553202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chance of Admit</th>\n",
              "      <td>0.042336</td>\n",
              "      <td>0.802610</td>\n",
              "      <td>0.791594</td>\n",
              "      <td>0.711250</td>\n",
              "      <td>0.675732</td>\n",
              "      <td>0.669889</td>\n",
              "      <td>0.873289</td>\n",
              "      <td>0.553202</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0868123a-7141-4932-9c15-1e178d3805f3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0868123a-7141-4932-9c15-1e178d3805f3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0868123a-7141-4932-9c15-1e178d3805f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#step 3\n",
        "y = admsn['Chance of Admit ']\n",
        "x = admsn[['Serial No', 'GRE Score', 'TOEFL Score', 'University Rating', ' SOP',\n",
        "       'LOR ', 'CGPA', 'Research']]"
      ],
      "metadata": {
        "id": "p6JCg4WjZLZ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "AxFFS0cAYI0K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state= 2529)"
      ],
      "metadata": {
        "id": "hICMPsSMGNbt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDnwy1EqGNdI",
        "outputId": "f5946dc0-225f-456a-b41e-ff74b1b69885"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((280, 8), (120, 8), (280,), (120,))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "ShceHqQZGNg3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "ofXv3aP6ILfU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-kAJ4gVILh5",
        "outputId": "3d00d424-6169-4302-cf39-7bbe59a7b7d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "i4pw4SA7QZbu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test, y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-cqEvUfQZfa",
        "outputId": "cd5a25da-a5d3-4dd6-8deb-4c98ac2962f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(244    0.63\n",
              " 366    0.74\n",
              " 163    0.68\n",
              " 379    0.71\n",
              " 182    0.68\n",
              "        ... \n",
              " 26     0.76\n",
              " 252    0.71\n",
              " 208    0.64\n",
              " 211    0.82\n",
              " 86     0.72\n",
              " Name: Chance of Admit , Length: 120, dtype: float64,\n",
              " array([0.71638511, 0.75610311, 0.68760789, 0.68124699, 0.56927168,\n",
              "        0.91423484, 0.90562753, 0.72569757, 0.79088749, 0.59675934,\n",
              "        0.59489304, 0.80774252, 0.54572472, 0.77132503, 0.8737274 ,\n",
              "        0.66751677, 0.61934136, 0.67447953, 0.73484064, 0.84425246,\n",
              "        0.62922545, 0.86090216, 0.82814605, 0.9151533 , 0.68755296,\n",
              "        0.63544487, 0.6134806 , 0.57723203, 0.51557048, 0.61715529,\n",
              "        0.52595747, 0.76388331, 0.69695739, 0.75953801, 0.62049587,\n",
              "        0.53097396, 0.40200383, 0.87065079, 0.92286173, 0.78952969,\n",
              "        0.71932513, 0.73604218, 0.70278395, 0.90704   , 0.80062706,\n",
              "        0.81781888, 0.91769447, 0.86667679, 0.58887763, 0.48098577,\n",
              "        0.62564848, 0.61917228, 0.8041983 , 0.46620308, 0.71182598,\n",
              "        0.74243792, 0.69141228, 0.63484399, 0.82789223, 0.6680648 ,\n",
              "        0.64158042, 0.93553342, 0.66537654, 0.65115639, 0.73074156,\n",
              "        0.69630576, 0.59954454, 0.91516057, 0.57456458, 0.90601273,\n",
              "        0.5702746 , 0.92658714, 0.62468054, 0.71689836, 0.5250502 ,\n",
              "        0.86364436, 0.60792138, 0.72722255, 0.66042681, 0.82080932,\n",
              "        0.7225671 , 0.89929811, 0.67637301, 0.49554676, 0.75688317,\n",
              "        0.64865133, 0.77128243, 0.57848782, 0.79141629, 0.67201156,\n",
              "        0.68439077, 0.68509744, 0.91922614, 0.67840876, 0.65244089,\n",
              "        0.64779263, 0.77551969, 0.67516585, 0.76765527, 0.60929075,\n",
              "        0.96500346, 0.69771574, 0.97109768, 0.8244446 , 0.80824716,\n",
              "        0.86627331, 0.65875764, 0.69147515, 0.60381244, 0.59431034,\n",
              "        0.81010835, 0.69414677, 0.76932107, 0.57984938, 0.54290832,\n",
              "        0.73859636, 0.70651852, 0.62741334, 0.84271712, 0.66928116]))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error"
      ],
      "metadata": {
        "id": "pCdlSCcKQ7Jj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ov4IUXERQ_p",
        "outputId": "5d059c72-a7f9-4549-e356-e114a642c3ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04308714723355852"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_percentage_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hPIloFkRRCu",
        "outputId": "dcbebb0f-d10f-4e3a-8f00-e4ec13981bb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.074004372761339"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KG_L5kuwR8VL",
        "outputId": "d5f9774a-bd04-4af4-8b96-2752b8758c16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003922607781791493"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    }
  ]
}