Chance_of_Admission.ipynb

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        "# **[YBI Foundation](https://www.ybifoundation.org/)**\n",
        "\n",
        "**[Join Telegram to Get Updates of all Future FREE Bootcamps and Courses](https://telegram.me/ybif_ybifoundation)**"
      ],
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        "# **Chance of Admission for Higher Studies**\n",
        "\n",
        "Predict the chances of admission of a student to a Graduate program based on:\n",
        "\n",
        "1. GRE Scores (290 to 340) \n",
        "2. TOEFL Scores (92 to 120) \n",
        "3. University Rating (1 to 5) \n",
        "4. Statement of Purpose (1 to 5) \n",
        "5. Letter of Recommendation Strength (1 to 5) \n",
        "6. Undergraduate CGPA (6.8 to 9.92) \n",
        "7. Research Experience (0 or 1) \n",
        "6. Chance of Admit (0.34 to 0.97)"
      ],
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      "source": [
        "**[Watch Video Tutorial](https://www.youtube.com/c/YBIFoundation?sub_confirmation=1)**"
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        "id": "FLRanr2j6yqA"
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      "source": [
        "# Step 1 : import library\n",
        "import pandas as pd"
      ],
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      "execution_count": 1,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Step 2 : import data\n",
        "admission = pd.read_csv('https://github.com/ybifoundation/Dataset/raw/main/Admission%20Chance.csv')"
      ],
      "metadata": {
        "id": "ueIYllMH6yqA"
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      "execution_count": 2,
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        "admission.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",
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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",
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              "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>3.500000</td>\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5ca7d4a-db84-4f0f-a3d0-d9e6cb88a275 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-b5ca7d4a-db84-4f0f-a3d0-d9e6cb88a275');\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",
              "          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": [
        "# Step 3 : define target (y) and features (X)"
      ],
      "metadata": {
        "id": "X1Ex022o6yqB"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "admission.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cc671a5-eb0b-47d7-aab9-d75600bbc411",
        "id": "2iBsXQom6yqB"
      },
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = admission['Chance of Admit ']"
      ],
      "metadata": {
        "id": "QHYrFO1W6yqB"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = admission.drop(['Serial No','Chance of Admit '],axis=1)"
      ],
      "metadata": {
        "id": "kRwBkZBz6yqB"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4 : train test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7, random_state=2529)"
      ],
      "metadata": {
        "id": "Jk1uAOTv6yqB"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shape of train and test sample\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4828dbc0-647c-43c7-dc69-2877bf16222b",
        "id": "XH-iRVS06yqB"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((280, 7), (120, 7), (280,), (120,))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5 : select model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "_rN-U2a56yqC"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6 : train or fit model\n",
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "a5082add-b003-4be9-c133-20a15a70e1d6",
        "id": "mbT2nuig6yqC"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "238f2863-e6f6-485b-ecad-d2fbe7e34050",
        "id": "2CzM_JZW6yqC"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.2831244932033998"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad72090c-501b-4452-85bc-d31cbd28857e",
        "id": "CrIUvWDV6yqC"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.00204057,  0.00287273,  0.00566887, -0.00380559,  0.01973175,\n",
              "        0.11314449,  0.02061553])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7 : predict model\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "lUhcfr9W6yqC"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "413803df-4bd8-48e3-c42a-3a489c58ec51",
        "id": "ybQFuyCI6yqC"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.71426327, 0.72534136, 0.69677103, 0.66566584, 0.57483872,\n",
              "       0.93087527, 0.93701113, 0.72361387, 0.81130158, 0.62223963,\n",
              "       0.59629648, 0.80084072, 0.52537944, 0.79174558, 0.84064992,\n",
              "       0.66429594, 0.65136589, 0.66990687, 0.75794085, 0.86072023,\n",
              "       0.66088101, 0.85570763, 0.84777425, 0.95033179, 0.68750762,\n",
              "       0.65907671, 0.65279623, 0.5709259 , 0.55895645, 0.57990205,\n",
              "       0.54497918, 0.7570717 , 0.69682571, 0.77286067, 0.64320811,\n",
              "       0.5183554 , 0.43816818, 0.84654064, 0.90398354, 0.80517781,\n",
              "       0.72218971, 0.72882587, 0.68145136, 0.88592237, 0.77208852,\n",
              "       0.78778085, 0.95526121, 0.88586486, 0.59980416, 0.50690214,\n",
              "       0.59947098, 0.63380406, 0.82841217, 0.44911724, 0.71068577,\n",
              "       0.77335748, 0.68851557, 0.64486026, 0.85537724, 0.65517768,\n",
              "       0.65046031, 0.90818978, 0.63422429, 0.68658606, 0.72150268,\n",
              "       0.69030545, 0.59381287, 0.93813035, 0.58997351, 0.91542587,\n",
              "       0.59283415, 0.93351713, 0.59478751, 0.71380389, 0.54346237,\n",
              "       0.84710913, 0.6084418 , 0.7257337 , 0.67545704, 0.81387503,\n",
              "       0.70259527, 0.88600461, 0.67084016, 0.53064995, 0.77790726,\n",
              "       0.65780713, 0.78970635, 0.54709634, 0.77924705, 0.66750436,\n",
              "       0.69363338, 0.69891086, 0.92185813, 0.70469056, 0.62554306,\n",
              "       0.62208829, 0.73828086, 0.67369114, 0.76391913, 0.61985049,\n",
              "       0.92865957, 0.70430038, 0.9828821 , 0.82502993, 0.78261009,\n",
              "       0.83438446, 0.66840368, 0.70165011, 0.64534281, 0.5715406 ,\n",
              "       0.80739359, 0.69273815, 0.80585447, 0.6102703 , 0.54641206,\n",
              "       0.76301749, 0.71080317, 0.6261331 , 0.83951248, 0.68578269])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8 : model accuracy\n",
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error"
      ],
      "metadata": {
        "id": "g4tDHm9dEUud"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38698b51-8e86-4e81-ce35-b985f747d598",
        "id": "-LJzLY91EUud"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04400128934232651"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_percentage_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2b84f78-a799-482e-bbbb-d2a5b2a313a0",
        "id": "tLfkMuUTEUue"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07575278864605438"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3beb0221-8464-4e48-da8e-2b65dadb3f0c",
        "id": "_qhSYJU-EUue"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.004038263715495693"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Don't Forget to Star and Watch on GitHub to Receive Updates**\n",
        "**Action 1: ⭐Star Repository as it make easy for you to find it again. You can see all the repositories and topics you have starred by going to your stars page.**\n",
        "\n",
        "**Action 2: 👁 Watch Repository and get notified of all future updates and activities in this repository.**\n",
        "\n",
        "**[Click Here to Visit Fundamental Repository on GitHub](https://github.com/YBIFoundation/Fundamentals)**"
      ],
      "metadata": {
        "id": "aeE0y9rd6yqD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "17Oo616Q6yqD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Don't Forget to Upvote NoteBook on Kaggle and Receive Updates** \n",
        "**[Click Here to Visit Kaggle](https://www.kaggle.com/ybifoundation)**"
      ],
      "metadata": {
        "id": "M2v7VQ2w6yqE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "6aX0qydQ6yqE"
      }
    }
  ]
}