AdmissionChancePredictor.ipynb

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      "name": "Copy of AdmissionChancePredictor.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJU7yydyqBbD"
      },
      "source": [
        "# Import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMFPkeeZlVwK"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCtrF02amqus"
      },
      "source": [
        "df = pd.read_csv('AdmissionPredict.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "IX1V-UvInDOq",
        "outputId": "bf3d27c2-c6e0-4aeb-8643-a38fc3f3fc1c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "<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>337.0</td>\n",
              "      <td>118.0</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>324.0</td>\n",
              "      <td>107.0</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>316.0</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>110.0</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>314.0</td>\n",
              "      <td>103.0</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      337.0        118.0  ...  9.65         1             0.92\n",
              "1           2      324.0        107.0  ...  8.87         1              NaN\n",
              "2           3      316.0          NaN  ...  8.00         1             0.72\n",
              "3           4        NaN        110.0  ...  8.67         1             0.80\n",
              "4           5      314.0        103.0  ...  8.21         0             0.65\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "b3a2sosom64s",
        "outputId": "e65d7ec8-9ec1-47f9-9c19-1fb25b4bbeea"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>378.000000</td>\n",
              "      <td>380.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>380.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>384.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>200.500000</td>\n",
              "      <td>316.759259</td>\n",
              "      <td>107.386842</td>\n",
              "      <td>3.087500</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>3.452500</td>\n",
              "      <td>8.604737</td>\n",
              "      <td>0.547500</td>\n",
              "      <td>0.724375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.614301</td>\n",
              "      <td>11.415599</td>\n",
              "      <td>6.048645</td>\n",
              "      <td>1.143728</td>\n",
              "      <td>1.006869</td>\n",
              "      <td>0.898478</td>\n",
              "      <td>0.599167</td>\n",
              "      <td>0.498362</td>\n",
              "      <td>0.142964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>100.750000</td>\n",
              "      <td>308.250000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.640000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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>8.640000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>300.250000</td>\n",
              "      <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",
              "      <td>9.080000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>340.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.920000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.970000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Serial No.   GRE Score  ...    Research  Chance of Admit\n",
              "count  400.000000  378.000000  ...  400.000000       384.000000\n",
              "mean   200.500000  316.759259  ...    0.547500         0.724375\n",
              "std    115.614301   11.415599  ...    0.498362         0.142964\n",
              "min      1.000000  290.000000  ...    0.000000         0.340000\n",
              "25%    100.750000  308.250000  ...    0.000000         0.640000\n",
              "50%    200.500000  317.000000  ...    1.000000         0.730000\n",
              "75%    300.250000  325.000000  ...    1.000000         0.830000\n",
              "max    400.000000  340.000000  ...    1.000000         0.970000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9veoT4Vfm84F",
        "outputId": "5a5b173c-716c-49f9-b12a-1544ffd0c70e"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<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          378 non-null    float64\n",
            " 2   TOEFL Score        380 non-null    float64\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               380 non-null    float64\n",
            " 7   Research           400 non-null    int64  \n",
            " 8   Chance of Admit    384 non-null    float64\n",
            "dtypes: float64(6), int64(3)\n",
            "memory usage: 28.2 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrCVoHJJqOUq"
      },
      "source": [
        "# Data Exploratory Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBPxexb5qjUJ"
      },
      "source": [
        "## Dealing With Missing Data\n",
        "Let's see which column has NaN values : "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yrit8TvKqQGl",
        "outputId": "515ad515-b764-4352-d4db-b5dd1a50a444"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Serial No.            0\n",
              "GRE Score            22\n",
              "TOEFL Score          20\n",
              "University Rating     0\n",
              "SOP                   0\n",
              "LOR                   0\n",
              "CGPA                 20\n",
              "Research              0\n",
              "Chance of Admit      16\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DjEIURuNWO6K"
      },
      "source": [
        "Let's drop NaN values in Chance of Admit column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lkaCpBveWEz8",
        "outputId": "21305395-2249-4515-d9d3-88929d7e3421"
      },
      "source": [
        "df = df[df['Chance of Admit'].notna()]\n",
        "df"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
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              "      <th>Chance of Admit</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>337.0</td>\n",
              "      <td>118.0</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>2</th>\n",
              "      <td>3</td>\n",
              "      <td>316.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.72</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</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",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>314.0</td>\n",
              "      <td>103.0</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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>330.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.34</td>\n",
              "      <td>1</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>395</td>\n",
              "      <td>329.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.23</td>\n",
              "      <td>1</td>\n",
              "      <td>0.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>396</td>\n",
              "      <td>324.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>9.04</td>\n",
              "      <td>1</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>398</td>\n",
              "      <td>330.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>9.45</td>\n",
              "      <td>1</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>399</td>\n",
              "      <td>312.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.78</td>\n",
              "      <td>0</td>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>400</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.66</td>\n",
              "      <td>1</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>384 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Serial No.  GRE Score  TOEFL Score  ...  CGPA  Research  Chance of Admit\n",
              "0             1      337.0        118.0  ...  9.65         1             0.92\n",
              "2             3      316.0          NaN  ...  8.00         1             0.72\n",
              "3             4        NaN        110.0  ...  8.67         1             0.80\n",
              "4             5      314.0        103.0  ...  8.21         0             0.65\n",
              "5             6      330.0        115.0  ...  9.34         1             0.90\n",
              "..          ...        ...          ...  ...   ...       ...              ...\n",
              "394         395      329.0        111.0  ...  9.23         1             0.89\n",
              "395         396      324.0        110.0  ...  9.04         1             0.82\n",
              "397         398      330.0        116.0  ...  9.45         1             0.91\n",
              "398         399      312.0        103.0  ...  8.78         0             0.67\n",
              "399         400      333.0        117.0  ...  9.66         1             0.95\n",
              "\n",
              "[384 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hO7in762SlZQ"
      },
      "source": [
        "## For filling NaN vlues, we have 2 options:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmPThRjlSlZQ"
      },
      "source": [
        "1) Filling with median vlue of each column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YF7E1awWSlZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b303d91-f3e7-4ad2-9a86-c6f95762c4b4"
      },
      "source": [
        "# we have missing data, only in 'GRE Score', 'TOEFL Score' and 'CGPA' columns.\n",
        "temp_df_nonan = df.copy()\n",
        "temp_df_nonan['GRE Score'].replace(np.nan, temp_df_nonan['GRE Score'].median(), inplace=True)\n",
        "temp_df_nonan['TOEFL Score'].replace(np.nan, temp_df_nonan['TOEFL Score'].median(), inplace=True)\n",
        "temp_df_nonan['CGPA'].replace(np.nan, temp_df_nonan['CGPA'].median(), inplace=True)\n",
        "temp_df_nonan.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 384 entries, 0 to 399\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Serial No.         384 non-null    int64  \n",
            " 1   GRE Score          384 non-null    float64\n",
            " 2   TOEFL Score        384 non-null    float64\n",
            " 3   University Rating  384 non-null    int64  \n",
            " 4   SOP                384 non-null    float64\n",
            " 5   LOR                384 non-null    float64\n",
            " 6   CGPA               384 non-null    float64\n",
            " 7   Research           384 non-null    int64  \n",
            " 8   Chance of Admit    384 non-null    float64\n",
            "dtypes: float64(6), int64(3)\n",
            "memory usage: 30.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJWK9-KUSlZS"
      },
      "source": [
        "2) Finding correlation and filling with mean of subset of each column: (we choose this method for other parts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVumT68aq6hd"
      },
      "source": [
        "### GRE Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnhnJXtpqigy",
        "outputId": "be1f93d0-2bdc-4b82-90a4-baf4673abd2d"
      },
      "source": [
        "df.corr()['GRE Score'].drop('GRE Score').sort_values(ascending = False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TOEFL Score          0.840993\n",
              "CGPA                 0.830727\n",
              "Chance of Admit      0.805076\n",
              "University Rating    0.671549\n",
              "SOP                  0.620963\n",
              "Research             0.588385\n",
              "LOR                  0.556729\n",
              "Serial No.          -0.105022\n",
              "Name: GRE Score, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJLSySwtrY1j"
      },
      "source": [
        "We will group the dataframe by University Rating(Because there's a high correlation between ) and calculate the average value for GRE Score per University Rating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrWaJoElrFtB",
        "outputId": "689a8895-9ecb-438d-9b23-0cd81ccb5232"
      },
      "source": [
        "Per_UR = df.groupby('University Rating').mean()['GRE Score']\n",
        "Per_UR"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "University Rating\n",
              "1    303.520000\n",
              "2    309.410526\n",
              "3    315.790323\n",
              "4    324.671875\n",
              "5    328.642857\n",
              "Name: GRE Score, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uia6RPfbrmoI"
      },
      "source": [
        "def fill_nan(uni_rating, second) :\n",
        "  if np.isnan(second) : \n",
        "    return Per_UR[uni_rating]\n",
        "  else :\n",
        "    return second\n",
        "\n",
        "df['GRE Score'] = df.apply(lambda row : fill_nan(row['University Rating'],row['GRE Score']),axis = 1)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjOGNxeHt5FQ",
        "outputId": "38416792-51b0-451f-a2dd-ffff49f0596b"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Serial No.            0\n",
              "GRE Score             0\n",
              "TOEFL Score          19\n",
              "University Rating     0\n",
              "SOP                   0\n",
              "LOR                   0\n",
              "CGPA                 20\n",
              "Research              0\n",
              "Chance of Admit       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOY9JHvxvWZX"
      },
      "source": [
        "### TOEFL Score Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmEc4vr4vYVt",
        "outputId": "8266814c-7a72-4b70-b644-07517e17734a"
      },
      "source": [
        "df.corr()['TOEFL Score'].drop('TOEFL Score').sort_values(ascending = False)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GRE Score            0.834685\n",
              "CGPA                 0.828364\n",
              "Chance of Admit      0.783667\n",
              "University Rating    0.689511\n",
              "SOP                  0.647810\n",
              "LOR                  0.556272\n",
              "Research             0.509609\n",
              "Serial No.          -0.153994\n",
              "Name: TOEFL Score, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aYNLvJfYts_",
        "outputId": "2ea5697a-2add-4cc2-8dfa-5bee4530e1fe"
      },
      "source": [
        "Per_UR = df.groupby('University Rating').mean()['TOEFL Score']\n",
        "Per_UR"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "University Rating\n",
              "1     99.240000\n",
              "2    103.729167\n",
              "3    107.000000\n",
              "4    111.530303\n",
              "5    113.800000\n",
              "Name: TOEFL Score, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAuBdYEVvbK2"
      },
      "source": [
        "df['TOEFL Score'] = df.apply(lambda row : fill_nan(row['University Rating'],row['TOEFL Score']),axis = 1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvJ9i0S0ZLoL",
        "outputId": "cf2cd30f-dcde-4515-ba9b-9cfad229a8f4"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Serial No.            0\n",
              "GRE Score             0\n",
              "TOEFL Score           0\n",
              "University Rating     0\n",
              "SOP                   0\n",
              "LOR                   0\n",
              "CGPA                 20\n",
              "Research              0\n",
              "Chance of Admit       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKQlUofhZmjB"
      },
      "source": [
        "### CGPA Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acUfQgEQZMuI",
        "outputId": "8fcd2eb4-9bda-4514-edbc-4226a5c5e5b9"
      },
      "source": [
        "df.corr()['CGPA'].drop('CGPA').sort_values(ascending = False)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Chance of Admit      0.872492\n",
              "GRE Score            0.824538\n",
              "TOEFL Score          0.824481\n",
              "University Rating    0.750312\n",
              "SOP                  0.720422\n",
              "LOR                  0.669002\n",
              "Research             0.527005\n",
              "Serial No.          -0.058785\n",
              "Name: CGPA, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsqhGCKQZztD",
        "outputId": "b8ecd36f-3c0f-486f-e0b9-a7f316457bd5"
      },
      "source": [
        "Per_UR = df.groupby('University Rating').mean()['CGPA']\n",
        "Per_UR"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "University Rating\n",
              "1    7.750000\n",
              "2    8.196064\n",
              "3    8.550000\n",
              "4    9.020746\n",
              "5    9.316909\n",
              "Name: CGPA, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgpATfO1Z-7W"
      },
      "source": [
        "df['CGPA'] = df.apply(lambda row : fill_nan(row['University Rating'],row['CGPA']),axis = 1)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omP1TLG7aDO2",
        "outputId": "3557e1b4-91f5-4b14-90bb-e16160a21004"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Serial No.           0\n",
              "GRE Score            0\n",
              "TOEFL Score          0\n",
              "University Rating    0\n",
              "SOP                  0\n",
              "LOR                  0\n",
              "CGPA                 0\n",
              "Research             0\n",
              "Chance of Admit      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyoaBmyka9Pd"
      },
      "source": [
        "## Scatterplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKtFbZrLbBLc"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhmhwPWQSlZX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "2c8cfe57-c7aa-43a2-dc8d-c71eeaacd5ec"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'Serial No.')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810ff05510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "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/",
          "height": 300
        },
        "id": "O9ev-jASbL3l",
        "outputId": "211ffd74-4590-4457-c065-d118b669f662"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'GRE Score')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810f39e390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TD5ewUjgPD8fGeQGtWTEfg0PDWjlH5cUUl3NUn5k+NeK4ruIFlBpEGhIEF6QiiMqva/tla1tHzzgvoDt/vhNnlE5Reg05yTk6OSw/D+IdlAbIiUAQXGDq6eNn8JnTtVUpmZ1SOJvcXes8d44PDONHz5+WgC57RzUunH3GhPKZrhymabppwX9kIxAEF5h4+vhdFUt37elT1SmZm+vLtCmcTdDl+ymbUoCOnn7c98xuRArz8KHzZozWA3CSeVRST7RCnW66tkw9N6ksFgwiDQmCC0w8ffyWkUxlle4+dQrnkZj2EkoqSwpw1zXNY6571zXNONo3oFwDL/MeiUFp6/H+IZGMUkigJwIiWgrgXgD5AB5g5q8nvd8I4IcAKu0+n2fmjUHaJAheMPH08bsqlu7am9/qMkrVfPhEP+bWuL/+ge5+PLJ5rxU4NjiM4qICPPDsm/jbK8/Co6suHrcGXuZ96Lg+lbYElKWOwDYCIsoHcB+AKwG0A9hCRBuYeVdCty8CeIyZ/42IzgGwEcDsoGwShMng1ovFjYzkJZ9/8rV119FJOqbBWLXlEbx+6ARWP/LSmHFmTSsdTS73m12do88CJpp3siuq02ckoCy1BCkNXQRgNzPvYeZBAI8CWJHUhwGU299XADgQoD2CkBIaKoqxZsXYXD9rVsxHQ0UxgNPPEJat3YQb79+MZWs34cm2TsRi7DTsOHSS0dm1ZcpcQ41VJb7MI2qXvLx+3fP4q4dfxPXrnsPj2/ajviyivW7cFTX5Mw0VxRJQlgYQs9kvn+uBia4FsJSZP2G/vgnAxcx8W0KfKIDfAKgCUArgCmbe6jRuS0sLt7a2BmKzIPjBtn3HsPrRl3D1whmjgVW/2L4fa284D4tmVmHP4RNYtnbTuLvdjasvM5Y94ieLRMno7a4+3PKDF8Zd//s3X2Q0vm4e/3LtItz04Avj7H/41ovx2Z+8rLzu8f4hXL/u+XGfWb9qMRbMqBw3h8Ri9BJQ5g9EtJWZW1Tvhe01dCOAHzDzt4joEgA/IqL5zDxGHCSiVQBWAUBjY2MIZgpB4qerpR/jT9aejp5+7O06NSawCsBo8jU/nyGoJKODvf0YHD59g0cEDA6z4/iDgyPYfqAHnb39iJZHsKC+Qj+P3gFtlLDuut0nh5RuotaaSFrpsAlyI9gPIDHlVYPdlsitAJYCADM/R0QRANUADiV2YuZ1ANYB1okgKIOF1OO3q+Vkx/fDHp1GX1dh6dt+Jp1TX1/tkqlzHx0cHMHj2w/gzp+PjSB+p2FCuJlVxdrrTp1SoC6TWVXsy5yFyRHkM4ItAJqIaA4RFQG4AcCGpD5/BHA5ABDR2QAiAA4HaJOQZgQdsRtGVK6uzGJztAJA8EnndC6ZOvfR7Qd6RjeBeP87f74TI7GYMolcYT4p28H660YK8pXvRQryfZmzMDkCOxEw8zAR3Qbg17BcQx9k5jYiWgOglZk3APgsgPuJ6G9hPTi+mYN6aCGkJX67WqrGN4my9cOegoI8bT0AwNkVVSdLmchVOpfMwyf6MbOyeJwEpCth2d6tL4Wpap9TXaq9ri7p3JG+AZwZm5pWEcRBS5XpSKDPCOyYgI1JbXcmfL8LwLuDtEFIb2rK9FGzfmAqk/gl2+jqAcRR6d9OZRtNEt5p5zB1iloCqpuqlrLKpyiTyFUWF6pLZDq4rTKrk85Vl05JqwjioKXKdGVCaYiI7nDTJgheyM9T57DP90m0NJVJwqwV4FfZRt0cjp0aUkpAQ8OsdBOtnlqk/Nn88WifJulcuXbtdD/n/uGRtIogDjO5YJi4ORH8Bazo4ERuVrQJWUjQx+SOHrX8cF5jJWZXT14acpJJ5lSrk5r5VXcA0K9fkGUbx8/Bkqae3NWp9fZZubAeZ1aXjtqzsL4CW/cdG/ez+dWODnzyPXNx5MQQvnntIuzvPonzZlZpks5Z183LI+3PWScnhRVBHLRUma5oNwIiuhHARwHMIaLEh7xlAI4GbZgQPqk4JteWO+ew92N8k4RtpjKME6ZSzzlRf8o26pK/6capLY+gqCgfLbPPGLd2iT+buMz2dwklKu+4vAm15VMmTDqn+zmnWwRx0B5d6YrTAfx/AXwLwKv2v/GvzwL4QPCmCWGTimNy0FKMacI2UxnGCVOpZyTmT9lG3XWnlRYpJaCF9RWu1u4jLQ2OMpvT74vu55BuEcSZWkZ0smhPBMy8F8BeAJekzhwhnUjFMdnPso26dqOEbT4mOzOVeg6f6NeuhU5yUeXv0V23s7cfy+dHMWdaCTp7B1BXPgULohUoKlK7cCZfNz9PXaLyj0f7MKdaL23F1043t3QqSZlu9qQKJ2no98x8KREdh+XaOfoWAGbmcs1HhSwhVcdkP8o2TiTpJI+v81bSeb5Ul04xnpeXpHA6byLV3K6YV4MNOw6MKSX5lZXzceHsKq0c9vQbh42C6xKve8fl71CO+9K+bpwaimGeJgAt/vui+zmnWwRxutmTCrTSEDNfav9bxszlCV9lsgnkBul4TPbLs0bnxQKw1rvFFL0coveuMZnz9gPjS0l+8fGd6O4b8qUsZPJ1H2ttH7c2q5c04Set7fjMYy8jP08tbWW7rJINuIojIKIqWOkiRvsz84tBGSWkB2Efk4P0rOno6cevdnRYufYHhlEypQD3P/umNljqzOpSnBOtMJKkdB5IBQV5WqlHhZPUo2tXjb9l71GjNUq+btzz5/6PteClP3ZjdnUpDnSfxIcvaLDyBvXqpS0hvZlwIyCiL8NyF90DIP5bwQCWBGeWkC6EdUzWSUB6zxqzHPzRigg+uCCKzyV5wMysLNZ6t5hKUgCU7/kVIBbVtNeVR5TjTyTduLnusZODqCopRGE+jVu7unK1tCWkP27Cdq4DMJeZ38vM77e/ZBMQAkUnh+g9a8zkFl2gWcmUAmWeoLJIobEkFXSA2IL6CqWtlaVqW02lG911dXmDTEthCumDG2loJ6xSkocm6igIfqGTQ5w8a0xkCV2gWVffgDJPkE5W0UlSB3v7cWpoxFjGitcTcBPkVlCQh6ubo5h1xlgvoJf2d6tTPhtKN6YeV05BekBu5vDJFNxsBF8D8BIR7QQwEG9k5uWBWSXkPE4eS354nzjlOFLlCdJ6GWnsHBphvN553EjGqis3k5+umFeDX7R1jPMaumhOlTa/kql0Y1IiUxekF5fJcjGHT6bgRhr6IYBvAPg6xgaWCUJgBO2xZJrjSNe/srRwnJ3f+PBC/OPPd4Ch+UxJgXJuIzGzIDed19Ch3sFApRvTID0nmSzbc/hkCm5OBCeZeW3glghCAkF7LJnmOHLqn2xnV98A9nadQt/gCH62td3VZ7wEuTl5Demkm7k1k3+IayoZHTquT0Od7Tl8MgU3G8EmIvoarKIyidKQuI8KRphGBJvKGCbj15ZHUFRwelMhAooKCDVlEeP+Kjvjd8u6PEqxGON4/xC6Tw6huLAAsRg7BKCZew2ZBgKqIpTj9ROcSKweMlEAYiqCEwVvuNkIzrP/XZzQJu6jghF+5dr3a/wr5tXg9iVN4/T1hopi5Ti6/o1VJeNsiUsn33jyFaxe0oS1T78xZqyGimI8vm3/uLGWL6jHPdedO+7a8Xw8ye1xr6HkcRbWq/vrZLXh4ZjSnpWLZig3A6e1drquiU1CaqFMKwjW0tLCra2tYZshGLLn8AksW7tp3B3h+lWLcf2658e1b1x9mZFkYDq+X+06O+OniqN9AyjMz8PJwZHR08WO/d3aayyYUYm3u/q0lcuS2+N38smV0HT9VWzbd0xrz6KZVa7XeuPqy0a9nlTXNbFJ8B8i2srMLar3nHINfcZpUGa+Z7KGCbmDrmSklyRvuohjk/G9tOtKXjZWlShllTOnT0VDhVUasqtvEEX5eWioKNZr/j39WDTTWQ5Lvm/LyyOURQpxcnAEZZHC0T+sJrKakz0LZriP7o7/zHTX9UvqE/zHSRoqs/+dB+BCnC48fw2AF4I0Ssg+dCUjG6rMIoJ1ssTZdWVG4+tcOHV6fENVsXL8mqlTtLJKLMbq0pCaCN+6CrM5+yWrmbqzmkYoeyFXS0aGhVPSubuZ+W4ADQDOZ+bPMvNnAVwAoDFVBgrZgS6SN1KQ70sCtuP9Q0bjV5YUKF07C/JI2R5jVo5/7NSQ0oWzraMH2w/0KEtDMlgZEdwcVdcF8CtCWUdztDzQCGUviLtpanHzsLgWwGDC60G7TUhjwjxWqzxQdJG8R/oGtG6iEyWdi1ZERqWaAxq54kjfAK6YV4OHb70Ynb39iJZHsEBThjFeOlGVjE5XUtFJVhmKsfK99mOnlNHLBQV5RvUF/JLVCgryjKKpvSaXM/mdzNWSkWHhZiN4CMALRPRf9uuVsILMhDQlzGO1zgPlgkZ1jnynHPw6CShSmIeqkiLctHjWqEeOLld+bVlEmbP/gpmVStfO+kp1Mrr6Sr18opN5hmOs/Ywqelm7djMrlePUeyhhqfu9UNnjJbpbh+nvZK6WjAyLCR2FmfmfANwC4Jj9dQszf9XN4ES0lIheI6LdRPR5xfv/l4hetr9eJ6Ju0wkI4wnzWN3WoY52Pd6vzpHvRQK64/ImfKSlYXQTANS58u+57lwc71dLN0dPDSkloDyQUgIqm6KOBl6oSfzWHK3AtNIi3HVN85j37rqmGdOmFhmtnc7Wssj4qGYva2qa7M6LBBTmtYWJcVWPwA4ee5GI5gL4KBE9zMzNTp8honwA9wG4EkA7gC1EtIGZdyWM+7cJ/W/H6ZgFYRKEeax28roxkRMO9vbjrJqp+MR75o6RaA7YEb6fvrxpzHXikb/f+4sWHO8fHs3B/9tXD2rt0UlDqv4Hjyfn+D/tHbR8QT1mTytFZ28/6sojWFhvyTwdPf14ZPNeS2YaHEZxUQEeePZNzKku0UYvm9h6XmOl0ia/5Ja8PFLKak5BgDq8XFtqG6QON/UI6gFcD+CjABbASkJ3g4uxLwKwm5n32OM8CmAFgF2a/jcCuMvFuMIEhHms1nqgVJjJCfWVEdx48awxEs1d1zRj1jSrXsC+7lPjrnPs5CA2v3UUa5/aPXoHOcNB0lHXHdD319UW+O1rh5SSR215BK8fOoHVj7w0Zix9jQQzW6dPVdukk1ucEu2pGB6OKWW15QvqtXPW/aH28jsptQ1Sh1YaIqJVRPQMgN8BmAbgVgAdtjfRDhdjzwCwL+F1u92mutYsAHMAPO3SbsGBMI/VOg8UnUeMju6+Idz9RNsYKeHuJ9owMMS457pz8cS2/Vi9ZLxc8pPW9tH+n3nsZRQVQCnP1JQXGdU1ME0I93ZXn/HPQbd28Ujh5HFMS0+aJtrTSVXbD5h7K4nUk944nQi+A+A5AB9l5lYAIKKgwpBvAPBTZlYWhiWiVQBWAUBjY256rpocxcM8Vus8UBKjXd3MoUMjJRyMe6zUlaH31CAumnMxjpwYwBmlRfjKL3aho6d/TP+9Xae08oxO6vEjIVxc8jApSem0dqZJ3lR1Dbwk2tN5DZlKjyL1pDdOG0EUwEcAfIuI6gA8BqDQYOz9sOocx2mw21TcAOBTuoGYeR2AdYCVYsLAhqzAixdQmMdqlQeK6RwmkphmTyvFk23Hx4x3x+VNOHxicHQzsKQetTyjk3ri9rjNwa8LQIsnljMN+FKtHeBPXYBzomXaJHgqnKQqL9KjSD3pi1NAWRczf5eZ3wvgcgDdAA4S0StE5MZraAuAJiKaQ0RFsP7Yb0juRETvBFAF6/QhKMiG4BrTOUwkManGu/epN/CRlobR/okJ29xKPaZeLLrx43fkQf7cTOsC6Mp8+iVVicyTubj1GmqHXZCGiM6Ci4fFzDxMRLcB+DWAfAAPMnMbEa0B0MrM8U3hBgCPcqZlv0shTh4XutKGOsIKNDP1GnHyxnEa77yZlXh01cVjpAeVBKQLlvLixaJrd5qzLj+RHxKgUylJE3nGVKry6/dIcgylHlcbQSLM/DqANS77bgSwMantzqTXXzK1IdfwUhpQ9R8nzEAzU6+RWIwdPVN0482aVjouME0lz8yrmWrkQQPopQ1du5OcpEtDbeqNYyIZeQkEM5Gq/EByDIWDm1KVQsh4KQ2oIkyJydRrZCJb3Y6nG+fU8IiRB42fc+7uU32xlMUAAB2xSURBVAe5efHGMbluJkg32SCDZiJOaajfzcx/IKIpzDyg6ycEj5fSgKo7NV2QltdAM9OKY6YBZU5zczuebpz2Y6eUOYV0HjRe16aqpBDrV12CoZERnFE6BbOnleI3uzq13ji6VNcmP59M9tCRHEPh4CQNrYWVafQ5AOenxhxBh6kEoEIXpBXVpD92wmtqZLdygpu5uRlPN05DVbEyp1Bd+eSD7nRrc37jGcjLI30K7HJ1qm4vNmWqh47kGAoHp4PwEBGtAzCDiNYmf6XKQEGPqQSgC9Lq7hsyvnbQqZH9kjd040QK8pU5hUZiTqO5YyJ5Q+eNM21qUWA2ZQqZLGtlMk4ngqsBXAHgAwC2psYcwQRTCUAXpNXZ249Fhtf2MzWyCr/kDS+eNXNrJncXPZG8YZr22Q+bMoVMlrUyGe1GwMxHADxKRK8w87YU2iQYYCIBOAVpmeLkETNrWjGuXjhjVOd+Ytv+SR3t3ToW655N+CGrmWAytjU1mvBzueRSmamyVibjxkeii4j+i4gO2V8/I6KGwC0TfMevPECA/gh/dm05bl/ShO/9fg++8/RuPLBpD25f0oTGqhKj8eM6+7K1m3Dj/ZuxbO0mPNnWiVhMvSuY9g9SgmisKlGuc3wN4nUHrl/3PP7q4Rdx/brn8Pi2/WioKFba1FhVYjQ3QTCFJorjIqL/BvAfAH5kN/05gD9j5isDtk1JS0sLt7a2hnHprCBeASs5QMgL8bvUxCP82119WLZ207i72o2rLzO6w9tz+ITROKb9dfb7cZe95/AJ3PKDF0ZPRczAL7bvx/dvvghnTp+KbfuO4fp1z4+zdf2qxVgwozKwNRVyGyLayswtqvfcBJTVMPP3E17/gIg+7Y9pQlDopARdgJAX6UF1hPfL/c80mtpL9LWXKltu1uhgbz/2dp0ak9MHwOgadPSo3UQ7e/qxaGZwa2oyByG3cLMRHCGiPwfwiP36RgBdwZkkTBbT6Ew/ozn90t69JFTTJUjzY24mazTRGjRUFSvdRGdUFSuvbVpHwI85CLmFG03g4wCuA9AJoAPAtbBKVwppiml0pp/RnEG7fZomVDNNLqfDZI0mWgOd62qkIF95bdM6An7MQcgtJjwRMPNeAMtTYIvgAdVR/6BDhKpfsoru2uOTvDnn4Nfhxu0zWhEZneORvgFcdXYtNhpEX9eXRbCjowedvQOoK5+CBdEKRCIFxmuqyj0Ut/1gbz9KivIxOBIbLVZz+MSA0qYjfQM4MzbVqI5A4xnukw5K1K6gwzjpnJA+6I76zfVlSukhWqGWSebVmssqADxFFpvg5PZZVVKEmxbPGi1g/8CmPaPXceMmWlc2BRt2dODODacTv61ZPh/XzK/DM7uPuF5TXdRvvGbCq53HccsPtrhab9M6Aqayl0TtCjok6VwGozvqd/cNKaWH7r4hZf/8PHNZJejIYh1x2eUjLQ2jm4DTdXQyzeETg6ObQPzzd27YiR2dvUZr6hT1q1sj3XoHLXtJ1K6gQ04EGYxOruhwaNdFFpsmtWOGcWSxae0EFXHZJfnONvE6iScCncT0yx0dys8f7FXLNro1PXyiH3Oq1fPSSTGm662rI2CadFCidgUdE24ERFQL4KsA6pn5g0R0DoBLmPl7gVsnOBKtUCcpmzVN7ZXSUKWOLNblqZ9IStBFFgfpvQOcll3cyhyqudWVT1F+vlbTrvP00cltS5vrjOsCOHkH+RUdLVG7ggo30tAPYFUZq7dfvw5A4gjSgJEYlHLFwBBrvVJMpAEnKcG0dKNf3jtubHPDgmgF1iwfG/27Zvl8LKgrN0pSp5Pb4g+GTWw09Q4SqUfwCzfSUDUzP0ZEXwBGS1COBGyX4IJDx/XSg0rGiHvWJJdtdKp+5eQBpJMZTEtDei23qcr17/Z0EYkUYPmCKOZUl+Bg7wBqE7yGrphXg4dvvRidvf2IlkewoL4CW/cd00pGTvKMUVJAB+8gVY0EkXoEv3CzEfQR0TTY+bGIaDGAnkCtElzhlGtfJWPUlkWMPHp0ZR7j/VUyg+4zuoAvv8ptxnP9mxCJFODCOdPGtA0Px7Bhx4FxZSRbZlWppbAJ5BnTGgwq7yCReoSgcSMNfQbABgBziegPAB4CcHugVgmuMM21f7xfL2Oo8BKApPuMzvMl3cpttnX0KMtIdp0YVMo2laWFvskzIvUIYeEmoOxFInovgHmw8uW+xszmlUyEMfiR88U01/4Bw1oBXgKQdJ/x4vliEvy219bkdacIt2ut83rq6NXLNn7JM9ku9Uieo/TFjdfQpwD8mJnb7NdVRHQjM/9r4NZlKX7mfDHxJtF59OikBy9eKX55vui8jHTBWC/t68apodi4NTRda23NBgfZxk95JlulHslzlN64kYY+yczd8RfMfAzAJ4MzKfsJWt4w9ejx4jWkwy/PF52XkSoYa/WSJvyktV25hqZrravZsLDebO2EsUieo/TGzcPifCIitgsXEFE+gCI3gxPRUgD3AsgH8AAzf13R5zoAX4L1MHobM3/Upe0Zi0neGi84SQwm0oMXqcIvzxedZBQPxpp2y0XYtPsImIEfPb8XHT39ADBuDU3lLV0ZyYKCvKyWbYJG8hylN242gicBrCeif7df/6Xd5oi9YdwH4EoA7QC2ENEGZt6V0KcJwBcAvJuZjxFRjekEMhFdIJgub40XdBKDqfRg2t8vz5eJgrGml03BA5v2TChbeZG3dDUbslW2SQWS5yi9cSMN/T2AZwD8tf31FIDPufjcRQB2M/MeZh4E8CiAFUl9PgngPltuAjMfcmt4JqMLBHPKW5MpBJ2GOj6O2+uIJ056ID+H9GbCUpWeBya6FsBSZv6E/fomABcz820JfR6HFan8bljy0ZeYedxpg4hWAVgFAI2NjRfs3bs3EJtTxXNvHsGN928e1/7oqotx0expnoKr0skTI14OMzGgzEs5zIlKSbotNemXPcLkCKo0qOCOSZWqJKJ3w9LwZ9n9CQAz85k+2FYAoAnA+wA0AHiWiBYkPpyGdbF1ANYBVs1iH64bKqaeMqmoLOYXEwWhmTCRFONGqvHTHmFyiLSWvri5LfoegHsAXArgQgAt9r8TsR9AosraYLcl0g5gAzMPMfNbsE4HTS7GzmhMPWXCCq7yQrrZlG72CEI64uZhcQ8z/8rD2FsANBHRHFgbwA0Akj2CHodVA/n7RFQN4CwAezxcK6Mw9ZTxM+AraNLNpnSzRxDSETcbwTNE9E0A/wlgIN7IzC86fchOTncbrMyl+QAeZOY2IloDoJWZN9jvXUVEuwCMAPg7Zu7yOJeMwo+0wk79w9LFvXiH6J5z+PH8IxXeKun4nEYQTJjwYTERPaNoZmZeEoxJzrS0tHBra2sYlw4cU81f1/+KeTXKxGkrF80IfDPwaw5+lbwM+jlKOj6nEQQVTg+LA/MaCops3ggAc88KVf8d+7tx/brnx90Fr1+1GItmVqXVHPYcPoFlazcpbVXNYePqy4wlnSC9VXT2e7FTEIJkUl5D9gB/AqAZwOh5mpnX+GNedmMqG/gR8KVLnNbZ048FM4KTYbzMQafhO5W81I070RyCuOeZ6BlEkLKXIPiFG/fR7wIoAfB+AA8AuBbACwHblRWEJRvoEqfpyir6JcN4QZ8gT19WU0XQEpMOpyR7YdkkCKa4EYzfxcwfA3CMme8GcAks7x5hAsJyXdQlTiuLFCrtaevoCc3FUp8gT10yUheJqlvroOfmlGQvLJsEwRQ30tAp+9+TRFQPoAtANDiTsodUuC6qJIaCgjwsX1CP2dNK0dnbj7ryCBY6lVt0kGFMy0ia4leCPC8Skx9zc0qyxwzfZC9BCBI3G8EviKgSwDcBvAgrS+gDgVqVJQTtuugkPfz2tUOuy0XWV6plmNoys0hnr/iRIM9UYjItkamjpkydZG/61AiI4IvsJQhBM6E0xMxfZuZuZv4ZrDQT72TmfwzetMwn6ERbptKDrlxk2ZQCpbxhWtoyTEwlJtMSmTqcpCG/ZC9BCBq3XkPvAjA73p+IwMwPBWhXVhB06UFTOcSpXKRK3phTXZoxEoapxGQaxa1jovoLfshe6Yh4PWUXbryGfgRgLoCXYUX/ApY8JBuBC4JMtOXF40YX0aySNzJNwjCRmPyS7Saqv+BXXYh0QoLosg83XkMtsArH/A0z325/rQ7aMGFi/JIeclHCSFXdhGxEEvllH25STPwEwGpm7kiNSc5ke2SxDt1RXJdTyI8I5cTAJ9U46SYPmNrjdY10wWGZKvOY4lRPY/GZ1SFYJLjBU2QxET0BSwIqA7CLiF7A2KRzy/02VFDjNTDJj5KUuvZ0kwe82GOyRhONn6kyjxek7GT2oT0RENF7nT7IzP8TiEUTkIsnglTk4/HLprBy7ARtT7rNN0zS7SZAcIfXXEP7AdQy8x+SBrsUQFrIRLmCn/l4grbJy7X9kJiCtkfqGpwmG7yehLE4bQT/D8AXFO099nvXBGKRMA5dPptoiEd0v+QBv+4ug7ZHF4yXq3JIrslh2Y6T11AtM+9IbrTbZgdmkTAOXdBSZWlhaB4rfnnL+OWBErQ9umC8bPYOEnIHpxNBpcN7xX4bIlioZAmnoKWwjuh5eYSrzq7F+lWLbY+lCJqjFaFJOl7kChMJSBeMl6tySFgV8IRgcNoIWonok8x8f2IjEX0CwNZgzcpNdLLEvNoybdBSWEf0WIx9SaXspweKH15AThKQyCEWw8MxPL5tfygV8IRgcPqpfRrALUT0OyL6lv31PwBuBXBHaszLLXSyRH5e+skS6SbpmCISkHfaOnpGNwHAWrsvPr4TbR09IVsmeEV7ImDmgwDeRUTvBzDfbv4lMz+dEstyEJ0s0dmbfrJEmJKOH4gE5B2nCniLZoZklDApJsw1xMzPAFAVsBd8xo1Mki4lpsOSdPzCyX6RgJzR5aCqq8hND6psQAS9NEInkzRWleDJtk4sW7sJN96/GcvWbsKTbZ2IxcLbFTI9x06m2x8mugp4zdGKkC0TvDJhrqFJDU60FMC9APIBPMDMX096/2ZYBW/2203fYWbHojfZHlmsylvzdldfWka1ZnqOnUy3P0ziXkOdPf2osz3G5EFxeuM1sniyF80HcB+AKwG0A9hCRBuYeVdS1/XMfFtQdmQaKlkirJKXE/1RzBYJJV3ktkyioCAPi2ZWyTOBLCGwjQDARQB2M/MeACCiRwGsAJC8EQgTEFbJy2zOHZOLcxYEHUGe5WYA2Jfwut1uS+bDRLSdiH5KRHJ/oSCskpfZnF8+F+csCDqCPBG44QkAjzDzABH9JYAfAliS3ImIVgFYBQCNjY2ptTAgTKSYsEpeZnNCtVTNWSJwhUwgyI1gP4DEO/wGnH4oDABg5q6Elw8A+GfVQMy8DsA6wHpY7K+ZqSfo3Pmm5GJ++VTMWSJwhUwhyN/GLQCaiGgOERUBuAHAhsQORBRNeLkcwCsB2pM2pJsskYuulKmYs0TgCplCYCcCZh4motsA/BqW++iDzNxGRGsAtDLzBgCriWg5gGEARwHcHJQ96US6STGpiu6dqNSjaYnJydQvSMWcJQJXyBQCfUbAzBsBbExquzPh+y9AXfMgq0lHKSZoV1Cv5TbdjuPF2yfoOUsErpApiFAZArkoxejksLaOHiOZLN1kNSckAlfIFML2GkoJfkgJfl/bj1z+YWOyrk7lNqtKivCh8xtA9kd/trVdK5Olm6zmREFBHlYumoGmmqkSgSukNVm/EYQZOOSXHJKOmK6rTg6rr4jgY5fMwr1PvTE6zh2XN6GuXC2fpKOs5oRE4AqZQNbfmoQpJfglh6Qjpuuqk8PKIoWjm0B8nHufegMjMeUwOSmrCULQZP2JIFVSgknZQ503SZjyhql8ZrquOi+dzW91aesCzK1xP066naTClCMFwZSs3whSISVoS0zWTFVeO5pm8oYX+czLuqq8dPwaJ52QPEZCppH10lAqpASdTHJqeAR3XN405tp3XN6EytLCtJI3vMhnfq1rNko9meTZJAhADpwIUuGho5NJ2o+dwkPP7cWtl54JIivd8UPP7cV5jZVpJW94kc/8kmgyReoxIZM8m+KIlJXbZP1GEItx4B46OnkjWhHBsZODuO+Z3WPa060colf5zK85pNNa+EGmeTaJlCVkvTSUimO6Tt5ojlZkhOyRjfJMmGTaeoqUJWT9iSAVx3QneSMTZI9MsTNTyLT1zEQpS/CXrN8IUnVM18kbuvYwEq05jZNt8kzYZNJ6ZpqUJfhP1m8E8WN6sv4Z5jHdVJP1S8MVLVhQkY7/R4TUQpxhlbtbWlq4tbXV6DPxu+B0OabvOXwCy9ZuGncHtnH1Zco7SNP+fl1XyB3S7f+I4D9EtJWZW1TvZf2JAEi/Y7qTJjt7WqnrCGVTDVe0YEFHuv0fEVJLTmwE6YZOk60rjyilm3OiZb5ouKIFC4KgIuvdR9MRnXvhSAxKN76RGCSKVxCEwJATQQh4ScCmc0c08Sby260x7GjUsK8vCNmCbAQhYZqATdXfixeQX1pw2B5IYV9fELIJkYbSCFPpJh1rLaQqGjXs6wtCNiEngjTCVLoJ0wsobA+ksK8vCNmEbARphol0E6YXUNgeSGFfXxCyiUClISJaSkSvEdFuIvq8Q78PExETkTLYQVATphdQ2B5IYV9fELKJwCKLiSgfwOsArgTQDmALgBuZeVdSvzIAvwRQBOA2ZnYMG/YSWZzNhBkRGnY0atjXF4RMIqzI4osA7GbmPbYRjwJYAWBXUr8vA/gGgL8L0JasJcyI0LCjUcO+viBkC0FKQzMA7Et43W63jUJE5wOYycy/DNAOQRAEwYHQHhYTUR6AewDc7KLvKgCrAKCxsTFYwwTBEAlsEzKdIDeC/QBmJrxusNvilAGYD+B3RAQAdQA2ENHy5OcEzLwOwDrAekYQoM2CYIQEtgnZQJDS0BYATUQ0h4iKANwAYEP8TWbuYeZqZp7NzLMBPA9g3CYgCOmMBLYJ2UBgGwEzDwO4DcCvAbwC4DFmbiOiNUS0PKjr5hqxGGPP4RN47s0j2HP4BGKx1B2Ywrx2uuAU2CYImUKgzwiYeSOAjUltd2r6vi9IW7KRMGUJkUQsJLBNyAYk11AGk8u5htIFCWwTsgFJMZHB5HKuoXTB79TeghAGshFkMLmcayidkMA2IdMRaSiDyeVcQ4Ig+EdguYaCQnINjSWXcw0JguCesHINCSlAJ0v4Fe3qNI5IIoKQHchGkIX45dopLqKCkBvIM4IsxC/XTnERFYTcQDaCLMSvaFeJmhWE3EA2giwk7tqZiBfXTr/GEQQhvZGNIAvxy7VTXEQFITcQ99E0Q+elY+oF5Jdrp7iICkJ2IO6jGYLOS+eqs2vxm1cOGnnv+OXaKS6igpD9iDSURui8dNo6esR7RxCEwJCNII3Qeel09Ij3jiAIwSEbQRqh89KJVoj3jiAIwSEbQRqh89JpjlaI944gCIEhXkNphs5LR7x3BEGYDOI1lEHovHTEe0cQhKAQaUgQBCHHkY1AEAQhx5GNQBAEIceRjUAQBCHHkY1AEAQhx8k491EiOgxgr8ePVwM44qM5mYDMOTeQOecGk5nzLGaernoj4zaCyUBErTo/2mxF5pwbyJxzg6DmLNKQIAhCjiMbgSAIQo6TaxvBurANCAGZc24gc84NAplzTj0jEARBEMaTaycCQRAEIYms2giIaCYRPUNEu4iojYjusNsXEdFzRLSDiJ4govKEz3yBiHYT0WtE9IHwrPcGEUWI6AUi2mbP+W67fQ4Rbbbntp6Iiuz2Kfbr3fb7s8O03xSH+d5mz4mJqDqhPxHRWvu97UR0fnjWe8Nhzj+2f293EtGDRFRot2fznL9nt20nop8S0VS7PaN/rwH9nBPeX0tEJxJe+zdnZs6aLwBRAOfb35cBeB3AOQC2AHiv3f5xAF+2vz8HwDYAUwDMAfAmgPyw52E4ZwIw1f6+EMBmAIsBPAbgBrv9uwD+2v7+bwB81/7+BgDrw56DT/M9D8BsAG8DqE7ovwzAr+zPLQawOew5+DjnZfZ7BOCRhJ9xNs+5PKHPPQA+b3+f0b/XTnO2X7cA+BGAEwn9fZtzVp0ImLmDmV+0vz8O4BUAMwCcBeBZu9t/A/iw/f0KAI8y8wAzvwVgN4CLUmv15GCL+F1Cof3FAJYA+Knd/kMAK+3vV9ivYb9/ORFlTGED3XyZ+SVmflvxkRUAHrI/9zyASiKKpshcX3CY80b7PQbwAoAGu082z7kXsE49AIph/a4DGf57DejnTET5AL4J4HNJH/Ftzlm1ESRiH5POg7WrtsFaNAD4CICZ9vczAOxL+Fi73ZZREFE+Eb0M4BCsje5NAN3MPGx3SZzX6Jzt93sATEutxZMjeb7MvNmhe1b+jBPnbEtCNwF40m7K6jkT0fcBdAJ4J4Bv290z/vca0M75NgAbmLkjqbtvc87KjcDWDX8G4NP2HcTHAfwNEW2FJRkNhmmf3zDzCDOfC+uO8CJY/0GyluT5EtH8sG0Kmgnm/K8AnmXmTeFYFwy6OTPzLQDqYZ34rw/RRN9RzPk9sG5ev+38ycmRdRuBfXf0MwA/Zub/BABmfpWZr2LmC2BpqW/a3ffj9OkAsBZ/fyrt9RNm7gbwDIBLYMkB8Qp0ifManbP9fgWArhSb6gsJ813q0C1bf8ZLAYCI7gIwHcBnErpl9ZztthEAj+K0zJs1v9fAmDm/H8A7AOwmorcBlBDRbrubb3POqo3A1se+B+AVZr4nob3G/jcPwBdhPTwFgA0AbrCfvs8B0ARLa80YiGg6EVXa3xcDuBLWndIzAK61u/0FgJ/b32+wX8N+/2lbY84INPN91eEjGwB8zPakWQygR3HETmt0cyaiTwD4AIAbmTmW8JFsnfNrRPQOu40ALMfpn31G/14D2jlvZeY6Zp7NzLMBnGTmd9gf8W/OXp8yp+MXgEthPTzaDuBl+2sZgDtgeRC9DuDrsAPp7M/8A6wTwmsAPhj2HDzMeSGAl+w57wRwp91+JqxNbTeAnwCYYrdH7Ne77ffPDHsOPs13NSwtfBjAAQAP2O0E4D77Z7wDQEvYc/BxzsP2vOK/6/H2rJwzrBvXP9hz2gngx7C9iDL999rp55zUJ9FryLc5S2SxIAhCjpNV0pAgCIJgjmwEgiAIOY5sBIIgCDmObASCIAg5jmwEgiAIOY5sBELOQES1RPQfRLSHiLaSlZH2T+333kdEPUT0MhG9SkT/kvC5m4nosP1e/Oscxfj/YGeN3G73uTiV8xMErxRM3EUQMh87AOlxAD9k5o/abbNgBSXF2cTMV9vBPC8R0X8x8x/s99Yz820O418C4GpY2W8HyEqFXTRJmwv4dL4oQQgMOREIucISAIPMHI8qBzPvZeZxOVyY+RSsAC2TRG1RAEeYecAe4wgzHwAAIrqQiP7XzjP/AhGV2bnnv09WjYyXiOj9dt+biWgDET0N4CkiKiWr1sALdr8VehMEwRtyIhByhWYAL7rpSERVsNKNPJvQfD0RXZrw+hJ7w4jzGwB3EtHrAH4L6wTxP2QVBFoP4Hpm3kJWUaRTsKLdmZkXENE7AfyGiM6yxzofwEJmPkpEX4WVOuDjdvqBF4jot8zcZ7wCgqBBTgRCTkJE99l36FsSmi8jom2wknn9mpk7E95bz8znJnwlbgJgK4/8BQBWATgMYD0R3QxgHoAOZt5i9+u15Z5LATxst70KYC+suhmAlX74qP39VQA+b6cm/h2stAKN/qyCIFjIiUDIFdpwOlMlmPlTto7fmtAn/oxgDoDniegxZn7Z7QXYyoj5OwC/I6IdsBKCbfVga+LdPgH4MDO/5mEcQXCFnAiEXOFpABEi+uuEthJVR7aq1X0dwN+7HZyI5hFRU0LTubDu8l8DECWiC+1+ZXbK4E0A/sxuOwvWXb7qj/2vAdxuP+wGEZ3n1iZBcItsBEJOwFZ2xZUA3ktEbxHRC7DK/On+2H8XwHvodEHw65PcR9+V1H8qgB8S0S4i2g6rHvaXmHkQVvGUb9uy03/Dknf+FUCefXJYD+Dm+IPmJL4Mq2ThdiJqs18Lgq9I9lFBEIQcR04EgiAIOY5sBIIgCDmObASCIAg5jmwEgiAIOY5sBIIgCDmObASCIAg5jmwEgiAIOY5sBIIgCDnO/wfYSc85ODo9bQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "OcoX2hV-bxS7",
        "outputId": "c713c66b-6ec4-4e0c-f9b1-5f465ac531fc"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'TOEFL Score')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810eecdb90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "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/",
          "height": 300
        },
        "id": "F-YrTf01cur0",
        "outputId": "66c83397-be05-4971-d0bc-b5b89753a194"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'University Rating')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810ee546d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "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/",
          "height": 300
        },
        "id": "QqbwLJrXc5Kd",
        "outputId": "534931bc-1602-4c2a-c7b7-c97a8ab8a677"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'SOP')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810ee56a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "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": {
        "id": "-v01QQY1SlZZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "27908176-a6cb-412a-d4ee-7b82c38eccc1"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'LOR ')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810edaad50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W0GXAHlXdq6rDwCPA6innKFAW/7kc2J9GPV7hs6XgIxfVlLJxdeLyeOPqFi6qKTvDb6aXaHSE9l0Rbn7gBb7w8O+4+YEXaN8VIRodcaoLoK40xB1XNfG9X+/lW0/t4f5n93LHVU3UOZ4ILq4pZWPblOeyrYXmGrd5F+P7dtfd+yw3ffd5rrv3WZ7s6GbMcee0TJDOry31wNuT7ncCl0855y+BX4jIF4B5wEfSqMcrfLYUfKSzb5BHt+6LJW4Nj1BUkMdDv9lL6+JKp1barkgf69oT9y7Wte9maVUx7166wJkugN3d/YHali2cR+uS+c50vXZogEe3nf5crqgtYdUid9ZQsn27P7jr/Vlv17pev94EPKiqfysi7wF+KCItqppgBovIWmAtQGNjowOZ6cHHCAXwc3nc0x9l274+tu37XcJx13sq3f3BtfV7+oecaRqnO0lGquuCeJG+KF29Q7zafWxCV1fvkPP9nrm8b5fOiaALmPy0NsSPTeZ24FoAVX1OREJAFXBg8kmqugnYBNDa2pr96zSH+BrW6uueSkNlcNZnvQf9CBrnB7eEbHDcErI+SavKugrHrSpLCgNfY1XzCh2qygzp3CPYCjSJyFIRKQBuBNqnnPN74MMAInIREAIOplGTcQZ8DWv1dU8lN0cCsz5zc91bfCdHNFDbyKjb71LDcR1TdQ2PuE3HHhwZDQxrjY6MOtWVCdK2IlDVERG5E/g5sdDQB1S1Q0Q2AttUtR34M+C7IvLviW0c36pq0cQu8XV57Oueyv7eJFmfvVEuaXAkKk4kyXPZ7dga2t8XbFlF+tzq6jw6GFhAcFnVPFrqK5xqS7ddm9Y9gnhOwJYpx9ZN+vll4H3p1GDMjOrSYAvGdbs+8HNPpc7jsNZwEjvNdZjmIk/ttIbKosACgvWOLatM2LVntIZE5IupHDOyA1/b9flKsvHK8yD6qyQU6588WduGtmZKQ26fzFENLtTnuk7fmAZbaWO4NSl86Ufwx8Sygydza8AxIwuI9CXPRnXZrs9X3p7GTmh2bCe8eWiQJ3dF+M7N76L3xEkqivN58NdvUlGUz4pad9qms9NY7EgU/tp8Pf1RLqwu4fMfWJ7wGstIPwIRuQn4DLBURCZv8pYCR2bl6oZ31CTJ+nQdneMrjfOD7YSG+W7tBIhlYz/35hGefu3QxLFQfg5fuuZCh6qgviLYsnJddM7XYnh1FSFuunxxwmts/Q3Ns6prujXib4C/Bf53/N/x258BfzhrCgyv8DU6x1cGh4MjYKLDro0Of1tC5ufmBNppBY5LivtapK/3+Ek2PN6R8Brb8HgHvccz0I9AVfcB+4D3zNrVDO/xNTrHV3yNzIFYfZrrLq6hcX4xPf1D1JQV0lJb6ryHQ1dvcjttZYM7y8rXIn3TvcZWzdI1prOGfq2qV4rIMUjYLRFAVdVtkRcjbfgYneMrdeXBNocPUUPR6AhP7O6ZKDMxXtOnbWXYaVE8X6NzqkuDbVHXEXNO+xGo6pXxf0tVtWzSrdQmAcOIUVGUH2gnVBa5tRMgeR2kXY57OEzYZ1PtNMcJZb5GzHnTj0BEKomVi5g4X1VfnDUVhpECw8Oj7NzfR3d/lHBZiJV15RQU5DrV1HNs6JTNMTxCcUHM5nhnYwXLqt1W0/S1DlKy3hKuE8p8jZjzoh+BiPwVsXDRvTAR6qvA1bOmwjDOwPDwKJt37mfdY5NsjtUtrLmkzulkEC4PBdoctR7U1m+o8DNxq7YsuKZPTZnbmj4+R8z50I/gU8ByVf2gqn4ofrNJwMgoO/f3TUwCELc5HtvNzv1ubY7RseDkqFH3QUOUFOYHaispdGtblYRykyS6uV3dzeWIuVSsod3EWkkeONOJhpEuupNETrguqXzgWLCugwNRlle73Ww/dDzYGjp8Yogm3NlWbxw8EZjoVlKYx0Vhd1FDOTnCRy+q4cdrryDSFyVcXkRzuMyLiDkfag39NfA7EdkNTJiLqto2ayoM4wwkjc5xbMH4XLq4urQw0BpaWOJWW7g8FJjodufVTQ5VxT5sf/FKj3cl2L2oNUSsufx/Br5GYmKZYWSMong25eRl+/obmikucBvS4XPp4qMnTgZaQ0dPzF4i0tmwYF5B4HO5oKTAqS5fS7D7UmvohKreO2tXNIyzYN+RQR5+PrG94f3PvMH84gu4qM6dndB5dDAw0sSH0sXJonO6PYjOCXoul1YVO43O8bUEeyZ0pTIRPCsif02sqcxka8jCR7MUH1tVhsuLeO3AAHc9fKpVpQ+JW+HyIuorCllRWzqRJVtfUehcF8SicxYvKOL6S+on9gge39HlRXRObXkB5UV5jI0pFUV51JYXOI/O8bULXiZ0pTIRvDP+7xWTjln4aJbia6vKFQtL2NjWclqW7IqFbmP1Vyws4VOtiQXBfNAFcHFtCXdc1XTamF1c63YTu640xEcuquNPf7g9QVed4w/cxspi7lnTwt2bT43XPWtaaKwsdqprPJpp6ntyNqOZ5HxrCNba2qrbtm1zLSNr2XtwgOvuffa0bx9b7nq/0+XxjreP8ldPdHDLe5dN2AkP/WYvf3F9M6sWVTrV9elNvz1tvH689gqnugC2vXWEz37v+dO0/ej2y2ldMt90TWHvwQFue/CFiRWUKjyxs4vv33qZ83Ir46v0c6n/JSLbVbU16LHpag19ebo/qqpfn5EK47zAV5800heltDA/wU4oLcynuy+atiSbVHUF1Yp3rQv8Dbn1VVdPf5R9hwcTEsoA5699SH/9r+msofG17Qrg3ZxqPH8D8EJa1BjO8dUnXVpVxLUrwwl2woa2ZpZUuS1UtnhBUWCt+EYP+hHUetqq0tcWmj63aU030xWd26CqG4AG4FJV/TNV/TPgXUBjpgQamcXX7Mr+wVHWtyfWZF/f3kH/oNswzRNDY4G14k940I+gKD+4VWVRvtuQ28IkocAhx7p8LTqXCVLZLK4BhifdH44fM7IQX/sRdPcPJbETHBdQS9JG0Id+BHsPnaDv+CAP3XYZPcdiEWAv7TvEm4fynLbRfOvwicDw0bIPLncacutr0blMkMpE8BDwgoj8Y/z+GmJJZkaW4mM/gnC5n4XKlswvTmINuY00AVhRW8zg8Ai3fP+FSdE5zTTVuNVWXxEKDAV23RLS56Jz6eaMix5V/Y/AbcDR+O02Vf1PqfxxEblWRF4VkT0i8pWAx/+riLwUv70mIr0z/Q8Yc4PSJIXKyorcFiqLnhwNtIaGTrrPLO49Mcq6KXbauvYO+hzbaXk5wa0q8x17ML7aopkgpX4E8eSxF0VkOfAZEfmRqjZP9zsikgvcB1wDdAJbRaRdVV+e9Hf//aTzv8CpnIU5gY+JWwAjI2N0RPoSCm+5bm/45qHBwEJlFUX5rKh1aCcksay6HVtWAD2e2mkJrSon9XBw3ary9KJzsbr/Prwn000q/QjqgE8DnwFWEitCd2MKf/syYI+q7o3/nUeA1cDLSc6/CVifwt/NCnxN3BoZGWPzjq7TkmrWrKp3OhmEy4sCC5V96SMXOtMEyS2r2nL3RedqPK37n7RVZaXbSCtfi85lgqTvbBFZKyJPA78CFgC3A5F4NNGuFP52PfD2pPud8WNB11oMLAWeSlH3eY+vBa46In0Tk8C4rrs376bDcXvDiuK8wEiTinlua+sX5uUG6irMc2tZASyYl8vGKXbaxrZmFsxzqy2UlxtYDC/keMx8fU9mgulWBN8CngM+o6rbAEQkXWnINwI/UdVA81JE1gJrARobsyNy1efErWSFylwmSO3vTV6obPECd+O178gJfrH7dMuqLJTn1OYAeP3AIL994xAP3PpuDg8MsaCkkJ9s/T3lRfksr3an7eBAsGV16PgQFzjsk+DrezITTDcRhIF/DfytiNQCjwIz+frVRazP8TgN8WNB3AjckewPqeomYBPESkzMQIO3+Jq4VVdRFKjLh4iOoEgT1+O1qLKIy5YtSEh0++KHm2hwbHNAzE7b0tHDP7wUmTgWys/hj9+31KEqf3s4WEJZAKp6WFW/raofBD4M9AI9IvKKiKQSNbQVaBKRpSJSQOzDvn3qSSLyB0AlsdXHnMHXCIXSwrzAiI7SkFsLxtfxQoNbVeLB15XmcBn3rGlJGLN71rTQHC53qsvXHg6WUHYGVLWTeEMaEbmQFDaLVXVERO4Efg7kAg+oaoeIbAS2qer4pHAj8Iieb9XvzhF/E7eSJ9Usdbg89rWNYCSJndDdH2WVI03j5OXlcH1zmMXzi+nuH6K2rJCV4XLnEWCdRwd5Ye9hvnPzuzh6/CTz5+Xz/V+/6byHgyWUzQBVfQ3YmOK5W4AtU46tm3L/L2eqIVvwMXHL16QaXyM6wuXBVprrFpoQiwB7oiPiXQRY44IiPtqSWDdq/Q3NLHJcn8nX134mmAOLHmMm+GrB+BrR4Ws0E/gbAXY8GpyEd3zIrTXk62s/E0xXhvp9qvovIlKoqu6zY4yM4GtSTU9/lMriAj5xacNEt62fbu90HtGxvzfK1r2xyJxDx4ZYWFrI/9z6e+fRTOBvBFh3/1Dgc+k6Cc9XuzYTTGcN3Uus0uhzwKWZkWO4xl8LJsQt71k8sTE7vpHn2oIJl4d497IqPvfg1gSbw7WumLYktpXjCLBFlcHP5aIK92Pmo12bCaazhk6KyCagXkTunXrLlEAjs/hqwYyOBUfnjDqu9nx4YDjQ5jg8MHyG30w/vkYNCRL4XIpk/zdvX5luRXA98BHgD4HtmZFjuMbXpJoDx4J1HRyIsrzaYQLeNFFDrsnLy2HNqnqaqkvo7otSG7f5XEcN+RxpNVdJOhGo6iHgERF5RVV3ZFCT4RBfE9181eVrt61xcnKE0lA+J4ZHKQ3le+F3+2pZgb+FINNNKl8NDovIP4rIgfjtpyLSkHZlhhN8jZzwVddFNSVsbEu0Xza2tXBxjXuPebyw4XX3PstN332e6+59lic7uhkbc5uys2Jh8JitWOiuvAT4O16ZQM6UxyUi/wz8PfDD+KHPAv+Hql6TZm2BtLa26rZt21xces4w/q3It8gJH3Vte+sIX/vZy9zy3mUTNZAe+s1evvKxi2ldMt+ptr0HB7ju3mdP++a95a73O7X5drx9lLse+R3XX1I/kbj1xM4u7r3xnaxaVOlMl6/jNVuIyHZVbQ16LJWEsmpV/f6k+w+KyJdmR5rhM77mevukq7s/SlfvEK92H5sIhezqHaLHgz0CX/d7In1R9h0eTEjcApyHtfo6XpB+yyqVieCQiHwWeDh+/ybg8KwpMLzC1z4JvupaNL8oMBTSh6Jz3u6reLpH4Ot4ZeK1n8oeweeATwHdQAT4JLHWlUYW4mv4qK+6To5oYCjkyVH3yxZf91V8DWv1dbwy8do/44pAVfcBbbN2RcNrfF0e+6orWfZupM+9NZSTI1zdtJAffu6yhKJzrvdV8vJy+PjFtTTOL6anf4iaskJW1rpvh+prZnEmXvszLjpnZDe+1or3ddm+qDJYV0Ol+1DI4eFR2ndHWPfYqaJzG1e3sOaSOgoK3HUDGx4e5fGObu90gZ+ZxZl47VvROSMBX2vF+7psH9PgGvY+bGjv3N838WELsW+R6x7bzc79bovO+arLVzLx2rcVgZFA59FBfrYrEmsJOTRCcWEe333mDee14n3tR9DVG1zDvqGymEsXO5VGdxJLwXVEU3d/lAurS/j8B5YnvMZc6/KVTFhWZ5wIRKQG+E9Anap+TEQuBt6jqt+bNRWGNzRUFvGxlWH+/Cc7EqJg6h1HwfhaDK+mrDCwhn1NmVsrDfzNel4yv5ibLl+c8Bpbf0MzjfOLnerymXRbVqlYQw8S6zJWF7//GmB5BFlKKC83MAomlOfWu/U1aqiyOJcNbYn9CDa0NVNZ7Ha8AFbWlbNx9ZQM3tUtXFLnNjpnZEwDC/WNzoEMXl9JxRqqUtVHReSrMNGC0q1hbKSNgwNDgXbCoeNDXIC7EgC+Rg29cXCQ59+I9yMYGGJhSawfQUVRPk017qw0gIKCXK5vrmHJglPROS21pc43ZH21rOYyqUwEx0VkAfF23CJyBWC7OllKdWmwnbCwxK2d4Gs006LKIppqyxL6EfhgpUEsOueJjh7vonN8TSiby6RiDX0ZaD7jglgAABVISURBVAeWi8i/AA8BX0irKsMZuTnBUTC5juPLfI1mGhoZC7TShkccN0rA3+gcXxPK5jKpJJS9KCIfBFYAAryqqifTrsxwQqQvOArmnY0VLKlyZ8F0Hh0M1OU6mmm/xwllvlowvvZJmMukEjV0B/A/VLUjfr9SRG5S1f+ednVGxqkuDQVGwbi2hsLlRUmic9zqqq8IttLCHtgcdeXB2nywYPLycli1qNJpkTnjFKlMwX+iqr3jd1T1KPAn6ZNkuMRXa6ggD9bfkBids/6GZgrzHZdLyMkJHK981wMGVBTlB2qrLMp3rMzwjVQ2i3NFRDTeuEBEcoGCVP64iFwLfAPIBe5X1a8FnPMp4C+JbUbvUNXPpKjdSAO+WkP7Dg/y8PP7Yolu8br/9z/zBvOLL+CisDtrqKt3UgLe8AjFBacS8FY2uI0a6jk2lPS5XFbttgmM4RepTARPAj8Wke/E7/9p/Ni0xCeM+4BrgE5gq4i0q+rLk85pAr4KvE9Vj4pI9Uz/A8bsUlMWbA25rukTLi/itQMD3PXw7yaO+WBz+JqAB/4+l4Z/pLJ+/b+Ap4F/G7/9EvjzFH7vMmCPqu5V1WHgEWD1lHP+BLgvbjehqgdSFW6kB19r+vgaaeJrAh74+1wa/pFK1NAY8Hfx20yoB96edL8TuHzKORcCxMNSc4G/VNXTVhsishZYC9DY2DhDGcZM8LWmj6+RJgcHhqgsLuATlzZMdCj76fZO5wl44O9zafhHKlFD7yPm4S+Ony+AquqyWbp+E3AV0AA8IyIrJ29OE7vYJmATxHoWz8J1jST4WtMH/Iw0qS0LBXYoq/HAfvH5uTT8IpWvU98Dvg5cCbwbaI3/eya6gMlv2Yb4scl0Au2qelJV3yRWx6gphb9tpAlfa/r4yrHoyUBr6FjUfaqNPZdGqqQyEfSp6s9U9YCqHh6/pfB7W4EmEVkqIgXAjcQylCezmdhqABGpImYV7U1dvjHbTFfTxzgdnxPK7Lk0UiWVqKGnReRvgH8AhsYPquqL0/1SvDjdncQql+YCD6hqh4hsBLapanv8sY+KyMvAKPAfUpxkjDThaycwgJGRMToifQl+t+s9Ap/r5vj8XBp+IXqGVkoi8nTAYVXVq9MjaXpaW1t127ZtLi49JxgZGWPzji7u3nyqUNk9a1pYs6re6Yeur7qi0RHad0VY1z6psFtbC20rw4RCbvs+jY0pT3Z02x6BAYCIbFfV1qDHUoka+tDsSzJ85fdHT/DNp15PSEL65lOvc2ljpdNyzx2RvolJAGIWx92bd9NUXcKqRZXOdL168Bj3/SpxvO771eusqHWrC/xtxm74R0pfWUTk40AzMLGmVNWN6RJluKOnP0p5KJ8VtaUTbQSffS3fed3/SF80MEyzuy/qNIoo0hdl3+HBhKQtwLmuqfjQQ3kyY2PKW4eP09MfpabMJijXpBI++m2gGPgQcD/wSeCFNOsyHFFXEQpsI+i6iFpDZVFgmGZ9hdsM3mR7BK6L4YG/1pCvuuYyqZir71XVW4CjqroBeA/xRDAj++g9fjKwjWDvcbfhkGOqgWGaY7j9qutrMTzwN3zUV11zmVSsocH4vydEpA44DITTJ8lwSSRJyGF3f5RVjjQB7O8NtoYivVEuaXCny9dieBCz+YLGzLXN52vb0blMKhPBEyJSAfwN8CKxKqH3p1WV4Yz6imCrI1zhqTXkuLhb0mJ4HlhDvmY9W1irf5zRGlLVv1LVXlX9KbEyE3+gqn+RfmmGC4TgfgQ5uLU6fC3uVlGcF2gNVcxzX/Pf16xnK4bnH6lGDb0XWDJ+voigqg+lUZfhiM7eaVpCOqyvf3BgKNBOcF3cbX9vNNAaWlpVzOIFbm2O6bKeL3EY0WRhrf6RStTQD4HlwEvEsn8hZg/ZRJCFJGsJ6TpT1lc7oaYsFGgNudYFUJfM5vMg6zknR1i2sMT2BDwhlaihVmKNY/6dqn4hfrsr3cIMN/ha999XO8FXXQClhXmBNl9pyL1tZfhFKtbQbqAWiKRZi+EBeXk5fPziWhrnF9PTP0RNWSEra93X9Dm9tn6sH4FrO8FXXQDd/cnbji61b+LGJJJOBCLyODELqBR4WUReILHoXFv65RmZZnh4lMc7uln32KTaOatbWHNJHQUF7jZmfa2t76susFaVRuokLTonIh+c7hdV9X+lRdEZsKJz6WXbW0f47PeeP81X/tHtl9O6ZL4zXXsPDnDdvc+epmvLXe936jP7qgssg9dI5GyLznUBNar6L1P+2JWYTZS1dCdJ9unpd1vD3tckJF+TtsBaVRqpM91E8N+ArwYc74s/dkNaFBlOCZcHR+e4TpDyNWooXB6ctOV6vMBv28rwi+l2AGtUddfUg/FjS9KmyHDKgnkFgQlSC0oKnOryNTpndIzApK3RsTP8Ygawmj5Gqky3Ipgue8htXn+W4GPHrUhflF/sjvCdm99F74mTVBTn8+Cv32RpVTFLqtxZHb4mIR04FmxZHRyIsrzarTXkq50GVobaN6abCLaJyJ+o6ncnHxSRzwPb0ysr+/G141a4PMRlyxbwpz/c7p3V4WMSkq+WFUB1abC2hSVutdkmtn9M94nzJeA2EfmViPxt/Pa/gNuBL2ZGXvaSrONWR6TPqS6frQ4f8dWyAsjNCa4blet20WmWlYckXRGoag/wXhH5ENASP/xPqvpURpRlOZEkdWBcd7by2erwEV8tK4i9xpIllLm0+Xy2rOYqqfQsfhoIamBvnAPJOlv5UNNn8YIirr+kfiIc8vEdXV5YHb7io2UF/iaU+WynzVUcLxLnLr7W9GmsLOYLVzfxvV/v5VtP7eH+Z/fyhaubaKwsdqrLmDm+2la+6prLJM0snpU/LnIt8A0gF7hfVb825fFbiTW86Yof+paqTtv0Jpsyi8ejhrr7otTGa9S4jhryOVPWmDnj0Tm+2Va+6spmzjaz+FwvmgvcB1wDdAJbRaRdVV+ecuqPVfXOdOnwmZwcoTSUz4nhUUpD+V68Ecy/nTk+h0L6alv5qmuukraJALgM2KOqewFE5BFgNTB1IpiT+BpCZ/7tzPD1eTSMmZBOH6IeeHvS/c74san8kYjsFJGfiIjDeJnM4msInfm3M8PX59EwZkI6VwSp8DjwsKoOicifAj8Arp56koisBdYCNDY2ZlZhmvDVgsnJET6yopof3X453f1RwmUhVtb5UV/fRwvG1+fRMGZCOieCLmDyN/wGTm0KA6CqhyfdvR/4L0F/SFU3AZsgtlk8uzLd4KsFMzIyRvuu/d5lPPtqwfj6PBrGTEjnO3sr0CQiS0WkALgRaJ98goiEJ91tA15Jox6v8NWC8TXj2VcLxtfn0TBmQtpWBKo6IiJ3Aj8nFj76gKp2iMhGYJuqtgN3iUgbMAIcAW5Nlx7f8DUj1deMZ18tGJ+tNPDTTjP8I617BKq6Bdgy5di6ST9/leCeB3MCH0PofM549tGC8dVKA3/tNMM/LLPYSMDXjGdfLRhfrTTw104z/MN11JDhGXl5OXz84loa5xfT0z9ETVkhK2vd90kwK23m+NxG0/ALmwiMBIaHR3m8o5t1j52yOjaubmHNJXUUFOQ61eajlVZXEWylhR1baeB3G03DL8waMhLYub9vYhKA2LfbdY/tZud+91aHj5QW5gXW/C8N5TtWZr0ljNSxFYGRQHeS6Jye/qgjRX7T3Z+85v9SxyuXA8eCrSHrLWFMxSYCI4FwkuicGrMTAqkuDa7577odJJg1ZKSOWUNGAiWhHDa0NSdYHRvamikN2UslCF/bQYJZQ0bq2IrASODNQ4M8uSvCd25+F70nTlJRnM+Dv36TiqJ8VtRWONXmY3KUr+0gwdqOGqljE4GRQLi8iOfePMLTrx2aOBbKz+FL11zoUJW/yVE+W0O+JuEZ/uHBAtbwCV8TynxNjvLZGvI1Cc/wD1sRGAnk5eWwZlU9TdUlXrXQ9LXWkM/WkK9JeIZ/2ERgnIaPLTR9tTlqykIU5J0aHxEoyBPnusbxMQnP8A+bCIwEfPXiGyuLuWdNy2nF3Rori51pGtf1haubvNNlGDPBAyfT8AlfvfjfHz3BN596nduvXMadV1/A7Vcu45tPvc7vj55wriuo6JxrXYYxE2xF4BAfwyF99eJ7+qPsOzyYEJ0DeKHLx/EyjJlgE4EjfLVgfPbiTZdhpAezhhzhqwXja8ih6TKM9DEnVgRmwaROTo7w0Ytq+PHaK4j0RQnHw0ddj9fpuopoDpd5octCNI3znayfCMyCmRljY8ovXunxbrx81QUWommc/2S9NWQWzMzwdbx81WUY2UDWrwh8tmB8tBR8HS9fdRlGNpD1E4GvFgz4aSn4Ol6+6jKMbCCt1pCIXCsir4rIHhH5yjTn/ZGIqIi0zrYGXy0YX/F1vHzVZRjZgKhqev6wSC7wGnAN0AlsBW5S1ZennFcK/BNQANypqtum+7utra26bdu0p5zGeNSQTxaMz/g6Xr7qMozzARHZrqqBX7bTaQ1dBuxR1b1xEY8Aq4GXp5z3V8B/Bv5DuoT4aMH4jK/j5asuwzjfSac1VA+8Pel+Z/zYBCJyKbBIVf8pjToMwzCMaXC2WSwiOcDXgVtTOHctsBagsbExvcIMb/ExMdAwsoF0TgRdwKJJ9xvix8YpBVqAX4kIQC3QLiJtU/cJVHUTsAliewRp1Gx4iq+JgYaRDaTTGtoKNInIUhEpAG4E2scfVNU+Va1S1SWqugT4LXDaJGAYYAllhpFO0rYiUNUREbkT+DmQCzygqh0ishHYpqrt0/8FwzhFT3+UyuICPnFpAxJfAPx0e6cllJ0Bs9OMVEjrHoGqbgG2TDm2Lsm5V6VTi3F+Ey4Pcct7FvONX74+YQ198cNN1JZZQlkyzE4zUiXraw0Z2cHoGBOTAMSsoW/88nVGx87wi3MYs9OMVLGJwDgvOHAsuNbQwYGoI0X+M119JsOYjE0ExnnBeK2hyVitoemxMTNSxSYC47zAag3NHBszI1XSVmsoXZxNrSEjO7BaQzPHxswYx1WtIeM8xfeQw/Psu4tTrD6TkQo2ERgJ+Bpy6Ksuw8gGbI/ASMDXkENfdRlGNmATgZGAryGHvuoyjGzAJgIjAV9DDn3VZRjZgE0ERgK+hhz6qsswsgELHzVOY2RkjI5IH5G+KOHyIprDZeTluf/O4GsopO9RVoYBFj5qzICxMeUXr/R4GZ3jYyikRTMZ2YD7r3mGV1h0zsyw8TKyAZsIjAQsOmdm2HgZ2YBNBEYCFp0zM2y8jGzAJgIjAYvOmRk2XkY2YFFDxmn4Gp3jKzZexvmARQ0ZM8LH6ByfsfEyznfMGjIMw5jj2ERgGIYxx7GJwDAMY45jE4FhGMYcxyYCwzCMOc55Fz4qIgeBfWf561XAoVmUM1uYrplhumaOr9pM18w4F12LVXVh0APn3URwLojItmRxtC4xXTPDdM0cX7WZrpmRLl1mDRmGYcxxbCIwDMOY48y1iWCTawFJMF0zw3TNHF+1ma6ZkRZdc2qPwDAMwzidubYiMAzDMKaQdROBiDwgIgdEZHeSx0VE7hWRPSKyU0Qu9UTXVSLSJyIvxW/rMqRrkYg8LSIvi0iHiHwx4JyMj1mKujI+ZiISEpEXRGRHXNeGgHMKReTH8fF6XkSWeKLrVhE5OGm8Pp9uXZOunSsivxORJwIey/h4pajL5Xi9JSK74tc9rdzyrL8nVTWrbsAHgEuB3Ukevw74GSDAFcDznui6CnjCwXiFgUvjP5cCrwEXux6zFHVlfMziY1AS/zkfeB64Yso5/w74dvznG4Efe6LrVuBbmX6Nxa/9ZeDvg54vF+OVoi6X4/UWUDXN47P6nsy6FYGqPgMcmeaU1cBDGuO3QIWIhD3Q5QRVjajqi/GfjwGvAPVTTsv4mKWoK+PEx2Agfjc/fpu60bYa+EH8558AHxaRtDYoSFGXE0SkAfg4cH+SUzI+Xinq8plZfU9m3USQAvXA25Pud+LBB0yc98SX9j8TkeZMXzy+JH8nsW+Tk3E6ZtPoAgdjFrcTXgIOAP+sqknHS1VHgD5ggQe6AP4obiX8REQWpVtTnP8G/DkwluRxJ+OVgi5wM14Qm8R/ISLbRWRtwOOz+p6cixOBr7xILAV8FfBNYHMmLy4iJcBPgS+pan8mrz0dZ9DlZMxUdVRV3wE0AJeJSEsmrnsmUtD1OLBEVS8B/plT38LThohcDxxQ1e3pvtZMSFFXxsdrEleq6qXAx4A7ROQD6bzYXJwIuoDJM3tD/JhTVLV/fGmvqluAfBGpysS1RSSf2Ift/1DVfwg4xcmYnUmXyzGLX7MXeBq4dspDE+MlInlAOXDYtS5VPayqQ/G79wPvyoCc9wFtIvIW8AhwtYj8aMo5LsbrjLocjdf4tbvi/x4A/hG4bMops/qenIsTQTtwS3zX/QqgT1UjrkWJSO24LyoilxF7btL+4RG/5veAV1T160lOy/iYpaLLxZiJyEIRqYj/XARcA/zvKae1A38c//mTwFMa3+FzqWuKh9xGbN8lrajqV1W1QVWXENsIfkpVPzvltIyPVyq6XIxX/LrzRKR0/Gfgo8DUaMNZfU9mXc9iEXmYWDRJlYh0AuuJbZyhqt8GthDbcd8DnABu80TXJ4F/KyIjwCBwY7rfDHHeB9wM7Ir7ywD/N9A4SZuLMUtFl4sxCwM/EJFcYhPPo6r6hIhsBLapajuxCeyHIrKHWIDAjWnWlKquu0SkDRiJ67o1A7oC8WC8UtHlarxqgH+Mf8fJA/5eVZ8UkX8D6XlPWmaxYRjGHGcuWkOGYRjGJGwiMAzDmOPYRGAYhjHHsYnAMAxjjmMTgWEYxhzHJgLDOAMiMhBwrFxEHopXf3wj/nN5/LElIjIYrxz5cvyx/MwrN4zUsInAMM6O7wF7VfUCVV0OvEli8bI34uUeVhLL+vyUA42GkRJZl1BmGOlGRC4gVm7g05MObwT2iMhyYHT8oKqOisgL+FPY0DBOw1YEhjFzLgZeUtWED3zgJSChAqqIhIDLgSczqtAwZoBNBIaRHpbHS2P0ABFV3elakGEkwyYCw5g5LwPvEJGJ90/853fEH4NTewTLgXfFa9YYhpfYRGAYM0RV9wC/A+6edPhu4MX4Y5PPPQR8Bfhq5hQaxsywicAwzkyxiHROun0ZuB24MB46+gZwYfxYEJvjf+P9mRJsGDPBqo8ahmHMcWxFYBiGMcexicAwDGOOYxOBYRjGHMcmAsMwjDmOTQSGYRhzHJsIDMMw5jg2ERiGYcxxbCIwDMOY4/z/PhvDXweCBg0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "LEGhfs7ddBnm",
        "outputId": "b5cfc7c0-8b88-4946-f114-fce6406e826b"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'CGPA')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810ed2ca10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "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/",
          "height": 300
        },
        "id": "eOb6t7lQdFiZ",
        "outputId": "d0769004-7bab-40f2-a53b-5a4943363a82"
      },
      "source": [
        "sns.scatterplot(data = df, y = 'Chance of Admit', x = 'Research')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810eca6fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "LrBOI7J7ejSo"
      },
      "source": [
        "It seems **CGPA** correlates more with Chance of Admission. if you ignore noisy points in plot, you can fit a straight line to example points of dataset in plot, approximately."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sY-TIMS8xUfM"
      },
      "source": [
        "# Training the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jA-TpWiIxbBN"
      },
      "source": [
        "## Train / Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KC52SXZyxZ1U"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMFnUOYOxgcb"
      },
      "source": [
        "X = df.drop(['Serial No.','Chance of Admit'],axis = 1)\n",
        "y = df['Chance of Admit']"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhYTxj19xy_R"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CXoTq9byxH-"
      },
      "source": [
        "## Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Irbu1VUGyT0_"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9AVUkccy3PA"
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GF6SxgFFzVx8"
      },
      "source": [
        "X_train_scaled = pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns)\n",
        "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=X_test.columns)\n",
        "\n",
        "X_train_mat = np.c_[np.ones(X_train.shape[0]), X_train['CGPA']]\n",
        "X_test_mat = np.c_[np.ones(X_test.shape[0]), X_test['CGPA']]\n",
        "\n",
        "X_train_mat_scaled = np.c_[np.ones(X_train_scaled.shape[0]), X_train_scaled['CGPA']]\n",
        "X_test_mat_scaled = np.c_[np.ones(X_test_scaled.shape[0]), X_test_scaled['CGPA']]"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YlS_X2cyzicd",
        "outputId": "7b52b8e9-5c38-43b9-896d-b4892ae3e214"
      },
      "source": [
        "X_train_mat_scaled"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [1.        , 0.68265683]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZKOmQiitt5z"
      },
      "source": [
        "## Performing Linear Regression Using the Normal Equation\n",
        "\n",
        "![NormalEquation.jpg](data:image/jpeg;base64,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)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iaRYx10nuINl",
        "outputId": "edd7fed9-0644-4d45-c3ba-6fa91a62ae1f"
      },
      "source": [
        "from numpy.linalg import inv\n",
        "\n",
        "X_train_transpose = np.transpose(X_train_mat)\n",
        "theta = np.matmul(np.matmul(inv(np.matmul(X_train_transpose, X_train_mat)),X_train_transpose),y_train)\n",
        "\n",
        "X_train_transpose = np.transpose(X_train_mat_scaled)\n",
        "theta_scaled = np.matmul(np.matmul(inv(np.matmul(X_train_transpose, X_train_mat_scaled)),X_train_transpose),y_train)\n",
        "\n",
        "# with non scaled train set\n",
        "print(theta)\n",
        "print('='*50)\n",
        "# with scaled train set\n",
        "print(theta_scaled)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1.11468397  0.21383338]\n",
            "==================================================\n",
            "[0.42491634 0.57948845]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mjx-Q_uHSlZf"
      },
      "source": [
        "### Plotting Predictor fucntion on scatter plot of data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a0MY7YitSlZf"
      },
      "source": [
        "def predict(theta, data):\n",
        "  return np.matmul(data,theta)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PQOwDhUSlZf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "8ff63035-717f-4feb-a430-45ad6ef89ea7"
      },
      "source": [
        "# scatter plot of training set\n",
        "plt.scatter(X_train_scaled['CGPA'], y_train,c='yellow')\n",
        "\n",
        "# predictor function (a line)\n",
        "plt.plot(X_train_scaled['CGPA'], predict(theta_scaled, X_train_mat_scaled), c='r')\n",
        "\n",
        "plt.xlabel('CGPA (scaled)')\n",
        "plt.ylabel('Chance of Admit')\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "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": {
        "id": "IIYPOEopSlZg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "6e387f6c-d86e-4a7a-a88f-c47ed72331a4"
      },
      "source": [
        "# scatter plot of test set\n",
        "plt.scatter(X_test_scaled['CGPA'], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "plt.plot(X_test_scaled['CGPA'], predict(theta_scaled, X_test_mat_scaled), c='green')\n",
        "\n",
        "plt.xlabel('CGPA (scaled)')\n",
        "plt.ylabel('Chance of Admit')\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "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": "markdown",
      "metadata": {
        "id": "KEixPXGT9Ms9"
      },
      "source": [
        "### Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqjELNjPvHBB"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error,explained_variance_score"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1m2Nfj4SlZk"
      },
      "source": [
        "predictions_scaled = predict(theta_scaled, X_test_mat_scaled)\n",
        "predictions = predict(theta, X_test_mat)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhxyU1UCSlZk"
      },
      "source": [
        "### evaluation without scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqBR-4sP6eRV",
        "outputId": "a032c5d2-ef8c-4c65-d403-70bf02f528da"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09045138251027339"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeYsF8CN6iY0",
        "outputId": "7671f08e-3c84-402b-9854-b2ab4b201dcb"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.667789910121453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBZKJ33sSlZl"
      },
      "source": [
        "### evaluation with scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKbYR6zaSlZl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1de6767e-46b4-4165-f1da-68cde40d3b39"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions_scaled))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0904513825102781"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYrkfrVfSlZl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3cb4404-61e4-484f-894c-6843fa7cae0f"
      },
      "source": [
        "explained_variance_score(y_test,predictions_scaled)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6677899101214168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_q_XIz4Vmh9"
      },
      "source": [
        "### Using Scikit Learn (only scaled values)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_jjC58p85Hn",
        "outputId": "4409a5ae-85b8-439a-c906-f7bad04f6a50"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg = LinearRegression().fit(pd.DataFrame(X_train_scaled['CGPA']), y_train)\n",
        "predictions = reg.predict(pd.DataFrame(X_test_scaled['CGPA']))\n",
        "predictions"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.72855973, 0.7584964 , 0.57032303, 0.45485301, 0.33938299,\n",
              "       0.5874297 , 0.98088311, 0.71359139, 0.69006972, 0.81409308,\n",
              "       0.95522311, 0.9295631 , 0.71145306, 0.66868638, 0.66868638,\n",
              "       0.63019638, 0.66868638, 0.71145306, 0.77560307, 0.67510138,\n",
              "       0.83119975, 0.59598304, 0.68151639, 0.5617697 , 0.7456664 ,\n",
              "       0.46768301, 0.65157971, 0.60239804, 0.94880811, 0.75421973,\n",
              "       0.87758215, 0.76704974, 0.7649114 , 0.6002597 , 0.84189142,\n",
              "       0.73711306, 0.74138973, 0.95949977, 0.69006972, 0.83119975,\n",
              "       0.90604143, 0.81836975, 0.44202301, 0.70289972, 0.9039031 ,\n",
              "       0.95949977, 0.89534976, 0.45912968, 0.60239804, 0.66013305,\n",
              "       0.66013305, 0.78843307, 0.56604636, 0.99585145, 0.83119975,\n",
              "       0.58956803, 0.88679643, 0.71572973, 0.71359139, 0.68579305,\n",
              "       0.87396642, 0.7328364 , 0.49761968, 0.64302638, 0.67510138,\n",
              "       0.66654805, 0.48051301, 0.87824309, 0.81836975, 0.64088804,\n",
              "       0.70289972, 0.7328364 , 0.84830642, 0.83761475, 0.79270974,\n",
              "       0.52755636, 0.68579305])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FY4Rwgj2SlZl"
      },
      "source": [
        "### Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBGPGCimSlZl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "d478d316-1f61-4fbc-f5b4-2e931e388ad6"
      },
      "source": [
        "# scatter plot of test set\n",
        "plt.scatter(X_test_scaled['CGPA'], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "plt.plot(X_test_scaled['CGPA'], predictions, c='green')\n",
        "\n",
        "plt.xlabel('CGPA (scaled)')\n",
        "plt.ylabel('Chance of Admit')\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "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": "markdown",
      "metadata": {
        "id": "Yt7inwLOSlZl"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvA8tsF3-wMC",
        "outputId": "40405739-251f-4928-9c7f-758106fa99b7"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09045138251027808"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynty1O9A-zPU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ce38186-de40-49a3-eac2-2ace02c88b27"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6677899101214173"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiiD63vCSlZm"
      },
      "source": [
        "## Training Model with 3 features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziEz7WI8SlZm"
      },
      "source": [
        "### we choose 'CGPA', 'GRE Score' and 'TOEFL Score' as 3 features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iENpwV0DSlZm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "517352a9-58b1-4890-d019-4c5ea0543cbd"
      },
      "source": [
        "reg = LinearRegression().fit(X_train_scaled[['GRE Score', 'TOEFL Score', 'CGPA']], y_train)\n",
        "predictions = reg.predict(X_test_scaled[['GRE Score', 'TOEFL Score', 'CGPA']])\n",
        "predictions"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.72476773, 0.7643534 , 0.5342631 , 0.44751534, 0.37978076,\n",
              "       0.57304369, 0.96023504, 0.66088914, 0.70831422, 0.82484739,\n",
              "       0.95674097, 0.94112815, 0.71470286, 0.70452154, 0.64136751,\n",
              "       0.60490541, 0.67420525, 0.72856302, 0.77018938, 0.66693167,\n",
              "       0.85062006, 0.63085835, 0.71143778, 0.55739525, 0.74016375,\n",
              "       0.5219926 , 0.65283892, 0.57636422, 0.96237998, 0.74908767,\n",
              "       0.81904258, 0.77253129, 0.76489842, 0.6155005 , 0.84812869,\n",
              "       0.75195882, 0.73698113, 0.97726659, 0.70916712, 0.8246062 ,\n",
              "       0.93311258, 0.77436641, 0.44578611, 0.71441482, 0.90817233,\n",
              "       0.96429495, 0.92519168, 0.44287934, 0.56858124, 0.65159768,\n",
              "       0.66640753, 0.76246544, 0.57635597, 0.99308857, 0.83587203,\n",
              "       0.57115214, 0.89458898, 0.76120738, 0.73015433, 0.66447527,\n",
              "       0.87896397, 0.73658623, 0.48019435, 0.63176064, 0.6642661 ,\n",
              "       0.68473268, 0.51160309, 0.86483919, 0.8176527 , 0.6474411 ,\n",
              "       0.66330993, 0.74884745, 0.85464403, 0.82678579, 0.80463421,\n",
              "       0.51292117, 0.68867712])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUcBbxA7SlZm"
      },
      "source": [
        "### Plotting\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIZ7yeg1TdZN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "289b19a8-965f-4a61-cbb6-50bcc0ff0673"
      },
      "source": [
        "import seaborn as sns\n",
        "# scatter plot of test set\n",
        "plt.plot(y_test,y_test,'r')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "plt.scatter(y_test, predictions, c='green')\n",
        "\n",
        "plt.xlabel('Actual Chance')\n",
        "plt.ylabel('Prediction')\n",
        "plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Im7ZsKjn2wsimyg+q4ZcuEDYBTJrZtpqzZjZAfkMYkUwprr3vC/M/JWLs9Y+tHiO3JEfPoh5yS3KMrR6L5TqSHWFrAY0AvzOzm8nXAnoPMBxbVCIp1d/bnx/+WVh6fEVuewYfeSm26xaGngp3H9uGnkDF4aRloTaFBzCzOcA7g5e/d/dnY4uqBm0KL4mrMsk7a3QmS49aGmtDnFuSq7oZzEDvAGsXrI3tujI11NoUvu4QkJntFfy7P9APPBn89AfHRLLhxRcrGv/PH5HfpSvuxh9qb/uo7SClHY2GgE4HPgOcU+U9bQov2VBjaed3OhhCYeip2nGRVtW9A3D3zwT/HlLlR42/TG333FPZ+K9Zk8gKH20HKXFoVAriI/Xed/crow1HJCVS9kCXtoOUONSdBDazwsbvryZfE+j/Ba8PAW5x9yPjDa+UJoEldhdfDJ/+dOmxTZtgxoxk4hGJQK1J4EalID4Z/PH1wN7u/lTw+rXApTHEKZKciHr9dUtFiKRI2AfBdis0/oE/kl8V1BYzO9zM7jezB83sy+2eT6QlRx0VWRmHWqUi9NCWpFHYBLDMzK4zs5PM7CTgl8CN7VzYzKYB5wPvB/YGjjOzvds5p0jTzOAXvyg91sZYf7VSEZu2bGJk2UjL5xSJS6gngd39FDP7MHBwcGipu1/V5rUPAB5094cBzOwnwNHAPW2eV6SxmCZ5tV5fuknYUhAAtwPPu/uNZjbTzHZ09+fbuPauwGNFrx8H3lH+ITMbJig70d+vNc8SgRhX+Gi9vnSTUENAZvYZ4AqgsEfwrsB/xhVUMXdf6u6D7j7Y19fXiUvKVNWBks1ary/dJOwcwD8BB5HfCQx3/2/aLwf9BLBb0eu5wTGRaLlXNvwf/3gs6/qH5g2x9KilDPQOYBgDvZ0pFSHSirBDQH91980W/J/IzKbTfjnoPwBvMLPdyTf8/wAc3+Y5RUol8EDX0LwhNfjSFcLeAdxsZmcCM8zs74GfAle3c2F33wqcAlwH3Av8H3df0845RbZ5/vnKxv/yy7VRi0iRsHcA/wP4R2A1cDJwDXBRuxd392uCc4lEJ2VlHETSqmECCNbrr3H3vYAL4w9JpEUrV8Jg2dPuDz0Ee+yRTDwiKdcwAbj7RPC0br+7azGzpJN6/SJNCzsH8CpgjZktM7OfF37iDEyyq6m9bxcvrmz8//pXNf4iIYSdA/hqrFFIYtJWuKypvW/V6xdpS6MtIV9pZguAjwJ7Af/l7jcXfjoSocQm6cJl1Xr6oWrpHHhg7A90iWRBo/0ALge2AL8lX7Rt3N1P61BsFbQfQLSS3Gi8vKcP+Sdmyxv/AsOYPGtSvX6RFrS0HwD5PQDmBSf4PnBbHMFJMpIsXFarpz/NpjHhExWfn1zosLBKr19EWtZoEnhL4ZfgwS2ZQmoVKOtE4bJaSWbCJypq6fjCsg/tvLMaf5EINEoA+5nZxuDneWDfwu9mtrETAUp8kixcVivJFGrnDPQO4AurNP7u8NxzcYcnkgl1E4C7T3P3nYKfHd19etHvO3UqSIlHkoXL6iWfoX2OZ+0XyuYmRkbU6xeJWN1J4LTRJPDUUnUJ6r4nVH6wi/43KpJGtSaBlQAkHf78Z3jVq0qP/eY38J73JBOPyBTS6iogkfhpaadIIsKWghCJ3n33VTb+zzyjxl+kQ3QHIMlQr18kcboDkM764Q8rG/+JCTX+IgnQHYB0jnr9IqmiOwCJ34knqnibSArpDkDiVd7wz5gBm6oXfBORzlICkHjMmAEvvVR6TD1+kVTREJBEz6y08f/EJ0oa/2Z2/GpqdzARaYruACQ6ISZ5m9nxq6ndwUSkaboDkPZNVtmo5Qc/qDrkE2rHrxY+KyLN0x2AtKfJpZ3NbEKT5IY1IlmgOwBpzfr1lY3/qlUNJ3rDbEJTGPd3qp+rExvWiGRBIgnAzM42s/vMbJWZXWVmOycRh7TIDObMKT3mDvPmNfzTRpvQFG9UX02nNqwRyYKk7gBuAPZx932BB4CvJBSHNOO22yp7/Rs2NLW8s9EmNNXG/Qs6uWGNSBYkMgfg7tcXvfw9cGwScUgTIizjMDRvqGYjXmt83zDWLljb0vVEpLo0zAF8Cri21ptmNmxmK8xsxbp16zoYlgBw/vmVjf/kZGwPdSW5Ub1I1sSWAMzsRjO7u8rP0UWfGQG2AjWf7nH3pe4+6O6DfX19cYUr1ZjBKaeUHnOvfjcQkSQ3qhfJmtiGgNz9ffXeN7OTgCOB+d5N+1JmwQc/CFdfXXqsQ/8VFc8FlOwVrHF/kcglsiewmR0OnAv8nbuHHtfRnsAdUN673203eFTr7kW6Wdr2BP42sD1wg+UbnN+7+2cTikVAtfpFMiipVUCvT+K6UkN543/qqfCtbyUTi4h0jEpBZJl6/SKZloZloJmSivLGW7dWNv5XXKHGXyRjlAA6qLjMgePbyht3NAmYwXbblR5zh2OO6VwMgVQkQ5EMUwLooETLG//xj5W9/vvvT6zXn4pkKJJxSgAdlFh5YzN4zWtKj7nDG98Y73XrUK1/keQpAXRQx8scLF9e2evftCkVY/2q9S+SPCWADupomQMzOPDA0mPujD14ZSrG3VXzRyR5SgAd1KgUciQuu6xm8bY0jbur5o9I8hIpBdEqlYJooMG6/tySXNWNVgZ6BxIptTy2ekw1f0Q6IG2lICRKX/sanHVW6bEqiT1t4+719gUQkfhpCKjbmZU2/ieeWHOSV+PuIlJMCSBlQj8cddhhlUM+7nDppTXPrXF3ESmmBJAioSdpzeCGG15+ffnloZZ2dmQSWkS6hiaBWxDX5GXDSdoQxds0sSoi5TQJHJFCL73wFGuhlw603dDWmox98k/jlY3/nXfCfvt1LDYRmXo0BNSkOEsYVJuM9YWw+evlB72i8Y87NhGZepQAmhTnUsriSdo5L+Qb/xLr1tUd60/bMk8RSTcNATWpv7e/6jh9FEspC8M0Q/ueUPlmiLmaOGMTkalHdwBN+sAbPtDU8abcd19l4795c+jibVrmKSLN0B1Ak67572uaOh5aBNszFu4gtApIRMJQAmhS5OPs110Hhx9eeqyNpbkqryAiYWkIqEmRllMwK238P/e5VNTqF5FsUAJoUiTj7OedV72Mw3e+E0GEIiLhKAE0qe1yCmawYMHLry+4YFut/jRs1CIi2aFSEJ1Sp2Rz+RO8kL+rCJtYVP5BROqpVQpiyt8BxN2zbnh+98qSzbffXjLWH/YJ3mrXStMuXyLSXRK9AzCz04FvAn3u/myjzzd7B9Buz7rt8x9zDFx5ZekfVfnPu2dRD07lccOYPGuy7rVmTJ/B+hfXV/xtUrt8iUj6pO4OwMx2Aw4DYqtTEHdtnFrnP+v6M/O9/uLG/4kn2tqopda1qjX+oPIPItJYkkNAi4EzoErXNyJx18apdp7HzoEHv1R23B1e97qa5wmzsqjZmFX+QUQaSSQBmNnRwBPufleIzw6b2QozW7Fu3bqmrhP3FojF5+l9MV+8be7zRR944YXINmqpFfPsGbNV/kFEWhLbHICZ3Qi8pspbI8CZwGHuvsHM1gKD3TwH8MJI6dDM+rfszuy7H277/NWuVe27gMo/iEhtteYAOj4JbGbzgGVAoSWbCzwJHODuT9f721aWgca6RPKppyqGdsbu/CFD+308mvOX0XJPEWlFahJARQAx3gHE6ogj4JqiAnDDw/mHukREUkZbQkblxRfhX/+1tPHvoofpREQKEn8QzN1zYXr/qXDzzbDvvvDNb8I73wl3363GX0S6VuIJoCts3Jiv1Pne98LkJCxbBsuXw1veknRkIiItUwJo5Je/zDf0S5fCF78Iq1bBoYcmHZWISNuUAGp59lk44QQ48kjYaSe45RY45xyYNSvpyEREIqEEUM4dfvITePOb4fLL80Xcbr8d3vGOpCMTEYmUVgEVe+IJ+Pzn4ec/h7/9W/j+92HevKSjEhGJhe4AIN/rv/BC2HtvuOGG/Cqf5cvV+IvIlKY7gIcegs98Bm66Kb/K58IL4fWvTzoqEZHYZfcOYGICzj0338tfuTL/FO+yZWr8RSQzsnkHcPfd8OlPw2235Vf5fPe7MHdu0lGJiHRUtu4ANm+GRYtg//3h4Yfhxz/OT/iq8ReRDMrOHcBtt+V7/XffDccfD0uWQF9f0lGJiCQmG3cA3/gGvOtd8NxzcPXVMDamxl9EMi8bCWDPPfMrfdasyY/5i4hIRoaAjjsu/yMiIttk4w5AREQqKAGIiGSUEoCISEYpAYiIZJQSgIhIRikBiIhklBKAiEhGKQGIiGSUuXvSMYRmZuuA8aTjCGkO8GzSQURM36l7TMXvpe/UugF3r6h/01UJoJuY2Qp3H0w6jijpO3WPqfi99J2ipyEgEZGMUgIQEckoJYD4LE06gBjoO3WPqfi99J0ipjkAEZGM0h2AiEhGKQGIiGSUEkAHmNnpZuZmNifpWNplZmeb2X1mtsrMrjKznZOOqVVmdriZ3W9mD5rZl5OOp11mtpuZ3WRm95jZGjM7LemYomJm08zsDjP7RdKxRMXMdjazK4L/P91rZu/qdAxKADEzs92Aw4BHk44lIjcA+7j7vsADwFcSjqclZjYNOB94P7A3cJyZ7Z1sVG3bCpzu7nsD7wT+aQp8p4LTgHuTDiJi5wG/cve9gP1I4PspAcRvMXAGMCVm2939enffGrz8PTA3yXjacADwoLs/7O6bgZ8ARyccU1vc/Sl3vz34/XnyDcquyUbVPjObCxwBXJR0LFExs17gYOD7AO6+2d3/3Ok4lABiZGZHA0+4+11JxxKTTwHXJh1Ei3YFHit6/ThToLEsMLMc8Dbg1mQjicQS8p2oyaQDidDuwDrgkmBo6yIzm9XpILKxKXyMzOxG4DVV3hoBziQ//NNV6n0nd/+/wWdGyA85jHUyNmnMzHYAfgYscPeNScfTDjM7EnjG3Vea2XuTjidC04H9gVPd/VYzOw/4MvDVTgchbXD391U7bmbzyGf5u8wM8kMlt5vZAe7+dAdDbFqt71RgZicBRwLzvXsfJHkC2K3o9dzgWFczs+3IN/5j7n5l0vFE4CDgg2b2AeCVwE5mdpm7n5BwXO16HHjc3Qt3aFeQTwAdpQfBOsTM1gKD7t7V1QzN7HDgXODv3H1d0vG0ysymk5/Enk++4f8DcLy7r0k0sDZYvqfxA+BP7r4g6XiiFtwBfMndj0w6liiY2W+Bf3T3+81sITDL3f+lkzHoDkCa9W1ge+CG4M7m9+7+2WRDap67bzWzU4DrgGnAxd3c+AcOAj4OrDazO4NjZ7r7NQnGJLWdCoyZ2SuAh4FPdjoA3QGIiGSUVgGJiGSUEoCISEYpAYiIZJQSgIhIRikBiIhklBKAdDUz+1BQaXWvEJ9dYGYz27jWSWb27Rrvvd/MVgSVOO8ws3OC45ea2bGtXlMkTkoA0u2OA34X/NvIAqDlBFCLme1D/vmIE4JKnIPAg1FfRyRqSgDStYKaN+8GPg38Q9HxaWb2TTO7O9i34FQz+2fgdcBNZnZT8Lm/FP3NsWZ2afD7UWZ2a9CTv9HMdmkQyhnAqLvfB+DuE+7+3aL3DzazW8zs4cLdgJntYGbLzOx2M1sdFA7EzHJBbfgLg5r+15vZjOC91wfx3BX83Z7B8X8xsz8E33VRG/+RSsYoAUg3O5p8PfUHgPVm9vbg+DCQA94a7Fsw5u7fAp4EDnH3Qxqc93fAO939beTLRJ/R4PP7ACvrvP9a8onqSOB/BcdeAj7s7vsDhwDnBKUcAN4AnO/ubwH+DBwTHB8Lju8HHAg8ZWaHBZ8/AHgr8HYzO7hBvCKASkFIdzuO/KYakG+ojyPfEL8P+F5h3wJ3/1OT550LXG5mrwVeATzSZpz/6e6TwD1FdxMG/FvQWE+SL0VdeO8Rdy+UclgJ5MxsR2BXd78KwN1fAggSwGHAHcHndyCfEH7TZsySAUoA0pXM7G+AQ4F5Zubk6/m4mTVTTKu4Dsori37/D+Bcd/95UIBsYYPzrAHeDtTa9+GvxaEH/w4BfcDb3X1LUCzwlVU+PwHMqHNtA/6nu1/QIEaRChoCkm51LPAjdx9w95y770a+p/4e8ttWnhxU/CwkC4DngR2LzvFHM3uzmXqXpp8AAADjSURBVPUAHy463svLpaFPDBHL2cCZZvbG4Ho9ZtaoQF4v+Tr3W8zsEGCg3oeDHb4eN7MPBdfYPljRdB3wqWA+BDPb1cxeHSJmESUA6VrHAVeVHftZcPwi8nswrzKzu4Djg/eXAr8qTAKTr7/+C+AW4Kmi8ywEfmpmK4GG5bvdfRX5FUb/28zuBe4G9mjwZ2PAoJmtBj4B3NfoOuQrff6zma0KYn6Nu18P/BhYHpzrCkqTnEhNqgYqIpJRugMQEckoJQARkYxSAhARySglABGRjFICEBHJKCUAEZGMUgIQEcmo/w9bLj/i9d8xKAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6oHqJnqSlZm"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSRWmossSlZm"
      },
      "source": [
        "#### as results below shows, MSE is lower than former part which means our model is predicting better"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1u6D-VhPSlZn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62334049-0778-44c1-8b1d-da8f19dc4a01"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08369232434585369"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTQA5MndSlZn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46c59aa1-24ca-459d-8287-9474e63b0628"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7163603184884166"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JP8hivI6SlZn"
      },
      "source": [
        "## Training Model with all features of training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crzkWjmQSlZn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b721f8ce-30d2-4e88-948d-85e0c36c190e"
      },
      "source": [
        "reg = LinearRegression().fit(X_train_scaled, y_train)\n",
        "predictions = reg.predict(X_test_scaled)\n",
        "predictions"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.69258208, 0.81044288, 0.51310898, 0.45419164, 0.41815956,\n",
              "       0.58656033, 0.96835885, 0.66388966, 0.73442086, 0.79897339,\n",
              "       0.94701322, 0.95539983, 0.71502732, 0.74225043, 0.67048096,\n",
              "       0.60906844, 0.64987854, 0.72660227, 0.78182432, 0.70040323,\n",
              "       0.83204485, 0.65202882, 0.7370366 , 0.56705457, 0.71456166,\n",
              "       0.54450931, 0.66018763, 0.59050255, 0.95158175, 0.75348727,\n",
              "       0.7898388 , 0.7682848 , 0.72111588, 0.63795012, 0.81181554,\n",
              "       0.78457268, 0.73335528, 0.93813201, 0.64606667, 0.81306901,\n",
              "       0.93743907, 0.80528176, 0.48915406, 0.69371513, 0.89432217,\n",
              "       0.96177364, 0.92125878, 0.44221111, 0.57322734, 0.61250893,\n",
              "       0.70244802, 0.77584558, 0.55531143, 0.99406201, 0.80993688,\n",
              "       0.54892312, 0.90795649, 0.76566316, 0.75064753, 0.63427477,\n",
              "       0.90600008, 0.77946092, 0.50299323, 0.64243595, 0.6647382 ,\n",
              "       0.68195785, 0.50693471, 0.83263111, 0.80717519, 0.63238343,\n",
              "       0.65395333, 0.72136173, 0.86819596, 0.85715903, 0.78372048,\n",
              "       0.52043994, 0.68695355])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "haIt5SuBSlZn"
      },
      "source": [
        "### Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "XNFw2hVVV9-V",
        "outputId": "0b4b1cc2-df9f-41fb-f6c1-5ccfdde709c4"
      },
      "source": [
        "# scatter plot of test set\n",
        "plt.plot(y_test,y_test,'r')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "plt.scatter(y_test, predictions, c='green')\n",
        "\n",
        "plt.xlabel('Actual Chance')\n",
        "plt.ylabel('Prediction')\n",
        "plt.show()"
      ],
      "execution_count": 91,
      "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": "markdown",
      "metadata": {
        "id": "TeWLdhEKSlZn"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQjRl_ypSlZn"
      },
      "source": [
        "#### as results below shows, MSE is lower than former part which means our model is predicting better"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALjOhOpkSlZn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f081640-a55a-454c-8fc6-02da7123a8f1"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08358009329950628"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoEHvrh1SlZn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af8f10e9-6e06-4ded-bcc6-2e5e645f14a6"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7176123476324897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ECiwmlLSlZo"
      },
      "source": [
        "# Section 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjqyJQv-SlZo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "f56062b1-da5a-43d7-b63e-1e3c6721fa51"
      },
      "source": [
        "sample_size = 250\n",
        "X = np.random.uniform(size=sample_size)\n",
        "y = 5 * np.sin(X * 2 * np.pi)\n",
        "y = y + np.random.normal(size=sample_size, loc=1)\n",
        "\n",
        "plt.scatter(X, y)\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('y')"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'y')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "m7zY_x-2SlZo"
      },
      "source": [
        "#### degree 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoJmGnMhSlZo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcc363e1-2243-42aa-e2ce-0d50b6b8d5c8"
      },
      "source": [
        "df = pd.DataFrame(np.c_[np.ones(sample_size), X], columns=[0,1])\n",
        "x_train, x_test, y_train, y_test = train_test_split(df, y, test_size=0.2, random_state=42)\n",
        "\n",
        "reg = LinearRegression().fit(x_train, y_train)\n",
        "predictions = reg.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.60324279,  1.68498026,  4.41799593,  5.19951873,  5.29747951,\n",
              "        3.56144803,  2.12479891,  4.15121652,  2.72833745,  1.12062602,\n",
              "        2.68689078, -2.41698525,  1.14006581,  3.40064082,  3.88352576,\n",
              "        5.39682555,  2.28566063, -0.74475563,  4.484272  ,  2.51893388,\n",
              "       -2.42874836, -1.41324169,  3.77418181,  3.05470066, -3.29964594,\n",
              "       -3.11379662,  0.4934083 ,  2.42387043, -3.12282776, -1.42114315,\n",
              "       -2.72894189, -0.52734725,  3.41523688, -0.688142  ,  4.84029345,\n",
              "       -1.63000505, -2.22559848,  1.21104433,  4.43676321, -0.89577663,\n",
              "        2.06081307,  3.84611454,  2.28629986,  0.04649934,  3.74182472,\n",
              "       -2.63785946,  3.27061852, -1.73458461,  2.23520681,  1.96343772])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rf2xNzg6SlZo"
      },
      "source": [
        "Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDQ0U0gPSlZo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "2e6e2f0e-6a52-479f-cad2-7f1cd673f85c"
      },
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "# scatter plot of training set\n",
        "axes[0].scatter(x_train[1], y_train, c='yellow')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "axes[0].plot(x_train[1], reg.predict(x_train), c='red')\n",
        "\n",
        "# scatter plot of test set\n",
        "axes[1].scatter(x_test[1], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "axes[1].plot(x_test[1], predictions, c='green')\n",
        "\n",
        "axes[0].set_xlabel('X(training)')\n",
        "axes[0].set_ylabel('y')\n",
        "axes[1].set_xlabel('X(test)')\n",
        "axes[1].set_ylabel('y')\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i6z5TRyLSlZo"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxaQeEKESlZo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "750bfce8-09d7-4d40-be46-e33bf392268c"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.285987474333589"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xP3LFW6SlZp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fefce832-8947-4925-ab73-d7a65a0d9534"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6077952835853988"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVA7Z5lzSlZp"
      },
      "source": [
        "#### Degree 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMkeHR1RSlZp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27269ead-3c4b-464f-c14a-ac75600d4354"
      },
      "source": [
        "df[2], df[3] = np.power(X, 2), np.power(X, 3)\n",
        "\n",
        "#split\n",
        "x_train, x_test, y_train, y_test = train_test_split(df, y, test_size=0.2, random_state=42)\n",
        "\n",
        "#scaling\n",
        "scaler = MinMaxScaler()\n",
        "x_train = scaler.fit_transform(x_train) + np.array([[1,0,0,0]])\n",
        "x_test = scaler.transform(x_test) + np.array([[1,0,0,0]])\n",
        "\n",
        "#regression\n",
        "reg = LinearRegression().fit(x_train, y_train)\n",
        "predictions = reg.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.88348875,  3.37321634,  4.50463859,  1.0957433 ,  0.50282982,\n",
              "        5.92347211,  4.44212364,  5.18067585,  5.52120471,  1.79483108,\n",
              "        5.46508633, -3.11449233,  1.85143893,  5.96555156,  5.63893345,\n",
              "       -0.13914255,  4.78024931, -3.04759801,  4.30066617,  5.20836033,\n",
              "       -3.08968364, -3.82433543,  5.76683986,  5.85301511,  0.03439323,\n",
              "       -0.85933423, -0.04193244,  5.04340118, -0.81892521, -3.82837285,\n",
              "       -2.30807594, -2.63647411,  5.96432414, -2.94700268,  2.94344054,\n",
              "       -3.88527184, -3.45953395,  2.05724365,  4.44837489, -3.29188118,\n",
              "        4.2989193 ,  5.68643708,  4.78152683, -1.28101725,  5.79839002,\n",
              "       -2.57608243,  5.95450015, -3.87604494,  4.67772046,  4.07226621])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yM1wLIcpSlZp"
      },
      "source": [
        "Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTLYQeL8SlZp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "db491ea7-6c81-4c78-edec-3fbf890b666b"
      },
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "# scatter plot of train set\n",
        "axes[0].scatter(np.transpose(x_train)[1], y_train, c='yellow')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_train)[1], reg.predict(x_train)), key=lambda x: x[0])))\n",
        "axes[0].plot(unziped[0], unziped[1], c='red')\n",
        "\n",
        "# scatter plot of test set\n",
        "axes[1].scatter(np.transpose(x_test)[1], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_test)[1], predictions), key=lambda x: x[0])))\n",
        "axes[1].plot(unziped[0], unziped[1], c='green')\n",
        "\n",
        "\n",
        "axes[1].set_xlabel('X(test)')\n",
        "axes[1].set_ylabel('y')\n",
        "axes[0].set_xlabel('X(train)')\n",
        "axes[0].set_ylabel('y')\n",
        "plt.show()\n"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wk_iMcgrSlZp"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eP6c2gJTSlZp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba9f2b88-7ffd-4609-a846-87aaf0a59b5c"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8798633168671822"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rFn-xfEWSlZp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c54ba12a-a0c4-42ff-8421-70ba4e634ca9"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9403255132354238"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJTuSZ0HSlZq"
      },
      "source": [
        "### Degree 7"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsjMWHbbSlZq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62c24e1a-e5ba-4cda-85df-9a4e380cb18d"
      },
      "source": [
        "df_mat = np.c_[df.to_numpy(), df.to_numpy() * np.c_[np.power(X, 4)]]\n",
        "print(df_mat.shape)\n",
        "#split\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_mat, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# #scaling\n",
        "scaler = MinMaxScaler()\n",
        "x_train = scaler.fit_transform(x_train) + np.array([[1,0,0,0,0,0,0,0]])\n",
        "x_test = scaler.transform(x_test) + np.array([[1,0,0,0,0,0,0,0]])\n",
        "\n",
        "#regression\n",
        "reg = LinearRegression().fit(x_train, y_train)\n",
        "predictions = reg.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.8223956 ,  3.76036783,  4.09986107,  1.60249983,  1.20418876,\n",
              "        5.66405551,  4.8662145 ,  4.72327913,  5.76514005,  1.96194919,\n",
              "        5.72909345, -2.68973397,  2.02831362,  5.80289568,  5.22965818,\n",
              "        0.77384176,  5.18178653, -3.41317284,  3.92752834,  5.54319099,\n",
              "       -2.66381984, -3.8803631 ,  5.39954541,  5.91138121, -0.082101  ,\n",
              "       -0.74415203, -0.20691719,  5.41010991, -0.71347048, -3.87962251,\n",
              "       -1.92064855, -3.04273064,  5.79254412, -3.32650372,  2.88311932,\n",
              "       -3.80756256, -3.07570436,  2.26880728,  4.0517574 , -3.60937754,\n",
              "        4.72651003,  5.29028825,  5.18293542, -1.62856174,  5.4454961 ,\n",
              "       -2.16249408,  5.87477733, -3.73410165,  5.0884421 ,  4.49923569])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GI6VOKB6SlZq"
      },
      "source": [
        "plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBdg1zS2SlZq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "78700729-989e-47af-d7bc-766d6130db6e"
      },
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "# scatter plot of train set\n",
        "axes[0].scatter(np.transpose(x_train)[1], y_train, c='yellow')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_train)[1], reg.predict(x_train)), key=lambda x: x[0])))\n",
        "axes[0].plot(unziped[0], unziped[1], c='red')\n",
        "\n",
        "# scatter plot of test set\n",
        "axes[1].scatter(np.transpose(x_test)[1], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_test)[1], predictions), key=lambda x: x[0])))\n",
        "axes[1].plot(unziped[0], unziped[1], c='green')\n",
        "\n",
        "\n",
        "axes[1].set_xlabel('X(test)')\n",
        "axes[1].set_ylabel('y')\n",
        "axes[0].set_xlabel('X(train)')\n",
        "axes[0].set_ylabel('y')\n",
        "plt.show()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rStf_VgDSlZq"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIix2e76SlZq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d93cfa18-e17d-42c8-f2b9-66e61cbe97c5"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8212012771584085"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5mT4SLiSlZq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b17015d-87cd-44e9-8bb7-f8c3cea9aac4"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9480628084084165"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdNo_CMCSlZr"
      },
      "source": [
        "### Degree 11"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSl__smkSlZr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "046764e6-c36a-4edf-d1df-5be255db10a5"
      },
      "source": [
        "df_mat = np.c_[df_mat, df.to_numpy() * np.c_[np.power(X, 8)]]\n",
        "print(df_mat.shape)\n",
        "#split\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_mat, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# #scaling\n",
        "scaler = MinMaxScaler()\n",
        "x_train = scaler.fit_transform(x_train) + np.array([[1,0,0,0,0,0,0,0,0,0,0,0]])\n",
        "x_test = scaler.transform(x_test) + np.array([[1,0,0,0,0,0,0,0,0,0,0,0]])\n",
        "\n",
        "#regression\n",
        "reg = LinearRegression().fit(x_train, y_train)\n",
        "predictions = reg.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 12)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.89349861,  3.90917505,  3.87485321,  1.84357297,  0.92739988,\n",
              "        5.79531957,  4.89679953,  4.64373112,  5.65553507,  1.99592712,\n",
              "        5.61877068, -2.78916825,  2.07083987,  5.89588217,  5.31338671,\n",
              "       -0.67235597,  5.1531511 , -3.18325382,  3.69460927,  5.44950551,\n",
              "       -2.75212822, -3.8074486 ,  5.52070204,  5.87156839, -0.17786263,\n",
              "       -0.55358929, -0.43381206,  5.33819912, -0.53356791, -3.81240804,\n",
              "       -1.70934826, -2.91145481,  5.89018982, -3.11699164,  2.95544191,\n",
              "       -3.89891957, -3.31569855,  2.34063766,  3.8228276 , -3.34874886,\n",
              "        4.78073647,  5.38926275,  5.15407686, -1.81801976,  5.57347783,\n",
              "       -2.03569023,  5.91985102, -3.89987151,  5.07786343,  4.58705696])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iyaRx3OISlZr"
      },
      "source": [
        "Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dT0Bw9JSlZr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "f5c91f13-33c7-415d-fe99-0581f96896d8"
      },
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "# scatter plot of train set\n",
        "axes[0].scatter(np.transpose(x_train)[1], y_train, c='yellow')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_train)[1], reg.predict(x_train)), key=lambda x: x[0])))\n",
        "axes[0].plot(unziped[0], unziped[1], c='red')\n",
        "\n",
        "# scatter plot of test set\n",
        "axes[1].scatter(np.transpose(x_test)[1], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_test)[1], predictions), key=lambda x: x[0])))\n",
        "axes[1].plot(unziped[0], unziped[1], c='green')\n",
        "\n",
        "\n",
        "axes[1].set_xlabel('X(test)')\n",
        "axes[1].set_ylabel('y')\n",
        "axes[0].set_xlabel('X(train)')\n",
        "axes[0].set_ylabel('y')\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tqc8lieGSlZr"
      },
      "source": [
        "evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5ywCB2BSlZr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ba70d44-b731-499a-eebb-8c8c116bf347"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8469189461485328"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdaFTZOiSlZr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1b4d49f-0859-4559-db4e-3e8cc54a9fb2"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9446716880610666"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yKQ-44PzSlZr"
      },
      "source": [
        "### Degree 16"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYhTuEX7SlZs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "497e1742-eb35-417a-c14c-d9dd48de5155"
      },
      "source": [
        "df_mat = np.c_[df_mat, df.to_numpy() * np.c_[np.power(X, 12)], np.c_[np.power(X, 16)]]\n",
        "print(df_mat.shape)\n",
        "#split\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_mat, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# #scaling\n",
        "scaler = MinMaxScaler()\n",
        "x_train = scaler.fit_transform(x_train) + np.array([[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])\n",
        "x_test = scaler.transform(x_test) + np.array([[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])\n",
        "\n",
        "#regression\n",
        "reg = LinearRegression().fit(x_train, y_train)\n",
        "predictions = reg.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 17)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.0438563 ,  3.95480826,  3.74641006,  1.85090603,  0.77415552,\n",
              "        5.8283128 ,  5.02710269,  4.83531933,  5.53029129,  1.93311897,\n",
              "        5.52032979, -2.62136346,  2.00906107,  5.77045149,  5.60128255,\n",
              "       -0.49986685,  5.26096449, -3.10937834,  3.50236151,  5.45660897,\n",
              "       -2.58940957, -3.96116001,  5.75308362,  5.6054407 ,  0.01387451,\n",
              "       -0.67107862, -0.47210171,  5.39515543, -0.63923239, -3.96732577,\n",
              "       -1.80164521, -2.81706395,  5.77765697, -3.03332409,  2.91816489,\n",
              "       -4.04296172, -3.13703905,  2.28422301,  3.67436837, -3.31613003,\n",
              "        4.90955662,  5.66352191,  5.26172141, -1.80170366,  5.7813051 ,\n",
              "       -2.03571982,  5.70145395, -4.00543978,  5.19699391,  4.70411933])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EuiqvtRtSlZs"
      },
      "source": [
        "Plotting\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNDjUn99SlZt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "9fa422cd-f334-47e6-da0d-a2f56bbe9416"
      },
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "# scatter plot of train set\n",
        "axes[0].scatter(np.transpose(x_train)[1], y_train, c='yellow')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_train)[1], reg.predict(x_train)), key=lambda x: x[0])))\n",
        "axes[0].plot(unziped[0], unziped[1], c='red')\n",
        "\n",
        "# scatter plot of test set\n",
        "axes[1].scatter(np.transpose(x_test)[1], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_test)[1], predictions), key=lambda x: x[0])))\n",
        "axes[1].plot(unziped[0], unziped[1], c='green')\n",
        "\n",
        "\n",
        "axes[1].set_xlabel('X(test)')\n",
        "axes[1].set_ylabel('y')\n",
        "axes[0].set_xlabel('X(train)')\n",
        "axes[0].set_ylabel('y')\n",
        "plt.show()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h53s8gVvSlZt"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeIRwImpSlZt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ba364e1-1772-408a-8a1c-db0c878376cc"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8476341811656453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pi2P_zhhSlZt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bfb37b5-70c4-4e5c-b8f5-548420fc9d20"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9446020022828876"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9qVR0uvSlZu"
      },
      "source": [
        "### Degree 20"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiLtVAPzSlZu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e5dc485-f53f-417b-e78a-682d46bb8398"
      },
      "source": [
        "df_mat = np.c_[df_mat, df.to_numpy() * np.c_[np.power(X, 17)]]\n",
        "print(df_mat.shape)\n",
        "#split\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_mat, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# #scaling\n",
        "scaler = MinMaxScaler()\n",
        "x_train = scaler.fit_transform(x_train) + np.array([[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])\n",
        "x_test = scaler.transform(x_test) + np.array([[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])\n",
        "\n",
        "#regression\n",
        "reg = LinearRegression().fit(x_train, y_train)\n",
        "predictions = reg.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 21)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.91794609,  4.01126095,  3.72382039,  1.59329203,  0.52854885,\n",
              "        5.60108799,  4.86615227,  5.05473764,  5.79996291,  2.00405484,\n",
              "        5.76560577, -2.60891174,  2.09294696,  5.60303309,  5.63744305,\n",
              "        0.66805119,  5.15838675, -3.23329867,  3.39751914,  5.55765857,\n",
              "       -2.56855979, -3.85828572,  5.66151058,  5.80739013,  0.05742296,\n",
              "       -0.75704055, -0.64811643,  5.40397985, -0.72421461, -3.86142587,\n",
              "       -1.66575854, -2.82180741,  5.59937891, -3.13356627,  2.93666845,\n",
              "       -3.92278115, -3.23521104,  2.40973933,  3.62730862, -3.46260348,\n",
              "        4.75116823,  5.65497571,  5.15954928, -1.69894065,  5.65614659,\n",
              "       -1.89677608,  5.66323832, -3.92557084,  5.06646196,  4.57584481])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tABfNOstSlZu"
      },
      "source": [
        "Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SkD4U0xSlZu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "f5f0e79a-5537-4ec6-a65e-e369d9904a02"
      },
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "# scatter plot of train set\n",
        "axes[0].scatter(np.transpose(x_train)[1], y_train, c='yellow')\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_train)[1], reg.predict(x_train)), key=lambda x: x[0])))\n",
        "axes[0].plot(unziped[0], unziped[1], c='red')\n",
        "\n",
        "# scatter plot of test set\n",
        "axes[1].scatter(np.transpose(x_test)[1], y_test)\n",
        "\n",
        "# output of predictor function (a line)\n",
        "unziped = list(zip(*sorted(zip(np.transpose(x_test)[1], predictions), key=lambda x: x[0])))\n",
        "axes[1].plot(unziped[0], unziped[1], c='green')\n",
        "\n",
        "\n",
        "axes[1].set_xlabel('X(test)')\n",
        "axes[1].set_ylabel('y')\n",
        "axes[0].set_xlabel('X(train)')\n",
        "axes[0].set_ylabel('y')\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZB6cBLlfSlZu"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4P-2DuTSlZu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a366a403-2054-460e-8ba4-61aa701cd178"
      },
      "source": [
        "np.sqrt(mean_squared_error(y_test,predictions))"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8053381071552244"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9syfFlaNSlZu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49b7bf43-e7f6-4869-9d13-8a5389d1163b"
      },
      "source": [
        "explained_variance_score(y_test,predictions)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9500396237580284"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    }
  ]
}