Chance_of_Admission_Prediction.ipynb

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        "# Chance of Admission for Higher Studies\n",
        "Predict the chances of admission of a student to a Graduate program based on:\n",
        "\n",
        "1. GRE Scores (290 to 340)\n",
        "2. TOEFL Scores (92 to 120)\n",
        "3. University Rating (1 to 5)\n",
        "4. Statement of Purpose (1 to 5)\n",
        "5. Letter of Recommendation Strength (1 to 5)\n",
        "6. Undergraduate CGPA (6.8 to 9.92)\n",
        "7. Research Experience (0 or 1)\n",
        "8. Chance of Admit (0.34 to 0.97)"
      ],
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      "source": [
        "# Step 1 : import library\n",
        "import pandas as pd"
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      "execution_count": 1,
      "outputs": []
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      "source": [
        "# Step 2 : import data\n",
        "admission = pd.read_csv('https://github.com/ybifoundation/Dataset/raw/main/Admission%20Chance.csv')"
      ],
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        "admission.head()\n"
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      "execution_count": 3,
      "outputs": [
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              "   Serial No  GRE Score  TOEFL Score  University Rating   SOP  LOR   CGPA  \\\n",
              "0          1        337          118                  4   4.5   4.5  9.65   \n",
              "1          2        324          107                  4   4.0   4.5  8.87   \n",
              "2          3        316          104                  3   3.0   3.5  8.00   \n",
              "3          4        322          110                  3   3.5   2.5  8.67   \n",
              "4          5        314          103                  2   2.0   3.0  8.21   \n",
              "\n",
              "   Research  Chance of Admit   \n",
              "0         1              0.92  \n",
              "1         1              0.76  \n",
              "2         1              0.72  \n",
              "3         1              0.80  \n",
              "4         0              0.65  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "admission",
              "summary": "{\n  \"name\": \"admission\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"Serial No\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 1,\n        \"max\": 400,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          210,\n          281,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GRE Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 290,\n        \"max\": 340,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          307,\n          335,\n          297\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOEFL Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 92,\n        \"max\": 120,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          94,\n          119,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"University Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" SOP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.006868641458697,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          4.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOR \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8984775482792571,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5.0,\n          3.5,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CGPA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5963170964964318,\n        \"min\": 6.8,\n        \"max\": 9.92,\n        \"num_unique_values\": 168,\n        \"samples\": [\n          8.15,\n          7.4,\n          9.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Research\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chance of Admit \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14260933017384092,\n        \"min\": 0.34,\n        \"max\": 0.97,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          0.92,\n          0.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Serial No          400 non-null    int64  \n",
            " 1   GRE Score          400 non-null    int64  \n",
            " 2   TOEFL Score        400 non-null    int64  \n",
            " 3   University Rating  400 non-null    int64  \n",
            " 4    SOP               400 non-null    float64\n",
            " 5   LOR                400 non-null    float64\n",
            " 6   CGPA               400 non-null    float64\n",
            " 7   Research           400 non-null    int64  \n",
            " 8   Chance of Admit    400 non-null    float64\n",
            "dtypes: float64(4), int64(5)\n",
            "memory usage: 28.2 KB\n"
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              "        Serial No   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
              "count  400.000000  400.000000   400.000000         400.000000  400.000000   \n",
              "mean   200.500000  316.807500   107.410000           3.087500    3.400000   \n",
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              "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
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              "\n",
              "             LOR         CGPA    Research  Chance of Admit   \n",
              "count  400.000000  400.000000  400.000000        400.000000  \n",
              "mean     3.452500    8.598925    0.547500          0.724350  \n",
              "std      0.898478    0.596317    0.498362          0.142609  \n",
              "min      1.000000    6.800000    0.000000          0.340000  \n",
              "25%      3.000000    8.170000    0.000000          0.640000  \n",
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              "  <div id=\"df-91d0894e-bed7-4071-9f4d-9490140b393a\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    }\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>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>200.500000</td>\n",
              "      <td>316.807500</td>\n",
              "      <td>107.410000</td>\n",
              "      <td>3.087500</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>3.452500</td>\n",
              "      <td>8.598925</td>\n",
              "      <td>0.547500</td>\n",
              "      <td>0.724350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.614301</td>\n",
              "      <td>11.473646</td>\n",
              "      <td>6.069514</td>\n",
              "      <td>1.143728</td>\n",
              "      <td>1.006869</td>\n",
              "      <td>0.898478</td>\n",
              "      <td>0.596317</td>\n",
              "      <td>0.498362</td>\n",
              "      <td>0.142609</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>308.000000</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>8.610000</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>9.062500</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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-91d0894e-bed7-4071-9f4d-9490140b393a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-91d0894e-bed7-4071-9f4d-9490140b393a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"admission\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Serial No\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 143.85727156983504,\n        \"min\": 1.0,\n        \"max\": 400.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          400.0,\n          200.5,\n          300.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GRE Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 116.59233338156497,\n        \"min\": 11.473646113500475,\n        \"max\": 400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          316.8075,\n          317.0,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOEFL Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 114.57254758734635,\n        \"min\": 6.069513777374126,\n        \"max\": 400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          107.41,\n          107.0,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"University Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140.45664706865134,\n        \"min\": 1.0,\n        \"max\": 400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0875,\n          3.0,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" SOP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140.39756402259295,\n        \"min\": 1.0,\n        \"max\": 400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.4,\n          3.5,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOR \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140.37527959785763,\n        \"min\": 0.8984775482792571,\n        \"max\": 400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.4525,\n          3.5,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CGPA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 138.83768591852123,\n        \"min\": 0.5963170964964318,\n        \"max\": 400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.598925,\n          8.61,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Research\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141.2176212184402,\n        \"min\": 0.0,\n        \"max\": 400.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5475,\n          1.0,\n          0.4983619785398314\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chance of Admit \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141.2005373652785,\n        \"min\": 0.14260933017384092,\n        \"max\": 400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.72435,\n          0.73,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3 : define target (y) and features (X)\n",
        "admission.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwzwlLK84pyq",
        "outputId": "03bc7be5-6200-4f5b-827d-7917cd4600e2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Serial No', 'GRE Score', 'TOEFL Score', 'University Rating', ' SOP',\n",
              "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = admission['Chance of Admit ']\n",
        "X = admission.drop(['Serial No','Chance of Admit '],axis=1)"
      ],
      "metadata": {
        "id": "j0K5n-CH4rPB"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4 : train test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7, random_state=2529)"
      ],
      "metadata": {
        "id": "JkejwIkY4stM"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shape of train and test sample\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQivLoJ34uKb",
        "outputId": "be8bdd61-2995-4b5a-b3b7-e476d2e4bfa1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((280, 7), (120, 7), (280,), (120,))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5 : select model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "B1sPnmEu4vJi"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6 : train or fit model\n",
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "YBMV-HXQ4waJ",
        "outputId": "fbf6aa00-3aee-4160-d785-345c1a2dd212"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbiAz40U4xbM",
        "outputId": "925f6adb-0200-4ae6-f071-60d7a1ced4cb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.2831244932033998"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2f1UJjx4yvY",
        "outputId": "b1d76420-4103-46a2-950e-7c48cddd7d80"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.00204057,  0.00287273,  0.00566887, -0.00380559,  0.01973175,\n",
              "        0.11314449,  0.02061553])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7 : predict model\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vt31vEbX4zyc",
        "outputId": "656e5c0d-18f6-4672-922e-a7e0a8bdfe73"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.71426327, 0.72534136, 0.69677103, 0.66566584, 0.57483872,\n",
              "       0.93087527, 0.93701113, 0.72361387, 0.81130158, 0.62223963,\n",
              "       0.59629648, 0.80084072, 0.52537944, 0.79174558, 0.84064992,\n",
              "       0.66429594, 0.65136589, 0.66990687, 0.75794085, 0.86072023,\n",
              "       0.66088101, 0.85570763, 0.84777425, 0.95033179, 0.68750762,\n",
              "       0.65907671, 0.65279623, 0.5709259 , 0.55895645, 0.57990205,\n",
              "       0.54497918, 0.7570717 , 0.69682571, 0.77286067, 0.64320811,\n",
              "       0.5183554 , 0.43816818, 0.84654064, 0.90398354, 0.80517781,\n",
              "       0.72218971, 0.72882587, 0.68145136, 0.88592237, 0.77208852,\n",
              "       0.78778085, 0.95526121, 0.88586486, 0.59980416, 0.50690214,\n",
              "       0.59947098, 0.63380406, 0.82841217, 0.44911724, 0.71068577,\n",
              "       0.77335748, 0.68851557, 0.64486026, 0.85537724, 0.65517768,\n",
              "       0.65046031, 0.90818978, 0.63422429, 0.68658606, 0.72150268,\n",
              "       0.69030545, 0.59381287, 0.93813035, 0.58997351, 0.91542587,\n",
              "       0.59283415, 0.93351713, 0.59478751, 0.71380389, 0.54346237,\n",
              "       0.84710913, 0.6084418 , 0.7257337 , 0.67545704, 0.81387503,\n",
              "       0.70259527, 0.88600461, 0.67084016, 0.53064995, 0.77790726,\n",
              "       0.65780713, 0.78970635, 0.54709634, 0.77924705, 0.66750436,\n",
              "       0.69363338, 0.69891086, 0.92185813, 0.70469056, 0.62554306,\n",
              "       0.62208829, 0.73828086, 0.67369114, 0.76391913, 0.61985049,\n",
              "       0.92865957, 0.70430038, 0.9828821 , 0.82502993, 0.78261009,\n",
              "       0.83438446, 0.66840368, 0.70165011, 0.64534281, 0.5715406 ,\n",
              "       0.80739359, 0.69273815, 0.80585447, 0.6102703 , 0.54641206,\n",
              "       0.76301749, 0.71080317, 0.6261331 , 0.83951248, 0.68578269])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8 : model accuracy\n",
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error\n",
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1vrV5D641I7",
        "outputId": "bf4ebf8f-54ac-46a4-c01b-d5e9562357fe"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04400128934232651"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_percentage_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wE8pyQAl43KS",
        "outputId": "a1f9f2dd-f46b-455d-d64d-3de2a6fa3cef"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07575278864605438"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fxTRumD44ah",
        "outputId": "811c0616-9c1c-4310-c2de-c14f49a2b5f7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.004038263715495693"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p9R2tLms45Ve"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}