{
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      "source": [
        "# Machine Learning\n",
        "\n",
        "\n",
        "* Machine learning is a subset of artificial intelligence (AI) that focuses on enabling computers to learn from data and make predictions or decisions without being explicitly programmed. In traditional programming, developers write code to provide instructions to computers on how to perform specific tasks. However, in machine learning, instead of programming explicit instructions, algorithms are trained on large datasets to recognize patterns and make predictions or decisions based on that data.\n",
        "*   Types of Machine Learning:\n",
        "  \n",
        "\n",
        "1.   Supervised Learning:  Learning from labeled data, where the computer is trained on input-output pairs.  Predicting whether a given email is spam or not spam based on its content and whether it was marked as spam by the user.\n",
        "2.   Unsupervised Learning: Finding patterns and structures in data without explicit supervision. Sorting different types of fruits into groups based on their similarities in size, color, and texture.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fj-HaH5PBiE3"
      },
      "source": [
        "### Linear Regression\n",
        "Linear regression is a statistical method used to model the relationship between two variables, typically denoted as X and Y. It assumes that there is a linear relationship between the independent variable X and the dependent variable Y.\n",
        "\n",
        "Example: Predicting Exam Scores\n",
        "Let's consider an example where we want to predict students' exam scores\n",
        "Y based on the number of hours they study X. We'll assume that there is a linear relationship between study hours and exam scores.\n",
        "\n",
        "Suppose our model equation is Y=5X+10.\n",
        "If a student studies for 6 hours(X = 6), we can predict their exam score using the equation: Y = 5(6) + 10 = 40"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "xRKsg7uiDyEB"
      },
      "outputs": [],
      "source": [
        "# importing library\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbfhyR34GMBw",
        "outputId": "cbc27605-f2a7-401a-d176-90a53a14d7b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "vBEd9zFGD4qC"
      },
      "outputs": [],
      "source": [
        "# Reading the .csv file\n",
        "data = pd.read_csv('/content/drive/MyDrive/MASHDEMY_AI_Summer Camp/LEVEL2_ML/scores.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KHtyAtCSGftV",
        "outputId": "2f6bcab8-c230-485d-ec84-2d68ce78468b"
      },
      "outputs": [
        {
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              "   Hours  Score\n",
              "0      8     90\n",
              "1      8     99\n",
              "2      9    100\n",
              "3      4     10\n",
              "4      5     23"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          9,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 5,\n        \"max\": 100,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          90,\n          12,\n          67\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# printing the first 5 rows\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UcU7OCJyGi4j",
        "outputId": "6eb1fb04-8007-45a0-a6ab-64068c5a23a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25, 2)\n"
          ]
        }
      ],
      "source": [
        "# printing the shape of the data\n",
        "print(data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EiVe7dJNGm_G",
        "outputId": "ca0c59a9-44ef-4ff8-c6d1-92b7ae2df496"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 25 entries, 0 to 24\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   Hours   25 non-null     int64\n",
            " 1   Score   25 non-null     int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 528.0 bytes\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# Getting the basic information about the data\n",
        "print(data.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVX5fDHDHdNM"
      },
      "source": [
        "Splitting the data into train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ECQa_eSwIR7E"
      },
      "outputs": [],
      "source": [
        "# Getting the independent(X) and dependent variables(Y)\n",
        "X = data[['Hours']]\n",
        "Y = data[['Score']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "T7FIjEShHbE0"
      },
      "outputs": [],
      "source": [
        "# Splitting the data into train and test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=23)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "-hnyar0nJTX0",
        "outputId": "4f93a049-b0e9-42f5-a8b2-28b34736f67d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Getting the relation ship between x_train and y_train\n",
        "plt.scatter(x_train, y_train, c='red')\n",
        "plt.title('Relationship Between x_train and y_train')\n",
        "plt.xlabel('x_train')\n",
        "plt.ylabel('y_train')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5jE-a87KKeC"
      },
      "source": [
        "Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "dPtMTfZtKMyz",
        "outputId": "46e61209-9119-4d3d-8d55-85a181b42585"
      },
      "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": 10
        }
      ],
      "source": [
        "# creating the object of  LinearRegression\n",
        "linear_regression = LinearRegression()\n",
        "# fitting the model(training the model)\n",
        "linear_regression.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WJt3RU1LZmo"
      },
      "source": [
        "Making the prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "-zmPu1RFLpDo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef1779ca-332d-4968-e9ed-7651dd39fe1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[93.02672293],\n",
              "       [82.29957806],\n",
              "       [71.57243319],\n",
              "       [50.11814346],\n",
              "       [60.84528833]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "y_predict = linear_regression.predict(x_test)\n",
        "y_predict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDHUFGvaLxzh"
      },
      "source": [
        "Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJCvtfWCL0UB",
        "outputId": "fa70b995-0064-4ffc-cc80-c330d0d9b891"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.19547188853278752\n"
          ]
        }
      ],
      "source": [
        "print(linear_regression.score(x_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtwlXw-6MhAI",
        "outputId": "4ba0b337-357c-4308-d9a5-4c22412d51a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[93.02672293]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "my_value = x_test.iloc[0,0]\n",
        "y_predict_new = linear_regression.predict(my_value.reshape(1,-1))\n",
        "print(y_predict_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "829XlcM2cQaP",
        "outputId": "49bd60ca-c04b-4e51-d335-64fed3589dfb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Check whether your prediction is aligned with the true value or not\n",
        "plt.scatter(x_test, y_test, c='red', label='Actual')\n",
        "plt.plot(x_test, y_predict, c='blue', label='Predicted')\n",
        "plt.title('Plot to check the prediction')\n",
        "plt.xlabel('x_test')\n",
        "plt.ylabel('y')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict_new[0][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REi-FEIjk0RT",
        "outputId": "fa853673-a941-488c-de27-57368e73efe4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.02672292545711"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_my_score(hours_of_study):\n",
        "  input_arr = [[hours_of_study]]\n",
        "  y_predict_new = linear_regression.predict(input_arr)\n",
        "  return int(y_predict_new[0][0])\n",
        "predict_my_score(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JS-ceSqykX-s",
        "outputId": "a87a2fdf-8f3c-4fdc-fc8b-26f5795211ec"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VB4zy6zSmZFo",
        "outputId": "bec0c470-ce95-443c-ecd5-7724ce52a190"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting gradio\n",
            "  Downloading gradio-4.37.2-py3-none-any.whl (12.3 MB)\n",
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            "\u001b[?25hCollecting aiofiles<24.0,>=22.0 (from gradio)\n",
            "  Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: altair<6.0,>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (4.2.2)\n",
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            "\u001b[?25hCollecting ffmpy (from gradio)\n",
            "  Downloading ffmpy-0.3.2.tar.gz (5.5 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting gradio-client==1.0.2 (from gradio)\n",
            "  Downloading gradio_client-1.0.2-py3-none-any.whl (318 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.2/318.2 kB\u001b[0m \u001b[31m35.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting httpx>=0.24.1 (from gradio)\n",
            "  Downloading httpx-0.27.0-py3-none-any.whl (75 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.23.4)\n",
            "Requirement already satisfied: importlib-resources<7.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from gradio) (6.4.0)\n",
            "Requirement already satisfied: jinja2<4.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.1.4)\n",
            "Requirement already satisfied: markupsafe~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.1.5)\n",
            "Requirement already satisfied: matplotlib~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.7.1)\n",
            "Requirement already satisfied: numpy<3.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (1.25.2)\n",
            "Collecting orjson~=3.0 (from gradio)\n",
            "  Downloading orjson-3.10.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, uvloop, ujson, tomlkit, semantic-version, ruff, python-multipart, python-dotenv, orjson, httptools, h11, dnspython, aiofiles, watchfiles, uvicorn, starlette, httpcore, email_validator, httpx, gradio-client, fastapi-cli, fastapi, gradio\n",
            "Successfully installed aiofiles-23.2.1 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.0 fastapi-cli-0.0.4 ffmpy-0.3.2 gradio-4.37.2 gradio-client-1.0.2 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 orjson-3.10.6 pydub-0.25.1 python-dotenv-1.0.1 python-multipart-0.0.9 ruff-0.5.0 semantic-version-2.10.0 starlette-0.37.2 tomlkit-0.12.0 ujson-5.10.0 uvicorn-0.30.1 uvloop-0.19.0 watchfiles-0.22.0 websockets-11.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n"
      ],
      "metadata": {
        "id": "KrJ_CTeUmMum"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interface = gr.Interface(predict_my_score, inputs = \"number\", outputs = \"number\")\n",
        "interface.launch(debug = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "BMxWguGTmmt_",
        "outputId": "aa718960-0f8a-4e47-b0ae-84480114462b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://770a3f8c0b6a49846a.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://770a3f8c0b6a49846a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://770a3f8c0b6a49846a.gradio.live\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle as pk\n",
        "pk.dump(linear_regression, open(\"score_predict.pkl\", \"wb\"))"
      ],
      "metadata": {
        "id": "Z9VhixulrE8z"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = pk.load(open(\"score_predict.pkl\", \"rb\"), encoding=\"bytes\")\n",
        "loaded_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "vI2WUJhfsJC3",
        "outputId": "a2d83c5f-f3df-4459-9b56-6dda1408a7f5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_my_score(hours_of_study):\n",
        "  input_arr = [[hours_of_study]]\n",
        "  y_predict_new = loaded_model.predict(input_arr)\n",
        "  return int(y_predict_new[0][0])\n",
        "predict_my_score(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldfNZjEDsrD4",
        "outputId": "04678260-2395-4a58-acc7-463ed1a2ab46"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}