2.Using_a_Model.ipynb

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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "text/plain": [
              "   boot_size  harness_size     sex  age_years\n",
              "0         39            58    male       12.0\n",
              "1         38            58    male        9.6\n",
              "2         37            52  female        8.6\n",
              "3         39            58    male       10.2\n",
              "4         38            57    male        7.8"
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          "metadata": {},
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      ],
      "source": [
        "import pandas\n",
        "!pip install statsmodels\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/graphing.py\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/doggy-boot-harness.csv\n",
        "\n",
        "# Load a file containing dog's boot and harness sizes\n",
        "data = pandas.read_csv('doggy-boot-harness.csv')\n",
        "\n",
        "# Print the first few rows\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf\n",
        "\n",
        "# Fit a simple model that finds a linear relationship\n",
        "# between boot size and harness size, which we can use later\n",
        "# to predict a dog's boot size, given their harness size\n",
        "model = smf.ols(formula = \"boot_size ~ harness_size\", data = data).fit()\n",
        "\n",
        "print(\"Model trained!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0KVrV-TFqO4",
        "outputId": "0db0be34-db1d-4538-c55f-80ec15148d0d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model trained!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "model_filename = './avalanche_dog_boot_model.pkl'\n",
        "joblib.dump(model, model_filename)\n",
        "\n",
        "print(\"Model saved!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyR0aVosHooi",
        "outputId": "c8851e99-38f3-4969-8dcb-3b0a80fdeead"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_loaded = joblib.load(model_filename)\n",
        "\n",
        "print(\"We have loaded a model with the following parameters:\")\n",
        "print(model_loaded.params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R41NR0stHrUU",
        "outputId": "9cd799bc-e2bf-42f9-fa03-ba05db332ca8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have loaded a model with the following parameters:\n",
            "Intercept       5.719110\n",
            "harness_size    0.585925\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's write a function that loads and uses our model\n",
        "def load_model_and_predict(harness_size):\n",
        "    '''\n",
        "    This function loads a pretrained model. It uses the model\n",
        "    with the customer's dog's harness size to predict the size of\n",
        "    boots that will fit that dog.\n",
        "\n",
        "    harness_size: The dog harness size, in cm \n",
        "    '''\n",
        "\n",
        "    # Load the model from file and print basic information about it\n",
        "    loaded_model = joblib.load(model_filename)\n",
        "\n",
        "    print(\"We've loaded a model with the following parameters:\")\n",
        "    print(loaded_model.params)\n",
        "\n",
        "    # Prepare data for the model\n",
        "    inputs = {\"harness_size\":[harness_size]} \n",
        "\n",
        "    # Use the model to make a prediction\n",
        "    predicted_boot_size = loaded_model.predict(inputs)[0]\n",
        "\n",
        "    return predicted_boot_size\n",
        "\n",
        "# Practice using our model\n",
        "predicted_boot_size = load_model_and_predict(45)\n",
        "\n",
        "print(\"Predicted dog boot size:\", predicted_boot_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BF3ubyJIBV_",
        "outputId": "aa55e6fe-6d2e-495d-8c29-69d0c26bfa47"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We've loaded a model with the following parameters:\n",
            "Intercept       5.719110\n",
            "harness_size    0.585925\n",
            "dtype: float64\n",
            "Predicted dog boot size: 32.08575356590478\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check_size_of_boots(selected_harness_size, selected_boot_size):\n",
        "    '''\n",
        "    Calculates whether the customer has chosen a pair of doggy boots that \n",
        "    are a sensible size. This works by estimating the dog's actual boot \n",
        "    size from their harness size.\n",
        "\n",
        "    This returns a message for the customer that should be shown before\n",
        "    they complete their payment \n",
        "\n",
        "    selected_harness_size: The size of the harness the customer wants to buy\n",
        "    selected_boot_size: The size of the doggy boots the customer wants to buy\n",
        "    '''\n",
        "\n",
        "    # Estimate the customer's dog's boot size\n",
        "    estimated_boot_size = load_model_and_predict(selected_harness_size)\n",
        "\n",
        "    # Round to the nearest whole number because we don't sell partial sizes\n",
        "    estimated_boot_size = int(round(estimated_boot_size))\n",
        "\n",
        "    # Check if the boot size selected is appropriate\n",
        "    if selected_boot_size == estimated_boot_size:\n",
        "        # The selected boots are probably OK\n",
        "        return \"Great choice! We think these boots will fit your avalanche dog well.\"\n",
        "\n",
        "    if selected_boot_size < estimated_boot_size:\n",
        "        # Selected boots might be too small \n",
        "        return \"The boots you have selected might be TOO SMALL for a dog as \"\\\n",
        "               f\"big as yours. We recommend a doggy boots size of {estimated_boot_size}.\"\n",
        "\n",
        "    if selected_boot_size > estimated_boot_size:\n",
        "        # Selected boots might be too big \n",
        "        return \"The boots you have selected might be TOO BIG for a dog as \"\\\n",
        "               f\"small as yours. We recommend a doggy boots size of {estimated_boot_size}.\"\n",
        "    \n",
        "\n",
        "# Practice using our new warning system\n",
        "check_size_of_boots(selected_harness_size=55, selected_boot_size=38)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "RB9sZx4iPWx_",
        "outputId": "42c2ffa7-667f-4bbd-a74e-45c897bae83d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We've loaded a model with the following parameters:\n",
            "Intercept       5.719110\n",
            "harness_size    0.585925\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Great choice! We think these boots will fit your avalanche dog well.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
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