chapter1.ipynb

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        "<a href=\"https://colab.research.google.com/github/iupui-soic/pybootcamp/blob/main/chapter1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
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      "source": [
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "lifesat = pd.read_csv(data_root + \"lifesat/lifesat.csv\")\n",
        "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
        "y = lifesat[[\"Life satisfaction\"]].values\n"
      ],
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        "lifesat.head()"
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          "data": {
            "text/plain": [
              "   Country  GDP per capita (USD)  Life satisfaction\n",
              "0   Russia          26456.387938                5.8\n",
              "1   Greece          27287.083401                5.4\n",
              "2   Turkey          28384.987785                5.5\n",
              "3   Latvia          29932.493910                5.9\n",
              "4  Hungary          31007.768407                5.6"
            ],
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              "      <th>Country</th>\n",
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              "      <td>Russia</td>\n",
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              "      <td>5.8</td>\n",
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              "      <th>1</th>\n",
              "      <td>Greece</td>\n",
              "      <td>27287.083401</td>\n",
              "      <td>5.4</td>\n",
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              "      <th>2</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
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              "      <td>Latvia</td>\n",
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              "      <td>5.9</td>\n",
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              "      <th>4</th>\n",
              "      <td>Hungary</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lifesat",
              "summary": "{\n  \"name\": \"lifesat\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Spain\",\n          \"United Kingdom\",\n          \"Slovenia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9631.452318546564,\n        \"min\": 26456.3879381321,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          36215.4475907307,\n          41627.129269425,\n          36547.7389559849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7656068482934606,\n        \"min\": 5.4,\n        \"max\": 7.6,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.8,\n          6.1,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 14
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        "lifesat.info()"
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        "outputId": "138c2f64-7f91-4017-a145-0259ce352243"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27 entries, 0 to 26\n",
            "Data columns (total 3 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   Country               27 non-null     object \n",
            " 1   GDP per capita (USD)  27 non-null     float64\n",
            " 2   Life satisfaction     27 non-null     float64\n",
            "dtypes: float64(2), object(1)\n",
            "memory usage: 776.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lifesat.plot(kind='scatter', grid=True,\n",
        "             x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n",
        "plt.axis([23_500, 62_500, 4, 9])\n",
        "plt.show()"
      ],
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      "execution_count": 11,
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a linear model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "rohhr05C3p6a",
        "outputId": "f9ccadc9-0fc2-4526-f35d-28dcf7157f29"
      },
      "execution_count": 12,
      "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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t0, t1 = model.intercept_[0], model.coef_[0][0]\n",
        "print(f\"θ0={t0:.2f}, θ1={t1:.2e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4Ughod25Qrj",
        "outputId": "5f37bb67-705f-4a40-e81f-78ccddbf810f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "θ0=3.75, θ1=6.78e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lifesat.plot(kind='scatter', figsize=(5, 3), grid=True, x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n",
        "\n",
        "X = np.linspace(lifesat[\"GDP per capita (USD)\"].min(), lifesat[\"GDP per capita (USD)\"].max(), 1000)\n",
        "Y = t0 + t1 * X\n",
        "plt.plot(X, Y, \"b\")\n",
        "plt.text(0.95, 0.9, f\"$\\\\theta_0$ = {t0:.2f}\", transform=plt.gca().transAxes, fontsize=12, ha='right')\n",
        "plt.text(0.95, 0.85, f\"$\\\\theta_1$ = {t1:.2e}\", transform=plt.gca().transAxes, fontsize=12, ha='right')\n",
        "plt.xlabel(\"GDP per capita (USD)\")\n",
        "plt.ylabel(\"Life satisfaction\")\n",
        "plt.title(\"Relationship between GDP per capita and Life satisfaction\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "YDGIi5Ws5cZ3",
        "outputId": "bb025d08-2aa5-4340-ae10-0fce044fadfa"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a prediction for Cyprus\n",
        "X_new = [[37_655.2]]  # Cyprus' GDP per capita in 2020\n",
        "print(model.predict(X_new))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBrKW_kX4rxB",
        "outputId": "c9021d6d-9b54-4f6a-fe00-922dcafdd395"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.30165767]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Replacing the Linear Regression model with k-Nearest Neighbors regression"
      ],
      "metadata": {
        "id": "Y32_67vB-cCw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "model1 = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)"
      ],
      "metadata": {
        "id": "zy23k1vj-bJg"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.c_[lifesat[\"GDP per capita (USD)\"]].reshape(-1, 1)\n",
        "y = np.c_[lifesat[\"Life satisfaction\"]].reshape(-1, 1)\n",
        "\n",
        "# Select a k-Nearest Neighbors regression model\n",
        "model1 = KNeighborsRegressor(n_neighbors=3)\n",
        "# Train the model\n",
        "model1.fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "print(model1.predict(X_new))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6gdOb_9-pRX",
        "outputId": "12be5300-29cc-4297-b238-03985544c8d2"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.33333333]]\n"
          ]
        }
      ]
    }
  ]
}