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              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
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                "text": [
                  "Coeficiente estimado: 0.89\n",
                  "Intercepto estimado: 0.00\n",
                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Predecir default de clientes, probabilidad de compra o éxito de inversión.\n",
                  "- Analizar cómo cambios en la variable independiente afectan la probabilidad de un evento.\n",
                  "- Útil para scoring crediticio, marketing y decisiones de riesgo.\n"
                ]
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              "<function __main__.simulador_regresion_logistica(n=100, coef=1.0, intercepto=0.0, ruido=1.0)>"
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "\n",
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simulador_regresion_logistica</b><br/>def simulador_regresion_logistica(n=100, coef=1.0, intercepto=0.0, ruido=1.0)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_3274/608637907.py</a>Simula una regresión logística y visualiza probabilidades.\n",
              "\n",
              "Parámetros:\n",
              "- n: número de observaciones\n",
              "- coef: coeficiente de la variable independiente\n",
              "- intercepto: intercepto del modelo\n",
              "- ruido: desviación del ruido para simular datos reales</pre></div>"
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      ],
      "source": [
        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import interact, FloatSlider, IntSlider\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# 🔹 Función simuladora de regresión logística\n",
        "def simulador_regresion_logistica(n=100, coef=1.0, intercepto=0.0, ruido=1.0):\n",
        "    \"\"\"\n",
        "    Simula una regresión logística y visualiza probabilidades.\n",
        "\n",
        "    Parámetros:\n",
        "    - n: número de observaciones\n",
        "    - coef: coeficiente de la variable independiente\n",
        "    - intercepto: intercepto del modelo\n",
        "    - ruido: desviación del ruido para simular datos reales\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar variable independiente\n",
        "    X = np.linspace(-10, 10, n).reshape(-1,1)\n",
        "\n",
        "    # 🔹 Generar probabilidad usando función logística\n",
        "    logits = intercepto + coef*X.flatten() + np.random.normal(0, ruido, n)\n",
        "    prob = 1 / (1 + np.exp(-logits))\n",
        "\n",
        "    # 🔹 Generar variable binaria según probabilidad\n",
        "    Y = np.random.binomial(1, prob)\n",
        "\n",
        "    # 🔹 Ajustar modelo de regresión logística\n",
        "    modelo = LogisticRegression()\n",
        "    modelo.fit(X, Y)\n",
        "    prob_pred = modelo.predict_proba(X)[:,1]\n",
        "\n",
        "    # 🔹 Visualización\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.scatter(X, Y, color='blue', alpha=0.6, label='Datos simulados')\n",
        "    plt.plot(X, prob_pred, color='red', linewidth=2, label='Probabilidades predichas')\n",
        "    plt.xlabel('Variable Independiente')\n",
        "    plt.ylabel('Probabilidad / Clase')\n",
        "    plt.title('Simulación de Regresión Logística')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(f\"Coeficiente estimado: {modelo.coef_[0][0]:.2f}\")\n",
        "    print(f\"Intercepto estimado: {modelo.intercept_[0]:.2f}\")\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Predecir default de clientes, probabilidad de compra o éxito de inversión.\")\n",
        "    print(\"- Analizar cómo cambios en la variable independiente afectan la probabilidad de un evento.\")\n",
        "    print(\"- Útil para scoring crediticio, marketing y decisiones de riesgo.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    simulador_regresion_logistica,\n",
        "    n=IntSlider(value=100, min=20, max=500, step=10, description='N° Observaciones'),\n",
        "    coef=FloatSlider(value=1.0, min=-5, max=5, step=0.1, description='Coeficiente'),\n",
        "    intercepto=FloatSlider(value=0.0, min=-5, max=5, step=0.1, description='Intercepto'),\n",
        "    ruido=FloatSlider(value=1.0, min=0, max=5, step=0.1, description='Ruido')\n",
        ")"
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