Monte_Carlo_Simulation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP3E0eLLCt0mBsJjTCeHqEn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VinaySingh561/Machine-learning-and-statstics/blob/main/Monte_Carlo_Simulation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "bzoeSfLPxeXr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Problem  - You are supposed to submit two reports to your manager , each will take 1 to 5 hours and 2 to 6 hours respectively. And you have to attend a crucial family function in 9 hours. Will you be able to make it on time?"
      ],
      "metadata": {
        "id": "jubg49pLQgK0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sims = 100000\n",
        "report_1 = np.random.uniform(1,5,sims) ## first report\n",
        "report_2 = np.random.uniform(2,6,sims) ## second report\n",
        "duration = report_1 + report_2   ## total time on making reports"
      ],
      "metadata": {
        "id": "UBLUBp4QxnVp"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(duration)\n",
        "plt.axvline(9,color = 'red')\n",
        "plt.show()\n",
        "print(\"Prob that you won't be able to make it on time {:.2f} %\".format((duration > 9).sum()*100/sims))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "roFDb2vJx1nx",
        "outputId": "19631537-9621-4b92-806a-0d02ad9f685f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prob that you won't be able to make it on time 12.48 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R7sSp8xIx3y6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}