Installation_Step.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "223bce7f-0ae0-4105-840f-6c52a82da666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.9.2-cp310-cp310-macosx_11_0_arm64.whl (7.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m0m\n",
      "\u001b[?25hCollecting fonttools>=4.22.0\n",
      "  Downloading fonttools-4.53.1-cp310-cp310-macosx_11_0_arm64.whl (2.2 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hCollecting contourpy>=1.0.1\n",
      "  Downloading contourpy-1.3.0-cp310-cp310-macosx_11_0_arm64.whl (249 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m249.2/249.2 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting pillow>=8\n",
      "  Downloading pillow-10.4.0-cp310-cp310-macosx_11_0_arm64.whl (3.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from matplotlib) (2.9.0.post0)\n",
      "Collecting pyparsing>=2.3.1\n",
      "  Downloading pyparsing-3.1.4-py3-none-any.whl (104 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.1/104.1 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.23 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from matplotlib) (2.1.1)\n",
      "Collecting kiwisolver>=1.3.1\n",
      "  Downloading kiwisolver-1.4.7-cp310-cp310-macosx_11_0_arm64.whl (64 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.3/64.3 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting cycler>=0.10\n",
      "  Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from matplotlib) (24.1)\n",
      "Requirement already satisfied: six>=1.5 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
      "Successfully installed contourpy-1.3.0 cycler-0.12.1 fonttools-4.53.1 kiwisolver-1.4.7 matplotlib-3.9.2 pillow-10.4.0 pyparsing-3.1.4\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8160046d-2c16-488a-b926-d5488b87639e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "xpts = np.array([0, 4])\n",
    "ypts = np.array([0, 100])\n",
    "\n",
    "plt.plot(xpts, ypts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "861607e2-b03e-4612-ba14-db350f183cdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-1.5.1-cp310-cp310-macosx_12_0_arm64.whl (11.0 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.0/11.0 MB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hCollecting scipy>=1.6.0\n",
      "  Downloading scipy-1.14.1-cp310-cp310-macosx_14_0_arm64.whl (23.1 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.1/23.1 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.19.5 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from scikit-learn) (2.1.1)\n",
      "Collecting threadpoolctl>=3.1.0\n",
      "  Downloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
      "Collecting joblib>=1.2.0\n",
      "  Downloading joblib-1.4.2-py3-none-any.whl (301 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.8/301.8 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
      "Successfully installed joblib-1.4.2 scikit-learn-1.5.1 scipy-1.14.1 threadpoolctl-3.5.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f947fa3-5bab-4f43-b382-43bc55146e88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ipykernel in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (6.29.5)\n",
      "Requirement already satisfied: packaging in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (24.1)\n",
      "Requirement already satisfied: debugpy>=1.6.5 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (1.8.5)\n",
      "Requirement already satisfied: nest-asyncio in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (1.6.0)\n",
      "Requirement already satisfied: comm>=0.1.1 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (0.2.2)\n",
      "Requirement already satisfied: psutil in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (6.0.0)\n",
      "Requirement already satisfied: pyzmq>=24 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (26.2.0)\n",
      "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (5.7.2)\n",
      "Requirement already satisfied: matplotlib-inline>=0.1 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (0.1.7)\n",
      "Requirement already satisfied: appnope in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (0.1.4)\n",
      "Requirement already satisfied: tornado>=6.1 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (6.4.1)\n",
      "Requirement already satisfied: traitlets>=5.4.0 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (5.14.3)\n",
      "Requirement already satisfied: ipython>=7.23.1 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (8.27.0)\n",
      "Requirement already satisfied: jupyter-client>=6.1.12 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipykernel) (8.6.2)\n",
      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (3.0.47)\n",
      "Requirement already satisfied: jedi>=0.16 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (0.19.1)\n",
      "Requirement already satisfied: stack-data in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (0.6.3)\n",
      "Requirement already satisfied: exceptiongroup in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (1.2.2)\n",
      "Requirement already satisfied: typing-extensions>=4.6 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (4.12.2)\n",
      "Requirement already satisfied: pexpect>4.3 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (4.9.0)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (2.18.0)\n",
      "Requirement already satisfied: decorator in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ipython>=7.23.1->ipykernel) (5.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from jupyter-client>=6.1.12->ipykernel) (2.9.0.post0)\n",
      "Requirement already satisfied: platformdirs>=2.5 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from jupyter-core!=5.0.*,>=4.12->ipykernel) (4.3.2)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from jedi>=0.16->ipython>=7.23.1->ipykernel) (0.8.4)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from pexpect>4.3->ipython>=7.23.1->ipykernel) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=7.23.1->ipykernel) (0.2.13)\n",
      "Requirement already satisfied: six>=1.5 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.12->ipykernel) (1.16.0)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from stack-data->ipython>=7.23.1->ipykernel) (2.4.1)\n",
      "Requirement already satisfied: executing>=1.2.0 in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from stack-data->ipython>=7.23.1->ipykernel) (2.1.0)\n",
      "Requirement already satisfied: pure-eval in /Users/shahinuralambhuiyan/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from stack-data->ipython>=7.23.1->ipykernel) (0.2.3)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install ipykernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a682877b-4694-42ee-a86c-49d1b4af7e87",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}