Homework Day 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "300b64ba",
   "metadata": {},
   "source": [
    "# HOMEWORK"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4243f3de",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "1. Develop the code where you can show the use of break and continue\n",
    "statement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b10e4c2",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"> Break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ac7dad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "j\n",
      "u\n",
      "p\n",
      "The end\n"
     ]
    }
   ],
   "source": [
    "for a in \"jupyter\":\n",
    "    if a == \"y\":\n",
    "        break\n",
    "    print(a)\n",
    "\n",
    "print(\"The end\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb6b5987",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">Continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac4321f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "j\n",
      "u\n",
      "p\n",
      "t\n",
      "e\n",
      "r\n",
      "The end\n"
     ]
    }
   ],
   "source": [
    "for a in \"jupyter\":\n",
    "    if a == \"y\":\n",
    "        continue\n",
    "    print(a)\n",
    "\n",
    "print(\"The end\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ec0636",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "2. Create program on <br>\n",
    "- Program to input, append and print the list element. <br>\n",
    "- Program – How do I get last element of list. <br>\n",
    "- Find the index of an item in the list. <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21f8950",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\\\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cd27fae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter number of elements : 5\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "lst = []\n",
    "number = int(input(\"Enter number of elements : \"))\n",
    "\n",
    "for h in range(0, number):\n",
    "    element = int(input())\n",
    "  \n",
    "    lst.append(element)\n",
    "      \n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a17fa7",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">Program – How do I get last element of list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5197556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The last element of the list is  0\n",
      "The index of 4 is 3\n"
     ]
    }
   ],
   "source": [
    "list = [1,2,3,4,5,6,7,8,9,0]\n",
    "last_element = list[-1]\n",
    "print(\"The last element of the list is \",last_element)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62bc044",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">Find the index of an item in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8dc3a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The index of 4 is\",list.index(4))"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "ad6432d0",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "3. Build Linear regression model on following dataset.\n",
    "</div>\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f729d617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 71]\n",
      " [ 89]\n",
      " [ 64]\n",
      " [ 74]\n",
      " [ 87]\n",
      " [ 93]\n",
      " [ 79]\n",
      " [ 81]\n",
      " [ 75]\n",
      " [ 72]\n",
      " [ 70]\n",
      " [ 66]\n",
      " [ 81]\n",
      " [ 78]\n",
      " [110]] [ 51  66  45  57  64  75  61  62  55  53  48  46  63  60 100]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "waist_cm = np.array([71,89,64,74,87,93,79,81,75,72,70,66,81,78,110]).reshape((-1, 1))\n",
    "weight_kg = np.array([51,66,45,57,64,75,61,62,55,53,48,46,63,60,100])\n",
    "print(waist_cm,weight_kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56fe3834",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression().fit(waist_cm, weight_kg)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a25767a",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg_pred = model.predict(waist_cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5db5fb25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(waist_cm,weight_kg)\n",
    "plt.plot(waist_cm,weight_kg_pred, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3108bce0",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">And also calculate the Standard Deviation for Weight_Kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "527236ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.21009714826756"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(weight_kg)"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}