29 Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e05d4b5-4db6-4395-985f-dfcecdfd5de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1d8dc23-4767-48b9-8393-44606fe4b7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# heights in cms\n",
    "x = np.array([[151],[174],[138],[186],[128],[136],[179],[163],[152],[131]])\n",
    "\n",
    "# weights in kgs\n",
    "y = np.array([63, 81, 56, 91, 47, 57, 76, 72, 62, 48])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb54f74a-ba2a-4270-bf00-9b246e8963f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x75e048867130>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Height vs Weight')\n",
    "plt.xlabel('Height')\n",
    "plt.ylabel('Weight')\n",
    "plt.grid()\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29ae814b-933e-49d9-aae5-f0a519ba0eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the class\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dedfc2a-f49f-46e6-a4fa-54e361e9875f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the object\n",
    "regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bad5a246-bfdb-43c7-8655-d4a6165fce9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the algorithm\n",
    "regressor.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f845e279-d894-4350-ac3d-514936517914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.67461045])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value of m\n",
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a92a3903-58c0-4069-b58e-4a0fc44fc9e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-38.45508707607699"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value of c\n",
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c285cb86-77af-4e6e-bab9-884e1fec55b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.467002923923"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "200 * 0.67461045 - 38.45508707607699"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36963523-085d-49d6-80d3-df2db6d24348",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_new = x * 0.67461045 - 38.45508707607699"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "10762286-0d89-46e1-b645-a67bf8862f18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x75e025421090>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Height vs Weight')\n",
    "plt.xlabel('Height')\n",
    "plt.ylabel('Weight')\n",
    "plt.grid()\n",
    "plt.scatter(x, y, color = 'red')\n",
    "plt.plot(x, y_new, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "605919cd-e821-4e84-858f-3b96ed05f0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed70fa97-95b8-4bd3-a25c-b8510fd31d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>63.411091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>78.927131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56</td>\n",
       "      <td>54.641155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>87.022457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>47.895051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>53.291934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76</td>\n",
       "      <td>82.300183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72</td>\n",
       "      <td>71.506416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62</td>\n",
       "      <td>64.085701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48</td>\n",
       "      <td>49.918882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actual  predicted\n",
       "0      63  63.411091\n",
       "1      81  78.927131\n",
       "2      56  54.641155\n",
       "3      91  87.022457\n",
       "4      47  47.895051\n",
       "5      57  53.291934\n",
       "6      76  82.300183\n",
       "7      72  71.506416\n",
       "8      62  64.085701\n",
       "9      48  49.918882"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame({\n",
    "    'actual': y,\n",
    "    'predicted': y_new.flatten()\n",
    "})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "648dc106-4a97-48b3-a8bd-557c32f362fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[63.41109087],\n",
       "       [78.92713122],\n",
       "       [54.64115502],\n",
       "       [87.02245662],\n",
       "       [47.89505052],\n",
       "       [53.29193412],\n",
       "       [82.30018347],\n",
       "       [71.50641627],\n",
       "       [64.08570132],\n",
       "       [49.91888187]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ead29ca6-bb96-4653-8072-bbd289724ca5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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