Plotting/Poly-LR-Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e4bb8a8",
   "metadata": {},
   "source": [
    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# Polynomial Linear Regression Plot Made Easy With Seaborn\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/polynomial-linear-regression-plot)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/posts/avi-chawla_python-datascience-activity-6993881347991703552-61DK?utm_source=share&utm_medium=member_desktop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4aa9fb58",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-01T20:48:32.491239Z",
     "start_time": "2022-12-01T20:48:30.603126Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6b99a6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-01T20:48:43.941242Z",
     "start_time": "2022-12-01T20:48:43.918692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>-19.778319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.176471</td>\n",
       "      <td>-20.439002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.352941</td>\n",
       "      <td>-20.849964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.529412</td>\n",
       "      <td>-21.727722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.705882</td>\n",
       "      <td>-22.769604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x          y\n",
       "0  3.000000 -19.778319\n",
       "1  3.176471 -20.439002\n",
       "2  3.352941 -20.849964\n",
       "3  3.529412 -21.727722\n",
       "4  3.705882 -22.769604"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 35\n",
    "x = np.linspace(3, 9, n)\n",
    "y = 0.8*x**2 - 10*x + 3 + 1*np.random.random(n)\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df['x'] = x\n",
    "df['y'] = y\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cea106ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-01T20:48:49.979009Z",
     "start_time": "2022-12-01T20:48:49.740211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd6305dfb80>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", \n",
    "           y=\"y\", \n",
    "           data=df, \n",
    "           order=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d42652f",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}