machinelearning

LinearRegressionSciLearn-checkpoint.ipynb (22127B)

---

 {
  "cells": [
   {
    "cell_type": "code",
    "execution_count": 9,
    "id": "997cfcd4-1cae-4e39-bc98-d74fee9c15de",
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from sklearn.linear_model import LinearRegression"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "id": "9cb40f1e-4273-4a7e-80d2-91bde63c1b5b",
    "metadata": {},
    "outputs": [],
    "source": [
     "data_root = 'https://github.com/ageron/data/raw/main/'\n",
     "lifesat = pd.read_csv(data_root + 'lifesat/lifesat.csv')\n",
     "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
     "y = lifesat[['Life satisfaction']].values"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "id": "6dab85c8-17f4-486a-9c0b-9539b5a31afc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "lifesat.plot(kind='scatter', grid=True,\n",
     "             x='GDP per capita (USD)', y='Life satisfaction')\n",
     "plt.axis([23_500, 62_500, 4, 9])\n",
     "plt.show()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "notebook",
    "language": "python",
    "name": "notebook"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.2"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
 }