{ "cells": [ { "cell_type": "markdown", "id": "799f31eb", "metadata": {}, "source": [ "线性回归预测的是连续数值，逻辑回归预测的是离散数值。" ] }, { "cell_type": "markdown", "id": "fc3c8eef", "metadata": {}, "source": [ "线性回归： \n", "1.预测天气 \n", "2.预测股票 \n", "3.预测房价 \n", "逻辑回归： \n", "1.邮件是否是垃圾邮件 \n", "2.猫狗分类 \n", "3.是否贷款" ] }, { "cell_type": "code", "execution_count": 2, "id": "0b62f4a6", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "efe20171", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"insurance_data.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "a0336e69", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agebought_insurance
0220
1250
2471
3520
4461
\n", "
" ], "text/plain": [ " age bought_insurance\n", "0 22 0\n", "1 25 0\n", "2 47 1\n", "3 52 0\n", "4 46 1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "50fb949d", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 7, "id": "e77e37f4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAeKElEQVR4nO3df3TV9X348VcC5AbBBCySAEahqy065EdBs5R6ejozc5yH1f06OdYVDv2xo6MOzXYmaRXadTWsHY71wMyk7dqdzUH1TNdWi2OxsOOalRHGqW4WpdLBURPgbOZi1MQln+8ffr2aAjYXE98JPB7n3GP43Pfn3vfH903u83zuzU1JlmVZAAAkUpp6AgDA2U2MAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUuNTT2AoBgYG4rnnnotzzz03SkpKUk8HABiCLMvi+PHjMXPmzCgtPfX5jzERI88991zU1NSkngYAcBoOHz4cF1xwwSmvHxMxcu6550bEawdTUVGReDYAwFDk8/moqakpPI+fypiIkddfmqmoqBAjADDG/Ly3WHgDKwCQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkFTRMfIv//IvsWzZspg5c2aUlJTEgw8++HP32blzZ7z//e+PXC4X73nPe+Ib3/jGaUyVd1RPT0RJyWuXnp7Us+FMdSY9zoZ6LMM9LuUcUxqJYxnu4x4LazhK1rroGOnp6YkFCxbE5s2bhzT+4MGDce2118aHP/zh2LdvX9xyyy3xyU9+Mh555JGiJwsAnHmK/ts011xzTVxzzTVDHt/a2hpz5syJDRs2RETEJZdcEo899lj8+Z//eTQ0NBR794y018v4zYX85q8nTXpn58OZ6Ux6nA31WIZ7XMo5pjQSxzLcxz0W1nCUrfWI/6G89vb2qK+vH7StoaEhbrnlllPu09vbG729vYV/5/P5kZoeP2vy5BO3VVW98XWWvXNz4cx1Jj3Ohnoswz0u5RxTGoljGe7jHgtrOMrWesTfwNrZ2RlVbz7AiKiqqop8Ph8vv/zySfdpaWmJysrKwqWmpmakpwkAJDLiZ0ZOR3NzczQ1NRX+nc/nBck75cUXX/tvT88bldzVNTpOz3LmOJMeZ0M9luEel3KOKY3EsQz3cY+FNRxlaz3iMVJdXR1dXV2DtnV1dUVFRUVMnDjxpPvkcrnI5XIjPTVO5mQPxEmTRtcPI8a+M+lxNtRjGe5xKeeY0kgcy3Af91hYw1G21iP+Mk1dXV20tbUN2rZjx46oq6sb6bsGAMaAos+MvPjii3HgwIHCvw8ePBj79u2L8847Ly688MJobm6OZ599Nv7mb/4mIiJuvPHG2LRpU/zRH/1RfPzjH49HH300vvWtb8VDDz00fEfB8Js0aXS8WY0z25n0OBvqsQz3uGKkvO/hNhLHMtzHPRbWcJSsddFnRvbs2ROLFi2KRYsWRUREU1NTLFq0KNauXRsREc8//3wcOnSoMH7OnDnx0EMPxY4dO2LBggWxYcOG+OpXv+rXegGAiIgoybJRkEQ/Rz6fj8rKyuju7o6KiorU0wEAhmCoz9/+Ng0AkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJDUacXI5s2bY/bs2VFeXh61tbWxe/futxy/cePGeN/73hcTJ06MmpqauPXWW+OVV145rQkDAGeWomNk27Zt0dTUFOvWrYu9e/fGggULoqGhIY4cOXLS8ffee2+sWbMm1q1bF08++WR87Wtfi23btsVnPvOZtz15AGDsKzpG7rrrrvjUpz4VK1eujEsvvTRaW1vjnHPOia9//esnHf+DH/wgli5dGh/96Edj9uzZcfXVV8f111//c8+mAABnh6JipK+vLzo6OqK+vv6NGygtjfr6+mhvbz/pPh/4wAeio6OjEB/PPPNMPPzww/Grv/qrp7yf3t7eyOfzgy4AwJlpfDGDjx07Fv39/VFVVTVoe1VVVfz4xz8+6T4f/ehH49ixY/HBD34wsiyL//u//4sbb7zxLV+maWlpic9//vPFTA0AGKNG/Ldpdu7cGXfeeWf85V/+Zezduzf+4R/+IR566KH4whe+cMp9mpubo7u7u3A5fPjwSE8TAEikqDMj06ZNi3HjxkVXV9eg7V1dXVFdXX3Sfe6444742Mc+Fp/85CcjIuKyyy6Lnp6e+N3f/d347Gc/G6WlJ/ZQLpeLXC5XzNQAgDGqqDMjZWVlsXjx4mhraytsGxgYiLa2tqirqzvpPi+99NIJwTFu3LiIiMiyrNj5AgBnmKLOjERENDU1xYoVK2LJkiVxxRVXxMaNG6OnpydWrlwZERHLly+PWbNmRUtLS0RELFu2LO66665YtGhR1NbWxoEDB+KOO+6IZcuWFaIEADh7FR0jjY2NcfTo0Vi7dm10dnbGwoULY/v27YU3tR46dGjQmZDbb789SkpK4vbbb49nn302zj///Fi2bFl88YtfHL6jAADGrJJsDLxWks/no7KyMrq7u6OioiL1dACAIRjq87e/TQMAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgqdOKkc2bN8fs2bOjvLw8amtrY/fu3W85/oUXXohVq1bFjBkzIpfLxXvf+954+OGHT2vCAMCZZXyxO2zbti2ampqitbU1amtrY+PGjdHQ0BD79++P6dOnnzC+r68vfuVXfiWmT58e999/f8yaNSv++7//O6ZMmTIc8wcAxriSLMuyYnaora2Nyy+/PDZt2hQREQMDA1FTUxM333xzrFmz5oTxra2t8eUvfzl+/OMfx4QJE05rkvl8PiorK6O7uzsqKipO6zYAgHfWUJ+/i3qZpq+vLzo6OqK+vv6NGygtjfr6+mhvbz/pPt/+9rejrq4uVq1aFVVVVTFv3ry48847o7+//5T309vbG/l8ftAFADgzFRUjx44di/7+/qiqqhq0vaqqKjo7O0+6zzPPPBP3339/9Pf3x8MPPxx33HFHbNiwIf7kT/7klPfT0tISlZWVhUtNTU0x0wQAxpAR/22agYGBmD59etxzzz2xePHiaGxsjM9+9rPR2tp6yn2am5uju7u7cDl8+PBITxMASKSoN7BOmzYtxo0bF11dXYO2d3V1RXV19Un3mTFjRkyYMCHGjRtX2HbJJZdEZ2dn9PX1RVlZ2Qn75HK5yOVyxUwNABijijozUlZWFosXL462trbCtoGBgWhra4u6urqT7rN06dI4cOBADAwMFLY99dRTMWPGjJOGCABwdin6ZZqmpqbYsmVLfPOb34wnn3wybrrppujp6YmVK1dGRMTy5cujubm5MP6mm26K//mf/4nVq1fHU089FQ899FDceeedsWrVquE7CgBgzCr6c0YaGxvj6NGjsXbt2ujs7IyFCxfG9u3bC29qPXToUJSWvtE4NTU18cgjj8Stt94a8+fPj1mzZsXq1avjtttuG76jAADGrKI/ZyQFnzMCAGPPiHzOCADAcBMjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQ1GnFyObNm2P27NlRXl4etbW1sXv37iHtt3Xr1igpKYnrrrvudO4WADgDFR0j27Zti6ampli3bl3s3bs3FixYEA0NDXHkyJG33O+nP/1p/OEf/mFceeWVpz1ZAODMU3SM3HXXXfGpT30qVq5cGZdeemm0trbGOeecE1//+tdPuU9/f3/ccMMN8fnPfz7e/e53v60JAwBnlqJipK+vLzo6OqK+vv6NGygtjfr6+mhvbz/lfn/8x38c06dPj0984hNDup/e3t7I5/ODLgDAmamoGDl27Fj09/dHVVXVoO1VVVXR2dl50n0ee+yx+NrXvhZbtmwZ8v20tLREZWVl4VJTU1PMNAGAMWREf5vm+PHj8bGPfSy2bNkS06ZNG/J+zc3N0d3dXbgcPnx4BGcJAKQ0vpjB06ZNi3HjxkVXV9eg7V1dXVFdXX3C+J/85Cfx05/+NJYtW1bYNjAw8Nodjx8f+/fvj1/4hV84Yb9cLhe5XK6YqQEAY1RRZ0bKyspi8eLF0dbWVtg2MDAQbW1tUVdXd8L4uXPnxuOPPx779u0rXH7t134tPvzhD8e+ffu8/AIAFHdmJCKiqakpVqxYEUuWLIkrrrgiNm7cGD09PbFy5cqIiFi+fHnMmjUrWlpaory8PObNmzdo/ylTpkREnLAdADg7FR0jjY2NcfTo0Vi7dm10dnbGwoULY/v27YU3tR46dChKS32wKwAwNCVZlmWpJ/Hz5PP5qKysjO7u7qioqEg9HQBgCIb6/O0UBgCQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkTitGNm/eHLNnz47y8vKora2N3bt3n3Lsli1b4sorr4ypU6fG1KlTo76+/i3HAwBnl6JjZNu2bdHU1BTr1q2LvXv3xoIFC6KhoSGOHDly0vE7d+6M66+/Pr7//e9He3t71NTUxNVXXx3PPvvs2548ADD2lWRZlhWzQ21tbVx++eWxadOmiIgYGBiImpqauPnmm2PNmjU/d//+/v6YOnVqbNq0KZYvXz6k+8zn81FZWRnd3d1RUVFRzHQBgESG+vxd1JmRvr6+6OjoiPr6+jduoLQ06uvro729fUi38dJLL8Wrr74a55133inH9Pb2Rj6fH3QBAM5MRcXIsWPHor+/P6qqqgZtr6qqis7OziHdxm233RYzZ84cFDQ/q6WlJSorKwuXmpqaYqYJAIwh7+hv06xfvz62bt0aDzzwQJSXl59yXHNzc3R3dxcuhw8ffgdnCQC8k8YXM3jatGkxbty46OrqGrS9q6srqqur33LfP/uzP4v169fHP//zP8f8+fPfcmwul4tcLlfM1ACAMaqoMyNlZWWxePHiaGtrK2wbGBiItra2qKurO+V+X/rSl+ILX/hCbN++PZYsWXL6swUAzjhFnRmJiGhqaooVK1bEkiVL4oorroiNGzdGT09PrFy5MiIili9fHrNmzYqWlpaIiPjTP/3TWLt2bdx7770xe/bswntLJk+eHJMnTx7GQwEAxqKiY6SxsTGOHj0aa9eujc7Ozli4cGFs37698KbWQ4cORWnpGydc7r777ujr64vf+q3fGnQ769ati8997nNvb/YAwJhX9OeMpOBzRgBg7BmRzxkBABhuYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCSEiMAQFJiBABISowAAEmJEQAgKTECACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkJUYAgKTECACQlBgBAJI6rRjZvHlzzJ49O8rLy6O2tjZ27979luPvu+++mDt3bpSXl8dll10WDz/88GlNdlgdORJRUvLa5ciRtx7b0/PG2J6et3/fQ729Yu53tM9xJI5luI8ZGDmpvl/9nBgTio6Rbdu2RVNTU6xbty727t0bCxYsiIaGhjhyiif0H/zgB3H99dfHJz7xifiP//iPuO666+K6666LJ5544m1PHgAY+0qyLMuK2aG2tjYuv/zy2LRpU0REDAwMRE1NTdx8882xZs2aE8Y3NjZGT09PfPe73y1s+6Vf+qVYuHBhtLa2Duk+8/l8VFZWRnd3d1RUVBQz3RO9Hk1Hj0bMm/fa1088EXH++a99PX36G2Nfr+ienoiqqte+7uqKmDTpta9f/+9QDfX2irnf0T7HkTiW4T5mYOSk+n71c2JUGOrz9/hibrSvry86Ojqiubm5sK20tDTq6+ujvb39pPu0t7dHU1PToG0NDQ3x4IMPnvJ+ent7o7e3t/DvfD5fzDTf2usPyjd7PUoiIt7cZpMnv/X+xXXc0G+vmPsd7XMciWMZ7mMGRk6q71c/J8aUol6mOXbsWPT390fVzzyhV1VVRWdn50n36ezsLGp8RERLS0tUVlYWLjU1NcVMEwAYQ4o6M/JOaW5uHnQ2JZ/PD1+QdHW99t9TvUzzZi+++Np/T3War1hDvb1i7ne0z3EkjmW4jxkYOam+X/2cGFOKipFp06bFuHHjouv1J/T/r6urK6qrq0+6T3V1dVHjIyJyuVzkcrlipjZ0b35PyOvOP//k20/2oJ006fQfzEO9vWLud7TPcSSOZbiPGRg5qb5f/ZwYU4p6maasrCwWL14cbW1thW0DAwPR1tYWdXV1J92nrq5u0PiIiB07dpxyPABwdin6ZZqmpqZYsWJFLFmyJK644orYuHFj9PT0xMqVKyMiYvny5TFr1qxoaWmJiIjVq1fHhz70odiwYUNce+21sXXr1tizZ0/cc889w3skxZo+fehvYJo0aXjf7DTU2yvmfkf7HEfiWIb7mIGRk+r71c+JMaHoGGlsbIyjR4/G2rVro7OzMxYuXBjbt28vvEn10KFDUVr6xgmXD3zgA3HvvffG7bffHp/5zGfi4osvjgcffDDmvfk3WACAs1bRnzOSwrB+zggA8I4Y6vO3v00DACQlRgCApMQIAJCUGAEAkhIjAEBSYgQASEqMAABJiREAICkxAgAkVfTHwafw+ofE5vP5xDMBAIbq9eftn/dh72MiRo4fPx4RETU1NYlnAgAU6/jx41FZWXnK68fE36YZGBiI5557Ls4999woKSlJPZ23lM/no6amJg4fPuzv6Iwi1mX0sjajk3UZvcbS2mRZFsePH4+ZM2cO+iO6P2tMnBkpLS2NCy64IPU0ilJRUTHqHyRnI+syelmb0cm6jF5jZW3e6ozI67yBFQBISowAAEmJkWGWy+Vi3bp1kcvlUk+FN7Euo5e1GZ2sy+h1Jq7NmHgDKwBw5nJmBABISowAAEmJEQAgKTECACQlRk5DS0tLXH755XHuuefG9OnT47rrrov9+/cPGvPKK6/EqlWr4l3veldMnjw5fvM3fzO6uroSzfjscffdd8f8+fMLHwZUV1cX3/ve9wrXW5fRYf369VFSUhK33HJLYZu1SeNzn/tclJSUDLrMnTu3cL11SefZZ5+N3/md34l3vetdMXHixLjssstiz549heuzLIu1a9fGjBkzYuLEiVFfXx9PP/10whmfPjFyGnbt2hWrVq2Kf/u3f4sdO3bEq6++GldffXX09PQUxtx6663xne98J+67777YtWtXPPfcc/Ebv/EbCWd9drjgggti/fr10dHREXv27Ilf/uVfjo985CPxn//5nxFhXUaDf//3f4+/+qu/ivnz5w/abm3S+cVf/MV4/vnnC5fHHnuscJ11SeN///d/Y+nSpTFhwoT43ve+F//1X/8VGzZsiKlTpxbGfOlLX4qvfOUr0draGj/84Q9j0qRJ0dDQEK+88krCmZ+mjLftyJEjWURku3btyrIsy1544YVswoQJ2X333VcY8+STT2YRkbW3t6ea5llr6tSp2Ve/+lXrMgocP348u/jii7MdO3ZkH/rQh7LVq1dnWeZ7JqV169ZlCxYsOOl11iWd2267LfvgBz94yusHBgay6urq7Mtf/nJh2wsvvJDlcrns7//+79+JKQ4rZ0aGQXd3d0REnHfeeRER0dHREa+++mrU19cXxsydOzcuvPDCaG9vTzLHs1F/f39s3bo1enp6oq6uzrqMAqtWrYprr7120BpE+J5J7emnn46ZM2fGu9/97rjhhhvi0KFDEWFdUvr2t78dS5Ysid/+7d+O6dOnx6JFi2LLli2F6w8ePBidnZ2D1qaysjJqa2vH5NqIkbdpYGAgbrnllli6dGnMmzcvIiI6OzujrKwspkyZMmhsVVVVdHZ2Jpjl2eXxxx+PyZMnRy6XixtvvDEeeOCBuPTSS61LYlu3bo29e/dGS0vLCddZm3Rqa2vjG9/4Rmzfvj3uvvvuOHjwYFx55ZVx/Phx65LQM888E3fffXdcfPHF8cgjj8RNN90Uv//7vx/f/OY3IyIK//+rqqoG7TdW12ZM/NXe0WzVqlXxxBNPDHqNlbTe9773xb59+6K7uzvuv//+WLFiRezatSv1tM5qhw8fjtWrV8eOHTuivLw89XR4k2uuuabw9fz586O2tjYuuuii+Na3vhUTJ05MOLOz28DAQCxZsiTuvPPOiIhYtGhRPPHEE9Ha2horVqxIPLvh58zI2/DpT386vvvd78b3v//9uOCCCwrbq6uro6+vL1544YVB47u6uqK6uvodnuXZp6ysLN7znvfE4sWLo6WlJRYsWBB/8Rd/YV0S6ujoiCNHjsT73//+GD9+fIwfPz527doVX/nKV2L8+PFRVVVlbUaJKVOmxHvf+944cOCA75mEZsyYEZdeeumgbZdccknhJbTX////7G82jdW1ESOnIcuy+PSnPx0PPPBAPProozFnzpxB1y9evDgmTJgQbW1thW379++PQ4cORV1d3Ts93bPewMBA9Pb2WpeErrrqqnj88cdj3759hcuSJUvihhtuKHxtbUaHF198MX7yk5/EjBkzfM8ktHTp0hM+MuKpp56Kiy66KCIi5syZE9XV1YPWJp/Pxw9/+MOxuTap30E7Ft10001ZZWVltnPnzuz5558vXF566aXCmBtvvDG78MILs0cffTTbs2dPVldXl9XV1SWc9dlhzZo12a5du7KDBw9mP/rRj7I1a9ZkJSUl2T/90z9lWWZdRpM3/zZNllmbVP7gD/4g27lzZ3bw4MHsX//1X7P6+vps2rRp2ZEjR7Issy6p7N69Oxs/fnz2xS9+MXv66aezv/u7v8vOOeec7G//9m8LY9avX59NmTIl+8d//MfsRz/6UfaRj3wkmzNnTvbyyy8nnPnpESOnISJOevnrv/7rwpiXX345+73f+71s6tSp2TnnnJP9+q//evb888+nm/RZ4uMf/3h20UUXZWVlZdn555+fXXXVVYUQyTLrMpr8bIxYmzQaGxuzGTNmZGVlZdmsWbOyxsbG7MCBA4XrrUs63/nOd7J58+ZluVwumzt3bnbPPfcMun5gYCC74447sqqqqiyXy2VXXXVVtn///kSzfXtKsizLUp6ZAQDObt4zAgAkJUYAgKTECACQlBgBAJISIwBAUmIEAEhKjAAASYkRACApMQIAJCVGAICkxAgAkJQYAQCS+n+c6suj7RLqHQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(df.age, df.bought_insurance, marker=\"+\", color='r')" ] }, { "cell_type": "code", "execution_count": 8, "id": "bf219340", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(27, 2)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 9, "id": "3a6ed8a7", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 10, "id": "c85065da", "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(df[['age']], df.bought_insurance, train_size=0.9, random_state=10)" ] }, { "cell_type": "code", "execution_count": 11, "id": "16437565", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24, 3)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(x_train), len(x_test)" ] }, { "cell_type": "code", "execution_count": 12, "id": "4d83ec8c", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 13, "id": "61312ccd", "metadata": {}, "outputs": [], "source": [ "lr = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 14, "id": "dac8d670", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr.fit(x_train, y_train)" ] }, { "cell_type": "code", "execution_count": 18, "id": "698480b6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([1, 1, 0], dtype=int64),\n", " 7 1\n", " 5 1\n", " 18 0\n", " Name: bought_insurance, dtype: int64)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr.predict(x_test), y_test" ] }, { "cell_type": "code", "execution_count": 15, "id": "89c91628", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr.score(x_test, y_test)" ] }, { "cell_type": "code", "execution_count": 19, "id": "452bb440", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.06470723, 0.93529277],\n", " [0.10327405, 0.89672595],\n", " [0.92775095, 0.07224905]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr.predict_proba(x_test)" ] }, { "cell_type": "code", "execution_count": 23, "id": "1246d4de", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\envs\\stark-lin\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n", " \"X does not have valid feature names, but\"\n", "D:\\envs\\stark-lin\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n", " \"X does not have valid feature names, but\"\n" ] }, { "data": { "text/plain": [ "(array([1], dtype=int64), array([0], dtype=int64))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr.predict([[53]]), lr.predict([[20]])" ] }, { "cell_type": "code", "execution_count": null, "id": "bc340fea", "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": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 5 }