From 1c561f0fe2c5ebed1cca39a7ca5dc30e033656ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=94=B0=E7=86=A0?= <13190684+tyvw@user.noreply.gitee.com> Date: Sat, 15 Jul 2023 01:50:18 +0000 Subject: [PATCH] =?UTF-8?q?=E7=AC=AC=E4=B8=83=E7=BB=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 田熠 <13190684+tyvw@user.noreply.gitee.com> --- .../第七组/第七组实践代码/病马分类.ipynb | 822 ++++++++++++++++++ 1 file changed, 822 insertions(+) create mode 100644 大众点评数据集分析/第七组/第七组实践代码/病马分类.ipynb diff --git a/大众点评数据集分析/第七组/第七组实践代码/病马分类.ipynb b/大众点评数据集分析/第七组/第七组实践代码/病马分类.ipynb new file mode 100644 index 0000000..daef5d7 --- /dev/null +++ b/大众点评数据集分析/第七组/第七组实践代码/病马分类.ipynb @@ -0,0 +1,822 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "062c9f2b", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve,roc_auc_score\n", + "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier,AdaBoostClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "# 中文正常显示\n", + "plt.rcParams['font.sans-serif'] = ['SimHei']\n", + "plt.rcParams['axes.unicode_minus'] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37cfbc42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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直肠温度脉搏呼吸频率红细胞体积总蛋白值y
038.5662845.08.40.0
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" + ], + "text/plain": [ + " 直肠温度 脉搏 呼吸频率 红细胞体积 总蛋白值 y\n", + "0 38.5 66 28 45.0 8.4 0.0\n", + "1 39.2 88 20 50.0 85.0 0.0\n", + "2 38.3 40 24 33.0 6.7 1.0\n", + "3 39.1 164 84 48.0 7.2 0.0\n", + "4 37.3 104 35 74.0 7.4 0.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"C:/Users/skey/Desktop/data/data_horse.csv\",encoding=\"gbk\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0e8a1d6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "y 1.000000\n", + "直肠温度 0.172629\n", + "总蛋白值 0.114651\n", + "呼吸频率 -0.036938\n", + "红细胞体积 -0.152119\n", + "脉搏 -0.276255\n", + "Name: y, dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## 可视化列的缺失值、唯一值、类型\n", + "df.corr()\n", + "# 可视化变量y与其他的相关性曲线\n", + "df.corr()['y'].sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "27bb1d29", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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直肠温度脉搏红细胞体积总蛋白值y
038.56645.08.40.0
139.28850.085.00.0
238.34033.06.71.0
339.116448.07.20.0
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" + ], + "text/plain": [ + " 直肠温度 脉搏 红细胞体积 总蛋白值 y\n", + "0 38.5 66 45.0 8.4 0.0\n", + "1 39.2 88 50.0 85.0 0.0\n", + "2 38.3 40 33.0 6.7 1.0\n", + "3 39.1 164 48.0 7.2 0.0\n", + "4 37.3 104 74.0 7.4 0.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 剔除相关性绝对值小于0.1的变量\n", + "df = df[df.columns[df.corr()['y'].abs()>0.1]]\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "263d36c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "直肠温度 0\n", + "脉搏 0\n", + "红细胞体积 0\n", + "总蛋白值 0\n", + "y 9\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0d9e3028", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#可视化每一个变量\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "for i in df.columns:\n", + " plt.figure(figsize=(10,5))\n", + " plt.title(i)\n", + " # 设置字体旋转角度\n", + " plt.xticks(rotation=90)\n", + " sns.countplot(df[i])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "adef0c7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "直肠温度 69\n", + "脉搏 26\n", + "红细胞体积 36\n", + "总蛋白值 42\n", + "y 9\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 我们认为这些变量中的0值都是不合理的，所以我们将除了y(最后一列)以外的变量中的0值都替换为np.nan\n", + "df[df.columns[:-1]] = df[df.columns[:-1]].replace(0,np.nan)\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b7c26b58", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "直肠温度 0\n", + "脉搏 0\n", + "红细胞体积 0\n", + "总蛋白值 0\n", + "y 9\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 用众数填充缺失值\n", + "# 观察发现直肠温度列是正太分布的，因此可以用均值填充缺失值，其他使用中位数\n", + "df['直肠温度'] = df['直肠温度'].fillna(df['直肠温度'].mean())\n", + "df[['脉搏','红细胞体积','总蛋白值']] = df[['脉搏','红细胞体积','总蛋白值']].fillna(df[['脉搏','红细胞体积','总蛋白值']].median())\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6ac51452", + "metadata": {}, + "outputs": [], + "source": [ + "## 删除缺失值\n", + "df = df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4318135a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QW2+9NbNmzUp3d3fq6uqydOnSkR4dAAA4iJQGBwcHaz3ErnrqqaeyYsWKdHV1ZeLEibt8XLlcTnNzcwYGBjJu3LhXcMIDT0f3HbUeAQCAl7HiE5fWeoT9yu60Qc0e8LAnpkyZkhkzZtR6DAAA4CBQs0eHAwAA7MvEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUKBmsfT888/nJz/5Sfr7+2s1AgAAwE7VJJbuvvvuHHvssZkzZ06OPvro3H333UmS3t7edHZ2ZsKECenu7s7g4GD1mPvvvz8nnnhiJk2alHnz5tVibAAA4CAy4rE0MDCQK6+8Mj/4wQ+yevXqzJ8/P93d3alUKpk5c2Y6OjqyfPny9PX1ZeHChUmS9evX54ILLsisWbOybNmyLFq0KPfdd99Ijw4AABxERjyWyuVyPvOZz6S9vT1Jcvrpp+fZZ5/NkiVLMjAwkHnz5mXq1Knp6enJbbfdliRZtGhRjjzyyHzoQx/KtGnT8uEPf7i6rUilUkm5XB6yAAAA7I4Rj6XW1tbMnj07SbJly5Z8+tOfzkUXXZRVq1alq6srY8aMSZK0t7enr68vSbJq1aq86U1vSqlUSpKcccYZWbFixU7f4/rrr09zc3N1aW1tfYU/FQAAcKCp2QMeVq1alSlTpuRb3/pWPvvZz6ZcLqetra26vVQqpb6+Pv39/cO2jRs3Lk888cROz33NNddkYGCguqxbt+4V/SwAAMCBp2ax1N7enu985zuZNm1a5syZk4aGhjQ1NQ3ZZ9SoUdm0adOwbTvW70xTU1PGjRs3ZAEAANgdNYulUqmUjo6O3H777fnyl7+clpaWrF+/fsg+GzZsSGNj47BtO9YDAAC8UkY8lu6///50d3dXXzc2NqZUKuXEE0/MsmXLquvXrl2bSqWSlpaWdHZ2Dtm2cuXKHHXUUSM6NwAAcHAZ8Vg6/vjjc/PNN+fmm2/OunXr8jd/8zc555xz8pa3vCXlcjkLFixIkvT09OTss89OfX19LrjggvzoRz/K9773vWzZsiU33nhjzj333JEeHQAAOIiMeCy96lWvype+9KX8/d//fU4++eRs2rQpd9xxRxoaGnLrrbfmAx/4QCZNmpR77rknH//4x5MkkyZNyqc//em85S1vyeTJk/Pwww/n2muvHenRAQCAg0hpcHBwsNZD/LqnnnoqK1asSFdXVyZOnDhk29q1a/PQQw/lzDPPzGGHHbbL5yyXy2lubs7AwICHPeymju47aj0CAAAvY8UnLq31CPuV3WmDhhGaaZdNmTIlM2bMKNzW1tY25BHiAAAAr5SaPQ0PAABgXyaWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAjWJpXvuuSevfvWr09DQkNNOOy0PPvhgkmTu3LkplUrV5bjjjqse09vbm87OzkyYMCHd3d0ZHBysxegAAMBBYsRj6ec//3kuu+yy3HDDDXn88cdz/PHHZ86cOUmS5cuXZ/Hixenv709/f39WrlyZJKlUKpk5c2Y6OjqyfPny9PX1ZeHChSM9OgAAcBAZ8Vh68MEHc8MNN+Sd73xnJk+enPe///1ZuXJltm7dmjVr1mT69OkZP358xo8fn7FjxyZJlixZkoGBgcybNy9Tp05NT09PbrvttpEeHQAAOIg0jPQbnn/++UNeP/zww5k2bVpWr16d7du357TTTsvjjz+es846KzfffHOOOeaYrFq1Kl1dXRkzZkySpL29PX19fTt9j0qlkkqlUn1dLpdfmQ8DAAAcsGr6gIcXX3wxn/rUp3LFFVekr68vJ5xwQu6888488MADaWhoyOWXX57kpdhpa2urHlcqlVJfX5/+/v7C815//fVpbm6uLq2trSPyeQAAgAPHiF9Z+nXXXXddDj300MyZMyeHHHJIZs+eXd120003pa2tLeVyOQ0NDWlqahpy7KhRo7Jp06ZMmDBh2HmvueaaXH311dXX5XJZMAEAALulZrF07733Zv78+fnxj3+cQw45ZNj2I444Itu3b8+TTz6ZlpaW9Pb2Dtm+YcOGNDY2Fp67qalpWFwBAADsjprchrd27drMmjUr8+fPz0knnZQk6e7uzl133VXdZ9myZamrq0tra2s6OzuzbNmyIcdXKpW0tLSM+OwAAMDBYcSvLG3evDnnn39+Lrzwwlx00UXZuHFjkpce2nDttddm8uTJ2bZtW6666qpceumlGTNmTKZPn55yuZwFCxbksssuS09PT84+++zU19eP9PgAAMBBYsRj6Tvf+U76+vrS19eXW265pbp+7dq1+aM/+qO87W1vS319fS655JL09PS8NGRDQ2699dbMmjUr3d3dqaury9KlS0d6dAAA4CBSGhwcHKz1ELvqqaeeyooVK9LV1ZWJEyfu8nHlcjnNzc0ZGBjIuHHjXsEJDzwd3XfUegQAAF7Gik9cWusR9iu70wY1fRre7poyZUpmzJhR6zEAAICDQE1/ZwkAAGBfJZYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACNYmle+65J69+9avT0NCQ0047LQ8++GCSpLe3N52dnZkwYUK6u7szODhYPeb+++/PiSeemEmTJmXevHm1GBsAADiIjHgs/fznP89ll12WG264IY8//niOP/74zJkzJ5VKJTNnzkxHR0eWL1+evr6+LFy4MEmyfv36XHDBBZk1a1aWLVuWRYsW5b777hvp0QEAgIPIXoulwcHBbNu27Tfu9+CDD+aGG27IO9/5zkyePDnvf//7s3LlyixZsiQDAwOZN29epk6dmp6entx2221JkkWLFuXII4/Mhz70oUybNi0f/vCHq9sAAABeCXsUS1deeWUqlcqQdffee29OPPHE33js+eefn8svv7z6+uGHH860adOyatWqdHV1ZcyYMUmS9vb29PX1JUlWrVqVN73pTSmVSkmSM844IytWrNjpe1QqlZTL5SELAADA7tijWPrnf/7nYbF08skn57//+7936zwvvvhiPvWpT+WKK65IuVxOW1tbdVupVEp9fX36+/uHbRs3blyeeOKJnZ73+uuvT3Nzc3VpbW3drbkAAAAadmfnO+64I8lLt9zddddd1atAg4ODuffee/O6171ut978uuuuy6GHHpo5c+bk2muvTVNT05Dto0aNyqZNm9LQ0DBk2471O3PNNdfk6quvrr4ul8uCCQAA2C27FUsLFixI8tJVn0WLFqWh4aXD6+rqMnXq1Hzxi1/c5XPde++9mT9/fn784x/nkEMOSUtLS3p7e4fss2HDhjQ2NqalpSXr168ftn5nmpqahoUXAADA7titWNrxBLq6urosXrw448aN26M3Xbt2bWbNmpX58+fnpJNOSpJ0dnbmlltuGbJPpVJJS0tLOjs7c9ddd1W3rVy5MkcdddQevTcAAMCu2KPvLL3vfe/b4ys3mzdvzvnnn58LL7wwF110UTZu3JiNGzfmzDPPTLlcrl696unpydlnn536+vpccMEF+dGPfpTvfe972bJlS2688cace+65e/T+AAAAu6I0+Ou//LobXnzxxTz99NP534cfc8wxL3vcPffck7e+9a3D1q9duzYPPPBAZs2aldGjR6euri5Lly6tXnn6p3/6p8ydOzeHHXZYxo8fn2XLlmXy5Mm7NGu5XE5zc3MGBgb2+GrYwaqj+45ajwAAwMtY8YlLaz3CfmV32mC3bsPb4aabbspf/MVf5MUXXxwSS6VS6Tf+1tKFF144LLB2OPbYY/Pzn/88K1asSFdXVyZOnFjddsUVV+Tcc8/NQw89lDPPPDOHHXbYnowOAACwS/boNrwPfehD+eQnP5kXXngh27dvry678qO0v8mUKVMyY8aMIaG0Q1tbW9785jcLJQAA4BW3R7E0duzY/P7v/34OOeSQvT0PAADAPmGPYukf/uEfcvnll2fNmjV7ex4AAIB9wh59Z2nu3Ll59tln097engkTJgz5YtQvfvGLvTYcAABArexRLC1cuHAvjwEAALBv2aNYamtr29tzAAAA7FP2KJaOPfbYlEql6iPAS6VSddveeCIeAABAre3RAx52PCZ8+/bt+dWvfpWlS5fmjW98Y7761a/u5fEAAABqY49i6deNHj06Z555Zr7+9a/nox/96N6YCQAAoOb+z7G0w7PPPpunn356b50OAACgpvb4AQ+//j2l7du358knn8yf/dmf7bXBAAAAammvPDq8VCrlqKOOytSpU/fGTAAAADW3R7fhnXXWWTnrrLMyevTorF+/PqNHjxZKAADAAWWPriw9/vjjufDCC/PII4/kqKOOyhNPPJHjjz8+99xzT4488si9PSMAAMCI26MrS+973/vyute9LuvXr8+DDz6YX/7ylzn99NPz3ve+d2/PBwAAUBN7dGXphz/8YVavXp2mpqYkSVNTU/72b/827e3te3U4AACAWtmjK0uvfe1rc/vttw9Zd/vtt+eUU07ZK0MBAADU2h5dWfrHf/zHnHvuuVm0aFHa2tryi1/8Ihs2bMh3vvOdvT0fAABATexRLJ1yyil55JFH8vWvfz3r1q3LH//xH2fGjBk59NBD9/Z8AAAANbFHt+H19fXlzDPPTH19fbq7u/Oxj30sr3/96/PII4/s7fkAAABqYo+fhnfWWWflnHPOSZL8+Mc/zvnnn58rrrhirw4HAABQK3t0G95Pf/rT/Mu//Euam5uTJIceemiuuuqqnHTSSXt1OAAAgFrZ46fhLVy4cMi6L3zhCzn55JP3xkwAAAA1t0dXlubPn583v/nNufPOO9PW1pZHH300zz33XL71rW/t7fkAAABqYo9i6bd/+7fzyCOPZPHixVm3bl0uueSSzJgxI+PGjdvb8wEAANTEHsVSkowbNy6zZs3am7MAAADsM/boO0sAAAAHOrEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUqFksPfPMM2lra8ujjz5aXTd37tyUSqXqctxxx1W39fb2prOzMxMmTEh3d3cGBwdrMDUAAHCwqEksPfPMMzn//POHhFKSLF++PIsXL05/f3/6+/uzcuXKJEmlUsnMmTPT0dGR5cuXp6+vLwsXLhz5wQEAgINGTWLpXe96Vy6++OIh67Zu3Zo1a9Zk+vTpGT9+fMaPH5+xY8cmSZYsWZKBgYHMmzcvU6dOTU9PT2677badnr9SqaRcLg9ZAAAAdkdNYumWW27J3Llzh6xbvXp1tm/fntNOOy2jR4/Oeeedl8ceeyxJsmrVqnR1dWXMmDFJkvb29vT19e30/Ndff32am5urS2tr6yv3YQAAgANSTWKpra1t2Lq+vr6ccMIJufPOO/PAAw+koaEhl19+eZKkXC4POaZUKqW+vj79/f2F57/mmmsyMDBQXdatW/fKfBAAAOCA1VDrAXaYPXt2Zs+eXX190003pa2tLeVyOQ0NDWlqahqy/6hRo7Jp06ZMmDBh2LmampqG7Q8AALA79tlHhx9xxBHZvn17nnzyybS0tGT9+vVDtm/YsCGNjY01mg4AADjQ7TOx1N3dnbvuuqv6etmyZamrq0tra2s6OzuzbNmy6ra1a9emUqmkpaWlFqMCAAAHgX3mNrxTTz011157bSZPnpxt27blqquuyqWXXpoxY8Zk+vTpKZfLWbBgQS677LL09PTk7LPPTn19fa3HBgAADlD7TCxdcsklWbNmTd72trelvr4+l1xySXp6epIkDQ0NufXWWzNr1qx0d3enrq4uS5cure3AAADAAa00ODg4WOshdtVTTz2VFStWpKurKxMnTtzl48rlcpqbmzMwMJBx48a9ghMeeDq676j1CAAAvIwVn7i01iPsV3anDfaZK0u7YsqUKZkxY0atxwAAAA4C+8wDHgAAAPYlYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAAChQs1h65pln0tbWlkcffbS6rre3N52dnZkwYUK6u7szODhY3Xb//ffnxBNPzKRJkzJv3rwaTAwAABxMahJLzzzzTM4///whoVSpVDJz5sx0dHRk+fLl6evry8KFC5Mk69evzwUXXJBZs2Zl2bJlWbRoUe67775ajA4AABwkahJL73rXu3LxxRcPWbdkyZIMDAxk3rx5mTp1anp6enLbbbclSRYtWpQjjzwyH/rQhzJt2rR8+MMfrm4DAAB4JdQklm655ZbMnTt3yLpVq1alq6srY8aMSZK0t7enr6+vuu1Nb3pTSqVSkuSMM87IihUrdnr+SqWScrk8ZAEAANgdNYmltra2YevK5fKQ9aVSKfX19env7x+2bdy4cXniiSd2ev7rr78+zc3N1aW1tXXvfgAAAOCAt888Da+hoSFNTU1D1o0aNSqbNm0atm3H+p255pprMjAwUF3WrVv3is0NAAAcmBpqPcAOLS0t6e3tHbJuw4YNaWxsTEtLS9avXz9s/c40NTUNCy8AAIDdsc9cWers7MyyZcuqr9euXZtKpZKWlpZh21auXJmjjjqqFmMCAAAHiX0mlqZPn55yuZwFCxYkSXp6enL22Wenvr4+F1xwQX70ox/le9/7XrZs2ZIbb7wx5557bo0nBgAADmT7zG14DQ0NufXWWzNr1qx0d3enrq4uS5cuTZJMmjQpn/70p/OWt7wlhx12WMaPH1/9DSYAAIBXQk1jaXBwcMjrCy64ID//+c+zYsWKdHV1ZeLEidVtV1xxRc4999w89NBDOfPMM3PYYYeN9LgAAMBBZJ+5srTDlClTMmPGjMJtbW1thY8dBwAA2Nv2me8sAQAA7EvEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFNjnYmnu3LkplUrV5bjjjkuS9Pb2prOzMxMmTEh3d3cGBwdrPCkAAHAg2+diafny5Vm8eHH6+/vT39+flStXplKpZObMmeno6Mjy5cvT19eXhQsX1npUAADgALZPxdLWrVuzZs2aTJ8+PePHj8/48eMzduzYLFmyJAMDA5k3b16mTp2anp6e3HbbbTs9T6VSSblcHrIAAADsjn0qllavXp3t27fntNNOy+jRo3Peeeflsccey6pVq9LV1ZUxY8YkSdrb29PX17fT81x//fVpbm6uLq2trSP1EQAAgAPEPhVLfX19OeGEE3LnnXfmgQceSENDQy6//PKUy+W0tbVV9yuVSqmvr09/f3/hea655poMDAxUl3Xr1o3URwAAAA4QDbUe4NfNnj07s2fPrr6+6aab0tbWlhNPPDFNTU1D9h01alQ2bdqUCRMmDDtPU1PTsP0BAAB2xz51Zel/O+KII7J9+/ZMmTIl69evH7Jtw4YNaWxsrNFkAADAgW6fiqXu7u7cdddd1dfLli1LXV1dXvva12bZsmXV9WvXrk2lUklLS0stxgQAAA4C+9RteKeeemquvfbaTJ48Odu2bctVV12VSy+9NOecc07K5XIWLFiQyy67LD09PTn77LNTX19f65EBAIAD1D4VS5dccknWrFmTt73tbamvr88ll1ySnp6eNDQ05NZbb82sWbPS3d2durq6LF26tNbjAgAAB7DS4ODgYK2H2FVPPfVUVqxYka6urkycOHGXjyuXy2lubs7AwEDGjRv3Ck544OnovqPWIwAA8DJWfOLSWo+wX9mdNtinriz9JlOmTMmMGTNqPQYAAHAQ2Kce8AAAALCvEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAECB/SqWent709nZmQkTJqS7uzuDg4O1HgkAADhA7TexVKlUMnPmzHR0dGT58uXp6+vLwoULaz0WAABwgGqo9QC7asmSJRkYGMi8efMyZsyY9PT05E//9E9z2WWXDdu3UqmkUqlUXw8MDCRJyuXyiM17oNhW2VzrEQAAeBn+H3f37Ph77cpdavtNLK1atSpdXV0ZM2ZMkqS9vT19fX2F+15//fX56Ec/Omx9a2vrKzojAACMtOZ/uKLWI+yXNmzYkObm5pfdZ7+JpXK5nLa2turrUqmU+vr69Pf3Z8KECUP2veaaa3L11VdXX2/fvj3PPfdcJk6cmFKpNGIzA7BvKZfLaW1tzbp16zJu3LhajwNADQwODmbDhg058sgjf+O++00sNTQ0pKmpaci6UaNGZdOmTcNiqampadi+48ePf6VHBGA/MW7cOLEEcBD7TVeUdthvHvDQ0tKS9evXD1m3YcOGNDY21mgiAADgQLbfxFJnZ2eWLVtWfb127dpUKpW0tLTUcCoAAOBAtd/E0vTp01Mul7NgwYIkSU9PT84+++zU19fXeDIA9hdNTU257rrrht2qDQBFSoP70S+7fu1rX8usWbMyevTo1NXVZenSpTnppJNqPRYAAHAA2q9iKUmeeuqprFixIl1dXZk4cWKtxwEAAA5Q+10sAQAAjIT95jtLAAAAI0ksAQAAFBBLAAAABRpqPQAAjISBgYFs2rQpjY2NaWlpSalUqvVIAOzjXFkC4IB1++2358wzz8zEiRNz/PHH5/Wvf32OO+64HHbYYbnwwgvz0EMP1XpEAPZhYgmAA9Jf/dVf5Utf+lLmz5+fZ599Nk8//XQee+yx9Pf3p7e3N0cccUTe+MY3pr+/v9ajArCP8uhwAA5IEydOzPLly9PW1rbTfQ4//PDccccdefOb3zyCkwGwv3BlCYAD0tSpU/PJT34yL7zwQuH222+/PZs3b05HR8cITwbA/sKVJQAOSL29vbngggtSLpfT1dWVtra2NDU1Zf369fm3f/u3bNiwIbfccktmzpxZ61EB2EeJJQAOWC+++GK++c1v5oEHHki5XE5DQ0NaWlrS2dmZ6dOnp76+vtYjArAPE0sAAAAFfGcJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAr8P7M8aAm+auyJAAAAAElFTkSuQmCC", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 再查看一下每个变量的分布\n", + "for i in df.columns:\n", + " plt.figure(figsize=(10,5))\n", + " plt.title(i)\n", + " # 设置字体旋转角度\n", + " plt.xticks(rotation=90)\n", + " sns.countplot(df[i])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c6d7e09c", + "metadata": {}, + "outputs": [], + "source": [ + "# 由于变量的取值范围不一样，因此需要对数据进行标准化\n", + "import time\n", + "X_train,X_test,y_train,y_test = train_test_split(df[df.columns[:-1]],df['y'],test_size=0.3,random_state=0)\n", + "X_train = StandardScaler().fit_transform(X_train)\n", + "X_test = StandardScaler().fit_transform(X_test)\n", + "\n", + "model_result = {}\n", + "\n", + "models = [RandomForestClassifier(),BaggingClassifier(),AdaBoostClassifier(),GaussianNB(),LogisticRegression(),DecisionTreeClassifier(),SVC(),KNeighborsClassifier()]\n", + "for model in models:\n", + " try:\n", + " model_name = str(model).split('(')[0]\n", + " start = time.time()\n", + " model.fit(X_train,y_train)\n", + " y_pred = model.predict(X_test)\n", + " end = time.time()\n", + " # 存储准确率和混淆矩阵\n", + " model_result[model_name] = [accuracy_score(y_test,y_pred),confusion_matrix(y_test,y_pred),end-start]\n", + " except Exception as e:\n", + " print(model_name,e)\n", + "\n", + "## 使用df保存模型的结果\n", + "df_result = pd.DataFrame(model_result).T\n", + "df_result.columns = ['accuracy_score','confusion_matrix','time']\n", + "\n", + "df_result.sort_values(by='accuracy_score',ascending=False,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "aebc5270", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0.,\n", + " 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1.,\n", + " 0., 1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0.,\n", + " 0., 0., 0., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 0., 1.,\n", + " 1., 0., 0., 1., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1., 1., 0., 1.,\n", + " 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n", + " 0., 0., 1., 1., 1., 1.])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#模型应用\n", + "RFC = RandomForestClassifier()\n", + "RFC.fit(X_train, y_train)\n", + "RFC.predict(X_test) " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "00e4c3eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0.,\n", + " 1., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0.,\n", + " 1., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0.,\n", + " 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 1., 0.,\n", + " 1., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.,\n", + " 1., 0., 1., 1., 1., 1., 0., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0.,\n", + " 1., 0., 0., 0., 1., 1.])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "BC = BaggingClassifier()\n", + "BC.fit(X_train, y_train)\n", + "BC.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2d37875f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 0.,\n", + " 0., 1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 1., 0., 1., 0., 0.,\n", + " 1., 0., 1., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0.,\n", + " 0., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 1., 0.,\n", + " 1., 0., 0., 1., 1., 1.])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ABC = AdaBoostClassifier()\n", + "ABC.fit(X_train, y_train)\n", + "ABC.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3640cc77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1.,\n", + " 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0.,\n", + " 0., 1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 0.,\n", + " 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 0., 1., 1., 0., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0.,\n", + " 0., 1., 0., 1., 1., 1.])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GNB = GaussianNB()\n", + "GNB.fit(X_train, y_train)\n", + "GNB.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c86a8c03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 0., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0.,\n", + " 0., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 0.,\n", + " 0., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 0.,\n", + " 1., 0., 0., 1., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 0., 1.,\n", + " 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n", + " 0., 0., 0., 1., 1., 1.])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LR = LogisticRegression()\n", + "LR.fit(X_train, y_train)\n", + "LR.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "234a3a8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1.,\n", + 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0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0.,\n", + " 1., 0., 0., 1., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 0., 1.,\n", + " 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n", + " 0., 0., 0., 1., 1., 1.])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svc = SVC()\n", + "svc.fit(X_train, y_train)\n", + "svc.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "58efdd86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 0.,\n", + " 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0., 1., 0., 0.,\n", + " 0., 0., 1., 0., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 0.,\n", + " 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 0., 1.,\n", + " 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n", + " 1., 0., 0., 1., 1., 1.])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "KNC = KNeighborsClassifier()\n", + "KNC.fit(X_train, y_train)\n", + "KNC.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "944ab14c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 绘制各个模型的准确率柱状图，并在柱状图上标注值\n", + "plt.figure(figsize=(10,5))\n", + "plt.title('各个模型的准确率')\n", + "sns.barplot(x=df_result.index,y=df_result['accuracy_score'])\n", + "for i in range(df_result.shape[0]):\n", + " plt.text(i,df_result['accuracy_score'][i],round(df_result['accuracy_score'][i],3),ha='center')\n", + "# 设置字体选择\n", + "plt.xticks(rotation=90)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c6b386b", + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}