├── 01_泰坦尼克号(titanic) ├── .ipynb_checkpoints │ └── kaggle泰坦尼克之灾-checkpoint.ipynb ├── README.md ├── datasets │ ├── test.csv │ └── train.csv └── kaggle泰坦尼克之灾.ipynb ├── 02_共享单车(Bike Sharing Demand) ├── .ipynb_checkpoints │ └── kaggle 共享单车项目可视化-checkpoint.ipynb ├── input │ ├── test.csv │ └── train.csv ├── kaggle 共享单车项目可视化.ipynb └── output │ ├── bike_predictions.csv │ └── sampleSubmission.csv ├── 03_Facebook广告投放(Sales Conversion Optimization) ├── README.md └── datasets │ └── KAG_conversion_data.csv └── README.md /01_泰坦尼克号(titanic)/.ipynb_checkpoints/kaggle泰坦尼克之灾-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# kaggle泰坦尼克之灾" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "步骤:\n", 15 | "\n", 16 | "一.导入数据包与数据集\n", 17 | "\n", 18 | "二.数据分析\n", 19 | "\n", 20 | "1. 总体预览:了解每列数据的含义,数据的格式等\n", 21 | "2. .数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n", 22 | "\n", 23 | "三.特征工程\n", 24 | "\n", 25 | "1. 根据业务,常识,以及第二步的数据分析构造特征工程.\n", 26 | "2. 将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n", 27 | "\n", 28 | "四.模型选择\n", 29 | "\n", 30 | "1. 根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n", 31 | "2. 比较各个模型的分数,然后取效果较好的模型作为基础模型.\n", 32 | "\n", 33 | "五.修改特征和模型参数\n", 34 | "\n", 35 | "1. 可以通过添加或者修改特征,提高模型的上限.\n", 36 | "2. 通过修改模型的参数,是模型逼近上限" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "## 一.导入数据包与数据集" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": null, 49 | "metadata": { 50 | "collapsed": true 51 | }, 52 | "outputs": [], 53 | "source": [ 54 | "# 导入相关数据包\n", 55 | "import numpy as np\n", 56 | "import pandas as pd\n", 57 | "import seaborn as sns\n", 58 | "import matplotlib.pyplot as plt\n", 59 | "%matplotlib inline" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "metadata": {}, 66 | "outputs": [], 67 | "source": [ 68 | "root_path = './datasets'\n", 69 | "\n", 70 | "train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))\n", 71 | "test = pd.read_csv('%s/%s' % (root_path, 'test.csv'))" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "## 二.数据分析" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "### 1.总体预览\n", 86 | "了解每列数据的含义,数据的格式等" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "metadata": {}, 93 | "outputs": [], 94 | "source": [ 95 | "train.head(5)" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": null, 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "# 返回每列列名,该列非nan值个数,以及该列类型\n", 105 | "train.info()" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": null, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "test.info()" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": null, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [ 123 | "# 返回数值型变量的统计量\n", 124 | "# train.describe(percentiles=[0.00, 0.25, 0.5, 0.75, 1.00])\n", 125 | "train.describe()" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "## 2..数据初步分析,使用统计学与绘图\n", 133 | "目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": null, 139 | "metadata": {}, 140 | "outputs": [], 141 | "source": [ 142 | "#存活人数\n", 143 | "train['Survived'].value_counts()" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "#### 1)数值型数据协方差,corr()函数\n", 151 | "来个总览,快速了解个数据的相关性" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": null, 157 | "metadata": {}, 158 | "outputs": [], 159 | "source": [ 160 | "#相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.\n", 161 | "train_corr = train.drop('PassengerId',axis=1).corr()\n", 162 | "train_corr" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": null, 168 | "metadata": {}, 169 | "outputs": [], 170 | "source": [ 171 | "#画出相关性热力图\n", 172 | "a = plt.subplots(figsize=(15,9))#调整画布大小\n", 173 | "a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)#画热力图" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "#### 2)各个数据与结果的关系\n", 181 | "进一步探索分析各个数据与结果的关系" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "##### ①Pclass,乘客等级,1是最高级\n", 189 | "结果分析:可以看出Survived和Pclass在Pclass=1的时候有较强的相关性（>0.5），所以最终模型中包含该特征。" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": null, 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "train.groupby(['Pclass'])['Pclass','Survived'].mean()" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": null, 204 | "metadata": {}, 205 | "outputs": [], 206 | "source": [ 207 | "train[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar()" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "##### ②Sex,性别\n", 215 | "结果分析:女性有更高的活下来的概率（74%）,保留该特征" 216 | ] 217 | }, 218 | { 219 | "cell_type": "code", 220 | "execution_count": null, 221 | "metadata": {}, 222 | "outputs": [], 223 | "source": [ 224 | "train.groupby(['Sex'])['Sex','Survived'].mean()" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": null, 230 | "metadata": {}, 231 | "outputs": [], 232 | "source": [ 233 | "train[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "metadata": {}, 239 | "source": [ 240 | "##### ③SibSp and Parch 兄妹配偶数/父母子女数\n", 241 | "结果分析:这些特征与特定的值没有相关性不明显，最好是由这些独立的特征派生出一个新特征或者一组新特征" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "train[['SibSp','Survived']].groupby(['SibSp']).mean()" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": null, 256 | "metadata": {}, 257 | "outputs": [], 258 | "source": [ 259 | "train[['Parch','Survived']].groupby(['Parch']).mean().plot.bar()" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "##### ④Age年龄与生存情况的分析.\n", 267 | "结果分析:由图,可以看到年龄是影响生存情况的. \n", 268 | "\n", 269 | "但是年龄是有大部分缺失值的,缺失值需要进行处理,可以使用填充或者模型预测." 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": null, 275 | "metadata": {}, 276 | "outputs": [], 277 | "source": [ 278 | "g = sns.FacetGrid(train, col='Survived',size=5)\n", 279 | "g.map(plt.hist, 'Age', bins=40)" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": null, 285 | "metadata": {}, 286 | "outputs": [], 287 | "source": [ 288 | "train.groupby(['Age'])['Survived'].mean().plot()" 289 | ] 290 | }, 291 | { 292 | "cell_type": "markdown", 293 | "metadata": {}, 294 | "source": [ 295 | "##### ⑤Embarked登港港口与生存情况的分析\n", 296 | "结果分析:C地的生存率更高,这个也应该保留为模型特征." 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": null, 302 | "metadata": {}, 303 | "outputs": [], 304 | "source": [ 305 | "sns.countplot('Embarked',hue='Survived',data=train)" 306 | ] 307 | }, 308 | { 309 | "cell_type": "markdown", 310 | "metadata": {}, 311 | "source": [ 312 | "⑥其他因素\n", 313 | "*在数据的Name项中包含了对该乘客的称呼，如Mr、Miss等，这些信息包含了乘客的年龄、性别、也有可能包含社会地位，如Dr、Lady、Major、Master等称呼。这一项不方便用图表展示，但是在特征工程中，我们会将其提取出来,然后放到模型中。\n", 314 | "\n", 315 | "*剩余因素还有船票价格、船舱号和船票号，这三个因素都可能会影响乘客在船中的位置从而影响逃生顺序，但是因为这三个因素与生存之间看不出明显规律，所以在后期模型融合时，将这些因素交给模型来决定其重要性。" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "## 三.特征工程" 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": null, 328 | "metadata": { 329 | "collapsed": true 330 | }, 331 | "outputs": [], 332 | "source": [ 333 | "#先将数据集合并,一起做特征工程(注意,标准化的时候需要分开处理)\n", 334 | "#先将test补齐,然后通过pd.apped()合并\n", 335 | "test['Survived'] = 0\n", 336 | "train_test = train.append(test)" 337 | ] 338 | }, 339 | { 340 | "cell_type": "markdown", 341 | "metadata": {}, 342 | "source": [ 343 | "### ①Pclass,乘客等级,1是最高级\n", 344 | "两种方式:一是该特征不做处理,可以直接保留.二是再处理:也进行分列处理(比较那种方式模型效果更好,就选那种)" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": null, 350 | "metadata": { 351 | "collapsed": true 352 | }, 353 | "outputs": [], 354 | "source": [ 355 | "train_test = pd.get_dummies(train_test,columns=['Pclass'])" 356 | ] 357 | }, 358 | { 359 | "cell_type": "markdown", 360 | "metadata": {}, 361 | "source": [ 362 | "### ②Sex,性别\n", 363 | "无缺失值,直接分列" 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": null, 369 | "metadata": { 370 | "collapsed": true 371 | }, 372 | "outputs": [], 373 | "source": [ 374 | "train_test = pd.get_dummies(train_test,columns=[\"Sex\"])" 375 | ] 376 | }, 377 | { 378 | "cell_type": "markdown", 379 | "metadata": {}, 380 | "source": [ 381 | "### ③SibSp and Parch 兄妹配偶数/父母子女数\n", 382 | "第一次直接保留:这两个都影响生存率,且都是数值型,先直接保存.\n", 383 | "\n", 384 | "第二次进行两项求和,并进行分列处理.(兄妹配偶数和父母子女数都是认识人的数量,所以总数可能也会更好)(模型结果提高到了)" 385 | ] 386 | }, 387 | { 388 | "cell_type": "code", 389 | "execution_count": null, 390 | "metadata": { 391 | "collapsed": true 392 | }, 393 | "outputs": [], 394 | "source": [ 395 | "#这是剑豪模型后回来添加的新特征,模型的分数最终有所提高了.\n", 396 | "train_test['SibSp_Parch'] = train_test['SibSp'] + train_test['Parch']" 397 | ] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": null, 402 | "metadata": { 403 | "collapsed": true 404 | }, 405 | "outputs": [], 406 | "source": [ 407 | "train_test = pd.get_dummies(train_test,columns = ['SibSp','Parch','SibSp_Parch']) " 408 | ] 409 | }, 410 | { 411 | "cell_type": "markdown", 412 | "metadata": {}, 413 | "source": [ 414 | "### ④Embarked \n", 415 | "数据有极少量(3个)缺失值,但是在分列的时候,缺失值的所有列可以均为0,所以可以考虑不填充.\n", 416 | "\n", 417 | "另外,也可以考虑用测试集众数来填充.先找出众数,再采用df.fillna()方法" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": null, 423 | "metadata": { 424 | "collapsed": true 425 | }, 426 | "outputs": [], 427 | "source": [ 428 | "train_test = pd.get_dummies(train_test,columns=[\"Embarked\"])" 429 | ] 430 | }, 431 | { 432 | "cell_type": "markdown", 433 | "metadata": {}, 434 | "source": [ 435 | "### ⑤ Name\n", 436 | "1.在数据的Name项中包含了对该乘客的称呼,将这些关键词提取出来,然后做分列处理.(参考别人的)" 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "execution_count": null, 442 | "metadata": {}, 443 | "outputs": [], 444 | "source": [ 445 | "#从名字中提取出称呼： df['Name].str.extract()是提取函数,配合正则一起使用\n", 446 | "train_test['Name1'] = train_test['Name'].str.extract('.+,(.+)').str.extract( '^(.+?)\\.').str.strip()" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": null, 452 | "metadata": { 453 | "collapsed": true 454 | }, 455 | "outputs": [], 456 | "source": [ 457 | "#将姓名分类处理()\n", 458 | "train_test['Name1'].replace(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer' , inplace = True)\n", 459 | "train_test['Name1'].replace(['Jonkheer', 'Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty' , inplace = True)\n", 460 | "train_test['Name1'].replace(['Mme', 'Ms', 'Mrs'], 'Mrs')\n", 461 | "train_test['Name1'].replace(['Mlle', 'Miss'], 'Miss')\n", 462 | "train_test['Name1'].replace(['Mr'], 'Mr' , inplace = True)\n", 463 | "train_test['Name1'].replace(['Master'], 'Master' , inplace = True)" 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": null, 469 | "metadata": { 470 | "collapsed": true 471 | }, 472 | "outputs": [], 473 | "source": [ 474 | "#分列处理\n", 475 | "train_test = pd.get_dummies(train_test,columns=['Name1'])" 476 | ] 477 | }, 478 | { 479 | "cell_type": "markdown", 480 | "metadata": {}, 481 | "source": [ 482 | "##### 从姓名中提取出姓做特征" 483 | ] 484 | }, 485 | { 486 | "cell_type": "code", 487 | "execution_count": null, 488 | "metadata": { 489 | "collapsed": true 490 | }, 491 | "outputs": [], 492 | "source": [ 493 | "#从姓名中提取出姓\n", 494 | "train_test['Name2'] = train_test['Name'].apply(lambda x: x.split('.')[1])\n", 495 | "\n", 496 | "#计算数量,然后合并数据集\n", 497 | "Name2_sum = train_test['Name2'].value_counts().reset_index()\n", 498 | "Name2_sum.columns=['Name2','Name2_sum']\n", 499 | "train_test = pd.merge(train_test,Name2_sum,how='left',on='Name2')\n", 500 | "\n", 501 | "#由于出现一次时该特征时无效特征,用one来代替出现一次的姓\n", 502 | "train_test.loc[train_test['Name2_sum'] == 1 , 'Name2_new'] = 'one'\n", 503 | "train_test.loc[train_test['Name2_sum'] > 1 , 'Name2_new'] = train_test['Name2']\n", 504 | "del train_test['Name2']\n", 505 | "\n", 506 | "#分列处理\n", 507 | "train_test = pd.get_dummies(train_test,columns=['Name2_new'])" 508 | ] 509 | }, 510 | { 511 | "cell_type": "code", 512 | "execution_count": null, 513 | "metadata": { 514 | "collapsed": true 515 | }, 516 | "outputs": [], 517 | "source": [ 518 | "#删掉姓名这个特征\n", 519 | "del train_test['Name']" 520 | ] 521 | }, 522 | { 523 | "cell_type": "markdown", 524 | "metadata": {}, 525 | "source": [ 526 | "### ⑥ Fare\n", 527 | "该特征有缺失值,先找出缺失值的那调数据,然后用平均数填充" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": null, 533 | "metadata": {}, 534 | "outputs": [], 535 | "source": [ 536 | "#从上面的分析,发现该特征train集无miss值,test有一个缺失值,先查看\n", 537 | "train_test.loc[train_test[\"Fare\"].isnull()]" 538 | ] 539 | }, 540 | { 541 | "cell_type": "code", 542 | "execution_count": null, 543 | "metadata": {}, 544 | "outputs": [], 545 | "source": [ 546 | "#票价与pclass和Embarked有关,所以用train分组后的平均数填充\n", 547 | "train.groupby(by=[\"Pclass\",\"Embarked\"]).Fare.mean()" 548 | ] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "execution_count": null, 553 | "metadata": { 554 | "collapsed": true 555 | }, 556 | "outputs": [], 557 | "source": [ 558 | "#用pclass=3和Embarked=S的平均数14.644083来填充\n", 559 | "train_test[\"Fare\"].fillna(14.435422,inplace=True)" 560 | ] 561 | }, 562 | { 563 | "cell_type": "markdown", 564 | "metadata": { 565 | "collapsed": true 566 | }, 567 | "source": [ 568 | "### ⑦ Ticket\n", 569 | "该列和名字做类似的处理,先提取,然后分列" 570 | ] 571 | }, 572 | { 573 | "cell_type": "code", 574 | "execution_count": null, 575 | "metadata": { 576 | "collapsed": true 577 | }, 578 | "outputs": [], 579 | "source": [ 580 | "#将Ticket提取字符列\n", 581 | "#str.isnumeric() 如果S中只有数字字符，则返回True，否则返回False\n", 582 | "train_test['Ticket_Letter'] = train_test['Ticket'].str.split().str[0]\n", 583 | "train_test['Ticket_Letter'] = train_test['Ticket_Letter'].apply(lambda x:np.nan if x.isnumeric() else x)\n", 584 | "train_test.drop('Ticket',inplace=True,axis=1)" 585 | ] 586 | }, 587 | { 588 | "cell_type": "code", 589 | "execution_count": null, 590 | "metadata": { 591 | "collapsed": true 592 | }, 593 | "outputs": [], 594 | "source": [ 595 | "#分列,此时nan值可以不做处理\n", 596 | "train_test = pd.get_dummies(train_test,columns=['Ticket_Letter'],drop_first=True)" 597 | ] 598 | }, 599 | { 600 | "cell_type": "markdown", 601 | "metadata": {}, 602 | "source": [ 603 | "### ⑧ Age\n", 604 | "1.该列有大量缺失值,考虑用一个回归模型进行填充.\n", 605 | "\n", 606 | "2.在模型修改的时候,考虑到年龄缺失值可能影响死亡情况,用年龄是否缺失值来构造新特征" 607 | ] 608 | }, 609 | { 610 | "cell_type": "code", 611 | "execution_count": null, 612 | "metadata": {}, 613 | "outputs": [], 614 | "source": [ 615 | "\"\"\"\n", 616 | "这是模型就好后回来增加的新特征\n", 617 | "考虑年龄缺失值可能影响死亡情况,数据表明,年龄缺失的死亡率为0.19.\n", 618 | "\"\"\"\n", 619 | "train_test.loc[train_test[\"Age\"].isnull()]['Survived'].mean()" 620 | ] 621 | }, 622 | { 623 | "cell_type": "code", 624 | "execution_count": null, 625 | "metadata": { 626 | "collapsed": true 627 | }, 628 | "outputs": [], 629 | "source": [ 630 | "# 所以用年龄是否缺失值来构造新特征\n", 631 | "train_test.loc[train_test[\"Age\"].isnull() ,\"age_nan\"] = 1\n", 632 | "train_test.loc[train_test[\"Age\"].notnull() ,\"age_nan\"] = 0\n", 633 | "train_test = pd.get_dummies(train_test,columns=['age_nan'])" 634 | ] 635 | }, 636 | { 637 | "cell_type": "markdown", 638 | "metadata": {}, 639 | "source": [ 640 | "### 利用其他组特征量，采用机器学习算法来预测Age" 641 | ] 642 | }, 643 | { 644 | "cell_type": "code", 645 | "execution_count": null, 646 | "metadata": {}, 647 | "outputs": [], 648 | "source": [ 649 | "train_test.info()" 650 | ] 651 | }, 652 | { 653 | "cell_type": "code", 654 | "execution_count": null, 655 | "metadata": {}, 656 | "outputs": [], 657 | "source": [ 658 | "#创建没有['Age','Survived']的数据集\n", 659 | "missing_age = train_test.drop(['Survived','Cabin'],axis=1)\n", 660 | "#将Age完整的项作为训练集、将Age缺失的项作为测试集。\n", 661 | "missing_age_train = missing_age[missing_age['Age'].notnull()]\n", 662 | "missing_age_test = missing_age[missing_age['Age'].isnull()]" 663 | ] 664 | }, 665 | { 666 | "cell_type": "code", 667 | "execution_count": null, 668 | "metadata": {}, 669 | "outputs": [], 670 | "source": [ 671 | "#构建训练集合预测集的X和Y值\n", 672 | "missing_age_X_train = missing_age_train.drop(['Age'], axis=1)\n", 673 | "missing_age_Y_train = missing_age_train['Age']\n", 674 | "missing_age_X_test = missing_age_test.drop(['Age'], axis=1)" 675 | ] 676 | }, 677 | { 678 | "cell_type": "code", 679 | "execution_count": null, 680 | "metadata": { 681 | "collapsed": true 682 | }, 683 | "outputs": [], 684 | "source": [ 685 | "# 先将数据标准化\n", 686 | "from sklearn.preprocessing import StandardScaler\n", 687 | "ss = StandardScaler()\n", 688 | "#用测试集训练并标准化\n", 689 | "ss.fit(missing_age_X_train)\n", 690 | "missing_age_X_train = ss.transform(missing_age_X_train)\n", 691 | "missing_age_X_test = ss.transform(missing_age_X_test)" 692 | ] 693 | }, 694 | { 695 | "cell_type": "code", 696 | "execution_count": null, 697 | "metadata": { 698 | "collapsed": true 699 | }, 700 | "outputs": [], 701 | "source": [ 702 | "#使用贝叶斯预测年龄\n", 703 | "from sklearn import linear_model\n", 704 | "lin = linear_model.BayesianRidge()" 705 | ] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": null, 710 | "metadata": {}, 711 | "outputs": [], 712 | "source": [ 713 | "lin.fit(missing_age_X_train,missing_age_Y_train)" 714 | ] 715 | }, 716 | { 717 | "cell_type": "code", 718 | "execution_count": null, 719 | "metadata": { 720 | "collapsed": true 721 | }, 722 | "outputs": [], 723 | "source": [ 724 | "#利用loc将预测值填入数据集\n", 725 | "train_test.loc[(train_test['Age'].isnull()), 'Age'] = lin.predict(missing_age_X_test)" 726 | ] 727 | }, 728 | { 729 | "cell_type": "code", 730 | "execution_count": null, 731 | "metadata": { 732 | "collapsed": true 733 | }, 734 | "outputs": [], 735 | "source": [ 736 | "#将年龄划分是个阶段10以下,10-18,18-30,30-50,50以上\n", 737 | "train_test['Age'] = pd.cut(train_test['Age'], bins=[0,10,18,30,50,100],labels=[1,2,3,4,5])\n", 738 | "\n", 739 | "train_test = pd.get_dummies(train_test,columns=['Age'])" 740 | ] 741 | }, 742 | { 743 | "cell_type": "markdown", 744 | "metadata": {}, 745 | "source": [ 746 | "### ⑨ Cabin\n", 747 | "cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模,也可以考虑直接舍去该特征" 748 | ] 749 | }, 750 | { 751 | "cell_type": "code", 752 | "execution_count": null, 753 | "metadata": { 754 | "collapsed": true 755 | }, 756 | "outputs": [], 757 | "source": [ 758 | "#cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模\n", 759 | "train_test['Cabin'] = train_test['Cabin'].apply(lambda x:str(x)[0] if pd.notnull(x) else x)\n", 760 | "train_test = pd.get_dummies(train_test,columns=['Cabin'])" 761 | ] 762 | }, 763 | { 764 | "cell_type": "code", 765 | "execution_count": null, 766 | "metadata": {}, 767 | "outputs": [], 768 | "source": [ 769 | "#cabin项缺失太多，只能将有无Cain首字母进行分类,\n", 770 | "train_test.loc[train_test[\"Cabin\"].isnull() ,\"Cabin_nan\"] = 1\n", 771 | "train_test.loc[train_test[\"Cabin\"].notnull() ,\"Cabin_nan\"] = 0\n", 772 | "train_test = pd.get_dummies(train_test,columns=['Cabin_nan'])\n", 773 | "train_test.drop('Cabin',axis=1,inplace=True)" 774 | ] 775 | }, 776 | { 777 | "cell_type": "markdown", 778 | "metadata": {}, 779 | "source": [ 780 | "### ⑩ 特征工程处理完了,划分数据集" 781 | ] 782 | }, 783 | { 784 | "cell_type": "code", 785 | "execution_count": null, 786 | "metadata": { 787 | "collapsed": true 788 | }, 789 | "outputs": [], 790 | "source": [ 791 | "train_data = train_test[:891]\n", 792 | "test_data = train_test[891:]\n", 793 | "train_data_X = train_data.drop(['Survived'],axis=1)\n", 794 | "train_data_Y = train_data['Survived']\n", 795 | "test_data_X = test_data.drop(['Survived'],axis=1)" 796 | ] 797 | }, 798 | { 799 | "cell_type": "markdown", 800 | "metadata": {}, 801 | "source": [ 802 | "## 四 建立模型" 803 | ] 804 | }, 805 | { 806 | "cell_type": "markdown", 807 | "metadata": {}, 808 | "source": [ 809 | "### 数据标准化\n", 810 | "1.线性模型需要用标准化的数据建模,而树类模型不需要标准化的数据\n", 811 | "\n", 812 | "2.处理标准化的时候,注意将测试集的数据transform到test集上" 813 | ] 814 | }, 815 | { 816 | "cell_type": "code", 817 | "execution_count": null, 818 | "metadata": { 819 | "collapsed": true 820 | }, 821 | "outputs": [], 822 | "source": [ 823 | "from sklearn.preprocessing import StandardScaler\n", 824 | "ss2 = StandardScaler()\n", 825 | "ss2.fit(train_data_X)\n", 826 | "train_data_X_sd = ss2.transform(train_data_X)\n", 827 | "test_data_X_sd = ss2.transform(test_data_X)" 828 | ] 829 | }, 830 | { 831 | "cell_type": "markdown", 832 | "metadata": {}, 833 | "source": [ 834 | "### 开始建模\n", 835 | "1.可选单个模型模型有随机森林,逻辑回归,svm,xgboost,gbdt等.\n", 836 | "\n", 837 | "2.也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法\n", 838 | "\n", 839 | "3.好的特征决定模型上限,好的模型和参数可以无线逼近上限.\n", 840 | "\n", 841 | "4.我测试了多种模型,模型结果最高的随机森林,最高有0.8." 842 | ] 843 | }, 844 | { 845 | "cell_type": "markdown", 846 | "metadata": {}, 847 | "source": [ 848 | "### 随机森林" 849 | ] 850 | }, 851 | { 852 | "cell_type": "code", 853 | "execution_count": null, 854 | "metadata": { 855 | "collapsed": true 856 | }, 857 | "outputs": [], 858 | "source": [ 859 | "from sklearn.ensemble import RandomForestClassifier\n", 860 | "\n", 861 | "rf = RandomForestClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6,oob_score=True)\n", 862 | "rf.fit(train_data_X,train_data_Y)\n", 863 | "\n", 864 | "test[\"Survived\"] = rf.predict(test_data_X)\n", 865 | "RF = test[['PassengerId','Survived']].set_index('PassengerId')\n", 866 | "RF.to_csv('RF.csv')" 867 | ] 868 | }, 869 | { 870 | "cell_type": "code", 871 | "execution_count": null, 872 | "metadata": { 873 | "collapsed": true 874 | }, 875 | "outputs": [], 876 | "source": [ 877 | "#随机森林是随机选取特征进行建模的,所以每次的结果可能都有点小差异\n", 878 | "#如果分数足够好,可以将该模型保存起来,下次直接调出来使用0.81339 'rf10.pkl'\n", 879 | "from sklearn.externals import joblib\n", 880 | "joblib.dump(rf, 'rf10.pkl')" 881 | ] 882 | }, 883 | { 884 | "cell_type": "markdown", 885 | "metadata": {}, 886 | "source": [ 887 | "### LogisticRegression" 888 | ] 889 | }, 890 | { 891 | "cell_type": "code", 892 | "execution_count": null, 893 | "metadata": { 894 | "collapsed": true 895 | }, 896 | "outputs": [], 897 | "source": [ 898 | "from sklearn.linear_model import LogisticRegression\n", 899 | "\n", 900 | "lr = LogisticRegression()\n", 901 | "\n", 902 | "from sklearn.grid_search import GridSearchCV\n", 903 | "\n", 904 | "param = {'C':[0.001,0.01,0.1,1,10],\"max_iter\":[100,250]}\n", 905 | "\n", 906 | "clf = GridSearchCV(lr,param,cv=5,n_jobs=-1,verbose=1,scoring=\"roc_auc\")\n", 907 | "\n", 908 | "clf.fit(train_data_X_sd,train_data_Y)\n", 909 | "\n", 910 | "#打印参数的得分情况\n", 911 | "clf.grid_scores_\n", 912 | "\n", 913 | "#打印最佳参数\n", 914 | "clf.best_params_\n", 915 | "\n", 916 | "#将最佳参数传入训练模型\n", 917 | "lr = LogisticRegression(clf.best_params_)\n", 918 | "\n", 919 | "lr.fit(train_data_X_sd,train_data_Y)\n", 920 | "\n", 921 | "#预测结果\n", 922 | "lr.predict(test_data_X_sd)\n", 923 | "\n", 924 | "#打印结果\n", 925 | "test[\"Survived\"] = lr.predict(test_data_X_sd)\n", 926 | "LS = test[['PassengerId','Survived']].set_index('PassengerId')\n", 927 | "\n", 928 | "#输出结果\n", 929 | "LS.to_csv('LS5.csv')" 930 | ] 931 | }, 932 | { 933 | "cell_type": "markdown", 934 | "metadata": {}, 935 | "source": [ 936 | "### SVM" 937 | ] 938 | }, 939 | { 940 | "cell_type": "code", 941 | "execution_count": null, 942 | "metadata": { 943 | "collapsed": true 944 | }, 945 | "outputs": [], 946 | "source": [ 947 | "from sklearn import svm\n", 948 | "svc = svm.SVC()\n", 949 | "\n", 950 | "clf = GridSearchCV(svc,param,cv=5,n_jobs=-1,verbose=1,scoring=\"roc_auc\")\n", 951 | "clf.fit(train_data_X_sd,train_data_Y)\n", 952 | "\n", 953 | "clf.best_params_\n", 954 | "\n", 955 | "svc = svm.SVC(C=1,max_iter=250)\n", 956 | "\n", 957 | "#训练模型并预测结果\n", 958 | "svc.fit(train_data_X_sd,train_data_Y)\n", 959 | "svc.predict(test_data_X_sd)\n", 960 | "\n", 961 | "#打印结果\n", 962 | "test[\"Survived\"] = svc.predict(test_data_X_sd)\n", 963 | "SVM = test[['PassengerId','Survived']].set_index('PassengerId')\n", 964 | "SVM.to_csv('svm1.csv')" 965 | ] 966 | }, 967 | { 968 | "cell_type": "markdown", 969 | "metadata": {}, 970 | "source": [ 971 | "### xgboost" 972 | ] 973 | }, 974 | { 975 | "cell_type": "code", 976 | "execution_count": null, 977 | "metadata": { 978 | "collapsed": true 979 | }, 980 | "outputs": [], 981 | "source": [ 982 | "import xgboost as xgb\n", 983 | "\n", 984 | "xgb_model = xgb.XGBClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6)\n", 985 | "\n", 986 | "xgb_model.fit(train_data_X,train_data_Y)\n", 987 | "\n", 988 | "test[\"Survived\"] = xgb_model.predict(test_data_X)\n", 989 | "XGB = test[['PassengerId','Survived']].set_index('PassengerId')\n", 990 | "XGB.to_csv('XGB5.csv')" 991 | ] 992 | }, 993 | { 994 | "cell_type": "markdown", 995 | "metadata": {}, 996 | "source": [ 997 | "### GBDT" 998 | ] 999 | }, 1000 | { 1001 | "cell_type": "code", 1002 | "execution_count": null, 1003 | "metadata": { 1004 | "collapsed": true 1005 | }, 1006 | "outputs": [], 1007 | "source": [ 1008 | "from sklearn.ensemble import GradientBoostingClassifier\n", 1009 | "\n", 1010 | "gbdt = GradientBoostingClassifier(learning_rate=0.7,max_depth=6,n_estimators=100,min_samples_leaf=2)\n", 1011 | "\n", 1012 | "gbdt.fit(train_data_X,train_data_Y)\n", 1013 | "\n", 1014 | "test[\"Survived\"] = gbdt.predict(test_data_X)\n", 1015 | "test[['PassengerId','Survived']].set_index('PassengerId').to_csv('gbdt3.csv')" 1016 | ] 1017 | }, 1018 | { 1019 | "cell_type": "markdown", 1020 | "metadata": {}, 1021 | "source": [ 1022 | "### 模型融合voting" 1023 | ] 1024 | }, 1025 | { 1026 | "cell_type": "code", 1027 | "execution_count": null, 1028 | "metadata": { 1029 | "collapsed": true 1030 | }, 1031 | "outputs": [], 1032 | "source": [ 1033 | "from sklearn.ensemble import VotingClassifier\n", 1034 | "\n", 1035 | "from sklearn.linear_model import LogisticRegression\n", 1036 | "lr = LogisticRegression(C=0.1,max_iter=100)\n", 1037 | "\n", 1038 | "import xgboost as xgb\n", 1039 | "xgb_model = xgb.XGBClassifier(max_depth=6,min_samples_leaf=2,n_estimators=100,num_round = 5)\n", 1040 | "\n", 1041 | "from sklearn.ensemble import RandomForestClassifier\n", 1042 | "rf = RandomForestClassifier(n_estimators=200,min_samples_leaf=2,max_depth=6,oob_score=True)\n", 1043 | "\n", 1044 | "from sklearn.ensemble import GradientBoostingClassifier\n", 1045 | "gbdt = GradientBoostingClassifier(learning_rate=0.1,min_samples_leaf=2,max_depth=6,n_estimators=100)\n", 1046 | "\n", 1047 | "vot = VotingClassifier(estimators=[('lr', lr), ('rf', rf),('gbdt',gbdt),('xgb',xgb_model)], voting='hard')\n", 1048 | "\n", 1049 | "vot.fit(train_data_X_sd,train_data_Y)\n", 1050 | "\n", 1051 | "vot.predict(test_data_X_sd)\n", 1052 | "\n", 1053 | "test[\"Survived\"] = vot.predict(test_data_X_sd)\n", 1054 | "test[['PassengerId','Survived']].set_index('PassengerId').to_csv('vot5.csv')" 1055 | ] 1056 | }, 1057 | { 1058 | "cell_type": "markdown", 1059 | "metadata": {}, 1060 | "source": [ 1061 | "### 模型融合stacking" 1062 | ] 1063 | }, 1064 | { 1065 | "cell_type": "code", 1066 | "execution_count": null, 1067 | "metadata": { 1068 | "collapsed": true 1069 | }, 1070 | "outputs": [], 1071 | "source": [ 1072 | "#划分train数据集,调用代码,把数据集名字转成和代码一样\n", 1073 | "X = train_data_X_sd\n", 1074 | "X_predict = test_data_X_sd\n", 1075 | "y = train_data_Y\n", 1076 | "\n", 1077 | "'''模型融合中使用到的各个单模型'''\n", 1078 | "from sklearn.linear_model import LogisticRegression\n", 1079 | "from sklearn import svm\n", 1080 | "import xgboost as xgb\n", 1081 | "from sklearn.ensemble import RandomForestClassifier\n", 1082 | "from sklearn.ensemble import GradientBoostingClassifier\n", 1083 | "\n", 1084 | "clfs = [LogisticRegression(C=0.1,max_iter=100),\n", 1085 | " xgb.XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\n", 1086 | " RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\n", 1087 | " GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)]\n", 1088 | "\n", 1089 | "#创建n_folds\n", 1090 | "from sklearn.cross_validation import StratifiedKFold\n", 1091 | "n_folds = 5\n", 1092 | "skf = list(StratifiedKFold(y, n_folds))\n", 1093 | "\n", 1094 | "#创建零矩阵\n", 1095 | "dataset_blend_train = np.zeros((X.shape[0], len(clfs)))\n", 1096 | "dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs)))\n", 1097 | "\n", 1098 | "#建立模型\n", 1099 | "for j, clf in enumerate(clfs):\n", 1100 | " '''依次训练各个单模型'''\n", 1101 | " # print(j, clf)\n", 1102 | " dataset_blend_test_j = np.zeros((X_predict.shape[0], len(skf)))\n", 1103 | " for i, (train, test) in enumerate(skf):\n", 1104 | " '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\n", 1105 | " # print(\"Fold\", i)\n", 1106 | " X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]\n", 1107 | " clf.fit(X_train, y_train)\n", 1108 | " y_submission = clf.predict_proba(X_test)[:, 1]\n", 1109 | " dataset_blend_train[test, j] = y_submission\n", 1110 | " dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1]\n", 1111 | " '''对于测试集，直接用这k个模型的预测值均值作为新的特征。'''\n", 1112 | " dataset_blend_test[:, j] = dataset_blend_test_j.mean(1)\n", 1113 | "\n", 1114 | "# 用建立第二层模型\n", 1115 | "clf2 = LogisticRegression(C=0.1,max_iter=100)\n", 1116 | "clf2.fit(dataset_blend_train, y)\n", 1117 | "y_submission = clf2.predict_proba(dataset_blend_test)[:, 1]\n", 1118 | "\n", 1119 | "\n", 1120 | "test = pd.read_csv(\"test.csv\")\n", 1121 | "test[\"Survived\"] = clf2.predict(dataset_blend_test)\n", 1122 | "test[['PassengerId','Survived']].set_index('PassengerId').to_csv('stack3.csv')" 1123 | ] 1124 | } 1125 | ], 1126 | "metadata": { 1127 | "kernelspec": { 1128 | "display_name": "Python 3", 1129 | "language": "python", 1130 | "name": "python3" 1131 | }, 1132 | "language_info": { 1133 | "codemirror_mode": { 1134 | "name": "ipython", 1135 | "version": 3 1136 | }, 1137 | "file_extension": ".py", 1138 | "mimetype": "text/x-python", 1139 | "name": "python", 1140 | "nbconvert_exporter": "python", 1141 | "pygments_lexer": "ipython3", 1142 | "version": "3.6.1" 1143 | } 1144 | }, 1145 | "nbformat": 4, 1146 | "nbformat_minor": 2 1147 | } 1148 | -------------------------------------------------------------------------------- /01_泰坦尼克号(titanic)/README.md: -------------------------------------------------------------------------------- 1 | # **泰坦尼克号** 2 | 3 | ## 比赛说明 4 | 5 | * [**泰坦尼克号**](https://www.kaggle.com/c/titanic) 的沉没是历史上最臭名昭着的沉船之一。1912年4月15日，在首航期间，泰坦尼克号撞上一座冰山后沉没，2224名乘客和机组人员中有1502人遇难。这一耸人听闻的悲剧震撼了国际社会，并导致了更好的船舶安全条例。 6 | * 沉船导致生命损失的原因之一是乘客和船员没有足够的救生艇。虽然幸存下来的运气有一些因素，但有些人比其他人更有可能生存，比如妇女，儿童和上层阶级。 7 | * 在这个挑战中，我们要求你完成对什么样的人可能生存的分析。特别是，我们要求你运用机器学习的工具来预测哪些乘客幸存下来的悲剧。 8 | 9 | ## 比赛分析 10 | 11 | - 分类问题：预测的是`生`与`死`的问题 12 | 13 | - 常用算法： K近邻(KNN)、 逻辑回归(LogisticRegression)、 随机森林(RandomForest)、 支持向量机(SVM)、 xgboost、 GBDT 14 | 15 | 16 | #### 步骤: 17 | **一. 数据分析** 18 | 19 | 1.下载并加载数据 20 | 21 | 2.总体预览:了解每列数据的含义,数据的格式等 22 | 23 | 3.数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备 24 | 25 | **二. 特征工程** 26 | 27 | 1.根据业务,常识,以及第二步的数据分析构造特征工程. 28 | 29 | 2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等) 30 | 31 | **三. 模型选择** 32 | 33 | 1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等. 34 | 35 | 2.比较各个模型的分数,然后取效果较好的模型作为基础模型. 36 | 37 | **四. 模型融合** 38 | 39 | **五. 修改特征和模型参数** 40 | 41 | 1.可以通过添加或者修改特征,提高模型的上限. 42 | 43 | 2.通过修改模型的参数,是模型逼近上限 44 | 45 | 46 | ## 一. 数据分析 47 | 48 | ### 数据下载和加载 49 | 50 | * 数据集下载地址： 51 | 52 | ---------- 53 | 54 | # 导入相关数据包 55 | import numpy as np 56 | import pandas as pd 57 | import seaborn as sns 58 | import matplotlib.pyplot as plt 59 | %matplotlib inline 60 | 61 | root_path = '/datasets' 62 | 63 | train = pd.read_csv('%s/%s' % (root_path, 'train.csv')) 64 | test = pd.read_csv('%s/%s' % (root_path, 'test.csv')) 65 | 66 | ### 特征说明 67 | 68 | - | survival | 生存 | 0 = No, 1 = Yes | 69 | - | pclass | 票类别-社会地位 | 1 = 1st, 2 = 2nd, 3 = 3rd | 70 | - | sex | 性别 | 71 | - | Age | 年龄 | 72 | - | sibsp | 兄弟姐妹/配偶 | 73 | - | parch | 父母/孩子的数量 | 74 | - | ticket | 票号 | 75 | - | fare | 乘客票价 | 76 | - | cabin | 客舱号码 | 77 | - | embarked | 登船港口 | C=Cherbourg, Q=Queenstown, S=Southampton | 78 | 79 | ---------- 80 | ### 特征详情 81 | 82 | train.head(5) 83 | 84 | ---------- 85 | 86 | # 返回每列列名,该列非nan值个数,以及该列类型 87 | train.info() 88 | 89 | 90 | RangeIndex: 891 entries, 0 to 890 91 | Data columns (total 12 columns): 92 | PassengerId 891 non-null int64 93 | Survived 891 non-null int64 94 | Pclass 891 non-null int64 95 | Name 891 non-null object 96 | Sex 891 non-null object 97 | Age 714 non-null float64 98 | SibSp 891 non-null int64 99 | Parch 891 non-null int64 100 | Ticket 891 non-null object 101 | Fare 891 non-null float64 102 | Cabin 204 non-null object 103 | Embarked 889 non-null object 104 | dtypes: float64(2), int64(5), object(5) 105 | memory usage: 83.6+ KB 106 | 107 | 108 | ---------- 109 | test.info() 110 | 111 | 112 | RangeIndex: 418 entries, 0 to 417 113 | Data columns (total 11 columns): 114 | PassengerId 418 non-null int64 115 | Pclass 418 non-null int64 116 | Name 418 non-null object 117 | Sex 418 non-null object 118 | Age 332 non-null float64 119 | SibSp 418 non-null int64 120 | Parch 418 non-null int64 121 | Ticket 418 non-null object 122 | Fare 417 non-null float64 123 | Cabin 91 non-null object 124 | Embarked 418 non-null object 125 | dtypes: float64(2), int64(4), object(5) 126 | memory usage: 36.0+ KB 127 | 128 | 129 | ---------- 130 | # 返回数值型变量的统计量 131 | # train.describe(percentiles=[0.00, 0.25, 0.5, 0.75, 1.00]) 132 | train.describe() 133 | 134 | ### 特征分析（统计学与绘图） 135 | 目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备 136 | 137 | 138 | # 存活人数 139 | train['Survived'].value_counts() 140 | 141 | 0 549 142 | 1 342 143 | Name: Survived, dtype: int64 144 | 145 | 146 | **数值型数据协方差,corr()函数** 147 | 148 | 来个总览,快速了解个数据的相关性 149 | 150 | # 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关. 151 | train_corr = train.drop('PassengerId',axis=1).corr() 152 | train_corr 153 | 154 | 155 | ---------- 156 | # 画出相关性热力图 157 | a = plt.subplots(figsize=(15,9))#调整画布大小 158 | a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)#画热力图 159 | 160 | **各个数据与结果的关系** 161 | 进一步探索分析各个数据与结果的关系 162 | 163 | - ① Pclass,乘客等级,1是最高级 164 | 165 | 166 | 结果分析:可以看出Survived和Pclass在Pclass=1的时候有较强的相关性（>0.5），所以最终模型中包含该特征。 167 | 168 | train.groupby(['Pclass'])['Pclass','Survived'].mean() 169 | 170 | 171 | ---------- 172 | train[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar() 173 | 174 | - ② Sex,性别 175 | 176 | 结果分析:女性有更高的活下来的概率（74%）,保留该特征 177 | 178 | train.groupby(['Sex'])['Sex','Survived'].mean() 179 | 180 | 181 | ---------- 182 | - ③ SibSp and Parch 兄妹配偶数/父母子女数 183 | 184 | 结果分析:这些特征与特定的值没有相关性不明显，最好是由这些独立的特征派生出一个新特征或者一组新特征 185 | 186 | train[['SibSp','Survived']].groupby(['SibSp']).mean() 187 | 188 | 189 | ---------- 190 | train[['Parch','Survived']].groupby(['Parch']).mean().plot.bar() 191 | 192 | ---------- 193 | 194 | - ④ Age年龄与生存情况的分析. 195 | 196 | 结果分析:由图,可以看到年龄是影响生存情况的. 197 | 198 | 但是年龄是有大部分缺失值的,缺失值需要进行处理,可以使用填充或者模型预测. 199 | 200 | g = sns.FacetGrid(train, col='Survived',size=5) 201 | g.map(plt.hist, 'Age', bins=40) 202 | 203 | 204 | 205 | 206 | ---------- 207 | train.groupby(['Age'])['Survived'].mean().plot() 208 | 209 | 210 | 211 | 212 | ---------- 213 | - ⑤ Embarked登港港口与生存情况的分析 214 | 215 | 216 | 结果分析:C地的生存率更高,这个也应该保留为模型特征. 217 | 218 | sns.countplot('Embarked',hue='Survived',data=train) 219 | 220 | 221 | 222 | - ⑥ 其他因素 223 | 224 | 在数据的Name项中包含了对该乘客的称呼，如Mr、Miss等，这些信息包含了乘客的年龄、性别、也有可能包含社会地位，如Dr、Lady、Major、Master等称呼。这一项不方便用图表展示，但是在特征工程中，我们会将其提取出来,然后放到模型中。 225 | 226 | 剩余因素还有船票价格、船舱号和船票号，这三个因素都可能会影响乘客在船中的位置从而影响逃生顺序，但是因为这三个因素与生存之间看不出明显规律，所以在后期模型融合时，将这些因素交给模型来决定其重要性。 227 | 228 | ## 二. 特征工程 229 | 230 | #先将数据集合并,一起做特征工程(注意,标准化的时候需要分开处理) 231 | #先将test补齐,然后通过pd.apped()合并 232 | test['Survived'] = 0 233 | train_test = train.append(test) 234 | 235 | ### 特征处理 236 | 237 | * ① Pclass,乘客等级,1是最高级 238 | 239 | 两种方式:一是该特征不做处理,可以直接保留.二是再处理:也进行分列处理(比较那种方式模型效果更好,就选那种) 240 | 241 | 242 | train_test = pd.get_dummies(train_test,columns=['Pclass']) 243 | 244 | 245 | * ② Sex,性别 246 | 247 | 无缺失值,直接分列 248 | 249 | 250 | train_test = pd.get_dummies(train_test,columns=["Sex"]) 251 | 252 | 253 | * ③ SibSp and Parch 兄妹配偶数/父母子女数 254 | 255 | 第一次直接保留:这两个都影响生存率,且都是数值型,先直接保存. 256 | 257 | 第二次进行两项求和,并进行分列处理.(兄妹配偶数和父母子女数都是认识人的数量,所以总数可能也会更好)(模型结果提高到了) 258 | 259 | #这是剑豪模型后回来添加的新特征,模型的分数最终有所提高了. 260 | train_test['SibSp_Parch'] = train_test['SibSp'] + train_test['Parch'] 261 | 262 | ---------- 263 | 264 | 265 | 266 | train_test = pd.get_dummies(train_test,columns['SibSp','Parch','SibSp_Parch']) 267 | 268 | 269 | * ④ Embarked 270 | 271 | 数据有极少量(3个)缺失值,但是在分列的时候,缺失值的所有列可以均为0,所以可以考虑不填充. 272 | 273 | 另外,也可以考虑用测试集众数来填充.先找出众数,再采用df.fillna()方法 274 | 275 | 276 | train_test = pd.get_dummies(train_test,columns=["Embarked"]) 277 | 278 | 279 | * ⑤ Name 280 | 281 | 1.在数据的Name项中包含了对该乘客的称呼,将这些关键词提取出来,然后做分列处理.(参考别人的) 282 | 283 | 284 | #从名字中提取出称呼： df['Name].str.extract()是提取函数,配合正则一起使用 285 | train_test['Name1'] = train_test['Name'].str.extract('.+,(.+)').str.extract( '^(.+?)\.').str.strip() 286 | 287 | 288 | ---------- 289 | 290 | #将姓名分类处理() 291 | train_test['Name1'].replace(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer' , inplace = True) 292 | train_test['Name1'].replace(['Jonkheer', 'Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty' , inplace = True) 293 | train_test['Name1'].replace(['Mme', 'Ms', 'Mrs'], 'Mrs') 294 | train_test['Name1'].replace(['Mlle', 'Miss'], 'Miss') 295 | train_test['Name1'].replace(['Mr'], 'Mr' , inplace = True) 296 | train_test['Name1'].replace(['Master'], 'Master' , inplace = True) 297 | 298 | ---------- 299 | 300 | #分列处理 301 | train_test = pd.get_dummies(train_test,columns=['Name1']) 302 | 303 | ---------- 304 | 305 | 306 | 2.从姓名中提取出姓做特征 307 | 308 | 309 | ---------- 310 | 311 | #从姓名中提取出姓 312 | train_test['Name2'] = train_test['Name'].apply(lambda x: x.split('.')[1]) 313 | 314 | #计算数量,然后合并数据集 315 | Name2_sum = train_test['Name2'].value_counts().reset_index() 316 | Name2_sum.columns=['Name2','Name2_sum'] 317 | train_test = pd.merge(train_test,Name2_sum,how='left',on='Name2') 318 | 319 | #由于出现一次时该特征时无效特征,用one来代替出现一次的姓 320 | train_test.loc[train_test['Name2_sum'] == 1 , 'Name2_new'] = 'one' 321 | train_test.loc[train_test['Name2_sum'] > 1 , 'Name2_new'] = train_test['Name2'] 322 | del train_test['Name2'] 323 | 324 | #分列处理 325 | train_test = pd.get_dummies(train_test,columns=['Name2_new']) 326 | 327 | 328 | ---------- 329 | 330 | 331 | #删掉姓名这个特征 332 | del train_test['Name'] 333 | 334 | 335 | * ⑥ Fare 336 | 337 | 该特征有缺失值,先找出缺失值的那调数据,然后用平均数填充 338 | 339 | 340 | #从上面的分析,发现该特征train集无miss值,test有一个缺失值,先查看 341 | train_test.loc[train_test["Fare"].isnull()] 342 | 343 | 344 | ---------- 345 | #票价与pclass和Embarked有关,所以用train分组后的平均数填充 346 | train.groupby(by=["Pclass","Embarked"]).Fare.mean() 347 | 348 | Pclass Embarked 349 | 1 C 104.718529 350 | Q 90.000000 351 | S 70.364862 352 | 2 C 25.358335 353 | Q 12.350000 354 | S 20.327439 355 | 3 C 11.214083 356 | Q 11.183393 357 | S 14.644083 358 | Name: Fare, dtype: float64 359 | 360 | 361 | ---------- 362 | 363 | #用pclass=3和Embarked=S的平均数14.644083来填充 364 | train_test["Fare"].fillna(14.435422,inplace=True) 365 | 366 | 367 | ---------- 368 | 369 | * ⑦ Ticket 370 | 371 | 该列和名字做类似的处理,先提取,然后分列 372 | 373 | 374 | #将Ticket提取字符列 375 | #str.isnumeric() 如果S中只有数字字符，则返回True，否则返回False 376 | train_test['Ticket_Letter'] = train_test['Ticket'].str.split().str[0] 377 | train_test['Ticket_Letter'] = train_test['Ticket_Letter'].apply(lambda x:np.nan if x.isnumeric() else x) 378 | train_test.drop('Ticket',inplace=True,axis=1) 379 | 380 | 381 | ---------- 382 | 383 | #分列,此时nan值可以不做处理 384 | train_test = pd.get_dummies(train_test,columns=['Ticket_Letter'],drop_first=True) 385 | 386 | 387 | * ⑧ Age 388 | 389 | 1.该列有大量缺失值,考虑用一个回归模型进行填充. 390 | 391 | 2.在模型修改的时候,考虑到年龄缺失值可能影响死亡情况,用年龄是否缺失值来构造新特征 392 | 393 | ---------- 394 | 395 | 396 | """ 397 | 这是模型就好后回来增加的新特征 398 | 考虑年龄缺失值可能影响死亡情况,数据表明,年龄缺失的死亡率为0.19 399 | """ 400 | train_test.loc[train_test["Age"].isnull()]['Survived'].mean() 401 | 402 | 0.19771863117870722 403 | 404 | 405 | ---------- 406 | 407 | # 所以用年龄是否缺失值来构造新特征 408 | train_test.loc[train_test["Age"].isnull() ,"age_nan"] = 1 409 | train_test.loc[train_test["Age"].notnull() ,"age_nan"] = 0 410 | train_test = pd.get_dummies(train_test,columns=['age_nan']) 411 | 412 | 413 | 利用其他组特征量，采用机器学习算法来预测Age 414 | 415 | train_test.info() 416 | 417 | 418 | Int64Index: 1309 entries, 0 to 1308 419 | Columns: 187 entries, Age to age_nan_1.0 420 | dtypes: float64(2), int64(3), object(1), uint8(181) 421 | memory usage: 343.0+ KB 422 | 423 | 424 | ---------- 425 | 426 | 427 | #创建没有['Age','Survived']的数据集 428 | missing_age = train_test.drop(['Survived','Cabin'],axis=1) 429 | #将Age完整的项作为训练集、将Age缺失的项作为测试集。 430 | missing_age_train = missing_age[missing_age['Age'].notnull()] 431 | missing_age_test = missing_age[missing_age['Age'].isnull()] 432 | 433 | ---------- 434 | 435 | #构建训练集合预测集的X和Y值 436 | missing_age_X_train = missing_age_train.drop(['Age'], axis=1) 437 | missing_age_Y_train = missing_age_train['Age'] 438 | missing_age_X_test = missing_age_test.drop(['Age'], axis=1) 439 | 440 | ---------- 441 | 442 | # 先将数据标准化 443 | from sklearn.preprocessing import StandardScaler 444 | ss = StandardScaler() 445 | #用测试集训练并标准化 446 | ss.fit(missing_age_X_train) 447 | missing_age_X_train = ss.transform(missing_age_X_train) 448 | missing_age_X_test = ss.transform(missing_age_X_test) 449 | 450 | ---------- 451 | 452 | #使用贝叶斯预测年龄 453 | from sklearn import linear_model 454 | lin = linear_model.BayesianRidge() 455 | 456 | ---------- 457 | 458 | lin.fit(missing_age_X_train,missing_age_Y_train) 459 | 460 | BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, 461 | fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, 462 | normalize=False, tol=0.001, verbose=False) 463 | 464 | ---------- 465 | 466 | #利用loc将预测值填入数据集 467 | train_test.loc[(train_test['Age'].isnull()), 'Age'] = lin.predict(missing_age_X_test) 468 | 469 | ---------- 470 | 471 | #将年龄划分是个阶段10以下,10-18,18-30,30-50,50以上 472 | train_test['Age'] = pd.cut(train_test['Age'], bins=[0,10,18,30,50,100],labels=[1,2,3,4,5]) 473 | 474 | train_test = pd.get_dummies(train_test,columns=['Age']) 475 | 476 | ---------- 477 | 478 | 479 | * ⑨ Cabin 480 | 481 | cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模,也可以考虑直接舍去该特征 482 | 483 | #cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模 484 | train_test['Cabin'] = train_test['Cabin'].apply(lambda x:str(x)[0] if pd.notnull(x) else x) 485 | train_test = pd.get_dummies(train_test,columns=['Cabin']) 486 | 487 | ---------- 488 | 489 | #cabin项缺失太多，只能将有无Cain首字母进行分类, 490 | train_test.loc[train_test["Cabin"].isnull() ,"Cabin_nan"] = 1 491 | train_test.loc[train_test["Cabin"].notnull() ,"Cabin_nan"] = 0 492 | train_test = pd.get_dummies(train_test,columns=['Cabin_nan']) 493 | train_test.drop('Cabin',axis=1,inplace=True) 494 | 495 | 496 | * ⑩ 特征工程处理完了,划分数据集 497 | 498 | ---------- 499 | 500 | train_data = train_test[:891] 501 | test_data = train_test[891:] 502 | train_data_X = train_data.drop(['Survived'],axis=1) 503 | train_data_Y = train_data['Survived'] 504 | test_data_X = test_data.drop(['Survived'],axis=1) 505 | 506 | ### 数据规约 507 | 508 | 1. 线性模型需要用标准化的数据建模,而树类模型不需要标准化的数据 509 | 2. 处理标准化的时候,注意将测试集的数据transform到test集上 510 | 511 | ---------- 512 | 513 | 514 | from sklearn.preprocessing import StandardScaler 515 | ss2 = StandardScaler() 516 | ss2.fit(train_data_X) 517 | train_data_X_sd = ss2.transform(train_data_X) 518 | test_data_X_sd = ss2.transform(test_data_X) 519 | 520 | 521 | ## 三. 建立模型 522 | 523 | ### 模型发现 524 | 525 | 1. 可选单个模型模型有随机森林,逻辑回归,svm,xgboost,gbdt等. 526 | 2. 也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法 527 | 3. 好的特征决定模型上限,好的模型和参数可以无线逼近上限. 528 | 4. 我测试了多种模型,模型结果最高的随机森林,最高有0.8. 529 | 530 | ### 构建模型 531 | 532 | - 随机森林 533 | 534 | ---------- 535 | 536 | from sklearn.ensemble import RandomForestClassifier 537 | 538 | rf = RandomForestClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6,oob_score=True) 539 | rf.fit(train_data_X,train_data_Y) 540 | 541 | test["Survived"] = rf.predict(test_data_X) 542 | RF = test[['PassengerId','Survived']].set_index('PassengerId') 543 | RF.to_csv('RF.csv') 544 | 545 | ---------- 546 | 547 | # 随机森林是随机选取特征进行建模的,所以每次的结果可能都有点小差异 548 | # 如果分数足够好,可以将该模型保存起来,下次直接调出来使用0.81339 'rf10.pkl' 549 | from sklearn.externals import joblib 550 | joblib.dump(rf, 'rf10.pkl') 551 | 552 | ---------- 553 | 554 | 555 | - LogisticRegression 556 | 557 | ---------- 558 | from sklearn.linear_model import LogisticRegression 559 | from sklearn.grid_search import GridSearchCV 560 | 561 | lr = LogisticRegression() 562 | param = {'C':[0.001,0.01,0.1,1,10],"max_iter":[100,250]} 563 | clf = GridSearchCV(lr,param,cv=5,n_jobs=-1,verbose=1,scoring="roc_auc") 564 | clf.fit(train_data_X_sd,train_data_Y) 565 | 566 | # 打印参数的得分情况 567 | clf.grid_scores_ 568 | 569 | # 打印最佳参数 570 | clf.best_params_ 571 | 572 | # 将最佳参数传入训练模型 573 | lr = LogisticRegression(clf.best_params_) 574 | lr.fit(train_data_X_sd,train_data_Y) 575 | 576 | # 预测结果 577 | lr.predict(test_data_X_sd) 578 | 579 | # 打印结果 580 | test["Survived"] = lr.predict(test_data_X_sd) 581 | LS = test[['PassengerId','Survived']].set_index('PassengerId') 582 | 583 | # 输出结果 584 | LS.to_csv('LS5.csv') 585 | 586 | ---------- 587 | 588 | - SVM 589 | 590 | ---------- 591 | from sklearn import svm 592 | svc = svm.SVC() 593 | 594 | clf = GridSearchCV(svc,param,cv=5,n_jobs=-1,verbose=1,scoring="roc_auc") 595 | clf.fit(train_data_X_sd,train_data_Y) 596 | 597 | clf.best_params_ 598 | 599 | svc = svm.SVC(C=1,max_iter=250) 600 | 601 | # 训练模型并预测结果 602 | svc.fit(train_data_X_sd,train_data_Y) 603 | svc.predict(test_data_X_sd) 604 | 605 | # 打印结果 606 | test["Survived"] = svc.predict(test_data_X_sd) 607 | SVM = test[['PassengerId','Survived']].set_index('PassengerId') 608 | SVM.to_csv('svm1.csv') 609 | 610 | 611 | ---------- 612 | 613 | 614 | - GBDT 615 | 616 | ---------- 617 | 618 | from sklearn.ensemble import GradientBoostingClassifier 619 | 620 | gbdt = GradientBoostingClassifier(learning_rate=0.7,max_depth=6,n_estimators=100,min_samples_leaf=2) 621 | 622 | gbdt.fit(train_data_X,train_data_Y) 623 | 624 | test["Survived"] = gbdt.predict(test_data_X) 625 | test[['PassengerId','Survived']].set_index('PassengerId').to_csv('gbdt3.csv') 626 | 627 | ---------- 628 | 629 | - xgboost 630 | 631 | 632 | ---------- 633 | 634 | import xgboost as xgb 635 | 636 | xgb_model = xgb.XGBClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6) 637 | xgb_model.fit(train_data_X,train_data_Y) 638 | 639 | test["Survived"] = xgb_model.predict(test_data_X) 640 | XGB = test[['PassengerId','Survived']].set_index('PassengerId') 641 | XGB.to_csv('XGB5.csv') 642 | 643 | 644 | ---------- 645 | 646 | 647 | ## 四 建立模型 648 | 649 | - 模型融合 voting 650 | 651 | 652 | ---------- 653 | 654 | from sklearn.ensemble import VotingClassifier 655 | 656 | from sklearn.linear_model import LogisticRegression 657 | lr = LogisticRegression(C=0.1,max_iter=100) 658 | 659 | import xgboost as xgb 660 | xgb_model = xgb.XGBClassifier(max_depth=6,min_samples_leaf=2,n_estimators=100,num_round = 5) 661 | 662 | from sklearn.ensemble import RandomForestClassifier 663 | rf = RandomForestClassifier(n_estimators=200,min_samples_leaf=2,max_depth=6,oob_score=True) 664 | 665 | from sklearn.ensemble import GradientBoostingClassifier 666 | gbdt = GradientBoostingClassifier(learning_rate=0.1,min_samples_leaf=2,max_depth=6,n_estimators=100) 667 | 668 | vot = VotingClassifier(estimators=[('lr', lr), ('rf', rf),('gbdt',gbdt),('xgb',xgb_model)], voting='hard') 669 | vot.fit(train_data_X_sd,train_data_Y) 670 | vot.predict(test_data_X_sd) 671 | 672 | test["Survived"] = vot.predict(test_data_X_sd) 673 | test[['PassengerId','Survived']].set_index('PassengerId').to_csv('vot5.csv') 674 | 675 | ---------- 676 | 677 | 678 | - 模型融合 stacking 679 | 680 | ---------- 681 | 682 | # 划分train数据集,调用代码,把数据集名字转成和代码一样 683 | X = train_data_X_sd 684 | X_predict = test_data_X_sd 685 | y = train_data_Y 686 | 687 | '''模型融合中使用到的各个单模型''' 688 | from sklearn.linear_model import LogisticRegression 689 | from sklearn import svm 690 | import xgboost as xgb 691 | from sklearn.ensemble import RandomForestClassifier 692 | from sklearn.ensemble import GradientBoostingClassifier 693 | 694 | clfs = [LogisticRegression(C=0.1,max_iter=100), 695 | xgb.XGBClassifier(max_depth=6,n_estimators=100,num_round = 5), 696 | RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True), 697 | GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)] 698 | 699 | # 创建n_folds 700 | from sklearn.cross_validation import StratifiedKFold 701 | n_folds = 5 702 | skf = list(StratifiedKFold(y, n_folds)) 703 | 704 | # 创建零矩阵 705 | dataset_blend_train = np.zeros((X.shape[0], len(clfs))) 706 | dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs))) 707 | 708 | # 建立模型 709 | for j, clf in enumerate(clfs): 710 | '''依次训练各个单模型''' 711 | # print(j, clf) 712 | dataset_blend_test_j = np.zeros((X_predict.shape[0], len(skf))) 713 | for i, (train, test) in enumerate(skf): 714 | '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。''' 715 | # print("Fold", i) 716 | X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test] 717 | clf.fit(X_train, y_train) 718 | y_submission = clf.predict_proba(X_test)[:, 1] 719 | dataset_blend_train[test, j] = y_submission 720 | dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1] 721 | '''对于测试集，直接用这k个模型的预测值均值作为新的特征。''' 722 | dataset_blend_test[:, j] = dataset_blend_test_j.mean(1) 723 | 724 | # 用建立第二层模型 725 | clf2 = LogisticRegression(C=0.1,max_iter=100) 726 | clf2.fit(dataset_blend_train, y) 727 | y_submission = clf2.predict_proba(dataset_blend_test)[:, 1] 728 | 729 | test = pd.read_csv("test.csv") 730 | test["Survived"] = clf2.predict(dataset_blend_test) 731 | test[['PassengerId','Survived']].set_index('PassengerId').to_csv('stack3.csv') 732 | 733 | 734 | 735 | 736 | 737 | -------------------------------------------------------------------------------- /01_泰坦尼克号(titanic)/datasets/test.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q 9 | 899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S 10 | 900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C 11 | 901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S 12 | 902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S 13 | 903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S 14 | 904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S 15 | 905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S 16 | 906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S 17 | 907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C 18 | 908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q 19 | 909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C 20 | 910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S 21 | 911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C 22 | 912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C 23 | 913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S 24 | 914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S 25 | 915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C 26 | 916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C 27 | 917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S 28 | 918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C 29 | 919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C 30 | 920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S 31 | 921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C 32 | 922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S 33 | 923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S 34 | 924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S 35 | 925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S 36 | 926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C 37 | 927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C 38 | 928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S 39 | 929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S 40 | 930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S 41 | 931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S 42 | 932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C 43 | 933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S 44 | 934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S 45 | 935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S 46 | 936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S 47 | 937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S 48 | 938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C 49 | 939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q 50 | 940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C 51 | 941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S 52 | 942,1,"Smith, Mr. Lucien Philip",male,24,1,0,13695,60,C31,S 53 | 943,2,"Pulbaum, Mr. Franz",male,27,0,0,SC/PARIS 2168,15.0333,,C 54 | 944,2,"Hocking, Miss. Ellen Nellie""""",female,20,2,1,29105,23,,S 55 | 945,1,"Fortune, Miss. Ethel Flora",female,28,3,2,19950,263,C23 C25 C27,S 56 | 946,2,"Mangiavacchi, Mr. Serafino Emilio",male,,0,0,SC/A.3 2861,15.5792,,C 57 | 947,3,"Rice, Master. Albert",male,10,4,1,382652,29.125,,Q 58 | 948,3,"Cor, Mr. Bartol",male,35,0,0,349230,7.8958,,S 59 | 949,3,"Abelseth, Mr. Olaus Jorgensen",male,25,0,0,348122,7.65,F G63,S 60 | 950,3,"Davison, Mr. Thomas Henry",male,,1,0,386525,16.1,,S 61 | 951,1,"Chaudanson, Miss. Victorine",female,36,0,0,PC 17608,262.375,B61,C 62 | 952,3,"Dika, Mr. Mirko",male,17,0,0,349232,7.8958,,S 63 | 953,2,"McCrae, Mr. Arthur Gordon",male,32,0,0,237216,13.5,,S 64 | 954,3,"Bjorklund, Mr. Ernst Herbert",male,18,0,0,347090,7.75,,S 65 | 955,3,"Bradley, Miss. Bridget Delia",female,22,0,0,334914,7.725,,Q 66 | 956,1,"Ryerson, Master. John Borie",male,13,2,2,PC 17608,262.375,B57 B59 B63 B66,C 67 | 957,2,"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)",female,,0,0,F.C.C. 13534,21,,S 68 | 958,3,"Burns, Miss. Mary Delia",female,18,0,0,330963,7.8792,,Q 69 | 959,1,"Moore, Mr. Clarence Bloomfield",male,47,0,0,113796,42.4,,S 70 | 960,1,"Tucker, Mr. Gilbert Milligan Jr",male,31,0,0,2543,28.5375,C53,C 71 | 961,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60,1,4,19950,263,C23 C25 C27,S 72 | 962,3,"Mulvihill, Miss. Bertha E",female,24,0,0,382653,7.75,,Q 73 | 963,3,"Minkoff, Mr. Lazar",male,21,0,0,349211,7.8958,,S 74 | 964,3,"Nieminen, Miss. Manta Josefina",female,29,0,0,3101297,7.925,,S 75 | 965,1,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C 76 | 966,1,"Geiger, Miss. Amalie",female,35,0,0,113503,211.5,C130,C 77 | 967,1,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C 78 | 968,3,"Miles, Mr. Frank",male,,0,0,359306,8.05,,S 79 | 969,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55,2,0,11770,25.7,C101,S 80 | 970,2,"Aldworth, Mr. Charles Augustus",male,30,0,0,248744,13,,S 81 | 971,3,"Doyle, Miss. Elizabeth",female,24,0,0,368702,7.75,,Q 82 | 972,3,"Boulos, Master. Akar",male,6,1,1,2678,15.2458,,C 83 | 973,1,"Straus, Mr. Isidor",male,67,1,0,PC 17483,221.7792,C55 C57,S 84 | 974,1,"Case, Mr. Howard Brown",male,49,0,0,19924,26,,S 85 | 975,3,"Demetri, Mr. Marinko",male,,0,0,349238,7.8958,,S 86 | 976,2,"Lamb, Mr. John Joseph",male,,0,0,240261,10.7083,,Q 87 | 977,3,"Khalil, Mr. Betros",male,,1,0,2660,14.4542,,C 88 | 978,3,"Barry, Miss. Julia",female,27,0,0,330844,7.8792,,Q 89 | 979,3,"Badman, Miss. Emily Louisa",female,18,0,0,A/4 31416,8.05,,S 90 | 980,3,"O'Donoghue, Ms. Bridget",female,,0,0,364856,7.75,,Q 91 | 981,2,"Wells, Master. Ralph Lester",male,2,1,1,29103,23,,S 92 | 982,3,"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)",female,22,1,0,347072,13.9,,S 93 | 983,3,"Pedersen, Mr. Olaf",male,,0,0,345498,7.775,,S 94 | 984,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27,1,2,F.C. 12750,52,B71,S 95 | 985,3,"Guest, Mr. Robert",male,,0,0,376563,8.05,,S 96 | 986,1,"Birnbaum, Mr. Jakob",male,25,0,0,13905,26,,C 97 | 987,3,"Tenglin, Mr. Gunnar Isidor",male,25,0,0,350033,7.7958,,S 98 | 988,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76,1,0,19877,78.85,C46,S 99 | 989,3,"Makinen, Mr. Kalle Edvard",male,29,0,0,STON/O 2. 3101268,7.925,,S 100 | 990,3,"Braf, Miss. Elin Ester Maria",female,20,0,0,347471,7.8542,,S 101 | 991,3,"Nancarrow, Mr. William Henry",male,33,0,0,A./5. 3338,8.05,,S 102 | 992,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43,1,0,11778,55.4417,C116,C 103 | 993,2,"Weisz, Mr. Leopold",male,27,1,0,228414,26,,S 104 | 994,3,"Foley, Mr. William",male,,0,0,365235,7.75,,Q 105 | 995,3,"Johansson Palmquist, Mr. Oskar Leander",male,26,0,0,347070,7.775,,S 106 | 996,3,"Thomas, Mrs. Alexander (Thamine Thelma"")""",female,16,1,1,2625,8.5167,,C 107 | 997,3,"Holthen, Mr. Johan Martin",male,28,0,0,C 4001,22.525,,S 108 | 998,3,"Buckley, Mr. Daniel",male,21,0,0,330920,7.8208,,Q 109 | 999,3,"Ryan, Mr. Edward",male,,0,0,383162,7.75,,Q 110 | 1000,3,"Willer, Mr. Aaron (Abi Weller"")""",male,,0,0,3410,8.7125,,S 111 | 1001,2,"Swane, Mr. George",male,18.5,0,0,248734,13,F,S 112 | 1002,2,"Stanton, Mr. Samuel Ward",male,41,0,0,237734,15.0458,,C 113 | 1003,3,"Shine, Miss. Ellen Natalia",female,,0,0,330968,7.7792,,Q 114 | 1004,1,"Evans, Miss. Edith Corse",female,36,0,0,PC 17531,31.6792,A29,C 115 | 1005,3,"Buckley, Miss. Katherine",female,18.5,0,0,329944,7.2833,,Q 116 | 1006,1,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63,1,0,PC 17483,221.7792,C55 C57,S 117 | 1007,3,"Chronopoulos, Mr. Demetrios",male,18,1,0,2680,14.4542,,C 118 | 1008,3,"Thomas, Mr. John",male,,0,0,2681,6.4375,,C 119 | 1009,3,"Sandstrom, Miss. Beatrice Irene",female,1,1,1,PP 9549,16.7,G6,S 120 | 1010,1,"Beattie, Mr. Thomson",male,36,0,0,13050,75.2417,C6,C 121 | 1011,2,"Chapman, Mrs. John Henry (Sara Elizabeth Lawry)",female,29,1,0,SC/AH 29037,26,,S 122 | 1012,2,"Watt, Miss. Bertha J",female,12,0,0,C.A. 33595,15.75,,S 123 | 1013,3,"Kiernan, Mr. John",male,,1,0,367227,7.75,,Q 124 | 1014,1,"Schabert, Mrs. Paul (Emma Mock)",female,35,1,0,13236,57.75,C28,C 125 | 1015,3,"Carver, Mr. Alfred John",male,28,0,0,392095,7.25,,S 126 | 1016,3,"Kennedy, Mr. John",male,,0,0,368783,7.75,,Q 127 | 1017,3,"Cribb, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q 893 | -------------------------------------------------------------------------------- /01_泰坦尼克号(titanic)/kaggle泰坦尼克之灾.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# kaggle泰坦尼克之灾" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "步骤:\n", 15 | "\n", 16 | "一.导入数据包与数据集\n", 17 | "\n", 18 | "二.数据分析\n", 19 | "\n", 20 | "1. 总体预览:了解每列数据的含义,数据的格式等\n", 21 | "2. .数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n", 22 | "\n", 23 | "三.特征工程\n", 24 | "\n", 25 | "1. 根据业务,常识,以及第二步的数据分析构造特征工程.\n", 26 | "2. 将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n", 27 | "\n", 28 | "四.模型选择\n", 29 | "\n", 30 | "1. 根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n", 31 | "2. 比较各个模型的分数,然后取效果较好的模型作为基础模型.\n", 32 | "\n", 33 | "五.修改特征和模型参数\n", 34 | "\n", 35 | "1. 可以通过添加或者修改特征,提高模型的上限.\n", 36 | "2. 通过修改模型的参数,是模型逼近上限" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "## 一.导入数据包与数据集" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": null, 49 | "metadata": { 50 | "collapsed": true 51 | }, 52 | "outputs": [], 53 | "source": [ 54 | "# 导入相关数据包\n", 55 | "import numpy as np\n", 56 | "import pandas as pd\n", 57 | "import seaborn as sns\n", 58 | "import matplotlib.pyplot as plt\n", 59 | "%matplotlib inline" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "metadata": {}, 66 | "outputs": [], 67 | "source": [ 68 | "root_path = './datasets'\n", 69 | "\n", 70 | "train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))\n", 71 | "test = pd.read_csv('%s/%s' % (root_path, 'test.csv'))" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "## 二.数据分析" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "### 1.总体预览\n", 86 | "了解每列数据的含义,数据的格式等" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "metadata": {}, 93 | "outputs": [], 94 | "source": [ 95 | "train.head(5)" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": null, 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "# 返回每列列名,该列非nan值个数,以及该列类型\n", 105 | "train.info()" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": null, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "test.info()" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": null, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [ 123 | "# 返回数值型变量的统计量\n", 124 | "# train.describe(percentiles=[0.00, 0.25, 0.5, 0.75, 1.00])\n", 125 | "train.describe()" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "## 2..数据初步分析,使用统计学与绘图\n", 133 | "目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": null, 139 | "metadata": {}, 140 | "outputs": [], 141 | "source": [ 142 | "#存活人数\n", 143 | "train['Survived'].value_counts()" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "#### 1)数值型数据协方差,corr()函数\n", 151 | "来个总览,快速了解个数据的相关性" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": null, 157 | "metadata": {}, 158 | "outputs": [], 159 | "source": [ 160 | "#相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.\n", 161 | "train_corr = train.drop('PassengerId',axis=1).corr()\n", 162 | "train_corr" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": null, 168 | "metadata": {}, 169 | "outputs": [], 170 | "source": [ 171 | "#画出相关性热力图\n", 172 | "a = plt.subplots(figsize=(15,9))#调整画布大小\n", 173 | "a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)#画热力图" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "#### 2)各个数据与结果的关系\n", 181 | "进一步探索分析各个数据与结果的关系" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "##### ①Pclass,乘客等级,1是最高级\n", 189 | "结果分析:可以看出Survived和Pclass在Pclass=1的时候有较强的相关性（>0.5），所以最终模型中包含该特征。" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": null, 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "train.groupby(['Pclass'])['Pclass','Survived'].mean()" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": null, 204 | "metadata": {}, 205 | "outputs": [], 206 | "source": [ 207 | "train[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar()" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "##### ②Sex,性别\n", 215 | "结果分析:女性有更高的活下来的概率（74%）,保留该特征" 216 | ] 217 | }, 218 | { 219 | "cell_type": "code", 220 | "execution_count": null, 221 | "metadata": {}, 222 | "outputs": [], 223 | "source": [ 224 | "train.groupby(['Sex'])['Sex','Survived'].mean()" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": null, 230 | "metadata": {}, 231 | "outputs": [], 232 | "source": [ 233 | "train[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "metadata": {}, 239 | "source": [ 240 | "##### ③SibSp and Parch 兄妹配偶数/父母子女数\n", 241 | "结果分析:这些特征与特定的值没有相关性不明显，最好是由这些独立的特征派生出一个新特征或者一组新特征" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "train[['SibSp','Survived']].groupby(['SibSp']).mean()" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": null, 256 | "metadata": {}, 257 | "outputs": [], 258 | "source": [ 259 | "train[['Parch','Survived']].groupby(['Parch']).mean().plot.bar()" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "##### ④Age年龄与生存情况的分析.\n", 267 | "结果分析:由图,可以看到年龄是影响生存情况的. \n", 268 | "\n", 269 | "但是年龄是有大部分缺失值的,缺失值需要进行处理,可以使用填充或者模型预测." 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": null, 275 | "metadata": {}, 276 | "outputs": [], 277 | "source": [ 278 | "g = sns.FacetGrid(train, col='Survived',size=5)\n", 279 | "g.map(plt.hist, 'Age', bins=40)" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": null, 285 | "metadata": {}, 286 | "outputs": [], 287 | "source": [ 288 | "train.groupby(['Age'])['Survived'].mean().plot()" 289 | ] 290 | }, 291 | { 292 | "cell_type": "markdown", 293 | "metadata": {}, 294 | "source": [ 295 | "##### ⑤Embarked登港港口与生存情况的分析\n", 296 | "结果分析:C地的生存率更高,这个也应该保留为模型特征." 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": null, 302 | "metadata": {}, 303 | "outputs": [], 304 | "source": [ 305 | "sns.countplot('Embarked',hue='Survived',data=train)" 306 | ] 307 | }, 308 | { 309 | "cell_type": "markdown", 310 | "metadata": {}, 311 | "source": [ 312 | "⑥其他因素\n", 313 | "*在数据的Name项中包含了对该乘客的称呼，如Mr、Miss等，这些信息包含了乘客的年龄、性别、也有可能包含社会地位，如Dr、Lady、Major、Master等称呼。这一项不方便用图表展示，但是在特征工程中，我们会将其提取出来,然后放到模型中。\n", 314 | "\n", 315 | "*剩余因素还有船票价格、船舱号和船票号，这三个因素都可能会影响乘客在船中的位置从而影响逃生顺序，但是因为这三个因素与生存之间看不出明显规律，所以在后期模型融合时，将这些因素交给模型来决定其重要性。" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "## 三.特征工程" 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": null, 328 | "metadata": { 329 | "collapsed": true 330 | }, 331 | "outputs": [], 332 | "source": [ 333 | "#先将数据集合并,一起做特征工程(注意,标准化的时候需要分开处理)\n", 334 | "#先将test补齐,然后通过pd.apped()合并\n", 335 | "test['Survived'] = 0\n", 336 | "train_test = train.append(test)" 337 | ] 338 | }, 339 | { 340 | "cell_type": "markdown", 341 | "metadata": {}, 342 | "source": [ 343 | "### ①Pclass,乘客等级,1是最高级\n", 344 | "两种方式:一是该特征不做处理,可以直接保留.二是再处理:也进行分列处理(比较那种方式模型效果更好,就选那种)" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": null, 350 | "metadata": { 351 | "collapsed": true 352 | }, 353 | "outputs": [], 354 | "source": [ 355 | "train_test = pd.get_dummies(train_test,columns=['Pclass'])" 356 | ] 357 | }, 358 | { 359 | "cell_type": "markdown", 360 | "metadata": {}, 361 | "source": [ 362 | "### ②Sex,性别\n", 363 | "无缺失值,直接分列" 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": null, 369 | "metadata": { 370 | "collapsed": true 371 | }, 372 | "outputs": [], 373 | "source": [ 374 | "train_test = pd.get_dummies(train_test,columns=[\"Sex\"])" 375 | ] 376 | }, 377 | { 378 | "cell_type": "markdown", 379 | "metadata": {}, 380 | "source": [ 381 | "### ③SibSp and Parch 兄妹配偶数/父母子女数\n", 382 | "第一次直接保留:这两个都影响生存率,且都是数值型,先直接保存.\n", 383 | "\n", 384 | "第二次进行两项求和,并进行分列处理.(兄妹配偶数和父母子女数都是认识人的数量,所以总数可能也会更好)(模型结果提高到了)" 385 | ] 386 | }, 387 | { 388 | "cell_type": "code", 389 | "execution_count": null, 390 | "metadata": { 391 | "collapsed": true 392 | }, 393 | "outputs": [], 394 | "source": [ 395 | "#这是剑豪模型后回来添加的新特征,模型的分数最终有所提高了.\n", 396 | "train_test['SibSp_Parch'] = train_test['SibSp'] + train_test['Parch']" 397 | ] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": null, 402 | "metadata": { 403 | "collapsed": true 404 | }, 405 | "outputs": [], 406 | "source": [ 407 | "train_test = pd.get_dummies(train_test,columns = ['SibSp','Parch','SibSp_Parch']) " 408 | ] 409 | }, 410 | { 411 | "cell_type": "markdown", 412 | "metadata": {}, 413 | "source": [ 414 | "### ④Embarked \n", 415 | "数据有极少量(3个)缺失值,但是在分列的时候,缺失值的所有列可以均为0,所以可以考虑不填充.\n", 416 | "\n", 417 | "另外,也可以考虑用测试集众数来填充.先找出众数,再采用df.fillna()方法" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": null, 423 | "metadata": { 424 | "collapsed": true 425 | }, 426 | "outputs": [], 427 | "source": [ 428 | "train_test = pd.get_dummies(train_test,columns=[\"Embarked\"])" 429 | ] 430 | }, 431 | { 432 | "cell_type": "markdown", 433 | "metadata": {}, 434 | "source": [ 435 | "### ⑤ Name\n", 436 | "1.在数据的Name项中包含了对该乘客的称呼,将这些关键词提取出来,然后做分列处理.(参考别人的)" 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "execution_count": null, 442 | "metadata": {}, 443 | "outputs": [], 444 | "source": [ 445 | "#从名字中提取出称呼： df['Name].str.extract()是提取函数,配合正则一起使用\n", 446 | "train_test['Name1'] = train_test['Name'].str.extract('.+,(.+)').str.extract( '^(.+?)\\.').str.strip()" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": null, 452 | "metadata": { 453 | "collapsed": true 454 | }, 455 | "outputs": [], 456 | "source": [ 457 | "#将姓名分类处理()\n", 458 | "train_test['Name1'].replace(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer' , inplace = True)\n", 459 | "train_test['Name1'].replace(['Jonkheer', 'Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty' , inplace = True)\n", 460 | "train_test['Name1'].replace(['Mme', 'Ms', 'Mrs'], 'Mrs')\n", 461 | "train_test['Name1'].replace(['Mlle', 'Miss'], 'Miss')\n", 462 | "train_test['Name1'].replace(['Mr'], 'Mr' , inplace = True)\n", 463 | "train_test['Name1'].replace(['Master'], 'Master' , inplace = True)" 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": null, 469 | "metadata": { 470 | "collapsed": true 471 | }, 472 | "outputs": [], 473 | "source": [ 474 | "#分列处理\n", 475 | "train_test = pd.get_dummies(train_test,columns=['Name1'])" 476 | ] 477 | }, 478 | { 479 | "cell_type": "markdown", 480 | "metadata": {}, 481 | "source": [ 482 | "##### 从姓名中提取出姓做特征" 483 | ] 484 | }, 485 | { 486 | "cell_type": "code", 487 | "execution_count": null, 488 | "metadata": { 489 | "collapsed": true 490 | }, 491 | "outputs": [], 492 | "source": [ 493 | "#从姓名中提取出姓\n", 494 | "train_test['Name2'] = train_test['Name'].apply(lambda x: x.split('.')[1])\n", 495 | "\n", 496 | "#计算数量,然后合并数据集\n", 497 | "Name2_sum = train_test['Name2'].value_counts().reset_index()\n", 498 | "Name2_sum.columns=['Name2','Name2_sum']\n", 499 | "train_test = pd.merge(train_test,Name2_sum,how='left',on='Name2')\n", 500 | "\n", 501 | "#由于出现一次时该特征时无效特征,用one来代替出现一次的姓\n", 502 | "train_test.loc[train_test['Name2_sum'] == 1 , 'Name2_new'] = 'one'\n", 503 | "train_test.loc[train_test['Name2_sum'] > 1 , 'Name2_new'] = train_test['Name2']\n", 504 | "del train_test['Name2']\n", 505 | "\n", 506 | "#分列处理\n", 507 | "train_test = pd.get_dummies(train_test,columns=['Name2_new'])" 508 | ] 509 | }, 510 | { 511 | "cell_type": "code", 512 | "execution_count": null, 513 | "metadata": { 514 | "collapsed": true 515 | }, 516 | "outputs": [], 517 | "source": [ 518 | "#删掉姓名这个特征\n", 519 | "del train_test['Name']" 520 | ] 521 | }, 522 | { 523 | "cell_type": "markdown", 524 | "metadata": {}, 525 | "source": [ 526 | "### ⑥ Fare\n", 527 | "该特征有缺失值,先找出缺失值的那调数据,然后用平均数填充" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": null, 533 | "metadata": {}, 534 | "outputs": [], 535 | "source": [ 536 | "#从上面的分析,发现该特征train集无miss值,test有一个缺失值,先查看\n", 537 | "train_test.loc[train_test[\"Fare\"].isnull()]" 538 | ] 539 | }, 540 | { 541 | "cell_type": "code", 542 | "execution_count": null, 543 | "metadata": {}, 544 | "outputs": [], 545 | "source": [ 546 | "#票价与pclass和Embarked有关,所以用train分组后的平均数填充\n", 547 | "train.groupby(by=[\"Pclass\",\"Embarked\"]).Fare.mean()" 548 | ] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "execution_count": null, 553 | "metadata": { 554 | "collapsed": true 555 | }, 556 | "outputs": [], 557 | "source": [ 558 | "#用pclass=3和Embarked=S的平均数14.644083来填充\n", 559 | "train_test[\"Fare\"].fillna(14.435422,inplace=True)" 560 | ] 561 | }, 562 | { 563 | "cell_type": "markdown", 564 | "metadata": { 565 | "collapsed": true 566 | }, 567 | "source": [ 568 | "### ⑦ Ticket\n", 569 | "该列和名字做类似的处理,先提取,然后分列" 570 | ] 571 | }, 572 | { 573 | "cell_type": "code", 574 | "execution_count": null, 575 | "metadata": { 576 | "collapsed": true 577 | }, 578 | "outputs": [], 579 | "source": [ 580 | "#将Ticket提取字符列\n", 581 | "#str.isnumeric() 如果S中只有数字字符，则返回True，否则返回False\n", 582 | "train_test['Ticket_Letter'] = train_test['Ticket'].str.split().str[0]\n", 583 | "train_test['Ticket_Letter'] = train_test['Ticket_Letter'].apply(lambda x:np.nan if x.isnumeric() else x)\n", 584 | "train_test.drop('Ticket',inplace=True,axis=1)" 585 | ] 586 | }, 587 | { 588 | "cell_type": "code", 589 | "execution_count": null, 590 | "metadata": { 591 | "collapsed": true 592 | }, 593 | "outputs": [], 594 | "source": [ 595 | "#分列,此时nan值可以不做处理\n", 596 | "train_test = pd.get_dummies(train_test,columns=['Ticket_Letter'],drop_first=True)" 597 | ] 598 | }, 599 | { 600 | "cell_type": "markdown", 601 | "metadata": {}, 602 | "source": [ 603 | "### ⑧ Age\n", 604 | "1.该列有大量缺失值,考虑用一个回归模型进行填充.\n", 605 | "\n", 606 | "2.在模型修改的时候,考虑到年龄缺失值可能影响死亡情况,用年龄是否缺失值来构造新特征" 607 | ] 608 | }, 609 | { 610 | "cell_type": "code", 611 | "execution_count": null, 612 | "metadata": {}, 613 | "outputs": [], 614 | "source": [ 615 | "\"\"\"\n", 616 | "这是模型就好后回来增加的新特征\n", 617 | "考虑年龄缺失值可能影响死亡情况,数据表明,年龄缺失的死亡率为0.19.\n", 618 | "\"\"\"\n", 619 | "train_test.loc[train_test[\"Age\"].isnull()]['Survived'].mean()" 620 | ] 621 | }, 622 | { 623 | "cell_type": "code", 624 | "execution_count": null, 625 | "metadata": { 626 | "collapsed": true 627 | }, 628 | "outputs": [], 629 | "source": [ 630 | "# 所以用年龄是否缺失值来构造新特征\n", 631 | "train_test.loc[train_test[\"Age\"].isnull() ,\"age_nan\"] = 1\n", 632 | "train_test.loc[train_test[\"Age\"].notnull() ,\"age_nan\"] = 0\n", 633 | "train_test = pd.get_dummies(train_test,columns=['age_nan'])" 634 | ] 635 | }, 636 | { 637 | "cell_type": "markdown", 638 | "metadata": {}, 639 | "source": [ 640 | "### 利用其他组特征量，采用机器学习算法来预测Age" 641 | ] 642 | }, 643 | { 644 | "cell_type": "code", 645 | "execution_count": null, 646 | "metadata": {}, 647 | "outputs": [], 648 | "source": [ 649 | "train_test.info()" 650 | ] 651 | }, 652 | { 653 | "cell_type": "code", 654 | "execution_count": null, 655 | "metadata": {}, 656 | "outputs": [], 657 | "source": [ 658 | "#创建没有['Age','Survived']的数据集\n", 659 | "missing_age = train_test.drop(['Survived','Cabin'],axis=1)\n", 660 | "#将Age完整的项作为训练集、将Age缺失的项作为测试集。\n", 661 | "missing_age_train = missing_age[missing_age['Age'].notnull()]\n", 662 | "missing_age_test = missing_age[missing_age['Age'].isnull()]" 663 | ] 664 | }, 665 | { 666 | "cell_type": "code", 667 | "execution_count": null, 668 | "metadata": {}, 669 | "outputs": [], 670 | "source": [ 671 | "#构建训练集合预测集的X和Y值\n", 672 | "missing_age_X_train = missing_age_train.drop(['Age'], axis=1)\n", 673 | "missing_age_Y_train = missing_age_train['Age']\n", 674 | "missing_age_X_test = missing_age_test.drop(['Age'], axis=1)" 675 | ] 676 | }, 677 | { 678 | "cell_type": "code", 679 | "execution_count": null, 680 | "metadata": { 681 | "collapsed": true 682 | }, 683 | "outputs": [], 684 | "source": [ 685 | "# 先将数据标准化\n", 686 | "from sklearn.preprocessing import StandardScaler\n", 687 | "ss = StandardScaler()\n", 688 | "#用测试集训练并标准化\n", 689 | "ss.fit(missing_age_X_train)\n", 690 | "missing_age_X_train = ss.transform(missing_age_X_train)\n", 691 | "missing_age_X_test = ss.transform(missing_age_X_test)" 692 | ] 693 | }, 694 | { 695 | "cell_type": "code", 696 | "execution_count": null, 697 | "metadata": { 698 | "collapsed": true 699 | }, 700 | "outputs": [], 701 | "source": [ 702 | "#使用贝叶斯预测年龄\n", 703 | "from sklearn import linear_model\n", 704 | "lin = linear_model.BayesianRidge()" 705 | ] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": null, 710 | "metadata": {}, 711 | "outputs": [], 712 | "source": [ 713 | "lin.fit(missing_age_X_train,missing_age_Y_train)" 714 | ] 715 | }, 716 | { 717 | "cell_type": "code", 718 | "execution_count": null, 719 | "metadata": { 720 | "collapsed": true 721 | }, 722 | "outputs": [], 723 | "source": [ 724 | "#利用loc将预测值填入数据集\n", 725 | "train_test.loc[(train_test['Age'].isnull()), 'Age'] = lin.predict(missing_age_X_test)" 726 | ] 727 | }, 728 | { 729 | "cell_type": "code", 730 | "execution_count": null, 731 | "metadata": { 732 | "collapsed": true 733 | }, 734 | "outputs": [], 735 | "source": [ 736 | "#将年龄划分是个阶段10以下,10-18,18-30,30-50,50以上\n", 737 | "train_test['Age'] = pd.cut(train_test['Age'], bins=[0,10,18,30,50,100],labels=[1,2,3,4,5])\n", 738 | "\n", 739 | "train_test = pd.get_dummies(train_test,columns=['Age'])" 740 | ] 741 | }, 742 | { 743 | "cell_type": "markdown", 744 | "metadata": {}, 745 | "source": [ 746 | "### ⑨ Cabin\n", 747 | "cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模,也可以考虑直接舍去该特征" 748 | ] 749 | }, 750 | { 751 | "cell_type": "code", 752 | "execution_count": null, 753 | "metadata": { 754 | "collapsed": true 755 | }, 756 | "outputs": [], 757 | "source": [ 758 | "#cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模\n", 759 | "train_test['Cabin'] = train_test['Cabin'].apply(lambda x:str(x)[0] if pd.notnull(x) else x)\n", 760 | "train_test = pd.get_dummies(train_test,columns=['Cabin'])" 761 | ] 762 | }, 763 | { 764 | "cell_type": "code", 765 | "execution_count": null, 766 | "metadata": {}, 767 | "outputs": [], 768 | "source": [ 769 | "#cabin项缺失太多，只能将有无Cain首字母进行分类,\n", 770 | "train_test.loc[train_test[\"Cabin\"].isnull() ,\"Cabin_nan\"] = 1\n", 771 | "train_test.loc[train_test[\"Cabin\"].notnull() ,\"Cabin_nan\"] = 0\n", 772 | "train_test = pd.get_dummies(train_test,columns=['Cabin_nan'])\n", 773 | "train_test.drop('Cabin',axis=1,inplace=True)" 774 | ] 775 | }, 776 | { 777 | "cell_type": "markdown", 778 | "metadata": {}, 779 | "source": [ 780 | "### ⑩ 特征工程处理完了,划分数据集" 781 | ] 782 | }, 783 | { 784 | "cell_type": "code", 785 | "execution_count": null, 786 | "metadata": { 787 | "collapsed": true 788 | }, 789 | "outputs": [], 790 | "source": [ 791 | "train_data = train_test[:891]\n", 792 | "test_data = train_test[891:]\n", 793 | "train_data_X = train_data.drop(['Survived'],axis=1)\n", 794 | "train_data_Y = train_data['Survived']\n", 795 | "test_data_X = test_data.drop(['Survived'],axis=1)" 796 | ] 797 | }, 798 | { 799 | "cell_type": "markdown", 800 | "metadata": {}, 801 | "source": [ 802 | "## 四 建立模型" 803 | ] 804 | }, 805 | { 806 | "cell_type": "markdown", 807 | "metadata": {}, 808 | "source": [ 809 | "### 数据标准化\n", 810 | "1.线性模型需要用标准化的数据建模,而树类模型不需要标准化的数据\n", 811 | "\n", 812 | "2.处理标准化的时候,注意将测试集的数据transform到test集上" 813 | ] 814 | }, 815 | { 816 | "cell_type": "code", 817 | "execution_count": null, 818 | "metadata": { 819 | "collapsed": true 820 | }, 821 | "outputs": [], 822 | "source": [ 823 | "from sklearn.preprocessing import StandardScaler\n", 824 | "ss2 = StandardScaler()\n", 825 | "ss2.fit(train_data_X)\n", 826 | "train_data_X_sd = ss2.transform(train_data_X)\n", 827 | "test_data_X_sd = ss2.transform(test_data_X)" 828 | ] 829 | }, 830 | { 831 | "cell_type": "markdown", 832 | "metadata": {}, 833 | "source": [ 834 | "### 开始建模\n", 835 | "1.可选单个模型模型有随机森林,逻辑回归,svm,xgboost,gbdt等.\n", 836 | "\n", 837 | "2.也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法\n", 838 | "\n", 839 | "3.好的特征决定模型上限,好的模型和参数可以无线逼近上限.\n", 840 | "\n", 841 | "4.我测试了多种模型,模型结果最高的随机森林,最高有0.8." 842 | ] 843 | }, 844 | { 845 | "cell_type": "markdown", 846 | "metadata": {}, 847 | "source": [ 848 | "### 随机森林" 849 | ] 850 | }, 851 | { 852 | "cell_type": "code", 853 | "execution_count": null, 854 | "metadata": { 855 | "collapsed": true 856 | }, 857 | "outputs": [], 858 | "source": [ 859 | "from sklearn.ensemble import RandomForestClassifier\n", 860 | "\n", 861 | "rf = RandomForestClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6,oob_score=True)\n", 862 | "rf.fit(train_data_X,train_data_Y)\n", 863 | "\n", 864 | "test[\"Survived\"] = rf.predict(test_data_X)\n", 865 | "RF = test[['PassengerId','Survived']].set_index('PassengerId')\n", 866 | "RF.to_csv('RF.csv')" 867 | ] 868 | }, 869 | { 870 | "cell_type": "code", 871 | "execution_count": null, 872 | "metadata": { 873 | "collapsed": true 874 | }, 875 | "outputs": [], 876 | "source": [ 877 | "#随机森林是随机选取特征进行建模的,所以每次的结果可能都有点小差异\n", 878 | "#如果分数足够好,可以将该模型保存起来,下次直接调出来使用0.81339 'rf10.pkl'\n", 879 | "from sklearn.externals import joblib\n", 880 | "joblib.dump(rf, 'rf10.pkl')" 881 | ] 882 | }, 883 | { 884 | "cell_type": "markdown", 885 | "metadata": {}, 886 | "source": [ 887 | "### LogisticRegression" 888 | ] 889 | }, 890 | { 891 | "cell_type": "code", 892 | "execution_count": null, 893 | "metadata": { 894 | "collapsed": true 895 | }, 896 | "outputs": [], 897 | "source": [ 898 | "from sklearn.linear_model import LogisticRegression\n", 899 | "\n", 900 | "lr = LogisticRegression()\n", 901 | "\n", 902 | "from sklearn.grid_search import GridSearchCV\n", 903 | "\n", 904 | "param = {'C':[0.001,0.01,0.1,1,10],\"max_iter\":[100,250]}\n", 905 | "\n", 906 | "clf = GridSearchCV(lr,param,cv=5,n_jobs=-1,verbose=1,scoring=\"roc_auc\")\n", 907 | "\n", 908 | "clf.fit(train_data_X_sd,train_data_Y)\n", 909 | "\n", 910 | "#打印参数的得分情况\n", 911 | "clf.grid_scores_\n", 912 | "\n", 913 | "#打印最佳参数\n", 914 | "clf.best_params_\n", 915 | "\n", 916 | "#将最佳参数传入训练模型\n", 917 | "lr = LogisticRegression(clf.best_params_)\n", 918 | "\n", 919 | "lr.fit(train_data_X_sd,train_data_Y)\n", 920 | "\n", 921 | "#预测结果\n", 922 | "lr.predict(test_data_X_sd)\n", 923 | "\n", 924 | "#打印结果\n", 925 | "test[\"Survived\"] = lr.predict(test_data_X_sd)\n", 926 | "LS = test[['PassengerId','Survived']].set_index('PassengerId')\n", 927 | "\n", 928 | "#输出结果\n", 929 | "LS.to_csv('LS5.csv')" 930 | ] 931 | }, 932 | { 933 | "cell_type": "markdown", 934 | "metadata": {}, 935 | "source": [ 936 | "### SVM" 937 | ] 938 | }, 939 | { 940 | "cell_type": "code", 941 | "execution_count": null, 942 | "metadata": { 943 | "collapsed": true 944 | }, 945 | "outputs": [], 946 | "source": [ 947 | "from sklearn import svm\n", 948 | "svc = svm.SVC()\n", 949 | "\n", 950 | "clf = GridSearchCV(svc,param,cv=5,n_jobs=-1,verbose=1,scoring=\"roc_auc\")\n", 951 | "clf.fit(train_data_X_sd,train_data_Y)\n", 952 | "\n", 953 | "clf.best_params_\n", 954 | "\n", 955 | "svc = svm.SVC(C=1,max_iter=250)\n", 956 | "\n", 957 | "#训练模型并预测结果\n", 958 | "svc.fit(train_data_X_sd,train_data_Y)\n", 959 | "svc.predict(test_data_X_sd)\n", 960 | "\n", 961 | "#打印结果\n", 962 | "test[\"Survived\"] = svc.predict(test_data_X_sd)\n", 963 | "SVM = test[['PassengerId','Survived']].set_index('PassengerId')\n", 964 | "SVM.to_csv('svm1.csv')" 965 | ] 966 | }, 967 | { 968 | "cell_type": "markdown", 969 | "metadata": {}, 970 | "source": [ 971 | "### xgboost" 972 | ] 973 | }, 974 | { 975 | "cell_type": "code", 976 | "execution_count": null, 977 | "metadata": { 978 | "collapsed": true 979 | }, 980 | "outputs": [], 981 | "source": [ 982 | "import xgboost as xgb\n", 983 | "\n", 984 | "xgb_model = xgb.XGBClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6)\n", 985 | "\n", 986 | "xgb_model.fit(train_data_X,train_data_Y)\n", 987 | "\n", 988 | "test[\"Survived\"] = xgb_model.predict(test_data_X)\n", 989 | "XGB = test[['PassengerId','Survived']].set_index('PassengerId')\n", 990 | "XGB.to_csv('XGB5.csv')" 991 | ] 992 | }, 993 | { 994 | "cell_type": "markdown", 995 | "metadata": {}, 996 | "source": [ 997 | "### GBDT" 998 | ] 999 | }, 1000 | { 1001 | "cell_type": "code", 1002 | "execution_count": null, 1003 | "metadata": { 1004 | "collapsed": true 1005 | }, 1006 | "outputs": [], 1007 | "source": [ 1008 | "from sklearn.ensemble import GradientBoostingClassifier\n", 1009 | "\n", 1010 | "gbdt = GradientBoostingClassifier(learning_rate=0.7,max_depth=6,n_estimators=100,min_samples_leaf=2)\n", 1011 | "\n", 1012 | "gbdt.fit(train_data_X,train_data_Y)\n", 1013 | "\n", 1014 | "test[\"Survived\"] = gbdt.predict(test_data_X)\n", 1015 | "test[['PassengerId','Survived']].set_index('PassengerId').to_csv('gbdt3.csv')" 1016 | ] 1017 | }, 1018 | { 1019 | "cell_type": "markdown", 1020 | "metadata": {}, 1021 | "source": [ 1022 | "### 模型融合voting" 1023 | ] 1024 | }, 1025 | { 1026 | "cell_type": "code", 1027 | "execution_count": null, 1028 | "metadata": { 1029 | "collapsed": true 1030 | }, 1031 | "outputs": [], 1032 | "source": [ 1033 | "from sklearn.ensemble import VotingClassifier\n", 1034 | "\n", 1035 | "from sklearn.linear_model import LogisticRegression\n", 1036 | "lr = LogisticRegression(C=0.1,max_iter=100)\n", 1037 | "\n", 1038 | "import xgboost as xgb\n", 1039 | "xgb_model = xgb.XGBClassifier(max_depth=6,min_samples_leaf=2,n_estimators=100,num_round = 5)\n", 1040 | "\n", 1041 | "from sklearn.ensemble import RandomForestClassifier\n", 1042 | "rf = RandomForestClassifier(n_estimators=200,min_samples_leaf=2,max_depth=6,oob_score=True)\n", 1043 | "\n", 1044 | "from sklearn.ensemble import GradientBoostingClassifier\n", 1045 | "gbdt = GradientBoostingClassifier(learning_rate=0.1,min_samples_leaf=2,max_depth=6,n_estimators=100)\n", 1046 | "\n", 1047 | "vot = VotingClassifier(estimators=[('lr', lr), ('rf', rf),('gbdt',gbdt),('xgb',xgb_model)], voting='hard')\n", 1048 | "\n", 1049 | "vot.fit(train_data_X_sd,train_data_Y)\n", 1050 | "\n", 1051 | "vot.predict(test_data_X_sd)\n", 1052 | "\n", 1053 | "test[\"Survived\"] = vot.predict(test_data_X_sd)\n", 1054 | "test[['PassengerId','Survived']].set_index('PassengerId').to_csv('vot5.csv')" 1055 | ] 1056 | }, 1057 | { 1058 | "cell_type": "markdown", 1059 | "metadata": {}, 1060 | "source": [ 1061 | "### 模型融合stacking" 1062 | ] 1063 | }, 1064 | { 1065 | "cell_type": "code", 1066 | "execution_count": null, 1067 | "metadata": { 1068 | "collapsed": true 1069 | }, 1070 | "outputs": [], 1071 | "source": [ 1072 | "#划分train数据集,调用代码,把数据集名字转成和代码一样\n", 1073 | "X = train_data_X_sd\n", 1074 | "X_predict = test_data_X_sd\n", 1075 | "y = train_data_Y\n", 1076 | "\n", 1077 | "'''模型融合中使用到的各个单模型'''\n", 1078 | "from sklearn.linear_model import LogisticRegression\n", 1079 | "from sklearn import svm\n", 1080 | "import xgboost as xgb\n", 1081 | "from sklearn.ensemble import RandomForestClassifier\n", 1082 | "from sklearn.ensemble import GradientBoostingClassifier\n", 1083 | "\n", 1084 | "clfs = [LogisticRegression(C=0.1,max_iter=100),\n", 1085 | " xgb.XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\n", 1086 | " RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\n", 1087 | " GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)]\n", 1088 | "\n", 1089 | "#创建n_folds\n", 1090 | "from sklearn.cross_validation import StratifiedKFold\n", 1091 | "n_folds = 5\n", 1092 | "skf = list(StratifiedKFold(y, n_folds))\n", 1093 | "\n", 1094 | "#创建零矩阵\n", 1095 | "dataset_blend_train = np.zeros((X.shape[0], len(clfs)))\n", 1096 | "dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs)))\n", 1097 | "\n", 1098 | "#建立模型\n", 1099 | "for j, clf in enumerate(clfs):\n", 1100 | " '''依次训练各个单模型'''\n", 1101 | " # print(j, clf)\n", 1102 | " dataset_blend_test_j = np.zeros((X_predict.shape[0], len(skf)))\n", 1103 | " for i, (train, test) in enumerate(skf):\n", 1104 | " '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\n", 1105 | " # print(\"Fold\", i)\n", 1106 | " X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]\n", 1107 | " clf.fit(X_train, y_train)\n", 1108 | " y_submission = clf.predict_proba(X_test)[:, 1]\n", 1109 | " dataset_blend_train[test, j] = y_submission\n", 1110 | " dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1]\n", 1111 | " '''对于测试集，直接用这k个模型的预测值均值作为新的特征。'''\n", 1112 | " dataset_blend_test[:, j] = dataset_blend_test_j.mean(1)\n", 1113 | "\n", 1114 | "# 用建立第二层模型\n", 1115 | "clf2 = LogisticRegression(C=0.1,max_iter=100)\n", 1116 | "clf2.fit(dataset_blend_train, y)\n", 1117 | "y_submission = clf2.predict_proba(dataset_blend_test)[:, 1]\n", 1118 | "\n", 1119 | "\n", 1120 | "test = pd.read_csv(\"test.csv\")\n", 1121 | "test[\"Survived\"] = clf2.predict(dataset_blend_test)\n", 1122 | "test[['PassengerId','Survived']].set_index('PassengerId').to_csv('stack3.csv')" 1123 | ] 1124 | } 1125 | ], 1126 | "metadata": { 1127 | "kernelspec": { 1128 | "display_name": "Python 3", 1129 | "language": "python", 1130 | "name": "python3" 1131 | }, 1132 | "language_info": { 1133 | "codemirror_mode": { 1134 | "name": "ipython", 1135 | "version": 3 1136 | }, 1137 | "file_extension": ".py", 1138 | "mimetype": "text/x-python", 1139 | "name": "python", 1140 | "nbconvert_exporter": "python", 1141 | "pygments_lexer": "ipython3", 1142 | "version": "3.6.1" 1143 | } 1144 | }, 1145 | "nbformat": 4, 1146 | "nbformat_minor": 2 1147 | } 1148 | -------------------------------------------------------------------------------- /03_Facebook广告投放(Sales Conversion Optimization)/README.md: -------------------------------------------------------------------------------- 1 | ## Facebook广告投放策略数据分析项目实践 2 | 本项目数据来自于Kaggle上的一个Facebook广告投放策略数据，项目主页地址： 3 | [Sales Conversion Optimization](https://www.kaggle.com/loveall/clicks-conversion-tracking/home) 4 | 5 | 项目使用的数据来自一个匿名组织的社交媒体广告活动,数据文件包含11个属性(列)，1143个观察值（行），下面是属性的描述： 6 | 7 | 1. ad_id: an unique ID for each ad. 8 | 9 | >>即每个广告的唯一标识 10 | 11 | 2. xyz_campaign_id: an ID associated with each ad campaign of XYZ company. 12 | 13 | >>即xyz公司的每次广告活动的唯一标识 14 | 15 | 3. fb_campaign_id: an ID associated with how Facebook tracks each campaign. 16 | 17 | >>即facebook用来跟踪每次广告活动的唯一标识 18 | 19 | 4. age: age of the person to whom the ad is shown. 20 | 21 | >>即广告展示对象的年龄 22 | 23 | 5. gender: gender of the person to whim the add is shown 24 | 25 | >>即广告展示对象的性别 26 | 27 | 6. interest: a code specifying the category to which the person’s interest belongs (interests are as mentioned in the person’s Facebook public profile). 28 | 29 | >>即facebook用户的兴趣标签，也就是facebook用户的个人主页所显示的兴趣标签。 30 | 31 | 7. Impressions: the number of times the ad was shown. 32 | 33 | >>即广告曝光次数。 34 | 35 | 8. Clicks: number of clicks on for that ad. 36 | 37 | >>即广告点击次数。 38 | 39 | 9. Spent: Amount paid by company xyz to Facebook, to show that ad. 40 | 41 | >>即xyz公司为每次广告展示向facebook支付的费用。 42 | 43 | 10. Total conversion: Total number of people who enquired about the product after seeing the ad. 44 | 45 | >>即用户看到广告之后向xyz公司咨询产品的次数。 46 | 47 | 11. Approved conversion: Total number of people who bought the product after seeing the ad. 48 | 49 | >>即用户看到广告之后购买产品的次数。 50 | 51 | 52 | --- 53 | 54 | 社交媒体广告活动营销是销售转换的一个主要来源, 本次分析聚焦两个问题： 55 | 56 | - 兴趣标签（interest）取值为多少的人点击率最高？ 57 | 58 | - 哪个群体的转化率最高？ 59 | 60 | 由于本次项目数据集不大，且数据质量很高，无需进行缺失值、异常值处理，故直接将其导入MySQL进行分析，本人使用的数据库管理软件是Mysql For Navicat Premium，分析内容分为两个部分：数据统计和投放效果分析。 61 | 62 | --- 63 | 64 | ### 一、数据统计 65 | 66 | 1、将数据集导入Mysql数据库，结果如下： 67 | ![1](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/yrapkc5xIrnCnqICQ4UqqhtSGpdN8WCYvbsAVlw0C0s!/b/dLYAAAAAAAAA&bo=wQOdAgAAAAARB20!&rf=viewer_4) 68 | 69 | 70 | 71 | 2、查看有多少个广告活动(compaign): 72 | ![2](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/*R9gxsbLPHOh3dt96uEiAsH37JCzRuLBRCUka9Vydgk!/b/dFIBAAAAAAAA&bo=.AIPAgAAAAARB8c!&rf=viewer_4) 73 | 74 | 75 | 76 | 3、计算每个广告活动（campaign）包含了多少个广告，按降序排列 77 | ![3](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/jDH*5itbngY5oBRbwT3PCBi8C5mK3oJLOF6gQaZ2PrU!/b/dAgBAAAAAAAA&bo=9gIOAgAAAAARB8g!&rf=viewer_4) 78 | 79 | 80 | 81 | 4、计算每个广告活动的总花费，按降序排列 82 | 83 | ![4](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/M5XckK*3KWyH5hB3dOSQTT.SWe36ecxX.rEH2EoUSvo!/b/dDYBAAAAAAAA&bo=9gIMAgAAAAARF9o!&rf=viewer_4) 84 | 85 | 86 | 87 | 5、以百分数形式显示xyz_campaign_id=916广告活动中每个广告的点击率CTR，并按降序排列，CTR=clicks/impressions 88 | ![5](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/GU8MN2N.wVmceRiE2L1VI*5SDT2baRQY4fR2*C*du5o!/b/dLYAAAAAAAAA&bo=.QIIAgAAAAARB8E!&rf=viewer_4) 89 | 90 | 91 | 92 | 6、以百分数形式显示xyz_campaign_id=916广告活动中每个广告的转化率CVR，并按降序排列，CVR= total_conversion/clicks 93 | ![6](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/h.z1qP1msT42MG8i2tTe0IRaIpWGRhwRKOmjVKZe6FM!/b/dLYAAAAAAAAA&bo=9QIQAgAAAAARF8U!&rf=viewer_4) 94 | 95 | --- 96 | 97 | ### 二、投放效果分析 98 | 99 | 1、 interest值为多少的人点击率最高？分别对3个不同的广告活动进行分析。 100 | 101 | - 对于xyz_campaign_id=916, interest=21的人点击率最高，分析过程如下： 102 | ![7](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/2wogMG2q3rVRnLXy3WHUu2hwk7xgWQfrTVtsJHD164g!/b/dDEBAAAAAAAA&bo=9gIRAgAAAAARB9c!&rf=viewer_4) 103 | 104 | 105 | - 对于xyz_campaign_id=936，interest=2的人点击率最高，分析过程同上，只需要将where语句中xyz_campain_code=916改为xyz_campain_code=936 106 | ![8](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/.B8FlbcfcpVnUsRixsu1Doizi3DYa.dcP4OmwOI4Y.A!/b/dLYAAAAAAAAA&bo=.AIMAgAAAAARF9Q!&rf=viewer_4) 107 | 108 | 109 | - 同理，对于xyz_campain_code=1178，分析可得interest=26的人点击率最高。 110 | ![9](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/PycCiOj08nV.XghHE.nD2xaN5aQ5BDoasNPyxLmHhQw!/b/dLYAAAAAAAAA&bo=9QIQAgAAAAARF8U!&rf=viewer_4) 111 | 112 | --- 113 | 114 | 2.男性还是女性更有可能完成转化？分别对3个不同的广告活动进行分析。 115 | 116 | - 对于xyz_campaign_code=916，男性更容易完成转化，分析过程如下： 117 | ![](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/Tlwowojpj7kO6Z*n71Y6drcXxbA1Ry*9tZtFgZCtJNg!/b/dDIBAAAAAAAA&bo=KAMUAgAAAAARBw0!&rf=viewer_4) 118 | 119 | 120 | 121 | - 对于xyz_campaign_code=936，男性更容易完成转化，分析过程同上，只需要将where语句中xyz_campain_code=916改为xyz_campain_code=936 122 | ![](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/oWmXZY9NtZpLY.yRog.*vj068oDSz26hn3I6UT6FWY0!/b/dDUBAAAAAAAA&bo=KgMNAgAAAAARFwY!&rf=viewer_4) 123 | 124 | 125 | - 同理，对于xyz_campain_code=1178，分析可得男性更容易完成转化。 126 | ![](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/oh2ZDQ3Tg.825WATCENsKiE4uz6Nf8jmYpS8.Q7UIZg!/b/dDQBAAAAAAAA&bo=JgMTAgAAAAARFxQ!&rf=viewer_4) 127 | 128 | 129 | --- 130 | 131 | 3、 进一步分析，哪个年龄段的男性转化率最高？分别对3个不同的广告活动进行分析。 132 | 133 | - 对于xyz_campaign_code=916，30-34岁的男性转化率最高，分析过程如下： 134 | ![13](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/38i201zy*CssGbxNwsRAIi0rQaFrxxTCoUqlL3QWl4A!/b/dEYBAAAAAAAA&bo=8gIMAgAAAAARB84!&rf=viewer_4) 135 | 136 | 137 | 138 | - 对于xyz_campaign_code=916，30-34岁的男性转化率最高，分析过程同上，只需要将where语句中xyz_campain_code=916改为xyz_campain_code=936。 139 | 140 | ![14](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/wDxsWB8TJupNTw2pTwiqGEdNjOr1onvt7Xp9q2pYHXQ!/b/dFQBAAAAAAAA&bo=9AISAgAAAAARF8Y!&rf=viewer_4) 141 | 142 | 143 | 144 | - 同理，对于xyz_campain_code=1178，分析可得30-34岁的男性更容易完成转化。 145 | 146 | ![15](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/RaUtjKtPDa8O3G8YT6ywuHYhhIHUHFWydIfqWv*txF8!/b/dDEBAAAAAAAA&bo=9gIMAgAAAAAKF8E!&rf=viewer_4) 147 | 148 | 149 | 150 | 4、 通过前两项分析可以看出30-34岁男性是转化率最高的群体，那么该群体的点击率如何呢？ 151 | 152 | ![16](http://m.qpic.cn/psb?/V10P3PxC0koPqQ/h09yipMdBbh97UBFg*q25Jq0LVDAzNSpGPimlR156y0!/b/dDYBAAAAAAAA&bo=JwMRAgAAAAARBwc!&rf=viewer_4) 153 | 154 | 155 | 156 | 由上图可知，30-34岁的男性虽然转化率很高，但是点击率并不高。这说明如果我们可以想办法提高这个群体的点击率的话，就能提高转化。比如针对这个群体设计更吸引他们的广告。 157 | 158 | 159 | 160 | 161 | 162 | 163 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 大数据竞赛项目实战清单 2 | 3 | - 01_泰坦尼克号(titanic) 【kaggle】 4 | - 02_共享单车(Bike Sharing Demand) 【Kaggle】 5 | - 03_Facebook广告投放(Sales Conversion Optimization) 【Kaggle】 --------------------------------------------------------------------------------