diff --git a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/4-模型预测及评估.ipynb b/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/4-模型预测及评估.ipynb
deleted file mode 100644
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--- a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/4-模型预测及评估.ipynb
+++ /dev/null
@@ -1,1540 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import lightgbm as lgb  # 模型\n",
-    "import pandas as pd  # 数据处理包\n",
-    "import numpy as np  # 数据处理包\n",
-    "from sklearn import metrics  # 混淆矩阵\n",
-    "from sklearn.model_selection import StratifiedKFold, GridSearchCV, train_test_split  # 分层五折验证包、寻找最优参函数、切分数据\n",
-    "from sklearn.metrics import accuracy_score, roc_curve, auc, confusion_matrix  # 准确率、roc计算、auc计算、混淆矩阵\n",
-    "import matplotlib.pyplot as plt  # 图形处理包\n",
-    "import itertools  # 处理混淆矩阵\n",
-    "import gc  # 处理缓存，有兴趣的可以搜搜怎么使用\n",
-    "import warnings  # 忽略普通警告，不打印太多东西\n",
-    "warnings.filterwarnings('ignore')\n",
-    "plt.rcParams['font.sans-serif']=['SimHei']  # 让图形可以显示中文\n",
-    "plt.rcParams['axes.unicode_minus']=False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
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-       "    user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "0  200001.0   20308.0   8.0                    0.0                    0.0   \n",
-       "1  200001.0   38604.0   9.0                    0.0                    0.0   \n",
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-       "3  200002.0    2866.0   9.0                    2.0                    0.0   \n",
-       "4  200002.0    3673.0   9.0                    0.0                    0.0   \n",
-       "\n",
-       "   action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "0                    1.0                    0.0                    0.0   \n",
-       "1                    1.0                    0.0                    0.0   \n",
-       "2                    1.0                    0.0                    0.0   \n",
-       "3                    0.0                    0.0                    0.0   \n",
-       "4                    0.0                    0.0                    0.0   \n",
-       "\n",
-       "   action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_4_mean  \\\n",
-       "0                    0.0                    0.0  ...          214.800000   \n",
-       "1                    0.0                    0.0  ...           59.033333   \n",
-       "2                    0.0                    0.0  ...          214.800000   \n",
-       "3                   10.0                   28.0  ...           59.033333   \n",
-       "4                    4.0                   30.0  ...           59.033333   \n",
-       "\n",
-       "   cate_action_5_mean  cate_action_6_mean  has_bad_comment  bad_comment_rate  \\\n",
-       "0          665.166667       185387.233333              1.0            0.0132   \n",
-       "1          106.600000        37389.133333              1.0            0.0250   \n",
-       "2          665.166667       185387.233333              1.0            0.0386   \n",
-       "3          106.600000        37389.133333              0.0            0.0000   \n",
-       "4          106.600000        37389.133333              1.0            0.0436   \n",
-       "\n",
-       "   comment_num_0  comment_num_1  comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            0.0            0.0            1.0  \n",
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-   "source": [
-    "data = pd.read_csv('data/test_set.csv')  # 读取训练数据\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
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-   "metadata": {},
-   "outputs": [
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-       "    user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "0  202633.0   12564.0   8.0                    1.0                    0.0   \n",
-       "1  218498.0  149854.0   8.0                    4.0                    0.0   \n",
-       "2  221842.0   75877.0   8.0                    3.0                    0.0   \n",
-       "3  222886.0  154636.0   8.0                   20.0                    1.0   \n",
-       "4  235240.0   38222.0   8.0                   30.0                    1.0   \n",
-       "\n",
-       "   action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "0                    0.0                    0.0                    0.0   \n",
-       "1                    0.0                    0.0                    0.0   \n",
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-       "3                    0.0                    0.0                    0.0   \n",
-       "4                    0.0                    0.0                    0.0   \n",
-       "\n",
-       "   action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_5_mean  \\\n",
-       "0                    2.0                    1.0  ...           20.866667   \n",
-       "1                    4.0                    2.0  ...           20.866667   \n",
-       "2                    5.0                   79.0  ...           20.866667   \n",
-       "3                   26.0                   10.0  ...           20.866667   \n",
-       "4                   28.0                   55.0  ...           20.866667   \n",
-       "\n",
-       "   cate_action_6_mean  has_bad_comment  bad_comment_rate  comment_num_0  \\\n",
-       "0              5167.6              1.0            0.0260            0.0   \n",
-       "1              5167.6              1.0            0.0403            0.0   \n",
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-       "3              5167.6              1.0            0.0208            0.0   \n",
-       "4              5167.6              1.0            0.0166            0.0   \n",
-       "\n",
-       "   comment_num_1  comment_num_2  comment_num_3  comment_num_4  label  \n",
-       "0            0.0            0.0            0.0            1.0    1.0  \n",
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-       "[5 rows x 237 columns]"
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-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
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-    "data = pd.read_csv('data/train_set.csv')  # 读取训练数据\n",
-    "data.head()"
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-   "cell_type": "code",
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-       "   cate_action_5_mean  cate_action_6_mean  has_bad_comment  bad_comment_rate  \\\n",
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-    "train_x = data.loc[:,data.columns != 'label']  # 将训练数据集分成特征和标签\n",
-    "train_y = data.loc[:,data.columns == 'label']\n",
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-       "0   8.0                    1.0                    0.0                    0.0   \n",
-       "1   8.0                    4.0                    0.0                    0.0   \n",
-       "2   8.0                    3.0                    0.0                    0.0   \n",
-       "3   8.0                   20.0                    1.0                    0.0   \n",
-       "4   8.0                   30.0                    1.0                    0.0   \n",
-       "\n",
-       "   action_before_3_4.0_x  action_before_3_5.0_x  action_before_3_6.0_x  \\\n",
-       "0                    0.0                    0.0                    2.0   \n",
-       "1                    0.0                    0.0                    4.0   \n",
-       "2                    0.0                    0.0                    5.0   \n",
-       "3                    0.0                    0.0                   26.0   \n",
-       "4                    0.0                    0.0                   28.0   \n",
-       "\n",
-       "   action_before_3_1.0_y  action_before_3_2.0_y  action_before_3_3.0_y  ...  \\\n",
-       "0                    1.0                    0.0                    0.0  ...   \n",
-       "1                    2.0                    0.0                    0.0  ...   \n",
-       "2                   79.0                    0.0                    0.0  ...   \n",
-       "3                   10.0                    0.0                    0.0  ...   \n",
-       "4                   55.0                    0.0                    0.0  ...   \n",
-       "\n",
-       "   cate_action_4_mean  cate_action_5_mean  cate_action_6_mean  \\\n",
-       "0                 8.4           20.866667              5167.6   \n",
-       "1                 8.4           20.866667              5167.6   \n",
-       "2                 8.4           20.866667              5167.6   \n",
-       "3                 8.4           20.866667              5167.6   \n",
-       "4                 8.4           20.866667              5167.6   \n",
-       "\n",
-       "   has_bad_comment  bad_comment_rate  comment_num_0  comment_num_1  \\\n",
-       "0              1.0            0.0260            0.0            0.0   \n",
-       "1              1.0            0.0403            0.0            0.0   \n",
-       "2              1.0            0.0245            0.0            0.0   \n",
-       "3              1.0            0.0208            0.0            0.0   \n",
-       "4              1.0            0.0166            0.0            0.0   \n",
-       "\n",
-       "   comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            1.0  \n",
-       "1            0.0            0.0            1.0  \n",
-       "2            0.0            0.0            1.0  \n",
-       "3            0.0            0.0            1.0  \n",
-       "4            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 234 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "del train_x['user_id']\n",
-    "del train_x['sku_id']\n",
-    "\n",
-    "train_x.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(14619, 234)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>user_id</th>\n",
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-       "      <th>cate</th>\n",
-       "      <th>action_before_3_1.0_x</th>\n",
-       "      <th>action_before_3_2.0_x</th>\n",
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-       "      <th>action_before_3_5.0_x</th>\n",
-       "      <th>action_before_3_6.0_x</th>\n",
-       "      <th>action_before_3_1.0_y</th>\n",
-       "      <th>...</th>\n",
-       "      <th>cate_action_4_mean</th>\n",
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-       "      <th>cate_action_6_mean</th>\n",
-       "      <th>has_bad_comment</th>\n",
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-       "      <td>200007.0</td>\n",
-       "      <td>26229.0</td>\n",
-       "      <td>9.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>200007.0</td>\n",
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-       "      <td>9.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>56.700000</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>200007.0</td>\n",
-       "      <td>126404.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 236 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "0  200005.0   67444.0   4.0                    2.0                    0.0   \n",
-       "1  200005.0   72967.0   4.0                   26.0                    1.0   \n",
-       "2  200007.0   26229.0   9.0                    2.0                    0.0   \n",
-       "3  200007.0   63315.0   9.0                    4.0                    0.0   \n",
-       "4  200007.0  126404.0   9.0                    4.0                    0.0   \n",
-       "\n",
-       "   action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "0                    0.0                    0.0                    0.0   \n",
-       "1                    0.0                    1.0                    0.0   \n",
-       "2                    0.0                    0.0                    0.0   \n",
-       "3                    0.0                    0.0                    0.0   \n",
-       "4                    0.0                    0.0                    0.0   \n",
-       "\n",
-       "   action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_4_mean  \\\n",
-       "0                    3.0                   26.0  ...           73.400000   \n",
-       "1                   30.0                    2.0  ...           73.400000   \n",
-       "2                    2.0                   12.0  ...           20.766667   \n",
-       "3                    3.0                   10.0  ...           20.766667   \n",
-       "4                    3.0                   10.0  ...           20.766667   \n",
-       "\n",
-       "   cate_action_5_mean  cate_action_6_mean  has_bad_comment  bad_comment_rate  \\\n",
-       "0          169.366667             48251.0              1.0            0.0821   \n",
-       "1          169.366667             48251.0              1.0            0.0196   \n",
-       "2           56.700000             12937.7              1.0            0.0198   \n",
-       "3           56.700000             12937.7              1.0            0.0476   \n",
-       "4           56.700000             12937.7              0.0            0.0000   \n",
-       "\n",
-       "   comment_num_0  comment_num_1  comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            0.0            0.0            1.0  \n",
-       "1            0.0            0.0            0.0            0.0            1.0  \n",
-       "2            0.0            0.0            0.0            0.0            1.0  \n",
-       "3            0.0            0.0            0.0            0.0            1.0  \n",
-       "4            0.0            0.0            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 236 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv('data/val_set.csv')  # 读取验证数据\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "      <th>action_before_3_2.0_x</th>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>200007.0</td>\n",
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-       "      <td>9.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
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-       "      <th>4</th>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 236 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "0  200005.0   67444.0   4.0                    2.0                    0.0   \n",
-       "1  200005.0   72967.0   4.0                   26.0                    1.0   \n",
-       "2  200007.0   26229.0   9.0                    2.0                    0.0   \n",
-       "3  200007.0   63315.0   9.0                    4.0                    0.0   \n",
-       "4  200007.0  126404.0   9.0                    4.0                    0.0   \n",
-       "\n",
-       "   action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "0                    0.0                    0.0                    0.0   \n",
-       "1                    0.0                    1.0                    0.0   \n",
-       "2                    0.0                    0.0                    0.0   \n",
-       "3                    0.0                    0.0                    0.0   \n",
-       "4                    0.0                    0.0                    0.0   \n",
-       "\n",
-       "   action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_4_mean  \\\n",
-       "0                    3.0                   26.0  ...           73.400000   \n",
-       "1                   30.0                    2.0  ...           73.400000   \n",
-       "2                    2.0                   12.0  ...           20.766667   \n",
-       "3                    3.0                   10.0  ...           20.766667   \n",
-       "4                    3.0                   10.0  ...           20.766667   \n",
-       "\n",
-       "   cate_action_5_mean  cate_action_6_mean  has_bad_comment  bad_comment_rate  \\\n",
-       "0          169.366667             48251.0              1.0            0.0821   \n",
-       "1          169.366667             48251.0              1.0            0.0196   \n",
-       "2           56.700000             12937.7              1.0            0.0198   \n",
-       "3           56.700000             12937.7              1.0            0.0476   \n",
-       "4           56.700000             12937.7              0.0            0.0000   \n",
-       "\n",
-       "   comment_num_0  comment_num_1  comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            0.0            0.0            1.0  \n",
-       "1            0.0            0.0            0.0            0.0            1.0  \n",
-       "2            0.0            0.0            0.0            0.0            1.0  \n",
-       "3            0.0            0.0            0.0            0.0            1.0  \n",
-       "4            0.0            0.0            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 236 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "val_x = data.loc[:,data.columns != 'label']  # 将验证数据集分成特征和标签\n",
-    "val_y = data.loc[:,data.columns == 'label']\n",
-    "val_x.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(171739, 236)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "val_x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}