add report_03_Fashion

report_02_Titanic/.ipynb_checkpoints/Titanic-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
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report_02_Titanic/README.md

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+# Titanic
+
+## Competition Description
+The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
+
+One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
+
+In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.
+
+## Practice Skills
+* Binary classification
+* Python & SKLearn
+
+## Data
+The data ziped into 'data.zip', please first extract it. There are two groups:
+
+* training set (train.csv)
+* test set (test.csv)
+
+The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the `ground truth`) for each passenger. Your model will be based on `features` like passengers' gender and class. You can also use feature engineering to create new features.
+
+The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.
+
+We also include `gender_submission.csv`, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.
+
+### Data description
+![data description1](images/data_description1.png)
+![data description2](images/data_description2.png)
+
+
+### Variable Notes
+pclass: A proxy for socio-economic status (SES)
+* 1st = Upper
+* 2nd = Middle
+* 3rd = Lower
+
+
+## Links
+* [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic)

report_02_Titanic/report_template.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report - 报告题目\n",
+    "\n",
+    "* 姓名\n",
+    "* 学号\n",
+    "\n",
+    "\n",
+    "## 任务简介\n",
+    "\n",
+    "这里简述一下任务是什么；数据的格式，包含了什么数据；最终的目标是什么\n",
+    "\n",
+    "## 解决途径\n",
+    "\n",
+    "主要包括：\n",
+    "1. 问题的思考，整体的思路\n",
+    "2. 选用的方法，以及为何选用这些方法\n",
+    "3. 实现过程遇到的问题，以及如何解决的\n",
+    "4. 最终的结果，实验分析\n",
+    "\n",
+    "要求：\n",
+    "1. 数据的可视化\n",
+    "2. 程序，以及各个部分的解释、说明\n",
+    "3. 结果的可视化，精度等的分析\n",
+    "\n",
+    "## 总结\n",
+    "总结任务实现过程所取得的心得等。"
+   ]
+  }
+ ],
+ "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": "3.5.2"
+  },
+  "main_language": "python"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
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