Machine Learning Course Home Page

This exercise will test your ability to read a data file and understand statistics about the data.

In later exercises, you will apply techniques to filter the data, build a machine learning model, and iteratively improve your model.

The course examples use data from Melbourne. To ensure you can apply these techniques on your own, you will have to apply them to a new dataset (with house prices from Iowa).

The exercises use a "notebook" coding environment. In case you are unfamiliar with notebooks, we have a 90-second intro video.

此练习将测试您读取数据文件和理解数据统计信息的能力。

在后面的练习中，您将应用技术来过滤数据、构建机器学习模型并迭代改进模型。

课程示例使用来自墨尔本的数据。为了确保您可以自己应用这些技术，您必须将它们应用到新的数据集（包含爱荷华州的房价）。

练习使用“笔记本”编码环境。如果您不熟悉“笔记本”，我们有一个 90 秒的介绍视频。

Exercises

练习

Run the following cell to set up code-checking, which will verify your work as you go.

运行以下单元格来设置代码检查，这将随时验证您的工作。

# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex2 import *
print("Setup Complete")

Setup Complete

Step 1: Loading Data

Read the Iowa data file into a Pandas DataFrame called home_data.

第 1 步：加载数据

将爱荷华州数据文件读入名为home_data的 Pandas DataFrame 中。

import pandas as pd

# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'

# Fill in the line below to read the file into a variable home_data
home_data = pd.read_csv(iowa_file_path)

# Call line below with no argument to check that you've loaded the data correctly
step_1.check()

Correct

# Lines below will give you a hint or solution code
#step_1.hint()
step_1.solution()

Solution:

home_data = pd.read_csv(iowa_file_path)

Step 2: Review The Data

Use the command you learned to view summary statistics of the data. Then fill in variables to answer the following questions

第 2 步：查看数据

使用您学到的命令查看数据的摘要统计信息。然后填写变量回答以下问题

# Print summary statistics in next line
home_data.info()


RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   int64  
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          91 non-null     object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     588 non-null    object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1423 non-null   object 
 31  BsmtCond       1423 non-null   object 
 32  BsmtExposure   1422 non-null   object 
 33  BsmtFinType1   1423 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1422 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    770 non-null    object 
 58  GarageType     1379 non-null   object 
 59  GarageYrBlt    1379 non-null   float64
 60  GarageFinish   1379 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1379 non-null   object 
 64  GarageCond     1379 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         7 non-null      object 
 73  Fence          281 non-null    object 
 74  MiscFeature    54 non-null     object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB

import time
# What is the average lot size (rounded to nearest integer)?
# 平均手数是多少（四舍五入到最接近的整数）？
avg_lot_size = round(home_data['LotArea'].mean())

# As of today, how old is the newest home (current year - the date in which it was built)
# 截至今天，最新房屋的年龄有多大（当年 - 建造日期）
newest_home_age = (time.localtime().tm_year - home_data['YearBuilt']).min()

print(avg_lot_size, newest_home_age)

# Checks your answers
step_2.check()

10517 14

Correct

#step_2.hint()
step_2.solution()

Solution:

# using data read from home_data.describe()
avg_lot_size = 10517
newest_home_age = 14

Think About Your Data

The newest house in your data isn't that new. A few potential explanations for this:

They haven't built new houses where this data was collected.
The data was collected a long time ago. Houses built after the data publication wouldn't show up.

If the reason is explanation #1 above, does that affect your trust in the model you build with this data? What about if it is reason #2?

How could you dig into the data to see which explanation is more plausible?

Check out this discussion thread to see what others think or to add your ideas.

考虑您的数据

您数据中最新的房屋并不是那么新。对此的一些可能的解释：

他们还没有在收集这些数据的地方建造新房子。
数据是很久以前收集的。数据发布后建造的房屋不会出现。

如果原因是上面的解释#1，这是否会影响您对使用此数据构建的模型的信任？如果这是原因#2呢？

您如何深入研究数据以查看哪种解释更合理？

查看此讨论主题，了解其他人的想法或添加您的想法。

Keep Going

You are ready for Your First Machine Learning Model.

继续前进

您已准备好 您的第一个机器学习模型。