【21天学习AI底层概念】day11 （kaggle新手入门教程）Your First Machine Learning Model
网址：https://www.kaggle.com/code/dansbecker/your-first-machine-learning-model
代码
一、选择训练集数据
1.检查数据
import pandas as pd
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'
melbourne_data = pd.read_csv(melbourne_file_path)
melbourne_data.columns
Index([‘Suburb’, ‘Address’, ‘Rooms’, ‘Type’, ‘Price’, ‘Method’, ‘SellerG’,
‘Date’, ‘Distance’, ‘Postcode’, ‘Bedroom2’, ‘Bathroom’, ‘Car’,
‘Landsize’, ‘BuildingArea’, ‘YearBuilt’, ‘CouncilArea’, ‘Lattitude’,
‘Longtitude’, ‘Regionname’, ‘Propertycount’],
dtype=‘object’)
2.丢弃不可用数据
# The Melbourne data has some missing values (some houses for which some variables weren't recorded.)
# We'll learn to handle missing values in a later tutorial.
# Your Iowa data doesn't have missing values in the columns you use.
# So we will take the simplest option for now, and drop houses from our data.
# Don't worry about this much for now, though the code is:
# dropna drops missing values (think of na as "not available")
melbourne_data = melbourne_data.dropna(axis=0)
3.选择prediction target
y = melbourne_data.Price
4.选择"Features"
melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']
X = melbourne_data[melbourne_features]
5.检查X，X可以理解成一个由rooms列、bathroom列…等列组成的数据集
X.describe()
输出一个表格，复制不过来格式…关于X的整体描述，平均值、最大最小值、标准差等
Rooms Bathroom Landsize Lattitude Longtitude
count 6196.000000 6196.000000 6196.000000 6196.000000 6196.000000
mean 2.931407 1.576340 471.006940 -37.807904 144.990201
std 0.971079 0.711362 897.449881 0.075850 0.099165
min 1.000000 1.000000 0.000000 -38.164920 144.542370
25% 2.000000 1.000000 152.000000 -37.855438 144.926198
50% 3.000000 1.000000 373.000000 -37.802250 144.995800
75% 4.000000 2.000000 628.000000 -37.758200 145.052700
max 8.000000 8.000000 37000.000000 -37.457090 145.526350
X.head()
输出一个表格，复制不过来格式…输出X的前几行数据
Rooms Bathroom Landsize Lattitude Longtitude
1 2 1.0 156.0 -37.8079 144.9934
2 3 2.0 134.0 -37.8093 144.9944
4 4 1.0 120.0 -37.8072 144.9941
6 3 2.0 245.0 -37.8024 144.9993
7 2 1.0 256.0 -37.8060 144.9954
二、训练模型
The steps to building and using a model are:
- Define: What type of model will it be? A decision tree? Some other type of model? Some other parameters of the model type are specified too.
- Fit: Capture patterns from provided data. This is the heart of modeling.
- Predict: Just what it sounds like
- Evaluate: Determine how accurate the model’s predictions are.
from sklearn.tree import DecisionTreeRegressor
# Define model. Specify a number for random_state to ensure same results each run
melbourne_model = DecisionTreeRegressor(random_state=1)
# Fit model
melbourne_model.fit(X, y)
print("Making predictions for the following 5 houses:")
print(X.head())
print("The predictions are")
print(melbourne_model.predict(X.head()))
输出预测数据，由于输入的X是训练数据集里的前几条，所以给出的y预测值也是和数据集里的sale_price完全一模一样
Making predictions for the following 5 houses:
Rooms Bathroom Landsize Lattitude Longtitude
1 2 1.0 156.0 -37.8079 144.9934
2 3 2.0 134.0 -37.8093 144.9944
4 4 1.0 120.0 -37.8072 144.9941
6 3 2.0 245.0 -37.8024 144.9993
7 2 1.0 256.0 -37.8060 144.9954
The predictions are
[1035000. 1465000. 1600000. 1876000. 1636000.]