Panduan Pembelajaran Mesin Python: Daripada asas sifar hingga tahap induk, impian AI anda bermula di sini

# 注释

# 变量

x = 5
y = "Hello, world!"

# 数据类型

print(type(x))# <class "int">
print(type(y))# <class "str">

# 控制结构

if x > 0:
print("x is positive.")
else:
print("x is not positive.")

# 函数

def my_function(x):
return x * 2

print(my_function(5))# 10

# 导入必要的库

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# 加载数据

data = pd.read_csv("data.csv")

# 划分训练集和测试集

X = data.drop("target", axis=1)# 特征数据
y = data["target"]# 标签数据

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 训练模型

model = LinearRegression()
model.fit(X_train, y_train)

# 评估模型

score = model.score(X_test, y_test)
print("准确率：", score)

# 预测

predictions = model.predict(X_test)

# 导入必要的库

from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LoGISticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier

# 加载数据

data = pd.read_csv("data.csv")

# 划分训练集和测试集

X = data.drop("target", axis=1)# 特征数据
y = data["target"]# 标签数据

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 训练模型

models = [
LinearRegression(),
LogisticRegression(),
DecisionTreeClassifier(),
SVC(),
RandomForestClassifier()
]

for model in models:
model.fit(X_train, y_train)

# 评估模型

score = model.score(X_test, y_test)
print(model.__class__.__name__, "准确率：", score)

# 导入必要的库

import Tensorflow as tf

# 定义神经网络模型

model = tf.keras.Sequential([
tf.keras.layers.Dense(100, activation="relu"),
tf.keras.layers.Dense(10, activation="softmax")
])

# 编译模型

model.compile(optimizer="adam", loss="sparse_cateGorical_crossentropy", metrics=["accuracy"])

# 训练模型

model.fit(X_train, y_train, epochs=10)

# 评估模型

score = model.evaluate(X_test, y_test)
print("准确率：", score[1])

# 预测

predictions = model.predict(X_test)