Steps and examples to implement linear regression algorithm using Python

Linear regression

is a common machine learning algorithm and a commonly used algorithm in artificial intelligence. It is a method for predicting a linear relationship between a numeric output variable and one or more independent variables. For example, you can use a linear regression model to predict housing prices based on the house's size, location, surroundings, etc.

The main idea is to describe the relationship between independent variables and output variables by building a linear model. The model can be expressed as:

y = a0 + a1*x1 + a2*x2 + … + an*xn

Among them, y is the output variable (also called the response variable), x1, x2,..., xn are independent variables (also called features), a0, a1, a2, …, an is the regression coefficient, used to express the influence of the independent variable on the output variable.

Goal

The goal is to find the optimal value of the regression coefficient so that the model best fits the data. A common method is the least squares method, which minimizes the sum of squares of the differences between the observed values ​​and the model's predicted values. Optimization algorithms such as gradient descent can be used to find the optimal values ​​of the regression coefficients.

Usage scenarios

can be used for many problems, such as predicting sales, stock prices, income, education levels, etc. It can also be used for multi-variable problems, such as predicting house prices, taking into account multiple factors such as the size, location, age, and number of bedrooms of the house.

Next, write a simple example of predicting house prices using linear regression:

Analysis:

The linear regression algorithm is based on statistical principles and the least squares method. By analyzing the training data Fit to predict test data. In the case of predicting house prices, the input variables to the model typically include important features such as the size of the house, the number of bedrooms, the number of bathrooms, and the number of garages. The linear regression model combines these variables to form a linear equation, and then finds the optimal coefficients based on the training data to best fit the training data.

When the model training is completed, the artificial intelligence can use the model to predict new home prices. Users only need to enter data on house characteristics, and the model will then generate predictions. In this way, AI can help buyers and sellers better understand the housing market and evaluate and sell their homes more valuable.

# 导入所需的库
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# 加载数据
data = pd.read_csv('house_prices.csv')
# 处理数据
X = data.iloc[:, :-1].values
y = data.iloc[:, 1].values
# 划分数据集，将数据分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 线性回归模型的实例化
lin_reg = LinearRegression()
# 训练模型
lin_reg.fit(X_train, y_train)
# 预测测试集的结果
y_pred = lin_reg.predict(X_test)
# 输出模型的评估结果
print('Coefficients: \n', lin_reg.coef_)
print('Mean squared error: %.2f' % np.mean((y_pred - y_test) ** 2))
> print('Variance score: %.2f' % lin_reg.score(X_test, y_test))

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