Explanation: This is a machine learning practical project (with data + code + documentation + video explanation). If you need data + code + documentation + video explanation, you can go directly to the end of the article to get it.
1. Project background
PSO is the English abbreviation of Particle Swarm Optimization (Particle Swarm Optimization), which is a population-based stochastic optimization technique proposed by Eberhart and Kennedy in 1995. Particle swarm algorithm imitates the flocking behavior of insects, herds of animals, flocks of birds and fish, etc. These groups look for food in a cooperative way, and each member of the group constantly changes by learning its own experience and the experience of other members. its search mode.
This project uses PSO particle swarm optimization CATBOOST classification algorithm to build a classification model.
2. Data acquisition
The modeling data for this time comes from the Internet (compiled by the author of this project), and the statistics of the data items are as follows:
The data details are as follows (partial display):
3. Data preprocessing
3.1 View data with Pandas tools
Use the head() method of the Pandas tool to view the first five rows of data:
key code:
3.2 Data missing view
Use the info() method of the Pandas tool to view data information:
As can be seen from the above figure, there are a total of 9 variables, no missing values in the data, and a total of 1000 data.
key code:
3.3 Data descriptive statistics
Use the describe() method of the Pandas tool to view the mean, standard deviation, minimum, quantile, and maximum of the data.
The key code is as follows:
4. Exploratory Data Analysis
4.1 y variable histogram
Use the plot() method of the Matplotlib tool to draw a histogram:
4.2 y=1 sample x1 variable distribution histogram
Use the hist() method of the Matplotlib tool to draw a histogram:
4.3 Correlation analysis
As can be seen from the figure above, the larger the value, the stronger the correlation. A positive value is a positive correlation, and a negative value is a negative correlation.
5. Feature engineering
5.1 Establish feature data and label data
The key code is as follows:
5.2 Dataset splitting
Use the train_test_split() method to divide according to 80% training set and 20% test set. The key code is as follows:
6. Construct PSO particle swarm optimization CATBOOST classification model
Mainly use the PSO particle swarm optimization algorithm to optimize the CATBOOST classification algorithm for object classification.
6.1 PSO particle swarm optimization algorithm to find the optimal parameter value
Iterate process data:
Optimal parameters:
6.2 Optimal parameter value construction model
7. Model Evaluation
7.1 Evaluation indicators and results
Evaluation indicators mainly include accuracy rate, precision rate, recall rate, F1 score and so on.
It can be seen from the above table that the F1 score is 0.902, indicating that the model works well.
The key code is as follows:
7.2 Classification report
As can be seen from the above figure, the F1 score of classification 0 is 0.90; the F1 score of classification 1 is 0.90.
7.3 Confusion Matrix
As can be seen from the above figure, there are 13 samples that are actually 0 and predicted to be not 0; 7 samples are actually predicted to be 1 and not 1, and the overall prediction accuracy is good.
8. Conclusion and Outlook
In summary, this project uses the PSO particle swarm optimization algorithm to find the optimal parameter values of the CATBOOST classification algorithm to build a classification model, which finally proves that the model we proposed works well. This model can be used for forecasting of everyday products.
# y变量柱状图
plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
# kind='bar' 绘制柱状图
df['y'].value_counts().plot(kind='bar')
plt.xlabel("y变量") # 设置x轴坐标名称
plt.ylabel("数量") # 设置y轴坐标名称
plt.title('y变量柱状图') # 设置标题名称
# ******************************************************************************
# 本次机器学习项目实战所需的资料，项目资源如下：
# 项目说明：
# 链接：https://pan.baidu.com/s/1c6mQ_1YaDINFEttQymp2UQ
# 提取码：thgk
# ******************************************************************************
if abs(params[0]) > 0: # 判断取值
depth = int(abs(params[0])/100) + 3 # 赋值
else:
depth = int(abs(params[0])/100) + 5 # 赋值
if abs(params[1]) > 0: # 判断取值
learning_rate = (int(abs(params[1])) + 1) / 10 # 赋值
else:
learning_rate = (int(abs(params[1])) + 1) / 10 # 赋值For more project practice, see the list of machine learning project practice collections:
List of actual combat collections of machine learning projects