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
The Sparrow Search Algorithm (SSA) is a new type of swarm intelligence optimization algorithm, which was proposed in 2020 and is mainly inspired by the foraging behavior and anti-predation behavior of sparrows.
In the process of sparrow foraging, it is divided into discoverers (explorers) and joiners (followers). Finders come to get food. In order to obtain food, sparrows can usually adopt two behavioral strategies of finder and joiner to forage. Individuals in the population monitor the behavior of other individuals in the population, and attackers in the population compete for food resources with high-intake peers to increase their predation rate. In addition, sparrow populations engage in anti-predation behavior when they perceive danger.
This project optimizes the CATBOOST classification model through the SSA intelligent sparrow search algorithm.
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 Check missing data
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 11 variables, and there are no missing values in the data, with 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. Build the SSA intelligent sparrow search algorithm to optimize the CATBOOST classification model
Mainly use the SSA intelligent sparrow search algorithm to optimize the CATBOOST classification algorithm for target classification.
6.1 SSA intelligent sparrow search algorithm to find the optimal parameter value
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 0.9158, 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 0.92; the F1 score of classification 1 is 0.92.
7.3 Confusion Matrix
As can be seen from the figure above, there are 10 samples that are actually 0 and predicted to be not 0; 6 samples are actually predicted to be 1 and not 1, and the overall prediction accuracy is good.
8. Conclusion and Outlook
To sum up, this paper uses the SSA intelligent sparrow search algorithm to find the optimal parameter values of the CATBOOST algorithm to build a classification model, and finally proves that the model we proposed works well. This model can be used for forecasting of everyday products.
# 定义边界函数
def Bounds(s, Lb, Ub):
temp = s
for i in range(len(s)):
if temp[i] < Lb[0, i]: # 小于最小值
temp[i] = Lb[0, i] # 取最小值
elif temp[i] > Ub[0, i]: # 大于最大值
temp[i] = Ub[0, i] # 取最大值
# ******************************************************************************
# 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
# ******************************************************************************
# y=1样本x1变量分布直方图
fig = plt.figure(figsize=(8, 5)) # 设置画布大小
plt.rcParams['font.sans-serif'] = 'SimHei' # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
data_tmp = data.loc[data['y'] == 1, 'x1'] # 过滤出y=1的样本
# 绘制直方图 bins：控制直方图中的区间个数 auto为自动填充个数 color：指定柱子的填充色
plt.hist(data_tmp, bins='auto', color='g')For more project practice, see the list of machine learning project practice collections:
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