Undergraduate graduation thesis do not know how to write, do not need an outline with algorithm model

The following answers are based on ChatGPT and GISer Liu:
Congratulations on starting your thesis! Here are some suggestions to help you build your thesis outline and algorithm model:

1.0 Introduction
1.1 Research background
1.2 Research Significance and purpose
1.3 Research status at home and abroad
1.4 Research methods and technical routes
1.5 Paper structure

2.0 Data acquisition and processing
2.1 Introduction of data sources
2.2 Data Preprocessing
2.3 Data cleaning and feature extraction
2.4 Data Exploration and analysis

3.0 Coupon distribution strategy analysis
3.1 Coupon Usage analysis
3.2 Coupon Distribution analysis
3.3 Correlation analysis between coupons and commodities

4.0 Data Mining and Modeling Based on Python
4.1 Introduction to Data mining algorithms
4.2 Model establishment
4.3 Model evaluation

5.0 Conclusion and Prospect
5.1 Conclusion
5.2 Prospect of Future Research Direction

For your data, the following are some commonly used algorithm models for reference:

• Association rule mining algorithm
  This algorithm can dig out the correlation between coupons and commodities, as well as the behavior rules of consumers to buy commodities.

• Clustering based analysis algorithm
  This algorithm can classify users according to their purchase behavior, so as to better analyze the coupon placement strategy for different types of users.

• Classification algorithm based on decision tree
  This algorithm can classify consumers' coupon use behavior and help you know when and what coupons are more attractive to users.

• Algorithm based on deep learning
  This algorithm can carry out deep learning of your data and learn the characteristics and rules of the data through the model to better help you analyze the coupon placement strategy.

Hope the above suggestions can help you, wish you a happy writing!

can analyze the release and use of discount volume, using association rule algorithm and logistic regression model for modeling. The
association rule algorithm is an algorithm to discover frequent item sets. The background of the algorithm is to discover frequently occurring sets in a specific environment, so that meaningful association rules can be found. Logistic regression is to predict the occurrence probability of an event according to the values of a set of characteristic variables. The association rule method and logistic regression model can compare and quantitatively evaluate the release and use of preferential papers, so as to give a reasonable and feasible solution to optimize the release and use of preferential papers.

Refer to GPT and own ideas, for data analysis based on Python coupon placement and use, you can use the following algorithms and models:

1 association rule mining algorithm: can dig out the correlation between different commodities, so as to help optimize the delivery strategy of coupons.

2 Clustering analysis algorithm: Users can be grouped according to factors such as purchasing behavior and using coupons to find similar user groups, so as to better understand user needs and optimize delivery strategy.

3 Decision tree algorithm: can be used to build a classification model to judge which factors have the greatest influence on the use of coupons by users.

4 Random forest algorithm: It can be used to build classification models. Compared with decision trees, it can reduce the risk of overfitting and improve the accuracy of model prediction.

5 Neural network algorithm: It can be used to build a deep learning model, analyze large amounts of data, identify hidden patterns and associations, and thus improve the prediction accuracy of the model.

In the actual analysis, the most appropriate algorithm and model can be selected according to the specific situation of the data. For example, if the data size is small, you can choose decision trees or random forest algorithms; If the data scale is large, you can choose the neural network algorithm. At the same time, different algorithms and models can be combined to obtain more accurate prediction results.

Here is an example of a simple Python-based association rule mining algorithm to find correlations between purchase behaviors:

# 导入必要的库
import pandas as pd
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules

# 读取数据
data = pd.read_csv('data.csv')

# 将数据进行编码
data_encoded = pd.get_dummies(data)

# 使用Apriori算法获取频繁项集
frequent_itemsets = apriori(data_encoded, min_support=0.1, use_colnames=True)

# 使用关联规则算法获取关联规则
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)

# 输出结果
print(rules)

In the code example above, we first read the data using the Pandas library and code the data so that we can obtain the frequent item set using the Apriori algorithm. Then the association rules algorithm in the Mlxtend library is used to obtain the association rules and output the results. In the association rule algorithm, lift is chosen as an evaluation indicator to measure the degree of association between two item sets.

It is important to note that this is only a simple example, which needs to be adjusted and optimized according to the specific situation of the data in practical application. At the same time, data preprocessing and feature selection should be carried out according to the specific characteristics of the algorithm and model, so as to improve the performance and accuracy of the model.
If it is helpful to you, please accept it, thank you.

This answer quotes ChatGPT

What algorithm can be used

1. Linear regression algorithm: It is helpful to analyze the relationship between the amount of coupons and the usage on different variables, so as to form an effective strategy. 2. Time series analysis algorithm: Through the analysis of the release and use of coupons in different time periods, so as to understand the release time, the best customer group and other information. 3. Decision tree algorithm: It can establish the model of placement and use of preferential papers and analyze the optimal results under different variables, so as to improve the use effect of preferential papers. 4. Clustering algorithm: It can classify potential customers and determine the characteristics of specific categories, so as to improve the efficiency of offering coupons.

Outline

Background and significance of Python coupon delivery and use: With the rapid development of e-commerce industry, the convenience of consumers continues to improve, becoming more flexible and changeable, businesses also seize the opportunity to develop various types of coupons, in order to attract customers, expand their own market position, in order to obtain rich interests. Thus, Python coupon delivery and use is a significant research direction. Research background: The release and use of Python coupons can help merchants make better use of coupons to promote consumers' purchase behavior, thus increasing the sales volume of enterprises. At the same time, it can also stimulate consumers' purchase desire and help consumers grasp shopping opportunities, thus saving consumers' purchase costs. In addition, the release and use of Python coupons can also provide reliable data support for merchants to explore customer needs, analyze application performance, protect applications and analyze customer behavior, thus promoting the establishment of a good relationship between merchants and customers and improving the market reputation of merchants. Research significance: The release and use of Python coupons can effectively improve marketing efficiency, help merchants save marketing costs, and provide consumers with discounts, thus promoting the development of e-commerce industry. At the same time, it can also provide merchants with effective customer service and narrow the relationship between merchants and customers.
Based on the data analysis of the release and use of preferential papers in Python, a prediction model can be built by combining machine learning with data mining algorithm, which is used to predict the results of users' release of preferential papers and analyze the use of preferential papers. The model can use clustering, regression analysis, neural network, support vector machine and other machine learning algorithms to obtain accurate prediction results.

Congratulations on choosing an interesting graduation topic! Here is a possible outline and the algorithm model that might be used:

I. Preface

• An introduction to the problems and background of this study

• State the importance of the problem and the purpose of the research

• A brief introduction of the main contributions of this paper
  II. Related work

• Review the existing data analysis studies on the release and use of coupons

• Summarize the main methods and conclusions of these studies
  III. Data preparation

• Characterizing the data source and the data set

• Data cleaning, processing and preprocessing
  IV. Data analysis

• Visualization of data and exploratory data analysis

• Modeling with Machine Learning and Data mining methods

• Model evaluation and result analysis
  V. Conclusions and Prospects

• Summarize the research results and their contributions

• Discuss the future research direction
  VI. Reference

Here are some possible algorithm models for this problem:

1. The random forest model
can be used for classification and regression problems. For this problem, it can be used for the prediction of coupon usage. Association rule mining
can be used to dig the correlation and rule of consumer purchase behavior, and then develop more accurate coupon delivery strategy
3. Cluster analysis
can be used to divide consumers into different groups and formulate different coupon strategies according to the consumption habits and behaviors of different groups
4. Factor analysis
can be used to analyze the factors of consumers' purchase decisions, help merchants understand consumer needs and adjust coupon placement strategies according to the needs.
The above algorithm models are just some examples. The specific algorithm models need to be analyzed and selected according to your research problems and data.

This answer quotes ChatGPT

The following algorithms and models can be adopted for data analysis based on the release and use of Python discount volumes:

Association rule mining algorithm: By analyzing users' purchasing behavior and the use of coupons, mining users' purchasing habits and preferences, so as to develop more accurate preferential strategies.

Prediction model based on regression analysis: analyze and model the use of users' coupons and delivery strategies through historical data, and formulate more accurate preferential delivery strategies.

Cluster analysis algorithm: Cluster users according to purchasing behaviors, preferences and other characteristics, so as to discover the characteristics of different user groups and formulate more accurate preferential strategies.

Decision tree algorithm: By building a decision tree model, it classifies and predicts users' purchase behaviors, preferences and other characteristics to develop more accurate preferential strategies.

The above algorithms and models can be implemented by using relevant libraries and tools in Python. For example, association rule mining can be implemented by using apriori algorithm in mlxtend library, regression analysis and decision tree can be implemented by using scikit-learn library. Cluster analysis can be implemented using scipy and scikit-learn libraries. When using these algorithms and models, appropriate features and parameters should be selected according to the actual situation, and the model should be optimized and adjusted to obtain better prediction effect

For data analysis based on Python coupon placement and usage, you may consider using the following algorithms and models:

• association rule algorithm: By mining the association relationship between commodities in the data, it can find some commodities that are often bought together and coupons that are often used at the same time, so as to optimize the delivery strategy of coupons.
• Clustering algorithm: By clustering users and commodities, users or commodities with similar purchase or use behaviors can be grouped, so as to push coupons in a targeted way.
• Decision tree algorithm: It can classify and predict the attributes and behaviors of users by constructing decision trees, so as to better deliver and predict the use of coupons.
• Random Forest algorithm: Random forest algorithm can be trained based on a large amount of data and can consider the interaction between multiple variables to get more accurate prediction results.
• Deep learning model: Deep learning models such as neural networks can be used to predict user behavior, so as to make better coupon placement and use prediction.

It should be noted that the above algorithms and models need to be selected and adjusted according to specific problems and data characteristics. At the same time, data cleaning, feature engineering, model evaluation and other issues need to be taken into account during data analysis.