Python Data Analysis Case study

Course Outline:
Lesson One: Power theft leakage user Identification system case
The traditional leakage current analysis is carried out by artificial detection, the dependence on people is too large, in order to improve the efficiency of the identification of leakage, power companies decided to first based on the user's meter data for preliminary automatic judgment, for the identification of the user to detect leakage of the artificial detection.

Lesson Two: Case Study of data analysis for public transport operations
A public transport company needs to conduct a mining analysis of operational data to identify useful information for decision-making purposes. According to the operation data of each line, the route clustering is carried out to find out the development characteristics of different lines. According to the historical cost of the circuit, the Basic Law of the circuit development is analyzed.

Lesson Three: Business District analysis case
With the coverage of traffic routes, different commercial districts in the city will show different traffic passenger volume characteristics. According to the characteristics of passenger traffic, the paper analyzes different city locations, divides the characteristics of different commercial districts, and analyzes the influence of the opening of new lines or stations on the location.

Lesson Four: Customer Value analysis Case study
In the information age, the focus of enterprise marketing has changed from product center to Customer center, and customer relationship has become the core problem of enterprise. Customer relationship Management is the key issue of customer classification, according to customer classification, to find the characteristics and value of different user groups, and then to different groups to provide different marketing strategies.

Lesson Five: A case study of targeted network ad delivery based on user behavior analysis
With the development of the network, people accept the information channels and forms more diversified, the network marketing means is increasingly more and more. The most important point of advertising delivery in network era is orientation. In order to conduct targeted advertising, we need to analyze the real needs of everyone based on massive amounts of data, then create unique behavioral profiles for everyone, analyze consumption according to the archives, and match the most suitable and effective ads

Lesson Six: E-commerce website user Behavior Analysis and recommendation system case
E-commerce platform is more developed, the face of more and more data. Customers face a large amount of data is difficult to timely access to their needs of information, will make the customer's goodwill to the site is reduced, resulting in the loss of users. In order to better meet the needs of users, according to the massive data of the network, study the user's interest preferences, analyze the user's needs and behavior, discover the user's point of interest, the appropriate products and services to recommend to users, thereby strengthening the site and the user's contact.

The seventh lesson: The text law discovers the case actual combat
The customer needs to learn the law according to the given sample set data, and use the learning rule to determine whether the new data has the same regularity as the data in the sample dataset.

Eighth lesson: E-Commerce product review sentiment analysis case
Analyze the customer's satisfaction with the product and the effective advice to the product according to the product's review data

Nineth Lesson: Fraud Trading Detection case
A company's sales staff is responsible for selling the company's products and regular sales reports, and the sales price can be set by the salesperson. The company wants to find out the mistakes and frauds in the transaction based on the sales report.

The tenth lesson: according to the product test data of defective discriminant analysis case
According to the product test data, analysis of normal products and defective distribution law, and the new product to identify the defective

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Python Data Analysis Case study