atlasmanyevgenii / practicum100-by-yandex

Course included such topics, as Data Preprocessing, Exploratory Data Analysis (EDA), Statistical Data Analysis (SDA), Data Collection and Storage (PostgreSQL), Business Analytics, Making Business Decisions Based on Data (Hypotheses testing), How to Tell a Story Using Data (Presentation and Data Visualization - Maplotlib, Seaborn, Plotly), Automation (Dash, Tableau), Forecasts and Predictions, 2 Integrated projects.

Project_01. Analyzing borrowers' risk of defaulting

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Our project is to prepare a report for a bank's loan division. We'll need to find out if a customer's marital status and number of children has an impact on whether they will default on a loan. The bank already has some data on customers' credit worthiness.

The main aim of the project is to build a credit score for a potential customer. Credit scoring is used to evaluate the ability of a potential borrower to repay their loan.

Plan of our work

Preprocess the data:

• Identify and fill in missing values
• Replace the real number data type with the integer type
• Delete duplicate data
• Categorize the data

Answer these questions:

• Is there a connection between having kids and repaying a loan on time?
• Is there a connection between marital status and repaying a loan on time?
• Is there a connection between income level and repaying a loan on time?
• How do different loan purposes affect on-time loan repayment?

Project_04. Identifying patterns of successful video games

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Project description

We are part of an analytics team at the online store Ice that sells video games worldwide. A variety of open sources provide us with user and expert reviews, genres, platforms (e.g. Xbox or PlayStation), and historical game sales data. Our goal is to identify the patterns that determine whether a game will succeed or fail so that we can predict which games will be successful in the future. Our insights will assist us in planning our advertising campaigns accordingly.

We have data up to the 2016 year. Currently, it is December 2016 and we are planning an advertising campaign for 2017.

Project structure

1. Data preprocessing
2. Data analysis (sales per platforms, genres, correlation between reviews and total sales)
3. Creating a user profile per region (top platforms, genres, critics and users reviews)
4. Testing hypotheses
5. General conclusion and recommendation

In conclusion, we advise focusing advertising campaigns on games for the PS4 in Europe and the XOne in North America. It is recommended that these games be Action and Shooter genres and have an ESRB rating of Everyone or 17+. In Japan, the best choice would be to use a game for portable device - the Nintendo 3DS - and games for it of Japanese production in the role-playing genre. Worth paying attention to games with good critics reviews.