Overview / Usage

** Steam Game Analysis Dashboard Overview**

​

Purpose:

The Steam Game Analysis Dashboard provides a comprehensive visualization of game data sourced from Steam, enabling stakeholders to make informed decisions about game development, marketing strategies, and user engagement. By leveraging data preprocessing techniques in Jupyter Notebook, the dashboard presents actionable insights derived from various game attributes.

​

Key Features:

​

1. Price Analysis:

   • Price Distribution: Visualize the distribution of game prices to identify pricing trends and outliers.

   • Price Categories: Analyze games categorized into segments like 'Free', 'Below $10', and 'Over $10' to understand market positioning.

​

1. Genre Insights:

   • Genre Breakdown: Explore the popularity of different game genres using pie charts or bar graphs.

   • Genre vs. Price: Compare average prices of games across various genres to assess market strategies.

​

1. Game Ratings:

   • Rating Distribution: Display histograms of game ratings to understand overall user satisfaction.

   • Correlation Analysis: Investigate correlations between game ratings and other features such as price and genre.

​

1. Free vs. Paid Games:

   • Comparative Analysis: Use side-by-side comparisons of free and paid games regarding average ratings, user reviews, and genre distribution.

​

1. Interactive Filters:

   • Dynamic Filtering: Allow users to filter games based on specific criteria like genre, price range, or rating to tailor their analysis.

   • Time-Based Analysis: Implement date filters to analyze trends over time, such as the release date and changes in user ratings.

​

1. User Engagement Metrics:

   • Sales Insights: If sales data is available, visualize total sales over time or per genre to assess market performance.

   • Review Analysis: Show the volume of reviews per game or genre to gauge user engagement.

​

Data Preprocessing:

The data for the dashboard is preprocessed in Jupyter Notebook using the following techniques:

• Data Cleaning: Handling missing values, outliers, and incorrect data types.

• Feature Engineering: Creating new features such as `Is_Free`, categorizing prices, and applying one-hot encoding for categorical variables (e.g., genres).

• Aggregation: Summarizing data to obtain insights, such as average ratings and total counts for different categories.

​

Technology Stack:

• Tableau: For creating interactive and visually appealing dashboards.

• Jupyter Notebook: For data preprocessing and analysis using Python libraries like pandas and NumPy.

• Data Source: The dataset is sourced from CSV exports, containing attributes like price, genre, rating, and more.

Methodology / Approach

Methodology for Steam Game Analysis Dashboard

​

The development of the Steam Game Analysis Dashboard follows a structured methodology that integrates data preprocessing, analysis, and visualization to derive actionable insights from the dataset. Below is an explanation of the approach, technology, frameworks, standards, and techniques used in this project.

​

1. Problem Identification

The primary objective of the dashboard is to provide insights into the Steam game market, enabling stakeholders to understand pricing strategies, genre popularity, user engagement, and overall game performance. Key questions include:

• What are the pricing trends for different genres?

• How do user ratings correlate with price and genre?

• What proportion of games are free, and how does this impact user engagement?

​

2. Data Collection

Data is collected from Steam's public API or through CSV exports. This dataset typically includes various attributes such as:

-** Game Title**: Name of the game.

• Price: Cost of the game.

• Genre: Type of game (e.g., Action, RPG).

• Rating: User rating scores.

• Release Date: Date when the game was launched.

​

3. Data Preprocessing

The preprocessing phase is conducted in Jupyter Notebook, employing Python libraries such as pandas and NumPy. The following techniques are used:

​

• Data Cleaning:

  • Handling missing values by imputation or removal.

  • Correcting data types (e.g., converting strings to numeric types).

  • Removing duplicates to ensure dataset integrity.

​

• Feature Engineering:

  • Creating new categorical features, such as `Is_Free` (binary indicator for free games).

  • Categorizing game prices into segments (e.g., 'Free', 'Below $10', 'Over $10').

  • One-hot encoding for categorical variables (e.g., genre) to facilitate numerical analysis.

​

• Data Aggregation:

  • Calculating aggregate statistics such as average ratings and total counts by genre or price category to facilitate comparison and analysis.

​

4. Data Analysis

After preprocessing, the analysis focuses on deriving insights from the cleaned dataset. Techniques employed include:

• Descriptive Statistics: Understanding data distributions, averages, and correlations.

• Comparative Analysis: Using statistical measures to compare categories (e.g., free vs. paid games).

• Trend Analysis: Identifying patterns over time in ratings or prices.

​

5. Data Visualization

The processed data is imported into Tableau for visualization. The dashboard is designed with the following principles in mind:

• User-Centric Design: Creating an intuitive layout that allows users to explore data interactively.

• Dynamic Filters: Implementing filters for genre, price range, and ratings to allow users to customize their views.

• Visual Appeal: Using appropriate charts (bar charts, pie charts, histograms) to represent data clearly and effectively.

​

6. Technology Stack

• Jupyter Notebook: For data preprocessing and analysis.

• Python Libraries:

  • pandas: For data manipulation and analysis.

  • NumPy: For numerical operations.

• Tableau: For creating interactive visualizations and dashboards.

• Data Source: CSV files.

​

7. Deployment and Iteration

Once the dashboard is created, it is shared with stakeholders for feedback. Based on user interactions and requirements, iterative improvements are made, including:

• Enhancing visualizations based on user feedback.

• Adding new data sources or features as necessary.

• Ensuring the dashboard remains up-to-date with the latest game data.

​

Conclusion

The methodology integrates various technologies and techniques to solve the problem of understanding the Steam game market. By employing a structured approach from data collection to visualization, the dashboard empowers stakeholders to make informed decisions based on comprehensive data analysis.

Documents and Presentations

Repository

https://github.com/SanjeyGM/Tableau/tree/main/Steam%20Dashboard