krishnaveni004/video-game-sales-analysis

Video Game Sales Analysis and Prediction 📈

Project idea: Video Game Sales Analysis and Prediction

I will be using Kaggle data available here to analyze and predict the ratings of video games based on 16 features. The dataset has 16,719 records, hence I will be using pandas and other visualization libraries to analyze the dataset and predict using Machine Learning models available through scientific libraries like scikit-learn, etc.

Tasks in the project:

• Data pre-processing
• Visualization to understand the data patterns
• Learn about different Machine Learning models used for the data pattern observed in Step 2
• Model building
• Evaluation of the performance of the built Model
• Results report

What can I Learn?

• pandas, NumPy, etc.
• Data processing technologies
• Visualization through libraries like matplotlib etc.
• About prediction techniques

Project Execution Plan

Week 4: Planning and Data Collection

Target: By end of Week 4, I will have completed data gathering and collecting information about important steps in data analysis, tools used, packages used, etc. from different blogs and articles on the net

Week 5: Data Inspection and Pre-processing

Target: By the end of Week 5, I would be completing the data preprocessing and data inspection portion of my Data Analysis project.

“GARBAGE IN, GARBAGE OUT”

Planned steps:

After reading different articles and blogs about pre-processing in data analysis projects, I have found the following steps crucial. It is been shown that “45% of a data scientist’s time is spent on data preparation tasks” - Datanami

• Data cleaning
  • Checking for missing values
  • Categorical v/s Numerical Data
  • Splitting Data into Training and Test datasets
  • Feature Scaling
• Data integration
  • In my project, this step is ignored, as my dataset is aggregated already by Kaggle
• Data reduction
  • Attribute selection
  • Numerosity reduction
  • Dimensionality reduction
• Data transformation
  • Aggregation
  • Normalization
  • Feature selection
  • Discreditization

Week 6: Visualization and Understanding Features

Target: By the end of Week 6, I will have done data visualization on my dataset and tried to understand the correlation between different features at my disposal. This would enable feature selection

“Data visualization is the language of decision making”

Planned steps:

• To analyze data in a chronological sense (by year)
  • Questions to answer:
    • How is the trend of video games over a period of time
    • Is the trend common across different geographical locations
• To visualize the prominence of Branding in video games (by publisher)
  • Questions to answer:
    • Who are the prominent players
    • How does Brand affect the ratings of video-games
• To visualize based on Genre and platform
  • Questions to answer:
    • Which genre dominates the industry
    • How many games are released in those genres
    • Does the platform play a role? PS2 vs PS4

Week 7: Learning and Experimentation

Target: To study different models used for the data patterns observed and experiment with those models on my dataset. Also, look for implementation tools, Google Colab?

Planned tasks:

• Implement different models
• Compare results
• Regression v/s Classification (Need to read more about it)

Week 8: Hyper-parameter Tuning

Target: Every model can be tuned to use certain parameters which affect model performance

“THE BRAIN of THE MODEL”

Planned tasks:

• Optimize performance through parameter tuning
• Interpret results
• What does the pattern convey? Can a prediction be made?
• How accurate is the model?

Week 9:

Planned tasks:

• Make a video for submission
• Prepare a report of the results

Results:

Dataset: Kaggle

Video Link: Panopto

How to run project? Install requirements specified in requirements.txt and run using jupyter

In case of numeric_only=True error, it is due to different version of pandas. Please use requirements.txt to install requirements. Alternatively, please remove numeric_only=True parameter.