Automobile Data Analysis

(as part of the Data Science Certification Program from IBM Cognitive Class)

Table of Contents

• Data Acquisition
• Data Wrangling
  • Identify and handle missing values
    • Identify missing values
    • Deal with missing values
    • Correct data format
  • Data standardization
  • Data Normalization
  • Binning
  • Dummy Variable
• Exploratory Data Analysis
  • Analyzing Individual Feature Patterns using Visualization
  • Descriptive Statistical Analysis
  • Basics of Grouping
  • Correlation and Causation
  • ANOVA

Extract of the dataset

Snapshots of the study

Conclusion

Important variables to take into account when predicting the car price :

Continuous numerical variables:

• Length
• Width
• Curb-weight
• Engine-size
• Horsepower
• City-mpg
• Highway-mpg
• Wheel-base
• Bore

Categorical variables:

• Drive-wheels