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Exploratory Data Analysis (EDA) Project

EDA for Car Price Prediction Model

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Descriptions :

• A .json file containing a dataset consisting of 15919 rows and 54 columns is provided.
• This dataset, scraped from the on-line car trading company in 2019, contains many features of 9 different car models.
• The features (variables) of this dataset are too messy and distored.

What is expected of you?

• Read the .json file and assign the dataset into a DataFrame using pandas.
• Implement all aspects of the EDA process to the dataset.
  • Fix corrupted data formats,
  • Handle with outliers and missing values,
    • Domain (automobiles) knowledge is important.
    • Always use the internet to do the research that you need.
    • Think carefully to decide whether a data is outliers or not. Examples :
      • There is no conventional car model with an average fuel consumption of 1 - 1.5 liters per 100 km.
      • Or you need to know that it cannot be a 300 euro car.
      • Or if there is only one car with 3 doors out of the 15919 cars, this is what you should pay attention to and examine.
  • Drop the columns / rows you determined unnecessary as a result of your analysis,
  • Use visualization tools while doing all these processes.
• As a result, get the dataset ready to provide an appropriate input to the ML models.
• Save cleaned dataset into a .csv file.

Need to Study :

• .str.method,
• .contains(),
• .extract(),
• .to_datetime(),
• .get_dummies(),
• .add_prefix(),
• .sample(),
• regex,
• .to_numeric(),
• .isin(),
• .corr().