Exploratory Data Analysis (EDA) Project
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EDA for Car Price Prediction Model
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 aDataFrame
usingpandas
. - 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()
.