Welcome to AutoScout Data Analysis Project. Auto Scout data which using for this project, scraped from the on-line car trading company in 2019, contains many features of 9 different car models. In this project, commonly used algorithms for Data Cleaning and Exploratory Data Analysis by using many Python libraries such as Numpy, Pandas, Matplotlib, Seaborn, Scipy analyzed clean dataset.
In this context, the project consists of 3 parts in general:
- The first part is related to 'Data Cleaning'. It deals with Incorrect Headers, Incorrect Format, Anomalies, and Dropping useless columns.
- The second part is related to 'Filling Data', in other words 'Imputation'. It deals with Missing Values. Categorical to numeric transformation is done as well.
- The third part is related to 'Handling Outliers of Data' via Visualization libraries. So, some insights will be extracted.