The "AutoScout Car Price Prediction EDA" project is an exploratory data analysis initiative focusing on car price prediction using a dataset scraped from an online car trading platform in 2022. The dataset encompasses 13 car makes, 594 models, and various features, offering a rich source for data exploration and cleaning. This project serves as a bridge between the Data Analysis and Data Visualization courses, with successful completion being a prerequisite for certification.
The primary objectives of the project are:
- To practice and enhance skills in data cleaning, visualization, and exploratory analysis.
- To prepare a clean and well-structured dataset suitable for predictive modeling in future machine learning projects.
- To solidify Python programming skills by using libraries like NumPy, Pandas, Matplotlib, Seaborn, and SciPy.
The project culminates in pushing the solution to a GitHub repository, showcasing the analysis as part of a professional portfolio.
The first stage focuses on preparing the dataset by addressing:
- Incorrect Headers: Renaming columns for clarity and consistency.
- Incorrect Formats: Standardizing data types across columns.
- Anomalies: Identifying and addressing inconsistencies in the data.
- Dropping Useless Columns: Removing irrelevant or redundant features to streamline analysis.
This stage involves:
- Filling missing values for both categorical and numerical features.
- Employing imputation techniques to ensure data completeness without compromising integrity.
- Transforming categorical data into numerical form through encoding for compatibility with analysis and modeling tools.
The third stage leverages visualization techniques to:
- Identify outliers using statistical and graphical methods.
- Extract meaningful insights by exploring relationships between variables.
- Make informed decisions about retaining, transforming, or removing outliers based on their impact on the dataset.
Through this project, participants will:
- Develop a deep understanding of exploratory data analysis and its significance in data science.
- Gain proficiency in cleaning, organizing, and analyzing real-world datasets.
- Strengthen Python programming skills by applying a variety of libraries and techniques.
- Learn to generate meaningful visualizations to support insights and decision-making.
- Prepare datasets suitable for predictive modeling, laying a foundation for machine learning applications.
Exploratory Data Analysis (EDA):
- Enables a thorough understanding of the dataset.
- Identifies missing data, outliers, and relationships between variables.
- Helps formulate and test hypotheses to guide future modeling efforts.
- Prepares datasets that align with business objectives and modeling requirements.
By iteratively exploring the dataset, data analysts and scientists can gain actionable insights that significantly impact business decisions and analysis outcomes.
Participants are required to:
- Complete the project as outlined above.
- Commit the solution to their GitHub repository as part of their professional portfolio.
- Submit the GitHub repository link via the designated platform.
This structured approach ensures both the completion of learning objectives and the development of a demonstrable portfolio artifact.