📅 2024-08-31 — Session: Constructed Comprehensive Car Attributes Dataframe
🕒 03:20–04:00
🏷️ Labels: Dataframe, Python, Data Manipulation, Car Attributes, Investment, SVD
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to construct a comprehensive dataframe by merging relevant car attributes from attributes_df with products_df for analysis purposes.
Key Activities:
- Developed Python code to extract, combine, and merge car attributes into a final dataframe.
- Calculated travel time for 23 kilometers using different speeds.
- Explored strategies for enhanced price data processing in investment decisions, focusing on data collection, advanced analytics, optimization, automation, and ethical considerations.
- Provided a detailed explanation of Singular Value Decomposition (SVD) for computing the kernel of a matrix.
- Processed vehicle data for price analysis, including categorizing mileage, rounding vehicle years, grouping by relevant columns, filtering groups, and calculating descriptive statistics.
- Implemented Python code to round and sort DataFrame columns.
Achievements:
- Successfully constructed a comprehensive dataframe suitable for analysis.
- Clarified strategies for data processing in investment decisions.
- Enhanced understanding of SVD applications in data science.
Pending Tasks:
- Further exploration of investment strategies leveraging enhanced data processing.
- Continued analysis of the used car market dynamics and communication strategies.