📅 2023-10-24 — Session: Enhanced Time Series Analysis and Data Handling
🕒 03:00–05:30
🏷️ Labels: Time Series, Data Analysis, Python, Linear Regression, Data Visualization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to improve the robustness and accuracy of time series analysis by addressing data handling issues and enhancing code for linear regression and visualization.
Key Activities
- Exponential Trend Detection: Implemented a method to detect exponential trends in time series data using linear regression.
- Data Handling Enhancements: Improved code to handle NaN and infinite values during logarithmic transformations, ensuring data integrity for linear regression.
- Data Resampling: Resampled time series data to a monthly frequency to ensure comparability across different datasets.
- DataFrame Manipulation: Converted and merged dictionaries into DataFrames using Pandas, with MultiIndex for alignment.
- Visualization Improvements: Enhanced plot aesthetics using Matplotlib and Seaborn for clearer data presentation.
Achievements
- Successfully implemented robust data handling techniques for linear regression, including NaN and infinite value management.
- Enhanced the visualization of time series data, improving interpretability.
- Developed a comprehensive workflow for converting and merging data structures, facilitating further analysis.
Pending Tasks
- Re-upload necessary data files to address code execution issues and data loss encountered during the session.
Technical Insights
- The use of MultiIndex in Pandas ensures proper alignment and manipulation of complex data structures.
- Aesthetic improvements in plots can significantly enhance the clarity and communication of data insights.