📅 2023-02-20 — Session: Developed and Enhanced Python Data Analysis Functions
🕒 07:10–08:10
🏷️ Labels: Python, Data Analysis, KNN, Regression, Simulation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to address a matrix multiplication error in regression analysis and to develop and enhance Python functions for data matching and analysis.
Key Activities
- Error Handling: Reflected on a matrix multiplication error encountered during regression analysis, emphasizing the importance of checking data shapes and consulting external resources for troubleshooting.
- KNN Matching Function: Developed a Python function to perform K-Nearest Neighbors (KNN) matching on treatment data, utilizing linear assignment for creating matched pairs across multiple data files.
- Regression Analysis: Implemented a function to perform regression analysis on matched pairs for estimating the average treatment effect, leveraging the statsmodels library for regression modeling.
- Data Simulation: Created code snippets for generating simulated datasets with covariates, outcome variables, and treatment effects using logistic and linear regression models.
- Data Generation Function: Developed a Python function to generate a DataFrame with simulated data, including treatment effects, using pandas and numpy.
- Modified KNN Function: Enhanced the KNN matching function to process a single DataFrame, detailing how to call the function and save matched pairs to a CSV file.
Achievements
- Successfully addressed the matrix multiplication error by reflecting on data shape compatibility.
- Developed robust Python functions for KNN matching and regression analysis, facilitating treatment effect estimation.
- Generated and validated simulated datasets for testing analysis functions.
Pending Tasks
- Further testing and validation of the modified KNN matching function on diverse datasets to ensure robustness and accuracy.