The code provided comprehensive data preprocessing and exploration script for a survey dataset related to masculinity. Here's a summary of the major steps performed in the script:
-
Data Loading and Overview:
- The script starts by loading the data from a CSV file and displaying basic information about the dataset, such as its shape and the first few rows.
-
Data Cleaning and Exploration:
- Various data cleaning and exploration techniques are applied, including checking for missing values, handling missing data, exploring unique values in columns, and visualizing the distribution of responses using bar plots, pie charts, and heatmaps.
- Different data types are inspected, and missing values are counted for each column.
-
Data Transformation:
- The script involves transforming categorical variables into numerical representations using mapping dictionaries and applying functions to convert categorical responses into ordinal values.
- Columns with categorical data are transformed into numerical representations suitable for machine learning algorithms.
- Missing values and irrelevant columns are dropped or handled based on specific criteria.
- Dummy encoding is applied to certain categorical variables.
-
Exploratory Data Analysis (EDA):
- Exploratory data analysis techniques, such as plotting histograms and bar plots, are used to analyze the distribution of responses across different survey questions.
- Geographic data visualization is performed using choropleth maps to represent respondents' locations.
-
Feature Engineering:
- Feature engineering techniques are applied to extract meaningful information from the data and prepare it for machine learning modeling.
- Categorical variables are converted into numerical representations, and missing values are handled appropriately.
-
Conclusion:
- The script concludes with a summary of the data preprocessing steps and the final dataset ready for further analysis or modeling tasks.