Excellent cell organization! For even cleaner output, consider using dataframe.head() instead of print statements when displaying DataFrames. This provides a more readable preview of the data.
Exploratory Data Analysis (EDA):
Informing Imputation:
While you have an imputation strategy, performing EDA beforehand could be beneficial. Analyzing the distribution of missing values (e.g., histograms) and identifying potential outliers can help guide the choice of an appropriate imputation technique.
Missingness and Dropping:
Some columns like "pool," "miscfeature," and "alley" have high percentages of missing values. Exploring the impact of these missing values on your analysis might be helpful. You could consider dropping these features entirely or imputing them based on your findings.
EDA Visualization:
Creating visualizations like correlation heatmaps can be a great way to understand relationships between features. This could replace printing the correlation DataFrame for a more interpretable output.
Documenting Findings:
Include clear summaries of your findings after each EDA step. This helps track your analysis and informs subsequent steps.
Univariate Analysis:
Aim to perform univariate analysis (exploring individual features) before imputation. This provides a clearer picture of the data distribution before handling missing values.
Data Preprocessing:
Encoding: It appears you might have repeated code for encoding categorical features. Consider refactoring your code to avoid redundancy and improve readability.
Overall:
Solid Project Effort! This is a really good attempt at the project. By incorporating these suggestions and adding comments and documentation to your code, you can take your work to the next level!
Code Organization:
Excellent cell organization! For even cleaner output, consider using dataframe.head() instead of print statements when displaying DataFrames. This provides a more readable preview of the data.
Exploratory Data Analysis (EDA):
Informing Imputation: While you have an imputation strategy, performing EDA beforehand could be beneficial. Analyzing the distribution of missing values (e.g., histograms) and identifying potential outliers can help guide the choice of an appropriate imputation technique.
Missingness and Dropping: Some columns like "pool," "miscfeature," and "alley" have high percentages of missing values. Exploring the impact of these missing values on your analysis might be helpful. You could consider dropping these features entirely or imputing them based on your findings.
EDA Visualization: Creating visualizations like correlation heatmaps can be a great way to understand relationships between features. This could replace printing the correlation DataFrame for a more interpretable output.
Documenting Findings: Include clear summaries of your findings after each EDA step. This helps track your analysis and informs subsequent steps.
Univariate Analysis: Aim to perform univariate analysis (exploring individual features) before imputation. This provides a clearer picture of the data distribution before handling missing values.
Data Preprocessing:
Encoding: It appears you might have repeated code for encoding categorical features. Consider refactoring your code to avoid redundancy and improve readability.
Overall: Solid Project Effort! This is a really good attempt at the project. By incorporating these suggestions and adding comments and documentation to your code, you can take your work to the next level!