Detecting multicollinearity (thanks to ChatGPT)

I just asked ChatGPT how to detect and handle multicollinearity (stg I probably already knew before). Here's the solution code:

import pandas as pd

import numpy as np

from statsmodels.stats.outliers_influence import variance_inflation_factor

# Load data into a pandas DataFrame

data = pd.read_csv('your_data.csv')

# Create a list of column names to check for multicollinearity

cols_to_check = ['feature1', 'feature2', 'feature3', ...]

# Create a new DataFrame with only the columns to check

data_to_check = data[cols_to_check]

# Calculate the VIF scores for each feature

vif_scores = pd.DataFrame()

vif_scores["Feature"] = data_to_check.columns

vif_scores["VIF"] = [variance_inflation_factor(data_to_check.values, i) for i in range(data_to_check.shape[1])]

# Print the VIF scores

print(vif_scores)

Outlier handling

I got this from ChatGPT again. This is a simple for loop to eliminate the outliers in a list of columns. We set an upper and lower limit of our choice, then delete the outliers in our dataset. (dataset name: sm_df in this example)

outlier_column_list = ["V1", "V4", "V5", "V6", "V7", "V8", "V10", "V11", "V12", "V13", "V14",
"V15", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V26"]

# Here we'll set upper and lower limits and then eliminate the outliers
# Loop through each column in outlier_column_list
for col in outlier_column_list:

    # Calculate the upper and lower limits
    upper_limit = sm_df[col].mean() + 2 * sm_df[col].std()
    lower_limit = sm_df[col].mean() - 2 * sm_df[col].std()
    
    # Replace outliers above the upper limit with NaN
    sm_df[col] = np.where(sm_df[col] > upper_limit, np.nan, sm_df[col])
    
    # Replace outliers below the lower limit with NaN
    sm_df[col] = np.where(sm_df[col] < lower_limit, np.nan, sm_df[col])

How to handle highly skewed data according to ChatGPT :)

 

import numpy as np

# assume sm_df is your pandas DataFrame

sm_df["A"] = sm_df["A"] - sm_df["A"].min() + 1

from scipy.stats import boxcox

# assume sm_df is your pandas DataFrame

sm_df["A"], _ = boxcox(sm_df["A"])

# assume sm_df is your pandas DataFrame

sm_df["A"] = (sm_df["A"] - sm_df["A"].mean()) / sm_df["A"].std()

import numpy as np

# assume sm_df is your pandas DataFrame

sm_df["A"] = sm_df["A"] - sm_df["A"].min() + 1

sm_df["A"] = np.log(sm_df["A"])

Some other information:

The choice of transformation depends on the characteristics of your data and the requirements of your analysis. For example, if your data has a strong right-skewness, a log transformation might be a better option than a Box-Cox or Yeo-Johnson transformation. Conversely, if your data has a strong left-skewness or a significant number of negative values, the Yeo-Johnson transformation might be more appropriate.