I ran an Ordinary Least Squares model and found the constant  / intercept is the more significant than all the other features. When constant is included, the R-squared is 45%, when I remove the constant, the R-squared drops to 29%.

The constant also has the lowest p-value compared to all the other features.

Moreover, I used StandardScaler to scale the features used.

Why would the intercept be so significant?

Example code:

model_2 = sm.OLS(df_reg_y.astype(float), sm.add_constant(X_scaled.astype(float))).fit()

This area circled in purple is the scatter plot of the target variable vs the most significant feature. The histogram below it is the distribution of the target variable.

Thanks in advance!

Edit: I realized that I forgot to scale the target variable. The issue was fixed after I scaled the features and target variable together.

I ran an Ordinary Least Squares model and found the constant/intercept is more significant than all the other features. When the intercept is included, the $R^2$ is 45%. When I remove the intercept, the $R^2$ drops to 29%.

The intercept also has the lowest p-value compared to all the other features.

Moreover, I used StandardScaler to scale the features used.

Why would the intercept be so significant?

Example code:

model_2 = sm.OLS(df_reg_y.astype(float), sm.add_constant(X_scaled.astype(float))).fit()

This area circled in purple is the scatter plot of the target variable vs the most significant feature. The histogram below it is the distribution of the target variable.

Edit: I realized that I forgot to scale the target variable. The issue was fixed after I scaled the features and target variable together.

I ran an Ordinary Least Squares model and found the constant / intercept is the more significant than all the other features. When constant is included, the R-squared is 45%, when I remove the constant, the R-squared drops to 29%.

The constant also has the lowest p-value compared to all the other features.

Moreover, I used StandardScaler to scale the features used.

Why would the intercept be so significant?

Example code:

model_2 = sm.OLS(df_reg_y.astype(float), sm.add_constant(X_scaled.astype(float))).fit()

I ran an Ordinary Least Squares model and found the constant / intercept is the more significant than all the other features. When constant is included, the R-squared is 45%, when I remove the constant, the R-squared drops to 29%.

The constant also has the lowest p-value compared to all the other features.

Moreover, I used StandardScaler to scale the features used.

Why would the intercept be so significant?

Example code:

model_2 = sm.OLS(df_reg_y.astype(float), sm.add_constant(X_scaled.astype(float))).fit()

This area circled in purple is the scatter plot of the target variable vs the most significant feature. The histogram below it is the distribution of the target variable.

Thanks in advance!

Edit: I realized that I forgot to scale the target variable. The issue was fixed after I scaled the features and target variable together.

I ran an Ordinary Least Squares model and found the constant / intercept is the more significant than all the other features. When constant is included, the R-squared is 45%, when I remove the constant, the R-squared drops to 29%.

The constant also has the lowest p-value compared to all the other features.

Moreover, I used StandardScaler to scale the features used.

Why would the intercept be so significant?

Example code:

model_2 = sm.OLS(df_reg_y.astype(float), sm.add_constant(X_scaled.astype(float))).fit()

Thanks in advance!

I ran an Ordinary Least Squares model and found the constant / intercept is the more significant than all the other features. When constant is included, the R-squared is 45%, when I remove the constant, the R-squared drops to 29%.

The constant also has the lowest p-value compared to all the other features.

Moreover, I used StandardScaler to scale the features used.

Why would the intercept be so significant?

Example code:

model_2 = sm.OLS(df_reg_y.astype(float), sm.add_constant(X_scaled.astype(float))).fit()