Important Question Regarding Missing Data Imputation for Purchase Probability Model

Hello everyone,

I have an important question regarding a machine learning task.

I’m testing sales data to predict the probability of purchase for a specific product. For the model to learn effectively, the dataset must contain negative samples (non-purchases), which is represented in the Target column (0/1).

After performing a natural cross join, I ended up with missing values in my dataset. Most of the values are manageable, but I’ve run into a specific challenge with some key missing data points:

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Invoice Number Column: I decided to drop this column.

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Branch Column: I chose to impute this with any branch since the products are similar across all locations, making the specific branch irrelevant for the prediction.

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Payment Method (Cash/Card): This will be NULL/NaN.

My biggest confusion is with the Date column. Should I:

1.

Set a fixed, placeholder date like 1900-01-01 (as I read somewhere)?

1.

Use NaN (Not a Number)?

I tried consulting AI tools, but the results were disastrous—conflicting advice, to say the least.

What is the best solution that won’t negatively impact the machine learning model’s performance?

Note: I plan to use AutoML, as I believe it will be the most suitable choice.

Any suggestions or experiences would be highly appreciated.

Thank you all!