# Can imbalance data create overfitting?

I am doing human activity recognition project. I have total of 12 classes. The class distribution look like this:

$$\color{red}{If \ you \ watch \ carefully, you \ can \ see \ that \ I \ have \ no \ data \ points \ for \ class \ 11 \ and \ class \ 8.}$$ Also, the dataset is highly imbalanced. So, I took minimum data points (in this case 2028) for all of the classes. Now my balanced data look like this:

After doing this it looks like a balance data. $$\color{red}{But \ still, \ I \ think \ it \ not, \ because \ I \ have \ zero \ datapoints \ for \ class \ 11 \ and \ class \ 8}$$. In my opinion the classes are still imbalance.

I am using CNN model to solve this activity project. My model summary is following:

The main problem is, my model starts overfitting heavily when I train it.

Is it due to my imbalance data( class 8 and 11 has zero data points) or something else?

$$\textbf{Hyperperameter:}$$

$$\textbf{features:}$$ X, Y, Z of mobile accelerometer

$$\textbf{frame size:}$$ 80

$$\textbf{optimizer:}$$ Adam, $$\textbf{Learning rate:}$$ 0.001

$$\textbf{Loss:}$$ Sparse categorical cross-entropy