Last 4-6 weeks, I have been learning and working for the first time on ML. Reading blogs, articles, documentations, etc. and practising. Have asked lot of questions here on Stack Overflow as well.
While I have got some amount of hands-on experience, but still got a very basic doubt (confusion) -- When I take my input data set with 1000 records, the model prediction accuracy is say 75%. When I keep 50000 records, the model accuracy is 65%.
1) Does that mean the model responds completely based on the i/p data being fed into?
2) If #1 is true, then in real-world where we don't have control on input data, how will the model work?
Ex. For suggesting products to a customer, the input data to the model would be the past customer buying experiences. As the quantity of input data increases, the prediction accuracy will increase or decrease?
Please let me know if I need to add further details to my question.
Edit - 1 - Below added frequency distribution of my input data:
Edit - 2 - Adding Confusion matrix and Classification report: