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Sales Prediction System using Machine Learning (Python Environment (Numpy…
Sales Prediction System using
Machine Learning
Things done
Feed Sample Sales Data
Develop Validation Set
Find best ARIMA configuration
validate Model
Finalize Model
Machine Learning
Time Series Forecast
ARIMA Model
Indicated by p, d, q parameters
q is the number of lagged forecast
errors in the prediction equation.
d is the number of nonseasonal
differences needed forstationarity
p is the number of
autoregressive terms
Most general model for forecasting
Data differencing
Python Environment
Numpy
Pandas
Scikit-learn
Statsmodel
Matplotlib
SciPy
Test Harness
Defining validation Set
Training data
Testing data
Develop method for Model Evaluation
Model Evaluation
Performance Measure
Root Mean Squared Error (RMSE)
Evaluate the performance of prediction