VideoGameSales

#Predicting Video games sales In this work the data is converted from categorical data into a numeric. Tis step is important since sklearn models doesn't accept the categorical data. Some of the features ahs been removed due to its irrelevancy. For example, year, name and rank. Creating a linear regression model based on the first 3 features. As it appear in the datawrangling notebook. The model couldn't generated good predictions. Then the model was fed with more feautres as in the "Trials" notebook, this model has improved to generat upto 96% accuacy.