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This project is based on a classification algorithm i.e. Naive Bayes which is run on a mobile dataset consisting of 2000 rows and 15 columns. It is a multi-class problem where mobile phones are classified in accordance with their price range. There are four classes of price ranging from 0 to 3, 0 indicating cheaper mobiles phones and 3 representing expensive mobile phones. Univariate analysis is conducted to understand individual predictors and bivariate analysis is conducted to infer relationship between predictors with other predictors and target variable. Important features are identified by Random Forest.
The main objectives of the project are: exploratory data analysis, pre-processing, feature extraction, price prediction of houses.
Implementing different model for Income Classification Python
Predictive analysis to find essential variables in customer booking
This is a regression problem which predicts plane arrival delay.