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<speak> This is our Jupyter notebook. <break strength="x-strong"/>So today we are going to perform various operations on this data set. <break strength="x-strong"/> We are going to do exploratory data analysis, and data visualization<break strength="x-strong"/> We are going to do feature engineering. <break strength="x-strong"/> we are going to check correlation between features.<break strength="x-strong"/> And finally, we will build the model. <break strength="x-strong"/> You can download the data set <break strength="strong"/>and notebook from code section. <break strength="x-strong"/>From code section, you will be able to download Jupyter notebook, test set, and train set. <break strength="x-strong"/> Download files <break strength="strong"/>and upload them to jupyter notebook.<break strength="x-strong"/> Or you can import data from jupyter notebook.<break strength="x-strong"/> Let's start our project. <break strength="x-strong"/> So here we will use these flight records to determine flight prices <break strength="strong"/>based on the below-given parameters. <break strength="x-strong"/> So the main business objective is the cost of misclassification can be very high.<break strength="x-strong"/> Because it a very big industry <break strength="x-strong"/> And if you predict a wrong price <break strength="strong"/>you will be in a huge loss<break strength="x-strong"/> We don't need any latency <break strength="strong"/>and here there is a strict latency concern. <break strength="x-strong"/> We should do it fast. <break strength="x-strong"/> The model should predict the value fast. <break strength="x-strong"/> First, we will import all the important libraries. <break strength="x-strong"/> Here we are using NumPy, pandas, matplotlib, seaborn and various sklearn libraries.<break strength="x-strong"/> We use Numpy and pandas for data analysis.<break strength="x-strong"/> We use matplotlib and seaborn for data visualization<break strength="x-strong"/> <break strength="x-strong"/> </speak>