The previous project on flight prediction has been optimized, by selecting more advanced feature selection techniques.

• Filter Method Variance Threshold Mutual Importance

• There are columns 'Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route', 'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops', 'Additional_Info', 'Price
• Price is the only Numerical column
• Total 11 columns
• There are 2 NAN values, and they are in same row (Air India) , they are Route and Total stops
• There are total 12 Airlines: ['IndiGo' 'Air India' 'Jet Airways' 'SpiceJet' 'Multiple carriers' 'GoAir' 'Vistara' 'Air Asia' 'Vistara Premium economy' 'Jet Airways Business' 'Multiple carriers Premium economy' 'Trujet']
• There are total 5 Sources: ['Banglore' 'Kolkata' 'Delhi' 'Chennai' 'Mumbai']
• Total 6 Destination: ['New Delhi' 'Banglore' 'Cochin' 'Kolkata' 'Delhi' 'Hyderabad']
• 5 types of stops: ['non-stop' '2 stops' '1 stop' '3 stops' '4 stops']
• Total 10 additional information
• This data is of year 2019
• April has the least no of flights
• June has the most no of flights

• R2 Score: 0.7870853276492524
• Mean absolute Score: 865.1498372819618
• Mean Square Score: 3568305.4385348638
• Root Mean Square Error: 1888.995881026442

• Clone the repository
• Create an environment
• Activate this environment
• Then use "python app.py"