Machine Learning in Aviation Flight Fare Prediction
This model we are developing aims to forecast flight costs depending on specified criteria. The data used in this is accessible for free on Kaggle. Given that the objective or label is the price (a continuous numeric value), this may be a regression concern. Given the many circumstances present at each unique time, airline firms utilise sophisticated algorithms to determine trip pricing. These techniques forecast flight prices by taking into account monetary, marketing, and numerous social elements. The number of people utilising aircraft has dramatically grown in recent years. Airlines find it challenging to keep up with pricing changes since they occur often due to many factors. We will try to solve this problem using machine learning as a result. By anticipating the pricing they will keep, this will assist airlines. Customers may also use it to forecast future flight costs and make travel plans accordingly.
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We will analyze the flight fare prediction using Machine Learning, in the dataset, we will be using a few necessary features to draw some predictions about the price of the flight like what type of airline it is, what is the arrival time, what is departure time, what is the duration of the flight, source, destination, and more.
first, we will see the data description
Airline: So this column will have all the types of airlines like Indigo, Jet Airways, Air India, and many more.
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Date_of_Journey: This column will let us know the date on which the passenger’s journey will start.
Source: This column holds the name of the place from where the passenger’s journey will start.
Destination: This column holds the name of the place where passengers wanted to travel.
Route: Here we can know about what is the route through which passengers have opted to travel from his/her source to their destination.
Arrival_Time:Arrival time is when the passenger will reach his/her destination.
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Duration:Duration is the whole period that a flight will take to complete its journey from source to destination.
Total_Stops:This will let us know how many places flights will stop there for the flight in the whole journey.
Additional_Info:In this column, we will get information about food, the kind of food, and other amenities.
Price:Price of the flight for a complete journey including all the expenses before onboarding.
First, we will import The Libraries that are required
Lets import the data and I'm using head() to view first five entries of the data
Then let's look into shape of the data and what are the columns we will be having and what kind of data types these features are holding using the shape and info functions
Now let's check do we have any null values are not
Since we have null values replacing with respective imputation technique
After removing the features which are not necessary let's look into the data
will do some exploratory data analysis to understand more about the data and here we can see the source of the flights are more when comes to delhi compared to any other city
Here we can check the price of the flights based on the source and destination and the number of stops
Performing the feature engineering on the data
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To calculate the time of flight that is the duration of the flight we have to find out the difference between Departure and Arrival time using that duration we can estimate the price of the ticket it will become one of the important feature
Since we have the categorical data im doing the respective encoding and appending those new features to the main dataframe
And the features that are not important that are not giving any information to predict the price im simply dropping or you can ignore
The dataset after doing all these operation its look like thins with 24 features
Since we are going with the regression we have to find the correlation between the predictors and the Target
I'm here using heatmap to find out the correlation among the features
Have not found any collinearity issue so we are good to go
Now we will split the data into predictors and Target here we have price is the target variable other than that we are taking it as input features
Now we will split the data frame into train and test,im giving the test data size as 30 percentage and for train it will automatically take as 70
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since we are going with regression make sure your data is normal for that i'm using standard scalar to make the data scale free or you can go with normalization as well
Now we will do the model training for that i'm importing Linear regression model from sk learn and fitting train data into model and using the test data to predict the price
As you can see we got R2 score as 0.54, we are using R2 score to check how good our model is fitting
it's not the good model, we can do lot more changes in the data and make improvement when comes to accuracy or R2 score like checking the normal distribution of the data and outlier treatment
To compare the actual price and the predicted price we can go with scatter plot and can check how good our predictions are
It has good similarity but we can do lot of improvements
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To evaluate the model of regression we are using measures like Mean absolute error, Mean square error and Root mean square error
we can see that results are not good since we are having so much error evalue obviously we can decrease this like we have discussed earlier.
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