Flight Fare Predictor Flight Fare Prediction is very useful for travel agencies as they can have an idea about the future fare trends and make their customers aware about them. This helps them to make decisions on whether to book flights for their clients or not. Flight Fare Prediction (having complex algorithms to calculate flight prices given various conditions present at that particular time) is a very interesting and useful project because it involves data analysis, machine learning and data science. We will use numpy for scientific computing with Python. It provides a rich array of tools such as linear algebra, Fourier transforms, statistical functions, and random number generation. Pandas to provide fast and flexible data structures for working with structured (tabular) data sets. joblib to provides tools to create shared memory jobs and implement lightweight pipelining in algorithmic code. kandi kit provides you with a fully deployable Flight Fare Prediction. Source code included so that you can customize it for your requirement.

Kit Deployment Instructions

Download, extract and double-click kit installer file to install the kit. Note: Do ensure to extract the zip file before running it. Follow below instructions to deploy and run the solution. 1. Download the 'kit_installer.zip' then unzip the folder. 2. Then Double-tap the 'kit_installer' and then command prompt opens, when you're prompted during the installation of the kit, press Y to launch the app automatically. 3. To run the app manually, press N when you're prompted and locate the zip file Flight-Fare-Prediction.zip. 4. Extract the zip file and navigate to the directory Flight-Fare-Prediction. 5. Open command prompt in the extracted directory Flight-Fare-Prediction and run the command 'pip install -r requirements.txt' & 'python app.py'. 6. Running on local URL: http://127.0.0.1:5000/.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Kit Solution Source

By using the below libraries you can create the Flight-Fare-Prediction. The entire solution is available as a package to download from the source code repository.

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc .