Flight-Fare-Price-Prediction-RandomForest

Predicting The Fare Price of Flights Using RandomFores Model. (Artificial Intelligence)

Requirement:

• sklearn
• numpy
• seaborn
• matplotlib
• sys
• pandas
• regex

Instruction:

Training Script is written according the specific columns format, you can view the format in org_dataset.csv file.

After you set your dataset file according to format given in org_dataset.csv format Next Step is to Clean the Dataset using Cleaning_Dataset.py Module, by providing the dataset_name and dataset_type (Training or Testing) as command line argument, dataset_type differentiate between training dataset and testing dataset (used for predicting price).

After you Cleaned You Dataset, Now its to Train your Model, run Train_Model Module and pass dataset_name as command line argument, and wait for a minutes. After completion Model Will be saved at Supported_data folder.

Now it's time to predict the price for specific input values, You need to run Predict_Price Module by passing any commands which is as follow:

• -m (m means insert data manually using terminal Input)
• -a (a means automatically, you have input values in dataset, you have pass it using this argument)

python Predict_price.py -a predict_dataset_name

(This will import predict_dataset_name file and perform operation automatically and will display the price

Example:

Run Following Command Using Command Prompt

Go Inside this repo after pulling or downloading this repo

Open Terminal in current folder (repo) and type following commands:

• python Cleaning_Dataset.py org_dataset Train
• python Train_Model.py Train_dataset
• python Predict_Price.py -a pre_dataset