/Flight-Price-Prediction

Primary LanguageJupyter Notebook

Flight-Price-Prediction

This is a Machine Learning and Big Data Project to analyse the flight booking dataset obtained from the “Ease My Trip” website to predict the flight prices based on some features.

Dataset

Flight Price Prediction

Prerequisites

  • Linux
  • Hadoop
  • Anaconda/Miniconda
  • Python, Numpy and Jupyter Notebook (must be inside conda env)
  • Pandas, Sklearn, Matplotlib and Seaborn

How to run

  • To explore the dataset open the notebook code/Data Analysis.ipynb
  • Modify the first 3 lines in code/train.sh and code/test.sh so it points to correct path, for example: 
    export HADOOP_HOME=~/Desktop/hadoop-3.3.2
export HADOOP_STREAMING=$HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-3.3.2.jar
export CONDA_PYTHON=/home/kareem/miniconda3/bin/python
  • Make sure hadoop is running
  • To train the linear regression model on hadoop, run:
    cd code && ./train.sh
    the trained model is saved in code/model.txt
  • To test the learned model on hadoop, run:
    cd code && ./test.sh
    the score is saved in code/score.txt

Results

You Can see the results and details in the pdf documentations found above.