nishantsbi / airticketpredicting

Machine Learning modeling for Air Ticket Predicting

Machine Learning for Air Ticket Predicting

Table of Contents:

• Instructions on the codes
• Code Structure
  • Classification
    • Specific Problem
    • Generalized Problem
  • Regression
    • Specific Problem
    • Generalized Problem
  • AI
  • HMM generalized model
  • Others

Instructions on the codes

Any theory about this project, please refer to my report. If you want to keep track of the result performance, please refer to the "Performance Record.xlsx" file.

I implemented many kinds of classifiers and regressors on this project in python.

And the features I used in classification and regression is described in the report.

The package I used during the project is described in requirements.txt.

This project defines two problems, one is the specific problem, the other one is the generalized problem. You can find the definition in the report (or in the representation draft).

And also, I provide the representation draft and ppt to you, it is compressed version of my report, which does not focus on mathematical formulas, but gives you the intuition of my project. And also, many details are not shown in the representation draft, please refer to my report if you want to know more details about this project.

Code Structure

Classification

• Use Classification to predict

Specific Problem

|-inputClf_small                  # the input for classification method
|-inputClf_GMMOutlierRemoval      # the input for classification method with outlier removal by EM
|-inputClf_KMeansOutlierRemoval   # the input for classification method with outlier removal by K-Means

# Classification methods
|-ClassificationBase.py           # The base class of the classification objects
	|-ClassificationAdaBoost.py     # AdaBoost class  
	|-ClassificationDecisionTree.py # Decision Tree class
	|-ClassificationKNN.py          # K nearest neighbot class
	|-ClassificationLinearBlend.py  # linear blending class
	|-ClassificationLogReg.py       # logistic regression class
	|-ClssificationNN.py            # neural networks class
	|-ClassificationPLA.py          # perceptron learning algorithm class
	|-ClassificationRandomForest.py # random forest algorithm class
	|-ClassificationSVM.py          # SVM class
	|-ClassificationUniformBlending.py # uniform blending algorithm class
# Classification test
|-mainAdaBoostClf.py
|-mainDecisionTreeClf.py
|-mainGeneralizeClf.py
|-mainKNNClf.py
|-mainLinearBlendClf.py
|-mainLogisticReg.py
|-mainNNClf.py
|-mainPLA.py
|-mainRandomForestClf.py
|-mainSVMClf.py
|-mainUniformBlendClf.py

Generalized Problem

# methods
|-inputGeneralClf_small              # the input for uniformGneralize method
|_inputGeneralClf_HmmParsed          # the input pattens are parsed from HMM Sequence Classification, used for HmmGeneralizeClf method       
|-ClassificationHmmGeneralize.py     # use hmm to do the generalized problem
|-ClassificationUniformGeneralize.py # use uniform blending to do the generalized problem
# test files
|-mainHmmGeneralizeClf.py
|-mainUniformGeneralize.py

Regression

• Use regression to predict.

Specific Problem

|-inputReg # input for regression methods
# Regression methods
|-RegressionBase.py # The base class of the regression objects
	|-RegressionAdaBoost.py     # AdaBoost class
	|-RegressionDecisionTree.py # Decision Tree class
	|-RegressionGaussianProcess.py # gaussian process class
	|-RegressionKNN.py          # K nearest neighbors class
	|-RegressionLinReg.py       # linear regression class
	|-RegressionNN.py           # neural networks class
	|-RegressionRandomForest.py # random forest class
	|-RegressionRidgeReg.py     # ridge regression class
	|-RegressionUniformBlend.py # Uniform Blending class
# Regression test
|-mainAdaBoostReg.py
|-mainDecision.py
|-mainGaussianProcess.py
|-mainLinReg.py
|-mainNNReg.py
|-mainRandomForestReg.py
|-mainRidgeReg.py
|-mainUniformBlendReg.py

Generalized Problem

There is no generalized problem method in regression, because the final preferred algorithm is AdaBoost-DecisionTree Classification.

AI(Aritificial Intelligence)

Use Artificial Intelligence to predict, here mainly Q-Learning.

# Artificial Intelligence methods
|-inputQLearning   # input for qlearning method
|-qlearn.py        # q learning class
|-mainQLearning.py # test for qlearning

HmmGeneralizeModel

It is used to generalize the patterns for the new routes.

|-HmmClassifier.py
|-mainHMM.py

Others

# utils
|-data_small   # input data crawled from an airplane company, json files. It is a 103 day period.
|-inputGeneralRaw   # the generalized problem input matrices parsed from data_small, and it is not price normalized(i.e. not in Euro currency)
|-inputSpecificRaw  # the specific problem input matrices parsed from data_small, and it is not price normalized(i.e. not in Euro currency)  
|-util.py      # util functions
|-load_data.py # load input from the raw json data
|-log.py       # log function, if you do not want to see some log info, please change the DEBUG variable in this file to 'False'   
|-priceBehaviorAnalysis.py # analyze the price behavior of several routes
|-plotOutlierRemoval.py    # plot the figure to illustrate outlier removal
|-plotNNUpdate.py          # plot the effect of different update method in NN
|-computeMoneySave.py      # used to compute the money save for every client on the average

# infos
|-requirements.txt         # package requirements
|-Performance Record.xlsx  # record the performance of various parameters

Citation

The repo is based on the following research articles:

• Lu, Jun. "Machine learning modeling for time series problem: Predicting flight ticket prices." arXiv preprint arXiv:1705.07205 (2017).