MovieDB - Movie Data Analysis Tool

Description

Although the name suggests a datastore gem, MovieDB is actually a ruby wrapper that inspects, cleans, transform and model imdb data and provides useful data analysis information, suggesting conclusion. The objective and usage is to provide a tool that can aide movie/film producers make statistical decisions based off archival imdb data.

Basic functions and Data Analysis:

• Data Analysis
• Exploratory Data Analysis
• Confirmatory Data Analysis

VERY IMPORTANT NOTE

Our budget feature depends on themoviedb gem.

Please follow the URL links for reading info.

• https://www.themoviedb.org
• https://github.com/ahmetabdi/themoviedb

Requirements

ruby 1.9.x

themoviedb api key

code formattted using TomDoc code documentation specification

Installation

Add this line to your application's Gemfile:

gem 'movieDB'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install movieDB

Require - loading the libraries

$ irb

> require 'movieDB'

> require 'MovieDB/data_export'

> require 'themoviedb'

Usage - Collect Movie Data From IMDb (3 Steps)

> MovieDB::Movie.clear_data_store         /* ONLY IF YOUR WANT TO EMPTY YOUR DATASTORE (ARRAY) */

> MovieDB::Movie.send(:get_multiple_imdb_movie_data, "2024544", "1800241", "0791314")

  /* YOU CAN ADD AS MANY IMDB UNIQUE NUMBERS. DO NOT EXCEED MAXIMUM REQUEST RATE.*/

> MovieDB::DataExport.export_movie_data

Exported Document

The exported movie data is stored in your reports directory.

$ open /reports/imdb_raw_data_20131216.xls

Usage - Analyse Data and Generate Statistic Results (4 Steps)

$ irb

> require 'MovieDB/data_analysis'

> require 'MovieDB/data_process'

> MovieDB::DataProcess.send(:basic_statistic, 'imdb_raw_data_20131216.xls')

Exported - Analyzed Data

The exported analyzed data is stored in your reports directory.

$ cd /reports/basic_statistic_20131216.xls

What's Next

More statistical computations coming soon:

:GaussNewtonAlgorithm

> Iteratively_Reweighted_Least_Squares
> Lack_Of_Fit_Sum_Of_Squares
> Least_Squares_Support_Vector_Machine
> Mean_Squared_Error
> Moving_Least_Sqares
> Non_Linear_Iterative_Partial_Least_Squares
> Non_Linear_Least_Squares
> Ordinary_Least_Squares
> Partial_Least_Squares_Regression
> Partition_Of_Sums_Of_Squares
> Proofs_Involving_Ordinary_Least_Squares
> Residual_Sum_Of_Squares
> Total_Least_Squares
> Total_Sum_Of_Squares

:EstimationOfDensity

> Cluster_Weighted_Modeling
> Density_Estimation
> Discretization_Of_Continuous_Features
> Mean_Integrated_Squared_Error
> Multivariate_Kernel_Density_Estimation
> Variable_Kernel_Density_Estimation

:ExploratoryDataAnalysis

> Data_Reduction
> Table_Diagonalization
> Configural_Frequency_Analysis
> Median_Polish
> Stem_And_Leaf_Display

> Data_Mining
  > Applied_DataMining
  > Cluster_Analysis
  > Dimension_Reduction
  > Applied_DataMining

> RegressionAnalysis
  > Choice_Modelling

  > Generalized_Linear_Model
    > Binomial_Regression
    > Generalized_Additive_Model
    > Linear_Probability_Model
    > Poisson_Regression
    > Zero_Inflated_Model

  > Nonparametric_Regression
  > Statistical_Outliers
  > Regression_And_Curve_Fitting_Software
  > Regression_Diagnostics
  > Regression_Variable_Selection
  > Regression_With_Time_Series_Structure
  > Robust_Regression
  > Choice_Modeling

> Resampling
  > Bootstrapping_Population

> Sensitivity_Analysis
  > Variance_Based_Sensitivity_Analysis
  > Elementary_Effects_Method
  > Experimental_Uncertainty_Analysis
  > Fourier_Amplitude_Sensitivity_Testing
  > Hyperparameter

> Time_Series_Analysis
  > Frequency_Deviation

Contact me

If you'd like to collaborate, please feel free to fork source code on github.

You can also contact me at albertmck@gmail.com

Copyright (c) 2013 Albert McKeever, released under MIT license