MovieDB - Movie Data Analysis Tool

Gem Version

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.

Requirements

ruby 1.9.x

themoviedb api key

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 [email protected]