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.
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 [email protected]
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