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raulcocera's Projects

box-office icon box-office

BoxOffice predicts movie box office revenues of feature length films to identify stock market opportunities in media properties. The tool is based on critic reviews, film characteristics, production budget, and what studio and players are involved. Producing a movie is a highly risky endeavor and studios rely on only a handful of extremely expensive movies every year to make sure they remain profitable. Box office hits and misses correspond to short-term changes in stock prices.

boxofficemojo icon boxofficemojo

A simple python module for scrapping movie information from www.boxofficemojo.com

estadistica-con-r icon estadistica-con-r

Apuntes personales sobre estadística, machine learning y lenguaje de programación R

fitnessgarage icon fitnessgarage

Sistema de bases de datos para control de usuarios del Gimnasio Fitness Garage.

gestiongimnasio icon gestiongimnasio

Ejemplo de Java Swing para la gestion de un gimnasio con funcionalidades basicas

gym icon gym

Sistemas para el control de un gimnasio desarrollado con java y mysql

ml-students-spark-python icon ml-students-spark-python

Machine Learning utilizando el dataset de estudiantes que se encuentra en https://archive.ics.uci.edu/ml/machine-learning-databases/00320/student.zip Para ello se utilizará PySpark

movie-data-analysis-project icon movie-data-analysis-project

Analysis of movie data using data from two public Kaggle CSV files, along with API calls to OMDb (The Open Movie Database). Data analysis includes both analysis of the movie industry as a whole, as well as specific actor analysis based on user's actor of choice.

movieclustering icon movieclustering

What makes a good movie? Most of the top-rated movies in the International movie database (IMDB) are critically acclaimed and are generally a safe bet in terms of commercial success. Naturally, it would be interesting to investigate if these top movies have some distinct features responsible for their high ratings. This project aims to find out the type of natural cluster that exists among the top 250 movies from IMDB. Unsupervised machine learning techniques will be employed, more specifically, clustering algorithms. Hopefully, these clusters will give us information to observe the recurrent pattern. To build our dataset we used OMDB's web API which is RESTful web service to obtain movie information. For plot summaries we scrapped a movie's plot from IMDB's website using BeautifulSoup Library in python. Our final extracted dataframe had 250 rows and 113 columns. The inputs to our dataframe were all categorical features which were one hot encoded. We first used dimensionality reduction techniques such as PCA which was followed by K-Means and DB-SCAN clustering to find inherent clusters in the data.

moviemania icon moviemania

A Project to analyze the relationship between a movie’s sentiment on Social Media and its crowd-sourced rating.

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