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Analytics data for looking : filter movies with drama genre, most rated movies, number of users and average rating for each age range,etc. Visualize the count and age of moviegoers with the Matplolib library and Show movies, age range, average rating.
This is one of my final projects for the HarvardX Data Science Professional Certificate Program. As the title suggests, it is on the GroupLense database colloquially known as MovieLens. The goal of the project is to predict ratings with a RMSE below .86490. I was able to surpass the goal with 3 different models. Happy reading!
A recommendation algorithm capable of accurately predicting how a user will rate a movie they have not yet viewed based on their historical preferences. The models and EDA are based on the 1M MOVIELENS dataset
Data analysis and movie recommendation of OpenMovie dataset by using the shell, Python, Cosine Similarity algorithm, Apache PySpark, and Apache Hadoop.
Data analysis on Big Data. Used various databases from 1M to 100M including Movie Lens dataset to perform analysis. Covers basics and advance map reduce using MongoDB.
Exploratory Dataset Analysis (EDA) will be uploaded to this repository. Libraries such as Pandas, Matplotlib, Seaborn and Plotly will be used for data analysis.
Data analysis on Big Data. Used various databases from 1M to 100M including Movie Lens dataset to perform analysis. Covers basics and advance map reduce using Hadoop.