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Project conducted in STAT 4355.001.S22. Utilized the R Programming Language to determine a multi-linear model fitting to predict the number of bike rentals. Determined the appropriate attributes that significantly influenced the number of bike rentals. Collaboration with three other classmates.

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Washington-D.C.-Bike-Rental-Prediction

Project conducted in STAT 4355.001.S22. Project consisted of obtaining the bike rental dataset from UCI Machine Learning Repository provided by the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at the University of Porto. Utilized the R Programming Language to determine a multi-linear model fitting to predict the number of bike rentals. Determined the appropriate attributes that significantly influenced the number of bike rentals. Collaboration with three other classmates.

Report: https://1drv.ms/b/s!AmZk6nE6De_nlF-S0CmnxBFYNzXy

UCI Machine Learning Repository Link:

https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset
Note: Only hour.csv is used

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Project conducted in STAT 4355.001.S22. Utilized the R Programming Language to determine a multi-linear model fitting to predict the number of bike rentals. Determined the appropriate attributes that significantly influenced the number of bike rentals. Collaboration with three other classmates.

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