Create a model to predict bicycle ridership
We're given the following dataset, from New York City Open Data. The dataset shows the counts of bicycle crossings for each bridge along the East River for the month of April, along with weather and precipitation data, cut by day. This data is used by the New York City Department of Transportation for planning purposes, among other things.
Using this data, create a model to determine whether or not you can predict bicycle traffic for the given bridges, based on the variables provided (day of week, temperature, precipitation, etc).
Below is code to import the dataset into a Google Colab or Jupyter notebook to help get you started:
# Here is code to pull the dataset and relevant libraries # into a Google Colab or Jupyter notebook to help get you started import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns df = pd.read_csv('https://raw.githubusercontent.com/erood/interviewqs.com_code_snippets/master/Datasets/nyc_east_river_bicycle_counts.csv') df.head()
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