bike sharing dataset linear regression

So our models do not perform better than the naive forecast when looking at this metric. Based on the visualizations so far, it wouldn’t be unreasonable for us to hypothesize that the weather-situation will affect the bike usage, with rainfall deterring usage. However, with some simple data manipulation (more on this in the next section), we can change this to represent the usage rate based on the temporal distance to 4 am, and find a somewhat linear fit (see below). Remove variables that you are not using. The correlation between the two factors is weak at best. This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information.

Although the curve for light rain or snow shows concavity, it is due to the smoothing of the data point by the ggplot function in R. The wind speed plot shows that although people enjoy gentle breeze in good weathers, the bike rental demand is significantly lower no matter the wind speed in light rain or snow weathers. This problem was hosted by Kaggle as a knowledge competition and was an opportunity to practice a regression problem on an easily manipulatable dataset.

"count" will be used as response variable here, and all other as predictor.A preliminary data cleaning is performed, converting hourly date variable to months, day of the week, and hour of the day. Use Git or checkout with SVN using the web URL. I then try and add spline smooth functions on the three weather variables, namely "temperature", "humidity" and "wind speed." GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Training set will be used to train statistical models and estimate coefficients, while testing set will be used to validate the model we build with the training set. We’ll use Now that we have chosen the best parameters, we can check the error again. There are many other predictive modelling methods I can employ, like time series etc. Unsurprisingly, there does seem to be strong positive correlations between the user count in an hour and the previous 2 lag values, and a moderate positive correlation between the user count in an hour and the 3rd lag value. That concludes our work here. We’ll work on a couple of graphs to try to see how data behaves. We found the dataset “Bike Sharing Dataset” under the index “regression” and chose the sub-dataset “day”. Higher humidity is correlated with higher chances of rainfall. In the data exploration and analysis, I will be using the training set for complete features and predictor variable.​The resulting dataset I will be using contains 10886 observations and 12 variables.

If you plot the Since we have categorical values in our data set, we need to ‘tell’ our algorithm that classes have equal weight for our analysis. This dataset contains daily counts of rented bicycles from the bicycle rental company Capital-Bikeshare in Washington D.C., along with weather and seasonal information.

Bike Sharing Dataset Data Set Download: Data Folder, Data Set Description. The first time in my life I saw a bike sharing system was in Paris. Using simple linear regression model, generalized linear model, and generalized addictive model, we successfully predict the bike sharing rental count with relatively high accuracy. The linear regression model predicted bike First of all, we can get rid of features that are not important for the model. We can obviously go further by spline smoothing these variables using pieces of range 1, but I would like to jump to prediction with what I have now.Predicting using the attributes from testing dataset and plot them against the true values Predicting using the attributes from testing dataset and plot them against the true values Here, I only used the third generalized addictive model in predicting. Our goal is to find a “break point” on the graph that will correspond to our optimal number of variables.We can also tune our algorithm by testing different parameters. The purpose of this summarization is to find a general relationship between variables regardless of which year the data is from (since the data spans two years and the business is growing. On Sundays and Saturdays, people use more the bikes during afternoon, while during work days, bikes are mostly used to go and come from work/school.Let’s now understand how casual and registered users use the vehicles. Bikes are more used on a time of the year and not on another. the future installation and expansion of bike sharing programs.

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