Machine learning datasets used in tutorials on MachineLearningMastery. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository contains a copy of machine learning datasets used in tutorials on MachineLearningMastery.
This repository was created to ensure that the datasets used in tutorials remain available and are not dependent upon unreliable third parties.
All regression and classification problem CSV files have no header line, no whitespace between columns, the target is the last column, and missing values are marked with a question mark character '? In many cases, tutorials will link directly to the raw dataset URL, therefore dataset filenames should not be changed once added to the repository.
We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page.
For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content. Code Pull requests Actions Security Insights. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Go back.
Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Jason Brownlee Added dataset for language modeling. Added dataset for language modeling. Git stats 64 commits. Failed to load latest commit information.Maybe they are too granular or not granular enough.
The Pandas library in Python provides the capability to change the frequency of your time series data. In this tutorial, you will discover how to use Pandas in Python to both increase and decrease the sampling frequency of time series data. Kick-start your project with my new book Time Series Forecasting With Pythonincluding step-by-step tutorials and the Python source code files for all examples. In the case of upsampling, care may be needed in determining how the fine-grained observations are calculated using interpolation.
In the case of downsampling, care may be needed in selecting the summary statistics used to calculate the new aggregated values. There are perhaps two main reasons why you may be interested in resampling your time series data:. For example, you may have daily data and want to predict a monthly problem.
You could use the daily data directly or you could downsample it to monthly data and develop your model. A feature engineering perspective may use observations and summaries of observations from both time scales and more in developing a model. The units are a sales count and there are 36 observations.
The original dataset is credited to Makridakis, Wheelwright, and Hyndman The timestamps in the dataset do not have an absolute year, but do have a month. We can write a custom date parsing function to load this dataset and pick an arbitrary year, such asto baseline the years from. Running this example loads the dataset and prints the first 5 rows. This shows the correct handling of the dates, baselined from Imagine we wanted daily sales information.
We would have to upsample the frequency from monthly to daily and use an interpolation scheme to fill in the new daily frequency.
The Pandas library provides a function called resample on the Series and DataFrame objects. This can be used to group records when downsampling and making space for new observations when upsampling.
Running this example prints the first 32 rows of the upsampled dataset, showing each day of January and the first day of February. We can see that the resample function has created the rows by putting NaN values in the new values.
We can see we still have the sales volume on the first of January and February from the original data. You may have domain knowledge to help choose how values are to be interpolated. A good starting point is to use a linear interpolation. This draws a straight line between available data, in this case on the first of the month, and fills in values at the chosen frequency from this line. Looking at a line plot, we see no difference from plotting the original data as the plot already interpolated the values between points to draw the line.
This creates more curves and can look more natural on many datasets.Disclaimer - The datasets are generated through random logic in VBA.
25 Shampoo Industry Statistics, Trends & Analysis
These are not real sales data and should not be used for any other purpose other than testing. I just want to clarify one thing. Anything published on this is completely copyright free.
You can use anything from this site without any obligation. You can even call the content from this site as your own. Hope, it clarifies.
There is absolutely no need to ask for permission for use. You can download sample csv files ranging from records to records. These csv files contain data in various formats like Text and Numbers which should satisfy your need for testing. All files are provided in zip format to reduce the size of csv file. Larges ones are also provided in 7z format apart from zip format to gain further reduction in size. The result data will be populated in Detail tab.
Can I please get a dataset of up torecords and around 30 columns? I can see the files above only have about 14columns. I am using a flat file source to read the data from […]. Hi, can I use your dataset for my github testing project? I would like to add my portofolio using these dataset. Ufak tefek […]. You can even download these sample CSV files to test it […]. And if anyone needs my help am here for them though am new in this.
It is named as […]. See Image below and get excel […]. Previous Next. This data set can be categorized under "Sales" category. Below are the fields which appear as part of these csv files as first line.
Lama August 21, at pm - Reply. Zouhair December 19, at pm - Reply. Import csv do sql server — m. Nicole January 16, at am - Reply.In this article, I focus on time series analysis and their forecast with R.
I will use two times series:. Both were downloaded from datamarket website. First we need to load the packages that will be used throughout the analysis.
These are the usual tidyversefor data manipulation and data visualisation, lubridate and stringr packages, for dealing with dates and strings, and the package forecast specific for time series analysis:. Let us first focus on shampoo sales. This dataset contains data on the sales of shampoo over a three year period. I downloaded the data and saved it on github so that it can be accesed straight from github and parsed to R as follows:.
When it comes to time series, the main data manipulation issue is usually related to the date and time format. Here the variable that indicates time is called Month and it is composed by a first part, before the -that seems to indicate the year year 1, year 2, year 3 and a second part, after the -that indicates the month month 1, month 2, etc. Did the software understand this format or did it not? We ask R for the format of the Month variable:. And the answer is that R did not quite get what we are talking about.
R believes it is a character. In order to make sure that the software will treat Month the way it should, let us do some small manipulations using the package lubridate.
From my knowledge there is no year-month format in Rso once we tell R that we are dealing with dates, it will automatically add the day:. First let us just plot the time series, showing the time on the x axis and the amount of sales on the y axis:.
Observing the above plot we can see that there seems to be fluctuations but there is an increasing linear trend. But how far does the dependence go? Shampoo sales in May, depend only on those of April, or do they depend on the sales over the whole season? Or do they just depend on the sales of May in the previous year? In order to answer such questions, we need to build a model that is able to deal with such dependencies. Standard statistical models assume indipendence of observations.
In time series this assumption does not hold. We call such dependence autocorrelation meaning that each observation is related to itself at the previous time. If there is autocorrelation, we need to include the dependent variable suitably lagged as predictive variables in the model.
ARIMA models are the most general class of models for forecasting time series. They have three components:. The first plot shows the autocorrelations. Each observation seems to be fairly correlated with the previous observations.
From the time series plot in figure 1, we also expect to find a drift, i. Moreover, looking at figure 1, the time series did not seem stationary, i. Hence, we expect the software will suggest to use an ARIMA model with an autoregressive component of order 1 or 2, no moving average component, a stationarity adjustment and a drift.
The forecast package in R contains a very useful function called auto. In our case, we will not set any contrainsts hence using the default:. The model R found is the one we expected from the graphical analysis: there is an autorogressive component of order 2, no moving average component and a drift.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
We use optional third-party analytics cookies to understand how you use GitHub. Learn more. You can always update your selection by clicking Cookie Preferences at the bottom of the page.
For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content. Code Pull requests Actions Security Insights. Permalink Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Sign up. Go to file T Go to line L Copy path. Raw Blame. Month Sales You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Accept Reject.
Essential cookies We use essential cookies to perform essential website functions, e. Analytics cookies We use analytics cookies to understand how you use our websites so we can make them better, e. Save preferences.The shampoo industry is the largest segment of the U. Ken Research. The top 3 manufacturers of hair care products in the U.
About 60, people are employed in the United States by the toiletries industry by about total manufacturers. California leads the United States in toiletry manufacturing, with nearly facilities currently in operation. New Jersey comes in second, with just under manufacturing companies. New York, Florida, and Pennsylvania round out the top 5. Statistics Brain. OGX was the leading shampoo brand in the United States inaccounting for 4. Suave Professionals was the second leading brand, accounting for 2.
Throughthe global pet grooming market, which includes pet shampoos, is expected to grow at a CAGR of 4. Business Wire. Mordor Intelligence. Beauty Matter.
In the shampoo industry, about 1 out of every 4 sales is of a conditioning product. Specialty stores are expected to be the lead distribution channel for the global shampoo industry. These stores are forecast to grow with a CAGR of 3. Inkwood Research. Additional brands and options in affordable categories has helped to drive sales to a point where it nearly matches revenues from the North American market.
The value of the U. In the overall hair care category, shampoo leads in every major market except for the United Kingdom and Japan. Shoppers in the U. Southeast Asia is the strongest market for shampoos, relative to other hair products. Herbal and botanical shampoos are the most popular type purchased in the global industry today. Most shampoos utilize argan oil, but shea butter, coconut oil, and olive oil are popular additives as well. As long as people will have a need to remove unwanted buildup from their hair safely, there will be a need for shampoo.
This industry has over two centuries of experience in its marketplace and is not going anywhere. What we will see in the future is a continuing diversity of branding from established names in the industry. As consumers look for niche shampoos that meet their unique hair needs, each brand will look to satisfy that demand with new brands, while keeping their manufacturing branding consistent. Pet shampoo products will continue to serve a niche area of this industry. Growth may be limited as demand is somewhat statistic, but innovation in this area could lead to large revenue gains.
Look for consumers to research new locations for shampoo purchases in the next 5-year period as well. Consumers will still typically purchase shampoo at local retail or grocery stores, but online purchases will certainly become stronger.
Ken Research 2. Ken Research 3. Ken Research 4. Statistic Brain 5.The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations.
It seems a perfect match for time series forecastingand in fact, it may be. In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem. Kick-start your project with my new book Deep Learning for Time Series Forecastingincluding step-by-step tutorials and the Python source code files for all examples.
This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this tutorial. The units are a sales count and there are 36 observations. The original dataset is credited to Makridakis, Wheelwright, and Hyndman The first two years of data will be taken for the training dataset and the remaining one year of data will be used for the test set. Models will be developed using the training dataset and will make predictions on the test dataset.
Each time step of the test dataset will be walked one at a time. A model will be used to make a forecast for the time step, then the actual expected value from the test set will be taken and made available to the model for the forecast on the next time step. This mimics a real-world scenario where new Shampoo Sales observations would be available each month and used in the forecasting of the following month.
How To Resample and Interpolate Your Time Series Data With Python
Finally, all forecasts on the test dataset will be collected and an error score calculated to summarize the skill of the model. The root mean squared error RMSE will be used as it punishes large errors and results in a score that is in the same units as the forecast data, namely monthly shampoo sales.
A good baseline forecast for a time series with a linear increasing trend is a persistence forecast. The persistence forecast is where the observation from the prior time step t-1 is used to predict the observation at the current time step t.
We can implement this by taking the last observation from the training data and history accumulated by walk-forward validation and using that to predict the current time step. We will accumulate all predictions in an array so that they can be directly compared to the test dataset.
The complete example of the persistence forecast model on the Shampoo Sales dataset is listed below. Note : Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome. Running the example prints the RMSE of about monthly shampoo sales for the forecasts on the test dataset. A line plot of the test dataset blue compared to the predicted values orange is also created showing the persistence model forecast in context.
Now that we have a baseline of performance on the dataset, we can get started developing an LSTM model for the data. For a time series problem, we can achieve this by using the observation from the last time step t-1 as the input and the observation at the current time step t as the output.
We can achieve this using the shift function in Pandas that will push all values in a series down by a specified number places. We require a shift of 1 place, which will become the input variables.
The time series as it stands will be the output variables. We can then concatenate these two series together to create a DataFrame ready for supervised learning. The pushed-down series will have a new position at the top with no value. A NaN not a number value will be used in this position.
It takes a NumPy array of the raw time series data and a lag or number of shifted series to create and use as inputs. We can test this function with our loaded Shampoo Sales dataset and convert it into a supervised learning problem. For more information on transforming a time series problem into a supervised learning problem, see the post:.
This means that there is a structure in the data that is dependent on the time. Specifically, there is an increasing trend in the data. The trend can be removed from the observations, then added back to forecasts later to return the prediction to the original scale and calculate a comparable error score. A standard way to remove a trend is by differencing the data.