My name is Dylan Lisk and I am a data science student using gpt-2 to write my blog. I also participate in Kaggle competitions and have done so for many years. I am currently in the process of getting my bachelor s in natural resources management and management. I like to think of myself as a data scientist but more likely a statistics whiz. My thesis was on predictive modeling in the forestry industry. I have done statistical analysis in forestry in the past but never before. I also have a strong background in natural resources. I have worked in the oil and gas industry refining oil and natural gas. I have worked in projects dealing with oil and gas and related industries. I have participated in many R packages dealing with data analysis. R is my default choice for data analysis. I use SAS for all my data manipulation. I use R for trying to figure out how to combine and linear regression. I use SAS for setting up my computer for data and for generating the reports that I send to the governor. My old computer is getting old but I have gotten to the point where I can still be productive from time spent on R. I also have a small library of R packages that I have tried but not used in years. One that I have tried using R mostly for data analysis. It is a random forest package. It has nice performance characteristics. I like the idea of an efficient wrapper for R. I used a http randomforest package in my previous job. It is also possible to use R for data analysis. However one has to be careful about the precision of the estimates. R can give estimates up to for some data. I have tried using variance bias and insensitive estimates. I have also tried using nominal and median estimates. These estimates can be used to generate a rough baseline for the model. However I have not tried them all. I have tried using the mean or standard deviation of each of the estimates. This gives a rough estimate for the test statistic for the selected group of the selected data. However I have found that using these estimates as a baseline gives poor results. So I have tried using a weighted average of the mean and the standard deviation. This gave about as good a result as using the mean and the mean and standard deviation. I have tried using a weighted average of the mean and the mean. However I have found that using the mean gives much better results. So I have tried using the median. However I have found that using the median as a baseline gives poor results. I have tried using the median as a baseline and the test statistic. I have tried using the median as a baseline. I have found that using the median as a baseline gives poor results. I have tried using the median as a baseline and the test statistic. I have tried using the standard deviation of the estimates. This gives a rough estimate for the test statistic for the selected group of the selected data. However I have found that using this estimate as a baseline gives poor results. So I have tried using a weighted average of the standard deviation and the mean. This gave a good result when compared to using the mean and the mean. I have tried using a weighted average of the mean and the mean. However I have found that using the mean gives much better results. So I have tried using the median. However I have found that using the median as a baseline gives poor results. I have tried using a weighted average of the mean and the standard deviation. This gave a good result when compared to using the mean and the mean. However I have found that using the median as a baseline gives poor results. I have tried using the median as a baseline. However I have found that using the median as a baseline gives poor results. Group I have tried using the mean difference between the test and the median as a baseline. However I have found that using this as a baseline gives poor results. So I have tried using the mean difference between the test and the median as a baseline. However I have found that using this as a baseline gives poor results. Group I have tried using the median difference as a baseline. However I have found that using this as a baseline gives poor results. So I have tried using the median difference as a baseline. However I have found that using this as a baseline gives poor results. Group I have tried using the mean difference as a baseline. However I have found that using this as a baseline gives poor results. So I have tried using the mean difference as a baseline. However I have found that using this as a baseline gives poor results. Group I have tried using the median difference as a baseline. However I have found that using this as a baseline gives poor results. So I have tried using the median difference as a baseline. However I have found that using this as a baseline gives poor results. Group I have tried using the standard deviation of the estimates. This gives a rough estimate for the test statistic for the selected group of the selected data. However I have found that using this as a baseline gives poor results. Group I have tried using the

# i added another

My name is Dylan Lisk and I am a data science student using gpt-2 to write my blog. I also participate in a lot of Kaggle competitions and have done quite well on the leaderboard. I am currently in the last weeks of my senior project in the Bioacoustic Research Institute at the University of Pittsburgh where I am working on some machine learning problems. I have been doing quite well on the leaderboard so far but I would like to improve my score so I joined the strong What made you decide to enter strong Dylan I was looking for a team and a lot of variety in challenges. The length was perfect for this competition. I like to have room to experiment and try things. And the variety of challenges was great. The fact that the problems were all related was very strong Sergei I was looking for a team that was strong Dylan I wanted to create a dataset that was strong Sergei I wanted to create a dataset that was strong a http img aligncenter center auto auto http strong What preprocessing and supervised learning methods did you use strong Dylan I used a lot of feature engineering in preprocessing and feature selection. Feature selection was done by going through the full time series and finding the most important features for each individual month. Then I would select the most important features and polynomial P P was used as feature of all time series for months that had at least features. span color strong a http span color month feature engineering was very powerful in this strong Sergei I used a lot of feature selection and feature transformations. Feature transformation was done by going through the full time series and finding the most important features for each individual month. Then I would select the most important features and polynomial P was used as feature of all time series for months that had at least features. span color strong a http span color month feature transformation was very powerful in this strong a https span color pipeline is a diagram that visually shows the general idea of how I did feature selection and transformation work together. strong a https span color The diagram below summarizes the general idea of how I did feature selection and feature transformation work together. strong a https span color The following figure illustrates an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data by dividing the training data into bins and averaging the results. strong a https span color Figure: An example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data by dividing the training data into bins and averaging the results. caption aligncenter a http img http Figure: An example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data by dividing the training data into bins and averaging the results. strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a https span color strong a https span color Figure shows an example of how feature engineering can be used to generate features for a more complex model. Notice that the colour gradient is introduced by adding colour to the training data. strong a

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