I want to build a model to help me build a team in fantasy premier league. There are two parts to the problem:
1) Predicting the player performances next week given the data for the last week and for the least season.
2) Using the result of the predictive model to build a team within a price of 100million euros.
For part 2), I was thinking of using either a 6D knapsack algorithm (2D for weight and number of items and the other 4 dimensions to make sure the appropriate number of players are picked from each category) or to use min cost max flow (not sure how I can add categories or restrict the number of players from each category).
For part 1) the only examples and papers I have come across either use models to predict whether or not a team will win or just classify the players as "good" or "bad". The second part of my problem requires that I predict a specific value for each player. At the moment I am thinking of using regression but I am not sure what kind of features I should use in this.
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I m making my first steps in AI and ML.
I choose myself a project, I want to fix with ML, but I m unsure which methode to use.
Business Case: A Customer can put offers and set a date he wants to receive his products.
He is able to change the amount of products he buys at every time.
I have to deal with the costs of unbuyed products and missing profit, in case I produced less than he wanted.
I have plenty of data from past transactions contianing the original amount of products ordered and the amount I sent to the costumer.
My goal is to get a predicitve analytics model which is able to tell me after a costumer changed the number of products from an order, how probably this change is final.
I m really new to this topic and are not quite getting all the information for the different methodes. I know classification and regression are the big players and can be implemented in different ways. But is one of those approaches fitting for my problem?
Many Thanks in advance.
You can go with a classification based approach. Since you goal is to predict whether the order change is final or not. The probability of happening that change can be derived from the accuracy/F1 score of your model. Higher the values, higher successful predictions. In laymen's terms think this as classifying whether the order is final or not.
You have to go for a regression approach if you're trying to predict a value based on the order change. For example if you want to predict what is the cost for the next order change, then you have to use regression.
As I understood your use case matches with the first scenario.
Let's say I have with me data containing
salary,
job profile,
work experience,
number of people in household,
other demographic etc ..
of multiple persons who visited my car dealership and I also have the data if he/she has bought a car from me or not.
I can leverage this dataset to predict if a new customer coming in is likely to buy a car or not. And let's say currently I am doing it using xgboost.
NOW, I have got additional data but it is a time series data of the monthly expenditure the person makes. Say I get the data for my training data too. Now I want to build a model which uses this time series data and the old demographics data(+ salary, age etc) to get to know if a customer is likely to buy or not.
Note: In the second part I have time series data of the monthly expenditure only. The other variables are at a point in time. For example I do not have the time series for Salary or Age.
Note2: I also have categorical variables like job profile which I would like to use in the model. But for this I do not know if the person has been in the same job profile or he has changed over from some other job profile.
As most of the data are specific to the person; except expenditure time series, so it is better to bring time series data at person level. This can be done by feature engineering like:
As #cmxu suggested take various statistical measures. It will be even more beneficial to take these statistical measures at different time intervals like say mean at last 2 days, 5 days, 7 days, 15 days, 30 day, 90 days, 180 days etc.
Create mixed features like:
a) ratio of salary vs expenditure statistical summery created in point 1 (choose appropriate interval)
b) salary per person household or avg monthly expenditure per household. etc.
With similar ideas you can easily create 100s or 1000s of features with your data and then feed all this data to XGBoost (which is easy to train and debug) or NN (more complicated to train).
Say I have a data set of students with features such as income level, gender, parents' education levels, school, etc. And the target variable is say, passing or failing a national exam. We can train a machine learning model to predict, given these values whether a student is likely to pass or fail (say in sklearn, using predict_prob we can say the probability of passing)
Now say I have a different set of information which has nothing to do with the previous data set, which includes the schools and percentage of students from that particular school who has passed that national exam last year and years before. say, schoolA: 10%, schoolB: 15%, etc.
How can I use this additional knowledge to improve my model. For sure this data is valuable. (Students from certain schools have a higher chance of passing the exam due to their educational facilities, qualified staff, etc.).
Do i some how add this information as a new feature to the data set? If so what is the recommend way. Or do I use this information after the model prediction and somehow combine these to get a final probability ? Obviously an average or a weighted average doesn't work due to the second data set having probabilities in the range below 20% which then drags the total probability very low. How do data scientist usually incorporate this kind of prior knowledge? Thank you
You can try different ways to add this data and see if your model will be able to learn on this set. More likely you'll see right away, that this additional data will just confuse the model. Mostly because you're already providing more precise data on each student of the school and the model has more freedom to use this information.
But artificial neural network training is all about continuous trials and errors, so you definitely should try to train it with all possible data you can imagine to see if it'll be able to get a descent error in the end.
Use the average pass percentage of the students' school as a new feature of each student is worth to try.
I have a decision tree that is trained on the columns (Age, Sex, Time, Day, Views,Clicks) which gets classified into two classes - Yes or No - which represents buying decision for an item X.
Using these values,
I'm trying to predict the probability of 1000 samples(customers) which look like ('12','Male','9:30','Monday','10','3'),
('50','Female','10:40','Sunday','50','6')
........
I want to get the individual probability or a score which will help me recognize which customers are most likely to buy the item X. So i want to be able to sort the predictions and show a particular item to only 5 customers who will want to buy the item X.
How can I achieve this ?
Will a decision tree serve the purpose?
Is there any other method?
I'm new to ML so please forgive me for any vocabulary errors.
Using decision tree with a small sample set, you will definitely run into overfitting problem. Specially at the lower levels of the decision, where tree you will have exponentially less data to train your decision boundaries. Your data set should have a lot more samples than the number of categories, and enough samples for each categories.
Speaking of decision boundaries, make sure you understand how you are handling data type for each dimension. For example, 'sex' is a categorical data, where 'age', 'time of day', etc. are real valued inputs (discrete/continuous). So, different part of your tree will need different formulation. Otherwise, your model might end up handling 9:30, 9:31, 9:32... as separate classes.
Try some other algorithms, starting with simple ones like k-nearest neighbour (KNN). Have a validation set to test each algorithm. Use Matlab (or similar software) where you can use libraries to quickly try different methods and see which one works best. There is not enough information here to recommend you something very specific. Plus,
I suggest you try KNN too. KNN is able to capture affinity in data. Say, a product X is bought by people around age 20, during evenings, after about 5 clicks on the product page. KNN will be able to tell you how close each new customer is to the customers who bought the item. Based on this you can just pick the top 5. Very easy to implement and works great as a benchmark for more complex methods.
(Assuming views and clicks means the number of clicks and views by each customer for product X)
A decision tree is a classifier, and in general it is not suitable as a basis for a recommender system. But, given that you are only predicting the likelihood of buying one item, not tens of thousands, it kind of makes sense to use a classifier.
You simply score all of your customers and retain the 5 whose probability of buying X is highest, yes. Is there any more to the question?
I'm working on a project where I need to predict future stats based on past stats of basketball players. I would like to be able to predict next season's statistics based on the statistics of the past three seasons (if there are three previous seasons to choose from). Does anyone have a suggestion for a good prediction algorithm I could use? The data is continuous and there can be anywhere between 5-14 dimensions (age, minutes, points, etc.)
Thanks!
Note: I'd really like to use the program Weka to do this.
Out of the box, random forest would likely give you a strong baseline, so I would start with this.
You can also try try linear regression, which is a simple yet relative effective method, but depending on the data might require a bit more tweaking (for example transforming some of the input and/or out variables).
Gradient boosting regression is another strong predictor, but typically also needs more tweaking to work well.
All of these algorithms have Weka implementations.
There obviously isn't one correct answer, but for anyone looking to do something similar, I'll better describe my problem and the solution that I've found. I created a csv file where each row is a different season, and each column contains a different attribute. For each attribute that I would like to predict, I have the stats for the current season and then another column for the stats for the previous season. The first (rookie) season will have 0 for all 'previous season' columns. With this data set, I loaded it into Weka and used a Multilayer Perceptron with the test-option set to Cross-Validation. I set the number of folds to somewhere between 80-90% of the number of seasons available.
Finally, to predict the next season's statistics, you add one more row to the end and input the last-season values with "?" in the columns that you would like to predict. If anyone would like a deeper example, I'd be glad to provide one.
I think also if you truly want to create an accurate prediction you have to look at player movement and if a player moves to a team with a losing record, do they increase their minutes to have a larger role which would inflate stats or move to a winning team for a lesser role where they could see a decrease in stats.