Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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17 views

How to train a CNN with several features but test with less features

I'm trying to train a model (with TensorFlow and Keras) to classify the soil based on the x,y, and z coordinates. I have a table 8321 x 10 where 8321 are the points in a mesh, and 10 are the features ...
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10 views

How do you optimise BPNN with PSO?

In the context of prediction, how would you optimise a backpropogation neural network with particle swarm optimisation?
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32 views

Machine Learning on Small Dataset with Huge Variation

In 2018 the NCAA allowed players to transfer school more freely, so now teams want an advantage by getting transfer players. I want to create a model that can predict which transfer players can be ...
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28 views

Building a relevant predictive model [closed]

I have a dataset on Covid and diet: https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset This dataset includes percentage of fat, food quantity, energy, protein intake from different types of ...
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Is it ok if i post process my ML model's output used to predict stock movement?

So I made a machine learning model which predicts stock movements which returns 1 for the price going up or 0 for the price going down. without the post processing the training accuracy and testing ...
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12 views

Could I use some elements of my target variable to predict it?

I'm trying to predict if a company will bankrupt, I use a dataset of 2020 and I manually created my target variable with the status of the company the status date, status reason to create my target ...
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How to predict a discrete dependent variable on a continuous scale using regression

I am trying to find the 'optimal' amount of a certain medicinal cream to be applied to a patient in order to minimize the days the patient has a rash. However, the data for the cream doses are of the ...
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14 views

How to predict delivery days for online retail products?

I am attempting to create a model that can predict how long it might take a product to arrive at a customer's address for an online retailer. In other words, the main objective is to predict the most ...
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6 views

How to properly measure forecast errors when predicting correlation coefficient?

My task is to accurately predict correlation coefficient value. I have some candidate models, and want to select the best one (with minimal forecast errors on validation dataset). I don't feel good ...
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43 views

How to evaluate model accuracy at tail of empirical distribution?

I am making a nonlinear regression on stationary dependent variable and I want to precisely forecast extreme values of this variable. So when my model predicts extreme values I want them to be highly ...
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1answer
20 views

How to strategize model training with new data coming in every day?

I have a mysql database in which new records are added every day to raw data. This raw data is cleaned and a ML model is trained with it once a week. What should be the best strategy to capture new ...
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Shifting The Result of Regression Model By N-Value

I am doing a multivariable regression problem, which predicts the frequency of a failure mode in a production system. In this problem, I used XGBRF for Regression as my ML model. These are the results ...
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1answer
79 views

How could we interpret a SI Scatter Index and RMSE?

SI is RMSE divided by the average value of the observed values (or the predicted values? am confused)? is SI = 25% acceptable? (is the model good enough? )
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Non-linear solver with RNN for MPC

Is it possible to use a non-linear solver to optimize the output of a recurrent neural network (RNN) by using a solver to find the optimal RNN inputs? For example, I want to optimize a RNN to a cost ...
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1answer
26 views

Prediction/Classification within each grup (Multi-Class / Multi-Label)

I would like to make a prediction of a list of products (>1000 in dataset) within a particular product category (>100 in dataset). Example: Select product categories (1 or many): fruits, ...
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Modeling events with an intermediate stage

For a lot of prediction problems, there's an intermediate stage which must occur for the target event to occur. For example, to graduate from college, one must first be accepted. For an internet ad to ...
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13 views

How can I combine the information from these two dataframes?

If one of my dataframes gives me some info about items: ...
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14 views

How to handle censored data?

We are building QSAR/SQPR models for ADME properties (related to drug discovery). Very simply put, our data are: molecular structures as the independent variables, and various properties measured for ...
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Develop a Scorecard Model with Orange 3.30

I'm a super fan user of Orange 3.30, actually I've beeing develop some Collection Strategys and some othe of CLI in my actuall work, and everything has been OK with all the decisions that I've being ...
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11 views

Making predictions based on the user's perspective

I have a dataframe containing Id's of tests and different features/characteristics of the tests (length, number of contents etc). The tests are either easy (=1) or hard (=0). Using these features, I ...
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24 views

Is Kalman filtering a suitable approach to predict data from a set of/or a single feature?

I have multiple repeats of a time series that I would like to use to train a model to predict future repeats. The time series contains feature data (easy to measure) and target data (hard to measure). ...
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41 views

When should I use neural networks?

I am struggling with this exercise. The objective is "to build a recommendation system that predicts the next video" viewed by a user, given the data provided. So, the dataset consists in ...
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11 views

How to train ARIMA model on multiple similar time series?

I am having 'business potential' values of 4000 cities (having generic names to ensure anonymity) for 72 months. The data for an individual city is just 72 months so I clustered the entire dataset ...
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22 views

How to go about predicting administrative fees?

We collect administrative fees from our customers based on many complex business rules albeit based on few variables. I have the history of fees colected through time (about 500 records for each ...
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29 views

A data in which an employee id is given in multiple months and many categorical features are there. To predict future retention. Recommend what to do?

I have this dataset in which we have to predict the retention of employees,i.e. how much will an employee stay in a company? This seems easy but the main obstruction here is that the same employee_id ...
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8 views

Supply partial inputs to trained model, model fills in missing inputs (for optimal solution)

Summary of issue: Given a model trained on some input and output data, I'd like to be able to then query the model with a subset of input data, and return a set of missing inputs that would give an ...
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Prediction for the next two years

I'm new to data science and I had formulate a question to be answered in my assignment which is "What type of livestock production that will increase in the next two years." Based on the ...
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99 views

Predicting Disease Drugs

I have a dataset in the format: ...
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How to estimate system time-to-failure without sensor data?

I'm working in the prediction of time-to-failure of vehicles. The available data are the vehicle characteristics, such as make, type of vehicle (truck, car, etc.), year of manufacture, weight, region ...
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What sort of analysis should be done in order to define our target outcome for modelling customer lapse?

I am trying to build a model to predict customer lapse and am required to define the target lapse definition myself. What sort of customer behavioural analysis should I do in order to define my ...
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19 views

Who will be churned in the next 4 months?

The task is predicting churn for a given time horizon (for example, 4 months or 6 months in the future). The standard approach predicts only that somebody will churn or not. Is there any approach that ...
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How to define churn prediction for period of time in the future (for example 4 months)

Task is churn prediction for customers who pay subscription for the service, in the next 4 months. The customer can pay subscription on monthly or yearly basis. If the customer doesn't pay in ...
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29 views

Residual plot understanding

I am trying to build a regression model to predict Gerrit code review delay (i.e the time between the creation time of the code review until the time of the last update.) For that, I used a random ...
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29 views

Predicted values scatter plot angle

Could you please come up with any possible explanation on why this happens when one uses a deep learning model to predict something? The points should be on a 45 degree angle but they seem to be on a ...
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28 views

How to use Lat/Lon in machine learning prediction?

I am research meteorologist working with hurricane model forecasts of track (millions of lat/lon pairs) and their verified lat/lon pairs (essentially where the hurricane actually went). With the ...
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generate synthetic data for multivariate data points

I have some data like.. ...
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1answer
38 views

Predictive Recency-Frequency-Monetary (RFM) through Classification of Customer Charateristics

I have an RFM model that I use to segment customers based on RFM score. What I would like to do is: Understand more about the ...
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11 views

How can we optimize a model to predict in the no shortest possible time (real time production model)?

I need to put a model in production and I have some questions: How can we measure the time it takes to predict? Let's consider data is ready (real time) and we need first to transform data than to ...
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1answer
47 views

which type of machine learning algorithms perform better at extrapolation (in general)

Assuming that: the problem lies in the field of natural science, i.e. relationships between variables are physics-based and does not change depending on context its a regression based model Would it ...
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1answer
22 views

creating a logistic regression model with coefficients

I am trying to understand the details of the logistic regression models and now I was wondering how the model can be created if you have the coefficients and intercepts. So I created a logistic ...
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1answer
22 views

Predict total responses to emails amongst multiple groups

I've got historical data about email characteristics (like time sent, length, topic etc.), and the respondents to these emails - I've got their IP, which is further linked to gender, domicile, ...
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1answer
15 views

Using machine learning or other methods for a fitting problem

I have a time series of data of when 'data events' are going to happen. e.g. 1.2, 4.6, 10.0, 17.3, 23.2, 24.3, 30.6, etc. I am trying to make predictions as to when the next 'data event' will happen. ...
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16 views

Making use of several time series in one LSTM model

I am working on a case where I want to do a multivariate and multi-step time series forecasting. I have hourly data that measures temperature at approximately 500 different devices. (the devices have ...
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1answer
15 views

Where are the predicted results located in this model?

I am new at this and am pretty sure this is a stupid question, but here it goes: Where can I see the results of a model's prediction? I did this course about deep learning, followed the tutorial, ran ...
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1answer
22 views

Forecasting profit based on allocation of labor and time-series data [closed]

Situation: a store sells services A & B, and we have historical data for daily sales/revenue/profit of each service. The store is interested in whether they should staff for more of service A or ...
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2answers
49 views

Selecting Training Set from Model CV Error

What I would like to do is recursively: Train the model on all data Remove the sample(s) with highest error Repeat until the remaining samples have an acceptable error The hypothesis is: "To ...
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1answer
56 views

"Up or down but not sideways" bimodal time series prediction - what is the best way to model it?

Say I have a time series (e.g. bitcoin price). I want to predict tomorrow's price, specifically tomorrow's % change in price from today. Let's say this is gaussian distributed, with the mean at 0%. If ...
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1answer
16 views

What are some deep learning models use in timeseries forecasting that include context from covariates?

I was going through the literature for time-series forecasting using DL and all the methods I read about only use the variable of interest at previous timesteps to predict the same variable at time ...
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1answer
55 views

Logistic Regression to model a rare event

I have a data set which has data on consumers and a flag for whether they have expressed interest in a product or not. I am looking to build a model using R which will be able to predict whether or ...
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2answers
79 views

LSTM model, poor performance

I have been working on a project on the demand for a product. I am using data from 2016 to train the LSTM model. The architecture is as follows: ...

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