Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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13 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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Image-to-image ML problem: one image input and one image output

Overview I'm trying to create a model that takes a "foot heatmap" (input image) and predicts a "shell heatmap" (true heatmap). My data contains foot heatmaps with a corresponding ...
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9 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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20 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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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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10 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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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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27 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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11 views

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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95 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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27 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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26 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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13 views

generate synthetic data for multivariate data points

I have some data like.. ...
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1answer
35 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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9 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
46 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
21 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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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
54 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
15 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
51 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
70 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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How to avoid Naïve Time Series Forecasting

I'm trying different deep learning models for time series prediction (Bitcoin Price), But the results are too good to Be true and I'm suspecting that the model is just learning to copy previous values ...
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Predicting labels greater than those in training set

I have to use a neural network to predict the value of a certain stock on the next day. I'm using an lstm net, feeding it with 7 days worth of data and using the 8th day price as target. I split the ...
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Qaulity assurance framework for ML/AI

I'd like to hear how organisations are applying a quality framework to ML/AI work. I'm struggling to find good content or good practice on this. By saying quality frameworks, for example, if I was ...
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34 views

How to use time series datasets that have the same time stamps and features across multiple locations for predicting energy output?

I have some datasets: One dataset is comprised of the overall solar energy/wind energy production of a country. The rest of the datasets contain weather data (temperature, pressure, wind speed, etc.) ...
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Rule of Thumb for number of observations required to train a model with n independent variables?

I am aware adding more features to a model leads to overfitting of a model. Is there a rule of thumb for minimum number of rows required to build a model with n features in order to build a ...
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18 views

LSTM Data Preparation Input Shape

I have a 2-D dataframe df: ...
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15 views

OLS Regression: Predicting to the certain total

I have a simple dataset: Rooms Price(in March) Single 20 Balcony 50 Triple 100 Couple 75 Family 150 Now, I can predict the values and set the ...
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21 views

Time-Series Cross-Validation for LSTM

Is it at all possible to separate my data into train/test sets with cross validation for time series data? I am experimenting with a LSTM model. Also, I am hoping to prevent data leakage/peaking in ...
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1answer
41 views

Labeling and aggregating features issue

I am trying build a simple binary classifier (some tree based algorithm for now) and my training data will have features aggregated at the user level. So I'll have a unique records of each user. These ...
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Why are the values of my Y predicted the same and R-Squared Negative in SupervisedDBNRegression, Neural Networks

My model is not outputting the results I expected. I don't quite know my way around ANN. After learning how to use SupervisedDBNClassification from https://github.com/albertbup/deep-belief-network I ...
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How to do online retraining of model on a single new data point/observation?

I am trying to investigate the effect on performance on old data and new data when a classifier is retrained on only the new observation when it is encountered. The aim is to retrain the classifier on ...
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1answer
292 views

'str' object has no attribute 'predict' [closed]

I am trying to deploy my ML model using flask. My model contains both categorical and numerical variables. Below is my model.py code:- ...
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1answer
37 views

In general, what are precision, recall, F1 that are reported in papers?

I used classification_report in sklearn library And, the picture below shows evaluation on my model (anomaly detector) In general, what are precision, recall, F1 ...
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Combining output (deciles or risk score) of 2 or more logistic regression based model into one single output SCORE so to use for any decision making

I have 3 different datasets in which customer can or cant be present in all 3 datasets. I have built 3 models for all 3 datasets which are working fine. Kindly note, i cant make a make model ...
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1answer
22 views

Random forest regression model improvement

I am working with vehicle occupancy prediction and I am very much new to this, I have used random forest regression to predict the occupancy values. Random forest jupyter notebook have around 48 M ...
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1answer
23 views

What methods are there for predicting a signal?

I have a large dataset of signals (composed of time series). All time series describe the same process, but each series has a different duration (number of points). Based on these time series, I want ...

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