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Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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2
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3answers
61 views

Dealing with no data

I am working on predictive maintenance and get temperature data from assets. In few months or few days asset remains down and we do not get temperature value. In this scenario i cannot fill data with ...
2
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1answer
50 views

How to get the survival duration prediction for each individual in the data by using the Kaplan-Meier method?

I am trying to learn how to use the Kaplan-Meier survival estimator model in the lifelines package. The documentation says that the KaplanMeierFitter.fit function ...
3
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2answers
87 views

Suggestion for stacked modelling in machine learning

I have built several models on the training dataset and i am not happy with the results and I wish to club them all together and generate a new model, so here is my idea as i already have the results ...
0
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0answers
8 views

Cross Sell, Up-Sell, Recommendation in Insurance Domain via Machine Learning [on hold]

I have recently started practising machine learning in Insurance Domain. I have been with a use case to develop models to cross-sell, up-sell insurance to existing customers and recommend to new ...
1
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0answers
18 views

Rescale prediction to correct dispersion due to correlation between response and residuals in Random Forrest Regression

I am using Random Forest Regression and I observe a strong positive correlation between the residuals ($\hat{u}$) and the response variable ($y$) which lead to a dispersion : predictions are ...
0
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1answer
29 views

Time series analysis [on hold]

I'm developing a prediction model that can predict the number of patients that will be admitted at a particular time in a hospital. My dataset has details like admission date, admission time and ...
0
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0answers
12 views

Optimal practices to group data by Customer ID for churn prediction

Here's a quite common problem and I read a couple of questions/answers on it, however I still having my doubts about what are the best practices for grouping data by Customer ID for churn prediction. ...
0
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1answer
17 views

Time Series segmentation

I have a time series graph that is segmented into a few parts based on the maintenance day. You can think of it like vertical lines appearing out of the x axis which symbolize maintenance at the date. ...
0
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2answers
39 views

Traditional Predictive Analytics vs Machine Learning Methods

What is the difference between traditional predictive analytics done using statistics and its tools and, one using machine learning and deep learning? How are we leveraging machine learning and deep ...
0
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1answer
21 views

Predicting churn - deal with missing dates in time series and improve modelling result

This is the follow up question for General approach on time series for customer retention/churn in retail. I have a time series of data in the following form: ...
2
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0answers
27 views

How exactly do probability distributions help modeling/making decisions?

I am an elementary/wanna-be statistician/data scientist from South Korea. I have been studying a variety of theories of mathematical statistics and different probability distributions. (I apologize ...
0
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0answers
10 views

Data Analysis on Data From Analytical Techniques

We have a set of data that are generated from analytical methods. In other words, data regarding the behavior of a system from different aeronautics and aerodynamics equations. We want to perform data ...
1
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0answers
17 views

How to deal with similar feature values but each indicates to a different information?

If I have a feature with replicated values but each of these values indicates a different piece of information. example: feature 'street name' with value 'A' which some of these 'A's are for Boston ...
1
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2answers
82 views

Forecasting/predicting techniques for qualitative data?

I have a food alert dataset composed of nominal qualitative variables, such as type of alert, country of origin, action taken, etc. as well as the date on which the alert was recorded. What ...
0
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1answer
27 views

Comparing different classification results with different trainig and test data

I have different samples with different sizes. The instances of each sample have different features in comparison to the instances from the other samples. For each sample i train my model and tested ...
0
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0answers
13 views

Is this a reasonable way to deal with known input data uncertainty for logistic regression predictions?

Suppose I want to use a logistic regression model to predict the class of N objects. Further, suppose the prediction is time sensitive: I need the prediction for each object on Day 1, but the value of ...
-1
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2answers
40 views

LSTM to predict Sin(x) from x

Hi I want to pass a series of values x1, x2... as input to the model to predict y1 = sin(x1), y2 = sin(x2)... -I created dataset: x=[0.1,0.2,...] and y=[sin(0.1),sin(0.2),...] -I normalize x in [0,1]...
0
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1answer
12 views

Is it possible to use an array of graph coordinates as an input variable?

Say I have 1000 graphs that shows sales every year for the last 10 years for 1000 different companies. And say each of those graphs belong to either domestic countries or foreign countries. Is it ...
1
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1answer
21 views

How do I deal with changing values in a categorical variable when I am aggregating customer records

My requirement is to build a model to predict if a new customer will return to their website. I need to analyze what drives customer repeat for both new and returning customers. The only information ...
0
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2answers
168 views

Retrieve user features in real time from UserId for prediction

Let's say I'm building an app like Uber and I want to predict the user's most likely destination based on the user's past history, current latitude/longitude, and time/date. Here is the proposed ...
6
votes
3answers
505 views

Data driven approach to define a churn user

I'm trying to define a churn prediction model for an online service (betting/gambling). A lot of papers talk about churn analysis/prediction for telco companies where defining a churn user is ...
1
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1answer
30 views

Predict correct answer among ten answers for a given question

I have a case study to solve where I am given a dataset of questions and its answers, there are ten answers for a particular question. It's a classification problem where correct answer is having <...
1
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2answers
29 views

What is the difference between a data-driven model and an empirical model?

Are they the same? Empirical models, per Wikipedia, are any kind of (computer) modelling based on empirical observations rather than on mathematically describable relationships of the system ...
1
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1answer
21 views

Minimum Possible Test MSE

I have a little confusion. What follows is from Introduction to Statistical Learning (2013) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. My understanding of what is going ...
1
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0answers
19 views

What is the best approach to train a multi-category regression model?

What is the best approach to train a multi-category regression model (using XBoost decision trees ensemble)? What are the ups and downs of each one? For example, if I want to train a model to predict ...
1
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1answer
30 views

How to deploy machine learning models as a chrome extension?

I have trained a stance detection model using SVMs. Wanted to know how can I deploy this as a chrome extensions. I do understand that the question is a bit broad but any links, suggestions etc. will ...
0
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1answer
29 views

How to approach data prediction problem

I'm new to ML and data science. I would really like high level advice how to approach the following problem. I need to predict if an engine will fail, what I've is a sensor that give a certain value ...
1
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0answers
21 views

Increase accuracy of occupancy prediction?

I have a project that's aimed to predict the amount of occupants at my local gym given the date and weather. Here's my Kaggle kernel I have two datasets, occupants on a given hour and weather on a ...
1
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1answer
19 views

Tree based method are robust against low probability feature space zones when using ML general interpretability methods?

I have this intuition but I'm not able to verify it. There are a lot of techniques to understand the effect of single features in ML models. Some take inspiration from counterfactual frameworks, like ...
0
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0answers
6 views

pattern recognition tool

From what i've seen while searching google and the site this is most likely way simpler than what normally get's asked but i've not been able to find anything that could help me with it. What i'm ...
2
votes
1answer
49 views

Model prediction on meshgrid in python

Suppose I have data with two independent variable $X_1$, $X_2$ and one dependent variable say $y$, as follows: $X_1$: $x_{1,1}$, $x_{1,2}$ , $x_{1,3}$ $X_2$: $x_{2,1}$, $x_{2,2}$, $x_{2,3}$ $y$: $...
1
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1answer
79 views

Machine learning method to predict event date

Let's say I have a big dataset consisting of variables including but not limited to the start/end date of loans, their notional amount, a loan prepayment indicator etc. My goal is to create a model ...
0
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1answer
25 views

Will historical data lead to target leakage?

I'm bulding a employee churn model. I've employee data from 2016 to 2019 (of people who stayed/left the company), my goal is to train using data from 2016 to 2018 and predict on 2019. Since there's ...
0
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0answers
5 views

SRGAN: How to adapt the model to the input image?

I wrote and trained my own SRGAN: so I obtained a generator’s model that takes 32x32 images as input and gives their improved 128x128 version as output… However, the end users of my Android app will ...
6
votes
2answers
362 views

What are some of the best practices for sharing data and models with colleagues?

As a data scientist who recently joined a new team, I wanted to ask the community how they share data and models among their colleagues. Currently I have to resort to storing data in some central ...
0
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1answer
28 views

How to predict consumer purchase in next 6 months?

I'm working on a model to predict a customer as being 'in-market' for a product in the next 6 months. The dataset has a wealth of information like lifestyle and demographic variables and previous ...
2
votes
2answers
63 views

How to normalize data from multiple sources?

I am trying to model an individuals' purchasing behavior using different data sources (ex: Zalando, Otto, etc.,). When I combine data sources, I see that the data across these channels is very ...
0
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1answer
43 views

How to restructure my dataset for interpretability without losing performance?

What I am doing: I am predicting product ratings using boosted trees (XGBoost) with a dataset in this format: What I want to do: I want to use SHAP TreeExplainer to interpret each prediction my ...
2
votes
2answers
45 views

Dealing with NaN for predictive models

I have data set that has data for patients: Arrival_Date : is when the patient has arrived Seen_By_Nurse : is number of minutes patient take to be seen by nurse since arrival when value is NaN it ...
1
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2answers
52 views

Which predictive model is appropriate?

I'm completely lost when trying to choose the type of predictive model for my problem. Is it autoregressive model, nonlinear time series, Markov Chain or other? Can someone please give me some advise? ...
0
votes
1answer
19 views

Will the performance of my NER model improve?

I am training a spacy model from scratch by creating a dataset of my own with format spacy needs it to be in, the model is an NER model and the entity i am trying to recognize is Food items. I have ...
0
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0answers
15 views

Beating Roulette with Neural Networks, YoloV3, and PyTorch

Background: I am in my last semester of electrical engineering, and I am working on my senior design project. The senior design project is a two-semester design project in which students outline, or ...
2
votes
1answer
46 views

How to merge personalized models together

Let's say I'm building an app like Uber and I want to predict the user's most likely destination based on the user's past history, current latitude-longitude, and current date and time. We have ...
0
votes
0answers
20 views

How to handle a data set with large number (about 75%) of binary variables?

I am doing a research right now and want to classify (predict) churns of costumers using machine learning. My data set consists of about 500,000 observations with 20 variables: 15 are binary, 2 ...
0
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2answers
30 views

Time series modelling

I have daily data for 2.5 years , but with more data points as 0, so when i excluded them in the cases which seems to be invalid. Can i use any other model than models used in time series or should i ...
4
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0answers
53 views

Model for Differing Number of Rows per Observation

Looking to build a response model (click or no click) on marketing data which displays varying number of offers to a person. I don't want to model which offer they click but do they click any of the ...
1
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2answers
28 views

ensuring that dependent variable decreases monotonically with independent variable

I have one key relationship between a numeric independent variable X and a numeric dependent variable Y, which is like a negative exponential function determined by 2 parameters. There are other ...
0
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1answer
56 views

Can we make two separate models vs one for classification?

Suppose I have a binary classification problem and my data is imbalanced, I can build a classification model using any of the algorithms and use an oversampling or undersampling technique to handle ...
1
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1answer
65 views

Need help on Time Series ARIMA Model

I'm working on forecasting daily volumes and have used time series model to check for data stationarity. However, I'm strugging at forecasting data with 90% accuracy. Right now variation is extremely ...