Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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68 views

Model to predict coronavirus (covid19) spread [closed]

im new in data sience and machine learning but i have some mathematical and statistics backgroud. I really just want some information about models (like papers or raw models). So if you have any ...
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1answer
111 views

TF Keras Text Processing - Classification Model

I'm trying to put together a script that classifies comments into either adequate or inadequate. I put a question up here earlier with all my code, but I think I've isolated the problem down into the ...
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1answer
28 views

Best way to evaluate performance for my case

I have dataset that looks like this ...
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0answers
49 views

Hedge fund rank on their returns or rating predictions modeling problem

Problem: Hi, I m a new machine learning practitioner. I have a dataset about hedge funds. It contains monthly hedge fund returns and some financial metrics. I calculated metrics for every month from ...
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22 views

Forecasting sales during time of epidemic

As we are going through a tough time because of the Coronavirus epidemic, is it possible to somehow include this affect of this in predicting sales as a time-series for next few weeks? I am new to ...
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1answer
37 views

Meaningful predictive analytics for small (n=114) dataset with just 1 explanatory variable and 1 response variable?

I am given an Excel pivot table that aggregates data from a somewhat sizable data source (a database table with 1.9m records and another of about 490k). The data within the Excel file consists of 3 ...
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1answer
41 views

What model do I use to predict a regression problem with timeseries data

Overall Goal To predict how much reagent "A" I started with in a reaction. Data: To predict this I have timeseries data of reagent "B". For each time step a measurement of reagent "B" is taken (the ...
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15 views

Predicting future of power consumption in repeating manufacturing process

I have this situation. We are tracking the power consumption of an industrial machine and by looking at the power consumption (in watt) we're trying to predict whenever something will break resulting ...
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10 views

What are and how can I unserstand/process geo levels id from this dataset?

I am using the dataset from this driven data competition: https://www.drivendata.org/competitions/57/nepal-earthquake/data/ There are three features (geo_level_id_1, geo_level_id_2, geo_level_id_3) ...
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19 views

HR Machine Learning: Treating/ Standardizing Part Time Employee Sums To Their Full Time Equivalents in Attrition Modeling

My data set consists of a subset of employees. Each employee has general HR information (typical standard hours, department, site, etc) along with punch card data which gives a clear picture of the ...
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1answer
32 views

Ideal strategy for multi variable regression attempting to maximize the target

I am trying to implement machine learning for the following data Data Input What I am trying to achieve is to keep the ad bid & cost per sale as low as possible while increasing sales. This is ...
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1answer
27 views

Using a past time series to predict how a present time series will pan out?

Let's say that I have past data indicating how some time series panned out. Now I also have the beginnings of a new time series that I expect to pan out in a similar trend to the old one. What are ...
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2answers
114 views

Determine statistically whether new product cannibalise old product by using data

Assume that $A$ is a cab company which offers online cab booking through their standard account. Recently, the company launched a pre-paid premium account with features such as discounted rides,...
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22 views

Clustering a dataset and creating a model per each cluster

I was wondering if it makes sense to cluster a dataset to find closely related data points and train a binary classification model for each of this clusters as they would be minidatasets. I'll ...
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20 views

Serializing a trained classification model into a set of actionable insights

I'm looking for ways to convert a trained classification model into a list of insights based on the resulting parameters of the model. To make an example, let's assume we trained a decision tree to ...
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1answer
19 views

I want to detect similar meaning in sentence “in my final year web based system” [closed]

When students submit a topic for his/her final year research, the system should be able to detect that this topic was carried on by some student or already exists. How can I be able to do it? If its ...
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126 views

RUL prediction without failures in historical data

I have faced in the past some problems of predictive maintenance where I had historical sensor data with failures. With this kind of dataset, you can calculate the RUL (Remaining Useful Life) and ...
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26 views

Plotting the decision boundary of different combination of 2 features from amongst a large number of features

How to plot the decision boundary of different combinations of 2 features from 107 feature data set? ...
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1answer
23 views

How do I build a model to predict which service a customer will use on an app?

There's an app with over 50 services. I have the data on the type of service a specific customer (they have a unique customer number) does on the app, the date, location, time, duration on a service ...
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1answer
32 views

Identifying and Accounting for trend/seasonality in Predictor Variables

I'm currently working with a dataset that has been collected over several years, and I suspect my predictor variables are changing over time for their predictive power. I could go back year by year ...
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54 views

Model-independent measures for feature importance given highly correlated features

I am currently working on a research project where the central question is which features drive the prediction of different models. The main issue is, that there is high (multi-)collinearity among ...
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1answer
27 views

How can you adjust a prediction based on features in the future being different than predicted?

I have a model that takes mostly cumulative data, and makes a prediction. It's not baseball, but I'm using this as a pretty accurate analogy. You put in all the totals so far, and it make a prediction ...
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2answers
142 views

How to train my model efficiently?

I am new to ML and have been reading online about training bottlenecks when there are frequent updates to data. Let's say I have a built a model based on a dataset of 10M records. Now, in another 2 ...
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2answers
48 views

What is an appropriate approach to sampling for probability of default using a classification model?

If we have a loan book and want to train the data to predict the probability of default, what is an appropriate way to sample the historical data to train the model, given that each account is open ...
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0answers
20 views

Clustering and producing final results to find next best customer to target(Ranked)

I have a problem where I need to cluster customer data that has all possible attributes to identify the next potential customer who can succeed the last customer in terms of buying a certain product. ...
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4answers
149 views

Machine learning approach for predicting set members

Say I have a large training dataset containing sets of 40 items each, and each item in the set is unique (so every training input is a set $S=\{i_1, i_2, ..., i_{40}\}$), and there are more than 40 ...
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1answer
35 views

Predicting the next occurrence based on binary

I have no experience in statistics or machine learning. I have a True/False binary array describing occupation of open public spaces ...
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1answer
52 views

Gaussian Naive Bayes (GaussianNB) classifier not working with large number of features

I'm trying to make a partial fitting with GuassianNB here's small snippet of my code ...
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1answer
62 views

How can I improve the accuracy of my model? (Cab Cancellation Prediction)

I am trying to predict based on several parameters like trip type, car type, source of booking, start time, lead time (start- book) and a few other params whether or not a customer will cancel. From ...
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1answer
80 views

Prediction for Current month based on last month's Labels

I have monthly data of loan installment repayment. The data contains basic features like salary, age, gender, credit score, etc. Along with the above features, I have the data for the last 6 ...
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2answers
1k views

Modelling data with Machine learning without a target variable

I want to understand the required steps that need to be taken into account while handling a dataset that does not have a target variable. I can do machine learning on top of a labeled dataset having ...
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1answer
732 views

Calculate confidence score of a neural network prediction

I am using a deep neural network model to make predictions. My problem is a classification(binary) problem. I wish to calculate the confidence score of each prediction. As of now, I use ...
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39 views

Calculate marginal probability distributions of a dataset

I have a dataset with 'n' features and a label(binary) corresponding each entry. I am using a predicitive model to predict these labels using those 'n' features. Now, I wish to know the marginal ...
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5answers
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In industry, what type of new data science algorithms does one develop?

I've seen several job descriptions for data science which include developing a novel algorithm to be a part of production environments. Can you give some input of what could be meant here exactly? ...
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1answer
33 views

Modifying a distribution by adding in samples incrementally

I would like to calculate the distribution (e.g., Gaussian) of a set of samples. However, I would also like to see how the distribution changes as I fit the samples into the distribution ...
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1answer
71 views

Machine learning solution approach to match loan repayments

I'm relatively new to the AI/ML space, but come from a programming background. The problem: I have a dataset of users transactions who have taken short-term loans from a single loan provider and I ...
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0answers
35 views

Improving a simple trig model

I have some data which I know is well approximated as a trig function, and I can fit it with scipy.optimize.curve_fit as follows: ...
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1answer
304 views

Finding lookalike customers for Digital Media targeting [closed]

I want to develop a model for digital marketing team. We have some set of ideal customers to whom we to send our promotions and have been converted. Now we want to pick past such cases and want to ...
2
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1answer
186 views

Find Net Reclassification Improvement/Index metric using Python

I am working on a binary classification problem with ~5k records and class proportion of 33:67. I have 60 features/variables in my dataset and finally I have come to about 10 variables based on ...
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1answer
43 views

How to evaluate performance of a new feature in a model?

I am working on a binary classification where I have 4712 records with Label 1 being 1554 records and Label 0 being 3558 records. When I tried multiple models based on 6,7 and 8 features, I see the ...
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1answer
73 views

Why results of statsmodel logreg is different from scikit-learn logreg?

I am trying to do a binary classification. I have only 6 input variables and one output variables. Label 1 is 1554 records and Label 0 is 3558 records. As you can see below, the metrics that I get ...
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4answers
971 views

How to impute Missing values not the usual way?

I have a dataset of 4712 records working on binary classification. Label 1 is 33% and Label 0 is 67%. I can't drop records because my sample is already small. Because there are few columns which has ...
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0answers
61 views

SVM C vs gamma hyperparameter tuning

While running SVC(), how we can hyperparameter tune C vs gamma combination? I could see changes in C and gamma are impacting the accuracy differently. Also, what I understand about C and gamma are: C ...
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2answers
61 views

Kernel selections in SVM

I want to understand the kernel selection rationale in SVM. Some basic things that I understand is if data is linear, then we must go for linear kernel and if it is non-linear, then others. But the ...
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1answer
56 views
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51 views

Time Series Classification for loan data

I have multiple columns for loan installment repayment. As there is a field for month of repayment, I want to predict if the customer is going to pay next month's installment or not. As I have ...
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2answers
87 views

Machine learning model bundled with a library vs. an API

I am thinking to "deploy" a machine learning model (in pickle it is sized 3 megabytes) and after discussing with my developer colleagues, they said it would be better if the model is packed as a ...
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0answers
104 views

Omnibus and R square improvements for OLS model

Checking on this community if any one can help with this problem posted on Cross Validated. Detailed question is as below: ...
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1answer
102 views

Why and how to match variables in logistic regression?

I have a dataset of ~4.7K records focused on binary classification with 60 features. class 1 is of 1554 records and class 2 is of 3558 records. Now I would like to find the risk factors that ...
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1answer
39 views

Threshold to consider new feature as a new finding to a model?

I am working on binary classification problem with 5K records and 60 features. Through feature selection, I narrowed it down to 14 features. In existing literature, I see that there are well-known 5 ...

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