Questions tagged [predictive-modeling]
Statistical techniques used for predicting outcomes.
989
questions
0
votes
1answer
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 ...
1
vote
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 ...
0
votes
1answer
28 views
1
vote
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 ...
1
vote
0answers
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 ...
1
vote
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 ...
1
vote
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 ...
1
vote
0answers
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 ...
1
vote
0answers
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) ...
1
vote
0answers
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 ...
2
votes
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 ...
1
vote
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 ...
4
votes
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,...
1
vote
0answers
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 ...
3
votes
0answers
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 ...
0
votes
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 ...
2
votes
0answers
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 ...
1
vote
0answers
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?
...
0
votes
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 ...
6
votes
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 ...
1
vote
0answers
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 ...
2
votes
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 ...
2
votes
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 ...
3
votes
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 ...
1
vote
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. ...
2
votes
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 ...
1
vote
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
...
2
votes
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
...
3
votes
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 ...
2
votes
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 ...
2
votes
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 ...
5
votes
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 ...
1
vote
0answers
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 ...
13
votes
5answers
1k views
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? ...
4
votes
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 ...
2
votes
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 ...
3
votes
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:
...
1
vote
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
votes
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 ...
2
votes
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 ...
1
vote
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 ...
10
votes
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 ...
1
vote
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 ...
2
votes
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 ...
3
votes
1answer
56 views
0
votes
1answer
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 ...
6
votes
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 ...
2
votes
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:
...
2
votes
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 ...
1
vote
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 ...
