Questions tagged [predictive-modeling]
Statistical techniques used for predicting outcomes.
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Prediction in multiclass classification
Context: I need to make an multiclass classification to predict what type of sentence(law) the case will have in the end.
Data: I Have several columns to predict the case:client, cause of action, ...
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Dynamic dosing recipe for accurate pH
I want to have a script for adjusting dosage of ingredients in each batch dynamically. Assuming that the requirement is to have a specific value of pH from each batch but with the variation of raw ...
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Growth (sales) forecast based on season
How to make a forecast based on last year (season)?
I have no experience in the field
Make a forecast of the sales in september 2023 based on last years sales
Example A
Sales august 2022 (last year): ...
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Create Dataset (ADS) to build predictve model for servey data
I have collected labeled survey data spanning 8 months in 2023, capturing satisfaction levels ranging from "very satisfied" to "very dissatisfied."
My objective is to develop a ...
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Dealing with varying predictive horizon
I know that the predictive horizon is the time window that runs from the observation of the data to the manifestation of the target variable.
But how can I deal with prediction if the time horizon ...
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Seeking guidance on data analysis techniques to estimate project LOE based on numeric counts of outputs produced
I’m looking for some basic guidance on where to focus my research in support of some data analysis I’m looking to perform.
The problem space is identifying a methodology for estimating Level of Effort ...
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Associating two variables in XGBoost model
I am trying to build a model that will predict a college football players probability of being drafted. I have multiple variables with different athletic measurements, but for the sake of example lets ...
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How to compare test vs train model performance
When comparing the test vs train model performance to ensure no overfitting (e.g., using AUC ROC as an example), is it better to select the model with the largest test score, or the model with the ...
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What does it mean order of input sequence does not matter for transformer self-attention head?
The need for positional encoding in transformer models is justified by permutation invariance of self-attention heads, because, without it, transformer wouldn't have any mechanism to take into account ...
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Modelling employee workload data
Wondering if I can get steer on this question.
I have a dataset with the columns -date,employee id, task id, volume of work completed as percentage (float) for that task, time taken to complete that ...
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Survival analysis on time series data for predictive maintenance
I want to train a survival analysis model for predictive maintenance on an asset (confidential, let's say it's a motor). The dataset consists of hourly readings of multiple sensors, the type of motor, ...
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ML with Python - extract information from user text
I'm a web developer, got a little experience with Python but none on ML.
Tour operator customer want to introduce AI/ML on his website, the goal is to have a single text input where user can prompt ...
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Prediction with 2years data
I have this data https://data.mendeley.com/datasets/3g8dtwbjjy/1 and I'm asked to make a prediction task but I don't quite understand how I should do that. I need to use 3different algorithms for it ...
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Fine-tuning Pretrained Models for Web DOM Interaction Prediction Task
I am currently working on a side project that involves predicting changes to a webpage's DOM based on user interactions. The idea is to input the initial DOM state and a user interaction, and predict ...
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improving Neural network regression model
I have the following toy data (which closely mimicks my original larger data used for the project):
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Time series prediction problem with other parameters
My data looks like this: time series of average house prices per day in 100 cities (maybe more) over a two-year period, each with a set of characteristic variables that do not change over time, such ...
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Help creating a SCORING model/formula.......am I on the right track?
Trying to create a basic machine scoring model, that takes in 4 parameters:
Number of maintenance events
Years of life left
Manufacturer support (bit - either yes or no)
Visual condition
The ...
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Simplex method for equality optimization
I have a linear model that goes as 0.1*x1 + 0.8*x2 + 3.4*x3 + 5.0*x4 + c and this linear model was generated by using a Linear Regression.
MAE is ~ 0.4
MSE is ~ 0....
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Are imbalanced data problems solvable? [closed]
I am working as a data scientist for the past 2 years where I have worked on problems related to binary classification, revenue prediction etc.
In the past two years, I have had 2 problems that ...
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How to treat missing values depending on what missing means
I have a dataset with quotes from an insurance company. I am trying to create a model to predict how much should the company charge the customer according to the different variables. Two of the ...
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Learning from aggregated data
Online and in the literature there seems to be a general consensus that training a machine learning model using aggregated data is harder and/or fundamentally different from training on raw event data....
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What model should be used with a time-series dataset of uneven data?
I'm working on building/selecting a model to predict the result of a sales lead: whether it's "SOLD" or "NOT SOLD".
My dataset consists of past leads with the following data:
...
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predicting temperature of something thermally isolated
new to data science/ML but experienced programmer in C/python for engineering/electronics.
I am trying to predict the temperature of a rotor inside an electric motor. I have a lot of data to feed into ...
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How to make a prediction if training time series have different lengths?
Description: I have 100 products. For each item I have the number of hours it has been used for each quarter of multiple years.
Item
2020_Q1
2020_Q2
2020_Q3
2020_Q4
2021_Q1
2021_Q2
2021_Q3
2021_Q4
...
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External factors in time series forecast of electricity production
I'm doing a machine learning time series forecast of electricity production shares by power plant (nuclear, coal-fired, gas, solar, wind, water etc.) in my country in 5 year horizon. I have historical ...
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Data organization
I want to model road accidents using Random Forest and Neural Networks, and predict where (Km) will the next accident occur. I chose 5 variables titles (each variable affects accidents) at the top of ...
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What is the steps to generate data from a plot and use them to have a predication using python?
I have a plot that represent a BH curve for magnatic material. The material have a behavior for each H value for two different temperature 25 C and 100 C. Figure 1. I need to extract the data for each ...
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how do i compute the predictive covariance matrix from the posterior samples?
I have generated with EMCEE some posterior samples from a statistical model whose likelihood is a multivariate gaussian. it's a regression problem.
can you explain me how I can use these samples to ...
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find the parameter that minimize a multivariate distribution
i was trying to use scipy's minimize scalar to find the value of the parameter T that minimize the negative log-likelihood of a multivariate distribution with covariance matrix C. if i understood ...
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How to cluster based on sensor data? - My first data science job
I'm on my first (real), data, programming job. As everyone can imagine, this can be quite hard and I learn a lot from it, given I am a data science student in university. However, I am completely ...
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Do models of social systems suffer from prediction drift?
Background
I've created a binary classification model that predicts the probability of fraud for a given sample.
The choice of threshold allows me to set how many frauds are captured in the training ...
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How can someone build a dataset for a "propensity to purchase" model?
Ok, this might seem a trivial question for some and it's not even a question, more like a discussion. I read the rules and I believe it's everything fine, so I'm gonna take my chances...
Here's the ...
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Differential equations, real time measurements of variables, and ML
TL;DR: We measure variable $x$ every $10$ minutes, solve a differential equation $\frac{\mathrm{d}y}{\mathrm{d}t}$ where $y=f(x)$. We are interested in the time it takes for the cumulative value of $y$...
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How can i implement an confucion matrix?
im trying to do a research but i need to make a confusion matrix how can i do that on this model?
https://www.kaggle.com/code/stpeteishii/race-classify-densenet201
Sorry im so so new to everything.
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What are some approaches to improve the classification of ONE particular instance of interest?
I'd like to know if there are some methods to correct a specific misclassified instance of interest (e.g. in a Logistic Regression or Random Forest).
Like maybe increasing the error for that ...
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What to do if your model's prediciton result wrong because of unlucky?
Have you ever had a situation where your model backtested with very good with historical data, and you also felt that your model was very logical? But when put it into practice case to predict the ...
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Sklearn predicts different results depending on the input length
Here is the problem: I fitted a Random Forest Classifier and saved it to a pickle file. However, when I predict with the entire dataset I get one result, and when run predict line by line (loop) I get ...
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How a Random forest "learns" or How loss (objective function value) is propagated back so that a random forest can "Improve"?
Every Blog and Youtube video talks about the same steps:
Choose that you have to build N number of tree and do the task 2-5 ...
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Treat multiple periods of huge outliers in time series data with weekly seasonality data
How can I model a time series data (average sales is around 20K) with weekly seasonality that has multiple recurring outlier periods for example 4 days of huge volumes (around 150K) in March and 14 ...
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how to define observation and event window
In a retail use case where I don't have a customer end date, so I tried to come up with a logic of taking the max difference within 1 year b/w consecutive purchases and based on a certain threshold ...
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Is there a way to use debt details without aggregating them to predict the probability of payment of every debt per debtor?
I edited my post for clarity, for the second time. Thx lpounng for the feedback.
I am seeking advice on predicting debt payment within a year. Each debt has its own carachteristic wich are not easy to ...
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What approach can be take to design a food menu intelligently?
I am given following problem, for which I am seeking for advice.
Problem Statement
Suppose you are given a catalogue of dishes with their cost and calories. Can you design an algorithm/model which can ...
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Is it normal for a SARIMA model to produce no residuals?
I'm working on my first time series project where I am required to produce predictions for financial data.
The raw data is below:
Clearly, there is a seasonality and downward trend, I used the ...
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Early anomaly detection / Failure prediction on time series
My problem here is that I want to predict failures in advance with respect to their occurrence. I have sensors mounted on my machine and with a certain frequency, they send data to my database. ...
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Refining AI problem statement - suggestions
I am looking for some guidance. My company is a electronic goods manufacturing company. We work with multiple distributors (around 7 distributors) across specific regions to sell our products. But ...
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Classification-Problem based on limited Dataset (Need keywords to search/ reading recommendations)
I have a dataset of about 200 test subjects, each with age, testscore and a one of two possible traits. I want to define some kind of function, where I input age and testscore, that predicts which of ...
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How to make a predictive model using a timeseries data consisted of binary information?
I have a set of data that is showing the state of an object as a function of time. I would like to know what and how I should be utilizing machine learning to predict the state of the object at some ...
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Which modeling technique is appropriate when I have nested/hierarchical data (individual and group) but user inputs will only be at the group level?
I am trying to create a predictive model that will be built on individual data, but user input will only exist at the group level. Reasoning is that I have 5 million rows of data at the individual ...
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Can Hip-Hop/music trend be estimated?
How can I determine how an "evolutionary" music album affects the development of its genre? Only two perspectives I can come up with: 1. The effect on the number of songs before and after(...
