Questions tagged [prediction]

prediction is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.

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577 views

What is the minimum amount of data required for sales prediction with ML

I have historical data from the MySQL DB which contains 33 months of data. The features in the data are state, depot, product type, purchase date, salesperson name, volume, and price. Using this ...
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1answer
65 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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1answer
111 views

Looking for a ML algorithm to predict a path based on millions of data

I have a dataset with following data format: 3 -> a -> b -> c -> d -> ikd a -> c -> 3 -> dk -> 2 -> l2i Each row represents a path from start to end. Let's take the ...
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1answer
3k views

Kalman filter for time series prediction

I have the information about the behaviour of 400 users across period of 1 months (30 days). Across those 30 days I measure 4 different information (let's call it A,B,C and D), hence I have a total of ...
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2answers
73 views

prediction using LSTM

i have training data from 2015-2017 and testing data of 2018. i have multiple variables my data is multivariate time series data.i want to predict 2019 data by using test data of 2018.is it possible? ...
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1answer
239 views

Why the first prediction of neural network in PyTorch is slower than following predictions?

So I have ResNet50 trained to classify images. For each prediction I track the time needed for it (input and model are moved to GPU): ...
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1answer
226 views

Why does CV yield lower score?

My training accuracy was better than my test accuracy, hence I thought my model was over-fitted and tried Cross-validation. The model further degraded. Is that my input data need to be sanitised ...
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1answer
66 views

predicition for a specific month

I am attempting to build a predictive model based on the past historical data. I have details of specific machine failure based on the past year data. I have data from some months of 2016 and from ...
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0answers
88 views

Prediction questions related to the dataset

I have been self-learning data science from different sources. I have a dataset which was sent to me by my friend from one of her college courses. The work is to be done in R preferably. Description: ...
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2answers
37 views

Classification with feature not available at time of model creation

I have problem statement to predict the probability of solving a task depending on multiple features for e.g. when the task was created, the time needed to work on a task, etc Please find a dummy ...
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1answer
8k views

XGBOOST : model.predict_proba() and model.predict() conflicting behaviour

I have two classes 1 and 2: The output of model.predict_proba(): [0.333,0.6667] The output of ...
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1answer
3k views

Making predictions / Loading model in TensorFlow 2.0

I use TensorFlow/Keras on a daily basis to make predictions for a project. Everything works fine but I was getting regular warnings about the transition to TensorFlow 2.0 and I thought this week I ...
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1answer
40 views

Pattern recogniser library for C# programs

I'm a regular user of the StackOverflow forum, but as this question is about a recommendation for libraries, which is supported at StackOverflow, and as my question is about data science libraries, I'...
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2answers
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Binary classification problem with imbalanced dataset, how to compare to random classifier

We have a very imbalanced dataset (2% of class 1). To the best of our knowledge, there is no baseline in the literature to the problem we want to solve - so we thought of comparing our performance to ...
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1answer
903 views

Predictions with arbitrairy sequence length for stateful RNN (LSTM/GRU) in Keras

I have time series data of the following properties: input shape: (num_timesteps, num_features) output shape: (num_timesteps, num_outputs) I reshape it to batch ...
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1answer
229 views

Machine Learning based Multivariate Time Series Prediction - How to create supervised data format

Q1: I have a multivariate time series dataset. For each timestep, there are 11 features and 1 output. I am going to use supervised ML to predect the output. I understand that in univariate cases, if ...
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20 views

Confusion matrix of 3*2

I would like to include confusion matrix in my research report. I have a binary classification problem. The positive class is further divided into two types for example: Real Positive and Obstruction ...
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1answer
1k views

How to extract the sample split (values) of decision tree leaves ( terminal nodes) applying h2o library

Sorry for a long story, but it is a long story. :) I am using the h2o library for Python to build a decision tree and to extract the decision rules out of it. I am using some data for training where ...
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1answer
14 views

How does an RNN differ from a CBOW model

CBOW: We are trying to predict the next word based on the context (defined as a certain window of words around the target word) RNN can also be used for predicting the next word in a sequence, where ...
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0answers
19 views

Multivariate regression - not enough data?

I have a table with data about 10 agriculture parcels. Each parcel has data in time regard the number of nutrients each parcel has received in each day and in the end I have the total number of ...
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1answer
18 views

What are more advanced techniques than ARIMA?

For timeseries predication cases, what other techniques are available in statistics or machine/deep learning other than MA (moving average), ARMA, and ARIMA?
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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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1answer
35 views

Dissecting performance issues with Random Forest

My task is to identify potential situation for trading and determine whether a candidate is going to succeed or not. I have a system in place to identify candidates, but there is a high rate of false ...
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0answers
27 views

How to consider the change in categorical variable in multiple linear regression?

I am building a multiple linear regression model to predict the mileage of tires and one of the independent variables is the wheel position. It is categorical and I could encode it to run the model ...
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3answers
308 views

How to insert two features in a model when a feature only applies to a certain group in the model

I'm building a machine learning model in Python to predict soccer player values. Consider the following feature columns of the dataframe: ...
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2answers
73 views

How to measure accuracy of a route prediction

I developed a new route prediction algorithm and I am trying to find a metric that informs on how well a prediction was. This metric is meant to be used offline, meaning that the goal is not to ...
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1answer
53 views

Find suitable locations using Machine Learning

Just for fun, I am currently trying to find suitable locations to deploy new stores. So what I did so far is to take the actual sites of current stores and to assign surrounding variables to it. These ...
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1answer
80 views

How to incorporate the uncertainty of the model coefficients in the prediction interval of a multiple linear regression

I'm dealing with modeling small experimental data sets. As most experimental work does not generate thousands of samples, but rather a handful, I need to be inventive about how to deal with this small ...
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1answer
13 views

What is predictive Horizon?

I understand the basics of what predictive Horizon means. When we predict in future, we set up a time window. In other words, it is used to determine how far ahead the model predicts the future. ...
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1answer
32 views

Service Request classification, questionnaire filling and call logging

I am very new to machine learning. I just went through some of the tutorials in Azure and completed one practice workflow(car price prediction). I hope I can ask basic questions here. Scenario : We ...
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1answer
124 views

How do I use rnn to forecast to n periods with limited data?

So this is my 1st time trying to run a small time-series dataset through an RNN, but after a lot of searching, I haven't been able to find, 1. How I can use this to forecast to n periods ? (like in ...
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2answers
271 views

How to cluster categorical and numerical data in the same dataset?

I have a dataset in which it contains both numerical and categorical data. This can be done using supervised learning algorithms, but I am eager to see how this data can be clustered using some ...
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2answers
426 views

Event prediction sequence

I need to create an app to predict events. I'll explain my requirements: I have a set of intermediate events (eg.: 1, 2, 3...9) and a set of final events (eg.: A for OK and B for KO). These events ...
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0answers
20 views

Using a randomForestSRC model to predict new values always produces the same value

I fit a multivariate random forest model using randomForestSRC, but when I try to use it to make predictions, it always prints out the same value. In the code below,...
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1answer
297 views

Include time as a variable in regression model

I am currently working on a regression problem which requires me to predict the costs of a fixed asset. I have used several variables to do so and derived a predicted cost. However, my superior has ...
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1answer
51 views

Predicting when component will fail having its parameters data

I have a component and I need to predict when it will wear out and will need replacement. I monitor, let's say 5 parameters of this component, each one is monitored for every run cycle. So, the ...
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1answer
35 views

How do you effectively predict the top 20% most likely customers to churn from a dataset?

I am looking to work out that if I have a dataset with 100,000 existing customers who didn't churn and 20,000 previous customers that churned in the past and the business objective is to target the 20%...
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2answers
288 views

How to read the predicted label of a Neural Netowork with Cross Entropy Loss? Pytorch

I am using a neural network to predict the quality of the Red Wine dataset, available on UCI machine Learning, using Pytorch, and Cross Entropy Loss as loss function. This is my code: ...
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4answers
634 views

Which type of model to use and what's my target variable

I am a beginner in Data Science field, so sorry if my question is too basic. The task is to build an ad bidding model for online marketing which allows you to deliver targeted ads to the right ...
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0answers
15 views

Prediction method when the time series is not sequential?

I have multivariate time series data consisting of monthly sales of contraceptives at various delivery sites in a certain country, between January 2016 and June 2019. The data looks as follows: The ...
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1answer
453 views

ML methods for prediction, using categorical variables and time

Most of the time series analysis tutorials/textbooks I found time series data, usually deal with continuous numerical variables. I am currently trying to solve a problem that deals with multivariate ...
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1answer
70 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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1answer
176 views

Is "adding the predictions to the real data for new training and prediction" a good idea for LSTM?

Considering we have trained our model with a lot of data for "many-to-one" prediction. Then we like to forecast the future data of next 10 days. So we use last 60 of existent data and predict the ...
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1answer
575 views

What does a predicted probability really mean, without considering the accuracy of the underlying model?

Say I've built a (completely unrealistic) classification model in Keras that gives me 1.00 accuracy. And next, I would like to use my model on some new, unseen data, and use ...
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2answers
62 views

Ingredients, Recipes and recipe ratings. I would like to predict the rating based on combination of ingredients

I would really appreciate some help on the first steps to my problem, suggestions of modeling techniques i could use or relevant research (i could not find any). I have a list of ingredients (150 in ...
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1answer
134 views

How to deal with different length entities in a Keras DataGenerator?

I'm solivng a prediction problem where I need to predict the demand of multiple articles based on their performance during the last 7 days. To get the most out of the data I am trying to implement a ...
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1answer
53 views

Regression performance varies hugely on shuffling training and testing data

I'm working on a regression problem to predict a variable y based on an input vector X with about 10 columns. To split the data for training and testing, I use the ...
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1answer
51 views

What metric should I use to achieve perfect score when choosing all possible results?

A guy told me that he can predict which player I would choose from Greece's Euro 2004 Champion football team. Assume my choice was random. He then goes ahead and names all the players of the team. He ...
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1answer
64 views

Random Forest. In a prediction less input is expected than features used

ValueError: Number of features of the model must match the input. Model n_features is 2 and input n_features is 7. I have the following error: I have not used any type of encoding(like one-hot etc.) I ...
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1answer
45 views

Keras: Provide One-Hot-Encoded input values to neural network

I have a dataframe which has two columns of interest: A and B with string values. I am trying to build a prediction model which takes in a set of values in A as input and predicts the corresponding B ...

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