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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Newbie in ML - Using error traces to predict issues

We have trace data from Jaeger which shows end-to-end information about requests/transactions/error codes. Jaeger UI/APIs are useful in debugging issues after they have happened. The requirement is to ...
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Genetic algorithms with severals keras model

I have a population of 50 individuals that are basically weights and biases. The genetic operators are operational from them in order to compute and assign new weights and biases to their models for ...
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Choosing a model to forecast parallel time series with multiple features

I have 6 websites, and I am trying to forecast the number of chat bots opened per hour for each website. The time forecast is 72 hours later. Data Format There are 15,000 data points (deseasonalised),...
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Linear regression returning negative values for house price prediction

I am trying to do a prediction of real estate (prices are in millions). The mean price for the dataset is 4 million. I do not have any negative values in my dataset,...
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How should a dataset looks like for Time series forecasting

What should a dataset look like for time series forecasting? Can I do time series forecasting with a dataset that contains apartments from ad sites obtained with: web scraping from 2018 to 2021 13 ...
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How to handle date in Random Forest prediction?

I want to predict the income for small business from 1/1/2018 to 1/1/2020 for a dataset from historical data(all my variables are numeric except for date) the start date of my data is 1/1/2012. The ...
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Weather impact on plant growth

I have a data set that includes the following and am using it to learn more about data science. I have googled a bunch - but can't seem to find any examples on what I am trying to do. I am trying to ...
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When to prioritize accuracy over precision?

I am working on a simple SVM project for the prediction of hepatitis c. I got my dataset from kaggle. When dealing with null values, I tried two ways, firstly by dropping data with null values, second ...
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Random Forest Classifier Output [closed]

Used a RandomForestClassifier for my prediciton model. But the output printed is either 0 or in decimals. What do I need to do for my model to show me 0 and 1's instead of decimals? Note: used feature ...
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Merging two datasets with different features for machine learning prediction

I'm trying to create a model which predicts Real estate prices with xgboost in machine learning, my question is : Can i combine two datasets to do it ? First dataset : 13 features Second dataset : ...
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Help with Time Series prediction

I'm a complete n00b to both this stackexchange and ML so please don't flame me too bad. I am trying to make a prediction from Time Series data. I have about 10 years worth of 1-minute resolution price ...
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How would I check the validity of covariates in my linear model on several hundred datasets?

I have this linear model with predictors that I need to prove are statistically significant and pass the necessary lm assumptions. I know for a single dataset, I can use various LM tests, but the ...
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massively imbalanced data

I am dealing with time series data with +200K (every minute for 6 months)record of gas turbine I am trying to early detect the fault (0 or 1-fault). The issues with the data are: 1.the fault occurred ...
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Get negative predicted value in Support Vector Regresion (SVR)

I am doing Covid-19 cases prediction using SVR, and getting negative values, while there should be no number of Covid-9 cases negative. Feature input that I was used is mobility factor (where have ...
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Where should I find electrolytic capacitor ageing data

I am trying to get a dataset of Electrolytic capacitors ageing and I am not being able to find one that shows the ripple current and the voltage in order to calculate its Equivalent Series Resistance (...
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Aggregated probability based on multiple predictions on independent samples using the same classifier

i have a understanding question regarding the interpretation of a aggregation of a machine learning classifier. Lets assume i have trained a binary classifier and it was validated with a accuracy of ...
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Prediction for package delivery to summarize packages

I have the problem, that a customer can buy something. Now I want to predict if the customer is buying another things in the next few days. So that you can summarize the packages and delivery not ...
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Categorical data preprocessing for training a algorithm

I have a training dataset where values of "Output" col is dependent on three columns (which are categorical [No ordering]). ...
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Creating a new prediction for the Keras.io BST model

The Keras.io example of a Transformer-based recommendation system is a great example for me to understanding neural networks in Keras. But how would you use the create_model_inputs() to get a new ...
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Low classification accuracy

I want to do a multi class classification with 6 classes. Whole dataset has 12750 and 56 features samples, so every class has 2125 samples. Before prediction I reduces amount of outliers by ...
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Successive Predictions

I am facing a problem which you could abstractly describe the following: I have a pool of possible customers. I want to know if a customer appears during one year. I want to predict the total revenue ...
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Plotting the confidence interval for a plot in python

I have a curve and I want to create the confidence interval for the curve. Here, I provide a simple example: ...
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Logistic Regression for prediction

I would like to ask about the theoretical approach of using Logistic Regression for customer data and more specifically Churn Prediction (in BigQuery and Python). I have my customer data for an online ...
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Is there suitable algorithm for prediction based on multi dimensional Dataset?

I'm currently working on predicting a variable based on some known variables, But I wonder if there's at least one algorithm for such a strange Dataset. The shape of the Dataset is like this: The ...
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How to predict an outcome of the game (next row) based on all previous games (rows)?

I'm a data science student and I've come across a fairly unusual dataset (to me, which explains the vague title). It's of the following form: STAT_1 STAT_2 ... HOME AWAY NEXT_HOME NEXT_AWAY ...
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Predicting Opportunity Win/Loss using Machine Learning

I have a dataset as below. These are closed opportunities where we have the outcome (won/lost). I want to predict whether the opportunity would be won/lost based on these features and also the time ...
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How to improve the accuracy of support vector machine algorithms in machine learning?

I am working with a machine learning project named "Diabetes prediction using support vector machine". In this project I have used Pima Indians Diabetes Database. Using SVM I have got 78% ...
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Dummy Predictors / Continuous Dependent Variable

I have a dataset with 50+ dummy coded variables that represent the purchases of an individual customer. Columns represent the products and the cell values 0 or 1, whether the product has been ...
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In stock prediction with LSTM, is there a need to get a dataset for a specific time period in order to predict future close price?

I am currently trying to predict the close price of the TSLA stock for March 2022 using LSTM model. Initially, I was using TSLA stock data starting from 2012 to of course March 2022. However, I was ...
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Fashion Compatibility Performance Evaluation: High in AUC but Low in FITB

I am a newbie in deep learning field. Still trying to understand how this works. But now I am working on fashion compatibility prediction. The most well-known performance evaluation in this task is ...
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forcasting anomaly in products

I have a question about the forecasting of anomalies. I would be very grateful if you could refer me to some papers that deal with this kind of problem or give me some hints to start with this problem....
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validation_split in time series data for lstm model

I have an LSTM network and I use it to predict. My whole data is an array with 10 rows and 1000 columns (10, 1000). I want to divide the data to train with size <...
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LSTM model can not work for n_ahead point prediction

I have an LSTM model for predicting the time series data. However, when I have tested the model with the data which are hidden from the training (but created based on the function which I have created ...
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Are Machine Learning Weather Prediction models better than classic weather forecast?

We all know that, there are weather prediction models and case studies. But I don't understand the reason, why people trust them rather than weather forecast on TV. I mean, what is advantages of ...
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Why am I getting different prediction result after every run?

I have a simple lstm model ...
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Prediction of failure of electrical components - Finetuning my approach

I am working on a personal project involving the prediction of the possible failure of electrical components. Considering I do this solo so I don't have any internal support and my own network does ...
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Probability Distribution on the Test Set

How can one interpret the probability distibution of the predictions for the target of the test set? For example if we wanted to interpret the plot below, can we say it is overfitted? Here x axis ...
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How to interpret a Regression Error Characteristic curve

How can I interpret the REC (Regression Error Characteristic) curve ? What is error tolerance and what is the area over the curve? What should be the accepted value for the error tolerance? How to ...
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How to use a model after cross_validation in predicting a test data?

I want to do the following: train a model using cross-validation use the model for prediction (test dataset) check the algorithmic bias towards some features values I wonder if what I am doing is ...
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The LTSM does not predict Apple Stock Close column well

I am using LSTM (Long Short Term Memory) to predict the Apple Stock Closing prices using the 3 previous days. My problem is that the model underestimate closing prices. The photo of the final result ...
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Does standard deviation of the time series data say anything about its predictability to us?

Assume we have three different time series from three different years. Could we claim that it will be more difficult to forecast the one with a high standard deviation after calculating their standard ...
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Predicting the users weekly booking activity using previous booking activity

I am trying to get the prediction over weekdays for particular user's Research problem is to predict the user's weekly schedule using historical bookings Let me introduce the dataset for better ...
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Discrepancy between cross-validation and un-seen data predictions

I am facing an issue with an imbalanced dataset. The dataset contains 20% targets and 80% non-targets. I am expecting a confusion matrix below when I give un-seen data to the trained model. ...
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What is the use of a 'supplemental' data

I have been working on a dataset from a drug chain supply company. It has one key dataset that has "historical data including Sales" (Sales is the target variable, that I ought to predict). ...
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How to fill missing values in a discrete column in sales predictions for a drug supply chain company

I have been working on a dataset that has data from a famous drug supply chain company. The first few records of the dataset look like the following; Another data accompanies this (primary) dataset. ...
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Prediction based on predicted data

may I ask if anyone of you knows well references on the prediction method based on the predicted data? By prediction based on the predicted data, I mean for example I have constructed a predictive ...
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What correlation is considered to be big for linear regression predictors?

It is well known that if two linear regression predictors highly correlate, it is bad for our model, but which correlation is considered to be big? Is it 0.5,0.6,0.8,0.9..? I have tried to find out ...
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Good model to predict color of the button to maximize site conversion

For example, we have a big internet store and we have Add to card button which can be blue or green. We want to show blue button to people who buy more with blue button and show a green button to ...
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Running model.fit multiple times for an LSTM?

I have time-series histogram data from many separate machine runs (see this post for detail). I am working to train an LSTM in order to predict the final histogram in a machine run based on the past ...
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Find the Outlier

I have data that contains points (geo coordinates of a random planet, integer pairs) that represent places where land is definitely there. Here is an example with ...
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