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# How can I predict the future data by past data?

By : Elavina
Date : September 14 2020, 11:00 PM
I hope this helps you . Try with these tutorials RNN or LSTM algorithms
code :

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## Advice on how to predict future time series data

By : max FIRMIN
Date : March 29 2020, 07:55 AM
Does that help Take a look at the statsmodels Time Series Analysis module. Time series models are often based around autocorrelation, and the module has the standard univariate (for individual time series) AR(p) and MA(p) models, as well as the combined version ARIMA that allows for unit roots. You'll also find multivariate (for various interrelated time series) VAR models.
And here's a time series tutorial for statistical analysis and forecasting using pandas and statsmodels.

## IF I wanted to predict future purchases in online shopping using historical data, do I need data science or data analysi

By : GenericException
Date : March 29 2020, 07:55 AM
Hope this helps it all falls in the category of data science (which is big data and data analysis). What you need for predictions and such stuff is some machine learning approach to data you have or can access about stuff you want to predict.
I'd recommend this, newest series of articles: https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12

## How to add new csv file data into training LSTM model to predict next future value using python

By : user3433022
Date : March 29 2020, 07:55 AM
I wish this help you Why do you reshape your inputs to have a final dimension of 1 in the snippet?
code :
dataset_test = pd.read_csv('data56.csv')
dataset_total = pd.concat((data8['x1'], dataset_test['x1']),axis=0)
inputs =dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)

dataset_test = pd.read_csv('data56.csv')
dataset_total = pd.concat((data8[['x1','x2','x3','x4']],
dataset_test[['x1','x2','x3','x4']]),axis=0)
inputs =dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,4)
inputs = sc.transform(inputs)


## Using the predict() function to make predictions past your existing data

By : Stephen - WiFi-Texas
Date : March 29 2020, 07:55 AM
wish of those help The predict function is going to predict the value of DirNDay based on the value of the other variables for that day. If you want it to predict DirNDay for a new day, then you need to provide it with all the other relevant variables for that new day.
It sounds like that's not what you're trying to do, and you need to create a totally different model which uses time (or day) to predict the values. Then you can provide predict with a new time and it can use that to predict a new DirNDay.

## How can i predict the future of this data

By : Mohit Kumar
Date : March 29 2020, 07:55 AM
will help you This is a regression task. You can use simple regression model (this is an example of predicting house prices in Boston):
code :
from sklearn import linear_model
import pandas as pd
from sklearn import datasets ## imports datasets from scikit-learn

# define the data/predictors as the pre-set feature names
df = pd.DataFrame(data.data, columns=data.feature_names)

# Put the target (housing value -- MEDV) in another DataFrame
target = pd.DataFrame(data.target, columns=["MEDV"])

X = df
y = target['MEDV']

lm = linear_model.LinearRegression()
model = lm.fit(X,y)

predictions = lm.predict(X)
print(predictions)[0:5]

import xgboost as xgb
import pandas as pd
from sklearn import datasets ## imports datasets from scikit-learn

# define the data/predictors as the pre-set feature names
df = pd.DataFrame(data.data, columns=data.feature_names)

# Put the target (housing value -- MEDV) in another DataFrame
target = pd.DataFrame(data.target, columns=["MEDV"])

X = df
y = target['MEDV']

lm = xgb.XGBRegressor()
model = lm.fit(X,y)

predictions = lm.predict(X)
print(predictions)[0:5]