Hello all, This code is written for multivariate (multiple independent variables x1,x2,x3..xn and a dependent variable y) time series analysis using logistic regression (correlation and prediction).
#Import Libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd #Import Dataset dataset = pd.read_csv(‘precipitation.csv’) x = dataset.iloc[:,[2,3]].values y =dataset.iloc[:,4].values #Split Training Set and Testing Set from sklearn.cross_validation import train_test_split x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.25) #Feature Scaling from sklearn.preprocessing import StandardScaler sc_X=StandardScaler() x_train=sc_X.fit_transform(x_train) x_test=sc_X.transform(x_test) #Training the Logistic Model from sklearn.linear_model import LogisticRegression classifier = LogisticRegression() classifier.fit(x_train, y_train) #Predicting the Test Set Result y_pred = classifier.predict(x_test) This code is based on one point location (one lat/long) datasets. Suppose, I am having gridded datasets (which has many points/locations, lat/long, varying in space and time) then How I will implement this code. I am not expertise in python. If somebody can help me in this? If somebody can give me an example or idea so I can implement this code as per my requirement. Thank you in advance. Vishu -- https://mail.python.org/mailman/listinfo/python-list
