使用 Pearson 相关和线性回归的简单预测 python

Question

我有这样的数据集

    Value   Month       Year 

    103.4   April       2006
    270.6   August      2006
    51.9    December    2006
    156.9   February    2006
    126.9   January     2006
    96.8    July        2006
    183.1   June        2006
    266.6   March       2006
    193.1   May         2006
    524.7   November    2006
    619.9   October     2006
    129     September   2006
    374.1   April       2007
    260.5   August      2007
    119.6   December    2007
    9.9     February    2007
    91.1    January     2007
    106.6   July        2007
    79.9    June        2007
    60.5    March       2007
    432.4   May         2007
    128.8   November    2007
    292.1   October     2007
    129.3   September   2007

value是一个地区的年降雨量。让我们称之为 DistrictA。我有 2006 年到 2014 年的数据集，我需要预测 DistrictA 未来 2 年的降雨量。我从 sklearn 库中选择皮尔逊相关和线性回归来预测数据。我很困惑，我不知道如何设置 X 和 Y。我是 Python 的新手，所以每个帮助都是 valuable.Thank 你

ps.. 我找到了这样的代码

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model

# Load the diabetes dataset
diabetes = datasets.load_diabetes()


# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean square error
print("Residual sum of squares: %.2f"
      % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))

# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')
plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
         linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()

当我打印 diabetes_X_train 它给了我这个

[[ 0.07786339]
 [-0.03961813]
 [ 0.01103904]
 [-0.04069594]
 [-0.03422907]...]

我假设这是从相关性和系数得到的 r 值。当我打印 diabetes_Y_train 它给了我这样的东西

[ 233.   91.  111.  152.  120.  .....]

我的问题是如何从降雨中获取 r 值并将其分配给 x 轴

Answer 1

没有最好的解决方案，但它有效。

小说明：我已经在列表中的索引上替换了月份，这是算法所必需的。我还用';'替换了空格分隔符分隔符，因为在不同的行中有不同数量的空格并且不方便。现在您的数据是：

Value;Month;Year 
103.4;April;2006
270.6;August;2006
51.9;December;2006

初始数据的文件是'data.csv'。

import pandas as pd
import sklearn.linear_model as ll

data = pd.read_csv('data.csv', sep=';')
X = data.ix[:,1:3]
y = data.ix[:,0]

month = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']

for i, m in enumerate(data.ix[:,1]):
    data.ix[i,1] = month.index(m)

X = data.ix[:,1:3]
lr = ll.LinearRegression()
lr.fit(X, y)

######### TEST DATA ##########
X_test = [[1, 2008], [2, 2008]]
X_test = pd.DataFrame(X_test, columns=['Month', 'Year'])

y_test = lr.predict(X_test)
print(y_test)

作为测试的结果，我得到了这个值

[69.23079837  80.63691725]

使用 Pearson 相关和线性回归的简单预测 python

simple prediction using Pearson correlation and linear regression with python

python

linear-regression

pearson-correlation