rainfall1.py

import pandas as pd
import numpy as np
import sklearn as sk
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

data = pd.read_csv("austin_final.csv")
X = data.drop(['PrecipitationSumInches'], axis = 1)
Y = data['PrecipitationSumInches']

Y = Y.values.reshape(-1, 1)
day_index = 798
days = [i for i in range(Y.size)]


clf = LinearRegression()

clf.fit(X, Y)
inp = np.array([[74], [60], [45], [67], [49], [43], [33], [45],
				[57], [29.68], [10], [7], [2], [0], [20], [4], [31]])
inp = inp.reshape(1, -1)
print('The precipitation in inches for the input is:', clf.predict(inp))
print("the precipitation trend graph: ")
plt.scatter(days, Y, color = 'b')
plt.scatter(days[day_index], Y[day_index], color ='r')
plt.title("Precipitation level")
plt.xlabel("Days")
plt.ylabel("Precipitation in inches")


plt.show()
x_vis = X.filter(['TempAvgF', 'DewPointAvgF', 'HumidityAvgPercent',
				'SeaLevelPressureAvgInches', 'VisibilityAvgMiles',
				'WindAvgMPH'], axis = 1)
print("Precipitation vs selected attributes graph: ")

for i in range(x_vis.columns.size):
	plt.subplot(3, 2, i + 1)
	plt.scatter(days, x_vis[x_vis.columns.values[i][:100]],
											color = 'b')

	plt.scatter(days[day_index],
				x_vis[x_vis.columns.values[i]][day_index],
				color ='r')

	plt.title(x_vis.columns.values[i])

plt.show()