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# create a linear model for poverty levels for each state
# something like percent_of_fpl * x = percent_of_population
# What is X?
from sklearn import linear_model
import csv
import numpy as np
# max of each level, ignoring the last column 400+ which gives no new information
levels = np.array([100, 199, 399]).reshape(-1, 1)
levels_to_predict = np.array([138]).reshape(-1, 1)
predictions = []
# Data from https://www.kff.org/other/state-indicator/distribution-by-fpl
# Downloaded CSV and deleted top line
with open('data/raw_data.csv') as csvfile:
filereader = csv.reader(csvfile)
next(filereader, None) # skip the headers
for row in filereader:
state = row[0]
percent_in_level = list(map(float, row[1:4]))
percent_in_level = np.array(percent_in_level).reshape(-1, 1)
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(levels, percent_in_level)
# Make predictions using the testing set
levels_prediction = regr.predict(levels_to_predict)
predictions.append([state, round(levels_prediction[0][0]*100, 2)])
with open("data/interpolation_output.csv", "w") as f:
writer = csv.writer(f)
writer.writerows(predictions)