LinearRegressionWithUnivariate.py

""" Method to read the csv file using Pandas and later use this data for linear regression. """
""" Better run with Python 3+. """

# Library to read csv file effectively
import pandas
import matplotlib.pyplot as plt
import numpy as np

# Method to read the csv file
def load_data(file_name):
	column_names = ['area', 'price']
	# To read columns
	io = pandas.read_csv(file_name,names=column_names, header=None)
	x_val = (io.values[1:, 0])
	y_val = (io.values[1:, 1])
	size_array = len(y_val)
	for i in range(size_array):
		x_val[i] = float(x_val[i])
		y_val[i] = float(y_val[i])
		return x_val, y_val

# Call the method for a specific file
x_raw, y_raw = load_data('area-price.csv')
x_raw = x_raw.astype(np.float)
y_raw = y_raw.astype(np.float)
y = y_raw

# Modeling
w, b = 0.1, 0.1
num_epoch = 100
converge_rate = np.zeros([num_epoch , 1], dtype=float)
learning_rate = 1e-3
for e in range(num_epoch):
	# Calculate the gradient of the loss function with respect to arguments (model parameters) manually.
	y_predicted = w * x_raw + b
	grad_w, grad_b = (y_predicted - y).dot(x_raw), (y_predicted - y).sum()
	# Update parameters.
	w, b = w - learning_rate * grad_w, b - learning_rate * grad_b
	converge_rate[e] = np.mean(np.square(y_predicted-y))

print(w, b)
print(f"predicted function f(x) = x * {w} + {b}" )
calculatedprice = (10 * w) + b
print(f"price of plot with area 10 sqmtr = 10 * {w} + {b} = {calculatedprice}")