Stochastic Gradient Descent for Linear Regression

Stochastic Gradient Descent for Linear Regression#

Predict the miles per gallon from the curb weight and engine size, using Stochastic Gradient Descent and a linear model with L2 regularization. You need to code up SGD yourself such as in here.

import pandas as pd

# Replace 'imports-85.csv' with the actual file path if it's not in the current directory
dataset_url = "https://raw.githubusercontent.com/plotly/datasets/master/imports-85.csv"

# Read the CSV file into a Pandas DataFrame
df = pd.read_csv(dataset_url)

# Now, you can work with the 'df' DataFrame as needed
pd.set_option('display.max_columns', None)  # Show all columns
df.head(10)

	symboling	normalized-losses	make	fuel-type	aspiration	num-of-doors	body-style	drive-wheels	engine-location	wheel-base	length	width	height	curb-weight	engine-type	num-of-cylinders	engine-size	fuel-system	bore	stroke	compression-ratio	horsepower	peak-rpm	city-mpg	highway-mpg	price
0	3	NaN	alfa-romero	gas	std	two	convertible	rwd	front	88.6	168.8	64.1	48.8	2548	dohc	4	130	mpfi	3.47	2.68	9.0	111.0	5000.0	21	27	13495.0
1	3	NaN	alfa-romero	gas	std	two	convertible	rwd	front	88.6	168.8	64.1	48.8	2548	dohc	4	130	mpfi	3.47	2.68	9.0	111.0	5000.0	21	27	16500.0
2	1	NaN	alfa-romero	gas	std	two	hatchback	rwd	front	94.5	171.2	65.5	52.4	2823	ohcv	6	152	mpfi	2.68	3.47	9.0	154.0	5000.0	19	26	16500.0
3	2	164.0	audi	gas	std	four	sedan	fwd	front	99.8	176.6	66.2	54.3	2337	ohc	4	109	mpfi	3.19	3.40	10.0	102.0	5500.0	24	30	13950.0
4	2	164.0	audi	gas	std	four	sedan	4wd	front	99.4	176.6	66.4	54.3	2824	ohc	5	136	mpfi	3.19	3.40	8.0	115.0	5500.0	18	22	17450.0
5	2	NaN	audi	gas	std	two	sedan	fwd	front	99.8	177.3	66.3	53.1	2507	ohc	5	136	mpfi	3.19	3.40	8.5	110.0	5500.0	19	25	15250.0
6	1	158.0	audi	gas	std	four	sedan	fwd	front	105.8	192.7	71.4	55.7	2844	ohc	5	136	mpfi	3.19	3.40	8.5	110.0	5500.0	19	25	17710.0
7	1	NaN	audi	gas	std	four	wagon	fwd	front	105.8	192.7	71.4	55.7	2954	ohc	5	136	mpfi	3.19	3.40	8.5	110.0	5500.0	19	25	18920.0
8	1	158.0	audi	gas	turbo	four	sedan	fwd	front	105.8	192.7	71.4	55.9	3086	ohc	5	131	mpfi	3.13	3.40	8.3	140.0	5500.0	17	20	23875.0
9	0	NaN	audi	gas	turbo	two	hatchback	4wd	front	99.5	178.2	67.9	52.0	3053	ohc	5	131	mpfi	3.13	3.40	7.0	160.0	5500.0	16	22	NaN

target_variable_column = df[['city-mpg']]
feature_columns = df[['curb-weight', 'engine-size']]
# Convert selected columns to a NumPy array
y = target_variable_column.values
X = feature_columns.values
print(X)

[[2548  130]
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