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]
 [2548  130]
 [2823  152]
 [2337  109]
 [2824  136]
 [2507  136]
 [2844  136]
 [2954  136]
 [3086  131]
 [3053  131]
 [2395  108]
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 [2710  164]
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 [3230  209]
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 [1488   61]
 [1874   90]
 [1909   90]
 [1876   90]
 [1876   90]
 [2128   98]
 [1967   90]
 [1989   90]
 [1989   90]
 [2191   98]
 [2535  122]
 [2811  156]
 [1713   92]
 [1819   92]
 [1837   79]
 [1940   92]
 [1956   92]
 [2010   92]
 [2024   92]
 [2236  110]
 [2289  110]
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 [1874   90]
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 [2910  140]
 [1918   92]
 [1944   92]
 [2004   92]
 [2145   98]
 [2370  110]
 [2328  122]
 [2833  156]
 [2921  156]
 [2926  156]
 [2365  122]
 [2405  122]
 [2403  110]
 [2403  110]
 [1889   97]
 [2017  103]
 [1918   97]
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 [2008   97]
 [2324  120]
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 [3095  181]
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 [3075  120]
 [3252  152]
 [3285  120]
 [3485  152]
 [3075  120]
 [3252  152]
 [3130  134]
 [1918   90]
 [2128   98]
 [1967   90]
 [1989   90]
 [2191   98]
 [2535  122]
 [2818  156]
 [2778  151]
 [2756  194]
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 [2800  194]
 [3366  203]
 [2579  132]
 [2460  132]
 [2658  121]
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 [2808  121]
 [2847  121]
 [2050   97]
 [2120  108]
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 [1985   92]
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 [2015   92]
 [2280   92]
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 [3110   92]
 [2081   98]
 [2109   98]
 [2275  110]
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 [2540  146]
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 [2326  122]
 [2480  110]
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 [2458  122]
 [2976  171]
 [3016  171]
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 [3151  161]
 [2261   97]
 [2209  109]
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 [2300  109]
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 [2661  136]
 [2579   97]
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 [3045  130]
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 [3217  145]
 [3062  141]]