Datasets:

phoenyx08
/

linear-regression-synthetic-set-1000

x float64 0.05 10	y float64 -3.13 53.1
3.745401	20.997409
9.507143	33.598279
7.319939	34.520777
5.986585	31.005976
1.560186	15.259605
1.559945	17.863712
0.580836	13.202537
8.661761	39.612065
6.01115	27.688197
7.080726	23.477736
0.205845	9.868548
9.699099	41.985283
8.324426	37.493387
2.123391	8.048126
1.81825	7.958591
1.834045	18.684922
3.042422	17.450703
5.247564	28.773979
4.31945	22.259668
2.912291	17.341801
6.118529	33.10627
1.394939	9.302061
2.921446	17.705667
3.663618	17.511288
4.5607	20.789968
7.85176	32.935298
1.996738	15.099251
5.142344	22.604462
5.924146	34.013291
0.464504	4.152728
6.075449	27.329712
1.705241	10.769689
0.650516	16.511695
9.488855	41.193768
9.65632	45.956344
8.083973	27.866105
3.046138	18.996733
0.976721	14.866678
6.84233	31.359576
4.401525	27.732739
1.220382	8.684896
4.951769	31.377929
0.343885	19.698089
9.093204	37.012021
2.5878	13.829787
6.625223	37.455202
3.117111	25.807748
5.20068	22.588081
5.467103	24.033926
1.848545	12.060983
9.695846	34.213209
7.751328	29.536389
9.394989	34.861759
8.948274	34.479969
5.979	27.753075
9.218742	40.436672
0.884925	17.84974
1.959829	8.86763
0.452273	13.504567
3.253303	17.316617
3.886773	20.356387
2.71349	19.871314
8.287375	30.392203
3.567533	21.398415
2.809345	17.664969
5.426961	28.456619
1.409242	13.378191
8.02197	47.353395
0.745506	6.420573
9.868869	38.886058
7.722448	30.912864
1.987157	11.177663
0.055221	4.006338
8.154614	41.486233
7.068573	38.842528
7.290072	29.661519
7.712703	29.832684
0.740447	11.948641
3.584657	16.785185
1.158691	14.220076
8.631034	38.223235
6.232981	21.236714
3.30898	26.318957
0.635584	18.203931
3.109823	14.820438
3.251833	16.442908
7.296062	33.965543
6.375575	30.986795
8.872127	41.345167
4.722149	33.578545
1.195942	10.301062
7.132448	27.972081
7.60785	26.730881
5.612772	22.990052
7.709672	33.818216
4.937956	33.255635
5.227328	22.707593
4.27541	23.082875
0.254191	7.807555
1.078914	16.718166

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Synthetic Linear Regression Dataset

This dataset consists of 1000 synthetic data points for training and evaluating simple linear regression models.

Usage

You can load this dataset manually using pandas:

import pandas as pd

df = pd.read_csv('synthetic_linear_data.csv')
print(df.head())

Downloads last month: 65

: A single input feature, randomly sampled from a uniform distribution between 0 and 10.

: Target value computed as:

Size of downloaded dataset files:

36.8 kB

Size of the auto-converted Parquet files:

19.8 kB

Number of rows:

1,000