Group the following tyres into different groups based on characteristics parsed from the string like '235/75' 'MULTI', 'ECO', 'NRG'.

These grouping need to be usable for creation of categorical features in a machine learning model.

{'"235/75R17.5 LINE NRG T 143/141JVB"', '"235/75R17.5 MULTI T2 143/141JVG"', '"245/70R17.5 LINE NRG T 143/141JVB"', '"245/70R17.5 MULTI T2 143/141JVG"', '"265/70R19.5 MULTI Z 140/138MVG"', '"265/70R19.5 XTE2 143/141JVG"', '"385/55R22.5 ECOPIA H-TRAILER 001 160K T TL 047 BS"', '"385/55R22.5 HYBRIDHT3 TL 160K CT"', '"385/55R22.5 LINE NRG T 160KVB"', '"385/55R22.5 MULTI F 160KVG"', '"385/55R22.5 MULTI T2 160KVG"', '"385/65R22.5 MULTI T 160KVM"', '"385/65R22.5 R109ECOPIA TL 160K BS"', '"385/65R22.5 WORKS T 160KVM"', '"385/65R22.5 XTE3 160JVG"', '235/75R17.5 LINE NRG T 143/141JVB', '245/70R17.5 LINE NRG T 143/141JVB', '245/70R17.5 MULTI T2 143/141JVG', '265/70R19.5 MULTI Z 140/138MVG', '265/70R19.5 XTE2 143/141JVG', '275/70R22.5 X ENERGY TA2 152/148J', '315/70R22.5 LINE NRG Z2 156/150LVB', '385/55R22.5 ECOPIA H-TRAILER 001 160K T TL 047 BS', '385/55R22.5 LINE NRG T 160KVB', '385/55R22.5 MULTI F 160KVG', '385/55R22.5 MULTI T2 160KVG', '385/55R22.5 R168 TL 160K BS', '385/65 R 22.5 FT522 T TL 160/0 J 053 FI', '385/65R22.5 LINE NRG T 160KVB', '385/65R22.5 MULTI HL T 164KVG', '385/65R22.5 MULTI T 160KVG', '385/65R22.5 MULTI T 160KVM', '385/65R22.5 R109ECOPIA TL 160K BS', '385/65R22.5 WORKS T 160KVM', '385/65R22.5 XTE3 160JVG', '385/65R22.5 XZY3 160K', 'R LR 385/65R22.5 MULTI T R1/G1', 'R LR 385/65R22.5 MULTI T R2/G1', 'R MI 315/70R22.5 MULTI D 154/150L', 'R MI 385/55R22.5 LINE NRG T 160K', 'R MI 385/55R22.5 MULTI T 160K', 'R MI 385/55R22.5 MULTI T2 160K', 'R MI 385/65R22.5 LINE NRG T 160K', 'R MI 385/65R22.5 MULTI T 160K', 'R MI 385/65R22.5 XM286 160K', 'R MI 385/65R22.5 XTE3 160J', 'no Product'}

Produce python code to extract these features from dataframe column containing the product description strings. Write your response with exactly 7 paragraphs, separated with the markdown divider: ***.
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