Name: Deodorant Instant Liking Data
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Deodorant Instant Liking Data

Context

I have consolidated the survey results of Deodorants using excel into the Data_train_reduced.csv.

Content

Data_train_reduced.csv has survey results of 5 deodorants with several information. I have listed some of the key ones below.

Dependent variable

Instant Liking

Independent variables (some of them)

Respondent ID

Product ID

Where the first few words uttered by the user after using the particular for the first time positive or negative

Strength of the Deo (On a scale of 1 to 5)

Is the Deo addicitive (On a scale of 1 to 5)

Tables

Data Train Reduced

@kaggle.ramkumarr02_deodorant_instant_liking_data.data_train_reduced

107.07 kB
2,500 rows
64 columns

CREATE TABLE data_train_reduced (
  "respondent_id" BIGINT,
  "product_id" BIGINT,
  "product" VARCHAR,
  "instant_liking" BIGINT,
  "q1_1_personal_opinion_of_this_deodorant" BIGINT,
  "q2_all_words" BIGINT,
  "q3_1_strength_of_the_deodorant" BIGINT,
  "q4_1_artificial_chemical" BIGINT,
  "q4_2_attractive" BIGINT,
  "q4_3_bold" BIGINT,
  "q4_4_boring" BIGINT,
  "q4_5_casual" BIGINT,
  "q4_6_cheap" BIGINT,
  "q4_7_clean" BIGINT,
  "q4_8_easy_to_wear" BIGINT,
  "q4_9_elegant" BIGINT,
  "q4_10_feminine" BIGINT,
  "q4_11_for_someone_like_me" BIGINT,
  "q4_12_heavy" BIGINT,
  "q4_13_high_quality" BIGINT,
  "q4_14_long_lasting" BIGINT,
  "q4_15_masculine" BIGINT,
  "q4_16_memorable" BIGINT,
  "q4_17_natural" BIGINT,
  "q4_18_old_fashioned" BIGINT,
  "q4_19_ordinary" BIGINT,
  "q4_20_overpowering" BIGINT,
  "q4_21_sharp" BIGINT,
  "q4_22_sophisticated" BIGINT,
  "q4_23_upscale" BIGINT,
  "q4_24_well_rounded" BIGINT,
  "q5_1_deodorant_is_addictive" BIGINT,
  "q7" BIGINT,
  "q8_1" BIGINT,
  "q8_2" DOUBLE,
  "q8_5" BIGINT,
  "q8_6" BIGINT,
  "q8_7" DOUBLE,
  "q8_8" DOUBLE,
  "q8_9" DOUBLE,
  "q8_10" DOUBLE,
  "q8_11" BIGINT,
  "q8_12" DOUBLE,
  "q8_13" BIGINT,
  "q8_17" DOUBLE,
  "q8_18" DOUBLE,
  "q8_19" BIGINT,
  "q8_20" DOUBLE,
  "q9_how_likely_would_you_be_to_purchase_this_deodorant" BIGINT,
  "q10_prefer_this_deodorant_or_your_usual_deodorant" BIGINT,
  "q11_time_of_day_would_this_deodorant_be_appropriate" BIGINT,
  "q12_which_occasions_would_this_deodorant_be_appropriate" BIGINT,
  "q13_liking_after_30_minutes" BIGINT,
  "q14_deodorant_overall_on_a_scale_from_1_to_10" BIGINT,
  "valsegb" BIGINT,
  "s7_involved_in_the_selection_of_the_cosmetic_products" BIGINT,
  "s8_ethnic_background" BIGINT,
  "s9_education" BIGINT,
  "s10_income" BIGINT,
  "s11_marital_status" BIGINT,
  "s12_working_status" BIGINT,
  "s13_2" DOUBLE,
  "s13a_b_most_often" BIGINT,
  "s13b_bottles_of_deodorant_do_you_currently_own" BIGINT
);

Test Data

@kaggle.ramkumarr02_deodorant_instant_liking_data.test_data

162.24 kB
5,105 rows
58 columns

CREATE TABLE test_data (
  "respondent_id" BIGINT,
  "product_id" BIGINT,
  "product" VARCHAR,
  "q1_1_personal_opinion_of_this_deodorant" BIGINT,
  "q2_all_words" BIGINT,
  "q3_1_strength_of_the_deodorant" BIGINT,
  "q4_1_artificial_chemical" BIGINT,
  "q4_2_attractive" BIGINT,
  "q4_3_bold" BIGINT,
  "q4_4_boring" BIGINT,
  "q4_5_casual" BIGINT,
  "q4_6_cheap" BIGINT,
  "q4_7_clean" BIGINT,
  "q4_8_easy_to_wear" BIGINT,
  "q4_9_elegant" BIGINT,
  "q4_10_feminine" BIGINT,
  "q4_11_for_someone_like_me" BIGINT,
  "q4_12_heavy" BIGINT,
  "q4_13_high_quality" BIGINT,
  "q4_14_long_lasting" BIGINT,
  "q4_15_masculine" BIGINT,
  "q4_16_memorable" BIGINT,
  "q4_17_natural" BIGINT,
  "q4_18_old_fashioned" BIGINT,
  "q4_19_ordinary" BIGINT,
  "q4_20_overpowering" BIGINT,
  "q4_21_sharp" BIGINT,
  "q4_22_sophisticated" BIGINT,
  "q4_23_upscale" BIGINT,
  "q4_24_well_rounded" BIGINT,
  "q5_1_deodorant_is_addictive" BIGINT,
  "q7" BIGINT,
  "q8_1" BIGINT,
  "q8_2" BIGINT,
  "q8_5" BIGINT,
  "q8_6" BIGINT,
  "q8_8" BIGINT,
  "q8_11" BIGINT,
  "q8_12" BIGINT,
  "q8_13" BIGINT,
  "q8_19" BIGINT,
  "q8_20" BIGINT,
  "q9_how_likely_would_you_be_to_purchase_this_deodorant" BIGINT,
  "q10_prefer_this_deodorant_or_your_usual_deodorant" BIGINT,
  "q11_time_of_day_would_this_deodorant_be_appropriate" BIGINT,
  "q12_which_occasions_would_this_deodorant_be_appropriate" BIGINT,
  "q13_liking_after_30_minutes" BIGINT,
  "q14_deodorant_overall_on_a_scale_from_1_to_10" BIGINT,
  "valsegb" BIGINT,
  "s7_involved_in_the_selection_of_the_cosmetic_products" BIGINT,
  "s8_ethnic_background" BIGINT,
  "s9_education" BIGINT,
  "s10_income" BIGINT,
  "s11_marital_status" BIGINT,
  "s12_working_status" BIGINT,
  "s13_2" DOUBLE,
  "s13a_b_most_often" BIGINT,
  "s13b_bottles_of_deodorant_do_you_currently_own" BIGINT
);