Collection Of Classification & Regression Datasets

pre_score Pre Score	age_in_years Age In Years	percentage_in_10_class Percentage In 10 Class	percentage_in_12_class Percentage In 12 Class	percentage_in_under_graduate Percentage In Under Graduate	percentage_mba Percentage MBA	post_score Post Score	gender Gender	state STATE	previous_degree Previous Degree	marital_status Marital Status	place_you_belong_to Place You Belong To	perceived_job_skill Perceived#Job#Skill	specialization Specialization
75	22	71	74.8	72	61	83.33333333	Male	Central Zone	Engineering	Single	Urban	prefered skills	Marketing
71.66666667	25	77.6	82.6	76.9	66.85	76.66666667	Male	Central Zone	Engineering	Single	Semi Urban	prefered skills	LOS
76.66666667	26	93.2	83.8	77	74.97	75	Female	Central Zone	Engineering	Single	Urban	desired skills	Finance
66.66666667	22	91.2	80	67	68.3	60	Male	Central Zone	Commerce	Single	Semi Urban	prefered skills	Finance
71.66666667	24	79.8	61.6	60.33	69.28	76.66666667	Female	Central Zone	Engineering	Single	Urban	prefered skills	Finance
83.33333333	23	67	62	67	63	80	Female	Central Zone	Commerce	Single	Urban	desired skills	Marketing
73.33333333	22	82	70	76.5	67	81.66666667	Male	Central Zone	Engineering	Single	Semi Urban	prefered skills	Marketing
85	26	86.4	80.4	70.22	70.48	76.66666667	Male	Central Zone	Engineering	Single	Semi Urban	desired skills	LOS
71.66666667	22	95	78.6	75.43	68.54	88.33333333	Male	Central Zone	Engineering	Single	Urban	prefered skills	Marketing
75	22	71	74.8	72	61	83.33333333	Male	Central Zone	Engineering	Single	Urban	prefered skills	Marketing


CREATE TABLE mba_admissions (
  "pre_score" DOUBLE,
  "age_in_years" BIGINT,
  "percentage_in_10_class" DOUBLE,
  "percentage_in_12_class" DOUBLE,
  "percentage_in_under_graduate" DOUBLE,
  "percentage_mba" DOUBLE,
  "post_score" DOUBLE,
  "gender" VARCHAR,
  "state" VARCHAR,
  "previous_degree" VARCHAR,
  "marital_status" VARCHAR,
  "place_you_belong_to" VARCHAR,
  "perceived_job_skill" VARCHAR,
  "specialization" VARCHAR
);