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Nifty 50 Stocks Price Dataset

Nifty 50 Stocks Prices

@kaggle.tadakasuryateja_nifty_50_stocks

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About this Dataset

Nifty 50 Stocks Price Dataset

A dataset containing prices of Nifty 50 Stocks. Contains open, high, low, close prices, volume of the stock.
This dataset is meant to use for predicting the stock prices in future.
You may use linear regression or neural networks.****

Tables

Itc

@kaggle.tadakasuryateja_nifty_50_stocks.itc
  • 187.4 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE itc (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Kotakbank

@kaggle.tadakasuryateja_nifty_50_stocks.kotakbank
  • 190.82 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE kotakbank (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Lt

@kaggle.tadakasuryateja_nifty_50_stocks.lt
  • 205.26 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE lt (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Lupin

@kaggle.tadakasuryateja_nifty_50_stocks.lupin
  • 187.09 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE lupin (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Mandm

@kaggle.tadakasuryateja_nifty_50_stocks.mandm
  • 245.79 KB
  • 5827 rows
  • 10 columns
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CREATE TABLE mandm (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Maruti

@kaggle.tadakasuryateja_nifty_50_stocks.maruti
  • 203.09 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE maruti (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Mcdowell N

@kaggle.tadakasuryateja_nifty_50_stocks.mcdowell_n
  • 5.67 KB
  • 10 columns
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CREATE TABLE mcdowell_n (
  "date" VARCHAR,
  "stock_name" VARCHAR,
  "n_1_open" VARCHAR,
  "n_2_high" VARCHAR,
  "n_3_low" VARCHAR,
  "n_4_close" VARCHAR,
  "n_5_adjusted_close" VARCHAR,
  "n_6_volume" VARCHAR,
  "n_7_dividend_amount" VARCHAR,
  "n_8_split_coefficient" VARCHAR
);

Nestleind

@kaggle.tadakasuryateja_nifty_50_stocks.nestleind
  • 199.67 KB
  • 4431 rows
  • 10 columns
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CREATE TABLE nestleind (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Ntpc

@kaggle.tadakasuryateja_nifty_50_stocks.ntpc
  • 167.19 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE ntpc (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Ongc

@kaggle.tadakasuryateja_nifty_50_stocks.ongc
  • 188.62 KB
  • 4431 rows
  • 10 columns
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CREATE TABLE ongc (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Powergrid

@kaggle.tadakasuryateja_nifty_50_stocks.powergrid
  • 138.41 KB
  • 3747 rows
  • 10 columns
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CREATE TABLE powergrid (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Reliance

@kaggle.tadakasuryateja_nifty_50_stocks.reliance
  • 202.84 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE reliance (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Sbin

@kaggle.tadakasuryateja_nifty_50_stocks.sbin
  • 194.76 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE sbin (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Sunpharma

@kaggle.tadakasuryateja_nifty_50_stocks.sunpharma
  • 198.92 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE sunpharma (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Tatamotors

@kaggle.tadakasuryateja_nifty_50_stocks.tatamotors
  • 215.51 KB
  • 4433 rows
  • 10 columns
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CREATE TABLE tatamotors (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Tatasteel

@kaggle.tadakasuryateja_nifty_50_stocks.tatasteel
  • 190.6 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE tatasteel (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Tcs

@kaggle.tadakasuryateja_nifty_50_stocks.tcs
  • 197.53 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE tcs (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Techm

@kaggle.tadakasuryateja_nifty_50_stocks.techm
  • 172.41 KB
  • 4021 rows
  • 10 columns
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CREATE TABLE techm (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Ultracemco

@kaggle.tadakasuryateja_nifty_50_stocks.ultracemco
  • 196.76 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE ultracemco (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Unionbank

@kaggle.tadakasuryateja_nifty_50_stocks.unionbank
  • 173.37 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE unionbank (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Vedl

@kaggle.tadakasuryateja_nifty_50_stocks.vedl
  • 188.19 KB
  • 4430 rows
  • 10 columns
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CREATE TABLE vedl (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Wipro

@kaggle.tadakasuryateja_nifty_50_stocks.wipro
  • 208.83 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE wipro (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

Zeel

@kaggle.tadakasuryateja_nifty_50_stocks.zeel
  • 200.64 KB
  • 4432 rows
  • 10 columns
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CREATE TABLE zeel (
  "date" TIMESTAMP,
  "stock_name" VARCHAR,
  "n_1_open" DOUBLE,
  "n_2_high" DOUBLE,
  "n_3_low" DOUBLE,
  "n_4_close" DOUBLE,
  "n_5_adjusted_close" DOUBLE,
  "n_6_volume" BIGINT,
  "n_7_dividend_amount" DOUBLE,
  "n_8_split_coefficient" DOUBLE
);

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