### Predict which stock will provide greatest rate of return

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![](9.jpg)

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This *intermediate* level data set has 750 rows and 16 columns.

This dataset contains weekly data for the Dow Jones Industrial Index. It has been used in computational investing research.

In this dataset, each record (row) is data for a week. Each record also has the percentage of return that stock has in the following week (percent_change_next_weeks_price).

Ideally, this could be used to determine which stock will produce the greatest rate of return in the following week.

This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, **clustering** and **regression/classification modelling techniques**.

Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:

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### Data Dictionary

| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |

| 2 | stock | Stock: the stock symbol* | Qualitative | INTC, INTC, BA | 0 |

| 3 | date | Date: the last business day of the work (this is typically a Friday) | Quantitative | 40564, 40683, 40620 | 0 |

| 4 | open | Open: the price of the stock at the beginning of the week | Quantitative | $21.03, $23.32, $71.17 | 0 |

| 5 | high | High: the highest price of the stock during the week | Quantitative | $21.2, $23.96, $71.23 | 0 |

| 6 | low | Low: the lowest price of the stock during the week | Quantitative | $20.62, $23.08, $67.34 | 0 |

| 7 | close | Close: the price of the stock at the end of the week | Quantitative | $20.82, $23.22, $69.1 | 0 |

| 8 | volume | Volume: the number of shares of stock that traded hands in the week | Quantitative | 218479469, 387571150, 29746370 | 0 |

| 9 | percent_change_price | Percent_Change_Price: the percentage change in price throughout the week | Quantitative | -0.998573, -0.428816, -2.90853 | 0 |

| 10 | percent_change_volume_over_last_wk | Percent_Change_Volume_Over_Last_Week: the percentage change in the number of shares of stock that traded hands for this week compared to the previous week | Quantitative | -20.29526016, 12.41924755, 16.3954667 | 4 |

| 11 | previous_weeks_volume | Previous_Weeks_Volume: the number of shares of stock that traded hands in the previous week | Quantitative | 274111012, 344755154, 25556296 | 4 |

| 12 | next_weeks_open | Next_Weeks_Open: the opening price of the stock in the following week | Quantitative | $21.03, $22.92, $70.29 | 0 |

| 13 | next_weeks_close | Next_Weeks_Close: the closing price of the stock in the following week | Quantitative | $21.46, $22.21, $73.34 | 0 |

| 14 | percent_change_next_weeks_price | Percent_Change_Next_Weeks_Price: the percentage change in price of the stock in the | Quantitative | 2.0447, -3.09773, 4.33917 | 0 |

| 15 | days_to_next_dividend | Following Week Days_to_next_dividend: the number of days until the next dividend | Quantitative | 13, 75, 54 | 0 |

| 16 | percent_return_next_dividend | Percent_Return_Next_Dividend: the percentage of return on the next dividend | Quantitative | 0.864553, 0.904393, 0.607815 | 0 |

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Stock Symbols:<br/>

3M MMM<br/>

American Express AXP<br/>

Alcoa AA<br/>

AT&T T<br/>

Bank of America BAC<br/>

Boeing BA<br/>

Caterpillar CAT<br/>

Chevron CVX<br/>

Cisco Systems CSCO<br/>

Coca-Cola KO<br/>

DuPont DD<br/>

ExxonMobil XOM<br/>

General Electric GE<br/>

Hewlett-Packard HPQ<br/>

The Home Depot HD<br/>

Intel INTC<br/>

IBM IBM<br/>

Johnson & Johnson JNJ<br/>

JPMorgan Chase JPM<br/>

Kraft KRFT<br/>

McDonald's MCD<br/>

Merck MRK<br/>

Microsoft MSFT<br/>

Pfizer PFE<br/>

Procter & Gamble PG<br/>

Travelers TRV<br/>

United Technologies UTX<br/>

Verizon VZ<br/>

Wal-Mart WMT<br/>

Walt Disney DIS<br/>

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### Acknowledgement

This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Dow Jones Index Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index).

The UCI page mentions the following publication as the original source of the data set:

*Brown, M. S., Pelosi, M. & Dirska, H. (2013). Dynamic-radius Species-conserving Genetic Algorithm for the Financial Forecasting of Dow Jones Index Stocks. Machine Learning and Data Mining in Pattern Recognition, 7988, 27-41*