39 lines
1.1 KiB
CSV
39 lines
1.1 KiB
CSV
# Sample dataset for training a machine learning model.
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# This dataset contains features and a target variable for demonstration purposes.
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# You can replace this with your own dataset.
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# Feature 1: Numerical feature representing age (e.g., of a customer).
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# Feature 2: Categorical feature representing location (e.g., city).
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# Feature 3: Binary feature representing whether a customer subscribed (1) or not (0).
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# Target Variable: Represents the outcome we want to predict (e.g., customer churn).
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age,location,subscribed,target
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25,New York,1,0
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30,Los Angeles,0,1
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40,Chicago,1,0
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22,Houston,0,0
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35,New York,1,1
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28,Los Angeles,0,0
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45,Chicago,1,1
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31,Houston,0,0
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27,New York,1,0
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33,Los Angeles,0,1
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42,Chicago,1,1
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24,Houston,0,0
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37,New York,1,0
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29,Los Angeles,0,0
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47,Chicago,1,1
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32,Houston,0,1
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26,New York,1,0
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34,Los Angeles,0,1
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41,Chicago,1,0
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23,Houston,0,0
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# Add more rows to create a robust dataset for training.
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# Consider increasing the number of rows and the diversity of data for better model performance.
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# Remember to clean and preprocess your data before training.
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# [ADD_MORE_DATA_HERE]
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# Example:
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# 50,San Francisco,1,1
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# 60,Seattle,0,0
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# 70,Boston,1,1
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# 80,Miami,0,0 |