ΚΙΝΟ Scaler (Διαίρεση με το 100 στα δεδομένα). (KINO Scaler (Division by 100 on the data))

Θα διαιρέσουμε τα δεδομένα με το 100 για να τα προετοιμάσουμε για την πρόβλεψη. We will divide the data by 100 to prepare it for prediction.

data = [

    [7, 8, 9, 11, 14, 15, 17, 23, 24, 30, 32, 33, 35, 51, 52, 56, 59, 64, 66, 74],
    [16, 20, 21, 28, 29, 33, 34, 36, 37, 38, 41, 44, 52, 63, 66, 71, 72, 74, 77, 80],
    [2, 11, 13, 22, 23, 27, 29, 30, 39, 42, 43, 50, 55, 62, 64, 68, 69, 71, 74, 75],
    [1, 3, 6, 8, 12, 13, 17, 19, 21, 26, 30, 31, 34, 41, 44, 45, 53, 58, 59, 60],
    [2, 5, 9, 10, 11, 13, 14, 17, 21, 24, 29, 30, 31, 37, 51, 55, 56, 63, 66, 80],
    [10, 12, 21, 23, 24, 34, 37, 41, 49, 50, 51, 56, 59, 60, 68, 71, 73, 76, 79, 80],
    [1, 7, 9, 11, 13, 17, 23, 26, 28, 36, 38, 39, 47, 50, 63, 68, 73, 74, 75, 77],
    [1, 2, 8, 11, 20, 24, 28, 30, 32, 34, 38, 43, 44, 47, 51, 58, 60, 64, 72, 76],
    [3, 4, 14, 19, 21, 23, 27, 29, 34, 35, 38, 48, 51, 55, 56, 60, 61, 70, 74, 76],
    [9, 10, 13, 14, 15, 17, 24, 26, 29, 32, 35, 40, 48, 55, 60, 61, 70, 76, 77, 80],

]

divided_data = [[num / 100 for num in sublist] for sublist in data]

for sublist in divided_data:
    line = ", ".join(f"{value:.2f}" for value in sublist)
    print(f"[{line}],")

Αποτελέσματα:
Result:

Έχω διαίρεσει τα δεδομένα του ΚΙΝΟ με το 100, προετοιμάζοντάς τα για πιθανή χρήση σε μοντέλα πρόβλεψης ή άλλες αναλυτικές διαδικασίες. I have divided the KINO data by 100, preparing it for potential use in predictive models or other analytical processes.

Αυτή η μετατροπή βοηθάει στην κανονικοποίηση των δεδομένων, κάνοντας τις τιμές να κυμαίνονται μεταξύ 0 και 1, πράγμα που είναι συχνά ωφέλιμο στην επεξεργασία δεδομένων και την εφαρμογή στατιστικών και μαθηματικών μοντέλων. This transformation helps normalize the data, making the values range between 0 and 1, which is often beneficial in data processing and the application of statistical and mathematical models.

[0.07, 0.08, 0.09, 0.11, 0.14, 0.15, 0.17, 0.23, 0.24, 0.30, 0.32, 0.33, 0.35,

 0.51, 0.52, 0.56, 0.59, 0.64, 0.66, 0.74],

[0.16, 0.20, 0.21, 0.28, 0.29, 0.33, 0.34, 0.36, 0.37, 0.38, 0.41, 0.44, 0.52,

 0.63, 0.66, 0.71, 0.72, 0.74, 0.77, 0.80],

[0.02, 0.11, 0.13, 0.22, 0.23, 0.27, 0.29, 0.30, 0.39, 0.42, 0.43, 0.50, 0.55,

 0.62, 0.64, 0.68, 0.69, 0.71, 0.74, 0.75],

[0.01, 0.03, 0.06, 0.08, 0.12, 0.13, 0.17, 0.19, 0.21, 0.26, 0.30, 0.31, 0.34,

 0.41, 0.44, 0.45, 0.53, 0.58, 0.59, 0.60],

[0.02, 0.05, 0.09, 0.10, 0.11, 0.13, 0.14, 0.17, 0.21, 0.24, 0.29, 0.30, 0.31,

 0.37, 0.51, 0.55, 0.56, 0.63, 0.66, 0.80],

[0.10, 0.12, 0.21, 0.23, 0.24, 0.34, 0.37, 0.41, 0.49, 0.50, 0.51, 0.56, 0.59,

 0.60, 0.68, 0.71, 0.73, 0.76, 0.79, 0.80],

[0.01, 0.07, 0.09, 0.11, 0.13, 0.17, 0.23, 0.26, 0.28, 0.36, 0.38, 0.39, 0.47,

 0.50, 0.63, 0.68,  0.73, 0.74, 0.75, 0.77],

[0.01, 0.02, 0.08, 0.11, 0.20, 0.24, 0.28, 0.30, 0.32, 0.34, 0.38, 0.43, 0.44,

 0.47, 0.51, 0.58, 0.60, 0.64, 0.72, 0.76],

[0.03, 0.04, 0.14, 0.19, 0.21, 0.23, 0.27, 0.29, 0.34, 0.35, 0.38, 0.48, 0.51,

 0.55, 0.56, 0.60, 0.61, 0.70, 0.74, 0.76],

[0.09, 0.10, 0.13, 0.14, 0.15, 0.17, 0.24, 0.26, 0.29, 0.32, 0.35, 0.40, 0.48,

 0.55, 0.60, 0.61, 0.70, 0.76, 0.77, 0.80],