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Update predictions_samarth_kumar (1).py #84

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5 changes: 3 additions & 2 deletions ...sions/SamarthKumar__-20240416T151447Z-001/SamarthKumar__/predictions_samarth_kumar (1).py
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Expand Up @@ -253,7 +253,7 @@
# Assuming you have x_projection_Ethereum containing close prices,
# linReg_prediction_Ethereum containing predicted values,
# and projection_dates containing corresponding dates.
linReg_prediction_Ethereum = [3129, 3156, 3181, 3189, 3171, 3114, 3105, 3110, 3126, 3152]
linReg_prediction_Ethereum = [2981, 3068, 3057, 3155, 3171, 3114, 3202, 3110, 3126, 3152]

# Example: Generating sample projection dates
projection_dates = pd.date_range(start='2024-04-17', periods=len(x_projection_Ethereum), freq='D')
Expand Down Expand Up @@ -294,6 +294,7 @@

# Example: Generating sample projection dates
projection_dates = pd.date_range(start='2024-04-17', periods=len(linReg_prediction_Arbitrum), freq='D')
linReg_prediction_Arbitrum = [1.11, 1.13, 1.12, 1.23, 1.20, 1.21, 1.18, 1.17, 1.18, 1.19]

# Define the half range
half_range = 0.125 # Half of 0.25 (25 points)
Expand Down Expand Up @@ -336,7 +337,7 @@
import pandas as pd
import numpy as np
import plotly.graph_objects as go
linReg_prediction_Chainlink = [14.69, 14.40, 14.55, 15.15, 16.70, 17.31, 18.00, 18.71, 18.78, 18.48]
linReg_prediction_Chainlink = [13.13, 13.55, 14.11, 14.97, 15.06, 15.34, 15.35, 15.56, 16.13, 16.83]
projection_dates = pd.date_range(start='2024-04-17', periods=len(linReg_prediction_Chainlink), freq='D')
half_range = 0.125 # Half of 0.25 (25 points)
lower_bounds = [value - half_range for value in linReg_prediction_Chainlink]
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