In this Data Science Project we will predict Bitcoin Price for the next 30 days with Machine Learning model Support Vector Machines(Regression).
You can download the data set we need for this task from here:
Let’s start with importing libraries
import numpy as np
import pandas as pd
df = pd.read_csv("bitcoin.csv")
df.head()
Remove the date column
df.drop(['Date'],1,inplace=True)
Now lets create a variable to predict ‘n’ days out there in the future
predictionDays = 30 # Create another column shifted 'n' units up df['Prediction'] = df[['Price']].shift(-predictionDays) # show the first 5 rows df.head()
To Show last 5 rows of new data set
df.tail()
# Create the independent dada set # Here we will convert the data frame into a numpy array and drp the prediction column x = np.array(df.drop(['Prediction'],1)) # Remove the last 'n' rows where 'n' is the predictionDays x = x[:len(df)-predictionDays] print(x)
#Output [[ 7881.84668 ] [ 7987.371582] [ 8052.543945] [ 8673.21582 ] [ 8805.77832 ] [ 8719.961914] [ 8659.487305] [ 8319.472656] [ 8574.501953] [ 8564.016602] [ 8742.958008] [ 8208.995117] [ 7707.770996] [ 7824.231445] [ 7822.023438] [ 8043.951172] [ 7954.12793 ] [ 7688.077148] [ 8000.32959 ] [ 7927.714355] [ 8145.857422] [ 8230.923828] [ 8693.833008] [ 8838.375 ] [ 8994.488281] [ 9320.352539] [ 9081.762695] [ 9273.521484] [ 9527.160156] [10144.55664 ] [10701.69141 ] [10855.37109 ] [11011.10254 ] [11790.91699 ] [13016.23145 ] [11182.80664 ] [12407.33203 ] [11959.37109 ] [10817.15527 ] [10583.13477 ] [10801.67773 ] [11961.26953 ] [11215.4375 ] [10978.45996 ] [11208.55078 ] [11450.84668 ] [12285.95801 ] [12573.8125 ] [12156.5127 ] [11358.66211 ] [11815.98633 ] [11392.37891 ] [10256.05859 ] [10895.08984 ] [ 9477.641602] [ 9693.802734] [10666.48242 ] [10530.73242 ] [10767.13965 ] [10599.10547 ] [10343.10645 ] [ 9900.767578] [ 9811.925781] [ 9911.841797] [ 9870.303711] [ 9477.677734] [ 9552.860352] [ 9519.145508] [ 9607.423828] [10085.62793 ] [10399.66895 ] [10518.17481 ] [10821.72656 ] [10970.18457 ] [11805.65332 ] [11478.16895 ] [11941.96875 ] [11966.40723 ] [11862.93652 ] [11354.02441 ] [11523.5791 ] [11382.61621 ] [10895.83008 ] [10051.7041 ] [10311.5459 ] [10374.33887 ] [10231.74414 ] [10345.81055 ] [10916.05371 ] [10763.23242 ] [10138.04981 ] [10131.05566 ] [10407.96484 ] [10159.96094 ] [10138.51758 ] [10370.82031 ] [10185.5 ] [ 9754.422852] [ 9510.200195] [ 9598.173828] [ 9630.664063] [ 9757.970703] [10346.76074 ] [10623.54004 ] [10594.49316 ] [10575.5332 ] [10353.30273 ] [10517.25488 ] [10441.27637 ] [10334.97461 ] [10115.97559 ] [10178.37207 ] [10410.12695 ] [10360.54688 ] [10358.04883 ] [10347.71289 ] [10276.79395 ] [10241.27246 ] [10198.24805 ] [10266.41504 ] [10181.6416 ] [10019.7168 ] [10070.39258 ] [ 9729.324219] [ 8620.566406] [ 8486.993164] [ 8118.967773] [ 8251.845703] [ 8245.915039] [ 8104.185547] [ 8293.868164] [ 8343.276367] [ 8393.041992] [ 8259.992188] [ 8205.939453] [ 8151.500488] [ 7988.155762] [ 8245.623047] [ 8228.783203] [ 8595.740234] [ 8586.473633] [ 8321.756836] [ 8336.555664] [ 8321.005859] [ 8374.686523] [ 8205.369141] [ 8047.526855] [ 8103.911133] [ 7973.20752 ] [ 7988.560547] [ 8222.078125] [ 8243.720703] [ 8078.203125] [ 7514.671875] [ 7493.48877 ] [ 8660.700195] [ 9244.972656] [ 9551.714844] [ 9256.148438] [ 9427.6875 ] [ 9205.726563] [ 9199.584961] [ 9261.104492] [ 9324.717773] [ 9235.354492] [ 9412.612305] [ 9342.527344] [ 9360.879883] [ 9267.561523] [ 8804.880859] [ 8813.582031] [ 9055.526367] [ 8757.788086] [ 8815.662109] [ 8808.262695] [ 8708.094727] [ 8491.992188] [ 8550.760742] [ 8577.975586] [ 8309.286133] [ 8206.145508] [ 8027.268066] [ 7642.75 ] [ 7296.577637] [ 7397.796875] [ 7047.916992] [ 7146.133789] [ 7218.371094] [ 7531.663574] [ 7463.105957] [ 7761.243652] [ 7569.629883] [ 7424.29248 ] [ 7321.988281] [ 7320.145508] [ 7252.034668] [ 7448.307617] [ 7546.996582] [ 7556.237793] [ 7564.345215] [ 7400.899414] [ 7278.119629] [ 7217.427246] [ 7243.134277] [ 7269.68457 ] [ 7124.673828] [ 7152.301758] [ 6932.480469] [ 6640.515137] [ 7276.802734] [ 7202.844238] [ 7218.816406] [ 7191.158691] [ 7511.588867] [ 7355.628418] [ 7322.532227] [ 7275.155762] [ 7238.966797] [ 7290.088379] [ 7317.990234] [ 7422.652832] [ 7292.995117] [ 7193.599121] [ 7200.174316] [ 6985.470215] [ 7344.884277] [ 7410.656738] [ 7411.317383] [ 7769.219238] [ 8163.692383] [ 8079.862793] [ 7879.071289] [ 8166.554199] [ 8037.537598] [ 8192.494141] [ 8144.194336] [ 8827.764648] [ 8807.010742] [ 8723.786133] [ 8929.038086] [ 8942.808594] [ 8706.245117] [ 8657.642578] [ 8745.894531] [ 8680.875977] [ 8406.515625] [ 8445.43457 ] [ 8367.847656] [ 8596.830078] [ 8909.819336] [ 9358.589844] [ 9316.629883] [ 9508.993164] [ 9350.529297] [ 9392.875 ] [ 9344.365234] [ 9293.521484] [ 9180.962891] [ 9613.423828] [ 9729.801758] [ 9795.943359] [ 9865.119141] [10116.67383 ] [ 9856.611328] [10208.23633 ] [10326.05469 ] [10214.37988 ] [10312.11621 ] [ 9889.424805] [ 9934.433594] [ 9690.142578] [10141.99609 ] [ 9633.386719] [ 9608.475586] [ 9686.441406] [ 9663.181641] [ 9924.515625] [ 9650.174805] [ 9341.705078] [ 8820.522461] [ 8784.494141] [ 8672.455078] [ 8599.508789] [ 8562.454102] [ 8869.669922] [ 8787.786133] [ 8755.246094] [ 9078.762695] [ 9122.545898] [ 8909.954102] [ 8108.116211] [ 7923.644531] [ 7909.729492] [ 7911.430176] [ 4970.788086] [ 5563.707031] [ 5200.366211] [ 5392.314941] [ 5014.47998 ] [ 5225.629395] [ 5238.438477] [ 6191.192871] [ 6198.77832 ] [ 6185.066406] [ 5830.254883] [ 6416.314941] [ 6734.803711] [ 6681.062988] [ 6716.44043 ] [ 6469.79834 ] [ 6242.193848] [ 5922.042969] [ 6429.841797] [ 6438.644531] [ 6606.776367] [ 6793.624512] [ 6733.387207] [ 6867.527344] [ 6791.129395] [ 7271.78125 ] [ 7176.414551] [ 7334.098633] [ 7302.089355] [ 6865.493164] [ 6859.083008] [ 6971.091797] [ 6845.037598] [ 6842.427734] [ 6642.109863] [ 7116.804199] [ 7096.18457 ] [ 7257.665039] [ 7189.424805] [ 6881.958496] [ 6880.323242] [ 7117.20752 ] [ 7429.724609]]
# Create the dependent data set # convert the data frame into a numpy array y = np.array(df['Prediction']) # Get all the values except last 'n' rows y = y[:-predictionDays] print(y)
#Output [10701.69141 10855.37109 11011.10254 11790.91699 13016.23145 11182.80664 12407.33203 11959.37109 10817.15527 10583.13477 10801.67773 11961.26953 11215.4375 10978.45996 11208.55078 11450.84668 12285.95801 12573.8125 12156.5127 11358.66211 11815.98633 11392.37891 10256.05859 10895.08984 9477.641602 9693.802734 10666.48242 10530.73242 10767.13965 10599.10547 10343.10645 9900.767578 9811.925781 9911.841797 9870.303711 9477.677734 9552.860352 9519.145508 9607.423828 10085.62793 10399.66895 10518.17481 10821.72656 10970.18457 11805.65332 11478.16895 11941.96875 11966.40723 11862.93652 11354.02441 11523.5791 11382.61621 10895.83008 10051.7041 10311.5459 10374.33887 10231.74414 10345.81055 10916.05371 10763.23242 10138.04981 10131.05566 10407.96484 10159.96094 10138.51758 10370.82031 10185.5 9754.422852 9510.200195 9598.173828 9630.664063 9757.970703 10346.76074 10623.54004 10594.49316 10575.5332 10353.30273 10517.25488 10441.27637 10334.97461 10115.97559 10178.37207 10410.12695 10360.54688 10358.04883 10347.71289 10276.79395 10241.27246 10198.24805 10266.41504 10181.6416 10019.7168 10070.39258 9729.324219 8620.566406 8486.993164 8118.967773 8251.845703 8245.915039 8104.185547 8293.868164 8343.276367 8393.041992 8259.992188 8205.939453 8151.500488 7988.155762 8245.623047 8228.783203 8595.740234 8586.473633 8321.756836 8336.555664 8321.005859 8374.686523 8205.369141 8047.526855 8103.911133 7973.20752 7988.560547 8222.078125 8243.720703 8078.203125 7514.671875 7493.48877 8660.700195 9244.972656 9551.714844 9256.148438 9427.6875 9205.726563 9199.584961 9261.104492 9324.717773 9235.354492 9412.612305 9342.527344 9360.879883 9267.561523 8804.880859 8813.582031 9055.526367 8757.788086 8815.662109 8808.262695 8708.094727 8491.992188 8550.760742 8577.975586 8309.286133 8206.145508 8027.268066 7642.75 7296.577637 7397.796875 7047.916992 7146.133789 7218.371094 7531.663574 7463.105957 7761.243652 7569.629883 7424.29248 7321.988281 7320.145508 7252.034668 7448.307617 7546.996582 7556.237793 7564.345215 7400.899414 7278.119629 7217.427246 7243.134277 7269.68457 7124.673828 7152.301758 6932.480469 6640.515137 7276.802734 7202.844238 7218.816406 7191.158691 7511.588867 7355.628418 7322.532227 7275.155762 7238.966797 7290.088379 7317.990234 7422.652832 7292.995117 7193.599121 7200.174316 6985.470215 7344.884277 7410.656738 7411.317383 7769.219238 8163.692383 8079.862793 7879.071289 8166.554199 8037.537598 8192.494141 8144.194336 8827.764648 8807.010742 8723.786133 8929.038086 8942.808594 8706.245117 8657.642578 8745.894531 8680.875977 8406.515625 8445.43457 8367.847656 8596.830078 8909.819336 9358.589844 9316.629883 9508.993164 9350.529297 9392.875 9344.365234 9293.521484 9180.962891 9613.423828 9729.801758 9795.943359 9865.119141 10116.67383 9856.611328 10208.23633 10326.05469 10214.37988 10312.11621 9889.424805 9934.433594 9690.142578 10141.99609 9633.386719 9608.475586 9686.441406 9663.181641 9924.515625 9650.174805 9341.705078 8820.522461 8784.494141 8672.455078 8599.508789 8562.454102 8869.669922 8787.786133 8755.246094 9078.762695 9122.545898 8909.954102 8108.116211 7923.644531 7909.729492 7911.430176 4970.788086 5563.707031 5200.366211 5392.314941 5014.47998 5225.629395 5238.438477 6191.192871 6198.77832 6185.066406 5830.254883 6416.314941 6734.803711 6681.062988 6716.44043 6469.79834 6242.193848 5922.042969 6429.841797 6438.644531 6606.776367 6793.624512 6733.387207 6867.527344 6791.129395 7271.78125 7176.414551 7334.098633 7302.089355 6865.493164 6859.083008 6971.091797 6845.037598 6842.427734 6642.109863 7116.804199 7096.18457 7257.665039 7189.424805 6881.958496 6880.323242 7117.20752 7429.724609 7550.900879 7569.936035 7679.867188 7795.601074 7807.058594 8801.038086 8658.553711 8864.766602 8988.59668 8897.46875 8912.654297 9003.070313 9268.761719 9951.518555 9842.666016 9593.896484 8756.430664 8601.795898 8804.477539 9269.987305 9733.72168 9328.197266 9377.013672 9670.739258 9726.575195 9729.038086 9522.981445 9081.761719 9182.577148 9180.045898]
# Split the data into 80% training and 20% testing from sklearn.model_selection import train_test_split xtrain, xtest, ytrain, ytest = train_test_split(x,y, test_size = 0.2) # set the predictionDays array equal to last 30 rows from the original data set predictionDays_array = np.array(df.drop(['Prediction'],1))[-predictionDays:] print(predictionDays_array)
#Output [[7550.900879] [7569.936035] [7679.867188] [7795.601074] [7807.058594] [8801.038086] [8658.553711] [8864.766602] [8988.59668 ] [8897.46875 ] [8912.654297] [9003.070313] [9268.761719] [9951.518555] [9842.666016] [9593.896484] [8756.430664] [8601.795898] [8804.477539] [9269.987305] [9733.72168 ] [9328.197266] [9377.013672] [9670.739258] [9726.575195] [9729.038086] [9522.981445] [9081.761719] [9182.577148] [9180.045898]]
Now we will create a Machine Learning Model
from sklearn.svm import SVR # Create and Train the Support Vector Machine (Regression) using radial basis function svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.00001) svr_rbf.fit(xtrain, ytrain)
#Output
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=1e-05,
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)Test the model
svr_rbf_confidence = svr_rbf.score(xtest,ytest)
print('SVR_RBF accuracy :',svr_rbf_confidence)#Output SVR_RBF accuracy : 0.80318203039572782
# print the predicted values svm_prediction = svr_rbf.predict(xtest) print(svm_prediction) print() print(ytest)
#Output [ 8580.88704057 8598.79383546 8939.94375214 8388.92621489 9102.56201779 8832.68229779 8329.30224101 8157.80057348 8602.29644729 8707.90682044 7643.06939601 8408.93105022 8917.45480981 8511.7652266 7834.15919638 8832.62858497 8381.02268219 7098.85213417 8805.2578118 7757.01224446 8791.58493431 8961.26396398 8218.28537299 10512.39752674 8505.95838523 8504.09557077 8416.46565526 8812.06086838 8565.94893198 8378.22399262 8585.8737782 7630.00945667 9602.30696397 8934.97064742 9812.06855777 8473.66659984 8408.82946381 10548.41305096 9362.68382564 8597.33711016 7730.30747013 7792.1701846 8840.84467855 9893.05484852 9725.48044015 8539.54247434 8566.45635477 8916.11467623 8105.74685394 9240.42186178 9606.02191396 8392.00381076 8878.46155081 8586.37388665 8307.42830793 8397.91702065 9446.57181741 8857.3956994 8599.40879784 9324.81811167 9685.85175143 8286.70385539 9344.79392774 8978.54603972 8431.46694919 9370.69251132 8513.0501515 9400.8871896 ] [ 7546.996582 9598.173828 8912.654297 10138.04981 9261.104492 7047.916992 10159.96094 5238.438477 8037.537598 7238.966797 7410.656738 8374.686523 10312.11621 7321.988281 7292.995117 6932.480469 8047.526855 6971.091797 8657.642578 7257.665039 9328.197266 8807.010742 7923.644531 9519.145508 10185.5 9630.664063 6867.527344 8804.880859 8620.566406 7531.663574 7909.729492 8745.894531 9795.943359 9269.987305 9342.527344 8192.494141 10530.73242 11862.93652 9267.561523 8245.915039 7411.317383 8079.862793 5922.042969 7334.098633 7218.816406 9729.324219 10181.6416 6793.624512 8909.954102 11959.37109 7642.75 10241.27246 11182.80664 8586.473633 9078.762695 7556.237793 9729.801758 10256.05859 8599.508789 9324.717773 11450.84668 6198.77832 8027.268066 8804.477539 10276.79395 8206.145508 8321.756836 8151.500488]
# Print the model predictions for the next 30 days svm_prediction = svr_rbf.predict(predictionDays_array) print(svm_prediction) print() #Print the actual price for bitcoin for last 30 days print(df.tail(predictionDays))
#Output
[7746.08637672 7835.76387711 8657.25433329 9554.67400231 9617.98954538
8917.61532094 8831.29326224 8885.55655284 8640.51491415 8841.78875835
8815.42825999 8602.53094625 8400.08270252 8426.55153101 8172.93870238
8395.85348921 8903.73403919 8811.70139747 8917.58878224 8402.31118948
8102.70537693 8518.83392876 8606.8071745 8195.93279966 8108.54622414
8106.38537126 8573.49097641 8410.28935674 8307.6380027 8307.33725309]
Price Prediction
337 7550.900879 NaN
338 7569.936035 NaN
339 7679.867188 NaN
340 7795.601074 NaN
341 7807.058594 NaN
342 8801.038086 NaN
343 8658.553711 NaN
344 8864.766602 NaN
345 8988.596680 NaN
346 8897.468750 NaN
347 8912.654297 NaN
348 9003.070313 NaN
349 9268.761719 NaN
350 9951.518555 NaN
351 9842.666016 NaN
352 9593.896484 NaN
353 8756.430664 NaN
354 8601.795898 NaN
355 8804.477539 NaN
356 9269.987305 NaN
357 9733.721680 NaN
358 9328.197266 NaN
359 9377.013672 NaN
360 9670.739258 NaN
361 9726.575195 NaN
362 9729.038086 NaN
363 9522.981445 NaN
364 9081.761719 NaN
365 9182.577148 NaN
366 9180.045898 NaN
