1. import pandas as pd
2. import numpy as np
3. import seaborn as sns
4. import matplotlib.pyplot as plt
5. from sklearn.metrics import mean_squared_error
6. from sklearn.model_selection import train_test_split
7. from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, OrdinalEncoder
8. from sklearn.model_selection import GridSearchCV
9. from sklearn.ensemble import RandomForestRegressor
10.  
11. data = pd.read_csv('bitcoinweek.csv')
12. data = data[::-1]
13. data.head(7)
14.  
15. corr = data.corr(numeric_only=True)
16. corr.round(2)
17.  
18. plt.figure(figsize=(10,8))
19. _ = sns.heatmap(corr.round(2), cmap="Greens",  cbar=True, annot=True, square=True)
20.  
21. data['Volume'] = data['Volume'].str.replace(',', '').astype(float)
22. data['Market Cap'] = data['Market Cap'].str.replace(',', '').astype(float)
23.  
24. data['Date'] = data['Date'].astype('datetime64[ns]')
25. data['Date'] = data['Date'].apply(lambda x: x.timestamp())
26.  
27. # Açılış değeri sütununu ekliyoruz
28. data['Open'] = data['Open'].str.replace(',', '').astype(float)
29.  
30. X = data.drop(['Market Cap', 'Opening Price'], axis=1)
31. y = data['Market Cap']
32.  
33. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
34.  
35. numerical_cols = list(data.select_dtypes(include=['int64', 'float64']).columns)
36. numerical_cols.remove('Market Cap')
37.  
38. data["Market Cap"].dtype
39.  
40. scaler = MinMaxScaler()
41. X_train[numerical_cols] = scaler.fit_transform(X_train[numerical_cols])
42. X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])
43.  
44. param_grid = {
45.     'n_estimators': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 200, 300]
46. }
47.  
48. model = RandomForestRegressor(random_state=42)
49.  
50. grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
51.  
52. grid_search.fit(X, y)
53.  
54. best_n_estimators = grid_search.best_params_['n_estimators']
55. print(f'En iyi n_estimators değeri: {best_n_estimators}')
56.  
57. model = RandomForestRegressor(n_estimators=5, random_state=42)
58.  
59. model.fit(X_train, y_train)
60.  
61. y_pred = model.predict(X_test)
62.  
63. mse = mean_squared_error(y_test, y_pred)
64. print(f'Mean Squared Error: {mse}')
65.