基于机器学习和奇异值分解SVD的电池剩余使用寿命预测（Python）

采用k-最近邻KNN和随机森林算法建立预测模型。

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC  # Support Vector Classifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, classification_report
from sklearn.decomposition import TruncatedSVD
from ydata_profiling import ProfileReport
from sklearn.metrics import mean_squared_error
import time


import seaborn as sns
from importlib import reload
import matplotlib.pyplot as plt
import matplotlib
import warnings




from IPython.display import display, HTML
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.io as pio


# Configure Jupyter Notebook
pd.set_option('display.max_columns', None) 
pd.set_option('display.max_rows', 500) 
pd.set_option('display.expand_frame_repr', False)
display(HTML("<style>div.output_scroll { height: 35em; }</style>"))

dataset = pd.read_csv('Battery_RUL.csv')

profile = ProfileReport(dataset)
profile

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y = dataset['RUL']