Machine Learning Notes - [Machine Learning Case] Applying Gaussian Mixture Model GMM and Grid Search GridSearchCV to improve classification accuracy on tabular data

1. Requirements and data set description

        This is a binary classification task evaluating classification accuracy (percentage of correctly predicted labels). The training set has 1000 samples and the test set has 9000 samples. Your prediction should be a 9000 x 1 vector. You'll also need an Id column (1 to 9000), and that should include a header. The format is as follows:

Id,Solution
1,0
2,1
3,1
...
9000,0

        Dataset download address

Link: https://pan.baidu.com/s/1Dy5uF_OAmCQC3G-71e-yEQ?pwd=tjzq 
Extraction code: tjzq

2. Import package and read data

import numpy as np 
import pandas as pd
from sklearn.metrics import accuracy_score 
from sklearn.model_selection import cross_val_score
import os


train_data = pd.read_csv('../input/train.csv',header = None)
train_labels = pd.read_csv('../input/trainLabels.csv',header = None)
test_data =  pd.read_csv('../input/test.csv',header = None)

        If the following model reports an error when running, you can try the following method

train_data = pd.read_csv('data-science-london-scikit-learn/train.csv',header