Taking everything into consideration, select the best-performing model and provide an analysis of the dataset. Generate appropriate visualizations to support your analysis and, finally, provide recommendations for the next steps for the company. (综合考虑,选择表现最佳的模型并对数据集进行分析。生成适当...
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shuffle=True, batch_size=batch_size, pin_memory=True, num_workers=2, drop_last=True, collate_fn=collator ) dataloaders['val'] = DataLoader( dataset=val_dataset, shuffle=False, batch_size=batch_size, pin_memory=True, num_workers=2, drop_last=False, collate_fn=collator ) return dataloade...
2) Plot the data. Run the next code cell without changes to load a GeoDataFrameworldcontaining country boundaries. #This dataset is provided in GeoPandasworld_filepath = gpd.datasets.get_path('naturalearth_lowres') world=gpd.read_file(world_filepath) world.head() Use theworldandworld_loansG...
我选择用于分析的 EDA 是 lorinc 的 Feature Extraction From Images，selfishgene 的 Visualizing PCA with Leaf Dataset 以及 Jose Alberto 的 Fast Image Exploration。第一步最好先仔细瞧一瞧树叶的图像。selfishgene 检查树叶标本 Jose 绘制出各个种类的树叶，并指出每个种类有 10 张图片。他还观察了同类树叶间的...
EDA-Exploratory Data Analysis 基于使用频率和数值特征 In [14]: 取出和用户的数值型字段信息: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # df_frequency = df[["Customer_Age","Total_Trans_Ct","Total_Trans_Amt","Months_Inactive_12_mon","Credit_Limit","Attrition_Flag"]] 效果同下 df_...
###缺失值处理fordatasetindata_cleaner:#用中位数填充 dataset['Age'].fillna(dataset['Age'].median(),inplace=True)dataset['Embarked'].fillna(dataset['Embarked'].mode()[0],inplace=True)dataset['Fare'].fillna(dataset['Fare'].median(),inplace=True)#删除部分数据 ...
# Initialize a male counter variablemale_count = 0# Initialize variable to store all the ages.ages = []# Loop over the paths and check for male images.for path in image_paths: path_split = path.split("_") if"0"== path_split[1]: ...
基于Kaggle⼼脏病数据集的数据分析和分类预测-StatisticalLearning统。。。基于Kaggle⼼脏病数据集的数据分析和分类预测-StatisticalLearning统计学习实验报告⼀、实验准备 本数据来源于kaggle,包含14个维度，303个样本，具体的变量说明如下表所⽰。变量名详细说明取值范围 target是否患有⼼脏病（分类变量）0=否，1...
for dataset in [train_df]: dataset['Relatives'] = dataset['SibSp'] + dataset['Parch'] axes = sns.factorplot('Relatives','Survived', data=train_df, aspect = 2.5) 有1-3个亲戚在船上,幸存率相对更高。 清洗数据 在11个特征中找出可用的,每一个特征先填充空缺值(如有),并完成分类。 1、年...