python-projectdata-analysis-pythondiwali-sales-analysis UpdatedFeb 22, 2024 Jupyter Notebook A collection of data analysis and visualization projects designed to uncover insights from diverse datasets. These projects include analyses on COVID-19 trends, stock trading patterns, housing market prices, IoT...
2、Kaggle Kaggle是一个以数据科学竞赛为主题的社区,也是一个数据科学项目的平台。在Kaggle上,你可以参与各种数据挖掘竞赛,还可以下载别人分享的数据集进行练手。Kaggle上有很多Python实战项目,例如分类问题、聚类问题、预测问题等,还有一些大型项目,如推荐系统、自然语言处理等。Kaggle上的Python实战项目大多数都是真实的...
kaggle.com/learn/python 4小时入门机器学习: kaggle.com/learn/machin 4小时了解深度学习: kaggle.com/learn/deep-l 3小时喜提SQL: kaggle.com/learn/sql 4小时get Pandas: kaggle.com/learn/pandas 7小时搞懂数据可视化: kaggle.com/learn/data-v 以上课程汇总: kaggle.com/learn/overvi 值得先码后看,祝你...
有趣的Python爬虫和Python数据分析小项目(Some interesting Python crawlers and data analysis projects) - QueraZ123/interesting-python
3. Advanced: Time series analysis, performance tuning 4. Practice: End-to-end projects with real datasets (e.g., Kaggle)生态位分析 Ecosystem Position 上游：数据采集（`requests`、`Scrapy`）下游：可视化（`Matplotlib`）、机器学习（`scikit-learn`）替代方案：`Polars`（更高性能）、`Dask`（分布式...
训练数据用来分析,并训练一个分类模型(Classification Model)。使用分类模型是因为目标变量是类别数据(Categorical Data),即存活和死亡。 test.csv可以称作样本外数据(out-of-sample data)或测试数据,测试数据中只有特征变量而没有目标变量。在本例中用我们训练的模型来预测结果,并上传到kaggle评估模型的...
Django provides an all-inclusive experience: you get an admin panel, database interfaces, anORM[object-relational mapping], and directory structure for your apps and projects out of the box. You should probably choose: 你应该选择: Flask, if you're focused on the experience and learning opportun...
Participate in Kaggle competitions and hackathons Pursue further specialization in areas like NLP, computer vision, or big data Why Python for Machine Learning? Python has become the language of choice for machine learning due to its simplicity, versatility, and extensive ecosystem of powerful librarie...
data_for_analysis_1['Seconds'] = data_for_analysis_1.Seconds.astype(str)进行统计学上的Shapiro'...
PUBG-juediqiusheng-data_analysis 内容:主要分析绝地求生72万场比赛的数据,并结合数据给出吃鸡攻略,用数据吃鸡! 对应微信公众号文章:《【20G】Kaggle数据集强势分析“绝地求生”,攻略吃鸡!》 适合人群:Python数据分析学习者、Pandas使用者、各位吃鸡观众 难度:★★★☆☆...