[计算机作业] 数据科学的作业，工作量应该小于 1 工作日，预算 800 元

帮朋友做一个数据科学相关的计算机作业，是对 YouTube 视频播放数据进行分析，完成了 Part 1-3 ，找一位懂数据分析和机器学习的老哥帮忙做一下 Part 4 ，工作量应该小于 1 工作日，预算 800 元，感兴趣的可以留一下联系方式

• PART 1 – Defining the Problem and Questions （已完成）
• PART 2 – Cleaning the Data （已完成）
• PART 3 – Carrying out an Exploratory Analysis （已完成）
• PART 4 - Developing ML and DL Prediction Model （未完成，需要做的是这部分）

已完成的代码示例： https://colab.research.google.com/drive/1MkSpgV_XZVUcNIT1gI-b7Abd0ET8uT0W?usp=sharing

PART 4 具体要求

When your data is ready for modelling, you can start building your prediction model. As a starting point for your implementation, consider the following steps:

• Convert the Pandas dataframes into NumPy arrays that can be used by scikit_learn.
• Create an array that extracts only the feature data that you want to work with.
• Normalize your data as some ML models require the input data to be normalized.
• Split your data into train and test or use K-Fold cross validation.
• Create a decision tree classifier and fit it to your training data.
• Display the resulting decision tree.
• Measure the accuracy of the resulting decision tree model using your test data.
• Create a random forest classifier, fit it to your data and measure the accuracy.
• Create a random forest classifier, fit it to your data and measure the accuracy.
• Create SVM with linear kernel classifier, fit it to your data and measure the accuracy.
• Create KNN classifier, fit it to your data and measure the accuracy.
• WriteaforlooptorunKNNwithKvaluesrangingfrom1to50andseeifKmakesa substantial difference. Make a note of the best performance you could get out of KNN.
• Use Keras to set up a neural network with 1 binary output neuron (for binary classification only) and see how it performs. You can run a large number of epochs to train the model if necessary.
• Try different neural network topologies by adding additional layers and use Dropout at each step to prevent overfitting.