[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

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这是一个创建于 725 天前的主题，其中的信息可能已经有所发展或是发生改变。

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.