Thanks!

[coderschoolreview]

The goal of this assignment was to introduce you to 2 main concepts in Machine Learning: Data Visualization and Exploratory Data Analysis. You learn how to query and clean data using pandas library in Python, make some plots which help to understand more about data with seaborn lib.

Things you did well:

• Discovered the usage of many new functions! unique, nunique, mean, max, min, head, value_counts. Great job!
• Clearly named variables. Makes code easy to read, especially for your instructor!

One minor tip:

qwert = data[qwer]['Purchase Price'] >95] qwert.value_counts().sum()

:( ko biết sai gì

Should be like that qwert = data[qwer]['Purchase Price'] >95 redundant "]" at the end

Overall, excellent work! You are demonstrating that you are understanding the material and doing a great job of applying it. Keep it up!

[coderschoolreview]

The goal of this assignment is for you to learn to build and evaluate a model using scikit-learn library in Python. You learn how to do Data pre-processing, splitting it into training and test sets, training your model using training sets and evaluating its performance on test sets.

Things you did well: Discovering the usage of many new functions!Understanding how to use data to train a model using library in Python. This is a very important step!

Some minor tips:

• When importing “matplotlib.pyplot as plt”, you should add “%matplotlib inline” below it.
• The correlation between each columns can be seen directly on the heatmap.
• You should take more features into your X before splitting it (instead of just 1 feature “Length of Membership”). In the “Predicting Test Data” section, you should only use the scatterplot for y_test and prediction using “plt.scatter(y_test,predictions)”.
• The meaning of those coefficients is that: when 1 unit increase in one feature, it can affects the change on customer total dollars spent, and the total changes is the value of coefficient.