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!
• You seem comfortable with Python and are not afraid to try new functions and libraries. Awesome!

One minor tip:

ecom[ecom['Lots']=='90 WT']['Purchase Price'] "Lots" isn't a correct column name, it should be "Lot"

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! You are familiar with extracting information from your data and model. Good job!

Some minor tips:

• To create coefficient DataFrame, you could code like below: coeffecients = pd.DataFrame(lm.coef_,X.columns) coeffecients.columns = ['Coeffecient'] coeffecients
• In the “Residual” section, you count the different between your y_test and prediction, and then put it into graph to see whether it is a normal distribution. residuals = y_test-predict sns.distplot(residuals,bins=10)