Thanks!

[coderschoolreview]

Could write in this style for cleaner code & easier to read:

condition 1 = dataframe['colA'] == 'Value 1'
condition 2 = dataframe['colA'] > 100 

#number of row in dataframe satisfy both condition 
sum(condition 1 & condition 2)

Overall most answer are correct, missing 2 questions :

• Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...)
• Implement a bar plot for top 5 most popular email providers/hosts

[coderschoolreview]

Missing question 16

For reference: Regression Metric

Answer

from sklearn.metrics import mean_absolute_error,mean_squared_error
mae = mean_absolute_error = (y_test,y_predict)
mse = mean_square_error = (y_test,y_predict)
rmse = mse**.5

[coderschoolreview]

Good work, all answer are correct

Didn't work cause folder "data" didn't exist. Could fix this by creating folder data in same folder with the notebook.

[coderschoolreview]

Assignment 4:

Visualization:

• Not necessary to use lmplot.
• Could use some subplot for neater visualization.
• Ref: Check out Mai's notebook: https://github.com/janetvn/coderschool_mc_w04_assignment
• Subplot ref : https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html

Model:

• Should not used Logistic Regression, since this is a multi class classification problem (more than 2 classes, we have 3 species)
• Check out this article for ref on Logistic Regression for this case
• In the future, try fine Tuning the model with GridSearchCV/RandomizedSearchCV

[coderschoolreview]

Thao Ho: Assignment 5

The goal of this assignment was to introduce you following concepts in Machine Learning:

• Data quality check: missing value, anomaly, ...
• Exploratory Data Analysis: ratio of class, relationship of feature with another and relationship of feature with target, and data visualization.
• Preprocessing data: handling imbalance dataset, dealing with categorical variable.
• Modeling & evaluation: building classifier and evaluate, Hyper Parameter Tuning.

Things you did well

• Checking data quality: check for missing value and using missingno to visualize
• EDA: Ratio of classes, finding out that data is imbalance, visualizing relationship between features
• Preprocessing: under sampling / over sampling, train test split

Things to work on

• Preprocessing categorical features. Try pandas.get_dummies
• GridSearchCV and RandomSearchCV: common practice is using RandomSearchCV to narrow down search range then fine tuning with GridSearchCV, can read this article
• Modeling & evaluate: train and evaluate multiple model by comparing classification report of models against each other

Minor tips

• Almost got it right in Modeling and Evaluation, one minor mistake when define function evaluate model, you should've write it like this:

  # Import confusion_matrix, classification_report
  from sklearn.metrics import classification_report, confusion_matrix
  
  # We create an utils function, that take a trained model as argument and print out confusion matrix
  # classification report base on X and y
  def evaluate_model(estimator, X, y, description): #missing `description` argument
    prediction = estimator.predict(X)
    np.set_printoptions(precision=2)
    model_name = type(estimator).__name__
    return {'name': model_name, 
            'recall': recall_score(y, prediction),
            'precision': precision_score(y, prediction),
           'description': description}

• Installing packages missingno (or any arbitrary package) on Win10:

  • Go to Anaconda Prompt
  • Type conda install -c conda-forge missingno

• Filtering series:

  • Your code:train_copy.isnull()

  • Could write like this to get column with null value only:

  ncols = train_copy.isnull().sum
  ncols[ncols!=0]

• Check if whole data frame have any null value:

  ​ train.isnull().any().any()

• For evaluation function, should print instead of return so when you loop through list of model and evaluate them, the result for each iteration printed to output.

[coderschoolreview]

---

Assignment 6

Goal of this Assignment

The goal of this assignment was to introduce you to following concepts:

• Unsupervised Learning (KMeans, Hierachical Clustering)
• PCA

You learn how to use PCA for dimension reduction, KMeans, and Hierarchical Clustering. Also you learn to visualize the result of both tenichque.

Things you did well:

• Ultilizing Pandas to read, summary and visualize data.
• Almost got everything right

To sum up:

• Great work! This showing you understood the concept and able to apply it. Keep it up!

Few minor tips

Could try this shorter version:

total_purchases = data.sum(axis=1)
purchase_percent = data.div(total_purchases, axis=0) * 100