Thanks!

[coderschoolreview]

How many people have the job title of "Lawyer" ?

select_lawyer = sal['Job'] == 'Lawyer'
sal[select_lawyer].shape[0]

Could've been done with

sum(sal['Job']=='Lawyer')

[coderschoolreview]

Overall : Good, all answers are correct.

[coderschoolreview]

All answer correct. Awesome

Didn't work because "data" folder doesnt exist. Could fix by creating one.

[anderpham]

Thanks teacher

[coderschoolreview]

Assignment 4:

EDA: Should include Exploratory Data Analysis into notebook.

For ref, check out Mai's notebook

Model:

• Should not use Linear Regression on this problem cause it's a multi class classification problem (you have more than 2 classes).
• You could still use Linear Regression using this method : one vs all class
• Doesn't make sense to calculate RMSE, cause this is a classification problem, use classification_report from sklearn.metrics

[coderschoolreview]

Anderpham: 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

• To know "which is the best combination of parameter":

  https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html

  # Using gridsearchcv, random forest model and this param grid to find the best combination of parameters 
  # Hint: example
  # https://stackoverflow.com/questions/30102973/how-to-get-best-estimator-on-gridsearchcv-random-forest-classifier-scikit
  
  param_grid = {
    'bootstrap': [True],
    'max_depth': [80, 90, 100, 110],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'min_samples_split': [8, 10, 12],
    'n_estimators': [100, 200, 300, 1000]
  }
  
  from sklearn.model_selection import GridSearchCV
  gcv = GridSearchCV(RandomForestClassifier(),param_grid=param_grid)
  gcv.fit(X,y)
  gcv.best_params_

• 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]

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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

To create a range of integer, use range(low,high,step)

k_values = range(1,20)

Almost correct, just need to add color of each cluster to plot by adding argument c=y_cluster to plt.scatter function

Plot out result using scatter using the first 2 components of PCA

Your code here

pca2 = PCA(n_components=2) pca2.fit(X) projected = pca2.fit_transform(X)

plt.figure(figsize=(25, 10))

plt.scatter(projected[:, 0], projected[:, 1], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('viridis', 10) c = y_cluster )

[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

Almost correct, just need to add color of each cluster to plot by adding argument c=y_cluster to plt.scatter function

pca2 = PCA(n_components=2)
pca2.fit(X)
projected = pca2.fit_transform(X)
plt.figure(figsize=(25, 10))
plt.scatter(projected[:, 0], projected[:, 1],
            edgecolor='none', alpha=0.5,
            cmap=plt.cm.get_cmap('viridis', 10)
            c = y_cluster
           )