Thanks!

[coderschoolreview]

Goal of this Assignment

The goal of this assignment was to introduce you to 2 main concepts in Machine Learning:

• Data Visualization
• 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 library.

Things you did well:

• You accomplished every questions. This is a great start !
• Your solution is very comprehensive and shows that you know how to utilize Pandas very well.
• You also perform the bonus dataset. Awesome !!!
• From the bonus dataset, you draw some conclusions from it. And the great thing is being curious about the dataset and make conclusions to confirm the hypothesis. Keep up !

To sum up:

• 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:

• You should take more features into your X (before splitting it), instead of just 1 feature “Time On App”.
• You don’t actually need to sort the X_train, X_test, y_train and y_test, unless you are trying to plot them.
• In the “Residual” section, you count the different between your y_test and predictions, and then put it into graph to see whether it is a normal distribution. So: residuals = y_test-predictions, and then plot it using: sns.distplot(residuals,bins=10)
• The meaning of those coefficients is that when 1 feature increase (decrease) by 1, it changes your outcome (in this case Yearly Amount Spent) by 1*value_of_coefficient.

[coderschoolreview]

Hi Hung Tuan,

Excellent work on your Assignment 3. You seem confident with performing tasks that include sentiment analysis.

Keep it up!

[coderschoolreview]

hyl0428: 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

• Aweomse, you knew magic function !pip install missingno, more on magic command here
• 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
• Modeling & evaluate: train and evaluate multiple model by comparing classification report of models against each other

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

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]

Please ignore previous comment, commented on wrong profile, terribly sorry

Hung Tuan: 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
• Modeling & evaluate: train and evaluate multiple model by comparing classification report of models against each other

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

Minor tips

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

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

Or, in the notebook type: !pip install 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:

• You accomplished every questions. This is a great start !
• Your solution is very comprehensive.

To sum up:

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

Few minor tips

For this question, there's a shorter version. (What if there's a hundred features?)

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

For this question. you're plotting 2 first features of original dataset, while the question requires to plot on 2 first component of PCA. Try this

plt.figure(figsize=(10, 7))
plt.scatter(pca_data['PC1'], pca_data['PC2'], c=cluster.labels_, cmap='rainbow');