Happiness Classification

We will be using the Happiness Classification Dataset to practice practice the basic steps of a classification machine learning model with scikit-learn. This Dataset is based on a survey conducted where people rated different metrics of their city on a scale of 5 and answered if they are happy or unhappy.

codevalidated

Separate the target and the features into two variables.

Store the features in X and the target in y. Both variables should be dataframes and keep the headers.

Despite there being various ways to solve this exercise, the results must be dataframes in order to be considered correct.

codevalidated

Use train_test_split to split the data into training and testing sets. Split the dataset in 80% training, 20% testing, and random_state=0.

Set the random_state parameter to a desired integer value for reproducibility.

Store the values in the variables in X_train,X_test,y_train and y_test.

codevalidated

Logistic Regression

Create an instance of the Logistic Regression and store the model in lr.

codevalidated

Train a LogisticRegression classifier

It's time to train the LogisticRegression using the training dataset.

codevalidated

Make predictions on the test set

Use the trained model to make predictions on the test data. Store the prediction in y_pred.

Happiness Classification

Project Activities

Separate the target and the features into two variables.

Use train_test_split to split the data into training and testing sets. Split the dataset in 80% training, 20% testing, and random_state=0.

Logistic Regression

Train a LogisticRegression classifier

Make predictions on the test set

Verónica Barraza

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