Blog | Data Science and Technology

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc.  Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropped e

  1. What is logistic regression? Logistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modelled using a logistic function. 2. What is the syntax for logistic regression? Library: sklearn.linear_model.LogisticRegression Define model: lr = LogisticRegression() Fit model: model = lr.fit(x, y) Predictions: pred = model.predict_proba(test) 3. How do you split the data in train / test? Library: sklearn.model_selection.train_test_split Syntax: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) 4. What is decision tree? Given a data of attributes together with its classes, a decision tree produces a sequence of rules that can be used to classify the data. 5. What is the syntax for decision tree classifier? Library: sklearn.tree.DecisionTreeClassifier Define model: dtc = DecisionTreeClassifier() Fit model: model = dtc.fit(x, y) Predictions: pred