Applied ML Analyst Interview Questions &amp; Answers

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Detect with box plots, the IQR rule (below Q1 - 1.5 IQR or above Q3 + 1.5 IQR), z-scores above 3, or models like Isolation Forest. Treatment depends on cause: fix data entry errors, cap values (winsorise), apply log transforms for skewed data, or use robust models and losses. Genuine extreme values, such as large fraud amounts, may be the most important signal and should not be removed blindly.

Why is feature scaling needed and which algorithms need it?

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Scaling puts features on comparable ranges so that one large-valued feature does not dominate distances or gradients. Distance and gradient based models need it: k-NN, k-means, SVM, PCA, linear and logistic regression with regularisation, and neural networks. Tree-based models like random forest and XGBoost split on thresholds, so they are largely unaffected by scaling.

What is PCA and how does it work?

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Principal Component Analysis finds new orthogonal axes, the principal components, that capture maximum variance in the data. It works by computing eigenvectors of the covariance matrix, or via SVD, after centring and usually standardising the data. Keeping the top components that explain, say, 95 percent of variance reduces dimensions, removes correlation and speeds up models, but the components are harder to interpret.

What is the difference between standardisation and normalisation?

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Standardisation (StandardScaler) transforms a feature to mean 0 and standard deviation 1 using (x - mean) / std; it keeps outliers but does not bound values. Min-max normalisation (MinMaxScaler) rescales to a fixed range such as 0 to 1 and is sensitive to outliers. RobustScaler uses median and IQR and is better when outliers are present. Always fit the scaler on training data only.

Describe a project where you improved a model's performance significantly.

Decision Trees and EnsemblesMedium

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Situation: a product-return prediction model had 0.68 AUC with logistic regression. Task: improve it for a warehouse pilot. Action: I engineered features like past return rate per customer and category, moved to LightGBM with early stopping, and tuned it with Optuna on time-based folds. Result: AUC rose to 0.81, and the pilot reduced avoidable returns in two categories. I documented each step's gain.

Describe a time you found a serious data quality issue late in a project.

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Situation: two days before a demo, I noticed sales for one region were doubled. Task: fix it without missing the deadline. Action: I traced it to a join that duplicated rows on a non-unique key, fixed the join, added row-count assertions to the pipeline and retrained. I informed my lead immediately with the impact. Result: the demo went ahead with correct numbers, and the assertions caught a similar issue later.

What is polynomial regression and what risk does it carry?

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Polynomial regression adds powers and interaction terms of features, such as x squared and x1 times x2, so a linear model can fit curves. It is still linear in its coefficients. The main risk is overfitting at high degrees, plus wild behaviour outside the training range. Use cross-validation to choose the degree, scale features, and combine it with Ridge regularisation.

How do you interpret a coefficient in a multiple linear regression?

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A coefficient is the expected change in the target for a one-unit increase in that feature, holding other features constant. For example, a coefficient of 2,500 on 'area in square metres' means each extra square metre adds about 2,500 to price. Interpretation breaks down with multicollinearity or when features are scaled, so report units clearly and check stability.

How do you save and load a scikit-learn model?

MLOps and DeploymentEasy

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Save the whole fitted pipeline so preprocessing travels with the model. import joblib joblib.dump(pipe, 'model_v1.joblib') pipe = joblib.load('model_v1.joblib') preds = pipe.predict(new_df) Load with the same scikit-learn version used for training, and never load pickle or joblib files from untrusted sources because they can execute code. ONNX or skops are safer portable options.

What is the difference between Ridge, Lasso and Elastic Net regression?

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All add a penalty on coefficient size to reduce overfitting. Ridge uses an L2 penalty (sum of squares), shrinking coefficients toward zero but rarely to zero, and handles multicollinearity well. Lasso uses L1 (sum of absolute values) and can set coefficients exactly to zero, performing feature selection. Elastic Net mixes both, which is useful when there are groups of correlated features.

What is DBSCAN and when is it better than k-means?

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DBSCAN groups points that lie in dense regions, defined by eps (neighbourhood radius) and min_samples, and labels points in sparse areas as noise. It finds arbitrarily shaped clusters, does not need k, and handles outliers. It struggles when clusters have very different densities and in high dimensions. HDBSCAN improves on it for varying densities.

What is association rule mining? Explain support, confidence and lift.

Clustering and Dimensionality ReductionEasy

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Association rule mining finds items that occur together, such as in market basket analysis. Support is how often an itemset appears in all transactions. Confidence of A to B is the share of transactions with A that also contain B. Lift is confidence divided by B's support; lift above 1 means A and B occur together more than by chance. Apriori and FP-Growth are common algorithms.

Write a minimal scikit-learn workflow to train and evaluate a model.

ML FundamentalsEasy

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The fit, predict, score pattern is the same for almost every estimator. from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score model = LogisticRegression(max_iter=1000) model.fit(X_train, y_train) preds = model.predict(X_test) print(accuracy_score(y_test, preds)) Use predict_proba when you need probabilities for thresholds or ranking.

How does k-means clustering work?

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k-means picks k initial centroids, assigns each point to its nearest centroid, recomputes each centroid as the mean of its points, and repeats until assignments stop changing. It minimises within-cluster sum of squares (inertia). It needs k in advance, scaled features, and assumes roughly spherical, similar-sized clusters. k-means++ initialisation and multiple runs with n_init reduce bad starting points.

Tell me about a time your analysis was not used by stakeholders. What did you learn?

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Situation: I built eight customer segments, but marketing did not use them. Task: understand why. Action: I met the team and learned the segments were hard to act on and lacked clear names. I reduced them to five, added a one-page profile and suggested campaign ideas for each. Result: two segments were used in the next campaign. I learned to involve users from the start.

What is multicollinearity and how do you detect it?

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Multicollinearity is high correlation between input features, which makes linear model coefficients unstable and hard to interpret, though predictions may remain fine. Detect it with a correlation matrix or the Variance Inflation Factor, where VIF above 5 to 10 signals a problem. Fix it by dropping one of the correlated features, combining them, using PCA, or applying Ridge regularisation.

How do you find and remove duplicate rows and check missing values in pandas?

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Quick data-quality checks before modelling: print(df.isna().sum().sort_values(ascending=False)) print(df.duplicated().sum()) df = df.drop_duplicates() df['age'] = df['age'].fillna(df['age'].median()) For modelling, do the median imputation inside a pipeline instead so the value is learned only from training data.

Apply PCA and check explained variance.

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from sklearn.decomposition import PCA import numpy as np pca = PCA().fit(Xs) cum = np.cumsum(pca.explained_variance_ratio_) n = np.argmax(cum >= 0.95) + 1 print(n, 'components explain 95 percent') X_red = PCA(n_components=0.95).fit_transform(Xs) Passing a float to n_components keeps enough components to reach that variance.

How do you check regression residuals visually in Python?

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import matplotlib.pyplot as plt res = y_test - pred fig, ax = plt.subplots(1, 2, figsize=(10, 4)) ax[0].scatter(pred, res, s=8); ax[0].axhline(0, color='red') ax[0].set_xlabel('Predicted'); ax[0].set_ylabel('Residual') ax[1].hist(res, bins=40) plt.show() A funnel shape suggests heteroscedasticity; a curve suggests a missing non-linear term.

Use statsmodels to get p-values for regression coefficients.

Clustering and Dimensionality ReductionEasy

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import statsmodels.api as sm Xc = sm.add_constant(X_train) ols = sm.OLS(y_train, Xc).fit() print(ols.summary()) The summary shows coefficients, standard errors, p-values, confidence intervals, R-squared and diagnostics. scikit-learn focuses on prediction and does not report p-values, so statsmodels is preferred for inference.

Run k-means and plot the elbow and silhouette scores.

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from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score Xs = StandardScaler().fit_transform(X) for k in range(2, 10): km = KMeans(n_clusters=k, n_init=10, random_state=42).fit(Xs) print(k, round(km.inertia_), round(silhouette_score(Xs, km.labels_), 3)) Look for the bend in inertia and a high silhouette value.

What is R-squared and what is adjusted R-squared?

Clustering and Dimensionality ReductionEasy

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R-squared is the share of variance in the target explained by the model, 1 - SS_res / SS_tot. It never decreases when you add features, even useless ones. Adjusted R-squared penalises extra features: 1 - (1 - R2)(n - 1)/(n - p - 1), so it only rises if a new feature improves fit more than expected by chance. R-squared can be negative on test data if the model is worse than predicting the mean.

Visualise a small decision tree and its rules.

Decision Trees and EnsemblesEasy

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from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree import matplotlib.pyplot as plt dt = DecisionTreeClassifier(max_depth=3, random_state=0).fit(X_train, y_train) print(export_text(dt, feature_names=list(X_train.columns))) plot_tree(dt, feature_names=X_train.columns, filled=True) plt.show() Shallow trees are a good way to explain rules to business teams.

How do you choose the number of clusters k?

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Common methods are the elbow method, plotting inertia against k and looking for the bend; the silhouette score, which measures how close points are to their own cluster versus the next one; and the gap statistic. Business usability matters too: four clear segments a marketing team can act on are better than nine statistically optimal but confusing ones.

What is feature engineering and why does it matter?

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Feature engineering is creating input variables that make patterns easier for a model to learn, using domain knowledge. Examples are extracting day of week from a date, ratios like debt-to-income, rolling averages of past purchases, or text length. On tabular problems good features often improve results more than switching algorithms. Features must use only information available at prediction time.

Validation AUC is 0.99 on a loan default model. What do you suspect?

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Such a high score on a hard problem usually signals leakage. I would check feature importances for a suspiciously dominant feature, look for fields recorded after the loan outcome such as collection status, verify preprocessing was fit only on training data, and check for duplicate customers across train and test. Then re-run with a time-based split to confirm a realistic score.

Two models have RMSE 120 and 125, but the second has much lower MAE. Which do you choose?

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It depends on error cost. The first model makes fewer big errors, while the second is better on typical cases but has some large misses. If large errors are expensive, such as stock-outs of costly items, choose the first. If typical accuracy matters more, choose the second. I would also check error distributions by segment and discuss with stakeholders before deciding.

What is one-hot encoding and when can it cause problems?

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One-hot encoding turns a categorical column into binary columns, one per category, so models do not assume order. It causes problems with high-cardinality features like city or product ID, creating thousands of sparse columns and higher memory and overfitting risk. Alternatives are target encoding, frequency encoding, hashing, or grouping rare categories into 'Other'. Use handle_unknown='ignore' for unseen categories.

How does logistic regression work?

Classification ModelsEasy

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Logistic regression computes a weighted sum of features and passes it through the sigmoid function, 1 / (1 + e^-z), to get a probability between 0 and 1. It is trained by minimising log loss. Coefficients represent change in log-odds per unit of a feature, and exp(coefficient) gives the odds ratio. A threshold, 0.5 by default, converts probabilities into class labels.

40 percent of values in an important income column are missing. What do you do?

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I would study the missingness pattern first, for example whether self-employed applicants skip it. I would add a binary was_missing feature, impute with the median within relevant groups or use IterativeImputer, and compare against models that handle missing values natively, like HistGradientBoosting or XGBoost. I would also raise the issue with the data owner so collection can improve.

How do you handle missing values in a dataset?

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First understand why values are missing: completely at random, at random, or not at random. Options are dropping rows or columns with heavy missingness, simple imputation with mean, median or mode, model-based imputation like KNNImputer or IterativeImputer, or a separate 'missing' category. Adding a missing-indicator column often helps because missingness itself can be predictive, such as income left blank.

What does it mean to deploy a machine learning model?

MLOps and DeploymentEasy

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Deployment makes a trained model available to produce predictions for real users or systems. It can be batch, scoring data on a schedule and writing results to a table, or real-time, serving predictions through a REST API, or streaming, scoring events as they arrive. It includes packaging the model with its preprocessing, setting up infrastructure, logging, monitoring and a retraining plan.

What is the difference between label encoding and ordinal encoding?

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Both map categories to integers. In scikit-learn, LabelEncoder is meant for the target variable, while OrdinalEncoder is for input features. Integer encoding implies an order, so it is fine for truly ordered values like low, medium, high, or for tree models, but it can mislead linear models on nominal data like colours. For nominal features with linear models, prefer one-hot encoding.

How do you set up a basic train-test split with scikit-learn?

ML FundamentalsEasy

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Use train_test_split with a fixed random_state for reproducibility and stratify for classification so class ratios match. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) For time series, avoid random splits and use the most recent period as the test set.

What are the assumptions of linear regression?

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Linear regression assumes a linear relationship between features and target, independent errors, constant error variance (homoscedasticity), normally distributed errors for valid inference, and little multicollinearity among features. Violations mainly hurt confidence intervals and coefficient interpretation more than raw predictions. Check them with residual plots, Q-Q plots, the Durbin-Watson test and VIF.

What is regularisation in general and why does it reduce overfitting?

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Regularisation adds a constraint or penalty that discourages overly complex models, such as large weights in linear models, deep trees or too many boosting rounds. It trades a little extra bias for a larger drop in variance, so test error falls. Examples include L1 and L2 penalties, dropout in neural networks, early stopping, pruning and limiting tree depth.

k-means gives one huge cluster and several tiny ones. What could be wrong?

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Likely causes are unscaled features, where one large-range feature dominates distance; heavy skew or outliers pulling centroids; or data that is not naturally spherical. I would log-transform and scale features, remove or cap extreme outliers, and try different k values. If shapes are irregular, DBSCAN, HDBSCAN or a Gaussian Mixture Model may fit better.

Fit a linear regression in scikit-learn and print RMSE and R-squared.

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from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score import numpy as np lr = LinearRegression().fit(X_train, y_train) pred = lr.predict(X_test) print('RMSE', np.sqrt(mean_squared_error(y_test, pred))) print('R2', r2_score(y_test, pred)) print(dict(zip(X_train.columns, lr.coef_))) Newer scikit-learn versions also provide root_mean_squared_error directly.

What is data leakage? Give two examples.