1. Sampling

A given population has been divided into 3 categories along with the number of samples. What will be the distribution of each category in the sample?

Category
A
B
C
Total

Pick ONE option:

1. A = 5, B = 4, C = 1
2. A = 10, B = 0, C = 0
3. A = 4, B = 3, C = 3
4. A = 0, B = 5, C = 5

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2. Kurtosis

Given the 3 distributions below, what is the correct order of kurtosis values?

Pick ONE option:

1. K<B<CK < B < C
2. C<A<BC < A < B
3. A=B=CA = B = C
4. C<B<AC < B < A

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3. Dealing with Imbalanced Dataset

What is not a good way to deal with an imbalanced dataset when building an ML model?

Pick ONE option:

1. Oversampling of the minority group
2. Create synthetic samples around the minority group
3. Use Recall, F1 Score for model performance evaluation
4. Use Accuracy for model performance evaluation

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4. Learning Curve

Which among the below is the purpose of a learning curve in machine learning?

Pick ONE option:

1. To visualize the model’s accuracy over training iterations
2. To visualize decision boundaries in feature space
3. To visualize feature importances
4. To visualize the training time of a model

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5. Evaluation Metrics

Which of the following is an example of a Regression Metric?

Pick ONE option:

1. Root Mean Squared Error (RMSE)
2. Mean Absolute Error (MAE)
3. Area Under the Receiver Operating Characteristic (ROC) Curve
4. R-squared (R²)

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6. SQL: Financial Asset Portfolio Evaluation

A financial firm manages a portfolio of assets comprising various assets like bonds, stocks, and commodities. They want to evaluate the return on investment (ROI) for each asset within a client's portfolio over a given year.

The statistic required is the annual ROI for each asset, given the initial investment amount, total returns for the year, and any additional investments made during the year.

The result should have the following columns:

• name: name of the client
• asset_name: name of the bond, stock, or commodity
• asset_type: type of the asset ("Bond", "Stock", or "Commodity")
• annual_roi: the annual return on investment for the asset as a percentage, calculated as: (cash_inflows - initial_investment - additional_investment) / initial_investment * 100 Rounded to two decimal places.

The result should be sorted in ascending order by name, then in ascending order by asset_name.

Note:

• Only assets that have an annual ROI greater than 5% should be included in the result.
• Include trailing zeros after the decimal in annual_roi, e.g., 8.80.

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7. Python: Predict Mobile Store Application Popularity

Business Logic

Mobile applications have revolutionized the way products and services are used. MobileStore is an online marketplace where businesses can host their mobile apps and users can download them. The more popular an app is, the higher the returns a business can expect.

The company requires a model to predict the popularity of an app uploaded to its marketplace. The target feature is popularity, with two values: "High" and "Low". Some of the data fields have bugs that need to be fixed.

Deliverables

The deliverables are well-documented Jupyter notebooks and a submissions.csv file with predictions.

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Data Sets and Schema

• train.json: Data used for model training in JSON format.
• test.csv: Data used for predictions.
• submission.csv: Populate this file with the results.
• sample_submission.csv: Sample reference of submission file data.

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Technical Specifications

1. Perform Exploratory Data Analysis and Feature Engineering.
2. Build and train a machine learning model to classify app popularity.

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