How to Solve Assignments on Building Your First Machine Learning Pipeline Using Dataiku

1. No-code capabilities – You can drag-and-drop your way to a full pipeline without deep programming knowledge.
2. AutoML – It automates the model selection, hyperparameter tuning, and evaluation process.
3. Integration with multiple data sources – From spreadsheets to databases to APIs, Dataiku makes data import/export easy.
4. Visualization and reporting – You can communicate results with clear charts and dashboards—critical for statistics assignments.

In other words, Dataiku makes it possible to focus on understanding statistical and machine learning concepts instead of getting bogged down in syntax errors.

Step 1: Understanding Your Assignment Requirements

A lot of students lose points because they jump straight into the tool without clarifying what’s being asked. When your assignment says “Build a Machine Learning Pipeline using Dataiku”, check:

• The goal: Are you predicting a numerical value (regression) or a category (classification)?
• The dataset: Is it provided (e.g., a COVID dataset) or do you need to source it yourself?
• Performance metrics: Are you expected to achieve a specific accuracy level (e.g., >90%)?
• Deliverables: Do you need to submit only the model file, or a full report including exploratory data analysis (EDA)?

In the COVID fatalities prediction example, you’ll likely be working on a regression task, where the target variable is the number of fatalities.

Step 2: Data Import and Integration in Dataiku

Skill in focus: Data Integration and Data Import/Export

Your first technical step will be loading the dataset into Dataiku. Here’s how:

1. Open your Dataiku project dashboard.
2. Click + Dataset → choose your data source type (CSV, Excel, SQL database, cloud storage, etc.).
3. If it’s a COVID dataset in CSV format, simply drag it into the workspace or upload via the file picker.
4. Preview the data to check column names, formats, and missing values.

💡 Tip for assignments: Document your data source (where it came from, when it was last updated) in your final report—this adds professionalism.

Step 3: Exploratory Data Analysis (EDA)

Skill in focus: Exploratory Data Analysis and Data Visualization Software

Before modeling, your assignment will expect you to understand the dataset’s structure and relationships. In Dataiku:

• Use the Statistics tab to see summary stats for each variable.
• Create histograms, box plots, and scatter plots to identify trends and outliers.
• Look for correlations between variables, especially between potential predictors and the target variable (fatalities).

Example: You might find that variables like population density or testing rate have a strong relationship with fatalities.

💡 Assignment tip: Always include at least 3–4 meaningful visualizations in your submission—they’re a quick way to earn marks.

Step 4: Data Cleansing and Preparation

Skill in focus: Data Cleansing and Data Manipulation

Good models come from clean data. Dataiku makes preprocessing simple:

1. Handle missing values: Replace with median/mean for numerical variables or mode for categorical ones.
2. Remove duplicates: If your dataset has repeated entries, they could distort results.
3. Normalize data: Some algorithms work better if features are scaled.
4. Create derived variables: Example—cases per 100,000 population.

For the COVID dataset, you may need to aggregate data by country or region to match the level at which you’re predicting fatalities.

Step 5: Building Your First Machine Learning Pipeline

Skill in focus: Machine Learning and Data Pipelines

With your data ready, it’s time to set up the pipeline:

1. Create a visual analysis in Dataiku.
2. Choose the target variable (fatalities).
3. Select the type of prediction (regression).
4. Let Dataiku’s AutoML recommend models—this will test multiple algorithms (e.g., Random Forest, Gradient Boosting).
5. Automatically split your data into training and test sets.

The beauty of AutoML is that you can build and compare models without coding. The pipeline takes care of preprocessing, training, and evaluation.

Step 6: Evaluating Model Performance

Skill in focus: Predictive Modeling

Your assignment might specify an accuracy target—for example, predicting COVID fatalities with more than 90% accuracy.

In regression tasks, accuracy isn’t measured in the same way as classification. You might instead look at:

• R² (coefficient of determination) – Aim for >0.90.
• Mean Absolute Error (MAE) – Lower is better.
• Root Mean Squared Error (RMSE) – Helps understand prediction error in original units.

💡 Assignment tip: Include a table comparing models along with their metrics; justify why you chose the final one.

Step 7: Exporting and Documenting Results

Skill in focus: Data Export and Data Visualization Software

Once you have your final model:

1. Export the pipeline diagram—it’s a great visual for your report.
2. Download performance charts and add them to your assignment.
3. Save the trained model if your instructor requires reproducibility.

Be sure to include a narrative explaining:

• How you prepared the data.
• Why you chose the final model.
• How well it performed.
• Potential improvements (e.g., adding new data sources).

Step 8: Common Pitfalls Students Should Avoid

From helping hundreds of students, we’ve seen patterns in where people lose marks:

• Ignoring EDA: Jumping straight to modeling without exploring the data.
• Not checking assumptions: Some models require normality, linearity, or independence.
• Overfitting: Achieving high accuracy on training data but poor performance on unseen data.
• Incomplete reporting: Submitting just the model without explanations or visuals.

Final Thoughts

Building your first machine learning pipeline in Dataiku is more than just “dragging and dropping” components—it’s about understanding each stage in the statistical modeling process.

By:

1. Clearly defining the goal,
2. Performing thorough EDA,
3. Preprocessing data effectively,
4. Using AutoML for model selection, and
5. Documenting everything carefully,

…you’ll not only meet your assignment’s technical requirements but also demonstrate statistical thinking—a skill your instructors will value highly.

If at any point you feel stuck—whether it’s understanding regression metrics, cleaning messy COVID data, or interpreting AutoML results—our team at StatisticsHomeworkHelper.com can step in to guide you.