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This assignment builds on your previous work and introduces you to predictive analytics through a forecasting method called a binary classifier. You will then work on how to visualize and understand a binary classifier.

• Receive an introduction to binary classifiers, logistic regression, and the results, including true-positive, false-positive, true-negative, and false-negative results
• Run a binary classification algorithm on our diabetes data
• Visualize the results in Tableau

For the following assignment, please review the instructions on how to download a blocked file in Chrome before you start your assignment.

• Assignment Instuctions: Unit 3 Self-Check Assignment 3 Diabetes Forecasting
• Google Colab Notebook: Diabetes_Classifier
• Dataset: Diabetes
• How to Download a Blocked File in Chrome 

 

Struggling with where to start this assignment? Follow this guide to tackle your assignment easily!

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Tutor’s Step-by-Step Guide: Unit 3 Self-Check Assignment 3 — Diabetes Forecasting

Step 1 — Understand the assignment goals

By the end of this task, you should be able to:

1. Explain the purpose of binary classifiers and logistic regression.

2. Apply logistic regression to a diabetes dataset.

3. Interpret outcomes using true positive, false positive, true negative, false negative.

4. Visualize your classifier results in Tableau.

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Step 2 — Key concepts refresher

• Binary classifier: Predicts outcomes that fall into one of two categories (e.g., diabetes = yes/no).

• Logistic regression: A machine learning algorithm that predicts probabilities for binary outcomes.

• Confusion matrix terms:

  • True Positive (TP): Model predicts diabetes = yes, and patient actually has diabetes.

  • False Positive (FP): Model predicts diabetes = yes, but patient does not have it.

  • True Negative (TN): Model predicts diabetes = no, and patient does not have it.

  • False Negative (FN): Model predicts diabetes = no, but patient actually has diabetes.

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Step 3 — Running the model in Google Colab

1. Open Google Colab: Upload or open the provided Diabetes_Classifier.ipynb notebook.

2. Load the dataset: Make sure the diabetes dataset (likely CSV) is correctly linked in Colab.

3. Import packages: Typical imports — pandas, numpy, sklearn (LogisticRegression, train_test_split, metrics).

4. Preprocess the data:

   • Handle missing values.

   • Normalize or standardize features if needed.

5. Split the data:

   • Training set (e.g., 70%)

   • Testing set (30%).

6. Train the model:

   from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)


7. Make predictions:

   y_pred = model.predict(X_test)


8. Evaluate results with confusion matrix:

   from sklearn.metrics import confusion_matrix, classification_report
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))


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Step 4 — Visualizing results in Tableau

1. Export results from Colab: Save predictions and true labels into a CSV.

   import pandas as pd
results = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
results.to_csv('diabetes_predictions.csv', index=False)


2. Upload CSV into Tableau.

3. Create visuals:

   • Confusion matrix heatmap (Actual vs. Predicted).

   • Bar chart showing counts of TP, FP, TN, FN.

   • ROC curve / precision-recall curve (optional, if Colab code generates probabilities).

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Step 5 — Writing up your submission

• Introduction: Define binary classifiers and logistic regression.

• Methods: Briefly describe how you ran the model in Colab.

• Results: Present confusion matrix outcomes.

• Visualization: Insert Tableau screenshot(s).

• Reflection: Explain what TP, FP, TN, FN mean for healthcare decision-making (e.g., the risk of false negatives in diabetes screening).

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Step 6 — Troubleshooting tips

• If Chrome blocks downloads: Follow your course-provided instructions for allowing downloads.

• If Colab errors appear: Check Python package versions (e.g., sklearn).

• If Tableau doesn’t recognize your CSV: Ensure headers are included (“Actual”, “Predicted”).

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Step 8 — Helpful Resources

• Google Colab guide: https://colab.research.google.com/notebooks/intro.ipynb

• Scikit-learn Logistic Regression docs: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

• FEMA Confusion Matrix Explanation (for classifiers): https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

• Tableau basics: https://www.tableau.com/learn/training

• Chrome blocked downloads fix: https://support.google.com/chrome/answer/2898334

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