Interview¶

Interviewer: "Can you explain the purpose of a train/test split in machine learning?"¶

The train/test split is a fundamental technique used in machine learning to evaluate how well a model generalizes to unseen data. The idea is to divide the available dataset into two parts:

The training set, which is used to train the model — meaning the model learns the patterns, features, and relationships from this data.

The test set, which is used only after the training is complete, to test how well the model performs on new, unseen data.

This helps in identifying whether the model is overfitting — that is, performing well on training data but poorly on new data — or if it’s generalizing well to different scenarios.

A common practice is to split the data into 80% for training and 20% for testing, but this can vary depending on the size and nature of the dataset.

💡 Code Walkthrough (Interview Explanation) Interviewer: "Can you show a simple example?"

Sure. Here’s a quick example using the Iris dataset with a Random Forest classifier:

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split dataset: 80% train, 20% test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Predict on test data
y_pred = model.predict(X_test)

# Evaluate model
accuracy = accuracy_score(y_test, y_pred)
print("Test Accuracy:", accuracy)

In this example, we first load the dataset and split it using train_test_split. Then we train a RandomForestClassifier on the training set. Finally, we test the model on the test set and measure accuracy using accuracy_score.

This way, we get a clear idea of how well our model might perform in a real-world scenario.

Great! Let's do a mock interview focused on model evaluation, starting from the train/test split, and moving into related topics like overfitting, cross-validation, and evaluation metrics.

🧪 MOCK INTERVIEW SESSION – MODEL EVALUATION¶

🎤 Interviewer¶

"Can you explain the purpose of train/test split in machine learning?"

✅ Your Answer¶

Yes, the train/test split is a fundamental technique used to evaluate the performance of a machine learning model.

We divide the dataset into two parts — the training set is used to train the model, and the test set is used to evaluate how well it generalizes to unseen data.

This helps ensure that the model doesn’t just memorize the training data (i.e., overfit), but can also make accurate predictions on new, real-world data.

🎤 Interviewer¶

"You mentioned overfitting. Can you explain what overfitting is and how to detect it?"

✅ Your Answer¶

Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and random fluctuations.

As a result, it performs very well on the training set but poorly on the test set.

We can detect overfitting by comparing training and test accuracy:

If training accuracy is high and test accuracy is low, it’s a sign of overfitting.

To prevent it, we can:

Use simpler models

Apply regularization

Get more training data

Use techniques like cross-validation for more robust evaluation

🎤 Interviewer¶

"Interesting. What is cross-validation, and how is it better than a simple train/test split?"

✅ Your Answer¶

Cross-validation is a more reliable method of evaluating a model, especially when the dataset is small. The most common type is k-fold cross-validation, where the data is split into k parts. The model is trained on k-1 parts and tested on the remaining part. This process repeats k times, and the results are averaged.

Compared to a simple train/test split, cross-validation:

Reduces variance in the evaluation

Provides a more stable and unbiased estimate of model performance

Helps ensure that every data point is used for both training and testing

🎤 Interviewer¶

"What metrics would you use to evaluate a classification model?"

✅ Your Answer¶

It depends on the problem, but commonly used metrics for classification include:

Accuracy: Ratio of correctly predicted instances. Good when classes are balanced.

Precision: How many predicted positives are actually positive. Useful when false positives are costly.

Recall: How many actual positives were correctly predicted. Important when missing positives is costly.

F1-score: Harmonic mean of precision and recall — good balance when data is imbalanced.

Confusion Matrix: Gives a detailed breakdown of true/false positives and negatives.

For probabilistic models, I might also look at ROC-AUC to evaluate performance at different thresholds.

🎤 Interviewer¶

"Let's say you get 95% accuracy on your model. Is that enough to consider your model good?"

✅ Your Answer¶

Not necessarily. Accuracy alone can be misleading — especially in imbalanced datasets.

For example, if 95% of the data belongs to class A and only 5% to class B, a model that predicts only class A will still get 95% accuracy, but it fails at detecting class B entirely.

In such cases, I would use metrics like precision, recall, and F1-score, and examine the confusion matrix to get a complete picture of the model’s performance.

That's totally okay — this is exactly what practice is for!

Let me give you a solid interview-style answer you can learn and rephrase in your own words later.

✅ Model Interview Answer:¶

A confusion matrix is a performance evaluation tool used for classification problems. It is a 2x2 table for binary classification that compares the actual labels with the predicted labels made by the model.

The matrix includes:

True Positives (TP): The model correctly predicted the positive class.

True Negatives (TN): The model correctly predicted the negative class.

False Positives (FP): The model predicted positive when it was actually negative. (Type I error)

False Negatives (FN): The model predicted negative when it was actually positive. (Type II error)

From the confusion matrix, we can calculate several key metrics:

Accuracy = (TP + TN) / (Total)

Precision = TP / (TP + FP)

Recall = TP / (TP + FN)

F1 Score = 2 × (Precision × Recall) / (Precision + Recall)

It helps us understand where the model is making mistakes — for example, whether it's misclassifying more positives or negatives — which is especially important when the classes are imbalanced.

📘 Pro Tip¶

In interviews, it’s okay to say:

“I’m not sure, but I’d like to learn about it.” That shows humility and a willingness to grow — qualities interviewers respect.

"Can you explain the difference between precision and recall? When would you prefer one over the other?"

Precision and recall are two important metrics used to evaluate the performance of classification models, especially when dealing with imbalanced datasets.

Precision is the ratio of true positives to all predicted positives. It answers: "Of all the instances the model predicted as positive, how many were actually correct?" Formula: Precision = TP / (TP + FP)

Recall is the ratio of true positives to all actual positives. It answers: "Of all the actual positive cases, how many did the model correctly identify?" Formula: Recall = TP / (TP + FN)

🧠 When to prefer one over the other: Use precision when false positives are costly. For example, in spam detection, you don’t want to wrongly mark a real email as spam.

Use recall when false negatives are more serious. For example, in disease detection, you want to catch as many actual positive cases as possible — missing one could be dangerous.

In many cases, we balance both using the F1-score, which is the harmonic mean of precision and recall.

What is the F1-score? The F1-score is a metric used to evaluate the performance of a classification model, especially when dealing with imbalanced datasets.

It is the harmonic mean of precision and recall, and provides a single score that balances both concerns:

F1 = 2 \times \frac{{\text{Precision} \times \text{Recall}}}{{\text{Precision} + \text{Recall}}} ]

If a model has high precision but low recall, or vice versa, the F1-score will be lower — so it rewards models that have a good balance of both.

It's especially useful when false positives and false negatives are both important to consider.

🔍 Example Use Case: In medical diagnosis or fraud detection, where missing a positive case or raising too many false alarms both have serious consequences, the F1-score gives a better picture of model performance than accuracy alone.

Excellent — this is a classic data analysis interview question often asked to test your analytical thinking, problem-solving approach, and business understanding.

🎯 Interview Question:¶

"Revenue dropped last month. What would you check?"

✅ Model Interview Answer (Detailed & Structured):¶

If revenue dropped last month, I would follow a structured approach to identify the root cause. Here’s how I would break it down:

🧩 1. Verify the Drop Is Real (Data Sanity Check)¶

First, I would ensure the data is correct and up-to-date:
Are we comparing the same time frames (e.g., full month vs partial month)?
Were there any issues in data pipelines, delays, or missing entries?
Are currency conversions or reporting time zones consistent?

📊 2. Break Revenue into Components¶

Revenue is typically calculated as:

\[ \text{Revenue} = \text{Number of Transactions} \times \text{Average Order Value (AOV)} \]

So, I would check:

🔢 Transaction Count – Did fewer customers buy something?
💸 AOV – Are people spending less per order?
🧾 Refunds or Cancellations – Did returns increase?

📈 3. Segment the Drop¶

Break down revenue by key dimensions to localize the issue:

By Product or Category – Did a specific product or service decline?
By Customer Segment – Are certain customer groups (new vs returning) behaving differently?
By Geography – Did the drop happen in a specific region?
By Channel – Did revenue drop in paid ads, organic search, referrals, etc.?

📅 4. Compare Trends Over Time¶

How does this month compare with:
Last month?
The same month last year (to account for seasonality)?
Are there any long-term trends or one-off events?

⚠️ 5. Look for External Factors¶

Market changes, economic conditions, competitor actions.
Did any promotions end?
Were there product stockouts or supply chain issues?
Did any website or app outages occur that may have affected sales?

🛠️ 6. Run Diagnostics with KPIs¶

I would check supporting KPIs such as:

Website traffic & conversion rate
Cart abandonment rate
Marketing campaign performance (CTR, ROI, etc.)
Customer acquisition and retention rates

🧠 7. Talk to Stakeholders¶

Collaborate with Sales, Marketing, and Product teams.
Any known pricing changes, feature rollouts, or customer feedback trends?

📌 Conclusion¶

By systematically checking these areas, I can isolate where the revenue drop came from and then provide data-backed recommendations to address the issue — whether it's boosting traffic, improving conversion rates, adjusting pricing, or fixing operational problems.

Awesome! Here's a hands-on example of how to investigate a monthly revenue drop using Python (Pandas). This is something you might do in a Jupyter Notebook or an interview take-home test.

📊 Revenue Drop Investigation – Python Notebook Walkthrough¶

Let’s assume you have an e-commerce transaction dataset like this:

order_id	customer_id	order_date	amount	product_category	region	channel
101	1	2024-03-01	200	Electronics	North	SEO
102	2	2024-04-01	150	Fashion	South	Paid Ads
...	...	...	...	...	...	...

✅ Step-by-Step Revenue Drop Analysis in Python¶

import pandas as pd

# Load data
df = pd.read_csv("ecommerce_transactions.csv", parse_dates=["order_date"])

# Add 'month' column for grouping
df["month"] = df["order_date"].dt.to_period("M")

# Step 1: Monthly revenue trend
monthly_revenue = df.groupby("month")["amount"].sum()
print(monthly_revenue)
monthly_revenue.plot(kind="bar", title="Monthly Revenue")

🔍 Step 2: Drill down — Check key components¶

# Number of orders per month
monthly_orders = df.groupby("month")["order_id"].nunique()

# Average Order Value (AOV)
monthly_aov = monthly_revenue / monthly_orders

print("Orders:\n", monthly_orders)
print("AOV:\n", monthly_aov)

📦 Step 3: Segment by category, region, or channel¶

# Compare last 2 months by category
df_last_2_months = df[df["month"].isin(df["month"].unique()[-2:])]

category_revenue = df_last_2_months.groupby(["month", "product_category"])["amount"].sum().unstack()
category_revenue.plot(kind="bar", title="Revenue by Category")

# Segment by region
region_revenue = df_last_2_months.groupby(["month", "region"])["amount"].sum().unstack()
region_revenue.plot(kind="bar", title="Revenue by Region")

📈 Step 4: Channel performance check¶

channel_revenue = df_last_2_months.groupby(["month", "channel"])["amount"].sum().unstack()
channel_revenue.plot(kind="bar", title="Revenue by Channel")

📌 Final Step: Summary Table¶

summary = pd.DataFrame({
    "Revenue": monthly_revenue,
    "Orders": monthly_orders,
    "AOV": monthly_aov
})
print(summary.tail(2))  # Last 2 months

💡 Interpretation:¶

From the plots and tables, you can:

Spot where the revenue dropped (channel, category, region, etc.)
See if it’s due to fewer orders, lower AOV, or both
Understand which segment contributed most to the change

Great! Here's the SQL version of how you'd investigate a drop in monthly revenue — using real-world queries that apply to most relational databases like PostgreSQL, MySQL, or BigQuery.

🧾 Assumptions:¶

You're working with a table named orders that contains the following fields:

orders(
  order_id INT,
  customer_id INT,
  order_date DATE,
  amount DECIMAL,
  product_category VARCHAR,
  region VARCHAR,
  channel VARCHAR
)

🔍 Step-by-Step SQL Analysis of Revenue Drop¶

✅ 1. Monthly Revenue Trend¶

SELECT
  DATE_TRUNC('month', order_date) AS month,
  SUM(amount) AS total_revenue
FROM orders
GROUP BY month
ORDER BY month;

📉 2. Orders and Average Order Value (AOV)¶

SELECT
  DATE_TRUNC('month', order_date) AS month,
  COUNT(DISTINCT order_id) AS total_orders,
  SUM(amount) AS total_revenue,
  ROUND(SUM(amount) * 1.0 / COUNT(DISTINCT order_id), 2) AS avg_order_value
FROM orders
GROUP BY month
ORDER BY month;

🧱 3. Breakdown by Product Category¶

SELECT
  DATE_TRUNC('month', order_date) AS month,
  product_category,
  SUM(amount) AS category_revenue
FROM orders
WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '2 months'
GROUP BY month, product_category
ORDER BY month, category_revenue DESC;

🗺️ 4. Breakdown by Region¶

SELECT
  DATE_TRUNC('month', order_date) AS month,
  region,
  SUM(amount) AS regional_revenue
FROM orders
WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '2 months'
GROUP BY month, region
ORDER BY month, regional_revenue DESC;

📣 5. Channel-wise Revenue¶

SELECT
  DATE_TRUNC('month', order_date) AS month,
  channel,
  SUM(amount) AS channel_revenue
FROM orders
WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '2 months'
GROUP BY month, channel
ORDER BY month, channel_revenue DESC;

🧾 6. Compare Two Specific Months¶

SELECT
  TO_CHAR(order_date, 'YYYY-MM') AS month,
  SUM(amount) AS total_revenue,
  COUNT(DISTINCT order_id) AS total_orders,
  ROUND(SUM(amount) * 1.0 / COUNT(DISTINCT order_id), 2) AS avg_order_value
FROM orders
WHERE order_date BETWEEN '2024-03-01' AND '2024-04-30'
GROUP BY month
ORDER BY month;

🧠 Optional Insights¶

You can also analyze:

🛒 High-return products (WHERE amount < 0)
🧍‍♂️ Revenue from new vs returning customers
📦 Stock availability logs (if available)

Got it! Here's a sample dataset schema and some example rows matching the SQL queries above — so you can load it into your SQL environment and try the queries yourself.

📋 Sample Data for `orders` Table¶

order_id	customer_id	order_date	amount	product_category	region	channel
101	1	2024-03-10	200	Electronics	North	SEO
102	2	2024-03-15	150	Fashion	South	Paid Ads
103	3	2024-04-01	300	Electronics	East	Organic
104	1	2024-04-05	100	Home & Garden	West	Referral
105	4	2024-04-20	250	Fashion	North	Paid Ads
106	5	2024-05-02	350	Electronics	South	SEO
107	6	2024-05-15	400	Sports	East	Organic
108	7	2024-05-20	150	Fashion	West	Paid Ads

How to Load This Sample Data into SQL (Example for PostgreSQL)¶

CREATE TABLE orders (
  order_id INT PRIMARY KEY,
  customer_id INT,
  order_date DATE,
  amount NUMERIC(10,2),
  product_category VARCHAR(50),
  region VARCHAR(50),
  channel VARCHAR(50)
);

INSERT INTO orders VALUES
(101, 1, '2024-03-10', 200, 'Electronics', 'North', 'SEO'),
(102, 2, '2024-03-15', 150, 'Fashion', 'South', 'Paid Ads'),
(103, 3, '2024-04-01', 300, 'Electronics', 'East', 'Organic'),
(104, 1, '2024-04-05', 100, 'Home & Garden', 'West', 'Referral'),
(105, 4, '2024-04-20', 250, 'Fashion', 'North', 'Paid Ads'),
(106, 5, '2024-05-02', 350, 'Electronics', 'South', 'SEO'),
(107, 6, '2024-05-15', 400, 'Sports', 'East', 'Organic'),
(108, 7, '2024-05-20', 150, 'Fashion', 'West', 'Paid Ads');

Next Steps¶

Run the SQL queries I shared earlier on this sample data.
You’ll be able to see revenue trends by month, category, region, and channel.
This will help you understand how to identify drops and segment revenue changes.

Got it! Here’s a clear step-by-step guide to load your sample data into Power BI and create key visuals to analyze the revenue drop, just like in your SQL/Python analysis.

How to Analyze Revenue Drop in Power BI¶

Step 1: Prepare Your Sample Data¶

Create a CSV file named orders.csv with these columns and sample rows:

order_id,customer_id,order_date,amount,product_category,region,channel
101,1,2024-03-10,200,Electronics,North,SEO
102,2,2024-03-15,150,Fashion,South,Paid Ads
103,3,2024-04-01,300,Electronics,East,Organic
104,1,2024-04-05,100,Home & Garden,West,Referral
105,4,2024-04-20,250,Fashion,North,Paid Ads
106,5,2024-05-02,350,Electronics,South,SEO
107,6,2024-05-15,400,Sports,East,Organic
108,7,2024-05-20,150,Fashion,West,Paid Ads

Step 2: Load Data into Power BI¶

Open Power BI Desktop.
Click Get Data > Text/CSV and select your orders.csv.
Click Load to import the data.

Step 3: Create Date and Month Columns¶

Go to the Data view.
Select the order_date column.
In the ribbon, use Column Tools > Date > Month to extract the month (or create a new column with DAX):

Month = FORMAT('orders'[order_date], "YYYY-MM")

Step 4: Build Key Visuals¶

1. Monthly Revenue Trend¶

Create a Line Chart or Column Chart.
Axis: Month
Values: Sum of amount
This shows how total revenue changes month to month.

2. Number of Orders and Average Order Value¶

Create a Card visual for Total Orders:

Total Orders = DISTINCTCOUNT('orders'[order_id])

Create another Card visual for Average Order Value (AOV):

AOV = DIVIDE(SUM('orders'[amount]), DISTINCTCOUNT('orders'[order_id]))

Or create a table visual with these measures grouped by Month.

3. Revenue by Product Category¶

Use a Stacked Column Chart.
Axis: Month
Legend: product_category
Values: Sum of amount

4. Revenue by Region¶

Use another Stacked Column Chart.
Axis: Month
Legend: region
Values: Sum of amount

5. Revenue by Channel¶

Repeat similar steps with a Stacked Column Chart for channel.

Step 5: Add Filters and Slicers¶

Add Slicers for filtering by Month, Region, Product Category, or Channel to drill down interactively.

Step 6: Interpret Your Report¶

Compare revenue across months and segments.
Spot the month with revenue drop.
Identify whether fewer orders or lower AOV caused it.
Look for which categories, regions, or channels contributed most to the drop.

Interview¶

Interviewer: "Can you explain the purpose of a train/test split in machine learning?"¶

🧪 MOCK INTERVIEW SESSION – MODEL EVALUATION¶

🎤 Interviewer¶

✅ Your Answer¶

🎤 Interviewer¶

✅ Your Answer¶

🎤 Interviewer¶

✅ Your Answer¶

🎤 Interviewer¶

✅ Your Answer¶

🎤 Interviewer¶

✅ Your Answer¶

✅ Model Interview Answer:¶

📘 Pro Tip¶

🎯 Interview Question:¶

✅ Model Interview Answer (Detailed & Structured):¶

🧩 1. Verify the Drop Is Real (Data Sanity Check)¶

📊 2. Break Revenue into Components¶

📈 3. Segment the Drop¶

📅 4. Compare Trends Over Time¶

⚠️ 5. Look for External Factors¶

🛠️ 6. Run Diagnostics with KPIs¶

🧠 7. Talk to Stakeholders¶

📌 Conclusion¶

📊 Revenue Drop Investigation – Python Notebook Walkthrough¶

✅ Step-by-Step Revenue Drop Analysis in Python¶

🔍 Step 2: Drill down — Check key components¶

📦 Step 3: Segment by category, region, or channel¶

📈 Step 4: Channel performance check¶

📌 Final Step: Summary Table¶

💡 Interpretation:¶

🧾 Assumptions:¶

🔍 Step-by-Step SQL Analysis of Revenue Drop¶

✅ 1. Monthly Revenue Trend¶

📉 2. Orders and Average Order Value (AOV)¶

🧱 3. Breakdown by Product Category¶

🗺️ 4. Breakdown by Region¶

📣 5. Channel-wise Revenue¶

🧾 6. Compare Two Specific Months¶

🧠 Optional Insights¶

📋 Sample Data for orders Table¶

How to Load This Sample Data into SQL (Example for PostgreSQL)¶

Next Steps¶

How to Analyze Revenue Drop in Power BI¶

Step 1: Prepare Your Sample Data¶

Step 2: Load Data into Power BI¶

Step 3: Create Date and Month Columns¶

Step 4: Build Key Visuals¶

1. Monthly Revenue Trend¶

2. Number of Orders and Average Order Value¶

3. Revenue by Product Category¶

4. Revenue by Region¶

5. Revenue by Channel¶

Step 5: Add Filters and Slicers¶

Step 6: Interpret Your Report¶

📋 Sample Data for `orders` Table¶