# 🔹 SECTION A – Dataset Understanding

## ✅ Step 1: Load the Dataset

1. Open **Power BI Desktop**
2. Click **Home → Get Data → Text/CSV**
3. Select `netflix_titles.csv`
4. Click **Load**

---

## 1️⃣ How many rows and columns?

### Steps:

1. Go to **Data View**
2. Bottom left corner shows total rows.
3. Go to **Transform Data → Power Query**
4. It shows number of columns.

Or:

👉 Transform Data → View → Column distribution

---

## 2️⃣ Which columns are categorical?

Categorical columns:

* type
* title
* director
* cast
* country
* rating
* listed_in

These are text-based columns.

---

## 3️⃣ Which column contains date information?

👉 `date_added`

### Steps to check:

1. Select column
2. Go to **Column Tools**
3. Check Data Type = Date

---

## 4️⃣ Difference between release_year and date_added

* `release_year` → Year content was originally released
* `date_added` → When Netflix added it to the platform

---

## 5️⃣ Why blank values?

Reasons:

* Missing information
* Not applicable (e.g., some TV shows may not have directors)
* Data entry issues

---

# 🔹 SECTION B – Data Cleaning & Preparation

## ✅ Step 2: Open Power Query

Home → Transform Data

---

## 6️⃣ Four Data Cleaning Steps

✔ Remove duplicates
✔ Change data types
✔ Handle missing values
✔ Split multi-value columns (like listed_in)

---

## 7️⃣ Handle Missing Director Values

### Option 1 (Recommended in exam):

Replace blanks with "Not Available"

Steps:

1. Select `director`
2. Replace Values
3. Replace null with `"Not Available"`

OR

Filter out blanks if required.

---

## 8️⃣ Why Change Data Types?

* Ensures correct calculations
* Enables proper visualizations
* Improves performance
* Prevents errors in DAX

Example:
`date_added` must be Date type.

---

## 9️⃣ Problem with Duration Column

It contains:

* “90 min”
* “2 Seasons”

This mixes numeric + text → cannot calculate properly.

### Solution:

Split into:

* Duration_Number
* Duration_Type

Steps:

1. Select duration
2. Split column by delimiter (space)
3. Rename columns

---

## 🔟 Why Split listed_in?

It contains multiple genres in one cell:
Example:
“Drama, Romantic, International”

Splitting helps:

* Better filtering
* Accurate genre analysis

Steps:

1. Select listed_in
2. Split by delimiter (,)
3. Use “Split into rows”

---

# 🔹 SECTION C – Basic Calculations (DAX)

Go to **Report View**

---

## 1️⃣1️⃣ Count Total Titles

Go to Modeling → New Measure

```DAX
Total Titles = COUNTROWS('netflix_titles')
```

Use in Card Visual.

---

## 1️⃣2️⃣ Number of Movies Only

```DAX
Total Movies = 
CALCULATE(
    COUNTROWS('netflix_titles'),
    'netflix_titles'[type] = "Movie"
)
```

---

## 1️⃣3️⃣ How Slicers Help?

Steps to add slicer:

1. Insert → Slicer
2. Drag `release_year` or `country`

They:

* Filter all visuals
* Make dashboard interactive

---

## 1️⃣4️⃣ Column vs Measure

| Column          | Measure                |
| --------------- | ---------------------- |
| Stored in table | Calculated dynamically |
| Static          | Changes with filters   |
| Row-level       | Aggregated             |

---

## 1️⃣5️⃣ Why KPI Cards?

They:

* Show important numbers
* Easy to understand
* Highlight performance

Example:

* Total Titles
* Total Movies
* Total TV Shows

---

# 🔹 SECTION D – Visualization & Interpretation

---

## 1️⃣6️⃣ Best Visual for Trend Over Years

📊 Line Chart

Steps:

1. Insert Line Chart
2. Axis → release_year
3. Values → Total Titles

Reason:
Shows growth clearly over time.

---

## 1️⃣7️⃣ Compare Movies vs TV Shows

📊 Clustered Column Chart or Pie Chart

Steps:

1. Axis → type
2. Values → Count of title

---

## 1️⃣8️⃣ Three Dashboard Design Practices

✔ Use consistent colors
✔ Avoid clutter
✔ Align visuals properly
✔ Use meaningful titles

---

## 1️⃣9️⃣ If Most Titles are TV-MA?

It indicates:

* Platform focuses on mature/adult audience
* More adult content
* Not primarily child-focused

---

## 2️⃣0️⃣ Three Business Insights from Netflix Dataset

1. Netflix content increased rapidly after 2015.
2. Movies are more than TV Shows.
3. Most content is rated TV-MA → adult-focused audience.

---

# 🎯 Final Dashboard Structure (For Exam)

Your dashboard should include:

✔ KPI Cards:

* Total Titles
* Total Movies
* Total TV Shows

✔ Line Chart:

* Titles by Release Year

✔ Bar Chart:

* Movies vs TV Shows

✔ Bar Chart:

* Top 10 Countries

✔ Slicer:

* Release Year
* Type
* Country

---

Vaishnavi, below are the answers written in **proper Power BI exam format** (the way you should write in your RGCMS paper).
You can directly copy this style in exam. 👇

---

# 📊 NETFLIX DATASET – POWER BI ANSWERS FORMAT

---

# 🔹 SECTION A – Dataset Understanding

### 1. How many rows and columns are in the dataset?

After loading the dataset in Power BI:

* Go to **Data View**
* Total rows: 8807
* Total columns: 12

---

### 2. Which columns are categorical?

Categorical columns are text-based fields:

* type
* title
* director
* cast
* country
* rating
* listed_in
* description

---

### 3. Which column contains date information?

The column containing date information is:

👉 `date_added`

(Data type changed to Date in Column Tools.)

---

### 4. Difference between release_year and date_added

* `release_year` → Year when the movie/TV show was originally released.
* `date_added` → Date when content was added to Netflix platform.

---

### 5. Why might some columns contain blank values?

* Missing information
* Data entry errors
* Information not available
* Not applicable (e.g., director missing)

---

# 🔹 SECTION B – Data Cleaning & Preparation

---

### 6. Four data cleaning steps performed:

1. Removed duplicate rows
2. Changed data types correctly
3. Replaced null values
4. Split multi-value columns (listed_in, cast)

---

### 7. Handling missing values in director column

In Power Query:

* Select director column
* Replace null values with “Not Available”

OR

* Filter out blank rows if required.

---

### 8. Why should we change data types correctly?

* Ensures accurate calculations
* Prevents errors in visuals
* Improves performance
* Enables correct DAX formulas

Example: date_added must be Date type.

---

### 9. Why is mixed duration column a problem?

The duration column contains:

* “90 min”
* “2 Seasons”

This mixes text and numeric values.

Problem:

* Cannot perform numerical analysis.
* Causes incorrect aggregations.

Solution:

* Split into Duration_Number and Duration_Type.

---

### 10. Purpose of splitting listed_in

The listed_in column contains multiple genres separated by commas.

Splitting helps:

* Better filtering
* Genre-wise analysis
* Accurate visualizations

---

# 🔹 SECTION C – Basic Calculations (DAX)

---

### 11. DAX formula to count total titles

```DAX
Total Titles = COUNTROWS('netflix_titles')
```

---

### 12. Calculate number of movies only

```DAX
Total Movies = 
CALCULATE(
    COUNTROWS('netflix_titles'),
    'netflix_titles'[type] = "Movie"
)
```

---

### 13. How do slicers help in dashboards?

Slicers:

* Filter data interactively
* Allow dynamic report analysis
* Improve user experience

Example: Slicer on release_year filters all visuals.

---

### 14. Difference between Column and Measure

| Column                | Measure                |
| --------------------- | ---------------------- |
| Calculated row by row | Calculated dynamically |
| Stored in table       | Not stored             |
| Static                | Changes with filters   |

---

### 15. Why are KPI cards useful?

* Highlight key metrics
* Easy to understand
* Improve dashboard readability

Example:

* Total Titles
* Total Movies
* Total TV Shows

---

# 🔹 SECTION D – Visualization & Interpretation

---

### 16. Best visual to show trend over years

Line Chart

Reason:
Shows increase/decrease over time clearly.

Axis → release_year
Values → Total Titles

---

### 17. Best visual to compare Movies vs TV Shows

Clustered Column Chart

Axis → type
Values → Count of title

---

### 18. Three good practices for clean dashboard design

1. Use consistent color theme
2. Avoid overcrowding visuals
3. Add proper titles and labels

---

### 19. If most titles are TV-MA, what does it indicate?

* Platform targets adult audience
* More mature content
* Less child-focused content

---

### 20. Three business insights

1. Netflix content increased rapidly after 2015.
2. Movies are more than TV Shows.
3. Majority content is rated TV-MA (adult audience focus).

---

# 🎯 Recommended Dashboard Layout (Exam Ready)

Top Row:

* Card → Total Titles
* Card → Total Movies
* Card → Total TV Shows

Middle:

* Line Chart → Titles by Year
* Column Chart → Movies vs TV Shows

Bottom:

* Bar Chart → Top Countries
* Slicer → Release Year

---

If you want, I can now give you:

✔ 10 Viva questions with answers
✔ Advanced DAX formulas
✔ Step-by-step full dashboard creation explanation

Tell me what you need next 😊