Find Interview Questions for Pleasant solutions

Right Answer:
A web server handles an HTTP request by following these steps:

1. **Receive Request**: The server listens for incoming HTTP requests on a specific port (usually port 80 for HTTP or port 443 for HTTPS).
2. **Parse Request**: It parses the request to extract the method (GET, POST, etc.), URL, headers, and body.
3. **Process Request**: The server determines how to respond based on the request. This may involve retrieving files, querying a database, or executing server-side scripts.
4. **Generate Response**: It creates an HTTP response, which includes a status code (like 200 for success), headers, and the requested content (like HTML, JSON, etc.).
5. **Send Response**: The server sends the response back to the client (usually a web browser) over the network.
6. **Log Request**: Optionally, the server logs the request details for monitoring and analysis.

Right Answer:
Outliers are data points that significantly differ from the rest of the dataset. They can skew results and affect statistical analyses. To handle outliers, you can:

1. Identify them using methods like the IQR (Interquartile Range) or Z-scores.
2. Remove them if they are errors or irrelevant.
3. Transform them using techniques like log transformation.
4. Use robust statistical methods that are less affected by outliers.
5. Analyze them separately if they provide valuable insights.

Right Answer:
Correlation is a statistical measure that indicates the extent to which two variables fluctuate together, while causation implies that one variable directly affects or causes a change in another variable.

Right Answer:
Some common data visualization techniques include:

1. Bar Charts
2. Line Graphs
3. Pie Charts
4. Scatter Plots
5. Histograms
6. Heat Maps
7. Box Plots
8. Area Charts
9. Tree Maps
10. Bubble Charts

Right Answer:
Data analysis is the process of inspecting, cleaning, and modeling data to discover useful information, draw conclusions, and support decision-making. It is important because it helps organizations make informed decisions, identify trends, improve efficiency, and solve problems based on data-driven insights.

Right Answer:
To handle missing data in a dataset, you can use the following methods:

1. **Remove Rows/Columns**: Delete rows or columns with missing values if they are not significant.
2. **Imputation**: Fill in missing values using techniques like mean, median, mode, or more advanced methods like KNN or regression.
3. **Flagging**: Create a new column to indicate missing values for analysis.
4. **Predictive Modeling**: Use algorithms to predict and fill in missing values based on other data.
5. **Leave as Is**: In some cases, you may choose to leave missing values if they are meaningful for analysis.