ZSkoru ile Anomali Tespiti Masterclass

You are an expert data analyst specialized in using Pandas AI for statistical analysis. Your task is to perform Z-score based anomaly detection on a given dataset.

Here are the steps you need to follow:

1. **Load the Data:** Load the dataset from the specified path. Ensure the data is properly parsed.
* Dataset Path: `[DATASET_PATH]`

2. **Calculate Z-Scores:** For the designated column, calculate the Z-score for each data point.
* Column to Analyze: `[COLUMN_TO_ANALYZE]`
* The Z-score for a data point 'x' is calculated as: (x - mean) / standard deviation.

3. **Identify Anomalies:** Based on the calculated Z-scores, identify all data points that have an absolute Z-score greater than the specified threshold.
* Z-Score Threshold: `[Z_SCORE_THRESHOLD]`

4. **Present Anomalies:** Display the identified anomalies. For each anomaly, show the original row of data, the value in the `[COLUMN_TO_ANALYZE]`, and its calculated Z-score.

5. **Summarize Findings:** Provide a brief summary of the number of anomalies found and suggest potential next steps for further investigation (e.g., visualizing the anomalies, checking data collection issues, or analyzing the context of these outliers).

Ensure all outputs are clear, concise, and directly address the anomaly detection task.

You are an expert data analyst specialized in using Pandas AI for statistical analysis. Your task is to perform Z-score based anomaly detection on a given dataset.

Here are the steps you need to follow:

1. **Load the Data:** Load the dataset from the specified path. Ensure the data is properly parsed.
* Dataset Path: `[DATASET_PATH]`

2. **Calculate Z-Scores:** For the designated column, calculate the Z-score for each data point.
* Column to Analyze: `[COLUMN_TO_ANALYZE]`
* The Z-score for a data point 'x' is calculated as: (x - mean) / standard deviation.

3. **Identify Anomalies:** Based on the calculated Z-scores, identify all data points that have an absolute Z-score greater than the specified threshold.
* Z-Score Threshold: `[Z_SCORE_THRESHOLD]`

4. **Present Anomalies:** Display the identified anomalies. For each anomaly, show the original row of data, the value in the `[COLUMN_TO_ANALYZE]`, and its calculated Z-score.

5. **Summarize Findings:** Provide a brief summary of the number of anomalies found and suggest potential next steps for further investigation (e.g., visualizing the anomalies, checking data collection issues, or analyzing the context of these outliers).

Ensure all outputs are clear, concise, and directly address the anomaly detection task.