**Handling Anomalies and Missing Data in Datasets**

**1. Anomalies (Outliers):**

**Definition:** Data points that differ significantly from other observations in the dataset.

**Detection:**

**Visual Inspection:**Scatter plots, Box plots.**Statistical Tests:**Z-score, IQR.

**Handling Techniques:** a. **Deletion:** Remove outlier data points.

- Pros: Quick and simple.
- Cons: May lose valuable information.

b. **Transformation:** Apply log or square root transformations to reduce variance.

c. **Capping:** Cap the outlier to a maximum/minimum value.

d. **Imputation:** Replace outliers with statistical measures such as mean, median, or mode.

e. **Binning:** Convert numerical variable into categorical bins.

**2. Missing Data:**

**Types of Missingness:** a. **MCAR (Missing Completely At Random):** Missingness is not related to any other variable. b. **MAR (Missing At Random):** Missingness is related to some other observed variable. c. **MNAR (Missing Not At Random):** Missingness is related to the missing data itself.

**Detection:**

- Use libraries like
`pandas`

(e.g.,`dataframe.isnull().sum()`

) or visualization tools like`missingno`

.

**Handling Techniques:** a. **Listwise Deletion:** Remove any row with a missing value.

- Pros: Simple.
- Cons: Risk of losing a lot of data.

b. **Pairwise Deletion:** Use available data for statistical analysis.

c. **Mean/Median/Mode Imputation:** Fill missing values with mean, median, or mode of the column.

- Good for MCAR.

d. **Forward/Backward Fill:** Use the previous or next data point to fill missing values. Useful for time series data.

e. **Model-Based Imputation:** Use regression, KNN, or other models to estimate and impute missing values.

f. **Multiple Imputation:** Generate multiple predictions for every missing value.

g. **Use Algorithms Robust to Missing Values:** Some algorithms (like XGBoost) can handle missing values.

**General Recommendations:**

**Understand Your Data:**Always explore and visualize your data before handling anomalies or missing values.**Consider Data’s Context:**Understand the potential real-world implications of removing or imputing data.**Validate:**After handling anomalies and missing values, validate the results using appropriate statistical tests or performance me