Add handling missing data page

Data_Manipulation/handling_missing_data.md

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+
+---
+title: Handling Missing Data
+parent: Data Manipulation
+has_children: false
+nav_order: 1
+mathjax: true
+---
+
+Introduction
+
+Handling Missing Data
+Missing data occurs when some observations in a dataset do not have recorded values for certain variables. This is common in surveys, experiments, and administrative datasets. Handling missing data is an important step in data cleaning. If missing values are ignored, they can lead to biased results or reduce the reliability of statistical models.
+This page introduces several common techniques for identifying and handling missing data.
+
+Keep in Mind
+- Missing data may occur because of survey non-response, data entry errors, or equipment failures.
+- Some statistical software automatically removes rows with missing values.
+- Removing missing data can reduce the sample size.
+- Imputation methods estimate missing values but may introduce bias if used incorrectly.
+
+Also Consider
+- Mean Imputation – replacing missing values with the mean of the variable.
+- Multiple Imputation – generating several possible values for missing data to reflect uncertainty.
+- Data Cleaning – identifying incorrect or inconsistent data.
+
+Python Example
+
+```python
+import pandas as pd
+
+# Load dataset
+data = pd.read_csv("data.csv")
+
+# Check missing values
+print(data.isnull().sum())
+
+# Drop rows with missing values
+data_clean = data.dropna()
+
+# Replace missing values with mean
+data['income'] = data['income'].fillna(data['income'].mean())
+```
+
+R Example
+
+```r
+data <- read.csv("data.csv")
+
+# Check missing values
+colSums(is.na(data))
+
+# Remove rows with missing values
+data_clean <- na.omit(data)
+
+# Replace missing values with mean
+data$income[is.na(data$income)] <- mean(data$income, na.rm = TRUE)
+```
+
+Excel Instructions
+
+1. Open your dataset in Excel.
+2. Select the column with missing values.
+3. Use the Filter tool to locate blank cells.
+4. Replace blank cells with a value such as the mean.
+5. Alternatively, remove rows containing missing values.
+
+