Stats-and-data-handling

Loading the Data:

Loaded the Titanic dataset and displayed the first few rows.

Data Cleaning:

Removed duplicate rows.

Handled outliers using the Z-score method, keeping rows where all numeric feature Z-scores are less than 3.

Corrected data types for columns if necessary.

Transformation:

Applied logarithmic and square root transformations to the 'Fare' column.

Normalization:

Used MinMaxScaler to normalize 'Fare' and 'Age' columns to the range [0, 1].

Standardization:

Used StandardScaler to standardize 'Fare' and 'Age' columns to zero mean and unit variance.

Encoding:

Applied One-Hot Encoding to the 'Embarked' column.

Applied Label Encoding to the 'Sex' column.

Imputation:

Filled missing values in 'Age' and 'Fare' columns with their respective means.

Handling Missing Data:

Dropped rows that contain any missing values.

Dimensionality Reduction:

Applied PCA to reduce the dataset to 2 components.

Data Integration:

For demonstration, combined the DataFrame with itself.

Sampling:

Randomly sampled 100 rows from the DataFrame.

This script demonstrates a range of data preprocessing techniques, providing a solid foundation for preparing data for analysis or machine learning tasks.