DPS(Data Processing and Data Statistics)

This repository contains practical exercises and mini-projects related to data preprocessing and data statistics.

📂 Structure

📂Data Practice/
Exercises focused on data preprocessing techniques.

Titanic_Data Cleaning: Data cleaning with Pandas, handling missing values, outliers, duplicates, text and datetime processing.
Salary_Data Transformation: Data transformation with Pandas, merging datasets, summarizing, handling missing values and outliers, aggregation, pivot tables, log transformation, one-hot encoding, scaling, PCA.
Speed Dating_Feature Engineering: Feature engineering with Pandas using the Speed Dating dataset to derive additional insights from the provided data.

📁 Data Project/
Projects that apply data preprocessing techniques to real-world datasets.

Segmentation_Project: Analysis based on RFM (Recency, Frequency, Monetary) segmentation.
TaxiFare_Project: Analysis based on data cleaning techniques.
Used Car Prices_Project: Analysis based on data transformation techniques.
Credit Transaction Anomaly Detection_Project: Analysis based on feature engineering techniques.

🛠️ Tech Stack
Pandas, Numpy, SQL, BigQuery, Jupyter Notebook, google colab

📊 Techniques

Missing value handling, Outlier detection, Duplicate removal, One-hot encoding, Scaling, Principal Component Analysis (PCA), feature engineering
Acquisition, Activation, Retention, Revenue(ARPU), Revenue(CLV), Distribution Visualization, One-Sample t-Test, Independent Sample t-Test, Paired Sample t-Test, Sampling, Confidence Interval, Hypothesis Testing, A/B Test