📊 Data Analytics Portfolio

Welcome to my data analytics portfolio!
This repository showcases my work in data cleaning, exploratory data analysis, and descriptive statistics using Python.
Each project represents an essential step in the data analysis workflow — transforming raw data into meaningful insights.

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🧩 Projects Overview

🔄 Data Analysis Pipeline (A1–A3)

A complete mini-project demonstrating the foundational stages of data analysis in Python.

Stages Included:

• Data Cleaning & Preparation (A1)
  Handling missing values, creating and structuring data frames, and preparing data for analysis.

• Exploratory Data Analysis (A2)
  Generating descriptive statistics, visualizing data distributions, and exploring relationships between variables.

• Relative Frequency Analysis (A3)
  Analyzing categorical and numerical distributions using frequency, relative frequency, and cumulative frequency methods.

Tools:
Python, Pandas, NumPy, Matplotlib, Seaborn

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🏠 Real Estate Price Prediction

An end-to-end data analysis project focused on understanding and predicting real estate prices.

Key Components:

• Data preprocessing and cleaning
• Exploratory analysis of property features
• Visualization of price trends and feature relationships
• Insight extraction to support pricing decisions

Tools:
Python, Pandas, Seaborn, Matplotlib, Jupyter Notebook

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📁 Exploratory Data Analysis Project Structure

ExploratoryDataAnalysis/
├── EDA_Workflow.ipynb
├── A1_Data_Cleaning.py
├── A2_Data_Visualization.py
├── A3_Relative_frequency_distribution.py
└── README.md