delvene43-ops/apartment-data-analysis-pandas-excel

Apartment Data Analysis using Python, Pandas and excel — exploring property prices, locations, and listing characteristics in Kenya through data cleaning, filtering, aggregation, and exploratory analysis. # apartment-data-analysis-pandas-excel Apartment Data Analysis using Python, Pandas and excel — exploring property prices, locations, and listing characteristics in Kenya through data cleaning, filtering, aggregation, and exploratory analysis. ## 📌 Project Overview This project explores an apartment listings dataset using **Python, Pandas and Excel** to identify patterns in property prices, locations, property types, and other listing characteristics. The project demonstrates how raw apartment data can be cleaned, explored, filtered, and transformed into meaningful insights that can support property-market analysis and decision-making. --- ## 🎯 Business Problem Apartment datasets can contain a large number of property listings with different locations, prices, property types, and characteristics. The objective of this project is to answer questions such as: * Which properties have the highest and lowest prices? * How do property prices vary by location? * What are the most common property types? * Which listings offer the lowest prices? * How can Pandas be used to efficiently explore and analyze real estate data? --- ## 📂 Dataset The dataset contains real estate listing information, including fields such as: * **Title** — property/listing title * **Location** — geographical location of the property * **Price** — listed property price * **Property Type** — type/category of property * **Bedrooms** — number of bedrooms * Other property listing attributes > **Note:** The exact columns may vary depending on the version of the dataset used in the analysis. --- ## 🛠️ Tools & Technologies * **Python** * **Pandas** * **Jupyter Notebook** * **Excel** * **Exploratory Data Analysis (EDA)** --- ## 🔎 Analysis Performed The analysis includes: ### 1. Data Inspection * Loaded the dataset using Excel and Pandas. * Examined the dataset structure. * Reviewed column names and data types. * Checked the available records and variables. ### 2. Data Selection Selected …