zect7/Russian_apartment_price

How to Use the Russian Apartment Price Dataset (Kvartis)

Dataset: main_data.csv GitHub Repository: zect-project/top_datasets) Size: 4,000 real estate listings (2025–2026 data) Target variable: real_price (price in Russian Rubles, ₽)

📋 Dataset Overview

This dataset contains detailed information about apartments for sale in five major Russian cities:

• —Moscow
• —Petersburg (St. Petersburg)
• —Novosibirsk
• —Yekaterinburg
• —Kazan

It is perfect for:

• —Price prediction (regression)
• —Market analysis
• —Feature importance studies
• —Learning CatBoost / handling categorical + numerical features
• —Russian real-estate research

🏠 Columns Description

Repair categories meaning:

• —not_needs — excellent condition, ready to move in
• —needs — requires cosmetic repairs
• —absolutely_needs — major renovation required

🚀 Quick Start

1. Load the data

import pandas as pd

df = pd.read_csv("main_data.csv")
print(df.shape)           # (4000, 7)
df.head()

Basic exploration

import matplotlib.pyplot as plt
import seaborn as sns

# Price distribution by city
sns.boxplot(data=df, x='city', y='real_price')
plt.title("Apartment Prices by City")
plt.ylabel("Price (₽)")
plt.xticks(rotation=45)
plt.show()

# Average price per m²
df['price_per_m2'] = df['real_price'] / df['m2']
print(df.groupby('city')['price_per_m2'].mean().round(0))

license: mit ---