india-housing-datasets 1.0.0

Standardized Indian housing datasets for data analysis, visualization, and machine learning practice.

🏠 Indian Housing Datasets - Python Library

A lightweight Python library providing standardized housing datasets for major Indian cities. Perfect for learning data science, practicing machine learning, and building housing price prediction models. All datasets are returned as pandas DataFrames for seamless integration with the Python data science ecosystem.

Ideal for students, beginners, and ML practitioners exploring Indian real estate data analysis, housing price prediction, and regression modeling.

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📦 Installation

Install via pip:

pip install india-housing-datasets

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🚀 Quick Start

Load a city's housing dataset and explore it as a pandas DataFrame:

from india_housing_datasets import load_housing

# Load Mumbai housing data
df = load_housing("mumbai")

# Explore the data
print(df.head())
print(df.info())
print(df.describe())

# Check for missing values
print(df.isnull().sum())

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📊 Visualization Example

Visualize relationships between housing features:

import matplotlib.pyplot as plt
from india_housing_datasets import load_housing

df = load_housing("bangalore")

# Scatter plot: Area vs Price
df.plot.scatter(x="area_sqft", y="price_lakhs", alpha=0.5, figsize=(10, 6))
plt.title("Housing Prices in Bangalore")
plt.xlabel("Area (sq ft)")
plt.ylabel("Price (Lakhs ₹)")
plt.show()

# Distribution of BHK types
df["bhk"].value_counts().plot(kind="bar", color="steelblue")
plt.title("Distribution of BHK Types")
plt.xlabel("BHK")
plt.ylabel("Count")
plt.show()

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🤖 Machine Learning Example

Build a simple housing price prediction model using linear regression:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, r2_score
from india_housing_datasets import load_housing

# Load Delhi housing data
df = load_housing("delhi")

# Prepare features and target
X = df[["area_sqft", "bhk", "bath", "age_years"]]
y = df["price_lakhs"]

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict and evaluate
y_pred = model.predict(X_test)
print(f"Mean Absolute Error: {mean_absolute_error(y_test, y_pred):.2f} Lakhs")
print(f"R² Score: {r2_score(y_test, y_pred):.2f}")

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🌆 Available Cities

The library currently supports housing datasets for the following Indian cities:

• Mumbai
• Delhi
• Bangalore
• Hyderabad
• Chennai
• Pune
• Ahmedabad
• Kolkata
• Jaipur
• Chandigarh

Load any city using:

df = load_housing("city_name")  # e.g., "mumbai", "delhi", "bangalore"

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📋 Dataset Schema

Each dataset contains the following standardized columns:

Column	Type	Description
city	string	Name of the city
locality	string	Locality/area within the city
area_sqft	integer	Built-up area in square feet
bhk	integer	Number of bedrooms (BHK)
bath	integer	Number of bathrooms
floor	integer	Floor number
age_years	integer	Age of the property in years
price_lakhs	float	Property price in lakhs (₹)

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💡 Use Cases

This library is designed for:

• Learning Data Science: Practice pandas, data cleaning, and exploratory data analysis (EDA)
• Housing Price Prediction: Build regression models to predict Indian real estate prices
• Data Visualization: Create charts and dashboards with matplotlib, seaborn, or plotly
• Machine Learning Practice: Experiment with feature engineering, model training, and evaluation
• Academic Projects: Use standardized datasets for coursework and research
• Portfolio Building: Showcase data science skills with Indian housing market analysis

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⚠️ Deprecation Notice

Important: Older fetch_* dataset functions are deprecated and will be removed in future versions.

Please migrate to the new API:

# ❌ Old (Deprecated)
from india_housing_datasets import fetch_mumbai_housing
df = fetch_mumbai_housing()

# ✅ New (Recommended)
from india_housing_datasets import load_housing
df = load_housing("mumbai")

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⚖️ Disclaimer

The datasets provided in this library are synthetically generated and standardized to resemble Indian housing markets. They are intended for educational purposes, data visualization practice, and machine learning experimentation only.

This data should not be used for:

• Real estate investment decisions
• Market analysis or research
• Commercial applications

For real-world applications, please use authentic data sources.

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👨‍💻 Author

Vishal Baghel
📧 [e-mail]
🌐 GitHub Repository

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📜 License

MIT License © 2025 Vishal Baghel

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

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⭐ Support

If you find this library helpful, please consider giving it a ⭐ on GitHub!