Boston Housing Price Prediction Model

Project Overview

This project focuses on predicting Boston housing prices using various features available in the dataset. The goal is to build a robust model that accurately estimates the median value of owner-occupied homes, providing valuable insights for real estate investment and valuation.

Key Features

• Data Exploration: Comprehensive analysis of the Boston Housing dataset to understand feature distributions and relationships.
• Predictive Modeling: Implementation of machine learning algorithms to predict housing prices.
• Model Evaluation: Rigorous evaluation using appropriate metrics to ensure accuracy and reliability.

Dataset Information

The dataset contains information on housing prices in the Boston area, with features such as:

• CRIM: Per capita crime rate by town
• ZN: Proportion of residential land zoned for large lots
• INDUS: Proportion of non-retail business acres per town
• CHAS: Charles River dummy variable
• NOX: Nitrogen oxides concentration
• RM: Average number of rooms per dwelling
• AGE: Proportion of old homes built prior to 1940
• DIS: Weighted distances to five Boston employment centers
• RAD: Index of accessibility to highways
• TAX: Property-tax rate per $10,000
• PTRATIO: Pupil-teacher ratio by town
• B: 1000(Bk - 0.63)^2 where Bk is the proportion of Black residents
• LSTAT: Percentage of lower status of the population
• MEDV: Median value of owner-occupied homes in $1000's (target)

Technologies Used

• Programming Language: Python
• Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn

Installation

1. Clone the repository:

   git clone https://github.com/SachinRawat1604/Boston-Housing-Price-Prediction-Model.git
   cd Boston-Housing-Price-Prediction-Model
   

2. Install dependencies:

   pip install pandas numpy scikit-learn matplotlib seaborn
   

Usage

1. Data Preparation: Load the boston.csv dataset into a pandas DataFrame.
2. Exploratory Data Analysis (EDA): Analyze the dataset to gain insights into feature distributions, correlations, and outliers.
3. Feature Engineering (Optional): Create new features or transform existing ones to improve model performance.
4. Model Training: Train a machine learning model (e.g., Linear Regression, Random Forest) using the prepared data.
5. Model Evaluation: Evaluate performance using metrics like Mean Squared Error (MSE) or R-squared.

Example Usage (Conceptual)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Load the dataset
data = pd.read_csv("boston.csv")

# Prepare features (X) and target (y)
X = data.drop("MEDV", axis=1)
y = data["MEDV"]

# 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
y_pred = model.predict(X_test)

# Evaluate
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

Contributing

Fork the repo, create a new branch for your feature, make changes, and submit a pull request!

License

This project is licensed under the MIT License.