🏠 Advanced House Price Prediction

📌 Project Overview

This project predicts house prices using advanced machine learning and feature engineering techniques.
The dataset contains multiple housing attributes such as property size, location features, and construction details.

The goal is to build a robust regression model that accurately predicts the SalePrice of houses by performing:

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Feature Selection
• Machine Learning Modeling

This project demonstrates an end-to-end data science pipeline used in real-world predictive modeling.

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🏗️ Project Architecture

Raw Data (train.csv, test.csv) │ ▼ Exploratory Data Analysis │ ▼ Data Cleaning (Missing Value Handling) │ ▼ Feature Engineering (Encoding, Transformations) │ ▼ Feature Selection (Lasso Regression) │ ▼ Model Training (Regression Models) │ ▼ Model Evaluation (RMSE / R² Score) │ ▼ Final Prediction Model

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📂 Project Structure

Advanced-House-Price-Prediction │ ├── data │ ├── train.csv │ ├── test.csv │ └── X_train.csv │ ├── notebooks │ ├── Exploratory Data Analysis.ipynb │ ├── Feature Engineering.ipynb │ └── Feature Selection.ipynb │ ├── outputs │ ├── selected_features.csv │ └── trained_model.pkl │ ├── requirements.txt ├── .gitignore └── README.md

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📊 Dataset

The dataset is taken from the Kaggle House Prices Competition.

It contains:

• 79 explanatory variables
• Residential homes located in Ames, Iowa

🎯 Target Variable

SalePrice

This variable represents the final selling price of the house.

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🔍 Exploratory Data Analysis (EDA)

EDA was performed to understand the structure and distribution of the dataset.

Key steps include:

• Checking dataset shape and data types
• Identifying missing values
• Distribution analysis of numerical features
• Correlation analysis
• Detecting outliers

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⚙️ Feature Engineering

Feature engineering improves model performance by transforming raw variables.

Techniques applied:

• Missing value imputation
• Rare category handling
• Label encoding
• Log transformation for skewed variables

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🎯 Feature Selection

Feature selection was performed to reduce dimensionality and keep only important predictors.

Method Used

• Lasso Regression + SelectFromModel

Example:

from sklearn.linear_model import Lasso
from sklearn.feature_selection import SelectFromModel

feature_sel_model = SelectFromModel(Lasso(alpha=0.005, random_state=0))
feature_sel_model.fit(X_train, y_train)
🤖 Machine Learning Models

The following regression models can be used:

Linear Regression

Lasso Regression

Ridge Regression

Random Forest Regressor

Gradient Boosting Regressor

📈 Model Evaluation Metrics

Model performance is evaluated using:

RMSE (Root Mean Squared Error)

MAE (Mean Absolute Error)

R² Score

These metrics measure how well the model predicts house prices.

🛠️ Technologies Used
Tool	Purpose
Python	Programming language
Pandas	Data manipulation
NumPy	Numerical operations
Scikit-learn	Machine learning
Matplotlib	Data visualization
Seaborn	Statistical visualization
Jupyter Notebook	Development environment
🚀 Project Workflow

1️⃣ Exploratory Data Analysis
2️⃣ Feature Engineering
3️⃣ Feature Selection
4️⃣ Model Training

🎯 Key Learning Outcomes

Through this project, the following skills were developed:

Real-world data preprocessing

Handling missing values

Feature engineering techniques

Feature selection using Lasso

Regression model development

End-to-end machine learning workflow

👩‍💻 Author

Sakshi Ingale