Project Overview
A real estate company operating across multiple cities wants to automate its house pricing process. Instead of manually estimating property values, the company aims to build a Machine Learning model that can accurately predict house prices based on historical housing data. This project implements a Linear Regression model to predict house prices using 13 property-related features including physical attributes, location details, and construction characteristics.
Dataset Description
- The dataset contains 13 features.
- Each row represents one residential house.
- Features include physical attributes, location details, and construction-related information.
- Target variable: House Price.
Objective
To build a supervised machine learning regression model that: -Analyzes housing data. -Performs necessary preprocessing. -Trains a Linear Regression model. -Evaluates model performance using standard regression metrics.
⚙️ Project Workflow
-
Data Loading Loaded the dataset using pandas and inspected its structure.
-
Exploratory Data Analysis (EDA) Analyzed summary statistics, checked missing values, and examined feature relationships.
-
Data Preprocessing Handled missing values and prepared numerical features for modeling.
-
Train-Test Split Split the dataset into training and testing sets for unbiased evaluation.
-
Model Training Trained a Linear Regression model using scikit-learn.
-
Model Evaluation Evaluated performance using: -Mean Absolute Error (MAE) -Root Mean Squared Error (RMSE) -R² Score
Model Performance
The Linear Regression model was able to capture the relationship between housing features and sale price effectively. Evaluation metrics indicate the model provides a reasonable predictive baseline for house pricing.
Technologies Used:
Python NumPy Pandas Scikit-learn
Learning Outcomes:
Practical implementation of regression modeling. Understanding of data preprocessing pipeline. Experience with real-world ML workflow. Model evaluation and performance interpretation.
📌 This project is part of my Machine Learning portfolio and preparation for open-source ML contributions.