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House Price Prediction using Machine Learning | End-to-End Regression Project
This video demonstrates an End-to-End Machine Learning regression project: Bangalore House Price Predictor.
The objective of this project is to predict the market price of residential properties in Bangalore, India, based on dynamic real estate features such as location, total square footage, number of bedrooms (BHK), and bathrooms.
This project and video showcase are tailored to serve as a core component of my professional data science portfolio, demonstrating my capabilities in data preprocessing, handling high-cardinality features, and deploying robust regression models.
💻 GitHub Repository: https://github.com/mahady13/Bengalore-House-Price-Project
🛠️ Key Technical Features
Exploratory Data Analysis (EDA): Analyzed real-world, noisy real estate data to handle missing values, format inconsistent data fields (e.g., converting "2-3 BHK" ranges into average numbers), and streamline features.
Feature Engineering & Dimensionality Reduction: Addressed high-cardinality spatial data by applying a frequency threshold to locations, reducing hundreds of unique areas into an 'Other' category to prevent overfitting.
Advanced Outlier Removal: Implemented domain-specific business logic and statistical thresholds (such as Mean and Standard Deviation criteria) to detect and remove price per square foot anomalies and invalid BHK/bathroom ratios.
Model Training & Hyperparameter Tuning: Developed multiple regression architectures utilizing Scikit-Learn. Employed GridSearchCV to test and optimize algorithms including Linear Regression, Lasso, and Decision Tree Regressor.
Streamlit Web UI Integration: Serialized the best-performing model and location maps into a Pickle binary file and built an interactive web application framework using Streamlit for real-time predictions.
💻 Tech Stack & Tools
Programming Language: Python
Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib
Development Environment: JupyterLab / Jupyter Notebook, PyCharm
Deployment & UI: Streamlit
Видео House Price Prediction using Machine Learning | End-to-End Regression Project канала Mohiuddin Mahady
The objective of this project is to predict the market price of residential properties in Bangalore, India, based on dynamic real estate features such as location, total square footage, number of bedrooms (BHK), and bathrooms.
This project and video showcase are tailored to serve as a core component of my professional data science portfolio, demonstrating my capabilities in data preprocessing, handling high-cardinality features, and deploying robust regression models.
💻 GitHub Repository: https://github.com/mahady13/Bengalore-House-Price-Project
🛠️ Key Technical Features
Exploratory Data Analysis (EDA): Analyzed real-world, noisy real estate data to handle missing values, format inconsistent data fields (e.g., converting "2-3 BHK" ranges into average numbers), and streamline features.
Feature Engineering & Dimensionality Reduction: Addressed high-cardinality spatial data by applying a frequency threshold to locations, reducing hundreds of unique areas into an 'Other' category to prevent overfitting.
Advanced Outlier Removal: Implemented domain-specific business logic and statistical thresholds (such as Mean and Standard Deviation criteria) to detect and remove price per square foot anomalies and invalid BHK/bathroom ratios.
Model Training & Hyperparameter Tuning: Developed multiple regression architectures utilizing Scikit-Learn. Employed GridSearchCV to test and optimize algorithms including Linear Regression, Lasso, and Decision Tree Regressor.
Streamlit Web UI Integration: Serialized the best-performing model and location maps into a Pickle binary file and built an interactive web application framework using Streamlit for real-time predictions.
💻 Tech Stack & Tools
Programming Language: Python
Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib
Development Environment: JupyterLab / Jupyter Notebook, PyCharm
Deployment & UI: Streamlit
Видео House Price Prediction using Machine Learning | End-to-End Regression Project канала Mohiuddin Mahady
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18 мая 2026 г. 3:02:10
00:11:54
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