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Category:

Machine Learning

Difficulty:

Beginner

Prerequisite(s):

Python

Skills to be Learned:

Feature engineering, Data analysis, Regression modeling

Housing Price Prediction

This project aims to develop a robust regression model to predict housing prices based on relevant features. The model will be trained on a real-world dataset of housing prices and features, and evaluated using standard machine learning metrics. The project will provide participants with hands-on experience in data analysis, feature engineering, and regression modeling.

Project Overview

The "Beginner's Guide to Housing Price Prediction" is a project-based course that aims to introduce beginners to the field of predictive modeling and data analysis using real-world housing price data. In this course, participants will learn the fundamentals of data analysis, feature engineering, and regression modeling through hands-on experience. The course focuses on predicting housing prices based on various features such as area, number of bedrooms, bathrooms, and more. By the end of the course, participants will have the skills to build and evaluate regression models for predicting housing prices.



Steps:

  1. Data Preparation:

    • Load and preprocess the housing price dataset, handling missing values and outliers.

    • Perform exploratory data analysis (EDA) to understand the data distribution and identify important features.

    • Split the dataset into training, validation, and test sets.

  2. Feature Engineering:

    • Create new features from existing features or transform existing features to improve model performance.

    • Encode categorical variables using one-hot encoding or label encoding.

    • Scale numerical features using standard scaling or min-max scaling.

  3. Model Development:

    • Implement and train various regression models, such as linear regression, random forest regressor, and support vector regressor.

    • Tune model hyperparameters using validation data to achieve optimal performance.

  4. Model Evaluation:

    • Evaluate the performance of trained models on the test set using standard metrics, such as R-squared and RMSE.

    • Compare the performance of different models and select the best model.

  5. Deployment:

    • Deploy the best model to production so that it can be used to predict housing prices for new data instances.

Required Skills:

  • Proficiency in Python programming

  • Familiarity with data analysis and machine learning concepts

  • Experience with using libraries such as Pandas, NumPy, and Scikit-Learn



Learning Outcomes:

Upon completing this project, participants will be able to:

  • Understand the principles of data analysis, feature engineering, and regression modeling.

  • Apply these principles to develop a robust regression model for predicting housing prices.

  • Evaluate the performance of trained models and select the best model for production.

  • Deploy the trained model to production to make predictions on new data.


This project will provide participants with hands-on experience in all aspects of the machine learning lifecycle, from data preparation to model development

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