Simple Linear Regression with Scikit-Learn: Predicting Boston Housing Prices
Discover the Power of Simple Linear Regression and Python to Predict Median Home Values in the Boston Housing Dataset
Simple Linear Regression with Scikit-Learn: Predicting Boston Housing Prices
Dive into the world of Simple Linear Regression using Scikit-Learn and Python in our comprehensive guide to predicting the median value of owner-occupied homes in the Boston housing dataset. In this in-depth project write-up, we’ll explore the following topics:
- Exploring the Boston housing dataset and its features
- Performing Exploratory Data Analysis (EDA) to understand the data
- Transforming variables to prevent underfitting
- Implementing Simple Linear Regression using Scikit-Learn in Python
- Analyzing the predictions and understanding the impact of various factors on housing prices
Join us on this enlightening journey as we unravel the intricacies of Simple Linear Regression and its impact on solving real-world problems like predicting Boston housing prices. Whether you’re an AI enthusiast or an experienced data scientist, this project write-up offers valuable insights and thorough analysis of building and solving the Boston housing prediction problem using Simple Linear Regression.
Boston Housing Prediction Problem
The Boston housing prediction problem involves predicting the median value of owner-occupied homes in the $1,000s using the Boston housing dataset. The dataset contains 506 rows and 14 features, totaling 7,804 values. This project focuses on understanding the dataset and its features, performing Exploratory Data Analysis (EDA), transforming variables to prevent underfitting, implementing Simple Linear Regression using Scikit-Learn in Python, and analyzing the predictions.
GitHub Repository Access
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Downloads
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Conclusion
In this project, we explored the Boston housing dataset, performed Exploratory Data Analysis (EDA) using graphical approaches, transformed variables to prevent underfitting, and implemented a Simple Linear Regression model using Scikit-Learn in Python. The best-performing model achieved an RMSE of 5.05 on the test data. This project demonstrates the power of Simple Linear Regression in solving real-world problems like predicting housing prices and offers insights into the impact of various factors on housing prices.
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