https://github.com/abhinav330/linear-regression-project-on-house-pricing
This Python notebook is a simple housing price prediction model using linear regression.
https://github.com/abhinav330/linear-regression-project-on-house-pricing
house-price-prediction kaggle-dataset linear-regression machine-learning
Last synced: 3 months ago
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This Python notebook is a simple housing price prediction model using linear regression.
- Host: GitHub
- URL: https://github.com/abhinav330/linear-regression-project-on-house-pricing
- Owner: Abhinav330
- Created: 2024-08-18T01:21:10.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-18T23:30:59.000Z (about 1 year ago)
- Last Synced: 2024-08-19T02:29:53.048Z (about 1 year ago)
- Topics: house-price-prediction, kaggle-dataset, linear-regression, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 628 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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# Linear-Regression-Project-on-House-Pricing
# Code Summary
This Python notebook is a simple housing price prediction model using linear regression. It imports necessary libraries, loads the 'USA_Housing.csv' dataset, preprocesses the data, visualizes it, builds a linear regression model, and allows for predicting house prices based on user input.
## Data Loading and Preprocessing
The script starts by importing essential libraries, loading the 'USA_Housing.csv' dataset using pandas, and performing data preprocessing:
- Drops the 'Address' column, which is not required for analysis.- Rounds the numeric columns to integers.
- Renames some columns for readability.
## Data Visualization
Data visualization is performed with various plots:
- A heatmap is used to check for missing values.
- Histograms and bar plots show the distribution of house prices and relationships between variables like 'House Age' and 'Price'.
- A heatmap of the correlation matrix visualizes feature correlations.
- A joint plot shows a hexbin plot and a kernel density estimate plot of 'Area Population' vs. 'Price'.
## Model Building
The code then proceeds to build a linear regression model:
- Splits the data into feature variables ('x') and the target variable ('y') using `train_test_split`.
- Imports `LinearRegression` from scikit-learn and initializes the model.
- Fits the model to the training data.
- Makes predictions on the testing data.
## Model Evaluation
The script evaluates the linear regression model's accuracy using the R-squared (R2) score:
- Imports `r2_score` from scikit-learn and calculates the R2 score.
- Prints the model's accuracy as a percentage.
- Plots the actual vs. predicted house prices for visual comparison.
## User Interaction
The script allows users to input values for 'Area Income,' 'House Age,' 'No. of rooms,' and 'Area Population' to predict the house price:
- Prompts users for input and creates a DataFrame with the input values.
- Uses the trained model to predict the house price based on user input.
- Displays the predicted price in both thousands of dollars and crores of rupees.
- Asks if the user wants to continue predicting house prices.
## References
- Chen, N. (2022). House Price Prediction Model of Zhaoqing City Based on Correlation Analysis and Multiple Linear Regression Analysis. Wireless Communications and Mobile Computing, 2022, pp.1–18. doi:.
- SASIKANTH (2018). USA Housing. [online] Kaggle.com. Available at: https://www.kaggle.com/datasets/kanths028/usa-housing [Accessed 18 Aug. 2024].
- scikit-learn developers (2019). sklearn.linear_model.LinearRegression — scikit-learn 0.22 documentation. [online] Scikit-learn.org. Available at: .