https://github.com/mzohaib364/house-price-prediction-model

End to End ML Project with Scikit Learn
https://github.com/mzohaib364/house-price-prediction-model

cross-validation decision-tree-regression linear-regression random-forest-regression scikit-learn

Last synced: about 2 months ago
JSON representation

End to End ML Project with Scikit Learn

• Host: GitHub
• URL: https://github.com/mzohaib364/house-price-prediction-model
• Owner: MZohaib364
• Created: 2024-09-01T17:54:19.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-15T13:12:42.000Z (4 months ago)
• Last Synced: 2024-11-21T16:14:51.157Z (about 2 months ago)
• Topics: cross-validation, decision-tree-regression, linear-regression, random-forest-regression, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 390 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# House Price Prediction Project Exploring scikit learn

## Overview

This project aims to predict house prices demonstrating a basic flow of ML projects and a little exposure to scikit learn for supervised learning. The goal is to develop a model that can accurately predict the median value of homes in different areas.

## Steps Followed

1. **Dataset Upload**

   - The dataset is uploaded and loaded into a Pandas DataFrame for further analysis and modeling.

2. **Data Exploration**

   - **Descriptive Statistics**: Summary statistics such as mean, standard deviation, min, and max values are calculated for each feature.

   - **Value Counts**: The distribution of categorical variables like `CHAS` is examined.

3. **Data Cleaning**

   - **Handling Missing Values**: Missing data is identified and appropriately handled to ensure a clean dataset for modeling.

   - **Outlier Detection**: Any outliers present in the data are detected and dealt with accordingly.

4. **Data Visualization**

   - **Histograms**: Used to display the distribution of each feature.

   - **Correlation Heatmaps**: Used to visualize the correlation between features.

   - **Scatter Plots**: Display relationships between individual features and the target variable `MEDV`.

5. **Feature Engineering**

   - **Feature Selection**: Relevant features are selected based on their correlation with the target variable.

   - **Feature Scaling**: Features are scaled to ensure they are on a comparable scale for the model.

6. **Splitting the Data**

   - The dataset is split into training and testing sets to allow for model training and subsequent evaluation on unseen data.

7. **Pipeline Creation**

   - A machine learning pipeline is created that combines data preprocessing steps with the model training process.

8. **Cross-Validation**

   - Cross-validation is performed to evaluate the model’s performance across different subsets of the data, providing a more robust assessment.

9. **Model Training**

   - Regression models (e.g., Linear Regression, Decision Trees) are trained on the training dataset.

10. **Model Testing**

    - The trained model is tested on the test dataset to evaluate its generalization performance.

11. **Model Evaluation**

    - The model’s performance is assessed using metrics such as RMSE and MAE to measure prediction accuracy.

12. **Results Visualization**

    - Visualizations such as predicted vs. actual value plots are used to compare the model’s predictions with the actual outcomes.

## Usage

To run the project:

1. Clone the repository.

2. Install the necessary dependencies.

3. Run the Jupyter notebook to follow the steps and build the model.

## Results

The model's performance was evaluated based on prediction accuracy, and further steps can be taken to improve the model by fine-tuning or using more advanced techniques.

## Conclusion

This project demonstrates a basic machine learning workflow for predicting house prices. It can be expanded further by experimenting with different models, hyperparameters, and more complex data preprocessing techniques.