https://github.com/gakas14/california_house_price_prediction

Build a model to predict the median house values in California.
https://github.com/gakas14/california_house_price_prediction

finance-and-housing gridsearchcv linear-regression machine-learning python randomforestregressor regression-models

Last synced: 8 days ago
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Build a model to predict the median house values in California.

• Host: GitHub
• URL: https://github.com/gakas14/california_house_price_prediction
• Owner: gakas14
• Created: 2021-11-12T04:49:34.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-04-12T05:56:29.000Z (over 2 years ago)
• Last Synced: 2023-11-28T06:30:04.972Z (12 months ago)
• Topics: finance-and-housing, gridsearchcv, linear-regression, machine-learning, python, randomforestregressor, regression-models
• Language: Jupyter Notebook
• Homepage:
• Size: 1.98 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# California_house_price_prediction

Analysis Tasks that we performed:

1. Build a model of housing prices to predict median house values in California using the provided dataset.

Using:Linear Regression.

      Decision Tree Regression.

      Random Forest Regression.

2. Train the model to learn from the data to predict the median housing price in any district, given all the other metrics.

3. Predict housing prices based on median_income and plot the regression chart for it.

In this project:

- We took a Quick Look at the Data Structure, to have a better understanding of the kind of data we are dealing with.

- We Create a Test Set(we picked 20% oof the dataset randomly) and set them aside.

- Discover and Visualize the Data to Gain Insights.

- Visualizing Geographical Data.

- Ckeking for Correlations.

- Attribute Combinations.

- Data Cleaning (handle the missing values using sklearn SimpleImputer class).

- Handling Text and Categorical Attributes.

- Custom Transformers (adds a combined attributes).

- Feature Scaling.

- Create a Transformation Pipelines with StandardScaler(), SimpleImputer().

- Select and Train a Model.

- Better Evaluation Using Cross-Validation.

- Fine-Tune the Model with Grid Search.

- Evaluate the System on the Test Set