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https://github.com/pb319/california_house-price-prediction
This is going to be my first end to end ML project implementation covering all required stages taking guidence from book called "Hands On Machine Learning".
https://github.com/pb319/california_house-price-prediction
Last synced: 2 months ago
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This is going to be my first end to end ML project implementation covering all required stages taking guidence from book called "Hands On Machine Learning".
- Host: GitHub
- URL: https://github.com/pb319/california_house-price-prediction
- Owner: pb319
- Created: 2024-06-04T04:10:26.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-07-15T15:37:56.000Z (6 months ago)
- Last Synced: 2024-07-15T19:03:19.204Z (6 months ago)
- Language: Jupyter Notebook
- Size: 3.84 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# California_House-Price-Prediction
This is my first end-to-end ML project implementation covering all required stages taking guidance from the book called "Hands-On Machine Learning"
## Table of Contents
- [Big Picture](https://github.com/pb319/California_House-Price-Prediction?tab=readme-ov-file#big-picture)
- [No Data Snooping](https://github.com/pb319/California_House-Price-Prediction?tab=readme-ov-file#no-data-snooping)
- [Exploratory Data Analysis](https://github.com/pb319/California_House-Price-Prediction?tab=readme-ov-file#exploratory-data-analysis)
- [Data Preparation](https://github.com/pb319/California_House-Price-Prediction?tab=readme-ov-file#data-prparation)
- [Model Selection and Training](https://github.com/pb319/California_House-Price-Prediction?tab=readme-ov-file#model-selection-and-training)
- [Model Fine Tuning](https://github.com/pb319/California_House-Price-Prediction?tab=readme-ov-file#model-fine-tuning)
- [Evaluation on Test Set](https://github.com/pb319/California_House-Price-Prediction?tab=readme-ov-file#evaluation-of-test-set)
## Big Picture
1. Problem Statement
- Welcome to Machine Learning Housing Corporation!
- Organization Objective: Replacing expensive, time-consuming and less effective manual prediction techniques with Machine Learning
2. Framing the problem
- Data: California Census Data
- A typical Univariate Multiple Regression task.
- Training Set is labelled hence **"Supervised Learning"**
- Data is small hence we shall opt for **"Batch Learning"**
2. Performance Measure
- Root Mean Square Error (RMSE) - l 2 Norm
- Mean Absolute Percentage Error (MAPE)
## No Data Snooping
1. Get the Data
- Overview and Primary Understanding
2. Test Set
- Firstly employed **Simple Random Sampling** to draw a test & train set using `Scikit- Learn`
- Secondly utilized **Stratified Sampling** by categorizing the whole datase on `median_income`
- Later we compared **Sampling Bias** from both the sampling techniques
## Exploratory Data Analysis
1. Creating Viualizations
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* There is a celar depiction of clusters in and around San Diego, Los Angeles, San Feancisco, etc.
* From the above figure we can see a general figure that `ocean_proximity` seems to be associated with `median_price_value`
* Bus still there are exceptions in North California, so we've to deploy some **feature engineering** here as well.
* Features such as **proximity to clusters** can also be checked.
***
2. Correlation Matrix and Scatter Plot
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* In General it shows a strong positive trend.
* Straight line at $500,000 reemphasize the **price_cap**
* Concerns are a few straight lines in and around `$450,000`, `$350,000`, `$280,000`, `$230,000` and so on in the below.
* We may remove the concerned districts.
## Data Preparation
1. Data Cleaning
* Missing value of `total_bedrooms` (1.01%) has been treated using **SimpleImputer** Class of Scikit Learn.
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2. Handling Text Attribute
* **OneHotEncoding** is used to handle `ocean_proximity` column
3. Feature Scaling and Transformation Pipeline
* Laid down a single transformation pipeline to transform both numeric and categorical attributes
* StandardScaler(), ColumnTransformer() classes have been utilized.
## Model Selection and Training
1. Model Selection
* `Linear Regression`, `Decision Tree Regressor`, and` Random Forest Regressor` models have been **fitted on the training set**.
3. Model Evaluation
* "K-Fold Cross Validation" depicts RSME scores of **68973.97, 69919.68, 50631.51** respectively.
* Random Forest Regressor looks very promising.
* Note: The score on the training set is still much lower than on validation sets, which means **still overfitting the training set**.
## Model Fine Tuning
1. Grid Search Cross Validation
* Deployed `GridSearchCV()` to fine-tune hyperparameters
* Got ---> `RandomForestRegressor(max_features= 6, n_estimators= 30)`
* RSME score slightly improved from 50631.51 to 50586.27 (K Fold Cross Validation)
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## Evaluation of Test Set
* K-Fold Cross Validation (RSME) score of `46811.29` having `[44833.99542182, 48708.38642949]` confidence interval with a 5% level of Significance.
* Mean Absolute Percetage Error(MAPE) score `0.1771`.
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🛡️ [Demonstration Video](https://youtu.be/dsufBxSrp88?si=edVnb4jONRBCy4RU)
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## Thank You So Much...🙏🙏