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| { | ||
| "default": true, | ||
| "MD013": { "line_length": 120 } | ||
| "MD013": { "line_length": 120, "tables": false } | ||
| } |
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| --- | ||
| title: End-to-End Machine Learning Project | ||
| author: Nikko Miu | ||
| date: 2023-09-04T15:00:00Z | ||
| tags: | ||
| - ai | ||
| - machine-learning | ||
| --- | ||
|
|
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| This basic ML project will follow the [Project Checklist]({{< relref "notes/ai-ml/project-checklist" >}}) | ||
| to create a simple ML application. | ||
| The project is to create a model that will predict the median housing price in a given district in California. | ||
| It is the first (most basic) project that will be completed from start to finish going through all of the steps of the checklist. | ||
|
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| <!--more--> | ||
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| ## Big Picture | ||
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| We're going to build a model of housing prices in California using the California census data. | ||
| This data has metrics such as the population, median income, median housing price, etc. for each block group in California. | ||
| A block group is the smallest geographical unit for which the US Census Bureau publishes sample data | ||
| (typically has a population of 600 to 3,000 people). | ||
| We will call them "districts" for short. | ||
|
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| ## Frame the Problem | ||
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| Knowing the objective will help us choose the right algorithm, performance measure, and so on. | ||
| Our model's output (a prediction of a district's median housing price) will be fed to another ML system, | ||
| along with many other signals. | ||
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| Currently, the district's housing prices are estimated manually by experts: | ||
| a team gathers up-to-date information about a district, | ||
| and when they cannot get the median housing price, they estimate it using complex rules. | ||
| This is costly and time-consuming, and their estimates are not great; | ||
| their typical error rate is about 15%. | ||
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| With this information in mind, we can determine the following: | ||
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| - This is a supervised learning task since we are given labeled training examples | ||
| (each instance comes with the expected output, i.e., the district's median housing price). | ||
| - This is a regression task since we are asked to predict a value. | ||
| - This is a univariate regression problem since we are only trying to predict a single value for each district. | ||
| If we were trying to predict multiple values per district, it would be a multivariate regression problem. | ||
| - This is a batch learning problem since we don't need to adjust to rapidly changing data, | ||
| and the data is small enough to fit in memory. | ||
| If the data was huge, we would need to use online learning instead. | ||
|
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| ## Select a Performance Measure | ||
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| A typical performance measure for regression problems is the Root Mean Square Error (RMSE). | ||
|
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| $$ | ||
| \text{RMSE}(\mathbf{X}, h) = \sqrt{\frac{1}{m} \sum_{i=1}^{m} \left(h(\mathbf{x}^{(i)}) - y^{(i)}\right)^2} | ||
| $$ | ||
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| It measures the standard deviation of the errors the system makes in its predictions. | ||
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| Both the RMSE and the Mean Absolute Error (MAE) are ways to measure the distance between two vectors: | ||
| the vector of predictions and the vector of target values. | ||
|
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| $$ | ||
| \text{MAE}(\mathbf{X}, h) = \frac{1}{m} \sum_{i=1}^{m} \left| h(\mathbf{x}^{(i)}) - y^{(i)} \right| | ||
| $$ | ||
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| Various distance measures are possible, but the RMSE is more sensitive to outliers than the MAE. | ||
| Other distance measures, or _norms_, are possible: | ||
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| - Computing the root of the sum of squares of the differences (RMSE) corresponds to the Euclidean norm: | ||
| it is the notion of distance you are familiar with. | ||
| It is also called the $\ell_2$ norm, noted $\|\cdot\|_2$. | ||
| - Computing the sum of absolutes (MAE) corresponds to the $\ell_1$ norm, which is also called the Manhattan norm | ||
| since it measures the distance between two points in a city if you can only travel along orthogonal city blocks. | ||
| - More generally, the $\ell_k$ norm of a vector $\mathbf{v}$ containing $n$ elements is defined as | ||
| $\|\mathbf{v}\|_k = \left(|v_0|^k + |v_1|^k + \cdots + |v_n|^k\right)^{1/k}$. | ||
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| The $\ell_0$ gives the number of nonzero elements in the vector, | ||
| and $\ell_\infty$ gives the maximum absolute value in the vector. | ||
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| The higher the norm index, the more it focuses on large values and neglects small ones. | ||
| This is why the RMSE is more sensitive to outliers than the MAE. | ||
| But when outliers are exponentially rare (like in a bell-shaped curve), the RMSE performs very well and is generally preferred. | ||
|
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| ## Check the Assumptions | ||
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| It's good practice to list and verify the assumptions that were made so far. | ||
| This can help to catch serious issues early on. | ||
| For example, if the downstream system that uses the output of our system assumes that the output is going to convert the | ||
| prices into categories (e.g., "cheap," "medium," or "expensive") and then use those categories instead of the actual prices, | ||
| then the problem should have been framed as a classification task, not a regression task. | ||
|
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| ## Get the Data | ||
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| It's time to get the data and explore it. | ||
| We will download the data from [here](https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.tgz). | ||
| We will create a `fetch_housing_data()` function to download the data and extract it into a `housing` directory in the workspace. | ||
|
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| ```python | ||
| from pathlib import Path | ||
| import pandas as pd | ||
| import tarfile | ||
| import urllib.request | ||
|
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| def fetch_housing_data(): | ||
| tarball_path = Path("datasets/housing.tgz") | ||
| if not tarball_path.is_file(): | ||
| Path("datasets").mkdir(parents=True, exist_ok=True) | ||
| url = "https://github.com/ageron/data/raw/main/housing.tgz" | ||
| urllib.request.urlretrieve(url, tarball_path) | ||
| with tarfile.open(tarball_path) as housing_tarball: | ||
| housing_tarball.extractall(path="datasets") | ||
| return pd.read_csv(Path("datasets/housing/housing.csv")) | ||
|
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| housing = fetch_housing_data() | ||
| ``` | ||
|
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| ## Looking at the Data | ||
|
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| Let's take a quick look at the top five rows using the `head()` method. | ||
|
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| ```python | ||
| housing.head() | ||
| ``` | ||
|
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| | | longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | | ||
| | ---- | --------- | -------- | ------------------ | ----------- | -------------- | ---------- | ---------- | ------------- | ------------------ | --------------- | | ||
| | 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY | | ||
| | 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY | | ||
| | 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY | | ||
| | 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 | NEAR BAY | | ||
| | 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY | | ||
|
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| Next we will use the `info()` method to get a quick description of the data, | ||
| in particular the total number of rows, and each attribute's type and number of non-null values. | ||
|
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| ```python | ||
| housing.info() | ||
| ``` | ||
|
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| ```text | ||
| <class 'pandas.core.frame.DataFrame'> | ||
| RangeIndex: 20640 entries, 0 to 20639 | ||
| Data columns (total 10 columns): | ||
| # Column Non-Null Count Dtype | ||
| --- ------ -------------- ----- | ||
| 0 longitude 20640 non-null float64 | ||
| 1 latitude 20640 non-null float64 | ||
| 2 housing_median_age 20640 non-null float64 | ||
| 3 total_rooms 20640 non-null float64 | ||
| 4 total_bedrooms 20433 non-null float64 | ||
| 5 population 20640 non-null float64 | ||
| 6 households 20640 non-null float64 | ||
| 7 median_income 20640 non-null float64 | ||
| 8 median_house_value 20640 non-null float64 | ||
| 9 ocean_proximity 20640 non-null object | ||
| dtypes: float64(9), object(1) | ||
| memory usage: 1.6+ MB | ||
| ``` |
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| let userInfo; | ||
|
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| export async function getUserInfo() { | ||
| if (userInfo) { | ||
| return userInfo; | ||
| } | ||
|
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| const resp = await fetch("/.auth/me"); | ||
| const { clientPrincipal } = await resp.json(); | ||
|
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| userInfo = clientPrincipal; | ||
|
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| return userInfo; | ||
| } | ||
|
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| export async function displayUserInfo() { | ||
| const clientPrincipal = await getUserInfo(); | ||
|
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| document.querySelector('.footer .user-info').innerHTML = ` | ||
| <div class="user-name"> | ||
| Currently signed in as ${clientPrincipal.userDetails} | ||
| </div> | ||
| <a href="/.auth/logout" class="logout"> | ||
| Sign Out | ||
| </a> | ||
| `; | ||
| } |