This project requires Python and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.
Template code is provided in the boston_housing.ipynb notebook file. You will also be required to use the included visuals.py Python file and the housing.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
In a terminal or command window, navigate to the top-level project directory boston_housing/ (that contains this README) and run one of the following commands:
ipython notebook boston_housing.ipynbor
jupyter notebook boston_housing.ipynbThis will open the Jupyter Notebook software and project file in your browser.
The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository.
Features
RM: average number of rooms per dwellingLSTAT: percentage of population considered lower statusPTRATIO: pupil-teacher ratio by town
Target Variable
4. MEDV: median value of owner-occupied homes
In this project, you will apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. You will first explore the data to learn how the census data is recorded. Next, you will apply a series of transformations and preprocessing techniques to manipulate the data into a workable format. You will then evaluate several supervised learners of your choice on the data, and consider which is best suited for the solution. Afterwards, you will optimize the model you've selected and present it as your solution to CharityML. Finally, you will explore the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given. predicted selling price to your statistics.
This project is designed to get you acquainted with the many supervised learning algorithms available in sklearn, and to also provide for a method of evaluating just how each model works and performs on a certain type of data. It is important in machine learning to understand exactly when and where a certain algorithm should be used, and when one should be avoided.
Things you will learn by completing this project:
- How to identify when preprocessing is needed, and how to apply it.
- How to establish a benchmark for a solution to the problem.
- What each of several supervised learning algorithms accomplishes given a specific dataset.
- How to investigate whether a candidate solution model is adequate for the problem.
This project uses the following software and Python libraries:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
For this assignment, you can find the finding_donors folder containing the necessary project files on the Machine Learning projects GitHub, under the projects folder. You may download all of the files for projects we'll use in this Nanodegree program directly from this repo. Please make sure that you use the most recent version of project files when completing a project!
This project contains three files:
finding_donors.ipynb: This is the main file where you will be performing your work on the project.census.csv: The project dataset. You'll load this data in the notebook.visuals.py: A Python file containing visualization code that is run behind-the-scenes. Do not modify
In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command jupyter notebook finding_donors.ipynb to open up a browser window or tab to work with your notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens. Follow the instructions in the notebook and answer each question presented to successfully complete the project. A README file has also been provided with the project files which may contain additional necessary information or instruction for the project.
Your project will be reviewed by a Udacity reviewer against the Finding Donors for CharityML project rubric. Be sure to review this rubric thoroughly and self-evaluate your project before submission. All criteria found in the rubric must be meeting specifications for you to pass.
When you are ready to submit your project, collect the following files and compress them into a single archive for upload. Alternatively, you may supply the following files on your GitHub Repo in a folder named student_intervention for ease of access:
- The
finding_donors.ipynbnotebook file with all questions answered and all code cells executed and displaying output. - An HTML export of the project notebook with the name report.html. This file must be present for your project to be evaluated.
Once you have collected these files and reviewed the project rubric, proceed to the project submission page.
When you're ready to submit your project, click on the Submit Project button at the bottom of the page.
If you are having any problems submitting your project or wish to check on the status of your submission, please email us at machine-support@udacity.com or visit us in the discussion forums.
You will get an email as soon as your reviewer has feedback for you. In the meantime, review your next project and feel free to get started on it or the courses supporting it!
In this project you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. You will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.
This project is designed to give you a hands-on experience with unsupervised learning and work towards developing conclusions for a potential client on a real-world dataset. Many companies today collect vast amounts of data on customers and clientele, and have a strong desire to understand the meaningful relationships hidden in their customer base. Being equipped with this information can assist a company engineer future products and services that best satisfy the demands or needs of their customers.
Things you will learn by completing this project:
- How to apply preprocessing techniques such as feature scaling and outlier detection.
- How to interpret data points that have been scaled, transformed, or reduced from PCA.
- How to analyze PCA dimensions and construct a new feature space.
- How to optimally cluster a set of data to find hidden patterns in a dataset.
- How to assess information given by cluster data and use it in a meaningful way.
A wholesale distributor recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week. Initial testing did not discover any significant unsatisfactory results, so they implemented the cheaper option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries, losing the distributor more money than what was being saved. You've been hired by the wholesale distributor to find what types of customers they have to help them make better, more informed business decisions in the future. Your task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
This project uses the following software and Python libraries:
You will also need to have software installed to run and execute a Jupyter Notebook.
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
For this assignment, you can find the customer_segments folder containing the necessary project files on the Machine Learning projects GitHub, under the projects folder. You may download all of the files for projects we'll use in this Nanodegree program directly from this repo. Please make sure that you use the most recent version of project files when completing a project!
This project contains three files:
customer_segments.ipynb: This is the main file where you will be performing your work on the project.customers.csv: The project dataset. You'll load this data in the notebook.visuals.py: This Python script provides supplementary visualizations for the project. Do not modify.
In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command jupyter notebook customer_segments.ipynb to open up a browser window or tab to work with your notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens. Follow the instructions in the notebook and answer each question presented to successfully complete the project. A README file has also been provided with the project files which may contain additional necessary information or instruction for the project.
Your project will be reviewed by a Udacity reviewer against the Creating Customer Segments project rubric. Be sure to review this rubric thoroughly and self-evaluate your project before submission. All criteria found in the rubric must be meeting specifications for you to pass.
When you are ready to submit your project, collect the following files and compress them into a single archive for upload. Alternatively, you may supply the following files on your GitHub Repo in a folder named customer_segments for ease of access:
- The
customer_segments.ipynbnotebook file with all questions answered and all code cells executed and displaying output. - An HTML export of the project notebook with the name report.html. This file must be present for your project to be evaluated.
Once you have collected these files and reviewed the project rubric, proceed to the project submission page.
When you're ready to submit your project, click on the Submit Project button at the bottom of the page.
If you are having any problems submitting your project or wish to check on the status of your submission, please email us at machine-support@udacity.com or visit us in the discussion forums.
You will get an email as soon as your reviewer has feedback for you. In the meantime, review your next project and feel free to get started on it or the courses supporting it!