(as provided by Udacity)
CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide
financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the
community, CharityML determined that every donation they received came from someone that was making more than $50,000
annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only
those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to
help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. Your goal will be evaluate
and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while
also reducing the total number of letters being sent.
Code is provided in the finding_donors.ipynb
notebook file.
Data visualization code is provided in the visuals.py
file.
Data is included in the census.csv
file.
The Project was approached in the following steps:
Each step has been discussed in detail in the finding_donors.ipynb
jupyter notebook.
- Exploring the Data (Basic Exploratory Data Analyis)
- Preparing the Data
- Transforming Skewed Continuous Features (Log transformation)
- Normalizing Numerical Features (MinMaxScaler)
- Shuffle and Split Data (train-test split)
- Evaluating Model Performance
- Set a naive predictor performance benchmark
- Explained Supervised Learning model with advantages, disadvantages and industry applications .
- Created a Training and Predicting Pipeline
- Implemented initial model performance
- Improving Results
- Chose the Best Model with reasoning
- Described the Model in Layman's Terms
- Model Tuning by Hyper-parameter Optimization
- Evaluated Final Model
- Feature Importance
- Observed feature relevance
- Extracted important features
- Importance of Feature Selection and its effects on the model
The modified census dataset consists of approximately 32,000 data points, with each datapoint having 13 features. This dataset is a modified version of the dataset published in the paper "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", by Ron Kohavi. You may find this paper online, with the original dataset hosted on UCI.
Features
age
: Ageworkclass
: Working Class (Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked)education_level
: Level of Education (Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool)education-num
: Number of educational years completedmarital-status
: Marital status (Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse)occupation
: Work Occupation (Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces)relationship
: Relationship Status (Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried)race
: Race (White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black)sex
: Sex (Female, Male)capital-gain
: Monetary Capital Gainscapital-loss
: Monetary Capital Losseshours-per-week
: Average Hours Per Week Workednative-country
: Native Country (United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands)
Target Variable
income
: Income Class (<=50K, >50K)