Python Developer

Job Description:

I need an Income predictor and someone who can explain how they did it.

Using a dataset ( the "Adult Data Set") from the UCI Machine-Learning Repository we can predict based on a number of factors whether or not someone's income will be greater than $50,000.

The technique

The approach is to create a 'classifier' - a program that takes a new example record and, based on previous examples, determines which 'class' it belongs to. In this problem we consider attributes of records and separate these into two broad classes, <=50K and >50K.

We begin with a training data set - examples with known solutions. The classifier looks for patterns that indicate classification. These patterns can be applied against new data to predict outcomes. If we already know the outcomes of the test data, we can test the reliability of our model. if it proves reliable we could then use it to classify data with unknown outcomes.

We must train the classifier to establish an internal model of the patterns that distinguish our two classes. Once trained we can apply this against the test data - which has known outcomes.

We take our data and split it into two groups - training and test - with most of the data in the training set.

We need to write a program to find the patterns in the training set.

Building the classifier

Look at the attributes and, for each of the two outcomes, make an average value for each one, Then average these two results for each attribute to compute a midpoint or 'class separation value'.

For each record, test whether each attribute is above or below its midpoint value and flag it accordingly. For each record the overall result is the greater count of the individual results (<=50K, >50K)

You'll know your model works if you achieve the same results as thee known result for the records. You should track the accuracy of your model, i.e how many correct classifications you made as a percentage of the total number of records.

Process overview

Create training set from data

Create classifier using training dataset to determine separator values for each attribute

Create test dataset

Use classifier to classify data in test set while maintaining accuracy score

The data

The data is presented in the form of a comma-delimited text file (CSV) which has the following structure:

Listing of attributes:

1. Age: Number.

2. Workclass: Can be one of -- Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.

3. fnlwgt: number. This is NOT NEEDED for our study.

4. Education: Can be one of -- Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. This is NOT NEEDED for our study.

5. Education-number: Number -- indicates level of education.

6. Marital-status: Can be one of -- Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.

7. Occupation: Can be one of -- 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.

8. Relationship: Can be one of -- Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.

9. Race: Can be one of -- White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.

10. Sex: Either Female or Male.

11. Capital-gain: Number.

12. Capital-loss: Number.


I'll give a full task if we get on

Færdigheder: Python, Machine Learning (ML), Software Arkitektur, Teaching/Lecturing

Om klienten:
( 0 bedømmelser ) Dublin, Ireland

Projekt ID: #35340418

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