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Machine Learning Explained
Machine Learning is the process of letting your machine use the data to learn the relationship between predictor variables and the target variable. It is one of the first steps toward becoming a data scientist.
There are two kinds of Machine Learning: supervised, and unsupervised learning. In supervised learning, there are two types: Regression and Classification.
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Types Of Data
- Nominal: used for labelling variables(m- male and f- female)
- Ordinal: used for measuring non-numeric with an order of the values(1-unhappy, 2-ok, 3- happy)
- Data Cleaning: In this data set, there are 2051 rows with 80 columns. So, there were a lot of missing and null values to be clean.
- Exploratory Data Analysis: The highest correlation between the sale price and features was the overall quality of the house.
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Feature Engineering
In order to predict the outcome variable (y variable) you need to turn all the object type columns into numeric in order to predict the Sale Price. So first, I converted all the ordinal columns into numeric by assigning them by numbers, and for all the other features I created dummy variables.
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Modelling
After creating all the features to predict the sale prices, using sklearn, we train-test-split the training data. In Regression, there are a lot of models you can choose from to get the best performing model. In this project, I ran Linear Regression, Ridge, and Lasso models.
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IDEAS CURATED BY
CURATOR'S NOTE
A machine learning example
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Alisa Thomas's ideas are part of this journey:
Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
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