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‎Decision Tree/Untitled.ipynb‎

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‎Decision Tree/utf-8''iris(1).csv‎

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sepal_length,sepal_width,petal_length,petal_width,species
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5.1,3.5,1.4,0.2,setosa
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4.9,3.0,1.4,0.2,setosa
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4.7,3.2,1.3,0.2,setosa
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4.6,3.1,1.5,0.2,setosa
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5.0,3.6,1.4,0.2,setosa
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5.4,3.9,1.7,0.4,setosa
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4.6,3.4,1.4,0.3,setosa
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5.0,3.4,1.5,0.2,setosa
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4.4,2.9,1.4,0.2,setosa
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4.9,3.1,1.5,0.1,setosa
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5.4,3.7,1.5,0.2,setosa
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4.8,3.4,1.6,0.2,setosa
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4.8,3.0,1.4,0.1,setosa
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4.3,3.0,1.1,0.1,setosa
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5.8,4.0,1.2,0.2,setosa
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5.7,4.4,1.5,0.4,setosa
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5.4,3.9,1.3,0.4,setosa
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5.1,3.5,1.4,0.3,setosa
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5.7,3.8,1.7,0.3,setosa
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5.1,3.8,1.5,0.3,setosa
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5.4,3.4,1.7,0.2,setosa
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5.1,3.7,1.5,0.4,setosa
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4.6,3.6,1.0,0.2,setosa
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5.1,3.3,1.7,0.5,setosa
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4.8,3.4,1.9,0.2,setosa
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5.0,3.0,1.6,0.2,setosa
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5.0,3.4,1.6,0.4,setosa
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5.2,3.5,1.5,0.2,setosa
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5.2,3.4,1.4,0.2,setosa
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4.7,3.2,1.6,0.2,setosa
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4.8,3.1,1.6,0.2,setosa
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5.4,3.4,1.5,0.4,setosa
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5.2,4.1,1.5,0.1,setosa
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5.5,4.2,1.4,0.2,setosa
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4.9,3.1,1.5,0.2,setosa
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5.0,3.2,1.2,0.2,setosa
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5.5,3.5,1.3,0.2,setosa
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4.9,3.6,1.4,0.1,setosa
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4.4,3.0,1.3,0.2,setosa
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5.1,3.4,1.5,0.2,setosa
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5.0,3.5,1.3,0.3,setosa
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4.5,2.3,1.3,0.3,setosa
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4.4,3.2,1.3,0.2,setosa
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5.0,3.5,1.6,0.6,setosa
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5.1,3.8,1.9,0.4,setosa
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4.8,3.0,1.4,0.3,setosa
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5.1,3.8,1.6,0.2,setosa
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4.6,3.2,1.4,0.2,setosa
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5.3,3.7,1.5,0.2,setosa
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5.0,3.3,1.4,0.2,setosa
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7.0,3.2,4.7,1.4,versicolor
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6.4,3.2,4.5,1.5,versicolor
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6.9,3.1,4.9,1.5,versicolor
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5.5,2.3,4.0,1.3,versicolor
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6.5,2.8,4.6,1.5,versicolor
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5.7,2.8,4.5,1.3,versicolor
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6.3,3.3,4.7,1.6,versicolor
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4.9,2.4,3.3,1.0,versicolor
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6.6,2.9,4.6,1.3,versicolor
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5.2,2.7,3.9,1.4,versicolor
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5.0,2.0,3.5,1.0,versicolor
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5.9,3.0,4.2,1.5,versicolor
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6.0,2.2,4.0,1.0,versicolor
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6.1,2.9,4.7,1.4,versicolor
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5.6,2.9,3.6,1.3,versicolor
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6.7,3.1,4.4,1.4,versicolor
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5.6,3.0,4.5,1.5,versicolor
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5.8,2.7,4.1,1.0,versicolor
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6.2,2.2,4.5,1.5,versicolor
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5.6,2.5,3.9,1.1,versicolor
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5.9,3.2,4.8,1.8,versicolor
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6.1,2.8,4.0,1.3,versicolor
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6.3,2.5,4.9,1.5,versicolor
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6.1,2.8,4.7,1.2,versicolor
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6.4,2.9,4.3,1.3,versicolor
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6.6,3.0,4.4,1.4,versicolor
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6.8,2.8,4.8,1.4,versicolor
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6.7,3.0,5.0,1.7,versicolor
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6.0,2.9,4.5,1.5,versicolor
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5.7,2.6,3.5,1.0,versicolor
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5.5,2.4,3.8,1.1,versicolor
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5.5,2.4,3.7,1.0,versicolor
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5.8,2.7,3.9,1.2,versicolor
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6.0,2.7,5.1,1.6,versicolor
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5.4,3.0,4.5,1.5,versicolor
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6.0,3.4,4.5,1.6,versicolor
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6.7,3.1,4.7,1.5,versicolor
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6.3,2.3,4.4,1.3,versicolor
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5.6,3.0,4.1,1.3,versicolor
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5.5,2.5,4.0,1.3,versicolor
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5.5,2.6,4.4,1.2,versicolor
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6.1,3.0,4.6,1.4,versicolor
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5.8,2.6,4.0,1.2,versicolor
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5.0,2.3,3.3,1.0,versicolor
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5.6,2.7,4.2,1.3,versicolor
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5.7,3.0,4.2,1.2,versicolor
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5.7,2.9,4.2,1.3,versicolor
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6.2,2.9,4.3,1.3,versicolor
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5.1,2.5,3.0,1.1,versicolor
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5.7,2.8,4.1,1.3,versicolor
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6.3,3.3,6.0,2.5,virginica
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5.8,2.7,5.1,1.9,virginica
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7.1,3.0,5.9,2.1,virginica
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6.3,2.9,5.6,1.8,virginica
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6.5,3.0,5.8,2.2,virginica
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7.6,3.0,6.6,2.1,virginica
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4.9,2.5,4.5,1.7,virginica
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7.3,2.9,6.3,1.8,virginica
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6.7,2.5,5.8,1.8,virginica
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7.2,3.6,6.1,2.5,virginica
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6.5,3.2,5.1,2.0,virginica
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6.4,2.7,5.3,1.9,virginica
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6.8,3.0,5.5,2.1,virginica
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5.7,2.5,5.0,2.0,virginica
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5.8,2.8,5.1,2.4,virginica
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6.4,3.2,5.3,2.3,virginica
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6.5,3.0,5.5,1.8,virginica
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7.7,3.8,6.7,2.2,virginica
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7.7,2.6,6.9,2.3,virginica
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6.0,2.2,5.0,1.5,virginica
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6.9,3.2,5.7,2.3,virginica
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5.6,2.8,4.9,2.0,virginica
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7.7,2.8,6.7,2.0,virginica
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6.3,2.7,4.9,1.8,virginica
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6.7,3.3,5.7,2.1,virginica
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7.2,3.2,6.0,1.8,virginica
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6.2,2.8,4.8,1.8,virginica
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6.1,3.0,4.9,1.8,virginica
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6.4,2.8,5.6,2.1,virginica
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7.2,3.0,5.8,1.6,virginica
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7.4,2.8,6.1,1.9,virginica
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7.9,3.8,6.4,2.0,virginica
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6.4,2.8,5.6,2.2,virginica
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6.3,2.8,5.1,1.5,virginica
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6.1,2.6,5.6,1.4,virginica
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7.7,3.0,6.1,2.3,virginica
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6.3,3.4,5.6,2.4,virginica
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6.4,3.1,5.5,1.8,virginica
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6.0,3.0,4.8,1.8,virginica
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6.9,3.1,5.4,2.1,virginica
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6.7,3.1,5.6,2.4,virginica
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6.9,3.1,5.1,2.3,virginica
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5.8,2.7,5.1,1.9,virginica
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6.8,3.2,5.9,2.3,virginica
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6.7,3.3,5.7,2.5,virginica
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6.7,3.0,5.2,2.3,virginica
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6.3,2.5,5.0,1.9,virginica
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6.5,3.0,5.2,2.0,virginica
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6.2,3.4,5.4,2.3,virginica
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5.9,3.0,5.1,1.8,virginica

‎Linear Regression/LinearRegressionMultipleVariables.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Linear Resgression Multiple variables"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import the Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sb"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Load the Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset=pd.read_csv('ex1data2.txt')\n",
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"X=dataset.iloc[:,:-1].values\n",
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"y=dataset.iloc[:,-1].values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[1600 3]\n",
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" [2400 3]\n",
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" [1416 2]\n",
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" [3000 4]\n",
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" [1985 4]\n",
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" [1534 3]\n",
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" [1427 3]\n",
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" [1380 3]\n",
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" [1494 3]\n",
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" [1940 4]\n",
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" [2000 3]\n",
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" [1890 3]\n",
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" [4478 5]\n",
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" [1268 3]\n",
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" [2300 4]\n",
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" [1320 2]\n",
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" [1236 3]\n",
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" [2609 4]\n",
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" [3031 4]\n",
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" [1767 3]\n",
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" [1888 2]\n",
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" [1604 3]\n",
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" [1962 4]\n",
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" [3890 3]\n",
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" [1100 3]\n",
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" [1458 3]\n",
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" [2526 3]\n",
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" [2200 3]\n",
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" [2637 3]\n",
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" [1839 2]\n",
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" [1000 1]\n",
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" [2040 4]\n",
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" [3137 3]\n",
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" [1811 4]\n",
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" [1437 3]\n",
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" [1239 3]\n",
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" [2132 4]\n",
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" [4215 4]\n",
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" [2162 4]\n",
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" [1664 2]\n",
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" [2238 3]\n",
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" [2567 4]\n",
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" [1200 3]\n",
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" [ 852 2]\n",
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" [1852 4]\n",
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" [1203 3]]\n"
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]
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}
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],
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"source": [
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"print(X)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[329900 369000 232000 539900 299900 314900 198999 212000 242500 239999\n",
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" 347000 329999 699900 259900 449900 299900 199900 499998 599000 252900\n",
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" 255000 242900 259900 573900 249900 464500 469000 475000 299900 349900\n",
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" 169900 314900 579900 285900 249900 229900 345000 549000 287000 368500\n",
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" 329900 314000 299000 179900 299900 239500]\n"
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]
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}
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],
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"source": [
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"print(y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Splitting the Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LinearRegression\n",
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"model=LinearRegression(normalize=True)\n",
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"model.fit(X_train,y_train)\n",
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"result=model.predict(X_test)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[266611.31683851 324085.31639041 438749.980843 327443.40737608\n",
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" 345745.46093557 335473.6249505 338350.81825691 329685.02578672\n",
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" 345505.01209594 511751.95879232 231965.49347897 353286.10757014\n",
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" 228942.29621667 276591.14491179]\n"
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]
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}
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],
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"source": [
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"print(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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