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190 lines
44 KiB
190 lines
44 KiB
4 years ago
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{
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"metadata": {
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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": "3.8.3-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3",
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"language": "python"
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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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"cells": [
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{
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"source": [
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"## Linear Regression Solution"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"source": [
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"Import needed libraries"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn import datasets, linear_model, model_selection\n"
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]
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},
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{
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"source": [
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"Load the diabetes dataset, divided into `X` data and `y` features"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"(442, 10)\n[ 0.03807591 0.05068012 0.06169621 0.02187235 -0.0442235 -0.03482076\n -0.04340085 -0.00259226 0.01990842 -0.01764613]\n"
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]
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}
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],
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"source": [
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"X, y = datasets.load_diabetes(return_X_y=True)\n",
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"print(X.shape)\n",
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"print(X[0])"
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]
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},
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{
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"source": [
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"Select just one feature to target for this exercise"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = X[:, np.newaxis, 2]\n"
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]
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},
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{
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"source": [
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"Split the training and test data for both `X` and `y`"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
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]
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},
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{
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"source": [
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"Select the model and fit it with the training data"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"LinearRegression()"
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]
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},
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"metadata": {},
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"execution_count": 6
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}
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],
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"source": [
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"model = linear_model.LinearRegression()\n",
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"model.fit(X_train, y_train)"
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]
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},
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{
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"source": [
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"Use test data to predict a line"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred = model.predict(X_test)\n"
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]
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},
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{
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"source": [
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"Display the results in a plot"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
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"metadata": {
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"needs_background": "light"
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}
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|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.scatter(X_test, y_test, color='black')\n",
|
||
|
|
"plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|