127 lines
3.1 KiB
Plaintext
127 lines
3.1 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "expected-characterization",
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"metadata": {},
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"source": [
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"# Ungraded Lab: Linear Regression using Scikit-Learn"
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]
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},
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{
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"cell_type": "markdown",
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"id": "gorgeous-lincoln",
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"metadata": {},
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"source": [
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"Now that you've implemented linear regression from scratch, let's see you can train a linear regression model using scikit-learn.\n",
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"\n",
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"## Dataset \n",
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"Let's start with the same dataset as before."
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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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"id": "mobile-firmware",
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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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"\n",
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"# X is the input variable (size in square feet)\n",
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"# y in the output variable (price in 1000s of dollars)\n",
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"X = np.array([1000, 2000])\n",
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"y = np.array([200, 400])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "offshore-lease",
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"metadata": {},
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"source": [
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"## Fit the model\n",
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"\n",
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"The code below imports the [linear regression model](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression) from scikit-learn. You can fit this model on the training data by calling `fit` function."
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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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"id": "monetary-tactics",
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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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"\n",
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"linear_model = LinearRegression()\n",
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"# We have to reshape X using .reshape(-1, 1) because our data has a single feature\n",
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"# If X has multiple features, you don't need to reshape\n",
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"linear_model.fit(X.reshape(-1, 1), y) "
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]
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},
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{
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"cell_type": "markdown",
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"id": "thick-seven",
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"metadata": {},
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"source": [
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"## Make Predictions\n",
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"\n",
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"You can see the predictions made by this model by calling the `predict` function."
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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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"id": "norwegian-variety",
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred = linear_model.predict(X.reshape(-1,1))\n",
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"\n",
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"print(\"Prediction on training set:\", y_pred)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "geographic-archive",
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"metadata": {},
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"source": [
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"## Calculate accuracy\n",
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"\n",
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"You can calculate this accuracy of this model by calling the `score` function."
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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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"id": "immune-password",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Accuracy on training set:\", linear_model.score(X.reshape(-1,1), y))"
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]
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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": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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