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‎ML_for_beginners/Project2_linear_regression/linear_regression.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0.5</td>\n",
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" <td>1.6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0.8</td>\n",
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" <td>2.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1.2</td>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1.4</td>\n",
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" <td>2.1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>2.1</td>\n",
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" <td>2.4</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>2.4</td>\n",
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" <td>2.6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>2.6</td>\n",
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" <td>2.1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>3.4</td>\n",
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" <td>2.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>4.0</td>\n",
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" <td>3.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1\n",
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"0 0.0 1.0\n",
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"1 0.5 1.6\n",
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"2 0.8 2.3\n",
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"3 1.2 2.0\n",
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"4 1.4 2.1\n",
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"5 2.1 2.4\n",
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"6 2.4 2.6\n",
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"7 2.6 2.1\n",
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"8 3.4 2.3\n",
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"9 4.0 3.0"
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]
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},
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"execution_count": 40,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"df = pd.DataFrame([\n",
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" [0, 1], \n",
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" [0.5, 1.6], \n",
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" [0.8, 2.3], \n",
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" [1.2, 2], \n",
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" [1.4, 2.1], \n",
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" [2.1, 2.4], \n",
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" [2.4, 2.6], \n",
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" [2.6, 2.1], \n",
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" [3.4, 2.3], \n",
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" [4, 3]])\n",
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"df "
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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": 41,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<AxesSubplot:xlabel='0', ylabel='1'>"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"df.plot.scatter(x=0, y=1)"
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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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"X = df[[0]]\n",
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"y = df[[1]]"
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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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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.28, random_state=0)\n"
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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": 34,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[3.23342267]])"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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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"reg = LinearRegression().fit(X, y)\n",
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"reg\n",
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"\n",
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"reg.score(X, y)\n",
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"reg.predict([[5]])"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "75a300ae82dd7b8f387c1777b66b2ec8c7a5f6d51d6392630ee9b10fab7f95f8"
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},
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"kernelspec": {
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"display_name": "Python 3.9.0 64-bit",
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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": "3.9.0"
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},
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"orig_nbformat": 4
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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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