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309 lines
22 KiB
Plaintext
309 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ae02d6c8",
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"metadata": {},
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"outputs": [],
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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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"import matplotlib.pyplot as plt\n",
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"from sklearn import linear_model"
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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": 3,
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"id": "6729b5ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"housing_price.csv\")"
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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": 4,
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"id": "717fda9f",
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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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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\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>area</th>\n",
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" <th>price</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>86.45</td>\n",
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" <td>117.0</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>91.57</td>\n",
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" <td>98.0</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>85.52</td>\n",
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" <td>114.0</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>103.60</td>\n",
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" <td>146.0</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>105.25</td>\n",
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" <td>106.0</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>99.00</td>\n",
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" <td>109.0</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>87.95</td>\n",
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" <td>91.5</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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" area price\n",
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"0 86.45 117.0\n",
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"1 91.57 98.0\n",
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"2 85.52 114.0\n",
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"3 103.60 146.0\n",
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"4 105.25 106.0\n",
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"5 99.00 109.0\n",
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"6 87.95 91.5"
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]
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},
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"execution_count": 4,
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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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"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": 5,
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"id": "8bae038f",
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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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"<matplotlib.collections.PathCollection at 0x193b20e02c8>"
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]
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},
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"execution_count": 5,
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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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\n",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib inline\n",
|
|
"plt.xlabel(\"area\")\n",
|
|
"plt.ylabel(\"price\")\n",
|
|
"plt.scatter(df.area, df.price, c='r')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "1dbdd68d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(7, 1)"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df[[\"area\"]].shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "c147b635",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"LinearRegression()"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model = linear_model.LinearRegression()\n",
|
|
"model.fit(df[[\"area\"]].values, df.price.values)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "cd1a7008",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(array([0.83809367]), 32.70161672527138)"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.coef_, model.intercept_"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "d7e74d63",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([115.93269948])"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.predict(np.array([[99.31]]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "4fffcc21",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([115.93269948])"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"price = model.coef_ * 99.31 + model.intercept_\n",
|
|
"price"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1859e8a2",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 保存模型"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "e29158e1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pickle"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "757f7233",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"with open(\"linear_model.pkl\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|