91 lines
1.8 KiB
Plaintext
91 lines
1.8 KiB
Plaintext
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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": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"from sklearn import linear_model\n",
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"from sklearn.metrics import classification_report as summary\n",
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"\n",
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"sns.set()\n",
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"sns.set(style=\"whitegrid\")\n",
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"tips = sns.load_dataset(\"tips\")\n",
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"plt.rcParams[\"figure.figsize\"] = (5,8)"
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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": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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"Boston = pd.read_csv(\"../../datasets/Boston.csv\")\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": 67,
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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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"[-0.95004935] 34.5538408793831\n",
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"0.5441462975864797\n"
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]
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}
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],
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"source": [
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"X = np.array(Boston[\"lstat\"]).reshape(-1,1)\n",
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"y = Boston[\"medv\"]\n",
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"model = linear_model.LinearRegression()\n",
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"model.fit(X, y)\n",
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"print(model.coef_, model.intercept_)\n",
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"print(model.score(X,y))\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": 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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"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": "3.8.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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