444 lines
85 KiB
Plaintext
444 lines
85 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": 43,
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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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"import statsmodels.api as sm\n",
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"import statsmodels.formula.api as smf\n",
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"from sklearn import metrics\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\"] = (8,5)"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"def precision_stats(model, y, X):\n",
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" pred_y = model.predict(X)\n",
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" coefficients = model.coef_\n",
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" intercept = model.intercept_\n",
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" MSE = metrics.mean_squared_error(y,pred_y)\n",
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" # these 3 are all the saem\n",
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" score = model.score(X,y)\n",
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" explained_var = metrics.explained_variance_score(y, pred_y)\n",
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" R2 = metrics.r2_score(y, pred_y)\n",
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" # residuals\n",
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" res = y - pred_y \n",
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" print(\"Residuals info\", res.describe())\n",
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" \n",
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" print(\"coefficients:\", coefficients)\n",
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" print(\"intercept\", intercept)\n",
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" \n",
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" print(\"MSE\", MSE)\n",
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" print(\"explained variance\", explained_var)"
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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": 6,
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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>Unnamed: 0</th>\n",
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" <th>crim</th>\n",
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" <th>zn</th>\n",
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" <th>indus</th>\n",
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" <th>chas</th>\n",
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" <th>nox</th>\n",
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" <th>rm</th>\n",
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" <th>age</th>\n",
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" <th>dis</th>\n",
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" <th>rad</th>\n",
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" <th>tax</th>\n",
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" <th>ptratio</th>\n",
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" <th>black</th>\n",
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" <th>lstat</th>\n",
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" <th>medv</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>1</td>\n",
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" <td>0.00632</td>\n",
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" <td>18.0</td>\n",
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" <td>2.31</td>\n",
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" <td>0</td>\n",
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" <td>0.538</td>\n",
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" <td>6.575</td>\n",
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" <td>65.2</td>\n",
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" <td>4.0900</td>\n",
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" <td>1</td>\n",
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" <td>296</td>\n",
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" <td>15.3</td>\n",
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" <td>396.90</td>\n",
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" <td>4.98</td>\n",
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" <td>24.0</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>2</td>\n",
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" <td>0.02731</td>\n",
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" <td>0.0</td>\n",
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" <td>7.07</td>\n",
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" <td>0</td>\n",
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" <td>0.469</td>\n",
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" <td>6.421</td>\n",
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" <td>78.9</td>\n",
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" <td>4.9671</td>\n",
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" <td>2</td>\n",
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" <td>242</td>\n",
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" <td>17.8</td>\n",
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" <td>396.90</td>\n",
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" <td>9.14</td>\n",
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" <td>21.6</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>3</td>\n",
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" <td>0.02729</td>\n",
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" <td>0.0</td>\n",
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" <td>7.07</td>\n",
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" <td>0</td>\n",
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" <td>0.469</td>\n",
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" <td>7.185</td>\n",
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" <td>61.1</td>\n",
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" <td>4.9671</td>\n",
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" <td>2</td>\n",
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" <td>242</td>\n",
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" <td>17.8</td>\n",
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" <td>392.83</td>\n",
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" <td>4.03</td>\n",
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" <td>34.7</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>4</td>\n",
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" <td>0.03237</td>\n",
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" <td>0.0</td>\n",
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" <td>2.18</td>\n",
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" <td>0</td>\n",
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" <td>0.458</td>\n",
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" <td>6.998</td>\n",
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" <td>45.8</td>\n",
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" <td>6.0622</td>\n",
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" <td>3</td>\n",
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" <td>222</td>\n",
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" <td>18.7</td>\n",
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" <td>394.63</td>\n",
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" <td>2.94</td>\n",
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" <td>33.4</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>5</td>\n",
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" <td>0.06905</td>\n",
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" <td>0.0</td>\n",
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" <td>2.18</td>\n",
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" <td>0</td>\n",
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" <td>0.458</td>\n",
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" <td>7.147</td>\n",
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" <td>54.2</td>\n",
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" <td>6.0622</td>\n",
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" <td>3</td>\n",
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" <td>222</td>\n",
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" <td>18.7</td>\n",
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" <td>396.90</td>\n",
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" <td>5.33</td>\n",
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" <td>36.2</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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" Unnamed: 0 crim zn indus chas nox rm age dis rad \\\n",
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"0 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 \n",
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"1 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 \n",
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"2 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 \n",
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"3 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 \n",
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"4 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 \n",
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"\n",
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" tax ptratio black lstat medv \n",
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"0 296 15.3 396.90 4.98 24.0 \n",
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"1 242 17.8 396.90 9.14 21.6 \n",
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"2 242 17.8 392.83 4.03 34.7 \n",
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"3 222 18.7 394.63 2.94 33.4 \n",
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"4 222 18.7 396.90 5.33 36.2 "
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]
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},
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"execution_count": 6,
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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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"Boston = pd.read_csv(\"../../datasets/Boston.csv\")\n",
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"Boston.head()"
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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": 28,
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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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"Residuals info count 5.060000e+02\n",
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"mean -4.437382e-15\n",
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"std 6.209603e+00\n",
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"min -1.516745e+01\n",
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"25% -3.989612e+00\n",
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"50% -1.318186e+00\n",
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"75% 2.033701e+00\n",
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"max 2.450013e+01\n",
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"Name: medv, dtype: float64\n",
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"coefficients: [-0.95004935]\n",
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"intercept 34.5538408793831\n",
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"MSE 38.48296722989415\n",
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"explained variance 0.5441462975864798\n"
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]
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}
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],
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"source": [
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"# "
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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": 65,
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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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" OLS Regression Results \n",
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"==============================================================================\n",
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"Dep. Variable: medv R-squared: 0.544\n",
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"Model: OLS Adj. R-squared: 0.543\n",
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"Method: Least Squares F-statistic: 601.6\n",
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"Date: Sat, 28 Mar 2020 Prob (F-statistic): 5.08e-88\n",
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"Time: 09:39:54 Log-Likelihood: -1641.5\n",
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"No. Observations: 506 AIC: 3287.\n",
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"Df Residuals: 504 BIC: 3295.\n",
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"Df Model: 1 \n",
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"Covariance Type: nonrobust \n",
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"==============================================================================\n",
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" coef std err t P>|t| [0.025 0.975]\n",
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"------------------------------------------------------------------------------\n",
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"Intercept 34.5538 0.563 61.415 0.000 33.448 35.659\n",
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"lstat -0.9500 0.039 -24.528 0.000 -1.026 -0.874\n",
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"==============================================================================\n",
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"Omnibus: 137.043 Durbin-Watson: 0.892\n",
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"Prob(Omnibus): 0.000 Jarque-Bera (JB): 291.373\n",
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"Skew: 1.453 Prob(JB): 5.36e-64\n",
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"Kurtosis: 5.319 Cond. No. 29.7\n",
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"==============================================================================\n",
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"\n",
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"Warnings:\n",
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"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
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]
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}
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],
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"source": [
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"results = smf.ols('medv ~ lstat', data=Boston).fit()\n",
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"print(results.summary())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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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": 66,
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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>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>Intercept</th>\n",
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" <td>33.448457</td>\n",
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" <td>35.659225</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>lstat</th>\n",
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" <td>-1.026148</td>\n",
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" <td>-0.873951</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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"Intercept 33.448457 35.659225\n",
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"lstat -1.026148 -0.873951"
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]
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},
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"execution_count": 66,
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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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"results.conf_int()"
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]
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|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f18fc4f2130>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 576x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"residu = results.predict(Boston[\"lstat\"]) - Boston[\"medv\"]\n",
|
||
|
|
"sns.scatterplot(\"lstat\" ,\"medv\",data=Boston)\n",
|
||
|
|
"sns.lineplot(Boston[\"lstat\"], results.predict(Boston[\"lstat\"]))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f18fc475340>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 576x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.scatterplot(residu, Boston[\"lstat\"])\n",
|
||
|
|
"# sns.lineplot(Boston[\"lstat\"], 0)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.8.2"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|