3838 lines
558 KiB
Plaintext
3838 lines
558 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "xwFyEsosINqT"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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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": 2,
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"metadata": {
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"id": "pKewSQysItJ-"
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},
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"outputs": [],
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"source": [
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"# https://www.statsmodels.org/stable/index.html\n",
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"import statsmodels.api as sm"
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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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"metadata": {
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"id": "Lz-DyAtNWsJR"
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},
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"outputs": [],
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"source": [
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"# Download Dataset from https://www.dropbox.com/scl/fo/v71bqw2zowgla028cwdh0/AEfemP4C8qQ2X5tTNXMCqUQ/Session%203?dl=0&preview=educationWage.xlsx&rlkey=rlkgo6o58ex2kjbiv4b7cr9nj&subfolder_nav_tracking=1\n",
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"# and add it to colab"
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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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"metadata": {
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"id": "0zM8FGMJXJ70"
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},
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"outputs": [],
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"source": [
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"educationWageDf = pd.read_excel(\"https://www.dropbox.com/scl/fi/u494o4buy26erbqi1p3xj/educationWage.xlsx?rlkey=7j2bgns66szpuc6xebfhgfha5&dl=1\")\n",
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"# educationWageDf = pd.read_excel(\"./educationWage.xlsx\")"
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 300
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},
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"id": "wsIgDGYcXT_z",
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"outputId": "df04b4a7-5823-4168-e65b-1a539afc94e4"
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},
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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>Education</th>\n",
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" <th>Wage</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>20</td>\n",
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" <td>160</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>18</td>\n",
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" <td>120</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>12</td>\n",
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" <td>70</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>16</td>\n",
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" <td>100</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>10</td>\n",
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" <td>65</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>23</td>\n",
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" <td>160</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</td>\n",
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" <td>40</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>5</td>\n",
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" <td>55</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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" Education Wage\n",
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"0 20 160\n",
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"1 18 120\n",
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"2 12 70\n",
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"3 16 100\n",
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"4 10 65\n",
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"5 23 160\n",
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"6 2 40\n",
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"7 5 55"
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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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"source": [
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"educationWageDf"
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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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"colab": {
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||
"base_uri": "https://localhost:8080/",
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"height": 204
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},
|
||
"id": "mE_jpf50ZSQ3",
|
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"outputId": "e928191e-8299-4b0d-de74-88905fc4f4b5"
|
||
},
|
||
"outputs": [
|
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{
|
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"data": {
|
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"text/plain": [
|
||
"pandas.core.frame.DataFrame"
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]
|
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},
|
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"execution_count": 6,
|
||
"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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"type(educationWageDf)"
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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": 7,
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"metadata": {
|
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
|
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"id": "sPAT8difZamr",
|
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"outputId": "200cdc13-fdfe-44f0-90cb-9bf0e72fb03c"
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},
|
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"outputs": [
|
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{
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"data": {
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"text/plain": [
|
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"(0 20\n",
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" 1 18\n",
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" 2 12\n",
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" 3 16\n",
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" 4 10\n",
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" 5 23\n",
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" 6 2\n",
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" 7 5\n",
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" Name: Education, dtype: int64,\n",
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" pandas.core.series.Series)"
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]
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},
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"execution_count": 7,
|
||
"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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"educationWageDf[\"Education\"], type(educationWageDf[\"Education\"])"
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]
|
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},
|
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{
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||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 472
|
||
},
|
||
"id": "L0294jIPa23E",
|
||
"outputId": "ac786e84-a4c5-4fdf-d1f8-a9c781f2f6dd"
|
||
},
|
||
"outputs": [
|
||
{
|
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"data": {
|
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"text/plain": [
|
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"<Axes: xlabel='Education', ylabel='Wage'>"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"educationWageDf.plot.scatter(\n",
|
||
" x = 'Education',\n",
|
||
" y = 'Wage',\n",
|
||
" xlim = (0, 25),\n",
|
||
" ylim = (0, 180),\n",
|
||
" grid = True\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "70sdSooaXIP6"
|
||
},
|
||
"source": [
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "5kGS_KvQxDnB"
|
||
},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "CAzIv4nUbLNs"
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "izbdCJO6bGgr",
|
||
"outputId": "3afc22ab-f6cd-42a3-f1bc-2ed7d9888438"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0 20\n",
|
||
" 1 18\n",
|
||
" 2 12\n",
|
||
" 3 16\n",
|
||
" 4 10\n",
|
||
" 5 23\n",
|
||
" 6 2\n",
|
||
" 7 5\n",
|
||
" Name: Education, dtype: int64,\n",
|
||
" pandas.core.series.Series)"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"educationWageDf[\"Education\"], type(educationWageDf[\"Education\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "qZTj0erZbNa4",
|
||
"outputId": "dc8383c8-059b-47e1-f8d6-2af3ff19f1b6"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"( const Education\n",
|
||
" 0 1.0 20\n",
|
||
" 1 1.0 18\n",
|
||
" 2 1.0 12\n",
|
||
" 3 1.0 16\n",
|
||
" 4 1.0 10\n",
|
||
" 5 1.0 23\n",
|
||
" 6 1.0 2\n",
|
||
" 7 1.0 5,\n",
|
||
" pandas.core.frame.DataFrame)"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sm.add_constant(educationWageDf[\"Education\"]), type(sm.add_constant(educationWageDf[\"Education\"]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"id": "3CZ6j-I1XH59"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"educationWageLiniarRgressionModel = sm.OLS(\n",
|
||
" educationWageDf[\"Wage\"],\n",
|
||
" sm.add_constant(educationWageDf[\"Education\"])\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"id": "OfbVQb61inFN"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"educationWageLiniarRgressionModelFit = educationWageLiniarRgressionModel.fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "QjoNi0wP_MiT",
|
||
"outputId": "0562af9c-3690-46fd-cdd8-24082338010b"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Wage R-squared: 0.906\n",
|
||
"Model: OLS Adj. R-squared: 0.890\n",
|
||
"Method: Least Squares F-statistic: 57.64\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 0.000272\n",
|
||
"Time: 01:23:10 Log-Likelihood: -32.114\n",
|
||
"No. Observations: 8 AIC: 68.23\n",
|
||
"Df Residuals: 6 BIC: 68.39\n",
|
||
"Df Model: 1 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 16.1358 11.887 1.357 0.223 -12.950 45.221\n",
|
||
"Education 6.0464 0.796 7.592 0.000 4.098 7.995\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 0.619 Durbin-Watson: 0.926\n",
|
||
"Prob(Omnibus): 0.734 Jarque-Bera (JB): 0.522\n",
|
||
"Skew: 0.213 Prob(JB): 0.770\n",
|
||
"Kurtosis: 1.823 Cond. No. 32.5\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/jasonjafari/gitHub/ml_models_deployments/venv/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py:531: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8\n",
|
||
" res = hypotest_fun_out(*samples, **kwds)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(educationWageLiniarRgressionModelFit.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x353978050>"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"educationWageLiniarRgressionModelFit\",\n",
|
||
" \"model\": educationWageLiniarRgressionModelFit,\n",
|
||
" \"description\": \"predict Wage based on Education with linear regression\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Education\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Wage\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "TYSiZnRL_tIx",
|
||
"outputId": "bf581098-bd2b-4ee8-eb05-22300b7840dd"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"const 16.135762\n",
|
||
"Education 6.046358\n",
|
||
"dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"educationWageLiniarRgressionModelFit.params"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"id": "gCeVAjLWd1zt"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# wage = b0 + b1 * Education"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "rgycwEPzibXT",
|
||
"outputId": "7b8a339e-4337-4cdf-a8c5-152d8041f8f9"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 137.062914\n",
|
||
"1 124.970199\n",
|
||
"2 88.692053\n",
|
||
"3 112.877483\n",
|
||
"4 76.599338\n",
|
||
"5 155.201987\n",
|
||
"6 28.228477\n",
|
||
"7 46.367550\n",
|
||
"dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predictedWage = educationWageLiniarRgressionModelFit.predict(sm.add_constant(educationWageDf[\"Education\"]))\n",
|
||
"predictedWage"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 300
|
||
},
|
||
"id": "BNQ2w_HAeGGq",
|
||
"outputId": "0c22f719-fd71-44d1-e9ea-3607b59b3f3a"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Education</th>\n",
|
||
" <th>Wage</th>\n",
|
||
" <th>predictedWage</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>20</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>137.062914</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>18</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>124.970199</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>70</td>\n",
|
||
" <td>88.692053</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>16</td>\n",
|
||
" <td>100</td>\n",
|
||
" <td>112.877483</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>65</td>\n",
|
||
" <td>76.599338</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>23</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>155.201987</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>40</td>\n",
|
||
" <td>28.228477</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>55</td>\n",
|
||
" <td>46.367550</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Education Wage predictedWage\n",
|
||
"0 20 160 137.062914\n",
|
||
"1 18 120 124.970199\n",
|
||
"2 12 70 88.692053\n",
|
||
"3 16 100 112.877483\n",
|
||
"4 10 65 76.599338\n",
|
||
"5 23 160 155.201987\n",
|
||
"6 2 40 28.228477\n",
|
||
"7 5 55 46.367550"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"educationWageDf['predictedWage'] = predictedWage\n",
|
||
"educationWageDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ooBEi1bmBE1F",
|
||
"outputId": "53ed63f4-88ec-4b48-e494-782811aa2b71"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([106.83112583])"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"testPredict = educationWageLiniarRgressionModelFit.predict([[1,15]])\n",
|
||
"testPredict"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"id": "hfmtTSDajJa1"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 718
|
||
},
|
||
"id": "FYmVGYFxjRJg",
|
||
"outputId": "5a08c13d-43b1-4ec7-92b3-cff806da38a0"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Plotting\n",
|
||
"plt.figure(\n",
|
||
" figsize=(8, 8)\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.scatter(\n",
|
||
" educationWageDf[\"Education\"],\n",
|
||
" educationWageDf[\"Wage\"],\n",
|
||
" color='blue',\n",
|
||
" alpha=0.9,\n",
|
||
" label='Data Points - scatter',\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.plot(\n",
|
||
" educationWageDf[\"Education\"],\n",
|
||
" educationWageDf[\"predictedWage\"],\n",
|
||
" color='red',\n",
|
||
" label='OLS Regression - predictedWage'\n",
|
||
")\n",
|
||
"plt.title('Education Level vs. Wage with OLS Regression')\n",
|
||
"plt.xlabel('Education Level(yr)')\n",
|
||
"plt.ylabel('Wage K')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"\n",
|
||
"plt.gca().set_xlim([0, 25])\n",
|
||
"plt.gca().set_ylim([0, 180])\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "trx50k1tBX0s"
|
||
},
|
||
"source": [
|
||
"# Another way"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "5G0wXkAFIxMd",
|
||
"outputId": "638eddd9-db57-42f7-e702-2d99e4ce18c7"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(array([20, 18, 12, 16, 10, 23, 2, 5]),\n",
|
||
" array([160, 120, 70, 100, 65, 160, 40, 55]))"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X = np.array([20, 18, 12, 16, 10, 23, 2, 5])\n",
|
||
"y = np.array([160, 120, 70 , 100, 65, 160, 40, 55])\n",
|
||
"X, y"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {
|
||
"id": "YTcd15wbK9AA"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = sm.add_constant(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Oksgjk2MKjL_",
|
||
"outputId": "05fff372-2217-40d5-ddae-6ae6f716f54a"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[ 1., 20.],\n",
|
||
" [ 1., 18.],\n",
|
||
" [ 1., 12.],\n",
|
||
" [ 1., 16.],\n",
|
||
" [ 1., 10.],\n",
|
||
" [ 1., 23.],\n",
|
||
" [ 1., 2.],\n",
|
||
" [ 1., 5.]])"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {
|
||
"id": "foBX_PxyJJj8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"model = sm.OLS(y, X).fit()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Da-TIkZpJMBB",
|
||
"outputId": "d5c347b5-2c1b-49bc-84cf-ecab21cef3f8"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: y R-squared: 0.906\n",
|
||
"Model: OLS Adj. R-squared: 0.890\n",
|
||
"Method: Least Squares F-statistic: 57.64\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 0.000272\n",
|
||
"Time: 01:23:11 Log-Likelihood: -32.114\n",
|
||
"No. Observations: 8 AIC: 68.23\n",
|
||
"Df Residuals: 6 BIC: 68.39\n",
|
||
"Df Model: 1 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 16.1358 11.887 1.357 0.223 -12.950 45.221\n",
|
||
"x1 6.0464 0.796 7.592 0.000 4.098 7.995\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 0.619 Durbin-Watson: 0.926\n",
|
||
"Prob(Omnibus): 0.734 Jarque-Bera (JB): 0.522\n",
|
||
"Skew: 0.213 Prob(JB): 0.770\n",
|
||
"Kurtosis: 1.823 Cond. No. 32.5\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/jasonjafari/gitHub/ml_models_deployments/venv/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py:531: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8\n",
|
||
" res = hypotest_fun_out(*samples, **kwds)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(model.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "8Evg53IMJQQD",
|
||
"outputId": "817500b1-4734-4d83-9347-ef1bdcd1ee7c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Coefficients: [16.13576159 6.04635762]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Coefficients:\", model.params)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Csteg6CiOiSv",
|
||
"outputId": "e3469200-48c1-4c56-8e46-fbdc0aac986b"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 46.36754967, 106.83112583])"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predictions = model.predict([[1, 5], [1, 15]])\n",
|
||
"predictions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"id": "xxZNODO1PC1m"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def predicWage(intercept, slope, yearsOfExperience):\n",
|
||
" return intercept + (slope * yearsOfExperience)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "8lYfVWyNPdLc",
|
||
"outputId": "7db3e8e0-1425-4179-dfc9-c48c5167cd4e"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"46.367549668874176"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predicWage(model.params[0], model.params[1], 5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "YnJzK9USPkcC",
|
||
"outputId": "01b40693-ade3-433a-de93-bc848a19506a"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"106.83112582781453"
|
||
]
|
||
},
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predicWage(model.params[0], model.params[1], 15)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "UxR96NeFLG9p"
|
||
},
|
||
"source": [
|
||
"# Another way"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {
|
||
"id": "y6-07nV0JoBa"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.linear_model import LinearRegression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 300
|
||
},
|
||
"id": "DJKKcxAFBlZi",
|
||
"outputId": "accbbe8f-e428-4dd1-83f6-9da8c3eb4fff"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Education</th>\n",
|
||
" <th>Wage</th>\n",
|
||
" <th>predictedWage</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>20</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>137.062914</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>18</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>124.970199</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>70</td>\n",
|
||
" <td>88.692053</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>16</td>\n",
|
||
" <td>100</td>\n",
|
||
" <td>112.877483</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>65</td>\n",
|
||
" <td>76.599338</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>23</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>155.201987</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>40</td>\n",
|
||
" <td>28.228477</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>55</td>\n",
|
||
" <td>46.367550</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Education Wage predictedWage\n",
|
||
"0 20 160 137.062914\n",
|
||
"1 18 120 124.970199\n",
|
||
"2 12 70 88.692053\n",
|
||
"3 16 100 112.877483\n",
|
||
"4 10 65 76.599338\n",
|
||
"5 23 160 155.201987\n",
|
||
"6 2 40 28.228477\n",
|
||
"7 5 55 46.367550"
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"educationWageDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {
|
||
"id": "0GMyNZTyB0tz"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"educationWageLiniarRgressionModel2 = LinearRegression()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "CMrTd4NsC3Am",
|
||
"outputId": "adb114b3-4853-4d1e-8982-c5aa615236da"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0 20\n",
|
||
" 1 18\n",
|
||
" 2 12\n",
|
||
" 3 16\n",
|
||
" 4 10\n",
|
||
" 5 23\n",
|
||
" 6 2\n",
|
||
" 7 5\n",
|
||
" Name: Education, dtype: int64,\n",
|
||
" pandas.core.series.Series)"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"educationWageDf[\"Education\"], type(educationWageDf[\"Education\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Flw3PPJKC0eI",
|
||
"outputId": "c7fd107a-295e-4e74-ac0d-fab8794f9da5"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"( Education\n",
|
||
" 0 20\n",
|
||
" 1 18\n",
|
||
" 2 12\n",
|
||
" 3 16\n",
|
||
" 4 10\n",
|
||
" 5 23\n",
|
||
" 6 2\n",
|
||
" 7 5,\n",
|
||
" pandas.core.frame.DataFrame)"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"educationWageDf[[\"Education\"]], type(educationWageDf[[\"Education\"]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {
|
||
"id": "ox_p7rYYB8nA"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"educationWageLiniarRgressionModel2Fit = educationWageLiniarRgressionModel2.fit(\n",
|
||
" educationWageDf[[\"Education\"]],\n",
|
||
" educationWageDf[\"Wage\"]\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "RxNZRBlhKpu5",
|
||
"outputId": "97fd2df3-121e-487d-89bd-eca5dccc3d5e"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Intercept: 16.135761589403984\n",
|
||
"Coefficient: 6.046357615894038\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Print the intercept and coefficient\n",
|
||
"print(\"Intercept:\", educationWageLiniarRgressionModel2Fit.intercept_)\n",
|
||
"print(\"Coefficient:\", educationWageLiniarRgressionModel2Fit.coef_[0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "xtqZ6ZRaQBgK",
|
||
"outputId": "7d2b6a50-998f-48e6-f711-4a245490e924"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/jasonjafari/gitHub/ml_models_deployments/venv/lib/python3.12/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 46.36754967, 106.83112583])"
|
||
]
|
||
},
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predictions1 = educationWageLiniarRgressionModel2Fit.predict(np.array([[5], [15]]))\n",
|
||
"predictions1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "e2cyeBEUhzx6"
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "BVJslxRTiCxx"
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ssUOw-bWQTMP",
|
||
"outputId": "a7f58e83-9a5f-4b68-cca9-95ab76e917ec"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"46.367549668874176"
|
||
]
|
||
},
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predicWage(educationWageLiniarRgressionModel2Fit.intercept_, educationWageLiniarRgressionModel2Fit.coef_[0], 5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "8cM6wdOdQYGK",
|
||
"outputId": "cfbd9022-bc83-4292-eac3-d24493fc1aa7"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"106.83112582781456"
|
||
]
|
||
},
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predicWage(educationWageLiniarRgressionModel2Fit.intercept_, educationWageLiniarRgressionModel2Fit.coef_[0], 15)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 300
|
||
},
|
||
"id": "uurMTqQpDi_A",
|
||
"outputId": "cd798e3d-1556-438f-d1ad-3b8d7acbd09f"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Education</th>\n",
|
||
" <th>Wage</th>\n",
|
||
" <th>predictedWage</th>\n",
|
||
" <th>predictedWage2</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>20</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>137.062914</td>\n",
|
||
" <td>137.062914</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>18</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>124.970199</td>\n",
|
||
" <td>124.970199</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>70</td>\n",
|
||
" <td>88.692053</td>\n",
|
||
" <td>88.692053</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>16</td>\n",
|
||
" <td>100</td>\n",
|
||
" <td>112.877483</td>\n",
|
||
" <td>112.877483</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>65</td>\n",
|
||
" <td>76.599338</td>\n",
|
||
" <td>76.599338</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>23</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>155.201987</td>\n",
|
||
" <td>155.201987</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>40</td>\n",
|
||
" <td>28.228477</td>\n",
|
||
" <td>28.228477</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>55</td>\n",
|
||
" <td>46.367550</td>\n",
|
||
" <td>46.367550</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Education Wage predictedWage predictedWage2\n",
|
||
"0 20 160 137.062914 137.062914\n",
|
||
"1 18 120 124.970199 124.970199\n",
|
||
"2 12 70 88.692053 88.692053\n",
|
||
"3 16 100 112.877483 112.877483\n",
|
||
"4 10 65 76.599338 76.599338\n",
|
||
"5 23 160 155.201987 155.201987\n",
|
||
"6 2 40 28.228477 28.228477\n",
|
||
"7 5 55 46.367550 46.367550"
|
||
]
|
||
},
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predictedWage2 = educationWageLiniarRgressionModel2Fit.predict(educationWageDf[[\"Education\"]])\n",
|
||
"educationWageDf['predictedWage2'] = predictedWage2\n",
|
||
"educationWageDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 718
|
||
},
|
||
"id": "30mmVCEuD4sx",
|
||
"outputId": "61e0c28a-6809-4a89-c670-ed767aaa7e60"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Plotting\n",
|
||
"plt.figure(\n",
|
||
" figsize=(8, 8)\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.scatter(\n",
|
||
" educationWageDf[\"Education\"],\n",
|
||
" educationWageDf[\"Wage\"],\n",
|
||
" color='blue',\n",
|
||
" alpha=0.9,\n",
|
||
" label='Data Points - scatter',\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.plot(\n",
|
||
" educationWageDf[\"Education\"],\n",
|
||
" educationWageDf[\"predictedWage\"],\n",
|
||
" color='red',\n",
|
||
" label='OLS Regression - predictedWage'\n",
|
||
")\n",
|
||
"plt.plot(\n",
|
||
" educationWageDf[\"Education\"],\n",
|
||
" educationWageDf[\"predictedWage2\"],\n",
|
||
" color='black',\n",
|
||
" label='sklearn Regression - predictedWage'\n",
|
||
")\n",
|
||
"plt.title('Education Level vs. Wage with OLS Regression')\n",
|
||
"plt.xlabel('Education Level(yr)')\n",
|
||
"plt.ylabel('Wage K')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"\n",
|
||
"plt.gca().set_xlim([0, 25])\n",
|
||
"plt.gca().set_ylim([0, 180])\n",
|
||
"\n",
|
||
"\n",
|
||
"equation = f'Wage = {educationWageLiniarRgressionModel2Fit.coef_[0]:.2f} * Education + {educationWageLiniarRgressionModel2Fit.intercept_:.2f}'\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.text(\n",
|
||
" 10, 120,\n",
|
||
" equation,\n",
|
||
" horizontalalignment='center',\n",
|
||
" verticalalignment='center',\n",
|
||
" fontsize=12,\n",
|
||
" color=\"green\",\n",
|
||
" bbox=dict(facecolor='white', alpha=0.5)\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "lNvoCcWRL1tW"
|
||
},
|
||
"source": [
|
||
"# Real dataset"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {
|
||
"id": "KHPW5DNvMUdz"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "gIC1S6uQQi_t"
|
||
},
|
||
"source": [
|
||
"Download it from [here](https://www.dropbox.com/scl/fi/1sc8ojfezlbrcaje42w0n/College.xlsx?rlkey=i3starhohiwkua8ekbjk3nb92&st=yd75jyvp&dl=0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {
|
||
"id": "Ksf536HkMQu5"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# collegeDf = pd.read_excel(\"./College.xlsx\")\n",
|
||
"collegeDf = pd.read_excel(\"https://www.dropbox.com/scl/fi/sqdbxs8c9r55s2qleal8t/College.xlsx?rlkey=c751oujzls8oxzwd0l89pcv5k&dl=1\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 206
|
||
},
|
||
"id": "Rjwma_7DNFzP",
|
||
"outputId": "ca99f39a-2b5b-44ac-e6dd-54fd610608f2"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>School</th>\n",
|
||
" <th>Earnings</th>\n",
|
||
" <th>Cost</th>\n",
|
||
" <th>Grad</th>\n",
|
||
" <th>Debt</th>\n",
|
||
" <th>City</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>St. Ambrose C (NC)</td>\n",
|
||
" <td>44800</td>\n",
|
||
" <td>22920</td>\n",
|
||
" <td>62</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Albion College (Albion, MI)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>23429</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Alfred University (Alfred, NY)</td>\n",
|
||
" <td>42300</td>\n",
|
||
" <td>19567</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>87</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Allegheny College (Meadville, PA)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>25147</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Beloit College (Beloit, WI)</td>\n",
|
||
" <td>37900</td>\n",
|
||
" <td>21979</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" School Earnings Cost Grad Debt City\n",
|
||
"0 St. Ambrose C (NC) 44800 22920 62 88 1\n",
|
||
"1 Albion College (Albion, MI) 45100 23429 73 92 0\n",
|
||
"2 Alfred University (Alfred, NY) 42300 19567 63 87 0\n",
|
||
"3 Allegheny College (Meadville, PA) 49200 25147 78 92 0\n",
|
||
"4 Beloit College (Beloit, WI) 37900 21979 78 93 1"
|
||
]
|
||
},
|
||
"execution_count": 46,
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>School</th>\n",
|
||
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|
||
" <th>Cost</th>\n",
|
||
" <th>Grad</th>\n",
|
||
" <th>Debt</th>\n",
|
||
" <th>City</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>Whittier College (Whittier, CA)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>33181</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>112</th>\n",
|
||
" <td>Widener University (Chester, PA)</td>\n",
|
||
" <td>51700</td>\n",
|
||
" <td>27387</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>Willamette University (Salem, OR)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>30312</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>114</th>\n",
|
||
" <td>Winthrop University (Rock Hill, SC)</td>\n",
|
||
" <td>36100</td>\n",
|
||
" <td>15311</td>\n",
|
||
" <td>54</td>\n",
|
||
" <td>76</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>Wittenberg University (Springfield, OH)</td>\n",
|
||
" <td>42700</td>\n",
|
||
" <td>26616</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>90</td>\n",
|
||
" <td>1</td>\n",
|
||
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||
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||
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||
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|
||
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|
||
"text/plain": [
|
||
" School Earnings Cost Grad Debt \\\n",
|
||
"111 Whittier College (Whittier, CA) 45100 33181 67 86 \n",
|
||
"112 Widener University (Chester, PA) 51700 27387 56 83 \n",
|
||
"113 Willamette University (Salem, OR) 49200 30312 78 93 \n",
|
||
"114 Winthrop University (Rock Hill, SC) 36100 15311 54 76 \n",
|
||
"115 Wittenberg University (Springfield, OH) 42700 26616 64 90 \n",
|
||
"\n",
|
||
" City \n",
|
||
"111 0 \n",
|
||
"112 0 \n",
|
||
"113 1 \n",
|
||
"114 1 \n",
|
||
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|
||
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|
||
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|
||
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|
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|
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|
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||
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|
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|
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||
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|
||
"(116, 6)"
|
||
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|
||
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||
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|
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|
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||
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|
||
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|
||
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|
||
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|
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|
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||
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|
||
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||
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|
||
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|
||
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|
||
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||
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|
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||
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|
||
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|
||
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|
||
"collegeDf.size"
|
||
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|
||
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{
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
" <th>Earnings</th>\n",
|
||
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|
||
" <th>Grad</th>\n",
|
||
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|
||
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|
||
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|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>116.000000</td>\n",
|
||
" <td>116.000000</td>\n",
|
||
" <td>116.000000</td>\n",
|
||
" <td>116.000000</td>\n",
|
||
" <td>116.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>46477.586207</td>\n",
|
||
" <td>25251.689655</td>\n",
|
||
" <td>65.681034</td>\n",
|
||
" <td>88.405172</td>\n",
|
||
" <td>0.508621</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>7341.876152</td>\n",
|
||
" <td>5387.152858</td>\n",
|
||
" <td>11.764796</td>\n",
|
||
" <td>6.838482</td>\n",
|
||
" <td>0.502095</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>32300.000000</td>\n",
|
||
" <td>9938.000000</td>\n",
|
||
" <td>32.000000</td>\n",
|
||
" <td>52.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>42300.000000</td>\n",
|
||
" <td>21674.250000</td>\n",
|
||
" <td>59.000000</td>\n",
|
||
" <td>86.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>45150.000000</td>\n",
|
||
" <td>24957.500000</td>\n",
|
||
" <td>67.000000</td>\n",
|
||
" <td>90.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>51000.000000</td>\n",
|
||
" <td>29489.750000</td>\n",
|
||
" <td>75.000000</td>\n",
|
||
" <td>93.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>74900.000000</td>\n",
|
||
" <td>35159.000000</td>\n",
|
||
" <td>86.000000</td>\n",
|
||
" <td>98.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Earnings Cost Grad Debt City\n",
|
||
"count 116.000000 116.000000 116.000000 116.000000 116.000000\n",
|
||
"mean 46477.586207 25251.689655 65.681034 88.405172 0.508621\n",
|
||
"std 7341.876152 5387.152858 11.764796 6.838482 0.502095\n",
|
||
"min 32300.000000 9938.000000 32.000000 52.000000 0.000000\n",
|
||
"25% 42300.000000 21674.250000 59.000000 86.000000 0.000000\n",
|
||
"50% 45150.000000 24957.500000 67.000000 90.000000 1.000000\n",
|
||
"75% 51000.000000 29489.750000 75.000000 93.000000 1.000000\n",
|
||
"max 74900.000000 35159.000000 86.000000 98.000000 1.000000"
|
||
]
|
||
},
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
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|
||
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|
||
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|
||
"source": [
|
||
"collegeDf.describe()"
|
||
]
|
||
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|
||
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|
||
"cell_type": "code",
|
||
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|
||
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|
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|
||
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|
||
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||
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||
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||
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|
||
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|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Plotting\n",
|
||
"plt.figure(\n",
|
||
" figsize=(8, 8)\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.scatter(\n",
|
||
" collegeDf[\"Cost\"],\n",
|
||
" collegeDf[\"Earnings\"],\n",
|
||
" color='blue',\n",
|
||
" alpha=0.9,\n",
|
||
" label='Data Points - scatter',\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.xlabel('Cost')\n",
|
||
"plt.ylabel('Earnings')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Puh0-DhgKp9i",
|
||
"outputId": "97c984fb-f216-4db1-8a61-e0a266ed5981"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"( const Cost\n",
|
||
" 0 1.0 22920\n",
|
||
" 1 1.0 23429\n",
|
||
" 2 1.0 19567\n",
|
||
" 3 1.0 25147\n",
|
||
" 4 1.0 21979\n",
|
||
" .. ... ...\n",
|
||
" 111 1.0 33181\n",
|
||
" 112 1.0 27387\n",
|
||
" 113 1.0 30312\n",
|
||
" 114 1.0 15311\n",
|
||
" 115 1.0 26616\n",
|
||
" \n",
|
||
" [116 rows x 2 columns],\n",
|
||
" pandas.core.frame.DataFrame)"
|
||
]
|
||
},
|
||
"execution_count": 52,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sm.add_constant(collegeDf['Cost']), type(sm.add_constant(collegeDf['Cost']))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {
|
||
"id": "54YBn53VNxQk"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"earningOthersOlsModelFit1 = sm.OLS(\n",
|
||
" collegeDf[\"Earnings\"],\n",
|
||
" sm.add_constant(collegeDf['Cost'])\n",
|
||
").fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "KbZxiP7zNyoY",
|
||
"outputId": "291d7bcc-2c3e-4ca6-8ab7-76b4af854f1a"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Earnings R-squared: 0.277\n",
|
||
"Model: OLS Adj. R-squared: 0.270\n",
|
||
"Method: Least Squares F-statistic: 43.61\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 1.33e-09\n",
|
||
"Time: 01:23:12 Log-Likelihood: -1177.9\n",
|
||
"No. Observations: 116 AIC: 2360.\n",
|
||
"Df Residuals: 114 BIC: 2365.\n",
|
||
"Df Model: 1 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 2.838e+04 2802.417 10.125 0.000 2.28e+04 3.39e+04\n",
|
||
"Cost 0.7169 0.109 6.604 0.000 0.502 0.932\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 11.840 Durbin-Watson: 1.841\n",
|
||
"Prob(Omnibus): 0.003 Jarque-Bera (JB): 19.875\n",
|
||
"Skew: 0.437 Prob(JB): 4.83e-05\n",
|
||
"Kurtosis: 4.830 Cond. No. 1.24e+05\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
|
||
"[2] The condition number is large, 1.24e+05. This might indicate that there are\n",
|
||
"strong multicollinearity or other numerical problems.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(earningOthersOlsModelFit1.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x355613890>"
|
||
]
|
||
},
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"earningOthersOlsModelFit1\",\n",
|
||
" \"model\": earningOthersOlsModelFit1,\n",
|
||
" \"description\": \"predict Earnings based on Cost\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Cost\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Earnings\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 424
|
||
},
|
||
"id": "Efg5OIl6KwDz",
|
||
"outputId": "880870cb-c2d4-4ff1-ad77-5103fd8a1083"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>School</th>\n",
|
||
" <th>Earnings</th>\n",
|
||
" <th>Cost</th>\n",
|
||
" <th>Grad</th>\n",
|
||
" <th>Debt</th>\n",
|
||
" <th>City</th>\n",
|
||
" <th>predictedEarning1</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>St. Ambrose C (NC)</td>\n",
|
||
" <td>44800</td>\n",
|
||
" <td>22920</td>\n",
|
||
" <td>62</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44806.067625</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Albion College (Albion, MI)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>23429</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>45170.954503</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Alfred University (Alfred, NY)</td>\n",
|
||
" <td>42300</td>\n",
|
||
" <td>19567</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>87</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>42402.402200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Allegheny College (Meadville, PA)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>25147</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>46402.537324</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Beloit College (Beloit, WI)</td>\n",
|
||
" <td>37900</td>\n",
|
||
" <td>21979</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44131.492866</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>Whittier College (Whittier, CA)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>33181</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>52161.871659</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>112</th>\n",
|
||
" <td>Widener University (Chester, PA)</td>\n",
|
||
" <td>51700</td>\n",
|
||
" <td>27387</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>48008.326334</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>Willamette University (Salem, OR)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>30312</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>50105.171359</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>114</th>\n",
|
||
" <td>Winthrop University (Rock Hill, SC)</td>\n",
|
||
" <td>36100</td>\n",
|
||
" <td>15311</td>\n",
|
||
" <td>54</td>\n",
|
||
" <td>76</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>39351.403080</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>Wittenberg University (Springfield, OH)</td>\n",
|
||
" <td>42700</td>\n",
|
||
" <td>26616</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>90</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>47455.619492</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>116 rows × 7 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" School Earnings Cost Grad Debt \\\n",
|
||
"0 St. Ambrose C (NC) 44800 22920 62 88 \n",
|
||
"1 Albion College (Albion, MI) 45100 23429 73 92 \n",
|
||
"2 Alfred University (Alfred, NY) 42300 19567 63 87 \n",
|
||
"3 Allegheny College (Meadville, PA) 49200 25147 78 92 \n",
|
||
"4 Beloit College (Beloit, WI) 37900 21979 78 93 \n",
|
||
".. ... ... ... ... ... \n",
|
||
"111 Whittier College (Whittier, CA) 45100 33181 67 86 \n",
|
||
"112 Widener University (Chester, PA) 51700 27387 56 83 \n",
|
||
"113 Willamette University (Salem, OR) 49200 30312 78 93 \n",
|
||
"114 Winthrop University (Rock Hill, SC) 36100 15311 54 76 \n",
|
||
"115 Wittenberg University (Springfield, OH) 42700 26616 64 90 \n",
|
||
"\n",
|
||
" City predictedEarning1 \n",
|
||
"0 1 44806.067625 \n",
|
||
"1 0 45170.954503 \n",
|
||
"2 0 42402.402200 \n",
|
||
"3 0 46402.537324 \n",
|
||
"4 1 44131.492866 \n",
|
||
".. ... ... \n",
|
||
"111 0 52161.871659 \n",
|
||
"112 0 48008.326334 \n",
|
||
"113 1 50105.171359 \n",
|
||
"114 1 39351.403080 \n",
|
||
"115 1 47455.619492 \n",
|
||
"\n",
|
||
"[116 rows x 7 columns]"
|
||
]
|
||
},
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predictedEarning1 = earningOthersOlsModelFit1.predict(sm.add_constant(collegeDf[\"Cost\"]))\n",
|
||
"collegeDf['predictedEarning1'] = predictedEarning1\n",
|
||
"collegeDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 676
|
||
},
|
||
"id": "HWPp6ZYeLA3W",
|
||
"outputId": "45e4214b-e148-4726-a4ca-a4fb1b4dfdbe"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(\n",
|
||
" figsize=(8, 8)\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.scatter(\n",
|
||
" collegeDf[\"Cost\"],\n",
|
||
" collegeDf[\"Earnings\"],\n",
|
||
" color='blue',\n",
|
||
" alpha=0.9,\n",
|
||
" label='Data Points - scatter',\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.plot(\n",
|
||
" collegeDf[\"Cost\"],\n",
|
||
" collegeDf[\"predictedEarning1\"],\n",
|
||
" color='red',\n",
|
||
" label='OLS 1'\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 424
|
||
},
|
||
"id": "VO6Yv3bgLyRv",
|
||
"outputId": "601d0a87-b32e-4957-bfdf-7b0749986842"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>School</th>\n",
|
||
" <th>Earnings</th>\n",
|
||
" <th>Cost</th>\n",
|
||
" <th>Grad</th>\n",
|
||
" <th>Debt</th>\n",
|
||
" <th>City</th>\n",
|
||
" <th>predictedEarning1</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>St. Ambrose C (NC)</td>\n",
|
||
" <td>44800</td>\n",
|
||
" <td>22920</td>\n",
|
||
" <td>62</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44806.067625</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Albion College (Albion, MI)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>23429</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>45170.954503</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Alfred University (Alfred, NY)</td>\n",
|
||
" <td>42300</td>\n",
|
||
" <td>19567</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>87</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>42402.402200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Allegheny College (Meadville, PA)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>25147</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>46402.537324</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Beloit College (Beloit, WI)</td>\n",
|
||
" <td>37900</td>\n",
|
||
" <td>21979</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44131.492866</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>Whittier College (Whittier, CA)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>33181</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>52161.871659</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>112</th>\n",
|
||
" <td>Widener University (Chester, PA)</td>\n",
|
||
" <td>51700</td>\n",
|
||
" <td>27387</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>48008.326334</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>Willamette University (Salem, OR)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>30312</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>50105.171359</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>114</th>\n",
|
||
" <td>Winthrop University (Rock Hill, SC)</td>\n",
|
||
" <td>36100</td>\n",
|
||
" <td>15311</td>\n",
|
||
" <td>54</td>\n",
|
||
" <td>76</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>39351.403080</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>Wittenberg University (Springfield, OH)</td>\n",
|
||
" <td>42700</td>\n",
|
||
" <td>26616</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>90</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>47455.619492</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>116 rows × 7 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" School Earnings Cost Grad Debt \\\n",
|
||
"0 St. Ambrose C (NC) 44800 22920 62 88 \n",
|
||
"1 Albion College (Albion, MI) 45100 23429 73 92 \n",
|
||
"2 Alfred University (Alfred, NY) 42300 19567 63 87 \n",
|
||
"3 Allegheny College (Meadville, PA) 49200 25147 78 92 \n",
|
||
"4 Beloit College (Beloit, WI) 37900 21979 78 93 \n",
|
||
".. ... ... ... ... ... \n",
|
||
"111 Whittier College (Whittier, CA) 45100 33181 67 86 \n",
|
||
"112 Widener University (Chester, PA) 51700 27387 56 83 \n",
|
||
"113 Willamette University (Salem, OR) 49200 30312 78 93 \n",
|
||
"114 Winthrop University (Rock Hill, SC) 36100 15311 54 76 \n",
|
||
"115 Wittenberg University (Springfield, OH) 42700 26616 64 90 \n",
|
||
"\n",
|
||
" City predictedEarning1 \n",
|
||
"0 1 44806.067625 \n",
|
||
"1 0 45170.954503 \n",
|
||
"2 0 42402.402200 \n",
|
||
"3 0 46402.537324 \n",
|
||
"4 1 44131.492866 \n",
|
||
".. ... ... \n",
|
||
"111 0 52161.871659 \n",
|
||
"112 0 48008.326334 \n",
|
||
"113 1 50105.171359 \n",
|
||
"114 1 39351.403080 \n",
|
||
"115 1 47455.619492 \n",
|
||
"\n",
|
||
"[116 rows x 7 columns]"
|
||
]
|
||
},
|
||
"execution_count": 58,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"collegeDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 437
|
||
},
|
||
"id": "at1I6sLoMJFu",
|
||
"outputId": "aec4f9c8-b2c0-48ff-f8ab-7f19585e8c84"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plt.figure()\n",
|
||
"ax = plt.axes(projection =\"3d\")\n",
|
||
"\n",
|
||
"# Creating plot\n",
|
||
"ax.scatter3D(\n",
|
||
" collegeDf[\"Cost\"],\n",
|
||
" collegeDf[\"Grad\"],\n",
|
||
" collegeDf[\"Earnings\"],\n",
|
||
" color = \"green\"\n",
|
||
")\n",
|
||
"plt.title(\"Cost,Grad -> Earnings\")\n",
|
||
"ax.set_xlabel('Cost')\n",
|
||
"ax.set_ylabel('Grad')\n",
|
||
"ax.set_zlabel('Earnings')\n",
|
||
"\n",
|
||
"# show plot\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "HzQFLcchNaDt",
|
||
"outputId": "5d676c4e-e4d2-4837-bcc1-98179415a3f0"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"( Cost Grad\n",
|
||
" 0 22920 62\n",
|
||
" 1 23429 73\n",
|
||
" 2 19567 63\n",
|
||
" 3 25147 78\n",
|
||
" 4 21979 78\n",
|
||
" .. ... ...\n",
|
||
" 111 33181 67\n",
|
||
" 112 27387 56\n",
|
||
" 113 30312 78\n",
|
||
" 114 15311 54\n",
|
||
" 115 26616 64\n",
|
||
" \n",
|
||
" [116 rows x 2 columns],\n",
|
||
" pandas.core.frame.DataFrame)"
|
||
]
|
||
},
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"collegeDf[['Cost', 'Grad']], type(collegeDf[['Cost', 'Grad']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "qBInUi_bsQ_x",
|
||
"outputId": "4235806e-71bb-41bd-d28b-f8ea2407c20a"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"( const Cost Grad\n",
|
||
" 0 1.0 22920 62\n",
|
||
" 1 1.0 23429 73\n",
|
||
" 2 1.0 19567 63\n",
|
||
" 3 1.0 25147 78\n",
|
||
" 4 1.0 21979 78\n",
|
||
" .. ... ... ...\n",
|
||
" 111 1.0 33181 67\n",
|
||
" 112 1.0 27387 56\n",
|
||
" 113 1.0 30312 78\n",
|
||
" 114 1.0 15311 54\n",
|
||
" 115 1.0 26616 64\n",
|
||
" \n",
|
||
" [116 rows x 3 columns],\n",
|
||
" pandas.core.frame.DataFrame)"
|
||
]
|
||
},
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sm.add_constant(collegeDf[['Cost', 'Grad']]), type(sm.add_constant(collegeDf[['Cost', 'Grad']]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {
|
||
"id": "PEQQ-E8dNQ9x"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"earningOthersOlsModelFit2 = sm.OLS(\n",
|
||
" collegeDf[\"Earnings\"],\n",
|
||
" sm.add_constant(collegeDf[['Cost', 'Grad']])\n",
|
||
").fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ETve670aOTNZ",
|
||
"outputId": "1d740ca1-fab3-4fdd-c574-052947d82800"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Earnings R-squared: 0.398\n",
|
||
"Model: OLS Adj. R-squared: 0.387\n",
|
||
"Method: Least Squares F-statistic: 37.37\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 3.50e-13\n",
|
||
"Time: 01:23:13 Log-Likelihood: -1167.2\n",
|
||
"No. Observations: 116 AIC: 2340.\n",
|
||
"Df Residuals: 113 BIC: 2349.\n",
|
||
"Df Model: 2 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 1.798e+04 3366.727 5.341 0.000 1.13e+04 2.47e+04\n",
|
||
"Cost 0.5131 0.108 4.741 0.000 0.299 0.728\n",
|
||
"Grad 236.6049 49.563 4.774 0.000 138.412 334.798\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 22.086 Durbin-Watson: 1.997\n",
|
||
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 33.660\n",
|
||
"Skew: 0.904 Prob(JB): 4.91e-08\n",
|
||
"Kurtosis: 4.923 Cond. No. 1.63e+05\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
|
||
"[2] The condition number is large, 1.63e+05. This might indicate that there are\n",
|
||
"strong multicollinearity or other numerical problems.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(earningOthersOlsModelFit2.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3555f8050>"
|
||
]
|
||
},
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"earningOthersOlsModelFit2\",\n",
|
||
" \"model\": earningOthersOlsModelFit2,\n",
|
||
" \"description\": \"predict Earnings based on Cost and Grad\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Cost\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Grad\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Earnings\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 424
|
||
},
|
||
"id": "Dn6EX65kOdtu",
|
||
"outputId": "f13fe9be-487f-4717-ebf6-7158d8753fa3"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>School</th>\n",
|
||
" <th>Earnings</th>\n",
|
||
" <th>Cost</th>\n",
|
||
" <th>Grad</th>\n",
|
||
" <th>Debt</th>\n",
|
||
" <th>City</th>\n",
|
||
" <th>predictedEarning1</th>\n",
|
||
" <th>predictedEarning2</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>St. Ambrose C (NC)</td>\n",
|
||
" <td>44800</td>\n",
|
||
" <td>22920</td>\n",
|
||
" <td>62</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44806.067625</td>\n",
|
||
" <td>44410.232770</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Albion College (Albion, MI)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>23429</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>45170.954503</td>\n",
|
||
" <td>47274.057585</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Alfred University (Alfred, NY)</td>\n",
|
||
" <td>42300</td>\n",
|
||
" <td>19567</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>87</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>42402.402200</td>\n",
|
||
" <td>42926.395289</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Allegheny College (Meadville, PA)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>25147</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>46402.537324</td>\n",
|
||
" <td>49338.597282</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Beloit College (Beloit, WI)</td>\n",
|
||
" <td>37900</td>\n",
|
||
" <td>21979</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44131.492866</td>\n",
|
||
" <td>47713.079376</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>Whittier College (Whittier, CA)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>33181</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>52161.871659</td>\n",
|
||
" <td>50858.231896</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>112</th>\n",
|
||
" <td>Widener University (Chester, PA)</td>\n",
|
||
" <td>51700</td>\n",
|
||
" <td>27387</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>48008.326334</td>\n",
|
||
" <td>45282.645045</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>Willamette University (Salem, OR)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>30312</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>50105.171359</td>\n",
|
||
" <td>51988.786671</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>114</th>\n",
|
||
" <td>Winthrop University (Rock Hill, SC)</td>\n",
|
||
" <td>36100</td>\n",
|
||
" <td>15311</td>\n",
|
||
" <td>54</td>\n",
|
||
" <td>76</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>39351.403080</td>\n",
|
||
" <td>38613.174391</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>Wittenberg University (Springfield, OH)</td>\n",
|
||
" <td>42700</td>\n",
|
||
" <td>26616</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>90</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>47455.619492</td>\n",
|
||
" <td>46779.880175</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>116 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" School Earnings Cost Grad Debt \\\n",
|
||
"0 St. Ambrose C (NC) 44800 22920 62 88 \n",
|
||
"1 Albion College (Albion, MI) 45100 23429 73 92 \n",
|
||
"2 Alfred University (Alfred, NY) 42300 19567 63 87 \n",
|
||
"3 Allegheny College (Meadville, PA) 49200 25147 78 92 \n",
|
||
"4 Beloit College (Beloit, WI) 37900 21979 78 93 \n",
|
||
".. ... ... ... ... ... \n",
|
||
"111 Whittier College (Whittier, CA) 45100 33181 67 86 \n",
|
||
"112 Widener University (Chester, PA) 51700 27387 56 83 \n",
|
||
"113 Willamette University (Salem, OR) 49200 30312 78 93 \n",
|
||
"114 Winthrop University (Rock Hill, SC) 36100 15311 54 76 \n",
|
||
"115 Wittenberg University (Springfield, OH) 42700 26616 64 90 \n",
|
||
"\n",
|
||
" City predictedEarning1 predictedEarning2 \n",
|
||
"0 1 44806.067625 44410.232770 \n",
|
||
"1 0 45170.954503 47274.057585 \n",
|
||
"2 0 42402.402200 42926.395289 \n",
|
||
"3 0 46402.537324 49338.597282 \n",
|
||
"4 1 44131.492866 47713.079376 \n",
|
||
".. ... ... ... \n",
|
||
"111 0 52161.871659 50858.231896 \n",
|
||
"112 0 48008.326334 45282.645045 \n",
|
||
"113 1 50105.171359 51988.786671 \n",
|
||
"114 1 39351.403080 38613.174391 \n",
|
||
"115 1 47455.619492 46779.880175 \n",
|
||
"\n",
|
||
"[116 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predictedEarning2 = earningOthersOlsModelFit2.predict(\n",
|
||
" sm.add_constant(collegeDf[['Cost', 'Grad']])\n",
|
||
")\n",
|
||
"collegeDf['predictedEarning2'] = predictedEarning2\n",
|
||
"collegeDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {
|
||
"id": "wZw7IpfVRHJx"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# if 'google.colab' in str(get_ipython()):\n",
|
||
"# get_ipython().run_line_magic('matplotlib', 'inline')\n",
|
||
"\n",
|
||
"# %matplotlib notebook\n",
|
||
"# # %matplotlib notebook\n",
|
||
"%matplotlib inline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 67,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 437
|
||
},
|
||
"id": "vSt3zKpiPYfJ",
|
||
"outputId": "45febe98-0078-49ec-9de6-f4bf941cf957"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Extracting coefficients\n",
|
||
"intercept = earningOthersOlsModelFit2.params['const']\n",
|
||
"coef_cost = earningOthersOlsModelFit2.params['Cost']\n",
|
||
"coef_grad = earningOthersOlsModelFit2.params['Grad']\n",
|
||
"\n",
|
||
"# Create 3D grid for plotting\n",
|
||
"cost_range = np.linspace(collegeDf['Cost'].min(), collegeDf['Cost'].max(), 100)\n",
|
||
"grad_range = np.linspace(collegeDf['Grad'].min(), collegeDf['Grad'].max(), 100)\n",
|
||
"cost_grid, grad_grid = np.meshgrid(cost_range, grad_range)\n",
|
||
"\n",
|
||
"# Calculate predicted earnings for each combination of cost and grad\n",
|
||
"earnings_predicted = intercept + coef_cost * cost_grid + coef_grad * grad_grid\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = plt.axes(projection =\"3d\")\n",
|
||
"\n",
|
||
"# Scatter plot of the actual data points\n",
|
||
"ax.scatter(collegeDf['Cost'], collegeDf['Grad'], collegeDf['Earnings'], color='blue', label='Actual Earnings')\n",
|
||
"\n",
|
||
"# Plotting the fitted plane\n",
|
||
"ax.plot_surface(cost_grid, grad_grid, earnings_predicted, color='red', alpha=0.5, label='Fitted Plane')\n",
|
||
"\n",
|
||
"# Labeling axes\n",
|
||
"ax.set_xlabel('Cost')\n",
|
||
"ax.set_ylabel('Grad')\n",
|
||
"ax.set_zlabel('Earnings')\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.title('Cost and Grad vs. Earnings with Fitted Plane')\n",
|
||
"\n",
|
||
"# Rotating the plot\n",
|
||
"# ax.view_init(elev=0, azim=0) # Set the elevation and azimuth angles\n",
|
||
"plt.show()\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 68,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 424
|
||
},
|
||
"id": "FMzTsqooWmsR",
|
||
"outputId": "f6aa23e3-fced-4a19-b113-3d0e471b768f"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>School</th>\n",
|
||
" <th>Earnings</th>\n",
|
||
" <th>Cost</th>\n",
|
||
" <th>Grad</th>\n",
|
||
" <th>Debt</th>\n",
|
||
" <th>City</th>\n",
|
||
" <th>predictedEarning1</th>\n",
|
||
" <th>predictedEarning2</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>St. Ambrose C (NC)</td>\n",
|
||
" <td>44800</td>\n",
|
||
" <td>22920</td>\n",
|
||
" <td>62</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44806.067625</td>\n",
|
||
" <td>44410.232770</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Albion College (Albion, MI)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>23429</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>45170.954503</td>\n",
|
||
" <td>47274.057585</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Alfred University (Alfred, NY)</td>\n",
|
||
" <td>42300</td>\n",
|
||
" <td>19567</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>87</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>42402.402200</td>\n",
|
||
" <td>42926.395289</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Allegheny College (Meadville, PA)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>25147</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>46402.537324</td>\n",
|
||
" <td>49338.597282</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Beloit College (Beloit, WI)</td>\n",
|
||
" <td>37900</td>\n",
|
||
" <td>21979</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44131.492866</td>\n",
|
||
" <td>47713.079376</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>Whittier College (Whittier, CA)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>33181</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>52161.871659</td>\n",
|
||
" <td>50858.231896</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>112</th>\n",
|
||
" <td>Widener University (Chester, PA)</td>\n",
|
||
" <td>51700</td>\n",
|
||
" <td>27387</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>48008.326334</td>\n",
|
||
" <td>45282.645045</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>Willamette University (Salem, OR)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>30312</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>50105.171359</td>\n",
|
||
" <td>51988.786671</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>114</th>\n",
|
||
" <td>Winthrop University (Rock Hill, SC)</td>\n",
|
||
" <td>36100</td>\n",
|
||
" <td>15311</td>\n",
|
||
" <td>54</td>\n",
|
||
" <td>76</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>39351.403080</td>\n",
|
||
" <td>38613.174391</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>Wittenberg University (Springfield, OH)</td>\n",
|
||
" <td>42700</td>\n",
|
||
" <td>26616</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>90</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>47455.619492</td>\n",
|
||
" <td>46779.880175</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>116 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" School Earnings Cost Grad Debt \\\n",
|
||
"0 St. Ambrose C (NC) 44800 22920 62 88 \n",
|
||
"1 Albion College (Albion, MI) 45100 23429 73 92 \n",
|
||
"2 Alfred University (Alfred, NY) 42300 19567 63 87 \n",
|
||
"3 Allegheny College (Meadville, PA) 49200 25147 78 92 \n",
|
||
"4 Beloit College (Beloit, WI) 37900 21979 78 93 \n",
|
||
".. ... ... ... ... ... \n",
|
||
"111 Whittier College (Whittier, CA) 45100 33181 67 86 \n",
|
||
"112 Widener University (Chester, PA) 51700 27387 56 83 \n",
|
||
"113 Willamette University (Salem, OR) 49200 30312 78 93 \n",
|
||
"114 Winthrop University (Rock Hill, SC) 36100 15311 54 76 \n",
|
||
"115 Wittenberg University (Springfield, OH) 42700 26616 64 90 \n",
|
||
"\n",
|
||
" City predictedEarning1 predictedEarning2 \n",
|
||
"0 1 44806.067625 44410.232770 \n",
|
||
"1 0 45170.954503 47274.057585 \n",
|
||
"2 0 42402.402200 42926.395289 \n",
|
||
"3 0 46402.537324 49338.597282 \n",
|
||
"4 1 44131.492866 47713.079376 \n",
|
||
".. ... ... ... \n",
|
||
"111 0 52161.871659 50858.231896 \n",
|
||
"112 0 48008.326334 45282.645045 \n",
|
||
"113 1 50105.171359 51988.786671 \n",
|
||
"114 1 39351.403080 38613.174391 \n",
|
||
"115 1 47455.619492 46779.880175 \n",
|
||
"\n",
|
||
"[116 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 68,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"collegeDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 69,
|
||
"metadata": {
|
||
"id": "fyEd2awfWvHR"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"earningOthersOlsModelFit3 = sm.OLS(\n",
|
||
" collegeDf[\"Earnings\"],\n",
|
||
" sm.add_constant(collegeDf[['Cost', 'Grad','Debt']])\n",
|
||
").fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x35691bef0>"
|
||
]
|
||
},
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"earningOthersOlsModelFit3\",\n",
|
||
" \"model\": earningOthersOlsModelFit3,\n",
|
||
" \"description\": \"predict Earnings based on Cost, Grad and Debt\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Cost\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Grad\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Debt\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Earnings\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "vmMvLc-nW1i0",
|
||
"outputId": "7d40fdab-a57f-40c3-c6e1-ad4d0733f87f"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Earnings R-squared: 0.402\n",
|
||
"Model: OLS Adj. R-squared: 0.386\n",
|
||
"Method: Least Squares F-statistic: 25.12\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 1.67e-12\n",
|
||
"Time: 01:23:13 Log-Likelihood: -1166.8\n",
|
||
"No. Observations: 116 AIC: 2342.\n",
|
||
"Df Residuals: 112 BIC: 2353.\n",
|
||
"Df Model: 3 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 1.182e+04 7735.631 1.528 0.129 -3507.686 2.71e+04\n",
|
||
"Cost 0.5050 0.109 4.644 0.000 0.290 0.720\n",
|
||
"Grad 192.6664 70.194 2.745 0.007 53.585 331.747\n",
|
||
"Debt 104.6573 118.283 0.885 0.378 -129.706 339.020\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 20.823 Durbin-Watson: 1.998\n",
|
||
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 31.211\n",
|
||
"Skew: 0.862 Prob(JB): 1.67e-07\n",
|
||
"Kurtosis: 4.867 Cond. No. 3.74e+05\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
|
||
"[2] The condition number is large, 3.74e+05. This might indicate that there are\n",
|
||
"strong multicollinearity or other numerical problems.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(earningOthersOlsModelFit3.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "-hrTLW63W8Dd",
|
||
"outputId": "9e03fe28-fe75-4dff-c322-ffbec2b5848d"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Earnings R-squared: 0.398\n",
|
||
"Model: OLS Adj. R-squared: 0.387\n",
|
||
"Method: Least Squares F-statistic: 37.37\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 3.50e-13\n",
|
||
"Time: 01:23:13 Log-Likelihood: -1167.2\n",
|
||
"No. Observations: 116 AIC: 2340.\n",
|
||
"Df Residuals: 113 BIC: 2349.\n",
|
||
"Df Model: 2 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 1.798e+04 3366.727 5.341 0.000 1.13e+04 2.47e+04\n",
|
||
"Cost 0.5131 0.108 4.741 0.000 0.299 0.728\n",
|
||
"Grad 236.6049 49.563 4.774 0.000 138.412 334.798\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 22.086 Durbin-Watson: 1.997\n",
|
||
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 33.660\n",
|
||
"Skew: 0.904 Prob(JB): 4.91e-08\n",
|
||
"Kurtosis: 4.923 Cond. No. 1.63e+05\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
|
||
"[2] The condition number is large, 1.63e+05. This might indicate that there are\n",
|
||
"strong multicollinearity or other numerical problems.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(earningOthersOlsModelFit2.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 424
|
||
},
|
||
"id": "BgNn0qPaYGK7",
|
||
"outputId": "2e79d89c-f027-4414-cd90-febe5b2ef9ef"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>School</th>\n",
|
||
" <th>Earnings</th>\n",
|
||
" <th>Cost</th>\n",
|
||
" <th>Grad</th>\n",
|
||
" <th>Debt</th>\n",
|
||
" <th>City</th>\n",
|
||
" <th>predictedEarning1</th>\n",
|
||
" <th>predictedEarning2</th>\n",
|
||
" <th>predictedEarning3</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>St. Ambrose C (NC)</td>\n",
|
||
" <td>44800</td>\n",
|
||
" <td>22920</td>\n",
|
||
" <td>62</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44806.067625</td>\n",
|
||
" <td>44410.232770</td>\n",
|
||
" <td>44548.538177</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Albion College (Albion, MI)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>23429</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>45170.954503</td>\n",
|
||
" <td>47274.057585</td>\n",
|
||
" <td>47343.527426</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Alfred University (Alfred, NY)</td>\n",
|
||
" <td>42300</td>\n",
|
||
" <td>19567</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>87</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>42402.402200</td>\n",
|
||
" <td>42926.395289</td>\n",
|
||
" <td>42943.384593</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Allegheny College (Meadville, PA)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>25147</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>46402.537324</td>\n",
|
||
" <td>49338.597282</td>\n",
|
||
" <td>49174.396984</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Beloit College (Beloit, WI)</td>\n",
|
||
" <td>37900</td>\n",
|
||
" <td>21979</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>44131.492866</td>\n",
|
||
" <td>47713.079376</td>\n",
|
||
" <td>47679.311039</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>Whittier College (Whittier, CA)</td>\n",
|
||
" <td>45100</td>\n",
|
||
" <td>33181</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>52161.871659</td>\n",
|
||
" <td>50858.231896</td>\n",
|
||
" <td>50484.047251</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>112</th>\n",
|
||
" <td>Widener University (Chester, PA)</td>\n",
|
||
" <td>51700</td>\n",
|
||
" <td>27387</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>48008.326334</td>\n",
|
||
" <td>45282.645045</td>\n",
|
||
" <td>45124.951685</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>Willamette University (Salem, OR)</td>\n",
|
||
" <td>49200</td>\n",
|
||
" <td>30312</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>50105.171359</td>\n",
|
||
" <td>51988.786671</td>\n",
|
||
" <td>51887.221654</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>114</th>\n",
|
||
" <td>Winthrop University (Rock Hill, SC)</td>\n",
|
||
" <td>36100</td>\n",
|
||
" <td>15311</td>\n",
|
||
" <td>54</td>\n",
|
||
" <td>76</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>39351.403080</td>\n",
|
||
" <td>38613.174391</td>\n",
|
||
" <td>37909.006123</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>Wittenberg University (Springfield, OH)</td>\n",
|
||
" <td>42700</td>\n",
|
||
" <td>26616</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>90</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>47455.619492</td>\n",
|
||
" <td>46779.880175</td>\n",
|
||
" <td>47009.552838</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>116 rows × 9 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" School Earnings Cost Grad Debt \\\n",
|
||
"0 St. Ambrose C (NC) 44800 22920 62 88 \n",
|
||
"1 Albion College (Albion, MI) 45100 23429 73 92 \n",
|
||
"2 Alfred University (Alfred, NY) 42300 19567 63 87 \n",
|
||
"3 Allegheny College (Meadville, PA) 49200 25147 78 92 \n",
|
||
"4 Beloit College (Beloit, WI) 37900 21979 78 93 \n",
|
||
".. ... ... ... ... ... \n",
|
||
"111 Whittier College (Whittier, CA) 45100 33181 67 86 \n",
|
||
"112 Widener University (Chester, PA) 51700 27387 56 83 \n",
|
||
"113 Willamette University (Salem, OR) 49200 30312 78 93 \n",
|
||
"114 Winthrop University (Rock Hill, SC) 36100 15311 54 76 \n",
|
||
"115 Wittenberg University (Springfield, OH) 42700 26616 64 90 \n",
|
||
"\n",
|
||
" City predictedEarning1 predictedEarning2 predictedEarning3 \n",
|
||
"0 1 44806.067625 44410.232770 44548.538177 \n",
|
||
"1 0 45170.954503 47274.057585 47343.527426 \n",
|
||
"2 0 42402.402200 42926.395289 42943.384593 \n",
|
||
"3 0 46402.537324 49338.597282 49174.396984 \n",
|
||
"4 1 44131.492866 47713.079376 47679.311039 \n",
|
||
".. ... ... ... ... \n",
|
||
"111 0 52161.871659 50858.231896 50484.047251 \n",
|
||
"112 0 48008.326334 45282.645045 45124.951685 \n",
|
||
"113 1 50105.171359 51988.786671 51887.221654 \n",
|
||
"114 1 39351.403080 38613.174391 37909.006123 \n",
|
||
"115 1 47455.619492 46779.880175 47009.552838 \n",
|
||
"\n",
|
||
"[116 rows x 9 columns]"
|
||
]
|
||
},
|
||
"execution_count": 73,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predictedEarning3 = earningOthersOlsModelFit3.predict(\n",
|
||
" sm.add_constant(collegeDf[['Cost', 'Grad', 'Debt']])\n",
|
||
")\n",
|
||
"collegeDf['predictedEarning3'] = predictedEarning3\n",
|
||
"collegeDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": []
|
||
},
|
||
"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": "3.12.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|