1907 lines
60 KiB
Plaintext
1907 lines
60 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/fi/zt2vtwhpz8ndblsxqdqx1/Salary_MIS.xlsx?rlkey=2uk6m7m9w90isv6zsynhhhpyv&st=gxumjns5&dl=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": "6rRHygNBIpgA"
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},
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"outputs": [],
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"source": [
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"sallaryMisDf = pd.read_excel(\"https://www.dropbox.com/scl/fi/zt2vtwhpz8ndblsxqdqx1/Salary_MIS.xlsx?rlkey=2uk6m7m9w90isv6zsynhhhpyv&st=gxumjns5&dl=1\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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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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"# sallaryMisDf = pd.read_excel(\"./Salary_MIS.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": 6,
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"metadata": {
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"id": "wsIgDGYcXT_z"
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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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"</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>Salary</th>\n",
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" <th>GPA</th>\n",
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" <th>MIS</th>\n",
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" <th>Statistics</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>72</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>66</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>72</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>115</th>\n",
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" <td>66</td>\n",
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" <td>3.27</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>116</th>\n",
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" <td>63</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <th>117</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>118</th>\n",
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" <td>64</td>\n",
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" <td>2.99</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>119</th>\n",
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" <td>66</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
|
||
"<p>120 rows × 4 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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||
" Salary GPA MIS Statistics\n",
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"0 72 3.53 1 0\n",
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"1 66 2.86 1 0\n",
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||
"2 72 3.69 0 0\n",
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"3 63 3.24 0 0\n",
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"4 65 3.21 0 0\n",
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".. ... ... ... ...\n",
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"115 66 3.27 0 0\n",
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||
"116 63 2.86 1 0\n",
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"117 78 3.04 1 1\n",
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||
"118 64 2.99 0 0\n",
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||
"119 66 3.65 0 0\n",
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||
"\n",
|
||
"[120 rows x 4 columns]"
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]
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},
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||
"execution_count": 6,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
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||
"sallaryMisDf"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 7,
|
||
"metadata": {
|
||
"id": "nw2BHv7PmpVU"
|
||
},
|
||
"outputs": [
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||
{
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||
"data": {
|
||
"text/plain": [
|
||
"(120, 4)"
|
||
]
|
||
},
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||
"execution_count": 7,
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||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
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],
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"source": [
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"sallaryMisDf.shape"
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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": 8,
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||
"metadata": {
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||
"id": "mWaKOoGvmrE8"
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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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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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||
" <th></th>\n",
|
||
" <th>Salary</th>\n",
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" <th>GPA</th>\n",
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||
" <th>MIS</th>\n",
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||
" <th>Statistics</th>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>120.000000</td>\n",
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||
" <td>120.000000</td>\n",
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" <td>120.000000</td>\n",
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||
" <td>120.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>mean</th>\n",
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||
" <td>69.875000</td>\n",
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||
" <td>3.242750</td>\n",
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||
" <td>0.316667</td>\n",
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||
" <td>0.341667</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
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||
" <td>6.594577</td>\n",
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||
" <td>0.493834</td>\n",
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||
" <td>0.467127</td>\n",
|
||
" <td>0.476257</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
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||
" <td>53.000000</td>\n",
|
||
" <td>2.410000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>65.750000</td>\n",
|
||
" <td>2.805000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>70.000000</td>\n",
|
||
" <td>3.280000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>73.250000</td>\n",
|
||
" <td>3.692500</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>88.000000</td>\n",
|
||
" <td>3.980000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
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||
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||
" </tbody>\n",
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||
"</table>\n",
|
||
"</div>"
|
||
],
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||
"text/plain": [
|
||
" Salary GPA MIS Statistics\n",
|
||
"count 120.000000 120.000000 120.000000 120.000000\n",
|
||
"mean 69.875000 3.242750 0.316667 0.341667\n",
|
||
"std 6.594577 0.493834 0.467127 0.476257\n",
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||
"min 53.000000 2.410000 0.000000 0.000000\n",
|
||
"25% 65.750000 2.805000 0.000000 0.000000\n",
|
||
"50% 70.000000 3.280000 0.000000 0.000000\n",
|
||
"75% 73.250000 3.692500 1.000000 1.000000\n",
|
||
"max 88.000000 3.980000 1.000000 1.000000"
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]
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||
},
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||
"execution_count": 8,
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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": [
|
||
"sallaryMisDf.describe()"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 9,
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||
"metadata": {
|
||
"id": "w-fAHOgMmyH5"
|
||
},
|
||
"outputs": [
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||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(120, 4)"
|
||
]
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||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
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],
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||
"source": [
|
||
"sallaryMisDf.shape"
|
||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
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||
"id": "MDlD1b-aY4Yc"
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},
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"outputs": [
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{
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" <th></th>\n",
|
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" <th>const</th>\n",
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" <th>GPA</th>\n",
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||
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|
||
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|
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|
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" const GPA MIS Statistics\n",
|
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"0 1.0 3.53 1 0\n",
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|
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|
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"[120 rows x 4 columns]"
|
||
]
|
||
},
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],
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|
||
"sm.add_constant(sallaryMisDf[[\"GPA\", \"MIS\", \"Statistics\"]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"id": "MjFUWOq2m6P3"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics = sm.OLS(\n",
|
||
" sallaryMisDf[\"Salary\"],\n",
|
||
" sm.add_constant(sallaryMisDf[[\"GPA\", \"MIS\", \"Statistics\"]])\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"id": "3yteijRmnabA"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatisticsFit = salaryBasedOnGpaMisStatistics.fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3473b7020>"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"salaryBasedOnGpaMisStatisticsFit\",\n",
|
||
" \"model\": salaryBasedOnGpaMisStatisticsFit,\n",
|
||
" \"description\": \"Predict Salary based on GPA MIS Statistics for sallaryMisDf\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"GPA\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"MIS\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Statistics\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Salary\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"id": "adXMPcPPndd1"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Salary R-squared: 0.795\n",
|
||
"Model: OLS Adj. R-squared: 0.790\n",
|
||
"Method: Least Squares F-statistic: 150.3\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 8.35e-40\n",
|
||
"Time: 01:24:53 Log-Likelihood: -300.92\n",
|
||
"No. Observations: 120 AIC: 609.8\n",
|
||
"Df Residuals: 116 BIC: 621.0\n",
|
||
"Df Model: 3 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 44.0072 1.860 23.662 0.000 40.324 47.691\n",
|
||
"GPA 6.6227 0.569 11.649 0.000 5.497 7.749\n",
|
||
"MIS 6.6071 0.595 11.098 0.000 5.428 7.786\n",
|
||
"Statistics 6.7309 0.591 11.391 0.000 5.561 7.901\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 1.144 Durbin-Watson: 2.164\n",
|
||
"Prob(Omnibus): 0.564 Jarque-Bera (JB): 0.758\n",
|
||
"Skew: -0.172 Prob(JB): 0.685\n",
|
||
"Kurtosis: 3.182 Cond. No. 24.4\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(salaryBasedOnGpaMisStatisticsFit.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"id": "H5PP4w6epEwm"
|
||
},
|
||
"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>Salary</th>\n",
|
||
" <th>GPA</th>\n",
|
||
" <th>MIS</th>\n",
|
||
" <th>Statistics</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>72</td>\n",
|
||
" <td>3.53</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>2.86</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>72</td>\n",
|
||
" <td>3.69</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>63</td>\n",
|
||
" <td>3.24</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>65</td>\n",
|
||
" <td>3.21</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>3.27</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>116</th>\n",
|
||
" <td>63</td>\n",
|
||
" <td>2.86</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>117</th>\n",
|
||
" <td>78</td>\n",
|
||
" <td>3.04</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>118</th>\n",
|
||
" <td>64</td>\n",
|
||
" <td>2.99</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>119</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>3.65</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>120 rows × 4 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Salary GPA MIS Statistics\n",
|
||
"0 72 3.53 1 0\n",
|
||
"1 66 2.86 1 0\n",
|
||
"2 72 3.69 0 0\n",
|
||
"3 63 3.24 0 0\n",
|
||
"4 65 3.21 0 0\n",
|
||
".. ... ... ... ...\n",
|
||
"115 66 3.27 0 0\n",
|
||
"116 63 2.86 1 0\n",
|
||
"117 78 3.04 1 1\n",
|
||
"118 64 2.99 0 0\n",
|
||
"119 66 3.65 0 0\n",
|
||
"\n",
|
||
"[120 rows x 4 columns]"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sallaryMisDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"id": "jgXOZuY4ocyq"
|
||
},
|
||
"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>Salary</th>\n",
|
||
" <th>GPA</th>\n",
|
||
" <th>MIS</th>\n",
|
||
" <th>Statistics</th>\n",
|
||
" <th>misXStatistics</th>\n",
|
||
" <th>misXStatistics1</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>72</td>\n",
|
||
" <td>3.53</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>2.86</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>72</td>\n",
|
||
" <td>3.69</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>63</td>\n",
|
||
" <td>3.24</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>65</td>\n",
|
||
" <td>3.21</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</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",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>3.27</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>116</th>\n",
|
||
" <td>63</td>\n",
|
||
" <td>2.86</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>117</th>\n",
|
||
" <td>78</td>\n",
|
||
" <td>3.04</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>118</th>\n",
|
||
" <td>64</td>\n",
|
||
" <td>2.99</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>119</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>3.65</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>120 rows × 6 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Salary GPA MIS Statistics misXStatistics misXStatistics1\n",
|
||
"0 72 3.53 1 0 0 0.0\n",
|
||
"1 66 2.86 1 0 0 0.0\n",
|
||
"2 72 3.69 0 0 0 0.0\n",
|
||
"3 63 3.24 0 0 0 0.0\n",
|
||
"4 65 3.21 0 0 0 0.0\n",
|
||
".. ... ... ... ... ... ...\n",
|
||
"115 66 3.27 0 0 0 0.0\n",
|
||
"116 63 2.86 1 0 0 0.0\n",
|
||
"117 78 3.04 1 1 1 1.0\n",
|
||
"118 64 2.99 0 0 0 0.0\n",
|
||
"119 66 3.65 0 0 0 0.0\n",
|
||
"\n",
|
||
"[120 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.transformers import transformersDict\n",
|
||
"sallaryMisDf[\"misXStatistics\"] = sallaryMisDf[\"MIS\"] * sallaryMisDf[\"Statistics\"]\n",
|
||
"# sallaryMisDf['misXStatistics1'] = sallaryMisDf.apply(lambda row: row['MIS'] * row['Statistics'], axis=1)\n",
|
||
"sallaryMisDf['misXStatistics1'] = sallaryMisDf.apply(transformersDict.get('MIS_X_Statistics'), axis=1)\n",
|
||
"\n",
|
||
"sallaryMisDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"id": "FwXG9Q54pbne"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics = sm.OLS(\n",
|
||
" sallaryMisDf[\"Salary\"],\n",
|
||
" sm.add_constant(\n",
|
||
" sallaryMisDf[[\n",
|
||
" \"GPA\",\n",
|
||
" \"MIS\",\n",
|
||
" \"Statistics\",\n",
|
||
" \"misXStatistics1\"\n",
|
||
" ]]\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"id": "w7hob-54phqv"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_misXStatisticsFit = salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics.fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3473d1e20>"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"salaryBasedOnGpaMisStatistics_Transfoms_misXStatisticsFit\",\n",
|
||
" \"model\": salaryBasedOnGpaMisStatistics_Transfoms_misXStatisticsFit,\n",
|
||
" \"description\": \"Predict Salary based on GPA MIS Statistics and interaction MIS * Statistics for sallaryMisDf\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"GPA\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"MIS\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Statistics\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"transformers\":[\n",
|
||
" {\n",
|
||
" \"name\": \"misXStatistics\",\n",
|
||
" \"transformer\": \"MIS_X_Statistics\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Salary\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"id": "NMNYYAespkAn"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Salary R-squared: 0.810\n",
|
||
"Model: OLS Adj. R-squared: 0.803\n",
|
||
"Method: Least Squares F-statistic: 122.2\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 1.87e-40\n",
|
||
"Time: 01:24:53 Log-Likelihood: -296.63\n",
|
||
"No. Observations: 120 AIC: 603.3\n",
|
||
"Df Residuals: 115 BIC: 617.2\n",
|
||
"Df Model: 4 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"===================================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"-----------------------------------------------------------------------------------\n",
|
||
"const 44.0993 1.803 24.464 0.000 40.529 47.670\n",
|
||
"GPA 6.7109 0.552 12.162 0.000 5.618 7.804\n",
|
||
"MIS 5.3250 0.725 7.343 0.000 3.889 6.761\n",
|
||
"Statistics 5.5350 0.704 7.861 0.000 4.140 6.930\n",
|
||
"misXStatistics1 3.4915 1.196 2.918 0.004 1.122 5.861\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 0.396 Durbin-Watson: 2.073\n",
|
||
"Prob(Omnibus): 0.820 Jarque-Bera (JB): 0.109\n",
|
||
"Skew: -0.013 Prob(JB): 0.947\n",
|
||
"Kurtosis: 3.146 Cond. No. 24.4\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(salaryBasedOnGpaMisStatistics_Transfoms_misXStatisticsFit.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"id": "ZnQnXfdRv7dP"
|
||
},
|
||
"outputs": [
|
||
{
|
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"data": {
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Salary</th>\n",
|
||
" <th>GPA</th>\n",
|
||
" <th>MIS</th>\n",
|
||
" <th>Statistics</th>\n",
|
||
" <th>misXStatistics</th>\n",
|
||
" <th>misXStatistics1</th>\n",
|
||
" <th>misXGpa</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
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|
||
" <tr>\n",
|
||
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||
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" <td>66</td>\n",
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||
" <td>1</td>\n",
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||
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|
||
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|
||
" <td>63</td>\n",
|
||
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||
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||
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" <tr>\n",
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|
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" <td>65</td>\n",
|
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" <td>0</td>\n",
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|
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>115</th>\n",
|
||
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|
||
" <td>3.27</td>\n",
|
||
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|
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|
||
" <tr>\n",
|
||
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|
||
" <td>63</td>\n",
|
||
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|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2.86</td>\n",
|
||
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|
||
" <tr>\n",
|
||
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|
||
" <td>78</td>\n",
|
||
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|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>3.04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>118</th>\n",
|
||
" <td>64</td>\n",
|
||
" <td>2.99</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
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|
||
" <td>0.00</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>119</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>3.65</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
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|
||
"</table>\n",
|
||
"<p>120 rows × 7 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Salary GPA MIS Statistics misXStatistics misXStatistics1 misXGpa\n",
|
||
"0 72 3.53 1 0 0 0.0 3.53\n",
|
||
"1 66 2.86 1 0 0 0.0 2.86\n",
|
||
"2 72 3.69 0 0 0 0.0 0.00\n",
|
||
"3 63 3.24 0 0 0 0.0 0.00\n",
|
||
"4 65 3.21 0 0 0 0.0 0.00\n",
|
||
".. ... ... ... ... ... ... ...\n",
|
||
"115 66 3.27 0 0 0 0.0 0.00\n",
|
||
"116 63 2.86 1 0 0 0.0 2.86\n",
|
||
"117 78 3.04 1 1 1 1.0 3.04\n",
|
||
"118 64 2.99 0 0 0 0.0 0.00\n",
|
||
"119 66 3.65 0 0 0 0.0 0.00\n",
|
||
"\n",
|
||
"[120 rows x 7 columns]"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# sallaryMisDf['misXGpa'] = sallaryMisDf.apply(lambda row: row['MIS'] * row['GPA'], axis=1)\n",
|
||
"sallaryMisDf['misXGpa'] = sallaryMisDf.apply(transformersDict.get('MIS_X_GPA'), axis=1)\n",
|
||
"\n",
|
||
"sallaryMisDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {
|
||
"id": "6CjgMmDAwEPw"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_misXGpa = sm.OLS(\n",
|
||
" sallaryMisDf[\"Salary\"],\n",
|
||
" sm.add_constant(\n",
|
||
" sallaryMisDf[[\n",
|
||
" \"GPA\",\n",
|
||
" \"MIS\",\n",
|
||
" \"Statistics\",\n",
|
||
" \"misXGpa\"\n",
|
||
" ]]\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {
|
||
"id": "VmYH7tHmwMzm"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_misXGpaFit = salaryBasedOnGpaMisStatistics_Transfoms_misXGpa.fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3473f2a20>"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"salaryBasedOnGpaMisStatistics_Transfoms_misXGpaFit\",\n",
|
||
" \"model\": salaryBasedOnGpaMisStatistics_Transfoms_misXGpaFit,\n",
|
||
" \"description\": \"Predict Salary based on GPA MIS Statistics and interaction misXGpa for sallaryMisDf\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"GPA\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"MIS\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Statistics\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"transformers\":[\n",
|
||
" {\n",
|
||
" \"name\": \"misXGpa\",\n",
|
||
" \"transformer\": \"MIS_X_GPA\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Salary\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {
|
||
"id": "rL8pX5dTwP8H"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Salary R-squared: 0.795\n",
|
||
"Model: OLS Adj. R-squared: 0.788\n",
|
||
"Method: Least Squares F-statistic: 111.8\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 1.11e-38\n",
|
||
"Time: 01:24:53 Log-Likelihood: -300.91\n",
|
||
"No. Observations: 120 AIC: 611.8\n",
|
||
"Df Residuals: 115 BIC: 625.8\n",
|
||
"Df Model: 4 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 44.1653 2.307 19.142 0.000 39.595 48.736\n",
|
||
"GPA 6.5737 0.709 9.278 0.000 5.170 7.977\n",
|
||
"MIS 6.1605 3.873 1.591 0.114 -1.511 13.832\n",
|
||
"Statistics 6.7350 0.594 11.330 0.000 5.558 7.912\n",
|
||
"misXGpa 0.1381 1.184 0.117 0.907 -2.206 2.483\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 1.114 Durbin-Watson: 2.167\n",
|
||
"Prob(Omnibus): 0.573 Jarque-Bera (JB): 0.727\n",
|
||
"Skew: -0.167 Prob(JB): 0.695\n",
|
||
"Kurtosis: 3.185 Cond. No. 57.3\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(salaryBasedOnGpaMisStatistics_Transfoms_misXGpaFit.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {
|
||
"id": "z-idrSTJwi90"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
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|
||
" vertical-align: middle;\n",
|
||
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|
||
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|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
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|
||
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|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
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|
||
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|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Salary</th>\n",
|
||
" <th>GPA</th>\n",
|
||
" <th>MIS</th>\n",
|
||
" <th>Statistics</th>\n",
|
||
" <th>misXStatistics</th>\n",
|
||
" <th>misXStatistics1</th>\n",
|
||
" <th>misXGpa</th>\n",
|
||
" <th>statisticsXGpa</th>\n",
|
||
" </tr>\n",
|
||
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|
||
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|
||
" <tr>\n",
|
||
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|
||
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|
||
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||
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||
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|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
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|
||
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|
||
" <td>1</td>\n",
|
||
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|
||
" <td>0</td>\n",
|
||
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|
||
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|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>72</td>\n",
|
||
" <td>3.69</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>63</td>\n",
|
||
" <td>3.24</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>65</td>\n",
|
||
" <td>3.21</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</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>115</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>3.27</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>116</th>\n",
|
||
" <td>63</td>\n",
|
||
" <td>2.86</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2.86</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>117</th>\n",
|
||
" <td>78</td>\n",
|
||
" <td>3.04</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>3.04</td>\n",
|
||
" <td>3.04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>118</th>\n",
|
||
" <td>64</td>\n",
|
||
" <td>2.99</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>119</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>3.65</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>120 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Salary GPA MIS Statistics misXStatistics misXStatistics1 misXGpa \\\n",
|
||
"0 72 3.53 1 0 0 0.0 3.53 \n",
|
||
"1 66 2.86 1 0 0 0.0 2.86 \n",
|
||
"2 72 3.69 0 0 0 0.0 0.00 \n",
|
||
"3 63 3.24 0 0 0 0.0 0.00 \n",
|
||
"4 65 3.21 0 0 0 0.0 0.00 \n",
|
||
".. ... ... ... ... ... ... ... \n",
|
||
"115 66 3.27 0 0 0 0.0 0.00 \n",
|
||
"116 63 2.86 1 0 0 0.0 2.86 \n",
|
||
"117 78 3.04 1 1 1 1.0 3.04 \n",
|
||
"118 64 2.99 0 0 0 0.0 0.00 \n",
|
||
"119 66 3.65 0 0 0 0.0 0.00 \n",
|
||
"\n",
|
||
" statisticsXGpa \n",
|
||
"0 0.00 \n",
|
||
"1 0.00 \n",
|
||
"2 0.00 \n",
|
||
"3 0.00 \n",
|
||
"4 0.00 \n",
|
||
".. ... \n",
|
||
"115 0.00 \n",
|
||
"116 0.00 \n",
|
||
"117 3.04 \n",
|
||
"118 0.00 \n",
|
||
"119 0.00 \n",
|
||
"\n",
|
||
"[120 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# sallaryMisDf['statisticsXGpa'] = sallaryMisDf.apply(lambda row: row['Statistics'] * row['GPA'], axis=1)\n",
|
||
"sallaryMisDf['statisticsXGpa'] = sallaryMisDf.apply(transformersDict.get('GPA_X_Statistics'), axis=1)\n",
|
||
"\n",
|
||
"sallaryMisDf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {
|
||
"id": "im61d1RUwpQJ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_statisticsXGpa = sm.OLS(\n",
|
||
" sallaryMisDf[\"Salary\"],\n",
|
||
" sm.add_constant(\n",
|
||
" sallaryMisDf[[\n",
|
||
" \"GPA\",\n",
|
||
" \"MIS\",\n",
|
||
" \"Statistics\",\n",
|
||
" \"statisticsXGpa\"\n",
|
||
" ]]\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"id": "WZ9eNcnMwvB3"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_statisticsXGpaFit = salaryBasedOnGpaMisStatistics_Transfoms_statisticsXGpa.fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3473f1040>"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"salaryBasedOnGpaMisStatistics_Transfoms_statisticsXGpaFit\",\n",
|
||
" \"model\": salaryBasedOnGpaMisStatistics_Transfoms_statisticsXGpaFit,\n",
|
||
" \"description\": \"Predict Salary based on GPA MIS Statistics and interaction misXGpa for statisticsXGpa\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"GPA\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"MIS\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Statistics\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"transformers\":[\n",
|
||
" {\n",
|
||
" \"name\": \"statisticsXGpa\",\n",
|
||
" \"transformer\": \"GPA_X_Statistics\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Salary\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {
|
||
"id": "P5MFMA4NwzcE"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Salary R-squared: 0.803\n",
|
||
"Model: OLS Adj. R-squared: 0.796\n",
|
||
"Method: Least Squares F-statistic: 116.9\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 1.44e-39\n",
|
||
"Time: 01:24:53 Log-Likelihood: -298.78\n",
|
||
"No. Observations: 120 AIC: 607.6\n",
|
||
"Df Residuals: 115 BIC: 621.5\n",
|
||
"Df Model: 4 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==================================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"----------------------------------------------------------------------------------\n",
|
||
"const 41.2856 2.267 18.215 0.000 36.796 45.775\n",
|
||
"GPA 7.4828 0.701 10.674 0.000 6.094 8.871\n",
|
||
"MIS 6.5400 0.588 11.118 0.000 5.375 7.705\n",
|
||
"Statistics 14.5988 3.891 3.752 0.000 6.892 22.306\n",
|
||
"statisticsXGpa -2.3890 1.168 -2.045 0.043 -4.703 -0.075\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 0.348 Durbin-Watson: 2.118\n",
|
||
"Prob(Omnibus): 0.840 Jarque-Bera (JB): 0.149\n",
|
||
"Skew: -0.079 Prob(JB): 0.928\n",
|
||
"Kurtosis: 3.068 Cond. No. 59.1\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(salaryBasedOnGpaMisStatistics_Transfoms_statisticsXGpaFit.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"id": "gJGNzwfdw-mg"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics_misXGpa_statisticsXGpa = sm.OLS(\n",
|
||
" sallaryMisDf[\"Salary\"],\n",
|
||
" sm.add_constant(\n",
|
||
" sallaryMisDf[[\n",
|
||
" \"GPA\",\n",
|
||
" \"MIS\",\n",
|
||
" \"Statistics\",\n",
|
||
" \"misXStatistics\",\n",
|
||
" \"misXGpa\",\n",
|
||
" \"statisticsXGpa\"\n",
|
||
" ]]\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {
|
||
"id": "NPGVE5cFxW-q"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics_misXGpa_statisticsXGpaFit = salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics_misXGpa_statisticsXGpa.fit()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x34741ec30>"
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from functions.exportModel import exportModel\n",
|
||
"exportModel({\n",
|
||
" \"modelName\": \"salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics_misXGpa_statisticsXGpaFit\",\n",
|
||
" \"model\": salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics_misXGpa_statisticsXGpaFit,\n",
|
||
" \"description\": \"Predict Salary based on GPA MIS Statistics and interaction misXStatistics, misXGpa, statisticsXGpa\",\n",
|
||
" \"modelType\": \"sm.OLS\",\n",
|
||
" \"baseRelativePath\": \"..\",\n",
|
||
" \"inputs\": [\n",
|
||
" {\n",
|
||
" \"name\": \"const\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"GPA\",\n",
|
||
" \"type\": \"float\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"MIS\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"Statistics\",\n",
|
||
" \"type\": \"binary\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"transformers\":[\n",
|
||
" {\n",
|
||
" \"name\": \"misXStatistics\",\n",
|
||
" \"transformer\": \"MIS_X_Statistics\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"misXGpa\",\n",
|
||
" \"transformer\": \"MIS_X_GPA\"\n",
|
||
" },\n",
|
||
" {\n",
|
||
" \"name\": \"statisticsXGpa\",\n",
|
||
" \"transformer\": \"GPA_X_Statistics\"\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" \"output\": {\n",
|
||
" \"name\": \"Salary\",\n",
|
||
" \"type\": \"int\"\n",
|
||
" }\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {
|
||
"id": "qRpqQP9LxaO-"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: Salary R-squared: 0.815\n",
|
||
"Model: OLS Adj. R-squared: 0.805\n",
|
||
"Method: Least Squares F-statistic: 83.09\n",
|
||
"Date: Sun, 09 Jun 2024 Prob (F-statistic): 4.15e-39\n",
|
||
"Time: 01:24:53 Log-Likelihood: -294.81\n",
|
||
"No. Observations: 120 AIC: 603.6\n",
|
||
"Df Residuals: 113 BIC: 623.1\n",
|
||
"Df Model: 6 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==================================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"----------------------------------------------------------------------------------\n",
|
||
"const 41.7092 2.481 16.809 0.000 36.793 46.625\n",
|
||
"GPA 7.4604 0.769 9.708 0.000 5.938 8.983\n",
|
||
"MIS 5.1669 3.757 1.375 0.172 -2.276 12.610\n",
|
||
"Statistics 12.6641 3.923 3.229 0.002 4.893 20.435\n",
|
||
"misXStatistics 3.3076 1.204 2.747 0.007 0.922 5.693\n",
|
||
"misXGpa 0.0512 1.158 0.044 0.965 -2.243 2.345\n",
|
||
"statisticsXGpa -2.1451 1.158 -1.853 0.066 -4.439 0.148\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 0.398 Durbin-Watson: 2.028\n",
|
||
"Prob(Omnibus): 0.820 Jarque-Bera (JB): 0.148\n",
|
||
"Skew: 0.067 Prob(JB): 0.928\n",
|
||
"Kurtosis: 3.108 Cond. No. 63.5\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(salaryBasedOnGpaMisStatistics_Transfoms_misXStatistics_misXGpa_statisticsXGpaFit.summary())"
|
||
]
|
||
},
|
||
{
|
||
"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
|
||
}
|