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Predicting Employee Productivity Using Tree Models Project Lab Solution
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predicting_employee_productivity_ML.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EDA (Exploratory Data Analysis)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"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>date</th>\n",
" <th>quarter</th>\n",
" <th>department</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>wip</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>idle_time</th>\n",
" <th>idle_men</th>\n",
" <th>no_of_style_change</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>8</td>\n",
" <td>0.80</td>\n",
" <td>26.16</td>\n",
" <td>1108.0</td>\n",
" <td>7080</td>\n",
" <td>98</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>59.0</td>\n",
" <td>0.940725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>finishing</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>NaN</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0</td>\n",
" <td>0.886500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>11</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>968.0</td>\n",
" <td>3660</td>\n",
" <td>50</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.5</td>\n",
" <td>0.800570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>12</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>968.0</td>\n",
" <td>3660</td>\n",
" <td>50</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.5</td>\n",
" <td>0.800570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>6</td>\n",
" <td>0.80</td>\n",
" <td>25.90</td>\n",
" <td>1170.0</td>\n",
" <td>1920</td>\n",
" <td>50</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>56.0</td>\n",
" <td>0.800382</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date quarter department day team targeted_productivity \\\n",
"0 1/1/2015 Quarter1 sweing Thursday 8 0.80 \n",
"1 1/1/2015 Quarter1 finishing Thursday 1 0.75 \n",
"2 1/1/2015 Quarter1 sweing Thursday 11 0.80 \n",
"3 1/1/2015 Quarter1 sweing Thursday 12 0.80 \n",
"4 1/1/2015 Quarter1 sweing Thursday 6 0.80 \n",
"\n",
" smv wip over_time incentive idle_time idle_men \\\n",
"0 26.16 1108.0 7080 98 0.0 0 \n",
"1 3.94 NaN 960 0 0.0 0 \n",
"2 11.41 968.0 3660 50 0.0 0 \n",
"3 11.41 968.0 3660 50 0.0 0 \n",
"4 25.90 1170.0 1920 50 0.0 0 \n",
"\n",
" no_of_style_change no_of_workers actual_productivity \n",
"0 0 59.0 0.940725 \n",
"1 0 8.0 0.886500 \n",
"2 0 30.5 0.800570 \n",
"3 0 30.5 0.800570 \n",
"4 0 56.0 0.800382 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"garments_worker_productivity.csv\")\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1197 entries, 0 to 1196\n",
"Data columns (total 15 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 1197 non-null object \n",
" 1 quarter 1197 non-null object \n",
" 2 department 1197 non-null object \n",
" 3 day 1197 non-null object \n",
" 4 team 1197 non-null int64 \n",
" 5 targeted_productivity 1197 non-null float64\n",
" 6 smv 1197 non-null float64\n",
" 7 wip 691 non-null float64\n",
" 8 over_time 1197 non-null int64 \n",
" 9 incentive 1197 non-null int64 \n",
" 10 idle_time 1197 non-null float64\n",
" 11 idle_men 1197 non-null int64 \n",
" 12 no_of_style_change 1197 non-null int64 \n",
" 13 no_of_workers 1197 non-null float64\n",
" 14 actual_productivity 1197 non-null float64\n",
"dtypes: float64(6), int64(5), object(4)\n",
"memory usage: 140.4+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"department\n",
"sweing 691\n",
"finishing 257\n",
"finishing 249\n",
"Name: count, dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"department\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['sweing', 'finishing ', 'finishing'], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"department\"].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a hidden space in some of the \"finishing\" values so we will need to clean this!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"quarter\n",
"Quarter1 360\n",
"Quarter2 335\n",
"Quarter4 248\n",
"Quarter3 210\n",
"Quarter5 44\n",
"Name: count, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"quarter\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that **quarter** in this dataset refers to a *portion of the month*, not a typical yearly quarter. Quarter 5 is for dates that fall on the 29th-31st."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"day\n",
"Wednesday 208\n",
"Sunday 203\n",
"Tuesday 201\n",
"Thursday 199\n",
"Monday 199\n",
"Saturday 187\n",
"Name: count, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"day\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are no Friday values. This is interesting to note, but most-likely not something we need to worry about."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>wip</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>idle_time</th>\n",
" <th>idle_men</th>\n",
" <th>no_of_style_change</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>691.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6.426901</td>\n",
" <td>0.729632</td>\n",
" <td>15.062172</td>\n",
" <td>1190.465991</td>\n",
" <td>4567.460317</td>\n",
" <td>38.210526</td>\n",
" <td>0.730159</td>\n",
" <td>0.369256</td>\n",
" <td>0.150376</td>\n",
" <td>34.609858</td>\n",
" <td>0.735091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.463963</td>\n",
" <td>0.097891</td>\n",
" <td>10.943219</td>\n",
" <td>1837.455001</td>\n",
" <td>3348.823563</td>\n",
" <td>160.182643</td>\n",
" <td>12.709757</td>\n",
" <td>3.268987</td>\n",
" <td>0.427848</td>\n",
" <td>22.197687</td>\n",
" <td>0.174488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.070000</td>\n",
" <td>2.900000</td>\n",
" <td>7.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.233705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.000000</td>\n",
" <td>0.700000</td>\n",
" <td>3.940000</td>\n",
" <td>774.500000</td>\n",
" <td>1440.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9.000000</td>\n",
" <td>0.650307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.000000</td>\n",
" <td>0.750000</td>\n",
" <td>15.260000</td>\n",
" <td>1039.000000</td>\n",
" <td>3960.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>34.000000</td>\n",
" <td>0.773333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>9.000000</td>\n",
" <td>0.800000</td>\n",
" <td>24.260000</td>\n",
" <td>1252.500000</td>\n",
" <td>6960.000000</td>\n",
" <td>50.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>57.000000</td>\n",
" <td>0.850253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>12.000000</td>\n",
" <td>0.800000</td>\n",
" <td>54.560000</td>\n",
" <td>23122.000000</td>\n",
" <td>25920.000000</td>\n",
" <td>3600.000000</td>\n",
" <td>300.000000</td>\n",
" <td>45.000000</td>\n",
" <td>2.000000</td>\n",
" <td>89.000000</td>\n",
" <td>1.120437</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" team targeted_productivity smv wip \\\n",
"count 1197.000000 1197.000000 1197.000000 691.000000 \n",
"mean 6.426901 0.729632 15.062172 1190.465991 \n",
"std 3.463963 0.097891 10.943219 1837.455001 \n",
"min 1.000000 0.070000 2.900000 7.000000 \n",
"25% 3.000000 0.700000 3.940000 774.500000 \n",
"50% 6.000000 0.750000 15.260000 1039.000000 \n",
"75% 9.000000 0.800000 24.260000 1252.500000 \n",
"max 12.000000 0.800000 54.560000 23122.000000 \n",
"\n",
" over_time incentive idle_time idle_men \\\n",
"count 1197.000000 1197.000000 1197.000000 1197.000000 \n",
"mean 4567.460317 38.210526 0.730159 0.369256 \n",
"std 3348.823563 160.182643 12.709757 3.268987 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 1440.000000 0.000000 0.000000 0.000000 \n",
"50% 3960.000000 0.000000 0.000000 0.000000 \n",
"75% 6960.000000 50.000000 0.000000 0.000000 \n",
"max 25920.000000 3600.000000 300.000000 45.000000 \n",
"\n",
" no_of_style_change no_of_workers actual_productivity \n",
"count 1197.000000 1197.000000 1197.000000 \n",
"mean 0.150376 34.609858 0.735091 \n",
"std 0.427848 22.197687 0.174488 \n",
"min 0.000000 2.000000 0.233705 \n",
"25% 0.000000 9.000000 0.650307 \n",
"50% 0.000000 34.000000 0.773333 \n",
"75% 0.000000 57.000000 0.850253 \n",
"max 2.000000 89.000000 1.120437 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see, for instance, that the `actual_productivity` column actually surpasses the limit of 1 that was indicated on the dataset description!\n",
"\n",
"The max value for `over_time` is very large. This is potentially something to consider."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>quarter</th>\n",
" <th>department</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>wip</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>idle_time</th>\n",
" <th>idle_men</th>\n",
" <th>no_of_style_change</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>1/20/2015</td>\n",
" <td>Quarter3</td>\n",
" <td>finishing</td>\n",
" <td>Tuesday</td>\n",
" <td>5</td>\n",
" <td>0.70</td>\n",
" <td>4.15</td>\n",
" <td>NaN</td>\n",
" <td>1440</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0</td>\n",
" <td>1.033570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>437</th>\n",
" <td>1/26/2015</td>\n",
" <td>Quarter4</td>\n",
" <td>finishing</td>\n",
" <td>Monday</td>\n",
" <td>3</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>NaN</td>\n",
" <td>1800</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10.0</td>\n",
" <td>1.059621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>456</th>\n",
" <td>1/27/2015</td>\n",
" <td>Quarter4</td>\n",
" <td>sweing</td>\n",
" <td>Tuesday</td>\n",
" <td>2</td>\n",
" <td>0.75</td>\n",
" <td>22.52</td>\n",
" <td>1635.0</td>\n",
" <td>6840</td>\n",
" <td>119</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>457</th>\n",
" <td>1/27/2015</td>\n",
" <td>Quarter4</td>\n",
" <td>sweing</td>\n",
" <td>Tuesday</td>\n",
" <td>3</td>\n",
" <td>0.75</td>\n",
" <td>22.52</td>\n",
" <td>1299.0</td>\n",
" <td>6840</td>\n",
" <td>119</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>477</th>\n",
" <td>1/28/2015</td>\n",
" <td>Quarter4</td>\n",
" <td>sweing</td>\n",
" <td>Wednesday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>478</th>\n",
" <td>1/28/2015</td>\n",
" <td>Quarter4</td>\n",
" <td>sweing</td>\n",
" <td>Wednesday</td>\n",
" <td>3</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1350.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>1/29/2015</td>\n",
" <td>Quarter5</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1416.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>518</th>\n",
" <td>1/31/2015</td>\n",
" <td>Quarter5</td>\n",
" <td>sweing</td>\n",
" <td>Saturday</td>\n",
" <td>3</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1136.0</td>\n",
" <td>6960</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58.0</td>\n",
" <td>1.000457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>519</th>\n",
" <td>1/31/2015</td>\n",
" <td>Quarter5</td>\n",
" <td>sweing</td>\n",
" <td>Saturday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1397.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>542</th>\n",
" <td>2/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>finishing</td>\n",
" <td>Sunday</td>\n",
" <td>8</td>\n",
" <td>0.65</td>\n",
" <td>4.15</td>\n",
" <td>NaN</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0</td>\n",
" <td>1.011562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>2/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Sunday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1396.0</td>\n",
" <td>6900</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.5</td>\n",
" <td>1.000671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>2/1/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Sunday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.94</td>\n",
" <td>1582.0</td>\n",
" <td>3060</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58.5</td>\n",
" <td>1.000402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>2/2/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Monday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.94</td>\n",
" <td>16882.0</td>\n",
" <td>7020</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58.5</td>\n",
" <td>1.000602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>580</th>\n",
" <td>2/3/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>finishing</td>\n",
" <td>Tuesday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>NaN</td>\n",
" <td>2400</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20.0</td>\n",
" <td>1.001417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>581</th>\n",
" <td>2/3/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Tuesday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1500.0</td>\n",
" <td>6900</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.5</td>\n",
" <td>1.000019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>599</th>\n",
" <td>2/4/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Wednesday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1263.0</td>\n",
" <td>6900</td>\n",
" <td>100</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.5</td>\n",
" <td>1.050281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>600</th>\n",
" <td>2/4/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Wednesday</td>\n",
" <td>3</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>968.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>618</th>\n",
" <td>2/5/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1300.0</td>\n",
" <td>6780</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>56.5</td>\n",
" <td>1.000446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>619</th>\n",
" <td>2/5/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1485.0</td>\n",
" <td>6900</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.5</td>\n",
" <td>1.000019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>636</th>\n",
" <td>2/7/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>finishing</td>\n",
" <td>Saturday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>NaN</td>\n",
" <td>3000</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25.0</td>\n",
" <td>1.050667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>637</th>\n",
" <td>2/7/2015</td>\n",
" <td>Quarter1</td>\n",
" <td>sweing</td>\n",
" <td>Saturday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1186.0</td>\n",
" <td>6900</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58.0</td>\n",
" <td>1.000019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>655</th>\n",
" <td>2/8/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>sweing</td>\n",
" <td>Sunday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1233.0</td>\n",
" <td>6900</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.5</td>\n",
" <td>1.000019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>674</th>\n",
" <td>2/9/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>finishing</td>\n",
" <td>Monday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>NaN</td>\n",
" <td>2160</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18.0</td>\n",
" <td>1.057963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>692</th>\n",
" <td>2/10/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>finishing</td>\n",
" <td>Tuesday</td>\n",
" <td>12</td>\n",
" <td>0.80</td>\n",
" <td>4.08</td>\n",
" <td>NaN</td>\n",
" <td>1080</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.0</td>\n",
" <td>1.004889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>711</th>\n",
" <td>2/11/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>finishing</td>\n",
" <td>Wednesday</td>\n",
" <td>4</td>\n",
" <td>0.70</td>\n",
" <td>4.15</td>\n",
" <td>NaN</td>\n",
" <td>1800</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15.0</td>\n",
" <td>1.033156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>712</th>\n",
" <td>2/11/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>finishing</td>\n",
" <td>Wednesday</td>\n",
" <td>12</td>\n",
" <td>0.80</td>\n",
" <td>4.08</td>\n",
" <td>NaN</td>\n",
" <td>1080</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.0</td>\n",
" <td>1.020000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>713</th>\n",
" <td>2/11/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>sweing</td>\n",
" <td>Wednesday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1557.0</td>\n",
" <td>0</td>\n",
" <td>90</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.5</td>\n",
" <td>1.000345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>714</th>\n",
" <td>2/11/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>sweing</td>\n",
" <td>Wednesday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1498.0</td>\n",
" <td>0</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>730</th>\n",
" <td>2/12/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1397.0</td>\n",
" <td>0</td>\n",
" <td>138</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.100484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>731</th>\n",
" <td>2/12/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>finishing</td>\n",
" <td>Thursday</td>\n",
" <td>4</td>\n",
" <td>0.70</td>\n",
" <td>4.15</td>\n",
" <td>NaN</td>\n",
" <td>1800</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15.0</td>\n",
" <td>1.096633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>732</th>\n",
" <td>2/12/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>sweing</td>\n",
" <td>Thursday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1327.0</td>\n",
" <td>0</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.5</td>\n",
" <td>1.000345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>749</th>\n",
" <td>2/14/2015</td>\n",
" <td>Quarter2</td>\n",
" <td>sweing</td>\n",
" <td>Saturday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1416.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>2/15/2015</td>\n",
" <td>Quarter3</td>\n",
" <td>finishing</td>\n",
" <td>Sunday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>NaN</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0</td>\n",
" <td>1.120437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767</th>\n",
" <td>2/15/2015</td>\n",
" <td>Quarter3</td>\n",
" <td>finishing</td>\n",
" <td>Sunday</td>\n",
" <td>2</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>NaN</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0</td>\n",
" <td>1.108125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>2/15/2015</td>\n",
" <td>Quarter3</td>\n",
" <td>sweing</td>\n",
" <td>Sunday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1420.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>785</th>\n",
" <td>2/16/2015</td>\n",
" <td>Quarter3</td>\n",
" <td>sweing</td>\n",
" <td>Monday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1422.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>803</th>\n",
" <td>2/17/2015</td>\n",
" <td>Quarter3</td>\n",
" <td>sweing</td>\n",
" <td>Tuesday</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>22.52</td>\n",
" <td>1445.0</td>\n",
" <td>6840</td>\n",
" <td>113</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57.0</td>\n",
" <td>1.000230</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date quarter department day team targeted_productivity \\\n",
"337 1/20/2015 Quarter3 finishing Tuesday 5 0.70 \n",
"437 1/26/2015 Quarter4 finishing Monday 3 0.75 \n",
"456 1/27/2015 Quarter4 sweing Tuesday 2 0.75 \n",
"457 1/27/2015 Quarter4 sweing Tuesday 3 0.75 \n",
"477 1/28/2015 Quarter4 sweing Wednesday 2 0.80 \n",
"478 1/28/2015 Quarter4 sweing Wednesday 3 0.80 \n",
"498 1/29/2015 Quarter5 sweing Thursday 2 0.80 \n",
"518 1/31/2015 Quarter5 sweing Saturday 3 0.80 \n",
"519 1/31/2015 Quarter5 sweing Saturday 2 0.80 \n",
"542 2/1/2015 Quarter1 finishing Sunday 8 0.65 \n",
"543 2/1/2015 Quarter1 sweing Sunday 2 0.80 \n",
"544 2/1/2015 Quarter1 sweing Sunday 1 0.80 \n",
"561 2/2/2015 Quarter1 sweing Monday 1 0.80 \n",
"580 2/3/2015 Quarter1 finishing Tuesday 2 0.80 \n",
"581 2/3/2015 Quarter1 sweing Tuesday 1 0.80 \n",
"599 2/4/2015 Quarter1 sweing Wednesday 2 0.80 \n",
"600 2/4/2015 Quarter1 sweing Wednesday 3 0.80 \n",
"618 2/5/2015 Quarter1 sweing Thursday 2 0.80 \n",
"619 2/5/2015 Quarter1 sweing Thursday 1 0.80 \n",
"636 2/7/2015 Quarter1 finishing Saturday 2 0.80 \n",
"637 2/7/2015 Quarter1 sweing Saturday 2 0.80 \n",
"655 2/8/2015 Quarter2 sweing Sunday 2 0.80 \n",
"674 2/9/2015 Quarter2 finishing Monday 2 0.80 \n",
"692 2/10/2015 Quarter2 finishing Tuesday 12 0.80 \n",
"711 2/11/2015 Quarter2 finishing Wednesday 4 0.70 \n",
"712 2/11/2015 Quarter2 finishing Wednesday 12 0.80 \n",
"713 2/11/2015 Quarter2 sweing Wednesday 2 0.80 \n",
"714 2/11/2015 Quarter2 sweing Wednesday 1 0.80 \n",
"730 2/12/2015 Quarter2 sweing Thursday 1 0.80 \n",
"731 2/12/2015 Quarter2 finishing Thursday 4 0.70 \n",
"732 2/12/2015 Quarter2 sweing Thursday 2 0.80 \n",
"749 2/14/2015 Quarter2 sweing Saturday 1 0.80 \n",
"766 2/15/2015 Quarter3 finishing Sunday 1 0.80 \n",
"767 2/15/2015 Quarter3 finishing Sunday 2 0.80 \n",
"768 2/15/2015 Quarter3 sweing Sunday 1 0.80 \n",
"785 2/16/2015 Quarter3 sweing Monday 1 0.80 \n",
"803 2/17/2015 Quarter3 sweing Tuesday 1 0.80 \n",
"\n",
" smv wip over_time incentive idle_time idle_men \\\n",
"337 4.15 NaN 1440 0 0.0 0 \n",
"437 3.94 NaN 1800 0 0.0 0 \n",
"456 22.52 1635.0 6840 119 0.0 0 \n",
"457 22.52 1299.0 6840 119 0.0 0 \n",
"477 22.52 1559.0 6840 90 0.0 0 \n",
"478 22.52 1350.0 6840 113 0.0 0 \n",
"498 22.52 1416.0 6840 113 0.0 0 \n",
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"543 22.52 1396.0 6900 113 0.0 0 \n",
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"599 22.52 1263.0 6900 100 0.0 0 \n",
"600 22.52 968.0 6840 113 0.0 0 \n",
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"619 22.52 1485.0 6900 113 0.0 0 \n",
"636 3.94 NaN 3000 0 0.0 0 \n",
"637 22.52 1186.0 6900 113 0.0 0 \n",
"655 22.52 1233.0 6900 113 0.0 0 \n",
"674 3.94 NaN 2160 0 0.0 0 \n",
"692 4.08 NaN 1080 0 0.0 0 \n",
"711 4.15 NaN 1800 0 0.0 0 \n",
"712 4.08 NaN 1080 0 0.0 0 \n",
"713 22.52 1557.0 0 90 0.0 0 \n",
"714 22.52 1498.0 0 113 0.0 0 \n",
"730 22.52 1397.0 0 138 0.0 0 \n",
"731 4.15 NaN 1800 0 0.0 0 \n",
"732 22.52 1327.0 0 113 0.0 0 \n",
"749 22.52 1416.0 6840 113 0.0 0 \n",
"766 3.94 NaN 960 0 0.0 0 \n",
"767 3.94 NaN 960 0 0.0 0 \n",
"768 22.52 1420.0 6840 113 0.0 0 \n",
"785 22.52 1422.0 6840 113 0.0 0 \n",
"803 22.52 1445.0 6840 113 0.0 0 \n",
"\n",
" no_of_style_change no_of_workers actual_productivity \n",
"337 0 8.0 1.033570 \n",
"437 0 10.0 1.059621 \n",
"456 0 57.0 1.000230 \n",
"457 0 57.0 1.000230 \n",
"477 0 57.0 1.000230 \n",
"478 0 57.0 1.000230 \n",
"498 0 57.0 1.000230 \n",
"518 0 58.0 1.000457 \n",
"519 0 57.0 1.000230 \n",
"542 0 8.0 1.011562 \n",
"543 0 57.5 1.000671 \n",
"544 0 58.5 1.000402 \n",
"561 0 58.5 1.000602 \n",
"580 0 20.0 1.001417 \n",
"581 0 57.5 1.000019 \n",
"599 0 57.5 1.050281 \n",
"600 0 57.0 1.000230 \n",
"618 0 56.5 1.000446 \n",
"619 0 57.5 1.000019 \n",
"636 0 25.0 1.050667 \n",
"637 0 58.0 1.000019 \n",
"655 0 57.5 1.000019 \n",
"674 0 18.0 1.057963 \n",
"692 0 9.0 1.004889 \n",
"711 0 15.0 1.033156 \n",
"712 0 9.0 1.020000 \n",
"713 0 57.5 1.000345 \n",
"714 0 57.0 1.000066 \n",
"730 0 57.0 1.100484 \n",
"731 0 15.0 1.096633 \n",
"732 0 57.5 1.000345 \n",
"749 0 57.0 1.000230 \n",
"766 0 8.0 1.120437 \n",
"767 0 8.0 1.108125 \n",
"768 0 57.0 1.000230 \n",
"785 0 57.0 1.000230 \n",
"803 0 57.0 1.000230 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df[\"actual_productivity\"] > 1]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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],
"text/plain": [
" date quarter department day team targeted_productivity \\\n",
"1 1/1/2015 Quarter1 finishing Thursday 1 0.75 \n",
"6 1/1/2015 Quarter1 finishing Thursday 2 0.75 \n",
"13 1/1/2015 Quarter1 finishing Thursday 10 0.65 \n",
"14 1/1/2015 Quarter1 finishing Thursday 8 0.75 \n",
"15 1/1/2015 Quarter1 finishing Thursday 4 0.75 \n",
"... ... ... ... ... ... ... \n",
"1192 3/11/2015 Quarter2 finishing Wednesday 10 0.75 \n",
"1193 3/11/2015 Quarter2 finishing Wednesday 8 0.70 \n",
"1194 3/11/2015 Quarter2 finishing Wednesday 7 0.65 \n",
"1195 3/11/2015 Quarter2 finishing Wednesday 9 0.75 \n",
"1196 3/11/2015 Quarter2 finishing Wednesday 6 0.70 \n",
"\n",
" smv wip over_time incentive idle_time idle_men \\\n",
"1 3.94 NaN 960 0 0.0 0 \n",
"6 3.94 NaN 960 0 0.0 0 \n",
"13 3.94 NaN 960 0 0.0 0 \n",
"14 2.90 NaN 960 0 0.0 0 \n",
"15 3.94 NaN 2160 0 0.0 0 \n",
"... ... ... ... ... ... ... \n",
"1192 2.90 NaN 960 0 0.0 0 \n",
"1193 3.90 NaN 960 0 0.0 0 \n",
"1194 3.90 NaN 960 0 0.0 0 \n",
"1195 2.90 NaN 1800 0 0.0 0 \n",
"1196 2.90 NaN 720 0 0.0 0 \n",
"\n",
" no_of_style_change no_of_workers actual_productivity \n",
"1 0 8.0 0.886500 \n",
"6 0 8.0 0.755167 \n",
"13 0 8.0 0.705917 \n",
"14 0 8.0 0.676667 \n",
"15 0 18.0 0.593056 \n",
"... ... ... ... \n",
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"1193 0 8.0 0.625625 \n",
"1194 0 8.0 0.625625 \n",
"1195 0 15.0 0.505889 \n",
"1196 0 6.0 0.394722 \n",
"\n",
"[506 rows x 15 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df[\"wip\"].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1000x1000 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.hist(figsize=(10,10))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- `idle_time` and `idle_men` appear to have few non-zero values. consider dropping"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"18\n",
"18\n"
]
}
],
"source": [
"print(len(df[(df[\"idle_time\"] > 0)]))\n",
"print(len(df[(df[\"idle_men\"] > 0)]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we can remove both this columns because there are very few non-zero values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data cleaning"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"department\n",
"sewing 691\n",
"finishing 506\n",
"Name: count, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# cleaning extra space in department column\n",
"df.loc[df[\"department\"] == \"finishing \", \"department\"] = \"finishing\"\n",
"df.loc[df[\"department\"] == \"sweing\", \"department\"] = \"sewing\"\n",
"df[\"department\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"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>quarter</th>\n",
" <th>department</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Quarter1</td>\n",
" <td>sewing</td>\n",
" <td>Thursday</td>\n",
" <td>8</td>\n",
" <td>0.80</td>\n",
" <td>26.16</td>\n",
" <td>7080</td>\n",
" <td>98</td>\n",
" <td>59.0</td>\n",
" <td>0.940725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Quarter1</td>\n",
" <td>finishing</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8.0</td>\n",
" <td>0.886500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Quarter1</td>\n",
" <td>sewing</td>\n",
" <td>Thursday</td>\n",
" <td>11</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>3660</td>\n",
" <td>50</td>\n",
" <td>30.5</td>\n",
" <td>0.800570</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" quarter department day team targeted_productivity smv \\\n",
"0 Quarter1 sewing Thursday 8 0.80 26.16 \n",
"1 Quarter1 finishing Thursday 1 0.75 3.94 \n",
"2 Quarter1 sewing Thursday 11 0.80 11.41 \n",
"\n",
" over_time incentive no_of_workers actual_productivity \n",
"0 7080 98 59.0 0.940725 \n",
"1 960 0 8.0 0.886500 \n",
"2 3660 50 30.5 0.800570 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# removing date column (due to short time frame, probably not useful for our model)\n",
"# removing idle_time and idle_men due to few non-zero values\n",
"# removing wip due to many null values\n",
"# removing no_of_style_change due to few non-zero values\n",
"df = df.drop([\"date\", \"idle_time\", \"idle_men\", \"wip\", \"no_of_style_change\"], axis=1)\n",
"\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"quarter\n",
"Quarter1 360\n",
"Quarter2 335\n",
"Quarter4 292\n",
"Quarter3 210\n",
"Name: count, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# merging Quarter 5 with Quarter 4 because it covers so few values on its own\n",
"df.loc[df[\"quarter\"] == \"Quarter5\", \"quarter\"] = \"Quarter4\"\n",
"df[\"quarter\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"quarter\n",
"1 360\n",
"2 335\n",
"4 292\n",
"3 210\n",
"Name: count, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# making quarter numeric\n",
"df.loc[df[\"quarter\"] == \"Quarter1\", \"quarter\"] = 1\n",
"df.loc[df[\"quarter\"] == \"Quarter2\", \"quarter\"] = 2\n",
"df.loc[df[\"quarter\"] == \"Quarter3\", \"quarter\"] = 3\n",
"df.loc[df[\"quarter\"] == \"Quarter4\", \"quarter\"] = 4\n",
"df[\"quarter\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1197 entries, 0 to 1196\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 quarter 1197 non-null int64\n",
"dtypes: int64(1)\n",
"memory usage: 9.5 KB\n"
]
}
],
"source": [
"df[\"quarter\"] = df[\"quarter\"].astype(\"int\")\n",
"df[[\"quarter\"]].info()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"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>quarter</th>\n",
" <th>department</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>sewing</td>\n",
" <td>Thursday</td>\n",
" <td>8</td>\n",
" <td>0.80</td>\n",
" <td>26.16</td>\n",
" <td>7080</td>\n",
" <td>98</td>\n",
" <td>59</td>\n",
" <td>0.940725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>finishing</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.886500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>sewing</td>\n",
" <td>Thursday</td>\n",
" <td>11</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>3660</td>\n",
" <td>50</td>\n",
" <td>30</td>\n",
" <td>0.800570</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" quarter department day team targeted_productivity smv \\\n",
"0 1 sewing Thursday 8 0.80 26.16 \n",
"1 1 finishing Thursday 1 0.75 3.94 \n",
"2 1 sewing Thursday 11 0.80 11.41 \n",
"\n",
" over_time incentive no_of_workers actual_productivity \n",
"0 7080 98 59 0.940725 \n",
"1 960 0 8 0.886500 \n",
"2 3660 50 30 0.800570 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# number of workers is currently a float, but can't have a fraction of a worker. convert to int\n",
"df[\"no_of_workers\"] = df[\"no_of_workers\"].astype(\"int\")\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"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>quarter</th>\n",
" <th>department</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>sewing</td>\n",
" <td>Thursday</td>\n",
" <td>8</td>\n",
" <td>0.80</td>\n",
" <td>26.16</td>\n",
" <td>7080</td>\n",
" <td>98</td>\n",
" <td>59</td>\n",
" <td>0.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>finishing</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.89</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" quarter department day team targeted_productivity smv \\\n",
"0 1 sewing Thursday 8 0.80 26.16 \n",
"1 1 finishing Thursday 1 0.75 3.94 \n",
"\n",
" over_time incentive no_of_workers actual_productivity \n",
"0 7080 98 59 0.94 \n",
"1 960 0 8 0.89 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# \"actual_productivity\" should feature two decimals, just like \"targeted_productivity\"\n",
"df[\"actual_productivity\"] = df[\"actual_productivity\"].round(2)\n",
"df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"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>quarter</th>\n",
" <th>department</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" <th>productive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>959</th>\n",
" <td>4</td>\n",
" <td>finishing</td>\n",
" <td>Thursday</td>\n",
" <td>10</td>\n",
" <td>0.70</td>\n",
" <td>2.90</td>\n",
" <td>3360</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.41</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>464</th>\n",
" <td>4</td>\n",
" <td>finishing</td>\n",
" <td>Tuesday</td>\n",
" <td>8</td>\n",
" <td>0.65</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.85</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>672</th>\n",
" <td>2</td>\n",
" <td>sewing</td>\n",
" <td>Sunday</td>\n",
" <td>7</td>\n",
" <td>0.70</td>\n",
" <td>24.26</td>\n",
" <td>6960</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0.36</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>321</th>\n",
" <td>3</td>\n",
" <td>sewing</td>\n",
" <td>Monday</td>\n",
" <td>6</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>4380</td>\n",
" <td>50</td>\n",
" <td>31</td>\n",
" <td>0.80</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>3</td>\n",
" <td>finishing</td>\n",
" <td>Saturday</td>\n",
" <td>9</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>1800</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0.83</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>307</th>\n",
" <td>3</td>\n",
" <td>sewing</td>\n",
" <td>Sunday</td>\n",
" <td>10</td>\n",
" <td>0.70</td>\n",
" <td>22.52</td>\n",
" <td>10080</td>\n",
" <td>40</td>\n",
" <td>56</td>\n",
" <td>0.70</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609</th>\n",
" <td>1</td>\n",
" <td>finishing</td>\n",
" <td>Wednesday</td>\n",
" <td>9</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.76</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1123</th>\n",
" <td>2</td>\n",
" <td>sewing</td>\n",
" <td>Sunday</td>\n",
" <td>8</td>\n",
" <td>0.60</td>\n",
" <td>30.48</td>\n",
" <td>6720</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>0.60</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>4</td>\n",
" <td>sewing</td>\n",
" <td>Sunday</td>\n",
" <td>9</td>\n",
" <td>0.70</td>\n",
" <td>18.79</td>\n",
" <td>3240</td>\n",
" <td>30</td>\n",
" <td>57</td>\n",
" <td>0.63</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>950</th>\n",
" <td>4</td>\n",
" <td>sewing</td>\n",
" <td>Thursday</td>\n",
" <td>3</td>\n",
" <td>0.75</td>\n",
" <td>29.40</td>\n",
" <td>6840</td>\n",
" <td>45</td>\n",
" <td>57</td>\n",
" <td>0.75</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" quarter department day team targeted_productivity smv \\\n",
"959 4 finishing Thursday 10 0.70 2.90 \n",
"464 4 finishing Tuesday 8 0.65 3.94 \n",
"672 2 sewing Sunday 7 0.70 24.26 \n",
"321 3 sewing Monday 6 0.80 11.41 \n",
"282 3 finishing Saturday 9 0.80 3.94 \n",
"307 3 sewing Sunday 10 0.70 22.52 \n",
"609 1 finishing Wednesday 9 0.75 3.94 \n",
"1123 2 sewing Sunday 8 0.60 30.48 \n",
"877 4 sewing Sunday 9 0.70 18.79 \n",
"950 4 sewing Thursday 3 0.75 29.40 \n",
"\n",
" over_time incentive no_of_workers actual_productivity productive \n",
"959 3360 0 8 0.41 False \n",
"464 960 0 8 0.85 True \n",
"672 6960 0 58 0.36 False \n",
"321 4380 50 31 0.80 True \n",
"282 1800 0 10 0.83 True \n",
"307 10080 40 56 0.70 True \n",
"609 960 0 8 0.76 True \n",
"1123 6720 0 56 0.60 True \n",
"877 3240 30 57 0.63 False \n",
"950 6840 45 57 0.75 True "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# setting new column for classifer based on whether targeted productivity was reached\n",
"df[\"productive\"] = df[\"actual_productivity\"] >= df[\"targeted_productivity\"]\n",
"df.sample(10, random_state=14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ML Prep"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"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>quarter</th>\n",
" <th>dept_sewing</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" <th>productive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>8</td>\n",
" <td>0.80</td>\n",
" <td>26.16</td>\n",
" <td>7080</td>\n",
" <td>98</td>\n",
" <td>59</td>\n",
" <td>0.94</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.89</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>11</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>3660</td>\n",
" <td>50</td>\n",
" <td>30</td>\n",
" <td>0.80</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>12</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>3660</td>\n",
" <td>50</td>\n",
" <td>30</td>\n",
" <td>0.80</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>6</td>\n",
" <td>0.80</td>\n",
" <td>25.90</td>\n",
" <td>1920</td>\n",
" <td>50</td>\n",
" <td>56</td>\n",
" <td>0.80</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>7</td>\n",
" <td>0.80</td>\n",
" <td>25.90</td>\n",
" <td>6720</td>\n",
" <td>38</td>\n",
" <td>56</td>\n",
" <td>0.80</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Thursday</td>\n",
" <td>2</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.76</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>3</td>\n",
" <td>0.75</td>\n",
" <td>28.08</td>\n",
" <td>6900</td>\n",
" <td>45</td>\n",
" <td>57</td>\n",
" <td>0.75</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>2</td>\n",
" <td>0.75</td>\n",
" <td>19.87</td>\n",
" <td>6000</td>\n",
" <td>34</td>\n",
" <td>55</td>\n",
" <td>0.75</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>1</td>\n",
" <td>0.75</td>\n",
" <td>28.08</td>\n",
" <td>6900</td>\n",
" <td>45</td>\n",
" <td>57</td>\n",
" <td>0.75</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" quarter dept_sewing day team targeted_productivity smv \\\n",
"0 1 1 Thursday 8 0.80 26.16 \n",
"1 1 0 Thursday 1 0.75 3.94 \n",
"2 1 1 Thursday 11 0.80 11.41 \n",
"3 1 1 Thursday 12 0.80 11.41 \n",
"4 1 1 Thursday 6 0.80 25.90 \n",
"5 1 1 Thursday 7 0.80 25.90 \n",
"6 1 0 Thursday 2 0.75 3.94 \n",
"7 1 1 Thursday 3 0.75 28.08 \n",
"8 1 1 Thursday 2 0.75 19.87 \n",
"9 1 1 Thursday 1 0.75 28.08 \n",
"\n",
" over_time incentive no_of_workers actual_productivity productive \n",
"0 7080 98 59 0.94 True \n",
"1 960 0 8 0.89 True \n",
"2 3660 50 30 0.80 True \n",
"3 3660 50 30 0.80 True \n",
"4 1920 50 56 0.80 True \n",
"5 6720 38 56 0.80 True \n",
"6 960 0 8 0.76 True \n",
"7 6900 45 57 0.75 True \n",
"8 6000 34 55 0.75 True \n",
"9 6900 45 57 0.75 True "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert department column to boolean\n",
"df = df.rename(columns={\"department\": \"dept_sewing\"})\n",
"df[\"dept_sewing\"] = df[\"dept_sewing\"].map({\"finishing\": 0, \"sewing\": 1}).astype(\"int64\")\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dept_sewing</th>\n",
" <th>day</th>\n",
" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>no_of_workers</th>\n",
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" <th>q_1</th>\n",
" <th>q_2</th>\n",
" <th>q_3</th>\n",
" <th>q_4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>959</th>\n",
" <td>0</td>\n",
" <td>Thursday</td>\n",
" <td>10</td>\n",
" <td>0.70</td>\n",
" <td>2.90</td>\n",
" <td>3360</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.41</td>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
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" <tr>\n",
" <th>464</th>\n",
" <td>0</td>\n",
" <td>Tuesday</td>\n",
" <td>8</td>\n",
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" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.85</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>672</th>\n",
" <td>1</td>\n",
" <td>Sunday</td>\n",
" <td>7</td>\n",
" <td>0.70</td>\n",
" <td>24.26</td>\n",
" <td>6960</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0.36</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>321</th>\n",
" <td>1</td>\n",
" <td>Monday</td>\n",
" <td>6</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>4380</td>\n",
" <td>50</td>\n",
" <td>31</td>\n",
" <td>0.80</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>0</td>\n",
" <td>Saturday</td>\n",
" <td>9</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>1800</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0.83</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>307</th>\n",
" <td>1</td>\n",
" <td>Sunday</td>\n",
" <td>10</td>\n",
" <td>0.70</td>\n",
" <td>22.52</td>\n",
" <td>10080</td>\n",
" <td>40</td>\n",
" <td>56</td>\n",
" <td>0.70</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609</th>\n",
" <td>0</td>\n",
" <td>Wednesday</td>\n",
" <td>9</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
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" <td>0.76</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1123</th>\n",
" <td>1</td>\n",
" <td>Sunday</td>\n",
" <td>8</td>\n",
" <td>0.60</td>\n",
" <td>30.48</td>\n",
" <td>6720</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>0.60</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>1</td>\n",
" <td>Sunday</td>\n",
" <td>9</td>\n",
" <td>0.70</td>\n",
" <td>18.79</td>\n",
" <td>3240</td>\n",
" <td>30</td>\n",
" <td>57</td>\n",
" <td>0.63</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>950</th>\n",
" <td>1</td>\n",
" <td>Thursday</td>\n",
" <td>3</td>\n",
" <td>0.75</td>\n",
" <td>29.40</td>\n",
" <td>6840</td>\n",
" <td>45</td>\n",
" <td>57</td>\n",
" <td>0.75</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dept_sewing day team targeted_productivity smv over_time \\\n",
"959 0 Thursday 10 0.70 2.90 3360 \n",
"464 0 Tuesday 8 0.65 3.94 960 \n",
"672 1 Sunday 7 0.70 24.26 6960 \n",
"321 1 Monday 6 0.80 11.41 4380 \n",
"282 0 Saturday 9 0.80 3.94 1800 \n",
"307 1 Sunday 10 0.70 22.52 10080 \n",
"609 0 Wednesday 9 0.75 3.94 960 \n",
"1123 1 Sunday 8 0.60 30.48 6720 \n",
"877 1 Sunday 9 0.70 18.79 3240 \n",
"950 1 Thursday 3 0.75 29.40 6840 \n",
"\n",
" incentive no_of_workers actual_productivity productive q_1 q_2 \\\n",
"959 0 8 0.41 False False False \n",
"464 0 8 0.85 True False False \n",
"672 0 58 0.36 False False True \n",
"321 50 31 0.80 True False False \n",
"282 0 10 0.83 True False False \n",
"307 40 56 0.70 True False False \n",
"609 0 8 0.76 True True False \n",
"1123 0 56 0.60 True False True \n",
"877 30 57 0.63 False False False \n",
"950 45 57 0.75 True False False \n",
"\n",
" q_3 q_4 \n",
"959 False True \n",
"464 False True \n",
"672 False False \n",
"321 True False \n",
"282 True False \n",
"307 True False \n",
"609 False False \n",
"1123 False False \n",
"877 False True \n",
"950 False True "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make quarter column into dummies (numeric order is *not* actually part of these values so they should be categorical)\n",
"df = pd.concat([df, pd.get_dummies(df[\"quarter\"], prefix=\"q\")], axis=1).drop([\"quarter\"], axis=1)\n",
"df.sample(10, random_state=14)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
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" <th>team</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
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" <th>actual_productivity</th>\n",
" <th>productive</th>\n",
" <th>q_1</th>\n",
" <th>q_2</th>\n",
" <th>q_3</th>\n",
" <th>q_4</th>\n",
" <th>Monday</th>\n",
" <th>Saturday</th>\n",
" <th>Sunday</th>\n",
" <th>Thursday</th>\n",
" <th>Tuesday</th>\n",
" <th>Wednesday</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>959</th>\n",
" <td>0</td>\n",
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" <td>False</td>\n",
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" <tr>\n",
" <th>464</th>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>672</th>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0.70</td>\n",
" <td>24.26</td>\n",
" <td>6960</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0.36</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>321</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0.80</td>\n",
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" <td>4380</td>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>1800</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0.83</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>307</th>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>0.70</td>\n",
" <td>22.52</td>\n",
" <td>10080</td>\n",
" <td>40</td>\n",
" <td>56</td>\n",
" <td>0.70</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <th>609</th>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.76</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1123</th>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>0.60</td>\n",
" <td>30.48</td>\n",
" <td>6720</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>0.60</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>0.70</td>\n",
" <td>18.79</td>\n",
" <td>3240</td>\n",
" <td>30</td>\n",
" <td>57</td>\n",
" <td>0.63</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>950</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0.75</td>\n",
" <td>29.40</td>\n",
" <td>6840</td>\n",
" <td>45</td>\n",
" <td>57</td>\n",
" <td>0.75</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dept_sewing team targeted_productivity smv over_time incentive \\\n",
"959 0 10 0.70 2.90 3360 0 \n",
"464 0 8 0.65 3.94 960 0 \n",
"672 1 7 0.70 24.26 6960 0 \n",
"321 1 6 0.80 11.41 4380 50 \n",
"282 0 9 0.80 3.94 1800 0 \n",
"307 1 10 0.70 22.52 10080 40 \n",
"609 0 9 0.75 3.94 960 0 \n",
"1123 1 8 0.60 30.48 6720 0 \n",
"877 1 9 0.70 18.79 3240 30 \n",
"950 1 3 0.75 29.40 6840 45 \n",
"\n",
" no_of_workers actual_productivity productive q_1 q_2 q_3 \\\n",
"959 8 0.41 False False False False \n",
"464 8 0.85 True False False False \n",
"672 58 0.36 False False True False \n",
"321 31 0.80 True False False True \n",
"282 10 0.83 True False False True \n",
"307 56 0.70 True False False True \n",
"609 8 0.76 True True False False \n",
"1123 56 0.60 True False True False \n",
"877 57 0.63 False False False False \n",
"950 57 0.75 True False False False \n",
"\n",
" q_4 Monday Saturday Sunday Thursday Tuesday Wednesday \n",
"959 True False False False True False False \n",
"464 True False False False False True False \n",
"672 False False False True False False False \n",
"321 False True False False False False False \n",
"282 False False True False False False False \n",
"307 False False False True False False False \n",
"609 False False False False False False True \n",
"1123 False False False True False False False \n",
"877 True False False True False False False \n",
"950 True False False False True False False "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# day column to dummies\n",
"df = pd.concat([df, pd.get_dummies(df[\"day\"], prefix=None)], axis=1).drop([\"day\"], axis=1)\n",
"df.sample(10, random_state=14)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dept_sewing</th>\n",
" <th>targeted_productivity</th>\n",
" <th>smv</th>\n",
" <th>over_time</th>\n",
" <th>incentive</th>\n",
" <th>no_of_workers</th>\n",
" <th>actual_productivity</th>\n",
" <th>productive</th>\n",
" <th>q_1</th>\n",
" <th>q_2</th>\n",
" <th>...</th>\n",
" <th>team_3</th>\n",
" <th>team_4</th>\n",
" <th>team_5</th>\n",
" <th>team_6</th>\n",
" <th>team_7</th>\n",
" <th>team_8</th>\n",
" <th>team_9</th>\n",
" <th>team_10</th>\n",
" <th>team_11</th>\n",
" <th>team_12</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>959</th>\n",
" <td>0</td>\n",
" <td>0.70</td>\n",
" <td>2.90</td>\n",
" <td>3360</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.41</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>464</th>\n",
" <td>0</td>\n",
" <td>0.65</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.85</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>672</th>\n",
" <td>1</td>\n",
" <td>0.70</td>\n",
" <td>24.26</td>\n",
" <td>6960</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0.36</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>321</th>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" <td>11.41</td>\n",
" <td>4380</td>\n",
" <td>50</td>\n",
" <td>31</td>\n",
" <td>0.80</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>0</td>\n",
" <td>0.80</td>\n",
" <td>3.94</td>\n",
" <td>1800</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>0.83</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>307</th>\n",
" <td>1</td>\n",
" <td>0.70</td>\n",
" <td>22.52</td>\n",
" <td>10080</td>\n",
" <td>40</td>\n",
" <td>56</td>\n",
" <td>0.70</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609</th>\n",
" <td>0</td>\n",
" <td>0.75</td>\n",
" <td>3.94</td>\n",
" <td>960</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.76</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1123</th>\n",
" <td>1</td>\n",
" <td>0.60</td>\n",
" <td>30.48</td>\n",
" <td>6720</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>0.60</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>1</td>\n",
" <td>0.70</td>\n",
" <td>18.79</td>\n",
" <td>3240</td>\n",
" <td>30</td>\n",
" <td>57</td>\n",
" <td>0.63</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>950</th>\n",
" <td>1</td>\n",
" <td>0.75</td>\n",
" <td>29.40</td>\n",
" <td>6840</td>\n",
" <td>45</td>\n",
" <td>57</td>\n",
" <td>0.75</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" dept_sewing targeted_productivity smv over_time incentive \\\n",
"959 0 0.70 2.90 3360 0 \n",
"464 0 0.65 3.94 960 0 \n",
"672 1 0.70 24.26 6960 0 \n",
"321 1 0.80 11.41 4380 50 \n",
"282 0 0.80 3.94 1800 0 \n",
"307 1 0.70 22.52 10080 40 \n",
"609 0 0.75 3.94 960 0 \n",
"1123 1 0.60 30.48 6720 0 \n",
"877 1 0.70 18.79 3240 30 \n",
"950 1 0.75 29.40 6840 45 \n",
"\n",
" no_of_workers actual_productivity productive q_1 q_2 ... \\\n",
"959 8 0.41 False False False ... \n",
"464 8 0.85 True False False ... \n",
"672 58 0.36 False False True ... \n",
"321 31 0.80 True False False ... \n",
"282 10 0.83 True False False ... \n",
"307 56 0.70 True False False ... \n",
"609 8 0.76 True True False ... \n",
"1123 56 0.60 True False True ... \n",
"877 57 0.63 False False False ... \n",
"950 57 0.75 True False False ... \n",
"\n",
" team_3 team_4 team_5 team_6 team_7 team_8 team_9 team_10 \\\n",
"959 False False False False False False False True \n",
"464 False False False False False True False False \n",
"672 False False False False True False False False \n",
"321 False False False True False False False False \n",
"282 False False False False False False True False \n",
"307 False False False False False False False True \n",
"609 False False False False False False True False \n",
"1123 False False False False False True False False \n",
"877 False False False False False False True False \n",
"950 True False False False False False False False \n",
"\n",
" team_11 team_12 \n",
"959 False False \n",
"464 False False \n",
"672 False False \n",
"321 False False \n",
"282 False False \n",
"307 False False \n",
"609 False False \n",
"1123 False False \n",
"877 False False \n",
"950 False False \n",
"\n",
"[10 rows x 30 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# team column to dummies\n",
"df = pd.concat([df, pd.get_dummies(df[\"team\"], prefix=\"team\")], axis=1).drop([\"team\"], axis=1)\n",
"df.sample(10, random_state=14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building the Tree"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.tree import plot_tree"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# Feature and target columns\n",
"\n",
"X = df.drop([\"actual_productivity\", \"productive\"], axis=1)\n",
"y = df[\"productive\"]\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, shuffle=True, random_state=24)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=24)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, random_state=24)</pre></div> </div></div></div></div>"
],
"text/plain": [
"DecisionTreeClassifier(max_depth=3, random_state=24)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tree = DecisionTreeClassifier(max_depth=3, random_state=24)\n",
"\n",
"tree.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"y_pred = tree.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing and Evaluating the Tree"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.85\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"print(\"Accuracy:\", round(accuracy_score(y_test,y_pred), 2))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([False, True])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tree.classes_"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 2000x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = [20.0, 8.0])\n",
"\n",
"# Plotting the tree with some specific parameters\n",
"\n",
"_ = plot_tree(tree, \n",
" feature_names=X.columns, \n",
" class_names=[\"Unproductive\", \"Productive\"],\n",
" filled=True, \n",
" rounded=False, \n",
" proportion=True, \n",
" fontsize=11) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- `smv`: standard minute value — the allocated time for a task \n",
"- `incentive`: represents the amount of financial incentive (in BDT) that enables or motivates a particular course of action"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 35, 24],\n",
" [ 13, 168]])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[True Negatives, False Positives],\n",
"\n",
"[False Negatives, True Positives]]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision: 0.88\n",
"Recall: 0.93\n",
"F1 Score: 0.9\n",
"----\n",
"Accuracy: 0.85\n"
]
}
],
"source": [
"from sklearn.metrics import precision_score, recall_score, f1_score\n",
"\n",
"print(\"Precision:\", round(precision_score(y_test, y_pred), 2))\n",
"print(\"Recall:\", round(recall_score(y_test, y_pred), 2))\n",
"print(\"F1 Score:\", round(f1_score(y_test, y_pred), 2))\n",
"print(\"----\")\n",
"print(\"Accuracy:\", round(tree.score(X_test, y_test), 2))"
]
},
{
"attachments": {
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8JweR0BpLfVvnio/4QMIZrVMXESo25QyveoHhUmwIHivhiZSJqmHX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\nsNEutxzIaU3mDjmmXNxslPcIULg8QcQtpXnX1TYjJZYazDcJCrgnLa9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pTdRPMJ4lX80s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DFUuBNhvW6rFrW6AdDYwrIwa9bwKEQEjpE5cqaNgOHgfusV3oGK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\ne9H8K1oNTSOoKaroy4UulgEh5FrtJ/qbm7YBTuOAAAAQDQB/45f/2Q==\n"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"<img src=\"attachment:a37e3c8a-212a-4a0a-ab27-9ceba6dc4249.jpg\" width=\"300\" height=\"200\">"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation Accuracy Scores: [0.85 0.88 0.81 0.87 0.87 0.82 0.72 0.76 0.84 0.79]\n",
"Mean Cross Validation Score: 0.82\n"
]
}
],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"\n",
"scores = cross_val_score(tree, X, y, cv = 10)\n",
"\n",
"print(\"Cross Validation Accuracy Scores:\", scores.round(2)) \n",
"print(\"Mean Cross Validation Score:\", scores.mean().round(2))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Cross Validated Precision: 0.85\n",
"Mean Cross Validated recall: 0.92\n",
"Mean Cross Validated F1: 0.88\n"
]
}
],
"source": [
"from sklearn.model_selection import cross_validate\n",
"\n",
"multiple_cross_scores = cross_validate(\n",
" tree, \n",
" X, y, cv=10, \n",
" scoring= (\"precision\", \"recall\", \"f1\") )\n",
"\n",
"print(\"Mean Cross Validated Precision:\", round(multiple_cross_scores[\"test_precision\"].mean(), 2))\n",
"print(\"Mean Cross Validated recall:\", round(multiple_cross_scores[\"test_recall\"].mean(), 2))\n",
"print(\"Mean Cross Validated F1:\", round(multiple_cross_scores[\"test_f1\"].mean(), 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.85\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"forest = RandomForestClassifier(\n",
" oob_score=True, \n",
" random_state=24\n",
" )\n",
"\n",
"forest.fit(X_train, y_train)\n",
"\n",
"y_pred_forest = forest.predict(X_test)\n",
"\n",
"print(\"Accuracy:\", round(accuracy_score(y_test,y_pred_forest), 2))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Out Of Bag Score: 0.83\n"
]
}
],
"source": [
"print(\"Out Of Bag Score:\", round(forest.oob_score_, 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0rc1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
@lydia-kisitu
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lydia-kisitu commented Sep 11, 2025

Thank you so much for your guidance, i really hope to have one on one with you, if possible for more help

@acstrahl
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acstrahl commented Sep 12, 2025

Thank you so much for your guidance, i really hope to have one on one with you, if possible for more help

@lydia-kisitu I'm not available for 1-1s, but please reach out to me in the Dataquest community where you can tag me as @Anna_Strahl if you have any questions about the project :)

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