About garmet employee

1. TITLE
PRODUCTIVITY PREDICTION OF
GARMENT EMPLOYEES
Under the guidance of:
Mrs.G.Vijaya Laxmi(Associative Professor)
Miss.J.Madhavi(Assistant Professor)
Department of Computer Science and Engineering in Artificial Intelligence and Machine Learning (CSE – AI & ML)

2. TEAM MEMBERS
D.Sharvani(20641A6610)
A.Archana(20641A6638)
K.Akhil(20641A6618)
B.Anirudh Reddy(20641A6622)

3. Introduction
 The Garment Industry is one of the key examples of the
industrial globalization of this modern era. It is a highly
labor-intensive industry with lots of manual processes.
 Satisfying the huge global demand for garment products is
mostly dependent on the production and delivery
performance of the employees in the garment
manufacturing companies.
 So, it is highly desirable among the decision-makers in the
garments industry to track, analyze, and predict the
productivity performance of the working teams in their
factories.
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4. Abstract
 The garment industry is one of the key examples of the
industrial globalization of this modern era. It is a highly labor-
intensive industry with lots of manual processes. Satisfying the
huge global demand for garment products is mostly dependent
on the production and delivery performance of the employees in
the garment manufacturing companies.
 So, it is highly desirable among the garment industry decision-
makers to track, analyze, and predict the productivity
performance of the working teams in their factories. This study
explores the application of state-of-the-art data mining
techniques for analyzing industrial data, revealing meaningful
insights, and predicting the productivity performance of the
working teams in a garment company.
 We have applied eight different data mining techniques with six
evaluation metrics as part of our exploration.
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5. Existing Systems
 Raw-Material Issue. Raw materials are
essential to the garment industry
 Inventory Management Issue
 Production Delays
 Order Processing Issue
 Garment Defects
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6. Proposed Systems
 If you are working in garment production or planning to open your
own garment company, you would need an all-in-one solution that
can help you to solve these challenges and issues and improve your
business productivity and performance. Garments ERP management
software is designed to help you overcome these operational
challenges. From planning, manufacturing, and order processing to
distribution, this robust software can easily handle all your
operational tasks and distribution activities.
 All manufacturing industries deal with various challenges and issues
in their product manufacturing processes. That is why today we will
discuss five common problems that all garment manufacturers face
that hamper their product quality and production performance.
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7. MERITS AND DEMERITS
MERITS
It is used to reduce work
pressure of an employee.
It helps in saving raw
materials.
It is used as a guide for those
who are starting new garment
industries.
By using this we can save
money.
It will help an industries to
gain profits.
DEMERITS
Retraining is not possible in
Machine Learning.
The data we give for training
should me accurate otherwise
the result will be incorrect.
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8. ALGORITHMS USED
K-Nearest Neighbors
algorithm
Linear Regression
Support Vector Machine
Decision Tree Using
Regression
Random Forest
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9. 9
Flow Chart

10. Requirements
Hardware Requirements
 RAM(8GB)
 ROM(64 bitprocessor)
 Windows 10
 HARD DISK 256GB
 I5 PROCESSOR
Software Requirements
 Python
 Python 3
 Libraries
 NumPy
 OpenCV, etc
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11. Code
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import sklearn
df = pd.read_csv('/content/drive/MyDrive/garments_worker_productivity.csv')
df.head()
df.shape
df.describe()
df2 = df.drop(['date', 'day'], axis=1)
df2.shape
df3 = df2.fillna({'wip': 0,})
df3.isnull().sum()
plt.figure(figsize=(10,5))
p = sns.boxplot(data = df3, orient ='v',width=0.8)
plt.xticks(rotation=90)
Q1 = df3.incentive.quantile(0.25)
Q3 = df3.incentive.quantile(0.75)
Q1, Q3
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12. IQR = Q3 - Q1
IQR
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
lower_limit, upper_limit
df3[(df3.incentive<lower_limit)|(df3.incentive>upper_limit)]
df4 = df3[(df3.incentive>lower_limit)&(df3.incentive<upper_limit)]
df4.shape
Q1 = df4.wip.quantile(0.25)
Q3 = df4.wip.quantile(0.75)
Q1, Q3
IQR = Q3 - Q1
IQR
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
lower_limit, upper_limit
df4[(df4.wip<lower_limit)|(df4.wip>upper_limit)]
df5 = df4[(df4.wip>lower_limit)&(df4.wip<upper_limit)]
df5.shape
Q1 = df5.over_time.quantile(0.25)
Q3 = df5.over_time.quantile(0.75)
Q1, Q3
IQR = Q3 - Q1
IQR
lower_limit = Q1 - 1.5*IQR
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13. upper_limit = Q3 + 1.5*IQR
lower_limit, upper_limit
df5[(df5.over_time<lower_limit)|(df5.over_time>upper_limit)]
df6 = df5[(df5.over_time>lower_limit)&(df5.over_time<upper_limit)]
df6.shape
lt.figure(figsize=(10,5))
p = sns.boxplot(data = df6, orient ='v',width=0.8)
plt.xticks(rotation=90)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for i in range(0, df6.shape[1]):
if df6.dtypes[i]=='object':
df6[df6.columns[i]] = le.fit_transform(df6[df6.columns[i]])
x = df6.drop(['actual_productivity'], axis=1)
y = df6.actual_productivity
x.shape, y.shape
y.head()
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x.iloc[:,:] = scaler.fit_transform(x.iloc[:,:])
x.head()
sns.pairplot(df,hue = "team")
plt.figure(figsize=(12, 10))
sns.heatmap(df6.corr(), annot=True,vmin=-1,vmax=1,cmap='viridis',square=True)
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14. df.iloc[:, 1:].plot.density(subplots=True, layout=(1,11), figsize=(20, 10), sharex = False)
df_wip = df[df.wip > 0]
sns.barplot(x="day", y="wip", data=df_wip)
df_diff = df["actual_productivity"] - df["targeted_productivity"]
sns.lineplot(x = df["date"], y = df_diff, ci = None)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30, random_state=42)
print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape)
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
algo = ['Linear regression','knr','SVr','Descision Tree','Random Forest']
accuracy = [linreg.score(x,y),knn.score(x,y),svr.score(x,y),dt.score(x,y),randf.score(x,y)]
ax.bar(algo[0],accuracy[0],color = 'g')
ax.bar(algo[1],accuracy[1],color = 'b')
ax.bar(algo[2],accuracy[2],color = 'y')
ax.bar(algo[3],accuracy[3],color = 'r')
ax.bar(algo[4],accuracy[4],color = 'y')
plt.xlabel('Classifiers ----->')
plt.ylabel('Accuracies ---->')
plt.title('ACCURACIES RESULTED')
plt.show()
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15. Output
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16. Modules
 Data Loading: Data loading is the process of reading data from a source, such
as a file or
database, and loading it into a program or application for further processing or
analysis. It involves accessing the data, transforming it into a format that the
program can use, and then loading it into memory or storage for use.
➢ Data Cleaning: Data cleaning is the process of identifying and correcting or
removing errors, inconsistencies, and inaccuracies in a dataset. It involves
analyzing and validating the data, detecting and correcting errors, handling missing
or duplicated values, and transforming the data into a usable format for analysis.
Data cleaning ensures that the data is accurate, complete, and reliable for further
analysis or modeling.
➢ Data Visualisation: Data visualization is the process of creating graphical
representations of data to help people understand and interpret complex
information. It involves transforming raw data into visual formats such as charts,
graphs, and maps, that can be easily interpreted and understood. Data
visualization helps to identify patterns, relationships, and trends in the data, and
communicate them effectively to stakeholders.
➢ Data Pre Processing: Data preprocessing refers to the process of preparing
and cleaning raw data before it can be used for analysis or modeling. Here are
some common steps in data preprocessing.
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17. CONCLUSION
In conclusion, this study has demonstrated the potential benefits of developing a
productivity prediction model for garment employees that can accurately forecast the
amount of clothes that need to be produced in the coming years based on input and
sales data from previous years. By utilizing machine learning algorithms such as linear
regression, decision trees, and neural networks, and data mining techniques, predictive
models can be developed that can improve the accuracy of demand forecasting and
production planning in the garment manufacturing industry. However, further research is
required to validate and optimize the proposed productivity prediction model and to
address challenges related to data quality, model interpretability, and implementation in
real-world settings. With continued research, the development of accurate productivity
prediction models can lead to improved operational efficiency, reduced costs, and
increased profitability in the garment manufacturing industry.
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18. REFERENCES
 Google
 Chatgpt
 Kaggle
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19. Thank You