How to Build and Evaluate a Deep Neural Network (DNN) for Predictive Maintenance Classification
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Condition for Building a Deep Neural Network (DNN) Model for Predictive Maintenance Classification
Description: A deep neural network (DNN) model to predict the target variable in a predictive maintenance dataset. It includes data preprocessing steps such as handling missing values, encoding categorical variables, scaling features, and splitting the data. The model is trained and evaluated with performance metrics like accuracy, F1 score, recall, and precision.
Step-by-Step Process
Dataset Loading: Load the dataset using pandas for further analysis and processing.
Handling Missing Values: Drop missing values or fill them with appropriate strategies.
Feature Scaling: Scale features using StandardScaler for uniform data distribution.
Model Building: Define and compile the DNN model using the Keras Model API.
Model Evaluation: Evaluate the model's effectiveness using metrics such as accuracy, F1 score, recall, and precision.
Sample Source Code
#Import Necessary Libraries
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense,Input
from tensorflow.keras.models import Model
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score, recall_score, precision_score
import warnings
warnings.filterwarnings("ignore")
# Rest of the DNN implementation
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