Using the project materials developed for the final project, create a presentation summarizing your project.Deliver a presentation to the class, highlighting key findings, recommendations, and the...

Predictive Maintenance Using Machine Learning: A Detailed Methodology
I. INTRODUCTION
The goal of predictive maintenance is to estimate the likelihood of an industrial equipment failure so that preventive maintenance can minimize unplanned downtime [1]. To forecast equipment failure, this project involves developing a machine-learning model utilizing past sensor data. Preventing malfunctions and optimizing maintenance schedules are the objectives.
II. DATASET OVERVIEW
Industrial equipment failure logs and sensor measurements constitute the dataset used in this study. Air temperature, process temperature, torque, rotational speed, and tool wear are important characteristics. The binary column Target, which represents the target variable, indicates whether a failure occu
ed (1) or not (0).
Columns:
· UDI: Unique Identifier.
· Product ID: ID of the product.
· Type: Categorical variable indicating product type.
· Air Temperature [K]: Air temperature in Kelvin.
· Process Temperature [K]: Process temperature in Kelvin.
· Rotational Speed [rpm]: Speed in revolutions per minute.
· Torque [Nm]: Torque in Newton-meters.
· Tool wear [min]: Tool wear in minutes.
· Target: Binary Failure target (0=No failure, 1=Failure).
Initial Data Inspection: The dataset is made up of 10 columns, no missing values were discovered at first. However, further data modifications produced missing values that were properly handled.
III. METHODOLOGY
3.1 Data Preprocessing:
Preparing the data was the initial stage in creating the predictive maintenance model. The dataset given comprised of several sensor readings and product information, including category factors, numerical variables, and the goal failure labels. Initial exploratory data analysis (EDA) indicated that several columns, such as UDI and Product ID, were identifiers rather than features that may add to the model’s predictive performance. As a result, these columns were removed since they included no information that might be used to anticipate equipment failure. Similarly, as the main objective of the classification model was to predict whether a failure happened rather than identifying the precise type of failure, the column Failure Type, which described the type of failure for failed units, was not explicitly employed as a feature in the model.
3.1.1 Dropping Unnecessary Columns:
UDI, Product ID, and Failure Type are examples of columns that were removed because they are either identifiers or not predictive for the cu
ent job.
data_cleaned = data.drop(['UDI', 'Product ID', 'Failure Type'], axis=1)
3.1.2 Handling Categorical Values:
A one-hot encoding process was used to transform the categorical Type column into a numerical format for model training. By assigning random numbers, one-hot encoding makes sure the model can handle categorical variables without adding bias.
data_cleaned =...