Predictive Maintenance Model For Automative Industry Using Random Forest Classifier | Inttrvu.ai
In this video, Mr. Rohit Mande is going to perform Random Forest Classifier for predicting need of maintenance for Automative Engines.
Machine learning (ML) plays a crucial role in automotive domain, from enhancing vehicle safety and efficiency to optimizing manufacturing processes. Here are some applications of ML in the automotive domain:
1.Self Driving Cars : Self-driving cars are a prime example ML applications. ML algorithms process vast amounts of sensor data to enable vehicles to navigate roads, perceive their surroundings, and make real-time decisions while adhering to traffic regulations.
2.Predictive Maintenance: ML models analyze data from various vehicle sensors to anticipate potential maintenance needs. This allows for proactive maintenance, preventing breakdowns, reducing repair costs, and ensuring optimal vehicle performance.
3.Supply Chain Optimization: ML can analyze complex data within the automotive supply chain, identifying bottlenecks and inefficiencies. This allows manufacturers to optimize logistics, streamline production, and reduce costs.
4.Vehicle Design and Manufacturing: ML can be employed to optimize vehicle design by analyzing data from simulations and real-world testing. This can lead to improvements in aerodynamics, fuel efficiency, and overall vehicle performance.
Random forest classifier
A Random Forest Classifier is a powerful machine learning algorithm widely used in various domains, including the automotive industry. It falls under the category of ensemble learning, which combines the predictions of multiple weaker models to create a single, more robust model. In the case of Random Forests, these weaker models are decision trees.
Basically
1.The algorithm first creates multiple decision trees, each trained on a random subset of the training data with replacement.
2.At each node of a decision tree, the algorithm randomly selects a subset of features and chooses the best split based on a specific criterion to separate the data.
3.This introduces randomness and prevents any individual tree from over fitting the training data.
4.When presented with a new data point, each tree in the forest makes a classification prediction.
5.The final prediction is made by taking the majority vote of the individual tree predictions.
Key benefits of Random Forest Classifier:
1.High accuracy: Ensemble learning often leads to better performance compared to single models like decision trees.
2.Robust to overfitting: The introduction of randomness during training helps prevent the model from memorizing the training data and improving its ability to generalize to unseen data.
3.Handles both categorical and numerical features: This makes it versatile for various applications.
4.Provides feature importance: Random forests can indicate which features are most influential in making predictions, aiding in understanding the model's behavior.
General applications of Random Forest:
1.Image classification: Random forests can be used to classify images into different categories, such as identifying objects in a photograph or distinguishing between different types of medical scans.
2.Spam filtering: By analyzing email content, a random forest classifier can distinguish between legitimate emails and spam messages.
3.Customer churn prediction: In various industries, including telecommunications and finance, random forests can be used to predict which customers are likely to churn (cancel their service) and take proactive measures to retain them.
4.Fraud detection: Random forest classifiers can be employed to identify fraudulent transactions in financial institutions or online marketplaces based on historical data and patterns.
Video Timestamps:
00:00 ML applications in Automotive Domain
00:57 Importing Libraries
01:34 Problem Statement
02:43 Reading Data
03:15 EDA
05:28 Visualising the data
07:22 Train Test Split the data
09:08 Fit and evaluate the model
10:42 Creating confusion matrix and predicting the result of model
11:23 Final Result
13:18 calculating the importance features
14:21 visualise the data that we have with respect to feature importance
About Us: Rohit Mande is Founder and CEO of inttrvu.ai. He has 10+ years of professional experience as a Data Scientist. In his previous role as 'Chief Data Scientist at Barclays' he was leading a team of Data Scientists. He has done his Masters from Technical University of Darmstadt, Germany in 2013-2015. He is also having published patent applications listed on Google Patents. He is passionate about helping people in transitioning to Data Science role.
Website : https://inttrvu.ai/
Instagram : https://www.instagram.com/inttrvu.ai/
LinkedIn : https://www.linkedin.com/in/rohit-mande-15a3a154/
Mail: info@inttrvu.ai
Contact Number: +91 7756043707
Address: Sr.No.19, Office no. 307, Acharya House, Plot No.24, 12/1, Bavdhan, Pune, Maharashtra 411021
#datascience #machinelearning #datasciencecourse #dataanalysis #automative #inttrvu.ai #datasciencecareer
In this video, Mr. Rohit Mande is going to perform Random Forest Classifier for predicting need of maintenance for Automative Engines.
Machine learning (ML) plays a crucial role in automotive domain, from enhancing vehicle safety and efficiency to optimizing manufacturing processes. Here are some applications of ML in the automotive domain:
1.Self Driving Cars : Self-driving cars are a prime example ML applications. ML algorithms process vast amounts of sensor data to enable vehicles to navigate roads, perceive their surroundings, and make real-time decisions while adhering to traffic regulations.
2.Predictive Maintenance: ML models analyze data from various vehicle sensors to anticipate potential maintenance needs. This allows for proactive maintenance, preventing breakdowns, reducing repair costs, and ensuring optimal vehicle performance.
3.Supply Chain Optimization: ML can analyze complex data within the automotive supply chain, identifying bottlenecks and inefficiencies. This allows manufacturers to optimize logistics, streamline production, and reduce costs.
4.Vehicle Design and Manufacturing: ML can be employed to optimize vehicle design by analyzing data from simulations and real-world testing. This can lead to improvements in aerodynamics, fuel efficiency, and overall vehicle performance.
Random forest classifier
A Random Forest Classifier is a powerful machine learning algorithm widely used in various domains, including the automotive industry. It falls under the category of ensemble learning, which combines the predictions of multiple weaker models to create a single, more robust model. In the case of Random Forests, these weaker models are decision trees.
Basically
1.The algorithm first creates multiple decision trees, each trained on a random subset of the training data with replacement.
2.At each node of a decision tree, the algorithm randomly selects a subset of features and chooses the best split based on a specific criterion to separate the data.
3.This introduces randomness and prevents any individual tree from over fitting the training data.
4.When presented with a new data point, each tree in the forest makes a classification prediction.
5.The final prediction is made by taking the majority vote of the individual tree predictions.
Key benefits of Random Forest Classifier:
1.High accuracy: Ensemble learning often leads to better performance compared to single models like decision trees.
2.Robust to overfitting: The introduction of randomness during training helps prevent the model from memorizing the training data and improving its ability to generalize to unseen data.
3.Handles both categorical and numerical features: This makes it versatile for various applications.
4.Provides feature importance: Random forests can indicate which features are most influential in making predictions, aiding in understanding the model’s behavior.
General applications of Random Forest:
1.Image classification: Random forests can be used to classify images into different categories, such as identifying objects in a photograph or distinguishing between different types of medical scans.
2.Spam filtering: By analyzing email content, a random forest classifier can distinguish between legitimate emails and spam messages.
3.Customer churn prediction: In various industries, including telecommunications and finance, random forests can be used to predict which customers are likely to churn (cancel their service) and take proactive measures to retain them.
4.Fraud detection: Random forest classifiers can be employed to identify fraudulent transactions in financial institutions or online marketplaces based on historical data and patterns.
Video Timestamps:
00:00 ML applications in Automotive Domain
00:57 Importing Libraries
01:34 Problem Statement
02:43 Reading Data
03:15 EDA
05:28 Visualising the data
07:22 Train Test Split the data
09:08 Fit and evaluate the model
10:42 Creating confusion matrix and predicting the result of model
11:23 Final Result
13:18 calculating the importance features
14:21 visualise the data that we have with respect to feature importance
About Us: Rohit Mande is Founder and CEO of inttrvu.ai. He has 10+ years of professional experience as a Data Scientist. In his previous role as ‘Chief Data Scientist at Barclays’ he was leading a team of Data Scientists. He has done his Masters from Technical University of Darmstadt, Germany in 2013-2015. He is also having published patent applications listed on Google Patents. He is passionate about helping people in transitioning to Data Science role.
Website : https://inttrvu.ai/
Instagram : https://www.instagram.com/inttrvu.ai/
LinkedIn : https://www.linkedin.com/in/rohit-mande-15a3a154/
Mail: info@inttrvu.ai
Contact Number: +91 7756043707
Address: Sr.No.19, Office no. 307, Acharya House, Plot No.24, 12/1, Bavdhan, Pune, Maharashtra 411021
#datascience #machinelearning #datasciencecourse #dataanalysis #automative #inttrvu.ai #datasciencecareer
