SKU: 80165366029

✅2015 RENAULT TRAFIC MK3 SL27 1.6D OIL FILTER HOUSING AND COOLER 152081926R A250

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Description

✅2015 RENAULT TRAFIC MK3 SL27 1.6D OIL FILTER HOUSING AND COOLER 152081926R A250Description: OIL FILTER HOUSING AND COOLER 152081926R 70375888 Vehicle details: RENAULT TRAFIC MK3 SL27 1. 6 dCi DIESEL 2015 6 SPEED MANUAL 150486 mileage Vehicle Identification No. VF12FL10252654839 Model TRAFIC III VU (F82) FGMB S040022 Date of production 2015 02 23 Engine Type R9M (450) C031831 R9M 1. 6 DCI DIESEL ENGINE Transmission PF6 (050) C024833 PF6 6 SPEED MANUAL GEARBOX BVM6 Version FG1E2 111SB6 Fuel GAZOLE (DIESEL) Please check pictures

Description:

OIL FILTER HOUSING AND COOLER

152081926R 70375888

Vehicle details:

RENAULT TRAFIC MK3 SL27

1.6 dCi DIESEL

2015 

6 SPEED MANUAL

150486 mileage

Vehicle Identification No.
VF12FL10252654839
Model
TRAFIC III VU (F82) FGMB - S040022
Date of production
2015-02-23
Engine Type
R9M (450) - C031831 R9M - 1.6 DCI DIESEL ENGINE
Transmission
PF6 (050) - C024833 PF6 - 6-SPEED MANUAL GEARBOX – BVM6
Version
FG1E2 111SB6

Fuel
GAZOLE (DIESEL)

Please check pictures carefully and ensure your part is the same. 

TERMS:

 

All ECU's we are selling are removed from a vehicle without any reprogramming or resetting. PLEASE MAKE SURE YOU WILL BE ABLE TO INSTALL, RESET OR PROGRAM IT ON YOUR OWN, IF NEEDED.

 

Engines

 

Engine need to be fitted by qualified mechanics for any warranty to be valid. We recommend a new timing belt / cam belt kit, as this is one of the most common reasons for engine failure. Any accessories that are left on engine are free of charge and warranty on them does not apply, warranty only apply for bare engine. We ship our engines and gearboxes with no oil. Please refill the engine/gearbox with manufacturers recommended oil grade before attempting to start the engine

 

Warranty and Returns

Please Note: Unwanted orders will be collected subject to a 25% charge depending on your order (to cover the carrier's charges) this also includes failed deliveries after 3 attempts and refused orders at the point of delivery. This does not apply to products which are faulty - these will be collected/replaced free of charge.

All items come with a 30 days warranty from the date of purchase. Warranty relates to the supplied part(s) only, it expressly excludes any consequential loss & fitting/ labour charges. If an item arrive faulty, damaged or was lost in the post, we will send you a replacement. If a replacement is not available, then a full refund will be issued. If you buy the item by mistake, you change your mind or don't check part number, you are welcome to send the item back at your expense. If any unauthorized alterations are found to have been made to a part then any return will be refused. Refunds are issued within 1-2 working days of part returned.

Please note Delivery includes a main land delivery to Scotland, Northern Ireland any Islands will be for extra charge. Please request delivery price to your location. Thanks.

Authorised Treatment Facility (ATF) Waste Permit No.
EPR/WE0582AC/A001

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SKU: 80165366029

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4.7 ★★★★★
Based on 12 reviews
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Product Reviews
P
Verified Purchase
Par
Omaha, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Alexandria, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Bozeman, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Fort Morgan, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Fort Morgan, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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