AI and ML - Creating, training and a model using python in jupyter notebook

Job Description:

Jupyter Notebook Contents

The various sections in the notebook should include code, code comments and appropriate

Markup cells describing your approach chosen.

In detail, sections should include the following:

Introduction and Problem Definition

- Textual description providing an overview over the data

- A discussion on why this problem is a regression problem

- A detailed problem statement question as discussed in Lecture 3.

Data Ingestion

- Code to load the data into a suitable format to be used in the notebook

- A description of the statistical data types for each field in the file [login to view URL]

Data Preparation

You should assume that exploratory data analysis has taken place and the following was


- Missing values are in the ‘temp’ and ‘atemp’ columns.

- The peak usage hours are: 7-9AM and 4-7PM on working days, and 10am-4pm on

non-working days.

- At night (10pm-4am) the bike rentals are low

- If the humidity or wind-speed is high, the number of rentals decreases.

Your data preparation steps should therefore include the following:

- Fill the missing values in the temperature columns automatically with values that

would most closely mirror the actual temperature.

- Create a new field that indicates whether it is a peak time or not

- Create a new field that indicates whether it is night time or not

- Remove all fields containing information about specific dates (‘yr’, ‘mnth’, ‘dteday’),

‘casual’ and ‘registered’ and any other variables that you deem irrelevant.

- A justification (and potential application) of whether you should use data binning or


- Suitable encoding of the data

Data Segregation

- Code and justification for the selection and application of a suitable data split

Model Training

- Selection of two different Regression models and justification why they are suitable.

Only one of those models should be Tree-Based (e.g. Random Forest or Decision


- Application of those models as a baseline on the data


- Utilisation of manual or automatic hyperparameter optimization and justification of

your choices to create “optimized” versions of each regression model

Model Evaluation

- Selection of appropriate regression metrics and a written outline why they are

suitable for this data

- A comparison of the baseline models to the “optimized” versions and an evaluation

of the results


- A conclusion and interpretation of the results and suggestion of potential


Færdigheder: Python, Machine Learning (ML), Kunstig intelligens

Om klienten:
( 31 bedømmelser ) Nairobi, Kenya

Projekt ID: #35457124

13 freelancere byder i gennemsnit $43 timen for dette job


Hello, I'm an accomplished data scientist with more than seven years of hands-on experience creating machine learning and artificial intelligence (AI) systems utilising a variety of tools and technologies, including R Flere

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AI and ML - Creating, training and a model using python in jupyter notebook Good morning , Hi I am a very experienced statistician, data scientist and academic writer. I have completed several PhD level thesis project Flere

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I read your description carefully and I can do this project at the top level. But I want you to increase budget a bit more. Python and Jupiter notebook are my first skills. Look at my reviews. Although I'm first on thi Flere

$50 USD på 1 dag
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