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Basic Machine Learning - Creating, training and evaluating 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

concluded:

- 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

not

- 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

Tree)

- Application of those models as a baseline on the data

4

- 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

Conclusion

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

improvement

Skills: Python, Machine Learning (ML), Artificial Intelligence

About the Client:
( 31 reviews ) Nairobi, Kenya

Project ID: #35462887

26 freelancers are bidding on average $39 for this job

datascientist90

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$300 USD in 6 days
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6.6
nrajuu07

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Actisoft2017

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shraboniadhikary

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(4 Reviews)
3.9
RoohiSalma

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(3 Reviews)
2.7
MoamenAG

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2.3
shabam2306

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aneetachacko

Being a data science graduate, I have theoretical knowledge and practical experience in data analysis and model building using python.

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prashant27197

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AlexanderGeorge5

Hi there, I am a data science even have some good knowledge in data analyst and I hade done a lot of similar projects you can see in my portfolio I can use jupyter notebook in python and I am very good in SQL too, usi More

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SamarthGeek

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alsaharia5

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Priya1995Verma

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themina20

Base on my experience and education i am a good fit for this role time and other thing we can discussed

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dmytromelnykdev

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ferasalshaheen

Hey I read the project description and I understand that you want to have a machine learning model built for a data set related to bike rental. In addition, I understand that there is some data manipulation, and analy More

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Shahram7373

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vattafynn2020

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venuboyina143

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JaimeSB

I've done a similar project lately so it should be fast to complete the task. I will also provide explanations (both written and within the code) to understand the structure :)

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