Nothing Special   »   [go: up one dir, main page]
Scribd Logo
Upload

Welcome to Scribd!

  • 15 views

    Machine Learning Project 1

    Uploaded by

    Fuad Kemal

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd

    Machine Learning Project 1

    Uploaded by

    Fuad Kemal
    15 views3 pages

    Copyright

    © © All Rights Reserved

    Available Formats

    DOCX, PDF, TXT or read online from Scribd

    Did you find this document useful?

    Is this content inappropriate?

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    15 views3 pages

    Machine Learning Project 1

    Uploaded by

    Fuad Kemal

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 3

    MACHINE LEARNING PROJECT ONE ( 30 % )

    1. Select title
    Define your title
    2. Problem definition
    Is it:-
     Classification
     Regression
     Clustering

    Define your problem in statement


    3. Data
    This may involve:-
     Sourcing
     Defining different parameters
     Talking to experts about it
    4. Evaluation
    The evaluation metric is something you might define at the start of a project
    5. Features
    Features are different parts of the data, during this step, you will want to start
    finding out what you can about the data.
    One of the most common way to do this is to create a data dictionary.
    6. Preparing the tools
    This is where you may want to consolidate every library you have used at the top
    of your note book. Especially the library’s you will likely take advantage during
    almost every structured data project.

     Pandas for data analysis


     NumPy for numerical operating
     Matplotlib / seaborn for plotting or data visualization
     Scikit-learn for machine learning modelling and evaluations
    7. Load data
    8. Data exploration ( Exploratory data analysis or EDA )
    Since EDA has no real set methodology, the following is a short check list you
    might want to walk through

    What question (s) are you trying to solve ?


    What kind of data do you have and how do you treat different types?
    What is missing from the data and how do you deal with it?
    How can you Compare different columns to each other, compare them to
    the target variable and correlation between in dependent variables?
     How can you add, change or remove features to get more out of your
    data?
    9. Modelling
     features and labels
     training and test split
     model choices
     model comparison
     hyperparameter tuning and cross-validation
    10. Evaluating your model, beyond the default score() evaluator
    For classification

     ROC curve and AUC score


     Confusion matrix
     Classification report
     Precision
     Recall
     F1-score
    For regression
     Mean Absolute Error (MAE)
     Root Mean Squared Error (RMSE)
    11. Feature importance
    Feature importance is another way of asking , “which features contributing most
    to the outcomes of the model?”
    12. Experimentation
    After trying a few different things, you’d ask yourselves did you meet the
    evaluation metric.
    Remember you defined one in step 4
    Did you achieve your evaluation if not?

    A good next step would be discuss with your team on your own different options
    of going for word.
     Could you collect more data?
     Could you try a better model?
     Could you improve the current model?

    13. Save the model

    If your model is good enough, how would you export it and share it with others?
    INSTRUCTIONS
     Write your project at jupyter note book
     You have to use google drive to back up your project
     You have to upload your project to GitHub repository and send for me its
    link
     Make it individually if you have special case communicate me
     It is out of 30% for your lab scores
     Submission date:- 22-09-2016 E.C

    You might also like