This video shows how to create, train, save, and deploy a logistic regression model that assesses the likelihood that a customer of an outdoor equipment company will buy a tent based on age, sex, marital status and job profession. After watching the video, try the step-by-step tutorial.

This videos shows you how to create a very simple Python Flask web application front end to call a deployed Logistic Regression model in real time. This video is a continuation of the logistic regression analysis video. After watching the video, try the step-by-step tutorial.

2 comments on"Build a Logistic Regression Model With Watson Machine Learning"

  1. Viral Ruparel May 31, 2018

    not getting api results in notes after giving the credentials.

  2. VidhyaSivakumar December 31, 2018

    # Call scoring endpoint with data payload

    scoring_header = {‘Content-Type’: ‘application/json’, ‘Authorization’: mltoken}

    payload = {“fields”: [“GENDER”,”AGE”,”MARITAL_STATUS”,”PROFESSION”],”values”: [[“M”, 20, “Single”, “Student”]]}

    scoring = requests.post(scoring_endpoint, json=payload, headers=scoring_header)

    print(scoring.text)

    {“trace”:”5e943881d813208ed0d2251a31c73644″,”errors”:[{“code”:”token_format_is_unsupported”,”message”:”Provided token should be in bearer token format”,”target”:{“type”:”header”,”name”:”Authorization”}}]}

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