Predict Bank Account Users With Machine Learning.

Even in your unknown,you have to take a leap of faith and just start moving toward what you want in life…

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Machine Learning is not the future, It’s the present.

Applications of machine learning

What is Machine Learning?

Types of Machine Learning

* Supervised Learning

  • Classification.
  • Regression.
  • Classification:It is a Supervised Learning task where output is having defined labels(discrete value). It can be either in binary classification, model predicts either 0 or 1 ; yes or no or be a multi class classification, model predicts more than one class.
  1. Binary or binomial classification: exactly two classes to choose between (usually 0 and 1, true and false, or positive and negative)
  2. Multiclass or multinomial classification: three or more classes of the outputs to choose from
  • Regression: It is a Supervised Learning task where output is having continuous value. Here we predict a value closer to our output value and then evaluate by calculating error value. The smaller the error the greater the accuracy of our prediction model.

* Unsupervised Learning

My SCA Mentorship Programme Project.

What Type of Supervised Learning is my Data?

Steps to Model

Step 1: Define Preprocessing Steps

  • Initial set_up: Imported important python packages needed to build the model.
  • Read the datasets: the train and test file using dataTest = pd.read_csv(‘Test_v2.csv’)
  • Check the information about the datasets : Used print( to know the type of data (i.e object or integer) in our dataset. There were more categorical data and since machine learning deals mostly with numerical data (binary). Categorical data are variables that contain label values rather than numeric values.
  • Encode categorical data: Adopted the use of one-hot encoding to encode the categorical datas using pandas.get dummies. One-hot encoding turns your categorical data into a binary vector representation. check out this link for more information on pandas get dummies.
  • Check for missing values: Most datasets comes with missing values , you can look up reasons for missing data and how they can be handled. Using the command null().sum() ,the dataset had no missing values.
  • Declare predictive feature and target variable: Allocated the variable ‘y’ to the predictive target and ‘X’ to the predictive features, after dropping the columns no longer needed to build my model.
  • Split dataset into training and test set.

Step2: Define the Model

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