Bank-Loan-Defaulter-Predictor
Python libraries that used in Machine Learning are:
Numpy, Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, Pandas, Matplotlib.
Machine learning is a field of computer science that gives computers the ability to learn without being programmed explicitly. The power of machine learning is that you can determine how to differentiate using models, rather than using human judgment. The basic steps that lead to machine learning and will teach you how it works are described below in a big picture:
Gathering data
Preparing that data
Choosing a model
Training
Evaluation
Hyper parameter tuning
Prediction.
Now the Question that arises is- How do we start?
The link is to create a system that answers a particular question. This question answering system called a model is created via a process termed as training. The main goal of training is to create an accurate model that answers our questions correctly, at least for most of the times. But in order to train a model, you also need to collect data on what you'd want to train on. This is where you start and then the rest follows. The detailed information to the next steps is given below:
- Gathering Data:
Once you know exactly what you want and the equipments are in hand, it takes you to the first real step of machine learning- Gathering Data. This step is very crucial as the quality and quantity of data gathered will directly determine how good the predictive model will turn out to be. The data collected is then tabulated and called as Training Data.
- Data Preparation:
After the training data is gathered, you move on to the next step of machine learning: Data preparation, where the data is loaded into a suitable place and then prepared for use in machine learning training. Here, the data is first put all together and then the order is randomized as the order of data should not affect what is learned.
This is also a good enough time to do any visualizations of the data, as that will help you see if there are any relevant relationships between the different variables, how you can take their advantage and as well as show you if there are any data imbalances present. Also, the data now has to be split into two parts. The first part that is used in training our model, will be the majority of the dataset and the second will be used for the evaluation of the trained model’s performance. The other forms of adjusting and manipulation like normalization, error correction, and more take place at this step.
- Choosing a model:
The next step that follows in the workflow is choosing a model among the many that researchers and data scientists have created over the years. Make the choice of the right one that should get the job done.
- Training:
After the before steps are completed, you then move onto what is often considered the bulk of machine learning called training where the data is used to incrementally improve the model’s ability to predict.
The training process involves initializing some random values for say A and B of our model, predict the output with those values, then compare it with the model's prediction and then adjust the values so that they match the predictions that were made previously.
This process then repeats and each cycle of updating is called one training step.
- Evaluation:
Once training is complete, you now check if it is good enough using this step. This is where that dataset you set aside earlier comes into play. Evaluation allows the testing of the model against data that has never been seen and used for training and is meant to be representative of how the model might perform when in the real world.
- Parameter Tuning:
Once the evaluation is over, any further improvement in your training can be possible by tuning the parameters. There were a few parameters that were implicitly assumed when the training was done. Another parameter included is the learning rate that defines how far the line is shifted during each step, based on the information from the previous training step. These values all play a role in the accuracy of the training model, and how long the training will take.
For models that are more complex, initial conditions play a significant role in the determination of the outcome of training. Differences can be seen depending on whether a model starts off training with values initialized to zeroes versus some distribution of values, which then leads to the question of which distribution is to be used. Since there are many considerations at this phase of training, it’s important that you define what makes a model good. These parameters are referred to as Hyper parameters. The adjustment or tuning of these parameters depends on the dataset, model, and the training process. Once you are done with these parameters and are satisfied you can move on to the last step.
- Prediction:
Machine learning is basically using data to answer questions. So this is the final step where you get to answer few questions. This is the point where the value of machine learning is realized. Here you can Finally use your model to predict the outcome of what you want.
The above-mentioned steps take you from where you create a model to where you Predict its output and thus acts as a learning path.
The 7 Steps of Machine Learning: