Real time prediction for multiclass classification using python and jupyter notebook
Hello friends. AI Sangam is back with other most trending video on real time heart disease prediction having multiclass values in the columns and removing a lot of abnormalities in the dataset.
The whole implementation is done using jupyter notebook for better visualization and understanding. You can fetch the dataset and elaborate steps for this from AI Sangam blog as
https://bit.ly/2KbhDZO
Please go through this and understand the things in better way. You
can also visit the main website for more details and services which AI Sangam provides.
You can read our different post in our blog to know latest trending topics.
Please see the various steps as below
Step1: Data downloaded from UCI repository is read using pandas. We are showing our approach. You may use anything else as you wish.
Step2: Missing values, string values and NaN values are removed using appropriate algorithm.
Step3: Normalization: This step is important when the data in the columns having many values which are very far from each other so it is difficult for machine to understand those values. So data except target is bounded to interval [0 1] so that machine can understand the data in better way.
Step4: Feature Extraction using PCA: PCA provides pair of Eigen values and Eigen vectors. Eigen vector represents direction and values represents the magnitude of variance. This method change the representation into principal components space representation.
Step5: Matrix obtained in the step4 is fed to feature selection algorithm which gives the rank of every column which is compared with the threshold and redundant columns are removed. Basic aim is to remove those variables or columns which have much correlation between them.
Step6: Spliting the data and target into training and testing phase: Once you have done with the above step, it is the time to split the data into training and testing phase. Model is trained with X_train and y_train, whereas the testing is done on the X_test. I represent X as data and y is target. You may your own representation.