On label encoding give a numeric (integer number) for each category. There are multiple way to handle categorical variable but most widely used techniques are label encoding and one host encoding. For understanding of machine learning algorithm categorical columns convert to numerical columns, this process called categorical encoding. The next equation displays the mean calculation:Įncoding is a pre-processing technique which represents data in such a way that computer can understand. On the mean imputation first calculate the particular features mean value and then replace the missing value with mean value. Mean imputation is the technique to replacing missing information with mean value. There are many imputation technique represent like mean, median, mode, k-nearest neighbours. Imputation is a way to handle missing data by replacing substituted value. In a normal distribution Z score represent 68% data lies on +/- 1, 95% data point lies on +/- 2, 99.7% data point lies on +/- 3 standard deviation. Z score provides information about data value is smaller or grater then mean that means how many standard deviations away from the mean value. Z score is a standard score in statistics. Outlier treatment with Z score is a common technique. Outlier need to treat because it may bias the entire result. Outlier is a data point which lies far from all other data point in a data set. This phase involved with outlier treatment, imputation, scaling, and transform data. These articles mainly focus to describe all algorithms which are going to implementation for better understanding.Ģ Phase 1 : Outlier treatment, Transform, Scaling, Imputation Structure of proposed process flow for two class problem combined with algorithm and sub-algorithm display on figure – 1. New method from phase 1 to phase 4 followed CRISP-DM methodology steps such as data collection, data preparation then phase 5 followed modelling and phase 6 followed evaluation and implementation steps. Then calculate the confusion matrix, ROC, AUC to find the best base algorithm. Phase 6: First, Prediction with validation data then evaluates with Test dataset which is fully unknown for these (Random forest, MLP classifier) two base algorithms.
As an example on this paper consider two class classification problems and also consider Random forest (Included CART – Classification and Regression Tree and GINI index impurity) and MLP classifier (Included (Relu, Sigmoid, binary cross entropy, Adam – Adaptive Moment Estimation) as base algorithms.
This phase also involve to find out best hyper parameter and sub-algorithm for each base algorithm. Phase 5: This Phase considering several base algorithms as a base model like CNN, RNN, Random forest, MLP, Regression, Ensemble method. Phase 4: On this Phase Training data set again partition into two more set (Training and Validation). But here for an example considering same feature reduction algorithm (LDA -Linear Discriminant analysis) on training and testing data set separately. Phase 3: This phase involved with reduction, selection, aggregation, extraction. As an example here SMOTE (synthetic minority oversampling technique) is considered. Phase 2: On this Phase training and testing data balance with same balancing algorithm but separately. Here as an example for outliner treatment, imputation, transformation, scaling consider accordingly Z score, mean, One hot encoding and Min Max Scaler. Phase 1: Involved with collection, outliner treatment, imputation, transformation, scaling, and partition dataset in to two sub-frames (Training and Testing). On this paper, proposed a process flow followed CRISP-DM methodology and has six steps where data understanding does not considered. And data mining play the vital part to solve, finding the hidden patterns and relationship from large dataset with business by using sophisticated data analysis tools like methodology, method, process flow etc.
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