One hot encoding was used to convert the data from categorical to numerical. One Hot Encoding : Each type of category is converted into a new feature with 0 for absent and 1 for present. The methods used are One Hot Encoding where each category is converted into different feature and the other is the label encoder which assigns values to each type of category. These categorical features must be converted into sets of binary. In this comparison, the NULLs were assigned with the unknown for categorical data and mean of the particular continuous feature was assigned to the NULLs in continuous data. are termed as categorical because it has different categories like male and female. Features like Age and Fare are termed as continuous (mostly numerical values) and cabin, sex, embarked etc. One is Continuous and the other is Categorical features. There are two kinds of features in this dataset.
Random forest vs neural network free#
Third, you can train the data which are free from the missing values from the first step and using regression technique to predict these missing values. Second, you can move them to a new category by assigning ‘U’ or unknown. There are 3 approaches in which you can solve this problem.įirst, you can drop or delete all the rows which has missing data to free your dataframe from the NULLs. The missing values will lead to misleading results when predicting because these values have no weight-age. The above data frame contains NULLs in the Cabin feature. Below is the feature importance graph showing weight-age distribution and comparison.Įvery data set you come across will contain NULL values or missing values. You can also consider a single category for a feature which accounts for the target class, like one among the cabins. So these type of features can be dropped while training the model. In the above graph, the port of embarkation accounts very less for the passenger’s survival, because the probability that the passenger has survived or not is around 0.5(50%) which does not hold any strong information for the prediction. So this is a feature which needs to be considered. From the graph we can see that the woman show a high rate of survival. Here the Man, Woman and Child are calculated by the Sex and Age features. We draw a graph of these, lets say its a bar graph like the picture below, Example: Draw a graph of Age vs Survived, Sex vs Survived, Passenger class vs Survived. In this situation, we need to start analyzing all the features with respect to the target. There will be an instance where some of the features will not account for the classification in the final result. The above features are called as X and the feature Y is expressed as Survived(1) or Deceased(0). Embarked – Port of embarkation (C = Cherbourg Q = Queenstown S = Southampton).Parch – number of parents or children on board related to the specific passenger.SibSp – number of siblings or spouses on board related to the specific passenger.
Random forest vs neural network full#
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