A proposed model ppt
- 1. A PROPOSED MODEL FOR PREDICTING EMPLOYEES’ PERFORMANCE USING DATA MINING TECHNIQUES: EGYPTIAN CASESTUDY By Rakesh Reddy Annarapu (ID 45325) Tinku Manivikesh Chukkapalli (ID 45195)
- 2. • HRM has a leading role in deciding the competitiveness and effectiveness for better continuation. • Organizations consider HRM as “people practices”. Therefore, it becomes the responsibility of the HRM to allocate the best employees to the appropriate job at the right time, train and qualify them, and build evaluation systems to monitor their performance and an attempt to preserve the potential talents of employees. • With the advancement and growth of technologies in business organizations, HR employees need not handle the massive amount of data manually anyfurther. • These data is very important for the decision makers, but there is a challenge to mine and get the best and useful data from these hugedata HRM
- 3. • Knowledge can be extracted through various methods and one of them is by using DM technique. • DM techniques provides an approach to utilize different DM tasks such as classification, association, and clustering used to extract hidden knowledge from huge amount of data. • Classification is a predictive DM technique, makes prediction about values of data using known results found from various data. • Classification technique is a supervised learning technique in DM and machine learning. • With classification, Predictive models have the specific target ofenabling us to predict the unknown values of variables. DM TECHNIQUES
- 4. • There are various data classification techniques such as DT, SVM, Naïve Bayes classifier, andothers. • The classification process is executed through using the three main classification technique. • Other techniques can also be used for classification such as Neural Network (NN), K-Nearest Neighbors (KNN), etc. • The C4.5 (J48) technique is one of the DT family. • In addition, it builds the tree for enhancing the predictionaccuracy. • Besides that, the models that are generated from the C4.5 (J48) are easily understandable because the extracted rules from the technique have a very explicit uncomplicated interpretation and has the advantage that does not need any field learning or parameter setting. DATACLASSIFICATION TECHNIQUES
- 5. • Naïve Bayes classifier or the Bayesian therom is another classification technique that is utilized for predicting a target class. • It depends on probabilities in its calculations, in addition, it provides a unique approach for realizing various learning algorithms that do not explicitly use probabilities. • SVMis considered as one of the most effective supervised machine learning techniques that has a straightforward structure and high ability for classification. • Moreover, SVM is recognized as the appropriate technique in machine learning and DM for classification particularly on both linear and non- linear decision margins where, high accuracy of model can be produced. CONTD…
- 6. • It has no ceiling on the number of attributes and depends on the kernel trick for building the model through expert knowledge on the problem via kernel adjustment. • Sequential Minimal Optimization (SMO) is a SVMalgorithm. It is recognized as an efficient classification technique in solving the problem of optimization. • SMO can be considered as the state–of–the–art approach in a non-linear SVM. • SVMwilltrain the dataset using SMO algorithm to build the prediction model. SVMADVANTAGES