Luis George

Areas Luis George is Knowledgeable in:

Techniques Luis George Uses:

Luis George's Problem Solving Skills:

1. Proficient user of power pivot for Business Intelligency
2. Proficient user of microsoft excel pivot tables for business models and dash boards development
3. I am a proficient user of R language programming and its applications to machine learning, data analysis, data mining

Luis George's Problem Solving Experience:

1. I have worked and used unsupervised learning to create models by finding groups of data using clustering algorithms with k-means. Data from a social network was used in this application to help marketers target adverting maximizing results and decreasing costs. Dummy coding techniques were used for coding missing values in the data. Some missing values were handled using imputing techniques. Training model on the data. Evaluate model performance and cluster analysis to interprete the results. Improving model performance by refining the quality of the clustering manipulating and aggregating variables using demographics information contained in the data.
2. I have worked and used Support Vector Machines to model transactional data collected by a large store chain to build a Market Basket Analysis Using Association Rules. Include preparing the data using a sparse matrix for the transaction data. Visualizing item support- using item frequency plots. Visualizing transaction data by plotting the sparse matrix. Training a model on the data. Evaluating model performance. Improving model performance sorting the set of association rules. Training subsets of association rules. Saving association rules to a file or to a data frame.
3. I have worked and used Regression trees and model trees to create a model for estimating the quality of wines. Including manipulating, exploring and preparing the data. Training a model on the data. Using graphs to visualize the decision tree. Evaluating model performance. Measuring performance with the mean absolute error. Improving model performance
4. I have worked and used ANNs to create machine models to simulate the strength of concrete. In the field of engineering, it is crucial to accurate estimates of the performance of building materials. In this simulation, I explored and prepared the data. Trained a model on the data. Evaluated model performance. Improved the model using more complex model topology capable of learning more difficult concepts.
5. I have worked and used in using Classification Using Decision Trees and Rules to identify poisonous mushroom with rule learners. Including manipulating the data (cleaning) . Training a model on the data. Evaluating model performance. Improving model performance
6. I have worked and used Regression Methods to create a machine model in R to predict medical expenses. Exploring and Preparing the data. Exploring relationships among features of the data. Visualizing relationships among features using a scatterplot matrix. Training a model on the data. Evaluating model performance. Improving model performance. Model specification adding non-linear relationships. Transformation- converting a numeric variable to a binary indicator. Model specification - adding interaction effects. Creating a improved regression model.
7. I have worked and used Classification Using Decision Trees and Rules to create models to analyze and identify risky bank loans using C5.0 decision trees.(using R). Including exploring and preparing the data. Creating random training and test datasets. Training a model on the data. Evaluating model performance. Improving model performance. Boasting the accuracy of decision trees.
8. I have used kNN machine learning algorithm for diagnosis of breast cancer (using R). Including normalizing numeric data. Data preparation- creating training and test datasets. Training a model on the data. Evaluating model performance. Improving model performance.
9. I have worked and used the Naïve Bayes algorithm to model applications to filter model phone spam. Including data manipulation and exploration. Data preparation: cleaning and standardization of text data. Data preparation- splitting text documents into words. Data preparation- creating training and test datasets. Visualizing text data- word clouds. Data preparation- creating indicator features for frequent words. Training a model on the data. Evaluating model performance. Improving model performance