Lakshmi Muthukumar

Data Analyst • Hillsboro, OR, US

Machine Learning

Data Science

Statistical Modeling

Statistical Analysis

Numerical Analysis

Web Scrapping

Data Mining

Available for hire fromNegotiable

Full-Time Roles

Contracts

Data Scientist | Machine Learning Engineer | Data Anayst

Data scientist who is proficient in Python, SQL, and Azure ML. I also have research experience in cheminformatics and AI which helps me solve problems logically.

Projects

Deep Learning: Baby Cry Prediction

Deep Learning: Baby Cry Prediction | Github | Streamlit App - Worked with Cappella(on Pangea), an early-stage MIT-founded startup building an AI-driven baby cry translator. - Data for baby cry translator-prediction app comes from donateacry-corpus by Gabor Veres. - Performed data exploration, data preprocessing, features scaling, features engineering using pandas, numpy, librosa(audio library) packages in python. - Librosa is an audio signal library in Python, and it was used to construct various spectrograms like Mel-Frequency Cepstral Coefficient, Chroma Energy Normalized, Spectral Centroid, Spectral Contrast, Spectral Rolloff, Zero Crossing Rate along with other audio features like tempo. - Exploratory analysis of the data revealed a class imbalance, and hence techniques for imbalanced data particularly SMOTE, i.e., oversampling the minority class was used to handle the class imbalance. - Built a multi class audio classification model with CNN in Keras Tensorflow to predict baby cry audio clips to translate in corresponding categories. - Achieved an accuracy of about 93 % using the model and proposed other deep learning methodologies for further improvement of the models along with the availability of more data not just in open-source platforms. - Created an AI powered app on Streamlit with the model created using deep learning technique for the purposes of demo. - Communicated the results of data collection, and requirements by interacting with other data sources and optimal strategy for taking best decisions. - Responsible for developing system models, prediction algorithms, solutions to prescriptive analytics problems and data mining techniques for the proposed business question. Environment: Python, Pandas, Numpy, Matplotlib, Librosa, Keras, Tensoflow, Streamlit, Scikit Learn Imblearn, Github. See More

A/B Testing: Student Performance - Test Score Analysis

1. Introduction: - The performance of high school students from all over the U.S. is under analysis - The data comes from Kaggle with 1000 observations and 8 variables. - Visual analysis, descriptive statistics, Shapiro-Wilk, independent sample t-test for hypothesis testing were done 2. Recommendations: - Women do better in literature and men do better in engineering/allied - All races perform differently in all the different categories - Parents with higher degree of education have children with better performance in all categories - We can infer that students with higher household incomes have better performance in the categories - We can infer that students with higher motivation to complete the test preparation course have better performance in the categories 3. Conclusions: - Sampling bias is common in such studies - Sample size for such a geographically diverse group should be considered - Data is generic, a location specific data might help improve the analysis - In addition to the current variable, internet access or access to external help can be an important variableSee More

Supervised Machine Learning: Credit Card Approval Prediction

1. Description: - Credit Card Approval Prediction: Used machine learning to determine the features that affect the approval of customer credit card!. This dataset is available from the UCI machine learning repository. - This data file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data. - This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values. 2. Machine Learning Models: (A). LOGISTIC REGRESSION (B). DECISION TREES (C). RANDOM FORESTS (D). SVM (E). STOCHASTIC GRADIENT DESCENT (F). KNN Top 2 Models for dataset: 1. Logistic Regression Hyperparameters were tuned, and these are the parameters used in the model are Kernel = ‘poly’, Degree = 3, Gamma = 'auto' Accuracy was 86% for this model 2. Random Forest Hyperparameters were tuned, and these are the parameters used in the model are criterion = ‘entropy’, max_depth = None, min_samples_split = 3 Accuracy was 85% for this model Variable Importance Plot indicates that Prior Default is the most important v 3. Conclusions: - It was found that approval status of the applicant is mostly correlated to priordefault. - Data was split into training and test set. Machine learning models were trained in training set and tested for accuracy on the test set. - Logistic Regression model does the best with an accuracy of 86% 6. Future Scope: - ML models accuracy is not high. A larger dataset with more information on the applicants will aid better prediction. - Lack of information about how the dataset was created may impact the prediction of approval status using only the generalized applicant data as predictors.See More

Unsupervised Learning: Credit Card Customer Segmentation

1. Dataset Description: - The dataset chosen for the current project is Credit Card Dataset from Kaggle. - This case requires to develop a customer segmentation to define marketing strategy. - The sample Dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. 2. Unsupervised Machine Learning: - The data was first standardized for clustering analysis. - Evaluation and Model Selection - Visualization of Clusters - Interpretation of Clusters Models: (A). KMeans - The tuned hyperparameters for KMeans are n_clusters = 3, n_init=1000, max_iter=400, init='k-means++’ - The silhouette score Kmeans is 0.2509 (B). Agglomerative Hierarchical - The tuned hyperparameters for Agglomerative Clustering are n_clusters = 3, affinity='euclidean’, linkage="ward" - The silhouette score Agglomerative Clustering is 0.1731 (C). DBSCAN - The tuned hyperparameters for DBSCAN are eps=4, metric="euclidean", and min_samples=3 - The silhouette score DBSCAN is 0.6239 (D). Gaussian Mixture - The tuned hyperparameters for Gaussian Mixture Models are n_clusters = 3, affinity='euclidean’, linkage="ward" - The silhouette score Gaussian Mixture Models is 0.1657 Final Model Selection for dataset: - We compared the silhouette scores of all the models. - From the silhouette scores, DBSCAN is the best model but PCA reveals a different story. DBSCAN clusters are not distinct and most values are clustered as noise. - KMeans is the best model for the current dataset 5. Conclusions: - We have performed data preprocessing, feature extraction with PCA, looked at various clustering metrics (inertias, silhouette scores), experimented with various Clustering algorithms and data visualizations. - We have segmented the customers into three smaller groups: the Active Users, the Cautious Spenders, and the Average Joe. See More

Work History

Instructor In charge

IntellicircleMay 2017 - Dec 2019 • 2 yrs 8 mosI was teaching, grading kids from K-4 to K-12 in math and coding.

Freelancer

Upwork- I am freelancing on a wide range of projects from web scraping, teaching Python, data analysis to machine learning projects

Education

Texas Tech University

Professional Degree, Chemical EngineeringJun 2010 - Mar 2015

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