NEEMA G

Data Analyst

Bangalore

adarv5@r.postjobfree.com

https://www.linkedin.com/in/neema-

g-b62b3817b/

Coding

R, Python

Statistics

Descriptive statistics, Probability

distributions, Inferential

statistics, Linear and Logistic

regression, Regularization, Storytelling

and Visualization

Machine Learning

Supervised Learning: Decision Trees,

Random Forest, Support Vector

Machines, K-Nearest Neighbours,

Naive Bayes Classifier, Time series

Unsupervised Learning: Clustering

Text Mining

Text mining concepts, Latent

Semantic Indexing and Search, Text

classification, Sentiment analysis.

AI and Decision Sciences

ANN, Deep learning, CNN and RNN

Big Data

Processing frameworks like Map-

Reduce, Spark, SQL, Sqoop, Hive

Immediate joinee

Data Analyst professional seeking challenging opportunity to work for an organization. Hardworking,Optimistic and Condent with good interpersonal skills.

2019 APR -

2019 JUN

Data Science Intern

INSOFE

Predictive & Descriptive analysis, Feature Engineering, Machine Learning modelling, Data Visualization

2014 - 2016 M.E IN COMMUNICATION SYSTEM

EASA COLLEGE OF ENGINEERING AND TECHNOLOGY

CGPA : 8.07

2010 - 2014 B.E IN ELECTRONICS AND COMMUNICATION

EXCEL ENGINEERING COLLEGE

CGPA : 7.42

SEP2018 -

MAR2019

PGP IN BIG DATA ANALYTICS AND OPTIMIZATION

INSOFE

APR2019 -

AUG2019

Machine Learning A-Z : Hands-On Python & R in Data Science online course

Udemy

Estimating Bias and Variance for different machine learning models The main objective of the project is to nd bias and variance for 5 different machine learning models and conclude which models are bias and variance sensitive. Estimated bias and variance on both regression and classication models.

Algorithms Used: Decision Tree, Random Forest, KNN, SVM(for both classication and regression), Linear Regression, Logistic Regression. Language Used: Python

Classication problem which had been described a tennis shot is a winner, an unforced error or a forced error

The given problem is Supervised learning and Multi class classication . Target variable 'outcome' is categorical with labels W/UE/FE. Algorithms Used: Decision Tree, Random Forest.

Highest accuracy got in Random Forest with 87%.

Languages Used: R

Predict The customer value for a retail store based on different quantitative and qualitative features

LINKED IN

Skills

Notice Period

Objective

Professional Experience

Education

Certifications

Data Science Projects

Predicting the "Customer Value" in a retail store as High/ Medium/ Low. Built different machine learning models to classify the customers as High/ Medium/ Low by using transactional and demographics data. Algorithms Used: Logistic Regression, Decision Tree, Random Forest, KNN, Naive Bayes.

Highest accuracy got in Random Forest with 68.3%

Languages Used: Python and R

Higgs Bosons particle detection

Classication problem which distinguishes a Signal Process which produces Higgs bosons and a background process which does not.

The features are kinematic properties measured by the particle detectors in the accelerator and the remaining features are high-level features derived by physicists to help discriminate between the two classes.

“Class” variable is target variable which is binary and has values 0&1 where 1 denotes a signal process which produces Higgs bosons and 0 denotes background process which does not. Removed some irrelevant features. Logistic Regression was applied because the target variable is a categorical

(binary).

Two models was generated one with step AIC + standardization and got an accuracy of 64%. Second was without step AIC and got less accuracy in that. Languages Used: R

Text Classification in health care services

Based on the Text in the summary and description of the call written in "converse" column, the ticket is to be classified to appropriate category(out of 21 categories). Preprocessing: Removing stop words, Lemmatization, Label Encoding, Tokenizer Algorithms Used: CNN, ANN, LSTM

Languages Used: Python

Highest accuracy got in ANN.

INDIAN SOCIETY FOR TECHNICAL EDUCATION

Participated in Srinivasa Ramanujan Mathematical Competitions 2012 Achievements

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