I’m a Fresher, I am looking forward towards new opportunity in the field of Analytics, Machine Learning, Natural Language Processing. That will provide me with ample scope for learning, enhance my skills and gain experience of the real corporate world and benefit the organization that I would serve.
Measure of Central tendency, Scatterplot, Histogram, Bar graph, Boxplot, Zest (one, two & anova), Chi-square test, Distributions (Binomial, Bernoulli, Normal, Poisson). Machine Learning:
Linear regression, Logistic Regression, K Nearest Neighbour’s, SVM, Naïve Bayes, Decision Trees, Ensemble methods.
Principle component analysis (PCA), Clustering, LDA, QDA, Association rules. Natural language Processing: Document, Corpus, Tokenizer, Bag of words, Lemmatization, Tf-Idf, Stemming, Sentiment analysis
Optimization algorithm: Gradient Descent
Deep Learning: Basic understanding of ANN, CNN, RNN Others:
Dummy variables, One hot encoding, Label encoding, Overfitting, Regularization (Lasso, Ridge
& Elastic net), Random state, Kfold, Cross validation, LOOCV, Bootstrap, Class imbalanced techniques, Standard Scaling, Forecasting, Text Mining. Programming Tools:
Data Visualization: Used Matplotlib and Seaborn Libraries (mainly used for PCA) to visualize the data
Exploratory Data Analysis: Used NumPy, Pandas and Stat models to perform data analysis Machine learning: Sci-kit learn and SciPy for model building R Programming
Funmodeling for dummy, car to know VIF, Boot strap is done with boot, glmnet for regularization and cross validation.
On goining: 1) BNP Paribas Cardif Claims Management: Objective of the project was to understand which of the factors contribute most to target columns and to predict which customers claims suitable for an accelerated approval. Data Visualization- Used Matplotlib, histogram, bar graph. Exploratory Data Analysis: Used NumPy, Pandas and to check milling values to perform data analysis.
Model Building: Sci-kit learn for model building.
Technology Used-Machine Learning (Classification), Decision Tree, Bagging, Random Forest, Logistic Regression,
2) Analysis of Breast cancer on python: Objective of the project was to understand which of the factors contribute most to diagnosis and to predict which patient will have benign cancer and malignant cancer.
Data Visualization- Used Matplotlib, histogram, bar graph, Seaborn Libraries, regularization techniques (Lasso used in R).
Exploratory Data Analysis: Used NumPy, Pandas to perform data analysis. Model Building: Sci-kit learn for model building.
Technology Used-Machine Learning (Classification), Logistic Regression, Confusion matrix, Python.
• INTERPERSONAL SKILLS
• Good learner
• Punctual, Confident.
• Interests: Want to think in a creative way.
• Languages: English, Telugu, Hindi
• Current Location: Hyderabad
SRM UNIVERSITY, Chennai, INDIA GPA: 87.05
B TECH in COMPUTER SCIENCE
Board of Intermediate, Hyderabad, INDIA GPA: 93.7
MPC from Krishna murthy Jr. college
Secondary school of education (SSC), Hyderabad, INDIA GPA: 75 Narayana Olympiad school
• Certificate in Data Science through Digital nest. PROJECT LINK
• GitHub: https://github.com/ShaliniL123/Cancer-Data-set-Project