Detail-oriented data science professional in machine learning, statistics, and data analysis. Proficient in Python, SQL, and data visualization tools. Experienced in analyzing complex datasets to drive insights and support decision-making. Quick learner, adaptable, and eager to apply knowledge to solve real-world challenges in data science.
Project : Marketing strategy to predict most
probable buyers using ML
This project aims to empower business to make
informed decisions and enhance their marketing
efforts, ultimately contributing to increased
profitability and customer retention.
Responsibilities:
· Understand, analyze, and interpret large data sets.
Develop advanced programs to extract the data
needed, prepare data for further analysis.
· Determine the meaning of the data, and Provide
data facts and insights. Use predictive modeling to
increase and optimize customer experiences,
revenue generation by targeting most probable
buyers.
Project : Using ML model segmentation or Grouping
Credit card Holder
Using ML model segmentation or Grouping Credit
card Holders which help business to find their
potential customer and many more marketing
strategy.
Responsibilities:
· use clustering model to do customer segmentation
based on purchase behavior of credit card holders.
Python/ML Packages: NumPy, Pandas, Sci-py, Scikit-learn, Seaborn, Matplotlib, Flask
Machine learning- Linear Regression, Logistic Regression, DEcision Tree, supervised and Unsupervised Algorithms , KNeighbor's Classifier, Support Vector Machine, Decision Tree, Random Forest, Gradient Descent, Ada Boost, Gradient Boosting, XGBoost, K-means Clustering
Deep Learning – Neural Networks, Deep Learning, ANN, CNN, DNN, Transfer Learning, Back Propagation, Linear Algebra, Activation & loss functions, optimisers, Tensorflow 2x, Keras
Techstat: BOW, TFIDF, word2vec, doc2vec, sent2vec,