Shuvam Shrivastwa

Data analyst with 3+ years of experience in analyzing complex datasets, identifying trends, and sharing patterns. Proficient in Statistical Analysis, SQL,Data-mining and Data Visualization. Seeking a challenging role to apply analytical expertise, and advanced predictive modeling. Motivated and quick to grasp emerging technologies, I aim to imbibe best practices in data analysis.

• Title : - Used Cars Price Predictions.

Objective : - Developed a machine learning model to predict used car prices based on extensive exploratory data analysis (EDA) and feature engineering.

Conclusion : - Utilized rich libraries in Python to perform EDA, data cleaning, handling outliers and preprocessing to enhance model performance. Implemented Linear Regression and XGBoost Regressor models for prediction.Fine-tuned model hyperparameters using grid search and cross-validation to optimize performance and achieved best accuracy.Evaluated model accuracy and generalization using metrics such as Mean Squared Error (MSE) and R-squared.

• Title : - Employee Attrition Prediction Project.

Tools/Algorithms Utilized : - Python , Pandas, Matplotlib, Seaborn, Decision Trees, Random Forest, XGBoost.

Description :- Employed Numpy, Pandas, Seaborn, and Matplotlib libraries to perform comprehensive Exploratory Data Analysis (EDA)and statistical techniques on employee attrition data in Jupyter Notebook; revealed key insights on attrition drivers, enabling informed decision-making to mitigate turnover risks.

Further, utilized Machine Learning Algorithms like Decision Trees, Random Forest and XGBoost for predictions and found the most relevant features affecting the attrition.Evaluated the model performances and did hyper-parameter tuning to get the best accuracy.

• Python for Advance Data Science and AI.
• Statistics and Machine Learning from Learnbay.
• SQL 5* Gold Badge from HackerRank.