Mohd Zaid
About
Aspiring Data Scientist with hands-on experience in machine learning, statistical modeling, and data analysis. Skilled in Python, SQL, and advanced ML algorithms (Logistic Regression, Decision Trees, Random Forest, ANN, CNN, RNN). Strong background in data preprocessing, feature engineering, and model evaluation across business and healthcare projects. Adept at building predictive models, visualizations, and scalable data solutions to deliver actionable insights and optimize decision-making.
Experience
Data Science Intern
M.A. Intelligence Classes
Gained hands-on experience in data analysis and visualization using Python, Pandas, NumPy, Matplotlib, and Seaborn. Applied machine learning techniques to real-world problems and explored computer vision with OpenCV.
Data Analyst Intern
Skillfied Mentor
Conducted exploratory data analysis (EDA) and data cleaning to uncover key business trends. Designed and implemented ML models (Logistic Regression, Decision Tree, Random Forest) for business and healthcare datasets. Built dynamic dashboards and interactive reports in Tableau. Completed five end-to-end projects involving data wrangling, statistical analysis, and visualization.
- Developed interactive dashboards and insights using Tableau and Power BI.
- Delivered predictive insights and model-driven recommendations for decision-making.
Education
Bachelor of Computer Application
Maharshi Dayanand University
Coursework: Computer Fundamentals, Web Technologies, Database Management System
Skills
Programming
Python Libraries
Machine Learning & AI
Data Visualization & BI Tools
Software Development Practices
Databases & Query Tools
Productivity Tools
Web & API Development
Data Analytics
Projects
Bank Marketing Inspection Test
Performed data cleaning by handling missing values and ensuring data integrity. Conducted EDA using summary statistics to identify key features. Created visualizations (e.g., histograms, correlation matrix) to understand variable relationships.
Bank Term Deposit
Cleaned and prepared dataset; applied Label Encoding for categorical variables. Built and compared Logistic Regression, Decision Tree, and Random Forest models to predict customer subscription. Evaluated model performance and selected the most accurate model for deployment.
Colorectal Cancer Prediction
Preprocessed medical dataset by handling missing values and encoding categorical variables. Developed models using Logistic Regression, Decision Tree, and Random Forest. Assessed model performance with accuracy and recall metrics; performed hyperparameter tuning.
Air Quality Index in Indian Cities
Conducted data cleaning and exploration on air quality datasets. Applied visualization techniques (heatmaps, correlation analysis). Implemented Logistic Regression and Decision Tree models to predict AQI categories. Compared model results and selected the best-performing predictor.
Certifications
6-Months Data Science Training Program (Online)
1.5 hr/day
4-Days Excel Master Class
2 hr/day
4-Days SQL Master Class
2 hr/day
30-Days Data Analyst Master Class (Online)
2 hr/day
40-Days Data Science Bootcamp & Internship Program (Offline)
5 hr/day
Languages
Intermediate