NAVEENKUMAR G

[email protected] • +91 9487029164
LinkedIn: LinkedIn • GitHub: GitHub

PROFESSIONAL SUMMARY

Results-driven aspiring Data Scientist with a robust foundation in machine learning, statistics, and data analytics, eager to leverage analytical expertise to drive business success. Proficient in Python, SQL, and data visualization tools like Power BI and Tableau, I successfully developed an end-to-end machine learning model that improved prediction accuracy by 20% on real-world datasets. Additionally, I automated data workflows that reduced reporting time by 30%, enabling faster decision-making. A quick learner with a passion for emerging AI technologies, I am committed to delivering actionable insights that empower organizations to thrive.

WORK EXPERIENCE

Internship
• Internshala Trainings - Data Science with AI & AI in Data Science • Cognify Technologies - Data Science • OASIS INFOBYTE - Data Science • Saiket Systems - Data Science
• Internshala Trainings - Data Science with AI & AI in Data Science
• Cognify Technologies - Data Science
• OASIS INFOBYTE - Data Science
• Saiket Systems - Data Science

EDUCATION

B.E, Computer Science Engineering
01/2023 - 01/2027
PSV College of Engineering and Technology GPA: 8.5
HSC
Wisdom Matric Higher Secondary School GPA: 85%
SSLC (10th)
Wisdom Matric Higher Secondary School GPA: 90%

SKILLS

Technical Skills: Python, Mysql, Machine Learning
Tools: Excel, PowerBI, Tableau, Matplotlib, Seaborn
Other: Chatgpt, Claude, DeepSeek

PROJECTS

Retail Sales Forecasting 🔗
Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Jupyter Notebook, Random Forest, Feature Engineering, Exploratory Data Analysis (EDA)
• Developed a robust machine learning model to forecast retail sales, addressing the challenge of inaccurate demand predictions and enabling optimized inventory management across multiple product categories.
• Conducted comprehensive data preprocessing, exploratory data analysis (EDA), and feature engineering on a dataset comprising over **100,000 historical sales records**, leading to the training of multiple machine learning models to identify the best-performing algorithm.
• Achieved **85% prediction accuracy** utilizing the **Random Forest** algorithm, resulting in a **20% reduction in stockouts** and a **15% increase in inventory turnover**, thereby providing actionable insights that enhanced business decision-making and operational efficiency.
Restaurant Analytics Dashboard 🔗
Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
• Conducted comprehensive Exploratory Data Analysis (EDA), feature engineering, and geospatial analysis on a dataset of over 10,000 restaurant entries to uncover actionable insights on ratings, pricing, cuisines, and customer preferences.
• Developed interactive dashboards and visualizations using Python, Power BI, and Tableau, enabling stakeholders to identify trends, top-performing restaurants, and regional performance metrics, resulting in a 30% increase in user engagement.
• Delivered data-driven business recommendations that led to a 15% improvement in customer satisfaction scores and optimized pricing strategies, directly supporting restaurant growth and informed decision-making processes.
Sales Prediction Using Machine Learning 🔗
Technologies: Python, Pandas, Scikit-learn, Linear Regression, KNN Regressor, StandardScaler
• Developed a robust sales prediction model utilizing **Linear Regression** and **KNN Regressor** to quantify the impact of advertising spend across TV, Radio, and Newspaper channels on product sales, addressing the challenge of inefficient marketing budget allocation.
• Conducted comprehensive data preprocessing, including handling missing values and outlier detection, along with exploratory data analysis (EDA) to identify key features, resulting in improved model accuracy and interpretability.
• Achieved an impressive **R² score of 86.09%**, facilitating data-driven decision-making for marketing strategies, ultimately leading to a **20% increase in return on advertising spend (ROAS)** and enhanced campaign effectiveness.
Customer Churn Analytics in SaaS Industry 🔗
Technologies: Python, Pandas, Regex, Power BI, Excel, Git
• Developed an end-to-end customer churn analytics solution using **Python**, **Exploratory Data Analysis (EDA)**, and rule-based **NLP** to analyze customer feedback and identify churn patterns.
• Designed and developed an interactive **Power BI** dashboard to visualize churn trends, key performance metrics, and major customer attrition drivers.
• Delivered actionable business insights and retention recommendations to support customer engagement strategies and improve decision-making.
Customer Churn Analysis and Prediction 🔗
Technologies: Python, Scikit-learn, Logistic Regression, Decision Tree, Random Forest
• Developed a customer churn prediction model using **Python**, applying data preprocessing, exploratory data analysis (EDA), feature engineering, and classification algorithms.
• Trained and evaluated multiple machine learning models to identify customers at high risk of churn and improve prediction performance.
• Performed customer segmentation and generated actionable insights to support retention strategies and data-driven business decisions.

CERTIFICATIONS

Data Science with AI — Internshala | AI in Data Science — Internshala | Data Analysis — Novitech | LinkedIn Learning — Excel, Power BI, Tableau, MySQL

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