Aniket Pingle

Junior Analyst

Mumbai, India 80 · Excellent

About

Junior Analyst with a B.Com in Accounting and Finance and hands-on expertise in Excel, SQL, Power BI, and Tableau. Built four end-to-end analytics projects covering data cleaning, validation, and dashboard reporting, each deployed live with clear summaries for stakeholders. Comfortable collecting, organizing, and analyzing data from multiple sources, tracking KPIs, and communicating findings to cross-functional teams.

Skills & Expertise (40)

Python Advanced

8.1/10

Years Exp

SQL Advanced

8.0/10

Years Exp

Model Evaluation Intermediate

7.5/10

Years Exp

Pandas Intermediate

7.5/10

Years Exp

scikit-learn Intermediate

7.5/10

Years Exp

Regression Intermediate

7.5/10

Years Exp

Classification Intermediate

7.5/10

Years Exp

Tableau Intermediate

7.0/10

Years Exp

Data Cleaning Intermediate

7.0/10

Years Exp

Power BI Intermediate

7.0/10

Years Exp

NumPy Intermediate

7.0/10

Years Exp

TensorFlow Intermediate

6.5/10

Years Exp

XGBoost Intermediate

6.5/10

Years Exp

Flask Intermediate

6.0/10

Years Exp

AWS Intermediate

6.0/10

Years Exp

Data Validation SMOTE GridSearchCV EDA Statistics Clustering KPI Tracking Attention to Detail MySql ROC-AUC RMSE MAE Feature Engineering Time Series Forecasting Google Sheets Excel Matplotlib Seaborn Streamlit Cloud Railway Hugging Face Spaces Render Azure Gradio Streamlit

Work Experience

Project

Customer Churn Prediction

Present - Present

Collected, cleaned, and validated customer data from multiple sources using SQL and Excel, identifying discrepancies and key patterns: contract type, tenure, and payment behavior. Built and optimized a Random Forest classifier achieving ROC-AUC of 0.83; handled class imbalance with SMOTE and fine-tuned using GridSearchCV. Deployed an interactive Streamlit application and built a Power BI dashboard to track KPIs and communicate insights clearly to stakeholders.

Project

Employee Attrition Prediction System

Present - Present

Performed data validation and quality checks on HR data using SQL, identifying discrepancies and key attrition drivers: overtime, salary bands, job satisfaction, and tenure. Benchmarked multiple ML models (Logistic Regression, Decision Tree, Random Forest) after handling class imbalance with SMOTE; evaluated on ROC-AUC, Accuracy, Precision, and Recall. Deployed a Flask web application on Render for real-time attrition risk scoring and built a Power BI dashboard for HR leadership retention insights.

Project

Sales Forecasting & Demand Prediction System

Present - Present

Collected and organized historical sales data using SQL and Excel to identify trends, patterns, and inconsistencies across quarterly growth and demand fluctuations. Trained and benchmarked 7 models including Ridge Regression, Random Forest, Prophet, ARIMA, SARIMA, LSTM, and GRU; selected Ridge Regression as the best production model. Built a Gradio web application and Tableau dashboard for real-time demand prediction and KPI visualization; deployed on Hugging Face Spaces.

Project

Customer Segmentation Analysis & Prediction System

Present - Present

Analyzed datasets using SQL to identify meaningful patterns and business insights across customer spending, income distribution, and purchasing behavior. Trained KMeans, Agglomerative, DBSCAN, and GMM clustering models with feature scaling; selected KMeans based on Silhouette Score evaluation. Built a Plotly Dash application for real-time segment prediction and revenue analysis; deployed a Tableau dashboard on Railway cloud platform.