Adidas Sales Analysis Dashboard

The Adidas Sales Analysis project aimed to provide a comprehensive understanding of Adidas' sales performance across various product categories, geographic regions, and customer segments. The project sought to uncover key insights and trends that could inform strategic decision-making.

Medical Insurance Analysis Dashboard

The dashboard provides a comprehensive analysis of key insurance metrics including: Key Performance Indicators and Demographics.

Sales Analysis

These dashboards provide a comprehensive and insightful overview of your business's sales and profitability, highlighting profit figures and ratios, allowing you to understand the profitability of different products or categories.

Crime Severity Classification and Prediction

Conducted comprehensive exploratory data analysis (EDA) techniques, including data grouping, feature distribution analysis, correlation. Developed predictive models to forecast future crime trends, employed feature engineering techniques to extract relevant features from data.

Crash Reporting - Injury Severity Prediction

Predicted the severity of the injury happened in a car crash at Los Angeles Classification Algorithms : Decision tree, K-Nearest Neighbors, SVM, Random Forest, Neural Networks Attribute Selection Methods: RFE, Wrapper Based Feature Selection, Filter, Chi-squared, Boruta

Telecom Customer Churn Analysis

Analysis of Customer churn and its influencing factors in the telecommunication Industry. Data pre-processing, Analysis of Categorical Variables & Numerical Variables, Central Limit Theorm. Sampling Methods: Random, Startified, Systematic

Disease Prediction System

This project uses machine learning to predict diseases from user symptoms. It combines multiple models for improved accuracy and features interactive predictions. Model performance is evaluated with accuracy metrics, confusion matrices, and classification reports.

Data Analysis With SQL : Optimizing Online Sports Retail Revenue

This SQL project optimizes revenue by analyzing missing values, comparing Adidas and Nike pricing, and calculating review-revenue correlations. It includes creating price range labels, analyzing review trends, and evaluating footwear vs. clothing performance. The insights help refine pricing strateg