Aakash J

Data science student with hands-on experience in data visualization, and predictive modeling using tools such as SQL, Tableau, and Scikit-learn. Proficient in programming language Python with a strong foundation in statistical analysis. Skilled in communicating insights to technical and non-technical stakeholders to drive effective decision-making. Seeking a challenging data science position to apply these skills and drive business growth.

1. Uncovering Urban Crime Dynamics - Machine Learning, Predicting the status of Crime Incidents in Los Angeles Country. 

Our project provides actionable insights for urban crime prevention using data-driven approaches. Among the models evaluated, RandomForest after smote, KNN after smote, and XGBoost after smote demonstrate strong predictive performance and effective handling of class imbalance. RandomForest after smote stands out as a promising candidate for its balanced accuracy, recall, and simplicity. Further analysis of dataset characteristics and feature interactions is essential for refining and deploying these models effectively in real-world scenarios, contributing to smarter crime prevention strategies in urban environments., Python, Matplotlib, Seaborn, 

2. Classification algorithms Predictive Modeling of Car Prices Understanding - Machine Learning.

The business objective is to optimize car pricing strategies based on key factors identified by the Decision Tree model, namely curb weight, engine size, fuel system type, and car brand. The goal is to use this information to maximize profitability and market competitiveness., Optimize car pricing strategies using a Decision Tree model. Collect and preprocess car attribute data focusing on curb weight, engine size, fuel system type, and car brand. Train the model to identify key factors impacting prices. Develop targeted pricing strategies based on these insights, adjusting prices and leveraging brand influence. Implement and monitor strategies to enhance market competitiveness and profitability., Python, Scikit-learn, Matplotlib or Seaborn, Excel or SQL, Tableau, Power BI.

3.  Zomato Predictive Analytics - Machine Learning.

Develop models to predict restaurant costs for two people and classify order types (online vs. offline) to optimize customer engagement and support new restaurant growth on Zomato., Utilize Python with libraries like pandas and Scikit-learn to preprocess and analyze the dataset. Train a Random Forest model to predict online vs. offline orders and conduct feature importance analysis to identify key factors influencing ordering behavior. Interpret the results to derive actionable insights, highlighting factors such as votes, cuisines, and cost for two people, and location. Evaluate model performance using metrics like accuracy and f1-score, selecting Random Forest for its robustness and ability to provide valuable business insights., Python, Scikit-learn, Matplotlib or Seaborn