Comprehensive Data Analysis and Response Time Optimization for City Service Requests
Python | Pandas | NumPy | Matplotlib | Plotly | Data Wrangling | Exploratory Data Analysis (EDA) | Geospatial Analysis | Time-Series Analysis | Economic Interpretation | Data Visualization | Machine Learning | Risk Management
Project Description
This project involved an extensive analysis of a large dataset comprising over 364,000 service request records from New York City's 311 system. Utilizing advanced statistical and data science methodologies, the primary objective was to assess the types and frequency of complaints, evaluate city-wise complaint distribution, and determine the optimal response time for each complaint type. Through the application of Ordinary Least Squares (OLS) regression, missing data treatment, and geospatial analysis, I provided data-driven insights and actionable strategies to optimize resource allocation and enhance response efficiency. These strategies were grounded in economic principles of cost minimization and service maximization, leveraging data analytics to improve overall city services performance.
Project Skills
Data Science: Applied statistical methods including OLS regression, missing value treatment, and exploratory data analysis (EDA) techniques to extract actionable insights. Used libraries such as Pandas, NumPy, and Matplotlib for data cleaning, manipulation, and visualization.
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Geospatial Analytics: Created scatter and hexbin plots to identify geographic hotspots for complaints across various boroughs, enhancing decision-making for resource allocation.
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Business Strategy: Employed economic principles such as demand elasticity and cost-benefit analysis to suggest optimal service allocation based on complaint frequency and type, ensuring efficient use of city resources.
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Data Visualization: Developed dynamic bar charts and frequency plots using Plotly and Matplotlib to visually represent city-wise complaint distribution and response times, aiding in better communication of key insights.
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Predictive Analytics: Utilized time-series analysis to estimate average response times for various types of complaints, offering recommendations to streamline city services based on historical data tren
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Statistical Analysis and Data Wrangling: By cleaning and transforming the dataset, I handled over 52 variables across 364,558 records. This involved detecting and treating missing values, removing unnecessary variables with over 80% missing data, and preparing the data for further analysis, demonstrating my ability to manage large, complex datasets.
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Geospatial and Business Insights: I visualized complaint concentration by city and borough using geospatial plotting, enabling management to better allocate resources where complaints were most frequent. I also identified top complaint categories, such as "Blocked Driveway" and "Illegal Parking," providing clear strategic insights for improving urban services.
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Optimization of Response Time: By calculating average response times across different types of complaints, I identified inefficiencies and suggested improvements. The data-driven analysis highlighted how faster response times could improve public satisfaction and service efficiency, directly tying to economic models of resource allocation.
Project Demostrates
I applied advanced data analytics techniques, including regression analysis, data cleaning, and time-series forecasting, to derive actionable business insights. By integrating business economics principles, I optimized cost-effectiveness and service delivery, combining statistical analysis with economic strategies to improve overall efficiency. Additionally, I communicated complex data-driven insights through clear and accessible visualizations and reports, ensuring that technical findings were aligned with business objectives to maximize impact for stakeholders.