Dynamic Dashboard: Insights into Manhattan Rental Prices

Dynamic Dashboard: Unlocking Insights into Manhattan Rental Prices

Fun Twist: Wondering how much to pay each guide in your apartment building, just like in the show “Only Murders in the Building” on Disney+? Our tool can help with that too!

In the bustling heart of New York City, finding the perfect apartment can be a daunting task. Rental prices in Manhattan vary widely based on many factors, including location, size, and amenities. To make this process more manageable and insightful, Insightful Data Technologies embarked on a journey to create a predictive model for rental apartment prices in Manhattan.

The Data Source

For this tools we use a Manhattan apartments dataset, a comprehensive collection of information on apartments available for rent in Manhattan.
The dataset encompassed many features, including location, size, number of bedrooms, number of bathrooms, floor, building age, and rent price. With 3,000 rows and 19 columns, this dataset provided a rich source of information for our analysis.

The dataset was meticulously curated from various sources, including StreetEasy, RentHop, and other real estate websites.
Before analysis, we performed data preprocessing tasks to ensure its quality. This involved removing duplicate entries, handling missing values, and converting categorical features into numerical representations.

Exploratory Data Analysis

Before diving into predictive modeling, we conducted exploratory data analysis (EDA) to understand the dataset better. Our team visualized the relationships between variables, identifying key factors influencing rental prices.

Predictive Modeling

With our top 5 features identified, we built a predictive model using the GradientBoostingRegressor.
This machine learning algorithm was chosen for its ability to capture complex relationships within the data and produce accurate predictions. Our model was trained on a subset of the dataset, and the remaining data were reserved for testing.

Dynamic Dashboard

One of the standout features of our project is the creation of a dynamic dashboard. This interactive dashboard allows users to explore the predictions and insights generated by our model in real time. Users can adjust input parameters, such as the number of bedrooms, location, and building age, to see how these factors instantly influence predicted rental prices.

Our dynamic dashboard provides an intuitive and user-friendly interface for anyone interested in understanding Manhattan’s rental market. It empowers renters, landlords, and investors to make informed decisions based on data-driven insights.

Results and Conclusion

In the final stage of our project, we evaluated our model’s performance and visualized the results. The model performed exceptionally well, accurately predicting Manhattan rental prices based on the selected features. This predictive tool can be invaluable for both renters and landlords, providing insights into the factors that influence rental fees in the city.

In conclusion, Insightful Data Technologies successfully leveraged its data analysis and machine learning expertise to develop a predictive model for Manhattan rental prices. We harnessed the power of Kaggle’s Manhattan apartments dataset, performed thorough exploratory data analysis, and built a robust predictive model. Our dynamic dashboard takes these insights to the next level by allowing users to interact with and explore the data in real time.

If you are navigating the competitive Manhattan rental market or seeking data-driven insights for your real estate investments, consider contacting us at Insightful Data Technologies. We are here to empower you with data and insights to make informed decisions in the world of real estate.

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