You can use link to the dataset in my Google Drive : https://drive.google.com/file/d/1HtPmVOFeYprczsDIi0eOyA8qBqLW_RUE/view
In my own data research, you'll find the following analyses:
Data Retrieval and Preprocessing:
- Download the real estate data for sales transactions from the provided link.
- Load the data into Python and perform necessary data preprocessing steps, such as handling missing values and data type conversions.
Data Overview:
- Provide a summary of the dataset, including the number of records, number of features, and basic statistics (e.g., mean, median, min, max) for key numeric variables.
- Describe the columns in the dataset and their respective meanings.
Time Series Analysis:
- Analyze the trends in real estate sales over time.
- Create visualizations, such as line plots or bar graphs, to illustrate the changes in sales over different time periods (e.g., months, quarters, years).
Property Type Analysis:
- Explore the different types of properties sold in Dubai.
- Determine the most common property types and visualize their distribution using pie charts or bar plots.
Price Distribution:
- Investigate the distribution of property prices in Dubai.
- Create a histogram to display the frequency distribution of property prices.
Location Analysis:
- Analyze the geographic distribution of real estate sales in Dubai.
- Use maps or geographical plots to visualize the areas with the highest sales volume.
Seasonal Patterns:
- Identify any seasonal patterns in real estate sales data.
- Explore if there are specific times of the year when sales tend to peak or drop.
Correlation Analysis:
- Investigate potential correlations between property prices and other relevant features in the dataset.
- Create correlation matrices and visualize correlations using heatmaps.
Price Prediction
- You can attempt to build a simple price prediction model based on property features like size, location, and property type.