This project involves analyzing Uber ride data to uncover patterns, trends, and insights that can aid in understanding customer behavior and optimizing services. The analysis covers areas like peak usage times, popular routes, and other key metrics using Python and data visualization techniques.
- Data Preprocessing: Cleaning and preparing the data for analysis, including handling missing values and outliers.
- Exploratory Data Analysis (EDA): Uncovering patterns and trends in the data, such as peak usage times, popular pickup/drop-off locations, and distance distribution.
- Data Visualization: Using charts and graphs to visually represent findings for easier interpretation.
- Insights and Observations: Providing insights based on the data analysis, such as identifying high-demand areas or hours.
- Python: Primary language used for data analysis and manipulation.
- Pandas: For data manipulation and preprocessing.
- Matplotlib & Seaborn: Libraries used to create visualizations.
- Jupyter Notebook: Development environment for organizing the analysis.
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Data Collection:
- Importing the Uber dataset, which contains information on pickup times, locations, and other ride details.
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Data Preprocessing:
- Cleaning the data by handling missing values and outliers.
- Formatting dates and times for time-based analysis.
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Exploratory Data Analysis:
- Analyzing ride patterns to find popular times, routes, and average trip distances.
- Examining data by day of the week, hour, and month to identify peak hours and seasonal trends.
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Data Visualization:
- Creating bar charts, line graphs, heatmaps, and other visualizations to represent key insights visually.
- Identifying correlations and patterns through visual analysis.
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Insights and Observations:
- Summarizing findings, such as peak hours, high-demand areas, and other patterns that Uber may use to enhance its services.